3 Mar 2017: Latest Measurement of Carbon Dioxide in the Atmosphere

Ann Morrison
By Ann Morrison March 20, 2017 15:56

Update as of 3 Mar 2017

John Moore, The Liberty Man, & Ann

Dr. Bill Deagle, Nutrimedical Report, & Ann


Biosecurity: H7N9v Bird Flu

Biosecurity: Superbug




Climate Change






Climate Change

Arctic Ozone Watch 20 February 2017

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National Aeronautics and Space Administration Goddard Space Flight Center



Antarctic Ozone Hole Watch 20 February 2017

Antarctic Ozone Hole Watch 28 February 2017




Jet Stream slower than normal over the U.S.

2017-03-02 1800 UTC at 250hPa




Current Large Fire Incidents 23 February 2017

34 Large Fires

Large Fires on2017-02-23




Carbon Dioxide Global Temperature Arctic Sea Ice Minimum Land Ice Sea Level

Carbon Dioxide LATEST MEASUREMENT: January 2017 405.92 ppm

Atmospheric CO2 levels in recent years


Carbon dioxide (CO2) is an important heat-trapping (greenhouse) gas, which is released through human activities such as deforestation and burning fossil fuels, as well as natural processes such as respiration and volcanic eruptions. The first chart shows atmospheric CO2 levels in recent years, with average seasonal cycle removed. The second chart shows CO2 levels during the last three glacial cycles, as reconstructed from ice cores.

The time series below shows global distribution and variation of the concentration of mid-tropospheric carbon dioxide in parts per million (ppm). The overall color of the map shifts toward the red with advancing time due to the annual increase of CO2.

CO2 676 April 2003


CO2 656 May 2004


CO2 573 March 2005


CO2 620 March 2006

CO2 617 April 2007


CO2 631 April 2008

CO2 607 April 2009

CO2 540 April 2004


CO2 644 April 2011


CO2 623 April 2012

CO2 630 April 2013


CO2 659 April 2014


Missions that observe CO2

  • Atmospheric Infrared Sounder (AIRS)
  • Orbiting Carbon Observatory (OCO-2)
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Accessed at https://climate.nasa.gov/vital-signs/carbon-dioxide/ on February 27, 2017.
NASA Earth Observatory
El Niño


Pacific Wind and Current Changes Bring Warm, Wild Weather

The GOES-West satellite observed four tropical cyclones roiling the Pacific on September 1 2015 during an El Niño event


The GOES-West satellite observed four tropical cyclones roiling the Pacific on September 1, 2015, during an El Niño event. (Image courtesy of the NASA/NOAA GOES Project.)

By Mike Carlowicz Design by Joshua Stevens February 14, 2017

If you want to understand how interconnected our planet is—how patterns and events in one place can affect life half a world away—study El Niño.

Episodic shifts in winds and water currents across the equatorial Pacific can cause floods in the South American desert while stalling and drying up the monsoon in Indonesia and India. Atmospheric circulation patterns that promote hurricanes and typhoons in the Pacific can also knock them down over the Atlantic. Fish populations in one part of the ocean might crash, while others thrive and spread well beyond their usual territory.

GOES-West satellite image of tropical cyclones.


During an El Niño event, the surface waters in the central and eastern Pacific Ocean become significantly warmer than usual. That change is intimately tied to the atmosphere and to the winds blowing over the vast Pacific. Easterly trade winds (which blow from the Americas toward Asia) falter and can even turn around into westerlies. This allows great masses of warm water to slosh from the western Pacific toward the Americas. It also reduces the upwelling of cooler, nutrient-rich waters from the deep—shutting down or reversing ocean currents along the equator and along the west coast of South and Central America.

The circulation of the air above the tropical Pacific Ocean responds to this tremendous redistribution of ocean heat. The typically strong high-pressure systems of the eastern Pacific weaken, thus changing the balance of atmospheric pressure across the eastern, central, and western Pacific. While easterly winds tend to be dry and steady, Pacific westerlies tend to come in bursts of warmer, moister air.

Illustration of atmospheric circulation.

Atmospheric circulation over the equator—the Walker circulation—changes substantially with the arrival of El Niño

Illustration by NOAA/Climate.gov

Atmospheric circulation over the equator—the Walker circulation—changes substantially with the arrival of El Niño. (Illustration by NOAA/Climate.gov)

Because of the vastness of the Pacific basin—covering one-third of the planet—these wind and humidity changes get transmitted around the world, disrupting circulation patterns such as jet streams (strong upper-level winds). We know these large-scale shifts in Pacific winds and waters initiate El Niño. What we don’t know is what triggers the shift. This remains a scientific mystery.

Illustration of the Pacific jet stream.

PHOTO - Winter La Nina patterns -


El Niño usually alters the Pacific jet stream, stretching it eastward, making it more persistent, and bringing wetter conditions to the western U.S. and Mexico. (NASA Earth Observatory illustration by Joshua Stevens.)

What is not a mystery is that El Niño is one of the most important weather-producing phenomena on Earth, a “master weather-maker,” as author Madeleine Nash once called it. The changing ocean conditions disrupt weather patterns and marine fisheries along the west coasts of the Americas. Dry regions of Peru, Chile, Mexico, and the southwestern United States are often deluged with rain and snow, and barren deserts have been known to explode in flowers. Meanwhile, wetter regions of the Brazilian Amazon and the northeastern United States often plunge into months-long droughts.

Chart showing increased rainfall during El Niño years.

Typically dry regions can experience nearly two times as much rain during a strong El Niño

NASA Earth Observatory chart by Joshua Stevens

Typically dry regions can experience nearly two times as much rain during a strong El Niño. (NASA Earth Observatory chart by Joshua Stevens, using data from the California-Nevada Climate Applications Program.)

El Niño events occur roughly every two to seven years, as the warm cycle alternates irregularly with its sibling La Niña—a cooling pattern in the eastern Pacific—and with neutral conditions. El Niño typically peaks between November and January, though the buildup can be spotted months in advance and its effects can take months to propagate around the world.

Though El Niño is not caused by climate change, it often produces some of the hottest years on record because of the vast amount of heat that rises from Pacific waters into the overlying atmosphere. Major El Niño events—such as 1972-73, 1982-83, 1997-98, and 2015-16—have provoked some of the great floods, droughts, forest fires, and coral bleaching events of the past half-century.

Chart showing temperatures over time.

El Niño years tend to be warmer than other years. (NASA Earth Observatory chart by Joshua Stevens, using data from the Goddard Institute for Space Studies.)

NASA, the National Oceanic and Atmospheric Administration (NOAA), and other scientific institutions track and study El Niño in many ways. From underwater floats that measure conditions in the depths of the Pacific to satellites that observe sea surface heights and the winds high above it, scientists now have many tools to dissect this l’enfant terrible of weather. The data visualizations on the next page show most of the key ways that we observe El Niño before, during, and after its visits.

Underwater Temperatures and Water Masses

The ocean is not uniform. Temperatures, salinity, and other characteristics vary in three dimensions, from north to south, east to west, and from the surface to the depths. With its own forms of underwater weather, the seas have fronts and circulation patterns that move heat and nutrients around ocean basins. Changes near the surface often start with changes in the depths.

The tropical Pacific receives more sunlight than any other region on Earth, and much of this energy is stored in the ocean as heat. Under neutral, normal conditions, the waters off southeast Asia and Australia are warmer and sea level stands higher than in the eastern Pacific; this warm water is pushed west and held there by easterly trade winds.

Temperature anomalies in the ocean depths reveal the fingerprints of El Niño-La Niña

NASA Earth Observatory/Joshua Stevens/Global Data and Assimilation Office

Temperature anomalies in the ocean depths reveal the fingerprints of El Niño and the La Niña that follows. (NASA Earth Observatory visualization by Joshua Stevens, using data from the Global Data and Assimilation Office.)   https://youtu.be/69N494UIlS8


But as an El Niño pattern develops and trade winds weaken, gravity causes the warm water to move east. This mass, referred to as the “western Pacific warm pool,” extends down to about 200 meters in depth, a phenomenon that can be observed by moored or floating instruments in the ocean: satellite-tracked drifting buoys, moorings, gliders, and Argo floats that cycle from the ocean surface to great depths. These in situ instruments (more than 3,000 of them) record temperatures and other traits in the top 300 meters of the global ocean.

The visualization above shows a cross-section of the Pacific Ocean from January 2015 through December 2016. It shows temperature anomalies; that is, how much the temperatures at the surface and in the depths ranged above or below the long-term averages. Note the warm water in the depths starting to move from west to east after March 2015 and peaking near the end of 2015. (The western Pacific grows cooler than normal.) By March 2016, cooler water begins moving east, sparking a mild La Niña in the eastern Pacific late in 2016, while the western Pacific begins to warm again.

Sea Surface Temperatures

For hundreds of years, the temperature near the water surface has been measured by instruments on ships, moorings and, more recently, drifters. Since the late 1970s, satellites have provided a global view of ocean surface temperatures, filling in the gaps between those singular points where floating measurements can be made.

Map comparing sea surface temperature anomalies before and during an El Niño.

El Niño is associated with above-average equatorial sea surface temperatures. El Niño’s signature warmth is apparent in the November 2015 map. (NASA Earth Observatory maps by Joshua Stevens, using data from Coral Reef Watch.)

Sea surface temperatures are measured from space by radiometers, which detect the electromagnetic energy (mostly light and heat) emitted by objects and surfaces on Earth. In the case of the oceans, satellite radiometers—such as the Advanced Very High Resolution Radiometer (AVHRR) on NOAA weather satellites and the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites—detect the strength of infrared and microwave emissions from the top few millimeters of the water.

The maps above show sea surface temperature anomalies in the Pacific from winter and fall of 2015. The maps do not depict absolute temperatures; instead, they show how much above (red) or below (blue) the surface water temperatures were compared to a long-term (30-year) average. The maps were built with data from a multi-satellite analysis assembled by researchers from NOAA, NASA, and the University of South Florida.

When deciding whether the Pacific is in an El Niño state, the climatologists at NOAA examine sea surface temperatures in the east-central tropical Pacific—referred to as the Niño 3.4 region (between 120° to 170° West). An El Niño is declared when the average temperature stays more than 0.5 degrees Celsius above the long-term average for five consecutive months. In 1997-98 and 2015-16, sea surface temperatures rose more than 2.5 degrees Celsius (4.5 degrees Fahrenheit) above the average.

Sea Surface Height

Sea level is naturally higher in the western Pacific; in fact, it is normally about 40 to 50 centimeters (15-20 inches) higher near Indonesia than off of Ecuador. Some of this difference is due to tropical trade winds, which predominantly blow from east to west across the Pacific Ocean, piling up water near Asia and Oceania. Some of it is also due to the heat stored in the water, so measuring the height of the sea surface is a good proxy for measuring the heat content of the water.

Animated map of sea surface height changes over time.

Water expands as it warms causing surface of the ocean to rise

NASA Earth Observatory/Patzert/NASA/JPL Ocean Surface Topograph

Water expands as it warms, causing the surface of the ocean to rise. (NASA Earth Observatory map by Joshua Stevens, using Jason-2 data provided by Akiko Kayashi and Bill Patzert, NASA/JPL Ocean Surface Topography Team.)

The animation above compares sea surface heights in the Pacific Ocean as measured by the altimeter on the OSTM/Jason-2 satellite and analyzed by scientists at NASA’s Jet Propulsion Laboratory. It shows sea surface height anomalies, or how much the water stood above or below its normal sea level. Shades of red indicate where the ocean was higher because warmer water expands to fill more volume (thermal expansion). Shades of blue show where sea level and temperatures were lower than average (water contraction). Normal sea-level conditions appear in white.

As you watch sea surface heights change through 2015, note the pulses of warmer water moving east across the ocean. When the trade winds ease and bursts of wind come out of the west, warm water from the western Pacific pulses east in vast, deep waves (Kelvin waves) that even out sea level a bit. As the warm water piles up in the east, it deepens the warm surface layer, lowering the thermocline and suppressing the natural upwelling that usually keeps waters cooler along the Pacific coasts of the Americas. (Look back at the underwater temperature animation to see this phenomenon.)

Ocean Color

As temperatures change due to El Niño, other effects ripple through the ocean. In the eastern Pacific, the surge of warm water deepens the thermocline, the thin layer that separates surface waters from deep-ocean waters. This thicker layer of warm water at the surface curtails the usual upwelling of cooler, nutrient-rich water—the water that usually supports rich fisheries in the region. This loss of the nutrient supply is evident in declining concentrations of sea surface chlorophyll, the green pigment present in most phytoplankton. Changes in water properties such as oxygen and carbon content also affect marine life.

Maps showing changes in chlorophyll concentration in response to El Niño.

Chlorophyll concentrations rise and fall with the presence of phytoplankton

Joshua Stevens/Stephanie Schollaert Uz/MODIS/NASA OceanColor Web/SeaDAS

Chlorophyll concentrations rise and fall with the presence of phytoplankton. During the 2015 El Niño, warming water temperatures changed where phytoplankton bloomed in the Pacific Ocean. (NASA Earth Observatory maps by Joshua Stevens and Stephanie Schollaert Uz, using data from MODIS, NASA OceanColor Web, and SeaDAS.)

The images above compare sea surface chlorophyll in the Pacific Ocean as observed in October 2014 and 2015. Shades of green indicate more chlorophyll and blooming phytoplankton. Shades of blue indicate less chlorophyll and less phytoplankton. (For a larger view of these maps, click here.)

Historic observations have shown that with less phytoplankton available, the fish that feed upon plankton—and the bigger fish that feed on the little ones—have a greatly reduced food supply. In most extreme El Niños, the decline in fish stocks has led to famine and dramatic population declines for marine animals such as Galapagos penguins, marine iguanas, sea lions, and seals.

Surface Winds

The behavior of the winds and waters are tightly intertwined in the Pacific basin during an El Niño event. “It is like the proverbial chicken-and-egg problem,” says Michael McPhaden of NOAA’s Pacific Marine Environmental Laboratory. “During an El Niño year, weakening winds along the equator lead to warming water surface temperatures that lead to further weakening of the winds.”

The image below shows the dominant direction of the winds and changes in their intensity near the ocean surface as observed by NASA’s RapidScat instrument. Arrows show how the primary wind direction changed from January 2015 to January 2016. The change in wind speed is represented by colors, with surface wind speeds increasing in teal-green areas and decreasing in purple areas.

Map showing changes in wind patterns as a result of El Niño.

During an El Niño, wind patterns shift all over the Pacific Ocean. Most significantly, they get weaker (purple) in the eastern tropical Pacific, allowing warm surface water to move toward the Americas (NASA Earth Observatory map by Joshua Stevens using RapidScat data from the Jet Propulsion Laboratory.)

The El Niño signal is evident in the eastward-blowing winds in the tropical western and central Pacific. Winds near the equator (5° North to 5° South) blew more forcefully from west to east in the western and central Pacific; meanwhile, the easterly (east to west) trade winds weakened near the Americas. These wind shifts allowed pulses of warm water to slosh from Asia toward the Americas over the course of 2015. The signal also shows up in a convergence in the eastern Pacific; that is, the winds in the tropics (23°N to 23°S) were generally moving toward the equator. This reflects intense convection, where warm surface waters promote intense evaporation and rising air. (See the Walker circulation illustration on page 1.) Consequently, new air masses move toward the equator to replace the rising air.

Other changes occurred well away from the equator; scientists refer to these as teleconnections. For instance, RapidScat detected a strong clockwise-rotating (anti-cyclonic) wind anomaly in the northeastern Pacific that may have been the result of stronger-than-normal atmospheric circulation (Hadley cell). That is, air that rose above the super-heated waters of the central tropical Pacific sank back to the surface at higher latitudes with more than usual intensity.

Cloudiness and Precipitation

By changing the distribution of heat and wind across the Pacific, El Niño alters rainfall patterns for months to seasons. As the warm ocean surface warms the atmosphere above it, moisture-rich air rises and develops into rain clouds. So while the majority of precipitation tends to occur over the west Pacific warm pool in neutral years, much more develops over the central and eastern Pacific during an El Niño event.

Maps showing cloud cover before and during an El Niño.

El Niño alters the amount and location of clouds over the Pacific

NASA Earth Observatory/Joshua Stevens/NASA Earth Observations

Just as El Niño influences ocean surface temperatures, it also alters the amount and location of clouds over the Pacific. (NASA Earth Observatory maps by Joshua Stevens, using data from the NASA Earth Observations.)

The globes show cloud fraction over the Pacific Ocean in January and November 2015 as measured by the MODIS instrument on NASA’s Aqua satellite. The data show how often and how much the sky was filled with clouds over a particular region. Cloudiness is a result of moisture rising from the ocean surface into the atmosphere. During an El Niño (November image), cloud cover increases in the eastern Pacific due to the warm water releasing more moisture and heat into the atmosphere. Those clouds can lead to more rain, but they also shade the water by day and trap heat near the surface at night.

The Human History of El Niño

El Niño was identified and named long before science caught up with the phenomenon. For centuries, Peruvian fishermen reaped a bounty off the Pacific coast of South America, where north- and west-flowing currents pulled cool, nutrient-rich water from the deep. But every so often, the currents would stop or turn around; warm water from the tropics would drive the fish away and leave the nets empty. These periodic warm spells were most noticeable around December or January—around the time of Christmas, the birth of “the boy child.”

Some of the first scientific descriptions of El Niño came during exchanges between the Lima Geographical Society and the International Geographic Congress in the 1890s. But the roots of El Niño stretch far back into history, long before the birth of Jesus of Nazareth or the arrival of Peruvian fisherman. The chemical signatures of warmer seas and increased rainfall have been detected in coral samples and in other paleoclimate indicators since the last Ice Age. This pattern of water and wind changes has been going on for tens of thousands of years.

Earth scientists, historians, and archaeologists have theorized that El Niño had a role in the demise or disruption of several ancient civilizations, including the Moche, the Inca, and other cultures in the Americas. But the recorded history of El Niño really starts in the 1500s, when European cultures reached the New World and met indigenous American cultures.

16th Century

Historical research has suggested that the Spanish conquest of the Incas and Peru may have been aided by El Niño conditions. When Francisco Pizarro first sailed from Panama along the west coast of South America in 1524, his progress was slowed and ultimately stopped by persistent south and southeasterly winds—which follow the pattern of the north-flowing coastal currents. In 1525-26, however, Pizarro got much farther down the coast, riding on favorable northeasterly winds, according to geographer Cesar Caviedes, author of El Niño in History.

Map showing the approximate routes of the conquests of Francisco Pizzaro.

1856 map by Alexander Keith Johnson/Creative Commons license courtesy of the David Rumsey Map Collection

The advance of Pizarro and his conquistadors was most successful during the El Niño of 1532

NASA Earth Observatory/Joshua Stevens

The expeditions of Francisco Pizarro provide hints that his conquest may have been aided by the winds of El Niño. The advance of Pizarro and his conquistadors was most successful during the El Niño of 1532. (NASA Earth Observatory map by Joshua Stevens.)

When Pizarro returned in 1531-32, his ships made haste down the coast, pushed along again by strong northeasterlies—the kind that blow in El Niño years. Once Spanish troops moved inland, they found blooming deserts, swollen rivers, and rainfall in the usually arid regions of Peru and Ecuador. The humid air and moist land allowed the conquistadors to sustain their long march and to avoid Incan settlements on the way to establishing a foothold in the country.

18th Century

Between 1789 and 1792, the monsoon in South Asia failed multiple times, according to historical and scientific records. There is evidence that several other climate patterns—some of them affected by or coinciding with Asian monsoon patterns and El Niño—influenced storm tracks and westerly winds near Europe. According to some researchers, the combination of climate anomalies and unusual weather led to crop failures in Europe and set the stage for some of the unrest that exploded in the French Revolution of 1789.

19th Century

In the book Late Victorian Holocausts, historian Mike Davis suggests that at least three great famines in the late 19th century were connected to El Niño. Extreme weather and the collapse of monsoon circulation—patterns documented by British and Indian officials, among others—led to great droughts and a few floods in 1876-78, 1896-97, and 1899-1900. Between 30 to 60 million people perished in India, China, and Brazil, among other countries; hundreds of millions suffered through hunger and social and political strife. Though European colonialism and the spread of laissez faire capitalism played important roles in these calamities, the global reach (teleconnections) of El Niño and La Niña likely spurred the great droughts, crop failures, and malaria outbreaks.

A historic map showing temperatures, currents, and known ship routes in 1856.

Temperatures, currents, ship routes

1856 map by Alexander Keith Johnson/Creative Commons license courtesy of the David Rumsey Map Collection

This 1856 map by Alexander Keith Johnson depicts temperatures, currents, and ship routes in the eastern Pacific, as they were known at the time. (Cropped image used under a Creative Commons license, courtesy of the David Rumsey Map Collection.)

20th Century

In the 1920s, a transplanted statistician and physicist from Britain began to piece together the big picture of this global weather-maker. While working as Director of Observatories in India and studying the monsoon, Gilbert Walker noted that “when pressure is high in the Pacific Ocean it tends to be low in the Indian Ocean from Africa to Australia; these conditions are associated with low temperatures in both these areas, and rainfall varies in the opposite direction to pressure.” He dubbed the alternating atmospheric weather pattern the “Southern Oscillation,” noting how highs over the tropical Pacific coincided with lows over the Indian Ocean, and vice versa.

It would be another four decades before Jacob Bjerknes—a Norwegian-born scientist who helped found the meteorology department at the University of California, Los Angeles—made the final connection between the alternating warm and cool patterns in Pacific waters and the atmospheric circulation described by Walker. The entire pattern came to be known as ENSO, or El Niño-Southern Oscillation, and it includes the sister phenomenon known as La Niña.

Photograph of numerous dead sardines on the coast of Chile.

chilean sardines

Armada de Chile

In April 2016, nearly 8,000 tons of sardines died and washed up along the coast of Chile, likely the result of El Niño related changes in the ocean. (Photographs courtesy of Armada de Chile.)

At least 26 El Niños were recorded in the 20th century, and each brought its own wrinkles that piqued the interest of scientists and sent ripples through economies. The El Niño of 1957-58, for instance, caused serious damage to the kelp forests off California. Another event in 1965-66 crashed the market for guano (fertilizer) in Peru and also spurred the use of soybeans for animal feed (instead of fish meal). In 1972-73, the anchovy population crashed, leading to the death of millions of sea birds and to destabilizing effects on the Peruvian economy and government.

In 1982-83, the first major El Niño to get significant real-time study, sea birds on Christmas Island abandoned their young and flew out over the Pacific in a desperate search for food. Nearly 25 percent of the fur seal and sea lion populations off Peru starved to death.

Photograph of the Santa Cruz River with strong waves and flooding.

Swollen with the rains of the 1983 El Niño the Santa Cruz River roils near Tucson Arizona

Peter L Kresan/University of Arizona/US Geological Survey 1983

Swollen with the rains of the 1983 El Niño, the Santa Cruz River roils near Tucson, Arizona. (Photograph courtesy of Peter L. Kresan, University of Arizona/U.S. Geological Survey.)

“To ask why El Niño occurs is like asking why a bell rings or a pendulum swings,” atmospheric scientist George Philander wrote in a 1999 paper. “It is a natural mode of oscillation. A bell, of course, needs to be struck in order to ring.” After nearly 100 years of investigation, scientists are still not sure what rings the bell; they just know that it rings.

   Related Reading
   Caviedes, César N. (2001) El Niño in History: Storming Through the Ages. University Press of Florida.
   Grove, R.H. (2007) Global Impact of the 1789-93 El Niño. Nature, 393, 318-19.
   Grove, R.H. (1998) The Great El Niño of 1789-93 and its Global Consequences. The Medieval History Journal, 10, (1-2) 75-98.
   Kessler, William, via NOAA Pacific Marine Environmental Laboratory (2003) Frequently (well, at least once) asked-questions about El Niño. Accessed July 23, 2016.
   Nash, J. Madeleine (2002) El Niño: Unlocking the Secrets of the Master Weather-Maker. Warner Books.
   Quinn, W.H. et al. (1987) El Niño occurrences over the past four and a half centuries. Journal of Geophysical Research-Oceans, 92, (C13) 14449–14461.
   Slate (2011, August 24) Weather and War. Accessed July 23, 2016.
   Wikipedia (2016) El Niño. Accessed July 23, 2016.
   What is El Niño?
   Fundamental Observations
   Human History of El Niño
The Earth Observatory is part of the EOS Project Science Office located at NASA Goddard Space Flight Center
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Accessed at http://earthobservatory.nasa.gov/Features/ElNino/?src=features-hp&eocn=home&eoci=feature on March 2, 2017.


Season Creep


Normal Curve Average increases over time

Paul Douglas

As climate change advances, spring is arriving much sooner, while winters are becoming shorter and milder. Changes in the timing of the seasons has been documented around the world — through studies on plant and animal development and life cycles, temperature and snow cover — and informally dubbed “season creep.” Season creep is an example of how small changes can have a big impact. Climate change disrupts the critically important timing of events, such as snow melt and spring bloom, upon which ecosystems and agricultural industries depend.

US trends and projections

Earlier/ warmer springs

Global warming drives season creep.[1] In the United States, studies have documented an advance in the timing of springtime lifecycle events across plant and animal species in response to increased temperatures.[2]

The continental US experienced widespread earlier green-up (when plants go from winter dormancy to photosynthesis) and last spring freeze dates over the period from 1920 to 2013.[3] From 1950 to 2013, green up and last spring freeze advanced by 4.2 and 7.9 days respectively.[3]

In the Northeast, the last spring frost is about a week earlier now than it was 30 years ago on average. This leads to to increased chances of frost damage since the start of growth for many plants has shifted even earlier than the last frost date.[4]

Longer/ warmer falls

Hardwood forests in the Northern Hemisphere are holding their green leaves for over a week longer than normal.[5]

The date of the first autumn freeze in the Pacific Northwest has been delayed 9 days since 1950.[6]

Drier, warmer autumn weather may be extending summer smog well into the fall in the Southeastern US.[7]

Shorter/ warmer winters

According to the 2014 US National Climate Assessment, “Observed long-term trends towards shorter, milder winters and earlier spring thaws are altering the timing of critical spring events such as bud burst and emergence from overwintering.”[1]

Global trends and projections

Earlier/ warmer springs

The IPCC states, “It is virtually certain that there will be more frequent hot and fewer cold temperature extremes over most land areas on daily and seasonal timescales, as global mean surface temperature increases.”[8] Recent warming of the Northern Hemisphere is well documented and typically greater in winter and spring than other seasons.[9]

In Eurasia and North America, the length of time in a calendar year when temperatures are consistently warm enough for agricultural activity lengthened by 10 days between 1982 and 2011.[10] In Eurasia, the growing season increased by 13 days, and in North America, it increased 6 days.[10] The increase closely tracks the pace of warming in the spring.[10]

Research indicates that natural variability can, at best, explain only one-third of the rate of “creep” in the arrival of spring in North America.[11]

Spring snow cover extent has decreased in the Northern Hemisphere. During the months of March and April from 1922 to 2012, snow cover decreased by 7 percent and the decline shows a strong negative correlation with land temperature.[8]

Longer/ warmer falls

In North America, the end of the growing season was delayed by 8.1 days from 1982 to 1999 and delayed by another 1.3 days from 2000 to 2008.[5]

Accessed at http://www.climatesignals.org/climate-signals/season-creep on February 27, 2017.


National Phenology Network Status of Spring 2017

The USA-NPN is tracking the start of the spring season across the country using models called the Spring Leaf and Bloom Indices.

Spring Leaf Index Anomaly 27 February 2017

National Phenology Network

How does this year stack up against the recent past?

In 2017, we see very large anomalies in the southeastern United States on the Spring Leaf Index map, where the Index was met up to three weeks earlier than what is typical for these locations.

The timing of leaf-out, migration, flowering and other seasonal phenomena in many species is closely tied to local weather conditions and broad climatic patterns. The Spring Index maps offered by USA-NPN shed light on plant and animal phenology, based on local weather and climate conditions.

The arrival of spring across the US so far 22 February 2017


Spring Indices: Indicators of phenological activity

How do you know when spring has begun? Is it the appearance of the first tiny leaves on the trees, or the first crocus plants peeping through the snow? The Spring Leaf Index is a synthetic measure of these early season events in plants, based on recent temperature conditions. This model allows us to track the progression of spring onset across the country.

The map at right shows locations that have reached the requirements for the Spring Leaf Index model (based on NOAA National Centers for Environmental Prediction Real-Time Mesoscale Analysis temperature products).

Re-use of Maps and Data

Content, maps and data accessible via usanpn.org are openly and universally available to all users. USA-NPN is not responsible for content or the use of the data. Content may be re-used and modified with appropriate attribution (e.g., “source: USA National Phenology Network, www.usanpn.org”). See our complete Content Policy and Data Use Policy.

Accessed at https://www.usanpn.org/data/spring on February 27, 2017.



Variability and predictability of the space environment as related to lower atmosphere forcing

Lower atmosphere forcing affects variability and predictability of the space environment

© 2017 American Geophysical Union/
Copyright © 1999 – 2017 John Wiley & Sons, Inc. All Rights Reserved


Authors:     H.-L. Liu,     First published: 24 September 2016Full publication history,    DOI: 10.1002/2016SW001450View/save citation
Volume 14, Issue 9 September 2016 Pages 634–658


The Earth’s thermosphere and ionosphere (TI) are characterized by perpetual variability as integral parts of the atmosphere system, with intermittent disturbances from solar and geomagnetic forcing. This review examines how the TI variability is affected by processes originating from the lower atmosphere and implications for quantifying and forecasting the TI. This aspect of the TI variability has been increasingly appreciated in recent years from both observational and numerical studies, especially during the last extended solar minimum. This review focuses on the role of atmospheric waves, including tides, planetary waves, gravity waves, and acoustic waves, which become increasingly significant as they propagate from their source region to the upper atmosphere. Recent studies have led to better understanding of how these waves directly or indirectly affect TI wind, temperature, and compositional structures; the circulation pattern; neutral and ion species transport; and ionospheric wind dynamo. The variability of these waves on daily to interannual scales has been found to significantly impact the TI variability. Several outstanding questions and challenges have been highlighted:

  • large, seemingly stochastic, day-to-day variability of tides in the TI
  • control of model error in the TI region by the lower atmosphere
  • increasing importance of processes with shorter spatial and temporal scales at higher altitudes.

Addressing these challenges requires model capabilities to assimilate observations of both lower and upper atmosphere and higher model resolution to capture complex interactions among processes over a broad range of scales and extended altitudes.

1 Introduction

The compositional and thermal structures of the Earth’s thermosphere and ionosphere (TI) are of central importance for space weather and space climate research, because they interact with the solar radiative and particulate inputs, determine the atmosphere drag on space vehicles and debris, and affect radio communication and GPS signals. The TI is a highly variable system displaying a broad range of temporal and spatial scales, and to understand, quantify, and forecast these variabilities are major objectives of space environment studies. The TI composition and energetics are primarily determined by solar radiation and can be strongly disturbed by radiative, magnetic, and particulate changes due to solar flares, coronal mass ejection (CME), geomagnetic solar storms, or magnetospheric substorms. The onsets of these events are determined by the solar and/or magnetospheric states, and their predictability thus depends on the predictability of the Sun, the solar wind, and the magnetosphere, though the TI responses to such large disturbances also depend on the state of the TI. These large events are intermittent, but the TI still displays persistent variabilities in their absence. These variabilities can have significant magnitude and can be disruptive to radio communication and GPS signals (e.g., ionospheric irregularities in E or F regions). Knowledge of the variabilities also enables better quantification of the prestorm conditions and leads to better prediction of storm time responses.

These variabilities of the TI during “quiet time” periods have been thought to be generally caused by perturbations originating from the lower atmosphere. For example, statistical studies of the critical frequency of the ionospheric F2 peak (foF2) data from over 100 stations by Forbes et al. [2000] found that under geomagnetic quiet conditions (Kp < 1) the F2 region peak plasma density variability (NmF2), measured via standard deviation, ranges from 25 to 35% of the mean for high-frequency (hours to 2days) and 15 to 20% of the mean for low-frequency (2–30days) components. With the very low level of geomagnetic forcing and minimal impact of F10.7 variability on the F region, these variabilities are thought to be from “meteorological forcing.” Rishbeth and Mendillo [2001] reached a similar conclusion, that the meteorological forcing is responsible for ~15% of the NmF2 standard deviation, by analyzing the total NmF2 standard deviation, the standard deviation of the Ap index, and sensitivity of the F2 region peak plasma density to Ap index. Pathways by which the lower atmosphere affects the upper atmosphere, however, were not well understood at that time. Thanks to both new observations, in particular those during the last extended solar minimum, and the use of numerical models that treat the whole atmosphere as an integrated system (see Akmaev [2011] for a review of whole atmosphere modeling), there has been rapid progress in our understanding. In this paper, we will review the recent development in our understanding of TI variability, and the implications for the predictability, as related to lower atmosphere forcing, especially through atmospheric waves. This review complements the review of this subject by Laštovicka [2006], by focusing more on progresses in numerical modeling studies, especially the ones within the last decade. The paper is organized as follows: Section 2 reviews the tidal and planetary-scale wave variability on long and short time scales, including the seemingly stochastic day-to-day tidal variability and how they affect the TI; section 3 discusses factors that impact predictability of the upper atmosphere and the need for whole atmosphere data assimilation; section 4 discusses the significant role that mesoscale processes, in particular gravity waves, play in the TI and the challenge and recent progress in quantifying gravity waves. A brief summary is presented in section 5.

2 Upper Atmosphere Variability as Related to Tides and Other Planetary-Scale Waves

Atmospheric waves of various scales play a key role in coupling the lower and upper atmosphere, because they can cause large atmospheric perturbations and can transport momentum, energy, and atmosphere constituents. Waves are generated when the essential atmosphere balance states, such as hydrostatic and geostrophic balances, are perturbed. Common examples include gravity waves, tides, Kelvin waves, and Rossby waves; see, e.g., Andrews et al. [1987] and Forbes [1995] for detailed discussions of these atmospheric waves. A brief summary of essential features of these waves are given in Table 1. A note on nomenclature is as follows: Rossby waves are also often referred to as planetary waves, while planetary-scale waves generally refer to tides, Rossby waves, and equatorial waves, especially the latter two in this review. When referring to tides, the following convention is adapted: tidal period in terms of harmonics of a day (D for diurnal, S for semidiurnal, T for terdiurnal, etc.), propagation direction (E for eastward propagating, W for westward propagating), and zonal wave number. For example, DW1 is for diurnal westward propagating tide with zonal wave number 1. A majority of wave sources are found in the troposphere, such as deep convection, frontal system, orography, diabatic heating/cooling, land-sea contrast, and instabilities. In addition, diabatic heating from absorption of solar ultraviolet (UV) radiation by stratospheric ozone is a source of atmospheric thermal tides. Adjustment processes associated with the winter stratospheric jet can excite gravity waves. Nonlinear interactions between primary waves can also generate secondary waves. The waves can propagate into the middle and upper atmosphere under favorable wind and temperature conditions and modulate the winds, thermal and compositional structures, and electrodynamics. The wave sources and the propagation condition (background wind and temperature) can vary significantly over space and time, and as a result waves in the upper atmosphere display large variability and contribute to the variability of the space environment. Since predictability reflects the strength of a signal as compared to the variability, in particular stochastic variability of a system, processes with well-defined periodicity, such as atmospheric waves, can thus enhance predictability, while variability of wave amplitudes and periods (and thus phases), on the other hand, may negatively affect predictability.

Table 1. A Brief Summary of the Major Atmospheric Waves of Interest to the Thermosphere and Ionosphere

  Primary Restoring Force Wave Sources Temporal/Spatial Scales Propagation
Solar thermal tides Buoyancy Solar radiative heating, Harmonics of a solar Migrating: westward
latent heat day/planetary following the Sun
not following the Sun
Lunar tides Buoyancy Lunar Harmonics of a lunar Following the
gravitational force day/planetary Moon
Rossby waves, Coriolis Tropospheric processes: Days to quasi- Westward relative
mixed Rossby- force/buoyancy topography, land-ocean stationary/planetary to background
gravity waves contrast, diabatic heating wind
Equatorial waves: Buoyancy/Coriolis Tropical Days/planetary Equatorially trapped
Kelvin waves, force tropospheric Kelvin waves: eastward
equatorial Rossby processes: deep Equatorial Rossby mixed
waves, equatorial convection Rossby-gravity waves:
mixed Rossby-gravity westward
waves, equatorial Equatorial inertio-gravity
inertio-gravity waves: eastward and
waves westward
Gravity waves Buoyancy Deep convection, Longer than Horizontal
orography, frontal buoyancy period and vertical
system, adjustment and less than
of jet, body inertial period/km to
forcing from thousands of kilometers
wave breaking
Acoustic waves Air pressure Deep convection, Shorter than buoyancy Horizontal and
orography period/km to vertical
hundreds of kilometers

2.1 Atmospheric Tides and Their Long-Term Variability

Atmospheric tides, including thermal and lunar gravitational tides with migrating and nonmigrating components, have been known to play an important role in the upper atmosphere and upper atmosphere variability [e.g., Forbes, 1995] and in ion-neutral coupling. Observations have shown that tidal signals can extend into the upper thermosphere, including the ionospheric dynamo region [e.g., Häusler et al., 2007; Häusler and Lühr, 2009; Forbes et al., 2008, 2009; Oberheide et al., 2009; Häusler et al., 2010], in spite of the strong molecular damping of the waves in the thermosphere. This likely results from the exponential density decrease and fast thermal conduction. Tides can cause temperature and density modulations and are responsible for the formation of midnight temperature maximum (MTM) and midnight density modulation (MDM) [Miyoshi et al., 2009; Akmaev et al., 2010; Lei et al., 2011; Ruan et al., 2014]. Tidal modulation of ion-neutral coupling is exemplified by the observational and modeling studies of the four wave structures in the equatorial ionosphere anomaly (EIA) and equatorial electrojet (EEJ) [Sagawa et al., 2005; Immel et al., 2006; Hagan et al., 2007; Lühr et al., 2008], and ionospheric responses during stratospheric sudden warming (SSW) [Goncharenko et al., 2010; Yue et al., 2010; Chau et al., 2012]. Tidal winds affect ion-neutral coupling through the ionosphere E and F region wind dynamo [Richmond et al., 1976; Forbes and Lindzen, 1976; Richmond and Roble, 1987; Millward et al., 2001; Liu et al., 2010a; Liu and Richmond, 2013], as well as by modulating plasma transport and ion-neutral collisions in the F region [Wan et al., 2012; Lei et al., 2014]. Tides with large amplitudes at middle to high latitudes (such as migrating and nonmigrating semidiurnal components), as well as those at lower latitudes, can contribute to the modulation of ionospheric electrodynamics [e.g., Liu and Richmond, 2013]. The relative importance of the E and F region wind dynamo can be affected by the solar activity, which determines the damping and penetration height of tidal waves [Oberheide et al., 2009; Liu et al., 2010b], as well as the Pederson and Hall conductivities [Liu and Richmond, 2013].

Upper atmosphere tides display large variability over long and short time scales [e.g., Forbes et al., 2008] and can thus contribute to the variability of the space environment on climate and weather scales. Tidal variability is caused by changes in wave forcing, propagation conditions (including resonance conditions), and interaction among tides and with other waves (e.g., other tides, planetary waves, and gravity waves). Over interannual time scales, tides in the mesosphere/lower thermosphere (MLT) region are modulated by El Niño and the Southern Oscillation (ENSO), as evidenced by observations [Lieberman et al., 2007; Warner and Oberheide, 2014] and by numerical simulations [Pedatella and Liu, 2012, 2013]. The numerical simulations determined that the ENSO-induced MLT variability is 10–30% and the ionospheric variability is 10–15%. ENSO-related changes in tropospheric diabatic heating is the primary cause of variability of diurnal migrating tide (DW1) and some of the nonmigrating diurnal tidal components (e.g., eastward wave 2 and 3, DE2 and 3). The DE2 and 3 are also affected by the background atmospheric changes during ENSO, while some other nonmigrating components are affected by nonlinear interaction with planetary waves (which changes during ENSO as found by Sassi et al. [2004] and Manzini et al. [2006]).

On interannual time scales, tides in the MLT region are also modulated by the quasi-biennial oscillation (QBO) [Burrage et al., 1996; Wu et al., 2008a, 2008b; Xu et al., 2009]. During the easterly/westerly phase (QBO-E/W), DW1 and DE3 are weaker/stronger and the migrating semidiurnal tide (SW2) is stronger/weaker. This dependence has been reproduced in whole atmosphere model simulations [e.g., Liu, 2014]. Since the propagation and resonance amplification of tides are found to be sensitively dependent on the zonal mean wind [Forbes and Vincent, 1989; Forbes and Zhang, 2012], it has been proposed that the QBO modulation could be caused by changes in vertical wavelength and dissipation and changes in ozone heating; however, numerical experiments using the Global Scale Wave Model (GSWM) suggested that neither could explain the QBO modulation of the diurnal migrating tide (DW1) [Hagan et al., 1999]. Some tidal components are also well known to display an annual and semiannual variation. For example, DW1 in the MLT reaches a primary maximum around March equinox and a secondary maximum around September equinox, and SW2 amplitude peaks in the winter hemisphere [Burrage et al., 1995; Wu et al., 2011]. McLandress [2002] demonstrated that DW1 is dependent on the latitudinal shear of the zonal mean zonal wind (thus, the zonal mean vorticity) in the summer mesosphere. The vorticity change alters the tidal propagation through modifying the equivalent depth of the tides.

2.2 Short-Term Tidal Variability and Tidal Interaction With Other Planetary-Scale Waves

Short-term variability (including variability with scales of day to day to a month) is of interest in space weather studies. Perturbations originating from the lower atmosphere can play an important role in the short-term variability of the upper atmosphere. For example, continuous lidar measurements by She et al. [2004] showed that the diurnal temperature amplitude in the MLT region can double or even triple from one day to the next [Liu et al., 2007]. According to the comparative modeling study by Liu et al. [2007], this rapid change could result from interaction between tides and planetary waves. Such interaction can excite diurnal nonmigrating components and modulate modes of migrating components and thus lead to changes of the total diurnal signal.

Quasi-stationary planetary waves (QSPWs) are the strongest planetary wave events in the winter stratosphere and mesosphere, and they can alter the stratospheric and mesospheric circulation pattern during stratospheric sudden warming (SSW) events [Scherhag, 1952; Matsuno, 1971]. Due to wave-mean flow interactions, the QSPWs undergo rapid increases and decreases during life cycles of SSWs (with time scales of weeks). Tides, including both thermal tides and lunar tides, have been shown to display large variability during SSWs as manifested in both the neutral atmosphere and ionosphere [e.g., Goncharenko et al., 2010; Fejer et al., 2010, 2011; Park et al., 2012; Yamazaki et al., 2012; Yamazaki, 2013; Wu and Nozawa, 2015; Chau et al., 2015], and possible mechanisms considered include nonlinearly interaction with the QSPWs, as well as changes in zonal mean zonal winds and tidal sources [e.g., Liu and Roble, 2002; Pancheva et al., 2009; Chang et al., 2009; Sridharan et al., 2012; Liu et al., 2010a; Fuller-Rowell et al., 2010; Forbes and Zhang, 2012]. Due to their large magnitude change in a short time duration, QSPW and SSW events provide exemplary cases to gain insights into the link of the lower and upper atmosphere through complex interactions among tides, planetary waves, and mean circulation. QSPW forcing on the mean wind may not always lead to a reversal of the stratospheric jet or even a minor SSW, but they can still contribute to MLT variability. One such example is the variability around the equinox transition period, when QSPWs start to become strong. Their interaction with the mean flow can lead to short-term changes of wind and temperature in the middle and upper atmosphere, termed “hiccup” by Matthias et al. [2015], with structures similar to SSW but smaller magnitudes.

In addition to QSPWs, traveling planetary waves (TPWs) and equatorial waves (EWs) and their interactions with tides and mean flow can also cause MLT variability. Well-known examples of TPWs and EWs of interest to the MLT include waves with periods of 5–7days (also referred to as 6.5days) [Hirota and Hirooka, 1984; Wu et al., 1994; Talaat et al., 2001, 2002; Lieberman et al., 2003; Sridharan et al., 2008] and quasi-2days [Muller and Nelson, 1978; Rodgers and Prata, 1981; Harris and Vincent, 1993; Wu et al., 1993; Garcia et al., 2005; Pancheva et al., 2006; Hecht et al., 2010] and ultrafast Kelvin waves (UFKW, Kelvin waves with periods of 3–4days) [Salby et al., 1984; Lieberman and Riggin, 1997; Gasperini et al., 2015]. Episodes of strong 5–7day wave and quasi-2day wave (QTDW) are observed in the MLT around equinox (5–7day wave) and around January-February and July-August (QTDW) [Talaat et al., 2001; Wu et al., 1993]. These window periods are determined by the wave propagation (wave guide) and amplification (baroclinic/barotropic instability) conditions, which in turn are dependent on the background winds [Plumb, 1983; Meyer and Forbes, 1997; Salby and Callaghan, 2001; Liu et al., 2004; Yue et al., 2012a]. In particular, the occurrence of QTDW with specific wave number (wave number 2, 3, or 4) may depend sensitively on the background wind conditions [Gu et al., 2016]. UFKWs, on the other hand, do not seem to have a clear seasonal dependence. TPWs may also become large around the time of SSW due to favorable propagation and amplification conditions [McCormack et al., 2009; Chandran et al., 2013; Sassi et al., 2013]. When these planetary waves become large, they can cause tidal variability either through nonlinear wave-wave interaction or through altering the background winds [Palo et al., 1999; Pancheva, 2006; Chang et al., 2011; Forbes and Moudden, 2012; Pedatella et al., 2012; Sassi and Liu, 2014; Moudden and Forbes, 2014].

2.3 Tidal Variability on Day-To-Day Scales

Knowledge of the tidal and planetary-scale wave interaction can enhance predictability of the upper atmosphere, since the planetary-scale waves up to the stratopause are generally well quantified and predicted. For example, the forecast experiment by Wang et al. [2014] demonstrated that the SSW event in 2009 could be captured 1–2weeks prior to the peak warming. Furthermore, since QSPWs and TPWs display clear seasonal dependence, tidal variability from tidal-planetary wave interaction would be seasonally dependent, too. A recent modeling study [Liu, 2014] suggests, however, that there is also an irregular or “stochastic” aspect of the day-to-day tidal variability. As shown in Liu [2014], amplitudes of migrating and nonmigrating tides in the upper atmosphere from the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X) simulations vary significantly from one day to the next. The day-to-day time scale is shorter than the time scales associated with planetary wave variability (weeks to a month), and it appears to be ubiquitous and does not show any clear seasonal, annual, or interannual dependence (while planetary waves usually do). The exact causes of the tidal day-to-day variability in the MLT have not been systematically studied, but there are several plausible ones: (1) It is known from previous studies that the atmosphere is a deterministic chaotic system that displays perpetual stochastic vacillations [Lorenz, 1969] and that the middle/upper atmosphere is no exception [Liu et al., 2009]. It is conceivable that the tidal day-to-day variability is a manifestation of the stochastic whole atmosphere system. (2) Tidal propagation is sensitively dependent on the winds as discussed earlier. Therefore, the tidal variability in the MLT is affected by the variability of global winds along the tidal propagation path from the source region to the MLT. (3) The tidal wave sources, which are often tied to processes in the troposphere weather system, are likely to be variable.

Since predictability is closely tied to the time scales of variabilities, there is a need to better quantify the latter for zonal mean zonal wind and tides in light of the second and third points. One way of measuring the time scales is to compute the correlation time of a quantity by integrating the autocorrelation function. Figure 1 shows the correlation time of the zonal mean zonal wind, as well as the amplitudes of three main tidal components (DW1, SW2, and DE3), calculated using detrended time series of these quantities from a 20-model year simulation of WACCM-X (detailed discussion can be found in Liu [2014]). The correlation time of zonal mean zonal wind is long in the stratosphere (weeks to months) and shorter in the troposphere, mesosphere, and thermosphere (less than 5days at most places). In the stratosphere, the main dynamical driving is by planetary waves, which vary on time scales of weeks or longer, and they are thus responsible for the longer correlation times. Dynamical processes with shorter time scales, on the other hand, becomes predominant in the mesosphere and lower thermosphere, and as a result these regions are more variable. In the upper thermosphere, the short correlation time of zonal mean zonal wind is a result of strong dissipative damping. The correlation time is determined by signal strength and signal variability. It is thus expected that the correlation time should follow the wave amplitude, yet should be reduced by the wave variability. It is seen from Figures 1b–1d that the tidal correlation time in general follows the structure of the respective tidal amplitude in the latitudinal direction but not necessarily in the vertical direction. For example, DW1 amplitude peaks in the lower thermosphere, but its correlation time is the longest in the stratosphere (~18days) and becomes shorter in the MLT (~3days). For DW1, the decrease of correlation time with altitude is consistent with that of the zonal mean zonal wind, which is an indication of the impact of the wind modulation. The correlation times of SW2 and DE3 are more complex, though they are still comparable with or shorter than the correlation time of zonal mean zonal wind in the MLT (maximums of 8days for SW2 and 4days for DE3). The wind modulation thus may still play an important role in the variability of these tides. The stochastic feature and the short correlation time of the diurnal tidal amplitude from the model is consistent with the autocorrelation analysis of observed diurnal tidal amplitudes by Phillips and Briggs [1991], though it is noted that the observations were made at one location (Buckland Park, 35°S, 138°E) and thus include all diurnal components.

Figure 1. Correlation time of zonal mean zonal wind and amplitudes

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Figure 1.

Correlation time (unit: day) of (a) zonal mean zonal wind and amplitudes of (b) migrating diurnal tide (DW1), (c) migrating semidiurnal tide (SW2), and (d) nonmigrating diurnal tide DE3. Contour intervals: Figure 1a: 2days for correlation time up to 10days and then 15, 20, 30, 40, and 50days; Figure 1b: 3days; Figures 1c and 1d: 1day.

The spatial correlation of tidal amplitudes is shown in Figure 2. To elucidate the differences in short and long-term variability, the detrended time series are decomposed into low-frequency components, obtained by smoothing the time series with a boxcar average over 10days, and high frequency components (referred to as day-to-day variable components). Figures 2a–2c are the correlation patterns for the high-frequency components, and Figures 2d–2f are for the low-frequency ones. The correlation coefficients for the day-to-day variable tides decay rapidly, dropping to 0 over a distance of ~20 km in altitude and 20–30° in latitude. Lagged correlation for the day-to-day variable tides has also been examined. With a lag of 2days, the correlation coefficients for DW1 are small (0.15) but still significant down to ~70 km, while the correlation coefficients for SW2 and DE3 are less than 0.1 below 80 km. No significant correlation is found beyond 2days for the day-to-day variability of these tides below 80 km. In contrast, significant correlation is found for long-term tidal variability down to the troposphere (DW1), stratosphere (SW2), and stratopause DE3). The correlation analysis thus suggests that the day-to-day tidal variability in the MLT is indeed more stochastic. It is not directly related to the variability of tidal sources in the troposphere and/or the stratosphere. Long-term tidal variability, on the other hand, is more closely related to tidal source variability.

Figure 2. Spatial correlation coefficients of tidal amplitudes with short-term and long-term variability.

2017 John Wiley & Sons, Inc. All Rights Reserved

Figure 2.

Spatial correlation coefficients of tidal amplitudes with (top row) short-term and (bottom row) long-term variability. (a and d) Migrating diurnal tide (DW1), (b and e) migrating semidiurnal tide (SW2), and (c and f) nonmigrating diurnal tide (DE3). The reference points are at the latitudes where the respective tidal amplitudes maximize in the MLT region. Statistically significant correlation is highlighted with gray shade. Contour intervals: 0.1.

Apart from interaction with a complex global wind system, including various large-scale waves, a potential source of tidal variability is that caused by tidal interaction with gravity waves [Walterscheid, 1981]. Previous studies using either mesoscale models or global scale models have demonstrated the interaction of these two types of waves [Liu and Hagan, 1998; Liu et al., 2000; Ortland and Alexander, 2006; Liu et al., 2014b]. Gravity wave instability and wave breaking, which are modulated by tidal temperature and winds, can lead to momentum deposition and heat flux change and can in turn modify the tidal amplitude and phase. Since the gravity waves are known to be highly variable spatially and temporally, their interaction with tides can be an important source of tidal variability. The variability due to gravity wave-tidal interactions, however, is still poorly quantified. The challenge for self-consistently studying such interaction stems from the large-scale difference between these two types of waves, which will be discussed later.

2.4 Ionospheric Variability Caused by Tides and Other Planetary-Scale Waves

There are several known ways in which atmospheric waves contribute to ionospheric variability. These include wave modulation of E and/or F region wind dynamo, ion-neutral coupling and field-aligned transport in the F region, and transport of neutral species in the mesosphere and lower thermosphere (which in turn affects neutral and plasma densities higher up).

Tidal waves can propagate deep into the middle and upper thermosphere with their large propagating speed and modulate the ionospheric structures. A numerical study by Millward et al. [2001] determined that the ionosphere prereversal enhancement (PRE) depends sensitively on the amplitude and phase of the semidiurnal tide. The variability of the tides can thus contribute to the variability of the ionosphere, as evidenced by observational and numerical studies of the ionosphere during SSW. As discussed earlier, tides undergo large variability during SSW, likely resulting from a combination of tidal-planetary wave and tidal-gravity wave interactions, changes of tidal propagation and resonance conditions, and changes of wave sources. Since tides can penetrate into the upper thermosphere, tidal wind changes lead to changes of the E and F region dynamo. Therefore, even though the QSPWs responsible for SSWs cannot usually impact the ionospheric E or F region, they can have indirect impact by modulating tidal variability [Liu et al., 2010a]. A thorough review of ionospheric variability during SSW is given by Chau et al. [2012].

In addition to affecting the wind dynamo, tides can also affect the ionospheric plasma density through ion-neutral coupling. Wan et al. [2012] and Lei et al. [2014] found that nonmigrating tides directly perturb the thermospheric neutral density. Through ion-neutral coupling, the thermospheric perturbations contribute to ionospheric variability, though these modeling studies found that this contribution is minor compared with the variability due to the wind dynamo.

Numerical studies by Müller-Wodarg and Aylward [1998], Yamazaki and Richmond [2013], and Jones et al. [2014] have found that tides can affect the transport in the mesosphere and lower thermosphere, mainly by changing the meridional and vertical circulation, and thus affect the thermospheric and ionosphere compositions. As shown by a recent numerical study by Pedatella et al. [2016], the MLT circulation change caused by short-term tidal change during SSW can be comparable or even dominant over the circulation changes caused by gravity wave forcing [Miyoshi et al., 2015]. The net effect is to decrease atomic oxygen and increase molecular oxygen and nitrogen, similar to enhanced eddy diffusion. The change extends to the upper thermosphere by the effective molecular diffusion. Although these numerical studies have focused on migrating diurnal and semidiurnal tides, the same mechanism is likely to be applicable to other migrating and also nonmigrating components with significant amplitudes in the MLT. On the other hand, tidal modulation of atomic oxygen recombination rate [Akmaev and Shved, 1980; Forbes et al., 1993] is found to be insignificant in the MLT region, probably due to the very long chemical lifetime of atomic oxygen therein [Yamazaki and Richmond, 2013].

TPWs can also cause ionospheric variability. Chen [1992] identified 2day oscillations in the equatorial ionization anomaly and proposed that it is caused by QTDW modulation of dynamo and field-aligned transport. Oscillations of F region electron density with periods 2 and 6.5days are identified from midlatitude ground-based observations and are assumed to be caused by TPWs [Altadill and Apostolov, 2001]. Analyses of concurrent neutral dynamics and ionospheric observations provided further evidence linking planetary-scale waves with ionospheric variability: Pancheva et al. [2006] related QTDW observed in MLT neutral wind measurements to quasi-2day oscillations of both F region electron density and ionospheric current; Gu et al. [2014] found clear correspondence between 6day wave from temperature and wind measurements by Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) and TIMED Doppler Interferometer (TIDI) on NASA’s Thermosphere Ionosphere Mesosphere Energetics Dynamics (TIMED) satellites with 6day oscillation in total electron content (TEC). Ionospheric variabilities have also been related to TPWs (and also UFKWs) with periods between 5 and 6days identified in MLT neutral winds and in National Centers for Environmental Prediction (NCEP) reanalysis data [Pancheva et al., 2008]. As noted by Pancheva et al. [2006], the ionospheric oscillations sometimes do not match neutral atmosphere TPWs exactly in the timing of the peak amplitudes or zonal wave number. For such events, it is possible that TPWs indirectly impact ionospheric variability, especially the F region, by modulating variability of tides, which can penetrate to higher altitudes.

Numerical studies have further elucidated the mechanisms how TPWs affect ionospheric wind dynamo. Numerical studies by Yue et al. [2012a, 2012b] found that although QTDWs can reach large amplitudes near the summer mesopause due to baroclinic/barotropic instability, they tend to decrease rapidly with altitude and thus do not significantly perturb E region dynamo. On the other hand, QTDWs can gain large amplitude in the equatorial lower thermosphere/E region as they propagate upward through the wave guide. This branch of the QTDWs, as shown in Yue et al. [2012b], perturbs the ionospheric electric potential, E × B drift, and electron density. The numerical simulation, however, found no evident tidal modulation by QTDW. Furthermore, since QTDWs originate from the winter lower atmosphere, they are likely to nonlinearly interact with the QSPWs. One of the child waves from the QTDW wave number 3 (W3) and QSPW wave number 1 interaction is QTDW wave number 2 (W2), and it has been found to become large in the MLT region and ionosphere [Pancheva et al., 2006; Tunbridge et al., 2011]. Numerical experiments by Gu et al. [2015] demonstrated that W2 penetrates to higher altitudes (probably due to its larger phase speed) and that W2 and W3 can have comparable contributions to the ionospheric E × B drift. UFKWs are also shown to propagate to high altitudes: a numerical study by Chang et al. [2010] found that a strong UFKW can be quite strong in the F region. The study determined that the UFKW affects ionosphere variability mainly through the ionospheric wind dynamo. This is consistent with the observation that UFKWs can affect the development of spread F by modulating the vertical drift [Abdu et al., 2015].

Like tidal waves, TPWs may affect thermospheric and ionospheric variability by species transport between the mesosphere and thermosphere. A numerical study by Yue and Wang [2014] found that the dissipation of QTDW in the MLT region leads to circulation change, which in turn enhances the vertical down-gradient transport and mixing near the turbopause. As a result, thermosphere O/N2 and F2 peak electron density decreases. Observational evidences of TPW effects on the transport have been provided from analysis of concurrent TIDI, Global Ultraviolet Imager (GUVI), and TEC measurements for multiple episodes of QTDW events [Chang et al., 2014] and 6.5 day wave events [Gan et al., 2015]. Both studies found that the column integrated O/N2 and TEC decrease during strong TPW events. The percentage TEC changes from both studies range between 15 and 25%, while the column integrated O/N2 changes are different (~6% in the case of QTDW and 16–24% in the case 6.5 day wave). These are in general agreement with model results by Yue and Wang [2014]. And they are also similar in magnitude to the changes caused by tidal waves [e.g., Yamazaki and Richmond, 2013]. Recent numerical studies using WACCM-X by Sassi et al. [2016] found that during periods of strong TPWs events (with periods between 3 and 10days), the dissipation of TPWs in the lower thermosphere reinforces the mean meridional circulation with stronger upwelling in the tropics and downwelling at high latitudes. This, along with fast molecular diffusion, reduces the O/N2 and neutral density at high latitudes while slightly increasing these quantities at low latitudes. It should be noted that electrodynamics are not considered in this simulation.

TPWs in the winter hemisphere can propagate equatorward and break at subtropical surf zone in the lower thermosphere, as shown by Sassi et al. [2016]. In addition to causing mean flow acceleration, the wave breaking also results in a meridional, down-gradient mixing of constituents. Although this horizontal diffusion is largest below 110 km, the effects on the compositional structure are shown to be significant throughout the thermosphere due to the strong molecular diffusion.

As discussed earlier, there is a stochastic aspect of tidal day-to-day variability, which is ubiquitous and independent of season. Its implication for the ionospheric day-to-day variability was explored by Liu et al. [2013]. Hourly wind and temperature outputs from WACCM-X, which displays large day-to-day tidal variability as shown in Liu [2014], were used to constrain (often referred to as nudging) the Thermosphere-Ionosphere-Mesosphere-Electrodynamics general circulation model (TIME-GCM) up to ~95 km. As a result of this nudging, migrating and nonmigrating tides in TIME-GCM also show large day-to-day variability with the standard deviation at 25% to 50% of the wave amplitudes. With solar forcing held at a constant minimum condition and geomagnetic forcing at a constant quiet condition, the model produces remarkable day-to-day variability in vertical and zonal E × B drifts and F2 peak electron density (NmF2). The standard deviations of the drifts and NmF2 from the model show clear local time and longitudinal dependence that are consistent with observations. The magnitudes of the standard deviation are 50% or more of those obtained from observations, consistent with the finding by Rishbeth and Mendillo [2001] that the meteorological driving may contribute comparably with geomagnetic forcing to the IT day-to-day variability. A numerical study by Fang et al.[2013] reached a similar conclusion with regard to tidal forcing and ionospheric longitudinal and day-to-day variability.

The simulated stochastic day-to-day ionospheric variability is reminiscent of the stochastic variability of the ionospheric current system observed by Briggs [1984]: the variations of the ionospheric current system seem to be random and are almost uncorrelated from one day to the next. Because the ionospheric wind dynamo and current system are determined by global winds in the thermosphere (weighted by electric conductivities), the lack of global coherence in tidal day-to-day variability, as shown in Figures 2a–2c, implies that the ionospheric day-to-day variability would be stochastic and would not correlate well with tidal variability at a single location. This is consistent with the observational finding by Phillips and Briggs [1991], that there is a lack of correlation between the variability of the ionospheric currents and the variability of tidal winds measured at Buckland Park, 35°S, 138°E. Yamazaki et al. [2014] demonstrated that the observed day to day of the EEJ can indeed be reproduced from TIME-GCM simulations when the model is constrained by realistic meteorological forcing up to 95 km.

Tidal waves thus play a key role in the thermosphere and ionosphere system, by directly perturbing winds, temperature, electrodynamics, and composition. Their day-to-day variability is thus important in studying the weather of the thermosphere/ionosphere system. Possible causes of the tidal day-to-day variability include dependence of wave propagation on background wind and temperature along the wave path, interactions with planetary waves and gravity waves, and variability of wave sources. In particular, the short autocorrelation time of background wind (Figure 1) and rapid decrease of spatial correlation of day-to-day tidal variability (Figures 2a–2c) demonstrate the challenge of deterministic calculation of tidal day-to-day variability from their sources and the need for constraining the whole atmosphere background.

3 Upper Atmosphere Predictability as Related to Lower Atmosphere Forcing

Climate and weather models are known to display features of deterministic chaos: any initial error will grow exponentially over time, due to complex feedback processes involving nonlinearity and/or instability in the system [e.g., Lorenz, 1963, 1969; Kalnay, 2003]. The error growth fundamentally limits the forecast capability of the models. It is thus important to study the predictability of the model systems, especially the rate of the error growth and processes determining the error growth, to design and optimize data assimilation schemes to improve forecast skills. Some middle and upper atmosphere models, such as the NCAR Thermosphere-Ionosphere-Mesosphere-Electrodynamics general circulation model (TIME-GCM), show little or no error growth, indicating that nonlinearity does not necessarily lead to chaos. Actually, a recent study by Shen [2014] found that nonlinear interaction between modes (as well as parameterized nonlinear eddy dissipation) can improve stability and predictability of a system. On the other hand, stratosphere and stratosphere-mesosphere simulations have shown vacillations between two different flow regimes when the specified tropospheric planetary wave forcing becomes large enough [Holton and Mass, 1976; Yoden, 1987a, 1987b; Scaife and James, 2000]. Using the UK Met Office (UKMO) Stratosphere and Mesosphere Model (SMM), Gray et al. [2003] performed ensemble simulations with different levels of tropospheric forcing, and they found that the error growth becomes more pronounced with increasing tropospheric forcing. This suggests that wave-mean flow interaction can be important for the error growth in the stratosphere and mesosphere. The error growth in the whole atmosphere context was examined by Liu et al. [2009] using WACCM. By performing identical twin type of experiments, with errors artificially introduced in the initial conditions, they found that the model results start to show clear deviation after 10–20days and that errors become comparable to internal model variability after 40–50days, regardless of the magnitude or scale of the initial error. The time scales are much shorter than those found in the SMM simulations by Gray et al. [2003] (50days and 100days, respectively). Liu et al. [2009] also found that the error growth is dependent on season and altitude: the largest error growth occurs in the winter stratosphere and both winter and summer MLT, and the smallest error growth in the summer stratosphere. To determine the role of the tropospheric forcing (as compared with wave-mean flow interaction) in error growth, one of the WACCM simulations with imperfect initial condition is repeated, but this time its troposphere is replaced every certain time (?t) by the base case (“truth”) simulation. It is found that if ?t is set to 1day or 5days, shorter than the characteristic error growth time (10–20days), the error in the middle and upper atmosphere in this simulation shows little growth (Figure 3). This study thus demonstrates that the troposphere plays a key role in controlling the error growth of the middle and upper atmosphere models. This has been confirmed later by a WACCM data assimilation experiment using the data assimilation research testbed (DART) ensemble adjustment Kalman filter and synthetic observations that are generated by sampling a WACCM simulation at the location of real observations [Pedatella et al., 2013]: Using data assimilation to constrain the lower atmosphere reduces the global root mean square error (RMSE) in zonal wind by up to 40% at MLT altitudes. Whole-atmosphere simulations with the lower atmosphere constrained either by data assimilation or nudging to reanalysis results during SSW periods have been able to reproduce tidal and ionospheric variabilities that agree with observations [e.g., Fuller-Rowell et al., 2011; Jin et al., 2012; Wang et al., 2014; Pedatella et al., 2014a].

Figure 3. RMS error in zonal wind between two identical twin WACCM simulations

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Figure 3.

Line a: RMS error in zonal wind between two identical twin WACCM simulations, with case “B” being the base case (“truth”) simulation and small perturbations introduced in the initial condition for case “A”. Lines b–d: RMS error in zonal wind in “hybrid” simulations with initial conditions from case A above pressure level ps on 10 December, model reinitialized by results from case B below ps every 24h, and ps set to 50hPa, 500hPa, and 700hPa, respectively. Lines e and f: similar to line b but the reinitialization period set to 5days and 20days, respectively. This demonstrates the control of error growth in the middle and upper atmosphere by the lower atmosphere in WACCM [Liu et al., 2009] (©American Meteorological Society. Used with permission).

Several other studies, however, exposed model biases in the mesosphere and thermosphere even when the troposphere and stratosphere are well constrained, mainly due to the incorrect representation of the gravity wave effects. Intercomparisons among four leading whole atmosphere models, GAIA (Ground-to-topside model of Atmosphere and Ionosphere for Aeronomy), HAMMONIA (Hamburg Model of the Neutral and Ionized Atmosphere), WAM (Whole Atmosphere Model), and WACCM-X, by Pedatella et al. [2014c] found that although the atmosphere below the stratopause is virtually identical among the four models for a targeted time period (January and February 2009) from data assimilation or nudging, the structure and temporal evolution of mean wind, temperature, and planetary waves in the mesosphere and thermosphere are significantly different in both hemispheres among the model results, as shown in Figure 4. For the zonal mean zonal wind, the height and strength of the wind reversal in the MLT region differ from model to model. GAIA, HAMMONIA, and WACCM-X all produced elevated stratopause following the peak SSW, but the heights, magnitudes, and descending rates of the elevated stratopause are different, while no clear elevated stratopause is seen in WAM simulation. SW2, which has been shown to strongly affect ionospheric variability, is qualitatively similar in these simulations, with a rather strong decrease around peak SSW, followed by a large increase. But the exact timing of the changes, as well as the wave amplitudes, are different in the simulations. Analysis of the model results suggests that differences in effective gravity wave forcing in the MLT are a major cause of the model differences in the mesosphere and thermosphere. This is hardly surprising, since gravity wave forcing is a major driver of MLT dynamics and there exist significant differences and uncertainties in the underlying physics and tuning among different parameterization schemes. Furthermore, Pedatella et al. [2014b] found that by only assimilating real observations of the lower atmosphere, WACCM-DART system does not improve the MLT temperature when compared with SABER and Microwave Limb Sounder (MLS) observations. A recent study by Siskind et al. [2015] also found that the WACCM simulation with its dynamics constrained up to 92 km by the Navy Operational Global Atmospheric Prediction System-Advanced Level Physics High Altitude (NOGAPS-ALPHA) yields a dramatic improvement in the simulated descent of enhanced nitric oxide and very low methane, over similar WACCM simulation but constrained up to 50 km by Modern-Era Retrospective Analysis for Research and Applications (MERRA). These appear to contradict previous results by Liu et al. [2009] and Pedatella et al. [2013]. It is noted, however, that in reaching the conclusion that constraining the lower atmosphere helps limiting the error growth in the middle and upper atmosphere, the underlying assumption in “identical twin” experiments by Liu et al. [2009] and Observation Simulation System Experiment (OSSE) by Pedatella et al. [2014a] is that the model physics is complete. This is certainly not the case: Subgrid processes, such as gravity waves and turbulence, are missing or poorly represented in these whole atmosphere models. On the other hand, it has been found that by better resolving gravity waves, the predictability of the stratosphere and mesosphere is enhanced [Ngan and Eperon, 2012]. To improve the representation of the upper atmosphere, therefore, it is necessary to better constrain the model by assimilating upper atmosphere observations, to improve gravity wave parameterization scheme, and/or to directly resolve gravity waves in the model.

Figure 4. Zonal mean zonal wind averaged from 1 January to 20 February 2009

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Figure 4.

Zonal mean zonal wind averaged from 1 January to 20 February 2009 for (a) GAIA, (b) HAMMONIA, (c) WAM, and (d) WACCM-X. GAIA, HAMMONIA, and WACCM-X are constrained by various reanalysis products up to 12hPa, 1hPa, and 0.002hPa, respectively, while WAM assimilates the standard lower atmosphere observations that influence model results up to 0.1hPa. The background states of the four models thus agree at least up to 12hPa. The zonal winds at higher altitudes, however, are notably different among the four models [Pedatella et al., 2014c] (©American Geophysical Union. Used with permission).

4 Importance of Mesoscale Processes for the Upper Atmosphere Variability and Predictability

Gravity waves (GWs) become increasingly important with height, primarily due to the exponential growth of wave amplitudes with the density decrease, as well as their ubiquity in the global atmosphere. Apart from causing large atmosphere perturbations, GWs can deposit wave momentum and interact with large-scale flows when dissipated, due to either instability or molecular damping. As a result, GWs can transfer momentum from their source region to their impact region, making them an important agent for lower and upper atmosphere coupling. GW instability and GW dissipation can also induce turbulent mixing and transport of heat and constituents. A comprehensive review of GWs can be found in Fritts and Alexander [2003]. GWs, especially the high-frequency components and secondary GWs from the dissipation of primary waves, can penetrate into the thermosphere and cause thermospheric and ionospheric disturbances [e.g., Hines, 1960; Vadas and Fritts, 2001; Vadas, 2007]. For example, one of the main motivations of the seminal paper by Hines [1960] was to explain traveling atmospheric disturbances (TADs) and traveling ionospheric disturbances (TIDs). Observed TIDs have been directly related to wave sources in the lower atmosphere, including deep convection, earthquake, and tsunami [e.g., Tsugawa et al., 2011; Nishioka et al., 2013; Azeem et al., 2015; Huba et al., 2015; Meng et al., 2015]. GWs are thought to drive ionospheric irregularities, such as the midlatitude sporadic E layer (Es) (probably along with semidiurnal tides) and equatorial spread F (ESF) [e.g., Kelley et al., 1981; Mathews, 1998; Haldoupis, 2012; Krall et al., 2013b]. They may thus play an important role in ion-neutral coupling and space weather applications. It is noted that wave sources that are capable of generating GWs can also excite acoustic waves at frequencies higher than the buoyancy frequency. Like GWs, the acoustic wave amplitudes increase with altitude and can thus cause large perturbations in the upper atmosphere [e.g., Walterscheid et al., 2003; Zettergren and Snively, 2013]. The acoustic waves can contribute to the thermospheric energetics through viscous heating [e.g., Hickey et al., 2001; Walterscheid and Hickey, 2005]. Due to their short temporal scales, direct observation of acoustic waves and assessment of their global impact are challenging. In global models, they are poorly represented due to limited spatial and temporal resolution, as well as the hydrostatic approximation employed by many.

4.1 Challenges to Quantifying Gravity Waves

GWs are a challenging multiscale problem: The wave spatial scales are determined by the many different sources with scales ranging from kilometers to thousands of kilometers and intrinsic wave periods between the buoyancy and inertial periods; the processes they impact range from turbulence to planetary-scale waves and general circulation. They are also highly variable, due to the variability of sources and the sensitive dependence of wave propagation on wind and temperature structures. To account for the broad range of scales and the variability is a stiff challenge for observations and global simulations. Ground-based observations, such as radar, lidar, all-sky imagers, radiosonde, and rockets, have been used to infer GW characteristics, seasonal variation, and vertical profiles of momentum fluxes [e.g., Fritts and Alexander, 2003]. Observations at single sites usually provide relatively high cadence and have been used for time/frequency domain analysis. They are generally limited in the spatial coverage and thus in resolving spatial scales of the waves, though they can be expanded by employing observational networks. For example, GW and their source information over North America have been deduced from U.S. radiosonde measurements [e.g., Wang and Geller, 2003; Gong and Geller, 2010]. Characteristics of gravity waves in the mesosphere, thermosphere, and ionosphere have been determined using a network of all-sky imagers by Shiokawa et al. [2000, 2009]. Concentric wave structures in total electron content (TEC) over Japan following the 2011 Tohoku Earthquake have been mapped with high resolution using a dense GPS receiver network by Tsugawa et al. [2011]. Using the first no-gap hydroxyl (OH) airglow all-sky imager network (consisting of six imagers), Xu et al. [2015] were able to measure multiple and complex GW activity in a contiguous area of 2000 km × 1400 km (east-west/north-south) over China. Extended spatial coverage of GWs has also been provided by airborne measurements, such as the long-duration balloon measurements (VORCORE over Antarctica [Hertzog et al., 2008]) and aircraft measurements (DEEPWAVE over New Zealand and Tasmania [Fritts et al., 2016]). For global coverage of GWs, analyses of satellite observations have provided increasingly comprehensive information on the global distribution and seasonal variation of GWs, including their energy density and absolute momentum flux, from the stratosphere to the lower thermosphere [Fetzer and Gille, 1994; Dewan et al., 1998; Tsuda et al., 2000; Eckermann and Preusse, 1999; Ern et al., 2004; Alexander et al., 2008; Wu and Eckermann, 2008; Hoffmann and Alexander, 2009; Ern et al., 2011; Gong et al., 2012; Sakanoi et al., 2011; Alexander, 2015; Miller et al., 2015]. Directional GW momentum fluxes in the stratosphere have been deduced recently by Wright et al. [2016], by combining the near-coincident Atmospheric Infrared Sounder (AIRS) and MLS measurements and taking advantage of their high horizontal and vertical resolution, respectively. These observations are helping better constrain parameterization schemes used in climate models [Alexander and Barnet, 2007; Alexander et al., 2010]. A recent review and intercomparison of various measurements of GWs (and comparisons with parameterized and resolved GWs from general circulation models) was given by Geller et al. [2013]. These measurement techniques are sensitive to different temporal and spatial scales, as discussed by Alexander and Barnet [2007].

In most general circulation models, GWs are either poorly resolved or not resolved at all, so the wave effects on the large-scale flows need to be parameterized. GW parameterization schemes for the stratosphere and mesosphere are reviewed by McLandress [1998], and Alexander et al. [2010] reviewed how the parameterization schemes could be better constrained by observations. By accounting for molecular dissipation, a few of the schemes have been extended into the thermosphere [Garcia et al., 2007; Yigit et al., 2008]. Dissipation of GWs in the thermosphere can lead to excitation of secondary GWs and has been studied by Vadas [2007]. With proper tuning, the parameterization schemes have been able to reproduce wind reversals and MLT temperature structures, mesosphere semi-annual oscillation (SAO), and QBO. Recent development of parameterization schemes starts to account for more realistic wave sources as related to deep convection and frontal systems, intermittency, and oblique wave propagation [e.g., Charron and Manzini, 2002; Richter et al., 2010; Kalisch et al., 2014], though there remain large uncertainties in specifying wave strength and spectral content. These in turn lead to uncertainties and biases in the models, as discussed in the previous section.

In spite of differences in physical assumptions and uncertainties of parameters involved, all parameterization schemes seek to quantify momentum deposition and eddy diffusion associated with GW dissipation. These partially address the needs to study coupling across scales and between the lower and upper atmosphere through GWs. The parameterization schemes do not, however, quantify processes that are dependent on GW perturbations, such as ionosphere ion-neutral coupling through ionospheric wind dynamo processes, modulation of polar stratospheric and mesospheric clouds, and temperature-sensitive chemical processes. Numerical simulations by Vadas and Liu [2013] and Liu and Vadas [2013] demonstrated that secondary GWs could cause large thermosphere and ionosphere perturbations, but the numerical model could only capture secondary waves with horizontal wavelengths larger than ~1000 km. The ability to study ionospheric impact by GWs with smaller scales is fundamentally limited in such model simulations.

4.2 Progress in Mesoscale-Resolving Global Models

In recent years increasing computing power has afforded increasing spatial resolution of general circulation models, including whole atmosphere models. For numerical weather prediction models, it is clearly imperative to resolve down to mesoscales. Current operational models in leading weather centers have reached down to ~10 km and the order of 1 km with nesting in their horizontal resolution. It is shown that tropical precipitation and tropical circulation improve with increasing model resolution [e.g., Lean et al., 2008; Jung et al., 2012]. There have also been exploratory efforts to increase resolution of climate models. The study by Bacmeister et al. [2014] using the NCAR CAM with quarter-degree resolution found that the model produces realistic tropical cyclone distributions and interannual variability.

Given the increasing significance of GWs with altitude, global models with extended vertical domain can benefit from increasing resolutions. Hamilton et al. [1999] demonstrated that the SKYHI model with higher spatial resolution (35 km horizontally with 160 levels between surface and 85 km) reduced the cold bias during early winter in the southern polar stratosphere and produced a QBO-like oscillation in the equatorial stratospheric zonal mean zonal winds. Koshyk et al. [1999] found that the divergence flow (largely associated with resolved GWs) in middle atmosphere models grew more rapidly with altitudes than the rotational flow above the lower stratosphere, and as a result the energy spectra became shallower at higher altitudes. Using an aquaplanet spectral model with triangular truncation at 106 of the spherical harmonic expansion and 63 vertical levels, or T106/L63 (corresponding to ~120 km horizontal resolution at the equator and 600m vertical resolution), Sato et al. [1999] resolved stratospheric inertial GWs with characteristics that are similar to observations. They also identified downward wave energy propagation in the winter stratosphere, likely from GW generation from the stratosphere polar night jet. Watanabe et al. [2008] developed a middle atmosphere GCM with T213/L256 spatial resolution (~62.5 km at the equator and 300m vertically) and demonstrated that the resolved waves drive a QBO-like oscillation in equatorial stratospheric zonal winds (with a period of ~15 months), and confirmed the vertical change of kinetic energy spectrum toward a shallower structure at high altitudes, as found by Koshyk et al. [1999]. The Japanese Atmospheric General circulation model for Upper Atmosphere Research (JAGUAR) is an extension of this middle atmosphere GCM to ~150 km and has been used to study GW forcing on the tides [Watanabe and Miyahara, 2009]. By using a whole atmosphere GCM that extends to the upper thermosphere with 1.1° × 1.1° horizontal resolution, Miyoshi et al. [2014] showed that the resolved GWs are significant in the thermosphere and that they are modulated by semidiurnal tides (between 100 and 200 km altitude) and diurnal tides (above 200 km). By analyzing results from the ECMWF T799/L91 operational forecast for the 2009 SSW period, Yamashita et al. [2010] found significant GW variability in the winter stratosphere associated with changes of the polar night jet and planetary waves during that time period. The GW excitation by the polar night jet is consistent with findings by Sato et al. [1999].

Recently, Liu et al. [2014a] developed a version of WACCM with horizontal resolution of ~25 km and 209 levels between the Earth surface and ~145 km, enabling the model to effectively resolve waves down to mesoscales (~200 km). GW energy density and its latitude-height structure from the simulations are in general agreement with those obtained from SABER measurements [Ern et al., 2011]. Figure 5 shows zonal mean GW momentum fluxes, averaged over January and July, calculated from this WACCM simulation. They are in general agreement with the momentum fluxes deduced from various satellite measurements [Geller et al., 2013]: a momentum flux maximum is found at middle to high latitudes in the winter hemisphere, especially in the Southern winter hemisphere, and a secondary maximum at subtropical latitudes in the summer hemisphere. The former is likely associated with GWs excited along storm tracks, over orography, and adjustment of stratospheric jet, while the latter is probably from tropospheric deep convection. The latter maximum in the summer hemisphere shifts to higher latitudes at higher altitudes and becomes comparable to and even larger than the winter peak. These features are consistent with the SABER analysis [Ern et al., 2011]. It is also noted from both observations and the simulations that momentum fluxes decrease rapidly with altitude, indicating continuous “peeling off” of the GW components with increasing altitudes. The MLT region is highly dynamic with shorter temporal and spatial scales and larger perturbation magnitudes compared with the lower atmosphere, which is consistent with the findings by Koshyk et al. [1999] and Watanabe et al. [2008] with regard to the vertical variation of energy spectra. The zonal forcing by resolved waves closes the jet in the summer mesosphere but at altitudes higher than climatology. In the winter mesosphere, the jet is slowed down but barely closed. It is thus still necessary to parameterize the forcing of GWs with scales smaller than 200 km. This is consistent with the findings by Hamilton et al. [1999] and Siskind [2014]. Siskind [2014] performed NOGAPS-ALPHA simulations with three different horizontal resolutions (T79, T239, and T479) and found that the vertical momentum fluxes increase significantly with spatial resolution (thus resolving greater fraction of GW spectrum), and the wave forcing does not yet converge at T479. As a result a cold bias at the winter stratopause and warm bias at the summer mesopause remain. It is not clear if a mesoscale-resolving whole atmosphere model with current GW parameterization schemes improves forecast skills over its coarse-resolution counterpart, but it is conceivable that with the wave spectrum to be parameterized being reduced by increasing resolution, the overall uncertainty and bias are reduced. The spatial and temporal variability are also better represented with increasing resolution.

Figure 5. Zonal mean momentum flux averaged over January and July at 30, 50, 70, and 90 km, calculated from mesoscale-resolving WACCM

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Figure 5.

Zonal mean momentum flux averaged over (left column) January and (right column) July at 30, 50, 70, and 90 km, calculated from mesoscale-resolving WACCM. Solid lines: averages of absolute momentum flux, including both zonal and meridional components; dotted lines: averages of signed zonal momentum flux; dash line: averages of signed meridional momentum flux.

As mentioned earlier, momentum deposition is one of the reasons why GWs are important for the upper atmosphere. Another reason is the large wave perturbations, which affect chemistry, dynamics, and electrodynamics. In the mesoscale-resolving WACCM simulations, although the resolved waves are still not sufficient for driving the observed circulation in the middle and upper atmosphere, the perturbations associated with these waves are much larger than those from coarser resolutions. Figures 6a and 6b are the probability density functions (PDFs) of horizontal winds and the vertical shear of these winds, respectively, in the equatorial lower thermosphere from the mesoscale-resolving WACCM simulations (solid lines) and those from ~2° resolution WACCM simulations (dotted lines). It is evident that the mesoscale-resolving simulation results have much larger dynamical ranges than the coarser resolution results. The vertical profiles of the shears (Figure 6c) at the equator show a clear maximum in the lower thermosphere with values exceeding 100ms-1 km-1. The magnitude and the location of the large shears are in good agreement with previous observations [Larsen, 2002] and could not be reproduced in coarser resolution models. As discussed earlier, the large winds and shears in the lower thermosphere have important implications for the stability, transport, and E region electrodynamics. By comparing the power spectrum densities from WACCM simulations from these two different resolutions (Figure 7), it is seen that the two generally agree at about zonal wave number 10 and lower at all three levels (the stratosphere, mesosphere, and lower thermosphere). At higher wave numbers, the power from ~2° resolution WACCM drops rapidly as compared to the mesoscale-resolving model. This suggests that ~2° resolution WACCM should be able to resolve most tidal waves and planetary waves, while processes with zonal scales of several thousand kilometers and less (including inertial gravity waves) will be underestimated. Due to the increasing significance of smaller-scale processes at higher altitudes, this underestimation becomes more severe in the MLT. From Figures 6 and 7 it is also clear that the smaller-scale processes are responsible for the large winds and shears in the MLT region.

Figure 6. Probability density function of horizontal winds and vertical shear of the horizontal winds at equatorial latitudes

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Figure 6.

Probability density function (PDF) of (a) horizontal winds and (b) vertical shear of the horizontal winds at equatorial latitudes and ~100 km from the mesoscale-resolving WACCM (solid) and WACCM with ~2° horizontal resolution. (c) Vertical profiles of the vertical wind shears at equatorial latitudes from the mesoscale-resolving WACCM.

Figure 7. Power spectrum densities (PSDs) of zonal wind over zonal wave number in the stratosphere, mesosphere, and lower thermosphere

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Figure 7.

Power spectrum densities (PSDs) of zonal wind over zonal wave number in the stratosphere, mesosphere, and lower thermosphere from the mesoscale-resolving WACCM (black lines) and WACCM with ~2° horizontal resolution (gray lines). The PSDs have been normalized by their respective values at wave number 1.

Apart from controlling the MLT circulation and causing large perturbations in winds and shears, GWs can also affect thermospheric energetics. Figure 8 shows the monthly mean, globally integrated total wave energy flux (including both that due to pressure work and advection of kinetic energy) over a year, calculated from the mesoscale-resolving WACCM model. At 20 km, the total wave flux is between 4 and 7 × 1012 W (TW), and at 100 km the total power drops to 100–150 × 109 W (GW). The near-two-decade drop in energy flux is generally consistent with the change of gravity wave kinetic energy density as measured by Balsley and Garello [1985]. The energy flux in the lower thermosphere is comparable to the daily average Joule power input to the upper atmosphere [Knipp et al., 2005]. Since the mesoscale-resolving WACCM does not capture the full wave spectrum, these values are likely underestimated by the model. It is also noted that the monthly mean total flux shows a semiannual cycle above the tropopause, with peaks in March and November in the stratosphere and January and July in the MLT.

Figure 8. Monthly mean, globally integrated total wave flux of energy, including both pressure work and advection of kinetic energy, over one year

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Figure 8.

Monthly mean, globally integrated total wave flux of energy, including both pressure work and advection of kinetic energy, over 1year, calculated from mesoscale-resolving WACCM. Log10 scale is used, and the unit is Gigawatts (109 W).

These progresses of mesoscale-resolving global models in producing GWs features similar to observations are encouraging, though understanding, quantifying and forecasting ionosphere and thermosphere system down to mesoscales will remain to be a challenging problem. A mesoscale-resolving whole atmosphere model with self-consistent ionospheric electrodynamics is not yet available and will require significant computing resources. Idealized GWs and GWs from regional high-resolution models have been used to drive ionosphere and plasmasphere models [Krall et al., 2013a, 2013b, Hysell et al., 2014; Wu et al., 2015], and it is found that GW wavelength and geometry, propagation direction with respect to the Earth magnetic field lines, spatial distribution, and their phase structure can affect the initiation of ESF. These findings suggest that the predictability of the GWs in the upper atmosphere have important implications for the predictability of ESF, and it will be important to explore what the effects are of a complex global wave system on the ionosphere and thermosphere system and how sensitive they are to different wave spectra.

The predictability of the upper atmosphere associated with mesoscale and GWs is a research area largely to be explored. Further progress will depend on better quantification of global GW distribution and temporal variability and better insights in the scale coupling and ion-neutral coupling. Novel development of numerical modeling, especially whole atmosphere modeling with mesh refinement capability, will provide valuable tools for the study. Lower and upper atmosphere observations can be assimilated into a whole atmosphere model to improve the specification and forecast of the upper atmosphere, as demonstrated by Polavarapu et al. [2005a], Hoppel et al. [2008], Eckermann et al. [2009], Pedatella et al. [2013, 2014b], and Wang et al. [2014]. However, whole atmosphere data assimilation also poses new challenges, as discussed by Polavarapu et al. [2005b], especially when mesoscale processes are better resolved. Adjustment during the assimilation step can generate spurious gravity waves, which albeit small locally [e.g., Yamashita et al., 2010] can attain large amplitudes in the upper atmosphere. If not treated properly, such waves will likely degrade the forecast skills across scales in the upper atmosphere.

5 Summary

Statistical analysis of ionospheric observations suggested that lower atmosphere forcing can contribute significantly to its variability (up to ~35%), and observations, especially during the recent solar minimum, have provided further and more direct evidence of lower and upper atmosphere coupling. Numerical simulations with realistic lower atmosphere variability corroborate the statistical results and have been used to investigate pathways of the coupling and its implications for the predictability of the space environment.

Atmospheric tides can cause perturbations from the lower thermosphere/ionospheric E region up to the thermosphere and ionosphere F region. They can modulate the ionospheric wind dynamo by directly perturbing winds in the dynamo region. They also affect the neutral and plasma densities, both through direct wave perturbations, and by inducing transport between the mesosphere and thermosphere. Tidal perturbations of the neutral and plasma densities can affect the response of the thermosphere and ionosphere to the geomagnetic forcing. In addition to migrating components, nonmigrating tides and lunar tides are recognized to play an important role in the upper atmosphere.

Tides are highly variable with temporal scales ranging from day to day to interannual. Tidal variability is likely caused by a combination of variability of wave sources, propagation in and interaction with variable atmospheric wind and temperature structures, and nonlinear interaction among tides, planetary waves, and gravity waves. Numerical simulations of tidal variability associated with ENSO, QBO, and semiannual variations are consistent with observations. Short-term tidal (including lunar components) variability and their thermosphere and ionosphere impact, for example during SSW, have been quantified quite well in comparison with observations. Both observations and numerical models have shown that tides can change significantly from day to day. The day-to-day tidal variability is a ubiquitous feature, is more persistent, and has shorter time scales than suggested by tidal-planetary wave interactions. It appears to be stochastic and characterized by short autocorrelation time (a few days) and distance (tens of kilometers in the vertical direction). It may be related to the vacillation of the atmosphere system, though the exact causes and its implication for upper atmosphere predictability need further elucidation.

Planetary waves and equatorial waves may propagate up to the lower thermosphere and E region ionosphere. Like tides, they can impact the thermosphere and ionosphere system by affecting the transport between the mesosphere and thermosphere and perturbing the ionospheric E region dynamo. Generally, they cannot propagate beyond the lower thermosphere due to strong molecular dissipation (and also critical layer filtering), but tidal waves propagating deep into the thermosphere and ionospheric F region could be modulated by planetary waves-tidal interactions. Planetary waves have been shown to modulate vertical and meridional transport in the mesosphere and lower thermosphere.

Gravity waves become increasingly important in the upper atmosphere, because of their global presence, their increasing amplitudes with altitude, and their fast propagation speeds (which makes them less vulnerable to molecular dissipation). Dissipating gravity waves deposit momentum flux and affect the mean circulation and large-scale waves. They can induce wave and turbulent fluxes of heat and constituents and alter the thermal and compositional structures of the upper atmosphere. The large wave perturbations cause thermospheric and ionospheric disturbances (e.g., TADs and TIDs) and may seed ionospheric irregularities. Since they usually have spatial scales comparable to or smaller than the grid sizes of most global models, the gravity waves are poorly resolved or not resolved at all. The wave effects on the mean circulation and thermal and compositional structures have thus been parameterized in general circulation models and have been tuned to reproduce the observed climatology. The use of gravity wave parameterization is found to be a major source of model biases, given the simplifications and uncertainties of various schemes. The direct modulation of physical, chemical, and electrodynamical processes by mesoscale wave perturbations, on the other hand, are not accounted for in global models due to coarse model resolutions.

The apparent stochasticity of day-to-day tidal variability, probably resulting from the sensitive dependence of tides on the wave sources, propagation conditions, and nonlinear wave-wave and wave-mean flow interactions, along with model biases introduced by parameterized gravity wave forcing, poses challenges for quantifying and forecasting the upper atmosphere variability as related to the lower atmosphere forcing. To address these challenges, it is necessary to constrain the state of the whole atmosphere by assimilating observations of both the lower atmosphere and upper atmosphere. Better understanding and quantification of the gravity wave impacts on the upper atmosphere variability will depend on improving representation of mesoscale processes.

I would like to thank Delores Knipp for her invitation to write this review and numerous discussions with her in developing this paper. She read the first draft of the paper and provided valuable comments. I also thank Markus Rapp, who, as the Editor-in-Chief of Journal of Atmospheric and Solar-Terrestrial Physics, invited me to write a review paper on the subject of lower and upper atmosphere coupling back in 2010. Although I failed to fulfill the assignment (sorry, Markus Rapp), partly because back then I felt the connection between the lower atmosphere forcing and the ionosphere variability was still somewhat unclear in my mind, his invitation did provide me with the initiative. I would like to acknowledge the support of an international team (led by Larisa Goncharenko) by the International Space Science Institute (Bern, Switzerland) to collaborate on studying the coupling of the lower/upper atmosphere during stratospheric sudden warming. The review is in part based on a lecture given in 2015 at NCAR Advanced Study Program Summer Colloquia on Climate, Space Climate, and Couplings Between. I acknowledge high-performance computing support on Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and Information Systems Laboratory and from the NASA Advanced Supercomputing (NAS) Division at Ames Research Center provided by NASA High-End Computing (HEC) Program. Simulation outputs used to produce Figures 1, 2, and 5-8 are available upon request. Figures 3 and 4 have been properly cited and referred to in the reference list. The work is partially supported by National Science Foundation grant AGS-1138784 and AFOSRFA9550-16-1-0050. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
© 2017 American Geophysical Union
Powered by Wiley Online Library
Copyright © 1999 – 2017 John Wiley & Sons, Inc. All Rights Reserved
Accessed at http://onlinelibrary.wiley.com/doi/10.1002/2016SW001450/full on February 28, 2017.


236 Atmospheric Radiation Modeling of Galactic Cosmic Rays Using LRO/CRaTER and the EMMREM Model with Comparisons to Balloon and Airline Based Measurements

Atmospheric Radiation Modeling of Galactic Cosmic Rays Using LRO-CRaTER and the EMMREM Model with Comparisons to Balloon and Airline Based Measurements 20170123

2017 John Wiley & Sons, Inc. All Rights Reserved

Monday, 23 January 2017
4E (Washington State Convention Center )
C. J. Joyce, University of New Hampshire, Durham, NH; and N. A. Schwadron, L. W. Townsend, W. C. deWet, J. K. Wilson, H. Spence, W. K. Tobiska, K. Shelton-Mur, A. Yarborough, J. Harvey, A. Herbst, A. Koske-Phillips, F. Molina, S. Omondi, C. Reid, D. Reid, J. Shultz, B. Stephenson, M. McDevitt, and T. Phillips

The current state of the Sun and solar wind, with uncommonly low densities and weak magnetic fields, has resulted in galactic cosmic ray fluxes that are elevated to levels higher than have ever before been observed in the space age. Given the continuing trend of declining solar activity, it is clear that accurate modeling of GCR radiation is becoming increasingly important in the field of space weather. Such modelling is essential not only in the planning of future manned space missions, but is also important for assessing the radiation risks to airline passengers, particularly given NASA’s plans to develop supersonic aircraft that will fly at much higher altitudes than commercial aircraft and thus be more vulnerable to radiation from GCRs. We provide an analysis of the galactic cosmic ray radiation environment of Earth’s atmosphere using measurements from the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) aboard the Lunar Reconnaissance Orbiter (LRO) together with the Badhwar-O’Neil model and dose lookup tables generated by the Earth-Moon-Mars Radiation Environment Module (EMMREM).

Newly available measurements of atmospheric dose rates from instruments aboard commercial and research aircraft enable evaluation of the accuracy of the model in computing atmospheric dose rates. Additionally, a newly available dataset of balloon-based measurements, including simultaneous balloon launches from California and New Hampshire, provide an additional means of comparison to the model.

When compared to the available observations of atmospheric radiation levels, the computed dose rates seem to be sufficiently accurate, falling within recommended radiation uncertainty limits.

– Indicates paper has been withdrawn from meeting
– Indicates an Award Winner
American Meteorological Society
Accessed at https://ams.confex.com/ams/97Annual/webprogram/Paper314393.html on March 1, 2017.


Moderate Geomagnetic Storm on 2 March 2017

Moderate Geomagnetic Storm G2 on 1 March 2017

NOAA/SWPC Boulder, Colorado USA

Satellite Environment 1 March 2017



Close approach of 2017 DR34 on 25 February 2017

Asteroid 2017 EA flew past Earth at 11,930 mi on 25 February 2017

2017 DR34 Orbit Diagram


sw170302_Asteroid 2017 EA flew past Earth at 11 930 miles
Posted by TW on March 02, 2017 in categories Featured articles, Near-Earth Objects
5asteroid 2017 EA flyby 2 March 2017


A newly discovered asteroid named 2017 EA flew past Earth at an extremely close distance of 0.05 LD (19 200 km / 11 930 miles) from the surface of our planet at 14:05 UTC on March 2, 2017. This is the 7th known asteroid to flyby Earth within 1 lunar distance since January 9, 2017.

2017 EA has an estimated diameter between 2 and 4.4 m (6.5 to 14.4 feet) and belongs to the Apollo group of asteroids. It was first observed at Catalina Sky Survey just a couple of hours before its close approach.

It flew past Earth at 14:05 UTC today at a speed (relative to the Earth) of 18.43 km/s.

[ Ephemeris | Orbit Diagram | Orbital Elements | Physical Parameters | Close-Approach Data ]

This is the 7th, and the closest, known asteroid to flyby Earth within 1 lunar distance since January 9, 2017:

  1. Asteroid 2017 DR34 – Distance: 0.57 LD – February 25
  2. Asteroid 2017 DG16 – Distance: 0.34 LD – February 23
  3. Asteroid 2017 BS32 – Distance: at 0.41 LD – February 2
  4. Asteroid 2017 BH30 – Distance: 0.17 LD – January 30
  5. Asteroid 2017 BX – Distance: 0.68 LD – January 25
  6. Asteroid 2017 AG13 – Distance: 0.53 LD – January 9
Asteroid 2017 EA on Minor Planet Center, NEO/JPL, ESA/SSA
Featured image: ESA/NEO Coordination Centre. Edit: TW
The Watchers — Watching the world evolve and transform
Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License
Accessed at https://watchers.news/2017/03/02/asteroid-2017-ea/ on March 3, 2017.


Environmental Impacts Near Earth

Atmospheric Types

NASA/Annotated by Ann Morrison



Volcanic Activity for the week of 22 February-28 February 2017

Smithsonian’s Global Volcanism Program and the US Geological Survey’s Volcano Hazards Program

Volcanic Activity for the week of 22 February-28 February 2017


The Weekly Volcanic Activity Report is a cooperative project between the Smithsonian’s Global Volcanism Program and the US Geological Survey’s Volcano Hazards Program. Updated by 2300 UTC every Wednesday, notices of volcanic activity posted on these pages are preliminary and subject to change as events are studied in more detail. This is not a comprehensive list of all of Earth’s volcanoes erupting during the week, but rather a summary of activity at volcanoes that meet criteria discussed in detail in the “Criteria and Disclaimers” section. Carefully reviewed, detailed reports on various volcanoes are published monthly in the Bulletin of the Global Volcanism Network.
  Name Location Activity
1. Etna Sicily (Italy) New
2. Pacaya Guatemala New
3. Piton de la Fournaise Reunion Island (France) New
4. Bagana Bougainville (Papua New Guinea) Ongoing
5. Bogoslof Fox Islands (USA) Ongoing
6. Cleveland Chuginadak Island (USA) Ongoing
7. Colima Mexico Ongoing
8. Dukono Halmahera (Indonesia) Ongoing
9. Ebeko Paramushir Island (Russia) Ongoing
10. Fuego Guatemala Ongoing
11. Kilauea Hawaiian Islands (USA) Ongoing
12. Langila New Britain (Papua New Guinea) Ongoing
13. Nevado del Ruiz Colombia Ongoing
14. Sabancaya Peru Ongoing
15. Sheveluch Central Kamchatka (Russia) Ongoing
16. Sinabung Indonesia Ongoing
17. Suwanosejima Ryukyu Islands (Japan) Ongoing


3 VA SIGMETs valid 2 March 2017 Volcanic Ash Hazards

VA SIGMETs valid 2 Mar 2017 Volcanic Ash Hazards



  1. VA ERUPTION SABANCAYA Begins: 2017-03-02T19:40:00Z Ends: 2017-03-03T01:50:00Z
  2. VA ERUPTION KLYUCHEVSK Begins: 2017-03-02T19:25:00Z Ends: 2017-03-03T00:40:00Z Elev 16,221
  3. VA ERUPTION SINABUNG Begins: 2017-03-02T16:45:00Z Ends: 2017-03-02T21:15:00Z Elev 09,824


Earthquakes for the week ending 2 March 2017

Earthquakes for the week ending 2 March 2017




UTC 2017-02-24 00:32:17 M 5.9 – 28.9 mi E of Kaputa, Zambia 16.6 mi depth

UTC 2017-02-24 00 32 17 M 5.9 - 28.9 mi E of Kaputa, Zambia 16.6 mi depth



UTC 2017-02-24 17:28:44 M 6.9 – 178 mi S of Ndoi Island, Fiji 258 mi depth

UTC 2017-02-24 17 28 44 M 6.9 - 178 mi S of Ndoi Island, Fiji 258 mi depth




Agreement inked between St. Louis Regional Freightway, Port of New Orleans

The Freightway hopes to capitalize on container-on-barge services

Leaders look to improve freight connections between St. Louis and New Orleans. © Copyright 2017 STLtoday.com

Leaders look to improve freight connections between St. Louis and New Orleans. Head of New Orleans port says there’s ‘no reason why we can’t connect the dots’

By Leah Thorsen St. Louis Post-Dispatch 19 hrs ago (0)

The St. Louis Regional Freightway and the Port of New Orleans on Thursday pledged to work together by “exchanging market and operational information with the goal of growing trade and building upon existing and new business relationships between the two regions and critical ports,” the Freightway said in a statement.

The two entities entered into a memorandum of understanding that includes joint marketing efforts. Discussions began in September for such a partnership when Gary LaGrange, president and chief executive of the New Orleans port, came to St. Louis.

About 500 million tons of cargo is handled by the lower Mississippi River.

Mary Lamie, the Freightway’s executive director, said in a statement that the partnership will help coordinate the two regions’ supply chains.

“We now have a framework to work more closely together to generate new business activity that will help accelerate the present level of economic growth by increasing revenues to the Port of New Orleans and optimizing the St. Louis region’s freight network,” she said.

The Freightway hopes to capitalize on container-on-barge services.

Leah Thorsen writes about transportation for the St. Louis Post-Dispatch. Email her at lthorsen@post-dispatch.com and follow her on Twitter: @leahthorsen
© Copyright 2017 STLtoday.com, 900 N. Tucker Blvd. St. Louis, MO
Accessed at http://www.stltoday.com/business/local/agreement-inked-between-st-louis-regional-freightway-port-of-new/article_7346fbcd-cdb3-5418-92a8-5d850f593396.html on February 24, 2017.


National Weather Service St. Louis, MO Weather Story Graphic

Severe Thunderstorms Likely 20170228


National Weather Service United States Department of Commerce
Weather Story Weather Forecast Office St. Louis, MO

The Storm Prediction Center has upgraded a large area from southeast and east central Missouri through Indiana to a MODERATE RISK of Severe Thunderstorms. A very warm and unstable air mass will evolve across the region this afternoon. This warm air may result in record high temperatures this afternoon, but more importantly is expected to produce favorable conditions for severe thunderstorms from later this afternoon through tonight. Severe thunderstorms are likely that will be capable of producing very large hail, damaging winds, and tornadoes. A few strong and long-track tornadoes are possible within the Moderate Risk Area. The majority of the severe weather is expected to occur after dark, so be prepared to take action! The public is urged to keep up to date with the latest forecasts and review their safety rules and safety plans now.

US Dept of Commerce
National Oceanic and Atmospheric Administration
National Weather Service
Central Region Headquarters
7220 NW 101st Terrace
Kansas City, MO 64153
Accessed at http://www.weather.gov/crh/weatherstory?sid=lsx&embed= on February 28, 2017.



Salem New Jersey Event 52581 Hot Standby

Salem New Jersey




Power Reactor Event Number: 52581
Facility: SALEM Region: 1 State: NJ
Unit: [1] [ ] [ ] RX Type: [1] W-4-LP,[2] W-4-LP
Notification Date: 02/28/2017 Notification Time: 16:24 [ET]
Event Date: 02/28/2017 Event Time: 10:00 [EST]
Last Update Date: 02/28/2017 Emergency Class: NON EMERGENCY
10 CFR Section: 50.72(b)(2)(i) – PLANT S/D REQD BY TS, 50.72(b)(3)(ii)(A) – DEGRADED CONDITION
Person (Organization): ANNE DeFRANCISCO (R1DO)


Unit SCRAM Code RX CRIT Initial PWR Initial RX Mode Current PWR Current RX Mode
1 N Y 28 Power Operation 0 Hot Standby


Event Text


“On February 28, 2017 at 0930 [EST], a containment visual inspection was performed to identify the source of elevated RCS [Reactor Coolant System] leakage. A leak was identified between 13RC6 and 13SS661, 13 RCS hot leg sample isolation valves at 1000 [EST]. These valves are manual isolation valves in the reactor coolant hot leg sample line. Leak isolation could not be initially verified and is considered RCS pressure boundary leakage. Salem Unit 1 entered Technical Specification, RCS operational leakage, for the existence of pressure boundary leakage.

“This event is being reported under the requirements of 10CFR50.72(b)(2)(i) for ‘The initiation of a plant shutdown required by Technical Specifications’ and 10CFR50.72(b)(3)(ii)(A) or ‘Any event of condition that results in the condition of the nuclear power plant, including its principal safety barriers being seriously degraded.’

“The unit was placed in mode 3 at 1554 [EST] on 02/28/2017.

“This condition has no impact on public health and safety.”

Per Technical Specifications, the unit is proceeding to mode 5. The leak rate at the time of shutdown was 0.33 gpm. This event has no effect on Unit 2.

The licensee has notified the NRC Resident Inspector. The licensee will be notifying the Lower Alloways Creek Township, the State of New Jersey and the State of Delaware.

Accessed at https://www.nrc.gov/reading-rm/doc-collections/event-status/event/2017/20170301en.html on March 2, 2017.


RADCON on 2 March None of Concern-Watch


RADCON none of concern-watch 20170302

© Copyright 2012-2014 Nuclear Emergency Tracking Center, LLC (netc.com).


© Copyright 2012-2014 Nuclear Emergency Tracking Center, LLC (netc.com).All information that is produced by netc.com websites belongs to Nuclear Emergency Tracking Center, LLC   (netc.com).
NETC.COM   © 2014



Spike in crime is a trend: U.S. Attorney General Sessions

Rising Crime Rate Fig 1 by Shannon Golden

by Shannon Golden


Tuesday, February 28, 2017 – 01:25

In a speech to the National Association of Attorneys General in Washington, U.S. Attorney General Jeff Sessions says the recent increase in violent crime is a trend caused by drugs and the undermining of police. Rough Cut (no reporter narration).


U.S. Attorney General Jeff Sessions said on Tuesday that the federal government should stop spending money to sue local police departments, signaling a sharp departure from the previous administration’s policy toward law enforcement exhibiting patterns of racism or excessive force.

In his speech to the National Association of Attorneys General in Washington, Sessions said the Justice Department should instead use its resources to help police figure out the best way to fight crime. He announced the formation of a Justice Department task force to look at deficiencies in current practices to combat crime and propose new legislation.

The Justice Department is still weighing whether it should impose reforms on the Chicago Police Department, which was the subject of a critical report by the Obama administration. Sessions said violent crime had risen since 2014, although it is down almost half since the early 1990s. Federal Bureau of Investigation crime statistics for 2015, the latest year for which complete data is available, showed violent crimes increased 3.9 percent from 2014, while property crimes declined by 2.6 percent. The rise in violent crime came after two years of decreases, not decades of declines as Sessions suggested. The Obama administration began several investigations into police departments that it said were unfairly targeting minorities and using excessive force. Videos of such incidents shared online have sparked protests in cities from Baltimore to Ferguson, Missouri.

© 2017 Reuters. All Rights Reserved.
Accessed at http://www.reuters.com/video/2017/02/28/spike-in-crime-is-a-trend-sessions?videoId=371210067 on March 1, 2017.


Active shooter risks are real — and in the news

Active Shooter Incidents by Location



Louise Esola, 2/23/2017 2:38:00 PM GRAPEVINE, Texas —

The morning after an intoxicated, belligerent man opened fire in a packed bar in Kansas, killing one person and injuring two, risk management experts told a packed house of restaurant and retail risk managers that active shooter risk is something businesses cannot ignore.

Lance Ewing, executive vice president for global risk management and client services at Katy, Texas-based Cotton Holdings Inc., pointed out the coincidental timing of his scheduled talk Thursday at the CLM & Business Insurance Retail, Restaurant, and Hospitality Conference in Grapevine, Texas, happening the day after the headline-grabbing incident at Austin’s Bar and Grill in Olathe, Kansas.

“You need to know what your plan A and plan B are … you need to practice this with your employees,” he said. “Your best option is to run, hide, or fight … but the best plan is to be prepared.”

As part of his presentation, Mr. Ewing highlighted a number of active shooter incidents, using Bureau of Labor Statistics data to map out where events are happening; the map and headlines show no locality or business is immune, he said — even in a large conference room in a large hotel, he added to the conference attendees sitting in such a space.

He also cited data that shows a lion’s share of active shooter incidents typically occurring in public establishments where people are having a meal or spending their vacation: 27 % of active shooter incidents occurred in retail establishments and 15% in hotels in 2015.

Meanwhile, other data shows a high turnover rate of employees in such industries — upward of 80% for restaurants — making it imperative for employers to constantly reinforce their emergency plan for employees in those industries who come and go.

A new front in preparing for such a tragedy is “vulnerability testing,” Mr. Ewing said. “We do it for cyber, why not this? … Can somebody walk into your business, pull out a weapon and begin shooting?… Know your situation.”

Because of greater awareness and the risk of active shooters — a catchphrase that lends itself to any time a person aims to attack people in an establishment by surprise — the insurance industry is now providing more products that help businesses grapple with such events, according to Jeffrey Mahon, Dallas-based claim executive for American International Group Inc.

Some products include money for funerals and counseling, training for employees, money to bring in outside experts to help workers understand what to do in the event of an attack, he said.

“Insurance will not stop an active shooter situation; all it will do (is help) with your post-incident,” he added.

Panelist Marsha Bonner, Irving, Texas-based vice president of risk management at FelCor Lodging Trust Inc., gave attendees insight on another active-shooter possibility: armed customers who are allowed, by law, to open carry weapons.

She encouraged businesses to create policies on how establishments address open carry — many in restaurants and hospitality cater to friendlier clientele with children, and opt to tell customers that they cannot have their weapon out in the open, she said.

“One chain restaurant has jackets and sweaters for people who open carry; that’s one practice,” she said.

“You want to educate your employees; you also want them to know how to respond,” she added. “What is our policy? Who’s going to do what and when?”

Accessed at http://www.businessinsurance.com/article/20170223/NEWS06/912312062/Active-shooter-risks-real-news-CLM-Business-Insurance-Retail-conference?utm_campaign=BI20170223BreakingNewsAlert&utm_medium=email&utm_source=ActiveCampaign on February 23, 2017.


Four Mosques Have Burned In Seven Weeks — Leaving Many Muslims and Advocates Stunned

The Islamic Center of the Eastside


“The short answer is we haven’t seen anything like this in the past.”

The Islamic Center of the Eastside AP

Posted on Feb. 28, 2017, at 5:23 p.m. Albert Samaha and Talal Ansari BuzzFeed News Reporter

On January 7, 2017, the Islamic Center of Lake Travis, in Austin, Texas, which had been under construction, caught on fire. A week later, on January 14, the Islamic Center of Eastside, in Bellevue, Washington, burned.

Two weeks after that, on January 27, several hours after President Donald Trump signed an executive order banning immigrants from seven Muslim-majority countries, a fire destroyed the Islamic Center of Victoria, in Texas.

Then, this past Friday, February 24, a small blaze broke out at the front entrance of the Daarus Salaam Mosque, near Tampa, Florida.

Authorities have ruled that three of the four fires were caused by arson. An official at the Travis County Fire Marshal told BuzzFeed News that the investigation into the cause of the fire at the Islamic Center of Lake Travis remains open.

“We’ve never seen four mosques burned within seven weeks of each other,” said Mark Potok, a senior fellow at the Southern Poverty Law Center, which tracks hate groups around the country. “It’s part of a whole series of dramatic attacks on Muslims.”

The mosque fires come amid increased fear about hate crimes against minority religious groups. In recent weeks, scores of bomb threats were called into Jewish community centers and schools around the country and graveyards in Jewish cemeteries in three states were vandalized. On Sunday, somebody threw a rock through a window of the Masjid Abu Bakr mosque in Denver. In Redmond, Washington, vandals destroyed the Muslim Association of Puget Sound mosque’s entrance sign on two occasions within two months of the election. Two days after the Inauguration, a woman shattered the windows of the Davis Islamic Center, in California, and left strips of raw bacon on a door handle. In January, a white nationalist fatally shot six people at a mosque in Quebec City, Canada. Last week, a white man shot two Indian men, one fatally, at a Kansas bar after making racial slurs, questioning their immigration status, and shouting, “Get out of my country.”

“The short answer is we haven’t seen anything like this in the past,” Potok said, referring to the overall surge in reported hate crimes across the country. “This is my 18th year here and I haven’t seen anything remotely like this.”

To have three mosque fires ruled arson within six weeks is highly unusual, said Corey Saylor, director of the Department to Monitor and Combat Islamophobia at the Council on American-Islamic Relations. “In normal times, I will see one to two mosque incidents of any type per month, and rarely is it arson,” he said. “I can tell you for sure I have not seen levels of violence like this since I started tracking this stuff” in 2009.

The fire at the Islamic Center of Lake Travis — which nearly two months later is still under investigation — destroyed the partially constructed frame. Community members began raising funds for the building four years earlier.

“There are a lot more people who are in support of us building this back again than people who oppose us but it takes one crazy guy to do something,” Shakeel Rashed, an executive board member of the Islamic Center of Lake Travis, told the Texas Tribune in January.

“Everybody believes we need to be more vigilant. When we start reconstruction we definitely want to plan the security of the place better, have more cameras,” Rashed said.

In Bellevue, Washington, six days before the inauguration, surveillance cameras caught a man walking toward the Islamic Center of Eastside while carrying a backpack and a gallon jug shortly before 2:45 am, the Seattle Times reported. Less than a minute later, the mosque was on fire. Investigators at the scene found a melted gallon jug and a gas can. Officers arrested Isaac Wayne Wilson, who remained at the scene, smelled of gasoline, and confessed to setting the blaze, according to police. Authorities said there was no evidence it was a hate crime. A year earlier, Wilson, who has a history of mental illness, had been convicted of misdemeanor assault after an incident at the mosque.

Hours after President Donald Trump signed the controversial executive order banning immigration from seven Muslim-majority countries, someone intentionally set fire to the Islamic Center of Victoria in Texas in the middle of the night, according to investigators, who have yet to identify a suspect. The blaze caused more than $500,000 in damage, and completely destroyed the 16-year-old mosque, shaking the Muslim American community in south Texas. The mosque’s president, Dr. Shahid Hashmi, told the Texas Tribune his community would forgive whoever set the fire, but added, “there’s no way we can forget. There’s no way our children can forget.”

The fire at the Daarus Salaam Mosque in Thonotosassa, Florida, on Friday was at least the third time in seven months that a mosque in the Tampa area had been set on fire, following incidents at the Islamic Education Center in July and the Masjid Omar mosque in August.

Firefighters responded to the February 24, 2017, fire shortly after 2 a.m. — four hours before congregants planned to gather for the early Friday morning prayer session, the Tampa Bay Times reported. Within hours, authorities announced that arson had caused the fire, though they did not specify what evidence led them to that conclusion. At a press conference, Tampa mayor Bob Buckhorn called the fire “no different than the wave of anti-Semitic attacks on Jewish community centers and synagogues and bomb threats that have been called in all across the country.”

“Whoever did this maybe intended to discourage us not to be part of this community,” Mazen Bondogji, a member of the mosque’s board, said at a press conference. “We are part of this community and we will stay.”

The number of reported anti-Islam hate crimes had already been on the rise before the presidential campaign picked up steam in 2016. According to a report by the Council on American-Islamic relations, there were 78 instances of mosques being targeted — counting arson, vandalism and other destruction — in 2015. By comparison, 2014 saw just 20 such incidents. A report released last year by Georgetown University’s Center for Muslim-Christian Understanding found that in 2015 there were eight instances of arson that targeted mosques, or businesses and homes associated with Muslims.

FBI data shows that the number of reported anti-Muslim hate crimes surged by 67% from 2014 to 2015 (2016 data is not yet available).

But in more recent months, the SPLC reported a rise in reported hate crimes following the election of Trump, who campaigned on promises to significantly reduce the number of Muslim immigrants allowed into the country.

“Donald Trump’s campaign and victory has emboldened people on the radical right or people who simply hate certain minority groups to act,” Potok said. “They feel that their views have been legitimized by the man who is president of the United States.”

© 2017 BuzzFeed, Inc
Accessed at https://www.buzzfeed.com/albertsamaha/four-mosques-burn-as-2017-begins?utm_term=.raerrXlVkn#.lvDNNZ4eKW on March 2, 2017.


Jewish headstones toppled in New York

One of the vandalized graves

Copyright © Yedioth Internet. All rights reserved.

One of the vandalized graves

In the third of a recent wave of desecrations of Jewish cemeteries, at least five headstones are knocked over in Rochester; the cemetery’s administrator says it’s uncertain if this was anti-Semitism.

Ynetnews|Published: 02.03.17 , 23:34

A Jewish cemetery in Rochester, New York, had at least five headstones toppled, New York and Jewish media reported on Thursday evening.

Despite the discovery of the toppled headstones at the Waad Hakolel Cemetery following the recent desecration of Jewish graves in Philadelphia and Missouri, the nonprofit responsible for the graveyard is not rushing to describe the vandalism as “a hate crime.”

Its president, Michael Phillips, told the Democrat & Chronicle, “I don’t want to label it anti-Semitism. I don’t think there’s any proof of that.”

Phillips pointed out that at the Waad Hakolel Cemetery (also known as the Stone Road Cemetery), there had been no defacing with swastikas or anti-Semitic graffiti.

New York Gov. Andrew Cuomo, who announced Thursday his upcoming trip to Israel, released a statement reading, “New York has zero tolerance for bias or discrimination of any kind, and we will always stand united in the face of anti-Semitism and divisiveness. It is repugnant to everything we believe as New Yorkers, and we will continue to do everything in our power to bring to justice those responsible for these cowardly attacks on the values we hold dear.”

The Rochester Police are investigating.

Copyright © Yedioth Internet. All rights reserved.
Accessed at http://www.ynetnews.com/articles/0,7340,L-4930106,00.html on March 2, 2017.


Biosecurity: H7N9v Bird Flu

As bird flu spreads, US concludes its vaccine doesn’t provide adequate protection

Officials wearing masks and protective suits proceed to cull chickens in Hong Kong on January 28, 2014, as Hong Kong began a mass cull of 20,000 chickens after the deadly H7N9 bird flu virus was discovered in poultry imported from mainland China, authorities said. Officials wearing masks and protective suits piled dead chickens into black plastic bags at the wholesale market in Hong Kong where the virus was discovered Monday, television footage showed.

Photo credit should read PHILIPPE LOPEZ/AFP/Getty Images

Officials wearing masks and protective suits proceed to cull chickens in Hong Kong in 2014. Philippe Lopez/AFP/Getty Images

By Helen Branswell @HelenBranswell March 1, 2017

With human infections from a bird flu virus surging in China, US officials charged with preparing the country for influenza pandemics have been assessing the state of an emergency stockpile of vaccines against that strain. The conclusion: The stored H7N9 vaccine doesn’t adequately protect against a new branch of this virus family, and a new vaccine is needed.

Rick Bright, who heads the Biomedical Advanced Research and Development Authority, or BARDA, said the H7N9 vaccine in the stockpile would not fend off a new family of these viruses that has emerged in China, known as the eastern or Yangtze River Delta lineage of the viruses.

The conclusion comes amid a sharp rise in human H7N9 infections this winter in China. There have been 460 cases reported since last fall — a third of the 1,258 H7N9 infections diagnosed since the virus was first spotted in early 2013. In addition, the virus has spread more broadly across the country, increasing the chances more people will be infected in future.

Adding to the concern is the fact that the virus is evolving and has essentially split into two groups that are now different enough that vaccine for one might not protect very well against viruses from the other. The US stockpile currently contains enough vaccines to inoculate about 12 million people against the older lineage of H7N9, the southern or Pearl River Delta viruses.

“The antibodies [generated by the stockpiled vaccine] now appear to be suboptimal in their reactivity to these new eastern lineage viruses. So that means we will need to make new vaccine that will induce an immune response that will be cross-reactive and neutralize this new lineage of viruses,” Bright said.

The vaccines in the pandemic flu stockpile are intended to protect first responders in the case one of the highest-risk bird flu viruses triggers a pandemic. BARDA’s policy is to maintain enough vaccine for each of these top threats to be able to vaccinate 20 million people. As each person would need both a primer and a booster dose, that means 40 million doses of vaccine for each viral threat.

The costs of producing that much vaccine is roughly $100 million, Bright said. Because vaccines against bird flu viruses don’t typically induce a strong immune reaction in people, they must be administered with an adjuvant, a compound that heightens the immune response. The adjuvant needed for this much vaccine adds roughly $100 million to the cost, he said.

Currently the stockpiled supplies of H7N9 are lower than they ought to be. Bright said some of the vaccine doses made when the vaccine was ordered in 2013 degraded and had to be discarded.

BARDA must now decide whether it needs to make a full order of vaccine to protect against the new H7N9 viruses and replenish the stores of the older vaccine. But a less expensive solution may be within reach, Bright noted.

Experts who advise the World Health Organization on which flu viruses should be used for seasonal and pandemic flu vaccines have been meeting this week in Geneva. Bright said there have been indications that some H7N9 viruses may be what’s called cross-reactive — if used in a vaccine, they may protect against both lineages of H7N9 viruses.

“We’re very hopeful that they will be able to identify a single or a few CVVs” — candidate vaccine viruses — “that will allow us to make vaccine that will cross-react against all of the H7N9 viruses, thereby reducing the amount of effort and of course cost that it would take to make sure that we are property prepared for H7N9,” Bright said.

The stockpile also contains several versions of vaccines against H5N1 bird flu viruses, the oldest of which was made about a decade ago.

All these vaccines are tested annually to make sure they are still potent. Until the United States began to stockpile pandemic vaccine, it was not known how long vaccine could be stored. Flu viruses evolve so often that leftover seasonal flu vaccine is not saved for use the following year. In fact, the terms of their licenses require that vaccines that are not used within the season for which they are made must be discarded.

Bright said BARDA is working with scientists at the University of Cambridge in Britain to try to figure out how to maximize use of the stored vaccine, conducting tests to see whether giving people different versions of a vaccine — an older and a newer H5N1 vaccine, for instance — could give people both effective and broader protection again the viruses in that family.

“We might still find there’s great value in that investment,” he said of the older vaccines in the stockpile.

Helen Branswell can be reached at helen.branswell@statnews.com
Follow Helen on Twitter @HelenBranswell
© 2017 STAT
Accessed at https://www.statnews.com/2017/03/01/bird-flu-vaccine-development/ on March 1, 2017.


Biosecurity: Superbug

These 12 superbugs pose the greatest threat to human health, WHO says

Two carbapenem-resistant Klebsiella pneumonias bacteria


Shown here are two carbapenem-resistant Klebsiella pneumonias bacteria, part of the family of germs known as Enterobacteriaceae. (National Institute of Allergy and Infectious Diseases)


World Health Organization releases list of antibiotic-resistant ‘priority pathogens’

By Lena H. Sun February 27

The World Health Organization published a list naming 12 superbugs that pose the greatest threat to human health on February 27, 2017, in a push for more research and drug development to fight these pathogens. (WHO via AP)

The World Health Organization announced its first list of antibiotic-resistant “priority pathogens” on Monday, detailing 12 families of bacteria that agency experts say pose the greatest threat to human health and kill millions of people every year.

The list is divided into three categories, prioritized by the urgency of the need for new antibiotics. The purpose is to guide and promote research and development of new drugs, officials said. Most of the pathogens are among the nearly two dozen antibiotic-resistant microbes that the U.S. Centers for Disease Control and Prevention warned in a 2013 report could cause potentially catastrophic consequences if the United States didn’t act quickly to combat the growing threat of antibiotic-resistant infections.

“This list is not meant to scare people about new superbugs,” said Marie-Paule Kieny, an assistant director-general at WHO. “It’s intended to signal research and development priorities to address urgent public health threats.”

Superbugs that the WHO considers the highest priority are responsible for severe infections and high mortality rates, especially among hospitalized patients in intensive care or using ventilators and blood catheters, as well as among transplant recipients and people undergoing chemotherapy. While these pathogens are not widespread, “the burden for society is now alarming,” she said.

Included in this highest-priority group is CRE, or carbapenem-resistant Enterobacteriaceae, which U.S. health officials have dubbed “nightmare bacteria.” In some instances, it kills up to 50 percent of patients who become infected. An elderly Nevada woman who died last year contracted an infection caused by CRE that was resistant to all 26 antibiotics available in the United States.

Also in the first-tier group is Acinetobacter baumannii; the infections tied to it typically occur in ICUs and settings with very sick patients. The other bacteria tagged as a critical priority: Pseudomonas aeruginosa, which can be spread on the hands of health-care workers or by equipment that gets contaminated and is not properly cleaned.

WHO’s list follows a summit on superbugs that world leaders held last fall — only the fourth time they had addressed a health issue at the U.N. General Assembly.

The list’s second and third tiers — the high and medium priority categories — cover bacteria that cause more common diseases, such as gonorrhea and food poisoning caused by salmonella. While not associated with significant mortality rates, “they have a dramatic health and economic impact, particularly in low-income countries,” Kieny said.

Although there has been renewed interest and research investment in antibiotics because of the growing threat that antibiotic resistance poses, much of the work is more focused on antibiotics with a broad range, she said. “We have to focus specifically for a much smaller range of bacteria,” targeting the three highest-priority pathogens, Kieny said.

Drug companies have also tended to focus more on Gram-positive bacteria that tend to colonize the skin of healthy individuals and generate less resistance, said Evelina Tacconelli, who heads the infectious diseases division at the University of Tübingen in Germany, which helped develop the WHO list. By comparison, Gram-negative bacteria more frequently colonize intestinal reservoirs and can cause sepsis and severe urinary tract infections, especially among elderly patients.

There have been no new classes of antibiotics discovered that have made it to market since 1984, according to the Pew Charitable Trust’s antibiotic-resistance project. And there aren’t enough drugs in the pipeline to meet future needs, Allan Coukell, senior director of health programs at Pew, said Monday.

Of the 40 antibiotics in clinical development in the United States, “fewer than half even have the potential to treat the pathogens identified by WHO,” he said. “And based on history, most of those will fail to reach the clinic for reasons of efficacy or safety. So the outlook is grim.”

Historical data show that generally only one of five drugs that reach the initial phase of testing in humans will receive approval from the Food and Drug Administration. Developing antibiotics to treat highly resistant bacterial infections is especially challenging because only a small number of patients contract these infections and meet the requirements to participate in traditional clinical trials.

Public health experts welcomed the announcement, including the need to address the problem in a comprehensive fashion.

While research and development are essential, “we cannot just discover and develop our way out of this crisis,” said Helen Boucher, an infectious diseases doctor at Tufts University and a spokeswoman for the Infectious Diseases Society of America.   Prevention, the appropriate use of antibiotics and surveillance are all necessary as part of a coordinated approach, she said.

In the United States, antibiotic-resistant infections kill an estimated 23,000 Americans each year, according to the CDC. Global estimates are difficult because there is no uniform way to include antimicrobial resistance in causes of death. But experts say that drug-resistant infections, especially those caused by the WHO’s highest-priority pathogens, double or triple the risk of death.

“We are talking about millions of people affected,” Tacconelli said.

Tuberculosis, whose resistance has been growing in recent years, was not included in the list because it is targeted by other dedicated programs, WHO said.

Here is the list from WHO:

Priority 1: Critical

  1. Acinetobacter baumannii, carbapenem-resistant
  2. Pseudomonas aeruginosa, carbapenem-resistant
  3. Enterobacteriaceae, carbapenem-resistant, ESBL-producing

Priority 2: High

  1. Enterococcus faecium, vancomycin-resistant
  2. Staphylococcus aureus, methicillin-resistant, vancomycin-intermediate and resistant
  3. Helicobacter pylori, clarithromycin-resistant
  4. Campylobacter spp., fluoroquinolone-resistant
  5. Salmonellae, fluoroquinolone-resistant
  6. Neisseria gonorrhoeae, cephalosporin-resistant, fluoroquinolone-resistant

Priority 3: Medium

  1. Streptococcus pneumoniae, penicillin-non-susceptible
  2. Haemophilus influenzae, ampicillin-resistant
  3. Shigella spp., fluoroquinolone-resistant
© 1996-2017 The Washington Post
Accessed at https://www.washingtonpost.com/news/to-your-health/wp/2017/02/27/these-12-superbugs-pose-the-greatest-threat-to-human-health-who-says/?utm_term=.34dbc1964cb4 on March 2, 2017.



Ann Morrison
By Ann Morrison March 20, 2017 15:56
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