How NASA Satellites Help Model the Future of Climate

NASA Reports 2022 Tied for 5th Warmest Year on Record, Continuing a TrendEarth s Earth science programs, visit: https://www.nasa.gov/earth This color-coded map in Robinson projection displays a progression of changing global surface temperature anomalies. Normal temperatures are shown in white. Higher than normal temperatures are shown in red and lower than normal temperatures are shown in blue. Normal temperatures are calculated over the 30 year baseline period 1951-1980. The final frame represents the 5 year global temperature anomalies from 2018-2022. This data visualization shows the 2022 global surface temperature anomaly compared with the 1951-1980 average. This data visualization shows only the 2022 global surface temperature anomalies on a rotating globe to highlight the La Niña. 2022 was one of the warmest on record despite a third consecutive year of La Niña conditions in the tropical Pacific Ocean. NASA scientists estimate that La Niña’s cooling influence may have lowered global temperatures about 0.11 degrees Fahrenheit from what the average would have been under more typical ocean conditions. Colortable is both degrees fahrenheit and degrees celsius. This image is the single year 2022 GISS temperature anomaly as compared with the 1951-1980 average. This version does not have any titles or text overlays, except for the corresponding colorbar. This frame sequence of color-coded global temperature anomalies in robinson projection display a progression of changing global surface temperatures anomalies in even degrees Fahrenheit. The first frame in this sequence represents the data from 1880-1884. The second frame represents 1881-1885, ...and the last frame represents 2018-2022. Higher than normal temperatures are shown in red and lower than normal are shown in blue. Normal temperatures are the average over the 30 year baseline period 1951-1980. This sequence of images are the corresponding date overlays for the 5 year rolling averages used in the first visualization on this page. This frame sequence of color-coded global temperature anomalies in degrees celsius is designed to be displayed on the Science on a Sphere projection system. Each image represents a unique 5 year rolling time period with no fades between datasets. Frame 1884 represents data from 1880-1884, frame 1885 represents data from 1881-1885,... frame 2022 represents data from 2018-2022. Higher than normal temperatures are shown in red and lower than normal are shown in blue. Normal temperatures are the average over the 30 year baseline period 1951-1980. This is the colorbar for the Science on a Sphere frameset above. It is in degrees celsius.

Earth’s global average surface temperature in 2021 tied with 2018 as the sixth warmest on record, according to independent analyses done by NASA and NOAA. Continuing the planet’s long-term warming trend, global temperatures in 2021 were 1.5 degrees Fahrenheit (or 0.85 degrees Celsius) above the average for NASA’s baseline period, according to scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York.Collectively, the past eight years are the top eight warmest years since modern record keeping began in 1880. This annual temperature data makes up the global temperature record – and it’s how scientists know that the planet is warming.GISS is a NASA laboratory managed by the Earth Sciences Division of the agency’s Goddard Space Flight Center in Greenbelt, Maryland. The laboratory is affiliated with Columbia University’s Earth Institute and School of Engineering and Applied Science in New York.For more information about NASA’s Earth science missions, visit: https://www.nasa.gov/earth This color-coded map in Robinson projection displays a progression of changing global surface temperature anomalies. Normal temperatures are shown in white. Higher than normal temperatures are shown in red and lower than normal temperatures are shown in blue. Normal temperatures are calculated over the 30 year baseline period 1951-1980. The final frame represents the 5 year global temperature anomalies from 2017-2021. Scale in degrees Fahrenheit. This data visualization shows the 2021 global surface temperature anomalies on a rotating globe to highlight the La Niña. La Niña has developed and is expected to last into early 2022. Despite the cooling influence of this naturally occurring climate phenomenon, temperatures in many parts of the world are above average. The year 2000 also saw a La Niña event of similar strength to that in 2021, but 2021 global temperatures was more than 0.75 degrees Fahrenheit hotter than 2000. This color-coded map in Robinson projection displays a progression of changing global surface temperature anomalies. Normal temperatures are shown in white. Higher than normal temperatures are shown in red and lower than normal temperatures are shown in blue. Normal temperatures are calculated over the 30 year baseline period 1951-1980. The final frame represents the 5 year global temperature anomalies from 2017-2021. Scale in degrees Celsius. This frame sequence is the corresponding date range for each frame in the sequence. Degrees Fahrenheit Colorbar Degrees Celsius Colorbar This frame sequence of color-coded global temperature anomalies in robinson projection display a progression of changing global surface temperatures anomalies in Fahrenheit. The first frame in this sequence represents the data from 1880-1884. The second frame represents 1881-1885, ...and the last frame represents 2017-2021. Higher than normal temperatures are shown in red and lower than normal are shown in blue. Normal temperatures are the average over the 30 year baseline period 1951-1980. This frame sequence of color-coded global temperature anomalies in degrees celsius is designed to be displayed on the Science on a Sphere projection system. Each image represents a unique 5 year rolling time period with no fades between datasets. Frame 1884 represents data from 1880-1884, frame 1885 represents data from 1881-1885,... frame 2021 represents data from 2017-2021. Higher than normal temperatures are shown in red and lower than normal are shown in blue. Normal temperatures are the average over the 30 year baseline period 1951-1980. This is the colorbar for the Science on a Sphere frameset above. It is in degrees celsius.

Tracking the aerosols carried on the winds let scientists see the currents in our atmosphere. This visualization follows sea salt, dust, and smoke from July 31 to November 1, 2017, to reveal how these particles are transported across the map.The first thing that is noticeable is how far the particles can travel. Smoke from fires in the Pacific Northwest gets caught in a weather pattern and pulled all the way across the US and over to Europe. Hurricanes form off the coast of Africa and travel across the Atlantic to make landfall in the United States. Dust from the Sahara is blown into the Gulf of Mexico. To understand the impacts of aerosols, scientists need to study the process as a global system.The Global Modeling and Assimilation Office (GMAO) at NASA s Earth observing satellites, the supercomputer simulations enhance our scientific understanding of specific chemical, physical, and biological processes.During the 2017 hurricane season, the storms are visible because of the sea salt that is captured by the storms. Strong winds at the surface lift the sea salt into the atmosphere and the particles are incorporated into the storm. Hurricane Irma is the first big storm that spawns off the coast of Africa. As the storm spins up, the Saharan dust is absorbed in cloud droplets and washed out of the storm as rain. This process happens with most of the storms, except for Hurricane Ophelia. Forming more northward than most storms, Ophelia traveled to the east picking up dust from the Sahara and smoke from large fires in Portugal. Retaining its tropical storm state farther northward than any system in the Atlantic, Ophelia carried the smoke and dust into Ireland and the UK.Computer simulations using the GEOS models allow scientists to see how different processes fit together and evolve as a system. By using mathematical models to represent nature we can separate the system into component parts and better understand the underlying physics of each.GEOS runs on the Discover supercomputer at the NASA Center for Climate Simulation (NCCS)For more information: NASA@SC17: Glimpse at the Future of Global Weather Prediction and Analysis at NASA Tracking aerosols over land and water from August 1 to November 1, 2017. Hurricanes and tropical storms are obvious from the large amounts of sea salt particles caught up in their swirling winds. The dust blowing off the Sahara, however, gets caught by water droplets and is rained out of the storm system. Smoke from the massive fires in the Pacific Northwest region of North America are blown across the Atlantic to the UK and Europe. This visualization is a result of combining NASA satellite data with sophisticated mathematical models that describe the underlying physical processes.Music: Elapsing Time by Christian Telford [ASCAP], Robert Anthony Navarro [ASCAP]Complete transcript available.Watch this video on the NASA Goddard YouTube channel. [Versión en español]Rastreando de aerosoles sobre tierra y agua desde el 1 de agosto hasta el 1 de noviembre de 2017. Los huracanes y las tormentas tropicales son obvios por las grandes cantidades de partículas de sal marina atrapadas en sus vientos arremolinados. Sin embargo, el polvo del Sahara queda atrapado por las gotas de agua y sale por lluvia del sistema de tormentas. El humo de los incendios masivos en la región noroeste del Pacífico de América del Norte cruza el Atlántico hacia el Reino Unido y Europa. Esta visualización es el resultado de combinar datos satelitales de la NASA con sofisticados modelos matemáticos que describen los procesos físicos subyacentes.Música: Elapsing Time por Christian Telford [ASCAP], Robert Anthony Navarro [ASCAP]Transcripción completa disponible Version without hurricane labels, dates, nor legend for color scales. Colorbars indicating the amount of smoke, sea salt, and dust (expressed as aerosol optical depth at 550 nm), on transparent background. Video of dates on transparent background.

2020 Tied for Warmest Year on Record, NASA Analysis ShowsEarth’s global average surface temperature in 2020 tied with 2016 as the warmest year on record, according to an analysis by NASA. Continuing the planet’s long-term warming trend, the year’s globally averaged temperature was 1.84 degrees Fahrenheit (1.02 degrees Celsius) warmer than the baseline 1951-1980 mean, according to scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York. 2020 edged out 2016 by a very small amount, within the margin of error of the analysis, making the years effectively tied for the warmest year on record.“The last seven years have been the warmest seven years on record, typifying the ongoing and dramatic warming trend,” said GISS Director Gavin Schmidt. “Whether one year is a record or not is not really that important – the important things are long-term trends. With these trends, and as the human impact on the climate increases, we have to expect that records will continue to be broken.”A Warming, Changing WorldTracking global temperature trends provides a critical indicator of the impact of human activities – specifically, greenhouse gas emissions – on our planet. Earth s topography is exaggerated by 10x. This frame sequence is the corresponding date range for each frame in the sequence. This 136 frame sequence of color-coded global temperature anomalies in robinson projection display a progression of changing global surface temperatures anomalies in Fahrenheit. The first frame in this sequence represents the data from 1880-1884. The second frame represents 1881-1885, ...and the last frame represents 2016-2020. Higher than normal temperatures are shown in red and lower than normal are shown in blue. Normal temperatures are the average over the 30 year baseline period 1951-1980. Degrees Fahrenheit Colorbar Degrees Celsius Colorbar This frame sequence of color-coded global temperature anomalies in degrees celsius is designed to be displayed on the Science on a Sphere projection system. Each image represents a unique 5 year rolling time period with no fades between datasets. Frame 1884 represents data from 1880-1884, frame 1885 represents data from 1881-1885,... frame 2020 represents data from 2016-2020. Higher than normal temperatures are shown in red and lower than normal are shown in blue. Normal temperatures are the average over the 30 year baseline period 1951-1980. Degrees Celsius horizontal colorbar

Methane is a powerful greenhouse gas that traps heat 28 times more effectively than carbon dioxide over a 100-year timescale. Concentrations of methane have increased by more than 150% since industrial activities and intensive agriculture began. After carbon dioxide, methane is responsible for about 23% of climate change in the twentieth century. Methane is produced under conditions where little to no oxygen is available. About 30% of methane emissions are produced by wetlands, including ponds, lakes and rivers. Another 20% is produced by agriculture, due to a combination of livestock, waste management and rice cultivation. Activities related to oil, gas, and coal extraction release an additional 30%. The remainder of methane emissions come from minor sources such as wildfire, biomass burning, permafrost, termites, dams, and the ocean. Scientists around the world are working to better understand the budget of methane with the ultimate goals of reducing greenhouse gas emissions and improving prediction of environmental change. For additional information, see the Global Methane Budget.The NASA SVS visualization presented here shows the complex patterns of methane emissions produced around the globe and throughout the year from the different sources described above. The visualization was created using output from the Global Modeling and Assimilation Office, GMAO, GEOS modeling system, developed and maintained by scientists at NASA. Wetland emissions were estimated by the LPJ-wsl model, which simulates the temperature and moisture dependent methane emission processes using a variety of satellite data to determine what parts of the globe are covered by wetlands. Other methane emission sources come from inventories of human activity. The height of Earth’s atmosphere and topography have been vertically exaggerated and appear approximately 50-times higher than normal in order to show the complexity of the atmospheric flow. As the visualization progresses, outflow from different source regions is highlighted. For example, high methane concentrations over South America are driven by wetland emissions while over Asia, emissions reflect a mix of agricultural and industrial activities. Emissions are transported through the atmosphere as weather systems move and mix methane around the globe. In the atmosphere, methane is eventually removed by reactive gases that convert it to carbon dioxide. Understanding the three-dimensional distribution of methane is important for NASA scientists planning observations that sample the atmosphere in very different ways. Satellites like GeoCarb, a planned geostationary mission to observe both carbon dioxide and methane, look down from space and will estimate the total number of methane molecules in a column of air. Aircraft, like those launched during NASA’s Arctic Boreal Vulnerability Experiment (ABOVE) sample the atmosphere along very specific flight lines, providing additional details about the processes controlling methane emissions at high latitudes. Atmospheric models help place these different types of measurements in context so that scientists can refine estimates of sources and sinks, understand the processes controlling them and reduce uncertainty in future projections of carbon-climate feedbacks. This 3D volumetric visualization shows the emission and transport of atmospheric methane around the globe between December 9, 2017 and December 1, 2018.Music: by Sam Dobson [PRS]Coming soon to our YouTube channel. This layer of the visualization includes the Earth with the global atmospheric methane emission and transport. The overlay with the date and colorbar is not included. This layer includes only the date and colorbar with transparency.

The monthly climatology dataset covers January 2001 to December 2018 as was created for the unveiling of the Global Precipitation Missions s (GPM) newly redesigned website This data visualization cycles through the monthly precipitation rates. Both the colorbar and corresponding months are burned into the movie. This data visualization cycles throught the monthly precipitation rates throughout the year. (The colorbar and dates are not included in this version in case an editor wants to include their own graphical overlays.) Date overlay match framed to the above data visualization to re-create the topmost example visualization if desired. Colorbar for average monthly precipitation rates shown in millimeters per hour. Cool colors (ie, blue and green) are areas that receive low amounts of precipitation for a given month. Warmer colors (yellow, orange, and red) receive higher amounts of precipitation for the given month. January Climatology February Climatology March Climatology April Climatology May Climatology June Climatology July Climatology August Climatology September Climatology October Climatology November Climatology December Climatology

In order to study the Earth as a whole system and understand how it is changing, NASA develops and supports a large number of Earth-observing missions. These missions provide Earth science researchers the necessary data to address key questions about global climate change.This visualization reveals that the Earth system, like the human body, comprises diverse components that interact in complex ways. Shown first, the Multi-Scale Ultra-High Resolution (MUR) sea surface temperature (SST) dataset combines data from the Advanced Very High-Resolution Radiometer (AVHRR), Moderate Imaging Spectroradiometer (MODIS) Terra and Aqua, and Advanced Microwave Spectroradiometer-EOS (AMSR-E) instruments. Constantly released into the Earth’s atmosphere, heat and moisture from the ocean and land influence Earth’s weather patterns—represented here as wind speeds from the Modern-Era Retrospective analysis for Research and Applications (MERRA) dataset. Moisture in the atmosphere—represented as water vapor (also from MERRA)—forms clouds (shown here using cloud layer data from the NOAA Climate Prediction Center) and precipitation. Precipitation (data from GPM IMERG) significantly impacts water availability, which influences soil moisture (data from NASA-USDA-FA) and ocean salinity.While scientists learn a great deal from studying each of these components individually, improved observational and computational capabilities increasingly allow them to study the interactions between these interrelated geophysical and biological parameters, leading to unprecedented insight into how the Earth system works—and how it might change in the future. All six time-synchronous datasets, individually and then layered two at a time

This visualization depicts changes in Antarctic land ice thickness as measured by the ICESat (2003-2009) and ICESat-2 (2018-) satellites. The camera zooms into a region near the Kamb ice stream to compare ICESat and ICESat-2 beam tracks. The beam intersections are highlighted to explain how the data at these points are used to measure how land ice has changed over time. After exploring a few regions in detail, the camera moves out to a global view and an ocean temperature dataset is revealed. The future response of the Antarctic Ice Sheet to changes in climate is the single largest source of uncertainty in projections of sea level rise. If the ice sheet melted completely it would raise sea levels by 57 meters, a process that would unfold over millennia. One key to understanding how the ice sheet will respond in the future is to observe and analyze how the ice sheet has reacted to changes in climate over the past decades, where satellites observations are available. One key to understanding ice sheet change is to examine records of elevation change that show where the ice sheet is thinning and thickening due to changing environment. Recent analysis of incredibly precise surface elevations collected by NASA’s ICESat and ICESat-2 satellite laser altimeters reveals complex patters of ice sheet and ice shelf (floating extensions of the ice sheets) change that are the combined consequence of changes in melting by the ocean, changes in precipitation and, changes at the bed of the glacier where the ice sheet slides across the underlying bedrock. The researchers do this my finding locations where tracks of measured elevation intersect, measuring the change in elevation and correct for changes in the average density of the surface using models. Coherent regional patters of elevation change reveal the underlying mechanism responsible causing ice sheet change. One of the most striking features in the data is the Kamb Ice Stream that once flowed rapidly into the Ross Ice Shelf but that stopped flowing due to an increase friction (resistance to flow) likely caused by changes in the availability of liquid water at its base. Strong patters of thinning are visible all along the Amundsen Cost where ice shelves are rapidly thinning in response to increased melting by warm ocean waters. Melting of ice shelves do not directly contribute to changes in sea level, since they are already floating, but they do indirectly impact how fast the grounded ice is able to flow into the ocean. Ice shelves are located at the fronts of the glaciers and help to regulate how fast the ice flows into the ocean. As the ice shelves thin they become less able to hold back the inland ice, causing the grounded glaciers to accelerate and thin. In the East, broad patters of thickening reveal that the East Antarctic Ice Sheet is growing most likely in response to increases in precipitation relative to some unknown time in the past. The thickening is strongest along the coast of Dronning Maud Land where enhanced moisture transport has resulted in increased snowfall. Despite the diversity of gains and losses, losses in the West (208 cubic kilometers of water per year or Gt) greatly outpace Gains (90 Gt per year) in the east resulting in a total Antarctic mass change loss of 118 Gt per year. This visualization depicts changes in Greenland land ice thickness as measured by the ICESat (2003-2009) and ICESat-2 (2018-) satellites. The camera zooms into a region near the Zachariae Isstrom glacier to compare ICESat and ICESat-2 beam tracks. The beam intersections are highlighted to explain how the data at these points are used to measure how land ice has changed over time. As the Greenland Ice Sheet responds to warming oceans and atmosphere it has become one of the largest contributors to sea level rise and will continue to be for the foreseeable future. Scientists are working to determine more precisely how much more ice will be lost and when that loss will occur. One key approach to doing this is to analyze changes in the ice sheets elevation over the past decades where satellite observations are available. By finding the intersection of elevation track measurements collected by NASA’s ICESat (2003-2009) and ICESat-2 (2018-) satellite laser altimeters, researchers are able to make very precise measurements of elevation change that can be converted to estimates of mass change after correcting for changes snow density using models. The combination of long time-span between measurements and the high accuracy of NASA’s satellite laser altimeters allows the researchers to make highly detailed maps of mass change that provide insights into the mechanism behind the ice sheets rapid rate of loss. Thinning can be seen around the periphery of the ice sheet where elevations are closest to sea level and rates of surface melting are highest. This pattern is punctuated by localized areas of extreme thinning where large glaciers come into contact with warm ocean waters. Unlike the uniform pattern of low-elevation thinning that is being driven by increased melting due to warmer summer air temperatures, these concentrated areas of thinning occur where outlet glacier have sped up. These glaciers have sped up in response to some combination of retreating ice front position, changes in the slipperiness at the bed of the glacier due to changes in liquid water at the ice-rock interface and due to change in the rate frontal melting due to an increase in the heat content of the ocean waters that come into contact with the glacier front. Juxtaposed on the pattern of rapid thinning along the periphery of the ice sheet is a broad pattern of thickening in the high-elevation interior of the ice sheet. This pattern of thickening suggests that increases in snowfall, relative to sometime in the past, are partly compensating for increased losses due to enhanced melt and accelerated glacier flow. Overall low-elevation losses greatly outpace high-elevation gains resulting in 3200 cubic kilometers of water (Gt) being lost from the ice sheets and entering the oceans, raising global mean sea level by 8.9 mm. This high resolution still image depicts changes in Antarctic land ice thickness as measured by the ICESat (2003-2009) and ICESat-2 (2018-) satellites. This high resolution still image depicts changes in Greenland land ice thickness as measured by the ICESat (2003-2009) and ICESat-2 (2018-) satellites.

This visualization was created as an introductory shot to video celebrating the 50th anniersary of Earth Day. The camera approaches the moon from the far side, with Earth behind the moon. The camera moves over the limb revealing Apollo-8, when Bill Anders took the iconic s Earth observing fleet in 1970 (the first Earth Day), then transition to the Earth observing fleet in 2020 (the 50th anniversary of Earth Day)This video is also available on our YouTube channel.

This visualization was created to be one of the final shots of a video celebrating the 50th anniversary of Earth Day. The camera starts under water off the coast of the Eastern United States showing layers of ocean currents from a computational model called ECCO-2. The camera slowly pulls back revealing the Gulf Stream, one of the most powerful ocean currents on Earth. The camera continues to pull back revealing NASA there are more levels near the surface than deeper.

This visualization shows the IMERG precipitation product for April, May, and June of 2014.This visualization was created in part to support Earth Day 2020 media releases. IMERG Visualization, With LabelsThis video is also available on our YouTube channel. IMERG Visualization, No Labels

This visualization shows groundwater storage as measured by the Gravity Recovery and Climate Experiment (GRACE) between August 2005 and June 2014 (the date range for the visualization was chosen for convenience rather than scientific significance).This visualization was created in part to support Earth Day 2020 media releases. GRACE Groundwater Storage, With LabelsThis video is also available on our YouTube channel. GRACE Groundwater Storage, No Labels

This visualization shows cloud cover as modeled by the GEOS-5 atmospheric model, using observations as its input, over the course of three days. The time period repeats halfway through the animation.This visualization was created in part to support Earth Day 2020 media releases. GEOS-5 Modeled Cloud Cover, With LabelsThis video is also available on our YouTube channel. GEOS-5 Modeled Cloud Cover, No Labels

NASA researchers have developed new satellite-based, weekly global maps of soil moisture and groundwater wetness conditions and one to three-month U.S. forecasts of each product. While maps of current dry/wet conditions for the United States have been available since 2012, this is the first time they have been available globally.Both the global maps and the U.S. forecasts use data from NASA and German Research Center for Geosciences’s Gravity Recovery and Climate Experiment Follow On (GRACE-FO) satellites, a pair of spacecraft that detect the movement of water on Earth based on variations of Earth’s gravity field. GRACE-FO succeeds the highly successful GRACE satellites, which ended their mission in 2017 after 15 years of operation. With the global expansion of the product, and the addition of U.S. forecasts, the GRACE-FO data are filling in key gaps for understanding the full picture of wet and dry conditions that can lead to drought.The satellite-based observations of changes in water distribution are integrated with other data within a computer model that simulates the water and energy cycles. The model then produces, among other outputs, time-varying maps of the distribution of water at three depths: surface soil moisture, root zone soil moisture (roughly the top three feet of soil), and shallow groundwater. The maps have a resolution of 1/8th degree of latitude, or about 8.5 miles, providing continuous data on moisture and groundwater conditions across the landscape.The new forecast product that projects dry and wet conditions 30, 60, and 90 days out for the lower 48 United States uses GRACE-FO data to help set the current conditions. Then the model runs forward in time using the Goddard Earth Observing System, Version 5 seasonal weather forecast model as input. The researchers found that including the GRACE-FO data made the resulting soil moisture and groundwater forecasts more accurate. GRACE Surface Water, Root Zone, and Groundwater Storage, Okovango Delta Region GRACE Surface Water, Root Zone, and Groundwater Storage, Australia GRACE Surface Water, Root Zone, and Groundwater Storage, Australian Drought Dec 2019 GRACE Surface Water, Root Zone, and Groundwater Storage, Europe GRACE Groundwater Storage, Whole Earth 2018 GRACE Data AssimilationFour images in dropdown menu, one for each month. 2018 GEOSV2 ForecastsFour images in dropdown menu, one for each month. 2019 GRACE Data AssimilationFour images in dropdown menu, one for each month. 2019 GEOSV2 ForecastsFour images in dropdown menu, one for each month. Percentile Color Bar, All Water Storage Percentile COlor Bar, Groundwater Storage Only

This visualization shows the global distribution of aerosols, generated by NASA’s GEOS-FP data assimilation system, from August 1, 2019 to January 14,2020—capturing the aerosols released by the extreme bushfires in Australia in December 2019 and January 2020 and how they are transported around the globe over the South Pacific Ocean.Different aerosol species are highlighted by color, including dust (orange), sea-salt (blue), nitrates (pink), sulfates (green), and carbon (red), with brighter regions corresponding to higher aerosol amounts. NASA s MODIS observations constrain regions with biomass burning as well as the aerosol optical depths in GEOS, capturing the prominent bushfires in Australia and transport of emitted aerosols well downstream over the South Pacific Ocean. Weather events including Hurricane Dorian in August – September 2019 and other tropical cyclones around the world, along with major fire events in South America and Indonesia in August - September 2019 are also shown.The local impacts of the Australian bushfires have been devastating to property and life in Australia while producing extreme air quality impacts throughout the region. As smoke from the massive fires has interacted with the global weather, the transport of smoke plumes around the global have accelerated through deep vertical transport into the upper troposphere and even the lowermost stratosphere, leading to long-range transport around the globe. Animation of global aerosols from August 1, 2019 to January 29, 2020 Time series starting in Dec 2019 and going through Jan 2020. Color tables for the different aerosols visualized. For More InformationSee [https://gmao.gsfc.nasa.gov/research/science_snapshots/2020/Australia_fires_smoke.php](https://gmao.gsfc.nasa.gov/research/science_snapshots/2020/Australia_fires_smoke.php)

Methane is a powerful greenhouse gas that traps heat 28 times more effectively than carbon dioxide over a 100-year timescale. Concentrations of methane have increased by more than 150% since industrial activities and intensive agriculture began. After carbon dioxide, methane is responsible for about 20% of climate change in the twentieth century. Methane is produced under conditions where little to no oxygen is available. About 30% of methane emissions are produced by wetlands, including ponds, lakes and rivers. Another 20% is produced by agriculture, due to a combination of livestock, waste management and rice cultivation. Activities related to oil, gas, and coal extraction release an additional 30%. The remainder of methane emissions come from minor sources such as wildfire, biomass burning, permafrost, termites, dams, and the ocean. Scientists around the world are working to better understand the budget of methane with the ultimate goals of reducing greenhouse gas emissions and improving prediction of environmental change. For additional information, see the Global Methane Budget.The NASA SVS visualization presented here shows the complex patterns of methane emissions produced around the globe and throughout the year from the different sources described above. The visualization was created using output from the Global Modeling and Assimilation Office, GMAO, GEOS modeling system, developed and maintained by scientists at NASA. Wetland emissions were estimated by the LPJ-wsl model, which simulates the temperature and moisture dependent methane emission processes using a variety of satellite data to determine what parts of the globe are covered by wetlands. Other methane emission sources come from inventories of human activity. The height of Earth’s atmosphere and topography have been vertically exaggerated and appear approximately 50-times higher than normal in order to show the complexity of the atmospheric flow. As the visualization progresses, outflow from different source regions is highlighted. For example, high methane concentrations over South America are driven by wetland emissions while over Asia, emissions reflect a mix of agricultural and industrial activities. Emissions are transported through the atmosphere as weather systems move and mix methane around the globe. In the atmosphere, methane is eventually removed by reactive gases that convert it to carbon dioxide. Understanding the three-dimensional distribution of methane is important for NASA scientists planning observations that sample the atmosphere in very different ways. Satellites like GeoCarb, a planned geostationary mission to observe both carbon dioxide and methane, look down from space and will estimate the total number of methane molecules in a column of air. Aircraft, like those launched during NASA’s Arctic Boreal Vulnerability Experiment (ABOVE) sample the atmosphere along very specific flight lines, providing additional details about the processes controlling methane emissions at high latitudes. Atmospheric models help place these different types of measurements in context so that scientists can refine estimates of sources and sinks, understand the processes controlling them and reduce uncertainty in future projections of carbon-climate feedbacks. This first 3D volumetric visualization focuses on several continents showing the emission and transport of atmospheric methane around the globe between January 1, 2017 and November 30, 2018. This video is also available on our YouTube channel. This second 3D volumetric visualization shows a global view of the methane emission and transport between December 1, 2017 and November 30, 2018. This visualizaion of the rotating global view is designed to be played in a continuous loop.This video is also available on our YouTube channel. This version of the first visualization shows the Earth and methane only. The date, colorbar and exaggeration are not displayed. This version of the second visualization shows the Earth and methane only. The date, colorbar and exaggeration are not displayed. A still image of the global atmospheric methane on December 25, 2017. This layer of the first visualization includes the date, colorbar and exaggeration with transparency. This layer of the second visualization includes the date, colorbar and exaggeration with transparency.

Earth’s atmosphere is mainly comprised of nitrogen and oxygen but also contains traces of hundreds of chemical compounds. While tiny in abundance, these chemicals have an outsized impact on humans and the environment due to their reactivity and toxicity. This visualization shows a computer simulation of the complexity of the chemical system of the atmosphere produced by NASA s a list of each of the chemical species shown and their groupings (ppbV=pars per billion by volume): 96 chemical species are shown from a GEOS atmospheric simulation Color bars

This visualization of the precipitation from IMERG, averaged over 90 days, shows how the broad patterns of global precipitation fluctuate during this particular time period. Unlike the climatology, the vigorous, relatively small-scale precipitation systems create a very precipitation and focuses attention on the systematic changes that are persistent, but not necessarily large enough to be readily noticed in the accumulations. 2015-2016 Precipitation Accumulation 2015-2016 Precipitation Anomalies featuring El Nino.

The Grand Average Climatology dataset covers June 2000 to May 2019. It shows the well-known structure of global precipitation: Intertropical Convergence Zone (ITCZ) near the Equator, South Pacific Convergence Zone (SPCZ) and smaller South Atlantic Convergence Zone (SACZ), relatively dry subtropical highs, and mid-latitude storm tracks. The relatively fine spatial resolution (0.1° lat./lon.) gives a more detailed picture than the previous NASA product (Tropical Rainfall Measuring Mission [TRMM] Multi-satellite Precipitation Analysis [TMPA], 0.25°), and its near-global coverage, better sampling in time, and improved algorithms provide wider coverage and more confidence in the results. The satellite input allows NASA researchers to estimate precipitation over both land and ocean, which networks of surface sensors do not provide. The most reliable estimates are provided over ocean; warm land is second-best, coastal areas are third, and snow/ice-covered regions are least certain. Grand Average Precipitation Climatology Colorbar for the IMERG Grand Average Climatology dataset. Cooler colors are areas that receive very little rain. Warm colors receive more rain. Alternative colorbar for the IMERG Grand Average Climatology with white outline and white text.

The daily climatology dataset covers January 2001 to December 2018, computed as a trailing 30-day average to reduce the random noise due to isolated big events. Notable features include the annual cycle of the InterTropical Convergence Zone (ITCZ) following the motion of the Sun (with a time lag) over both land and ocean, the seasonal shift of the Asian Monsoon between South Asia in the boreal summer and Australia in the boreal winter, the North American Monsoon in the late boreal summer in northern Mexico and southwestern U.S., and the dry summer/wet winter pattern in the Mediterranean Sea area and the west coast of the U.S. Example composite showing the daily climatology along with the appropriate month and colorbar. Daily Climatology with a 30 day trailing average. Colorbar for IMERG Daily Climatology. Cooler colors are areas that receive very little precipitation. Warmer colors receive more precipitation. Alternative IMERG daily climatology colorbar. Text and colorbar outline are white. Blank Earth Map used for compositing. Month overlay layer for compositing.

The Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center uses the Goddard Earth Observing System (GEOS) modeling and data assimilation system to produce gridded estimates of the atmospheric state by combining short-term forecasts with observations from numerous observing systems. While the GEOS system is under continual development, it is periodically frozen and used to reprocess the modern satellite era, which begins in about 1980. This period specifically has been the focus of the second version of the Modern-Era Retrospective analysis for Research and Applications (MERRA-2). The modern satellite era in the context of MERRA-2 stems from the launch of the NASA/NOAA Television InfraRed Observational Satellite N-series (TIROS-N) satellite. This satellite served as the space platform for the first of the TIROS Operational Vertical Sounder (TOVS) series, which included TIROS-N and NOAA-6 through NOAA-14. The series of TOVS observations included global infrared and microwave radiance observations that provided the first comprehensive space-based observations that served as the remotely sensed backbone of the assimilation system. These observations, along with wind estimates from geostationary satellites and the global surface and upper air conventional observing network (e.g. surface reporting stations, radiosondes, aircraft measurements) provide the observations for the beginning of MERRA-2 in 1980.The observing system has advanced substantially since the launch of TIROS-N. Both satellite and conventional observations have increased in both quality and quantity over the course of the past four decades. In 1980, the median number of observations assimilated over a six hour period was 175,000. In 2018, this number has approached 5 million. The transition from the TOVS to the ATOVS (Advanced TOVS) observing system, which began in 1998 with the launch of the NOAA-15 platform, provided better horizontal and vertical resolution, along with improved observational quality. NASA’s Atmospheric Infrared Sounder (AIRS) instrument on the EOS-Aqua spacecraft provided yet another major advance in remote sensing of Earth, providing the first well-calibrated hyperspectral infrared radiance observations of the atmosphere, leading to a massive increase in the number of observations available to constrain the system. Designed as a research instrument, AIRS has been adopted by international operational weather prediction centers in their analysis and forecasting systems and also provides a key part of the meteorological observing system for MERRA-2. The demonstrable value of NASA’s AIRS observations also provided the impetus for developing hyperspectral infrared radiance instruments by the weather agencies, with the Infrared Atmospheric Sounding Interferometers (IASI) on the EUMETSAT Metop spacecraft and the Cross-track Infrared Sounders (CrIS) on the NASA-NOAA Suomi-NPP and JPSS platforms providing massive boosts in the number of available observations for use in weather analysis and forecasting. These measurements all provide critical inputs to the observing system used in MERRA-2. One of the fundamental scientific goals of the GMAO reanalysis projects is to provide the optimal estimate atmospheric state in a manner that is consistent over time. These animations illustrate how different the observing system were in 1980 compared to today. On the one hand, these animations demonstrate the critical role that NASA has played in developing the observing systems that are used in satellite measurements, including the enhancements of the spacecraft observations between 1980 and the present time. They also highlight one of the great challenges in producing consistent long-term records of the atmospheric state in MERRA-2 and other reanalyses: technological advances lead to larger numbers of higher quality observations. Even though the underlying assimilation systems remain frozen over time, the great challenge is to overcome the impacts of an ever improving suite of observations. Meteorological Observing Systems, 1980 and 2018. Data is revealed within a moving 1.5 hour window centered on the time shown. Sistemas de observación meteorológica, 1980 y 2018. Los datos se muestran en una ventana móvil de 1,5 horas centrada en el tiempo indicado. Meteorological Observing Systems, 1980. Data is revealed within a moving 1.5 hour window centered on the time shown. Sistemas de observación meteorológica, 1980. Los datos se muestran en una ventana móvil de 1,5 horas centrada en el tiempo indicado. Meteorological Observing Systems, 2018. Data is revealed within a moving 1.5 hour window centered on the time shown. Sistemas de observación meteorológica, 2018. Los datos se muestran en una ventana móvil de 1,5 horas centrada en el tiempo indicado. Individual Meteorological Observing Systems, 1980. Each image represents 6 hours of observations, centered on 12Z. Dropdown menu contains complete set. Individual Meteorological Observing Systems, 2018. Each image represents 6 hours of observations, centered on 12Z. Dropdown menu contains complete set.

Carbon dioxide is the most important greenhouse gas released to the atmosphere through human activities. It is also influenced by natural exchange with the land and ocean. This visualization provides a high-resolution, three-dimensional view of global atmospheric carbon dioxide concentrations from September 1, 2014 to August 31, 2015. The visualization was created using output from the GEOS modeling system, developed and maintained by scientists at NASA. The height of Earth’s atmosphere and topography have been vertically exaggerated and appear approximately 400 times higher than normal to show the complexity of the atmospheric flow. Measurements of carbon dioxide from NASA’s second Orbiting Carbon Observatory (OCO-2) spacecraft are incorporated into the model every 6 hours to update, or “correct,” the model results, called data assimilation.As the visualization shows, carbon dioxide in the atmosphere can be mixed and transported by winds in the blink of an eye. For several decades, scientists have measured carbon dioxide at remote surface locations and occasionally from aircraft. The OCO-2 mission represents an important advance in the ability to observe atmospheric carbon dioxide. OCO-2 collects high-precision, total column measurements of carbon dioxide (from the sensor to Earth’s surface) during daylight conditions. While surface, aircraft, and satellite observations all provide valuable information about carbon dioxide, these measurements do not tell us the amount of carbon dioxide at specific heights throughout the atmosphere or how it is moving across countries and continents. Numerical modeling and data assimilation capabilities allow scientists to combine different types of measurements (e.g., carbon dioxide and wind measurements) from various sources (e.g., satellites, aircraft, and ground-based observation sites) to study how carbon dioxide behaves in the atmosphere and how mountains and weather patterns influence the flow of atmospheric carbon dioxide. Scientists can also use model results to understand and predict where carbon dioxide is being emitted and removed from the atmosphere and how much is from natural processes and human activities. Carbon dioxide variations are largely controlled by fossil fuel emissions and seasonal fluxes of carbon between the atmosphere and land biosphere. For example, dark red and orange shades represent regions where carbon dioxide concentrations are enhanced by carbon sources. During Northern Hemisphere fall and winter, when trees and plants begin to lose their leaves and decay, carbon dioxide is released in the atmosphere, mixing with emissions from human sources. This, combined with fewer trees and plants removing carbon dioxide from the atmosphere, allows concentrations to climb all winter, reaching a peak by early spring. During Northern Hemisphere spring and summer months, plants absorb a substantial amount of carbon dioxide through photosynthesis, thus removing it from the atmosphere and change the color to blue (low carbon dioxide concentrations). This three-dimensional view also shows the impact of fires in South America and Africa, which occur with a regular seasonal cycle. Carbon dioxide from fires can be transported over large distances, but the path is strongly influenced by large mountain ranges like the Andes. Near the top of the atmosphere, the blue color indicates air that last touched the Earth more than a year before. In this part of the atmosphere, called the stratosphere, carbon dioxide concentrations are lower because they haven’t been influenced by recent increases in emissions. Carbon Dioxide from the GEOS-5 modelThis video is also available on our YouTube channel. Volumetric Color bar

NASA’s Global Modeling and Assimilation Office (GMAO) uses the Goddard Earth Observing System Model, Version 5 Data Assimilation System (GEOS­-5 DAS) to produce global numerical weather forecasts on a routine basis. GMAO forecasts play important roles in managing NASA’s fleet of science satellites and in researching the impact of new satellite observations. In order to provide timely information about the state of the atmosphere for NASA instrument teams and researchers, the GMAO runs the GEOS-­5 DAS four times each day in real time. For each forecast, it is necessary to provide accurate initial conditions that drive the GEOS-­5 forecasts. To do this, the best estimate of the full, three-dimensional atmospheric state is determined by combining the latest observations and a short-term, 6-­hour forecast—a process known as data assimilation. The GEOS-­5 DAS assimilates more than 5 million observations during each 6-hour assimilation period.These observations are assembled from a number of sources from around the globe, including NASA, NOAA, EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), commercial airlines, the US Department of Defense, and many others. Similarly, each observation type has its own sampling characteristics. It can be seen in the animation how different observation types have different strategies. One of the main challenges of data assimilation is to understand how all these observations are alike, how they differ, and how they interact with each other.Funding for the development of the GEOS-5 model and data assimilation system development comes from NASA s contribution to the Joint Center for Satellite Data Assimilation.The GEOS-5 DAS runs at the NASA Center for Climate Simulation, which is funded by NASA’s High-End Computing Program.For More Information:http://gmao.gsfc.nasa.gov/http://www.nccs.nasa.gov/images/data_assim_story_072815.pdf This animation shows the global observations assimilated into the GEOS-5 data assimilation system over 6 hours. Data assimilation occurs four times per day. This animation shows the global observations (top-left) assimilated into the GEOS-5 DAS over 6 hours and various subsets of these observations. Sources include NASA observations (top-center), NOAA and EUMETSAT operational temperature and moisture sounders (top-right), conventional upper air observations (middle-left), conventional surface observations (middle-center), satellite-derived ozone measurements (middle-right), satellite-derived wind measurements (bottom-left), and GPS radio occultation (bottom-center). Legend showing the observation types assimilated into the GEOS-5 data assimilation system.