What's New with Earth Today

The Global Precipitation Measurement (GPM) mission produces NASA's most comprehensive global rain and snowfall product to date, called the Integrated Multi-satellite Retrievals for GPM (IMERG). It is computed using data from the GPM constellation of satellites — a network of international satellites that currently includes the GPM Core Observatory, GCOM-W1, NOAA-18, NOAA-19, DMSP F-16, DMSP F-17, DMSP F-18, Metop-A, and Metop-B. The global IMERG dataset provides precipitation rates for the entire world every 30 minutes. Although the process to create the combined dataset is intensive, the GPM team creates a preliminary, near-real-time dataset of precipitation within several hours of data acquisition. This visualization shows the most currently available precipitation data from IMERG, depicting how rain and snowstorms move around the planet. As scientists work to understand all the elements of Earth's climate and weather systems, and how they could change in the future, GPM provides a major step forward in providing comprehensive and consistent measurements of precipitation for scientists and a wide variety of user communities.

This visualization shows the daily Arctic sea ice and seasonal land cover change progressing through time, with a single frame rendered for each day (available from the drop-down of each image window), and an animation created from these frames. The Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water "SHIZUKU" (GCOM-W1) satellite. Two JAXA datasets are used in this animation: the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature. In this visualization sea ice changes from day to day, with the amount of ice shown being determined by the AMSR2 sea ice concentration data. A running 3-day minimum is used, with a minimum threshhold concentration of 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month. The numerical portion of the frame filename begins with the four-digit year, followed by the three-digit day of the year for that frame.

This visualization shows the daily Arctic sea ice and seasonal land cover change progressing through time, with a single frame rendered for each day (available from the drop-down of each image window), and an animation created from these frames. The Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water "SHIZUKU" (GCOM-W1) satellite. Two JAXA datasets are used in this animation: the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature. In this visualization sea ice changes from day to day, with the amount of ice shown being determined by the AMSR2 sea ice concentration data. A running 3-day minimum is used, with a minimum threshhold concentration of 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month. The numerical portion of the frame filename begins with the four-digit year, followed by the three-digit day of the year for that frame.

This image shows the current orbits of NASA's fleet of Earth observing spacecraft. Satellite orbits are generated using today's two-line element sets (TLEs). This website is updated every 30 minutes. Spacecraft included: • Aqua • Aura • CALIPSO: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation • CYGNSS-1: Cyclone Global Navigation Satellite System 1 • CYGNSS-2: Cyclone Global Navigation Satellite System 2 • CYGNSS-3: Cyclone Global Navigation Satellite System 3 • CYGNSS-4: Cyclone Global Navigation Satellite System 4 • CYNGSS-5: Cyclone Global Navigation Satellite System 5 • CYGNSS-7: Cyclone Global Navigation Satellite System 7 • CYGNSS-8: Cyclone Global Navigation Satellite System 8 • Cloudsat • GPM: Global Precipitation Measurement • GRACE-FO-1: Gravity Recovery and Climate Experiment Follow On-1 • GRACE-FO-2: Gravity Recovery and Climate Experiment Follow On-2 • ICESat-2 • ISS: International Space Station • Landsat 8 • Landsat 9 • OCO-2: Orbiting Carbon Observatory-2 • SMAP: Soil Moisture Passive Active • Suomi NPP: Suomi National Polar-orbiting Partnership • Sentinel-6 Michael Freilich • SWOT • Terra The clouds used in this version are from a high resolution GEOS model run at 10 minute time steps interpolated down to the per-frame level. The timeframe for this model does not match the date in this fleet visualization, so the clouds shown do not represent actual conditions for today.

Nitrogen dioxide can impact the respiratory system, and it also contributes to the formation of other pollutants including ground-level ozone and particulates. The gas is produced primarily during the combustion of gasoline in vehicle engines and coal in power plants. Air pollution has decreased even though population and the number of cars on the roads have increased. The shift is the result of regulations, technology improvements and economic changes, scientists say. This visualization shows tropospheric column concentrations of nitrogen dioxide as detected by the Ozone Monitoring Instrument (OMI) on NASA's Aura satellite, averaged yearly from 2005-2022.

Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its "minimum" before colder weather begins to cause ice cover to increase. An analysis of satellite data by NASA and the National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder shows that the 2021 minimum extent, which was likely reached on Sept. 18, measured 1.80 million square miles (4.67 million square kilometers). The Japan Aerospace Exploration Agency (JAXA) provides many water-related products derived from data acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument aboard the Global Change Observation Mission 1st-Water "SHIZUKU" (GCOM-W1) satellite. Two JAXA datasets used in this animation are the 10-km daily sea ice concentration and the 10 km daily 89 GHz Brightness Temperature. In this animation, the daily Arctic sea ice and seasonal land cover change progress through time, from the yearly maximum ice extent on February 25 2022, through its minimum on September 18 2022. Over the water, Arctic sea ice changes from day to day showing a running 3-day minimum sea ice concentration in the region where the concentration is greater than 15%. The blueish white color of the sea ice is derived from a 3-day running minimum of the AMSR2 89 GHz brightness temperature. The yellow boundary shows the minimum extent averaged over the 30-year period from 1981 to 2010. Over the terrain, monthly data from the seasonal Blue Marble Next Generation fades slowly from month to month.

Satellite-based passive microwave images of the sea ice have provided a reliable tool for continuously monitoring changes in the Arctic ice since 1979. Every summer the Arctic ice cap melts down to what scientists call its "minimum" before colder weather begins to cause ice cover to increase. This graph displays the area of the minimum sea ice coverage each year from 1979 through 2022. In 2022, the Arctic minimum sea ice covered an area of 4.16 million square kilometers (1.6 million square miles). This visualization shows the expanse of the annual minimum Arctic sea ice for each year from 1979 through 2022 as derived from passive microwave data.

After leaving the Caribbean, Hurricane Fiona became both the strongest and the first major hurricane of the 2022 Atlantic hurricane season as it made its way northward through the western Atlantic. Fiona began as an African easterly wave that moved across the tropical Atlantic in the direction of the Caribbean. While still about 800 miles east of the Leeward Isles, this wave organized into a tropical depression on September 14th. Later that same day, the depression strengthened and became Tropical Storm Fiona. Fiona remained a moderate tropical storm as it passed through the Leeward Isles on the evening of the 16th near Guadeloupe with maximum sustained winds reported at 50 mph by the National Hurricane Center (NHC). After entering the northeastern Caribbean, Fiona took a west-northwest track in the direction of Puerto Rico and began to slowly intensify, becoming a Category 1 hurricane at 11 am (AST) on the 18th just before making landfall at 3:20 pm (AST) near Punta Tocon with maximum sustained winds estimated at 85 mph. After passing over the southwest tip of Puerto Rico, Fiona emerged over the Mona Passage before making landfall in the Dominican Republic early the next morning at 3:30 am AST on the 19th near Boca de Yuma with sustained winds of 90 mph. After passing over northeast Hispaniola, Fiona took a northwest track as it re-emerged into the Atlantic around midday on the 19th in the direction of the Turks and Caicos Islands. As it moved away from Hispaniola, Fiona was able to overcome some moderate southwesterly wind shear and began to intensify over warm waters, becoming a Category 2 storm later that afternoon. Early on the morning of the 20th at 2am (AST) , Fiona became a major Category 3 hurricane with sustained winds reported at 115 mph as it born down on Grand Turk Island. After passing near Grand Turk Island, Fiona intensified further becoming a Category 4 storm with sustained winds of 130 mph early on the morning of the 21st as it headed due north about 755 miles southwest of Bermuda. Fiona maintained its intensity throughout the day on the 21st as it continued northward over the open western Atlantic well east of the US East Coast. Fiona continued to maintain its intensity on the 22nd as it accelerated northward ahead of a deep upper-level trough of low pressure over the northeastern US, passing west and northwest of Bermuda. Around this time, at about 05:30 UTC (1:30 AST) on the morning of the 23rd, the GPM Core Observatory satellite passed over the center of Fiona when the center was located about 185 miles due west of Bermuda. Corresponding images of surface rain rates estimated from the GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) (inner swath) show a large, intense outer rainband wrapping nearly completely around the storm well away from the center. This is generally separated by an area of weak rain with another area of more intense rain located closer to the center wrapping around the southern, eastern and western parts of the storm. This type of structure is generally characteristic of a large, intense storm that has passed its peak intensity. Over time, hurricane wind fields tend to expand away from the center. Echo top heights derived from the GPM DPR provide a 3D perspective of the precipitation within Fiona. The brighter red areas show that thunderstorms are still active in the southern and eastern portions of the eyewall and helping to maintain its intensity, which was still reported at 130 mph by NHC. However, soon after these images were taken, Fiona began to weaken due to the effects of increasing vertical wind shear. Fiona is forecast to continue to weaken and transition to a post tropical storm but remain near hurricane intensity as closes in on the Canadian Maritimes.

Super Typhoon Nanmadol became one of the strongest typhoons to threaten Japan since records began in 1951. Nanmadol began as a tropical disturbance, basically an area of active thunderstorms, on September 11th southeast of Iwo Jima about midway between Tokyo and Guam. After moving to the southwest for 2 days, this disturbance became better organized and formed into a depression on the 13th. The system then made a counterclockwise loop, moving first back to the northeast before turning back again towards the west. Over this time, the system slowly intensified, becoming Tropical Storm Nanmadol just before its westward turn. At this point, Nanmadol responded to a favorable environment, including sea surface temperatures (SSTs) in the area running 0.5 to over 1.0OC above normal due in part to the ongoing La Niña, and began to steadily intensify as it headed for the southern part of Japan, becoming a typhoon on the afternoon of the 15th, a category 3 typhoon on the morning of the 16th , and a category 4 super typhoon on the evening of the 16th with maximum sustained winds estimated at 150 mph by the Joint Typhoon Warning Center (JTWC). It was during this transition from typhoon to super typhoon, at 7:57 UTC (4:57 pm JST) 16 September, that the NASA / JAXA GPM Core Observatory satellite overflew Nanmadol, providing a detailed look into the storm’s structure. With its array of active and passive sensors, the GPM Core Observatory satellite is ideal for monitoring and studying tropical cyclones. This data visualization shows Nanmadol in the West Pacific beginning on the 15th of September 2022 as the storm was moving northwest towards southern Japan, though still far from landfall. The animation first shows a time loop of surface rainfall estimates from NASA’s IMERG precipitation product overlaid on IR data from the CPC global cloud cover composite. At the start of the loop at 07:41 UTC, IMERG shows that Nanmadol is already a well-defined typhoon having a distinct eye with moderate to heavy rain wrapping completely around the center. IMERG also reveals the size of Nanmadol’s large cyclonic (counterclockwise) circulation with curved rainbands wrapping around the center of low pressure well away from the center in nearly all directions. Over the course the IMERG loop, Nanmadol strengthened from a category 1 typhoon to a category 4 super typhoon. The second part of the visualization shows a detailed look into the structure and intensity of the precipitation within Nanmadol. Surface rainfall estimates from the GPM Microwave Imager (GMI) show heavy rain (in red) wrapping around the western and southern portions of the storm well away from the center as well as rainbands approaching the coast of Japan well to the north (green areas). GPM’s Dual-frequency Precipitation Radar (DPR) actively scanned the storm to provide a 3D perspective of its precipitation. Areas shaded in blue show frozen precipitation aloft. This is mainly in the form of snow but can also be graupel, which are rimed snow particles, and frozen drops, which are both present in the cores of active thunderstorms. Moreover, the structure and height to which these particles extend can provide additional information on future trends. The DPR shows that Nanmadol’s eyewall is both deep and complete, creating what is known as a “stadium effect” with a ring of tall towers surrounding a void in the center, which is the eye. This structure is only associated with mature and intense tropical cyclones and suggests Nanmadol will either maintain its intensity or strengthen. The tall towers result from strong thunderstorm updrafts generating and carrying the particles aloft. Associated with this are intense rain shafts (shown in magenta) that extend down towards the surface. Together these features suggest strong thunderstorms are actively releasing heat into Nanmadol’s core circulation and priming the storm for possible further intensification. At the time of the GPM overpass, Nanmadol’s maximum sustained winds were estimated at 130 mph by JTWC. Just over 4 hours later at 1200 UTC September 16th, they were estimated at 150 mph, making Nanmadol a super typhoon. Nanmadol would go on to reach a peak intensity of 155 mph before starting to weaken as it neared the Japanese coast. Nanmadol made landfall on the 18th as a category 3 storm with sustained winds of around 110 mph near Kagoshima city on the island of Kyushu. Nanmadol then turned to the northeast and moved along the west coast of Honshu before crossing back out into the Pacific east of Japan. The storm has brought heavy rains to much of Japan and is so far being blamed for 2 deaths.

The heat of the sun forces evaporation at the ocean's surface, which puts water vapor into the atmosphere but leaves minerals and salts behind, keeping the ocean salty. The salinity of the ocean also varies from place to place, because evaporation varies based on the sea surface temperature and wind, rivers and rain storms inject fresh water into the ocean, and melting or freezing sea ice affects the salinity of polar waters.

This visualization highlights data from a new fire detection and tracking approach (Chen et al., 2022) based on near-real time active fire detections from the VIIRS sensor on the Suomi-NPP satellite. Every 12 hours, the fire tracking algorithm uses new active fire detections to update the total fire perimeter and estimate the position of active fire lines where the fire may continue to spread. Yellow lines indicate the new fire fronts from active fire data (red points) every 12 hours. This approach provides a detailed perspective on the behavior of the Dixie fire, the largest fire in California history. The fire tracking data identify periods of rapid fire expansion, spot fires from blowing embers outside of the large fire perimeter, and active fire detections within the perimeter from continued flaming and smoldering behind the active fire fronts. In total, the Dixie fire burned for more than 100 days, including more than a month of fire activity after the perimeter was contained in mid-September. For more details, see the paper here.

This visualization highlights data from a new fire detection and tracking approach (Chen et al., 2022) based on near-real time active fire detections from the VIIRS sensor on the Suomi-NPP satellite. Every 12 hours, the fire tracking algorithm uses new active fire detections to update the total fire perimeter and estimate the position of active fire lines where the fire may continue to spread. Yellow lines indicate the new fire fronts from active fire data (red points) every 12 hours. This approach provides a detailed perspective on the behavior of the Caldor fire, just the second fire in California history to cross the Sierra Nevada Mountains. The fire tracking data identify periods of rapid fire expansion and active fire detections within the perimeter from continued flaming and smoldering behind the active fire fronts. In total, the Caldor fire burned for more than 80 days through the El Dorado National Forest, threatening the communities of Meyers and South Lake Tahoe. The fire continued to burn for more than a month after the perimeter was contained in mid-September. For more details, see the paper here.

According to a July 29 2022 report from the International Food Security and Nutrition Working Group, the worst drought conditions in 70 years across the Horn of Africa have more than 16 million people coping with a shortage of drinking water. Yields of key crops are down for the third year in a row, milk production is in decline, and more than 9 million livestock animals have been lost due to a lack of water and suitable forage land. At the same time, regional conflicts, COVID-19, locusts, and the Ukraine War have caused price spikes and shortages of basic commodities. An estimated 18 to 21 million people now "face high levels of acute food insecurity" in Ethiopia, Kenya, and Somalia. These animations depict root zone and surface soil moisture observations and forecasts from the NASA Hydrological Forecast and Analysis System (NHyFAS). Reds depict areas with soil moisture percentages below the average, while blues reflect areas that are above average (often due to passing storms). The first 27 seconds of the animation show soil moisture from August 2020 through June 2022. The final 10 seconds show forecasts for July through December 2022, including the next rainy season. Root zone moisture is critical for long term crop growth. New seedlings are mostly dependent on surface water, but then as plants grow and sink deeper roots, they are sustained by moisture in the top layer of the soil.

The Gulf of Maine North Atlantic Time Series (GNATS) is a transect time series across the widest part of the Gulf of Maine, between Portland, Maine and Yarmouth, Nova Scotia. It was started in 1998, funded by the Office of Biology and Biogeochemistry at NASA. It has two primary goals: (a) the calibration and validation of ocean color satellite remote sensing and (b) the development of a time series of physical, chemical, biological, biogeochemical and bio-optical variables with which to evaluate climate change. As of 2021, the GNATS data set includes results from 215 cruises. This visualization shows a small subset of variables collected from the first 20 years of GNATS (1998-2018 inclusive; 204 cruises in total), showing the location and temperature across the GNATS transect (vertical drops of expendable bathythermographs plus Moving Vessel Profiler measurements), autonomous Slocum Electric Glider missions (beginning 2008) and surface phytoplankton primary productivity. Since 1998, GNATS has documented extreme drought years, some of the wettest years in the last century, extreme warming events and an overall average decreasing primary production (60% mean decline). All of these results show the Gulf of Maine coastal shelf sea in transition, during our current geological epoch known as the Anthropocene. The visualization zooms into the Gulf of Maine region revealing sea surface temperatures (SST) that vary seasonally. Next GNATS transects of temperature data are shown over time as the data are collected. These data were collected from a mobile platform on a ferry. Next, data from a Slocum Electric Glider (an underwater vehicle) are shown. Once all of the Glider data are shown, all data are faded out except for "Henry Mission #4" data which were acquired between April 28 and May 21 of 2009. Then as a comparison, data from "Henry Mission #17" are shown which were acquired data between April 21 and May 12 of 2017. The change shows significantly more warm water in the deeper parts of the Gulf of Maine. These differences are similar to what the data show as a whole. The Gulf of Maine is warming. Finally, the visualization zooms out showing Sea Surface Temperature from a global perspective. The GNATS data used in this study are available from NASA SEABASS; https://seabass.gsfc.nasa.gov/experiment/GNATS doi: 10.5067/SeaBASS/GNATS/DATA001 See also: https://youtu.be/n6kX8liqDJU

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We gratefully acknowledge the captains, crews and staff of the various ships used in the GNATS program since 1998: M/S Scotia Prince, HSV The CAT, M/S Nova Star, HSV Alakai, R/V Connecticut, R/V Argo Maine, R/V Endeavor, F/V Ella and Sadie. We would also like to thank numerous former employees of Bigelow Laboratory who participated in the 204 GNATS cruises shown here: Danielle Alley, Amanda Ashe, Emily Booth, Rosaline Campbell, Susanne Dunford, Colin Fischer, Dr. Joaquim Goes, James Johnson, Laura Lubelczyk, Emily Lyczkowski, Elise Olson, Abby Onos, Carlton Rauschenberg, Dr. Bob Vaillancourt, Dr. Laura Windecker, Dr. Meredith White, Heather-Anne Wright and Amy Wyeth.

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Research ships traveling from Hawaii into the north Pacific Ocean have measured quantities of tiny organisms in the water called Prochlorococcus using an instrument called SeaFlow. These organisms are phytoplankton that, like plants, turn carbon dioxide into oxygen. Prochlorococcus is both the smallest and most abundant photosynthesizing organism on the planet. Scientists observed a falloff in the quantity of these organisms as the ships moved northward; however, the falloff did not happen where the water cooled as the scientists expected. This was a bit of a mystery. The scientists hypothesized that the drop off was actually due to competition with other tiny organisms like heterotrophic bacteria and their shared predator, a type of zooplankton. They coded this relationship into a computational model of the oceans called Darwin. Sure enough, when the predator-prey relationship was included in the model, the drop off occurred in the same place as the ship measurements. This demonstrates a powerful combination of using computational models with the scientific method.

Features in Earth’s atmosphere, spawned by the heat of the Sun and the rotation of the Earth, transport water and energy around the globe. Clouds and precipitation shown here are from NASA’s MERRA-2 reanalysis, a retrospective blend of a weather model and conventional and satellite observations. Within the mid-latitudes, winds move clouds from west to east. Within the tropics easterly trade winds converge along the equator to create a moisture rich cluster of clouds, convection, and precipitation called the intertropical convergence zone, or ITCZ. Disturbances in its flow transport immense amounts of moisture and energy from the tropics to the poles. Studies have shown that atmospheric rivers account for the vast majority of the poleward transport of water vapor. The American Meteorological Society defines an atmospheric river as “a long, narrow, and transient corridor of strong horizontal water vapor transport that is typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone.” A common measure for the strength of an atmospheric river is the integrated water vapor transport, or the amount of moisture that is moved from one place to another by the flow of the atmosphere. The blue shading shown here gives a three-dimensional view of the water vapor transport. Tropical moisture is pulled in from the ITCZ and in this example, converges with other moisture sources to form an atmospheric river. The feature then travels towards the west coast of the United States as a sub-class of atmospheric rivers commonly referred to as the “pineapple express” due to its origin near Hawai’i. The atmospheric river is guided by the semi-permanent sub-tropical high pressure off the coast of California and the Baja Peninsula as well as the Aleutian low in the Gulf of Alaska. The pressure gradient between the clockwise flow of the Californian high and the counterclockwise flow of the Aleutian low funnel the atmospheric moisture into a narrow corridor. The more intense the pressure gradient is, the stronger the winds are that transport the water vapor. Extreme rainfall has also been associated with the more intense gradients. Much of the moisture stays close to the surface but the rising motion of the low pressure to the north results in the air cooling, condensing the water vapor into a liquid. Precipitation over the ocean falls along the feature’s cold front on its northern side. Another way that air can rise and condense into precipitation is through orographic lift. When air encounters the mountains along the west coast of the United States, it is forced upwards. The rising air becomes saturated, causing rain and snow to fall, particularly on the windward side of the mountain. The flow of air continues eastward, depleted of its moisture. The precipitation that falls because of atmospheric rivers is important for the hydrologic cycle in the western United States. The winter buildup of the snowpack provides valuable freshwater resources. Despite being beneficial at times, atmospheric river induced precipitation can also be destructive. The occurrence of extreme atmospheric river precipitation events, such as the one that occurred in this example, can result in widespread flooding and mudslides. Atmospheric rivers are not unique to the west coast of North America and occur around the globe, including Europe, New Zealand, the Middle East, Greenland, and Antarctica. The study of global phenomenon such as atmospheric rivers over the past four decades is made possible through NASA’s MERRA-2 reanalysis, a spatially and temporally consistent blend of satellite and conventional observations with a numerical model. With a dataset that provides hourly information around the globe since 1980, there is still much that can be learned about Earth’s atmosphere and the transport of water and energy around the globe.

After growing through the fall and winter, sea ice in the Arctic appears to have reached its annual maximum extent. The image above shows the ice extent—defined as the total area in which the ice concentration is at least 15 percent—at its 2023 maximum, which occurred on March 6. On this day the extent of the Arctic sea ice cover peaked at 14.62 million square kilometers (5.64 million square miles), making it the fifth lowest yearly maximum extent on record. This year’s maximum is 1.03 million sq km below the 1981-2010 average Arctic maximum of 15.65 million sq km. The trend in the maximum is -41,200 sq km per year or -2.6 % per decade relative to the 1981-201 average.

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

NASA’s Orbiting Carbon Observatory, 2 (OCO-2) provides the most complete dataset tracking the concentration of atmospheric carbon dioxide (CO₂), the main driver of climate change. Every day, OCO-2 measures sunlight reflected from Earth’s surface to infer the dry-air column-averaged CO₂ mixing ratio and provides around 100,000 cloud-free observations. Despite these advances, OCO-2 data contain many gaps where sunlight is not present or where clouds or aerosols are too thick to retrieve CO₂ data. In order to fill gaps and provide science and applications users a spatially complete product, OCO-2 data are assimilated into NASA’s Goddard Earth Observing System (GEOS), a complex modeling and data assimilation system used for studying the Earth’s weather and climate. GEOS is also informed by satellite observations of nighttime lights and vegetation greenness along with about 1 million weather observations collected every hour. These data help scientists infer CO₂ mixing ratios even when a direct OCO-2 observation is not present and provide additional information on the altitude of CO₂ plumes that the satellite is not able to see. Together, OCO-2 and GEOS create one of the most complete pictures of CO₂. The visualization featured on this page shows the atmosphere in three dimensions and highlights the accumulation of CO₂ during a single calendar year. Every year, the world’s vegetation and oceans absorb about half of human CO₂ emissions, providing an incredibly valuable service that has mitigated the rate of accumulation of greenhouse gases in the atmosphere. However, around 2.5 parts per million remain in the atmosphere every year causing a steady upward march in concentrations that scientists have tracked since the 1950s at surface stations. The volumetric visualization starts in June 2020, showing all of the model’s values of global CO₂. All 3d cells of the model are opaque, revealing a solid brick of data. During the month of June 2020, the higher values of CO₂ coalesce around the equatorial belt. By mid-July 2020 the visualization reduces the opacity of lower CO₂ values between 385 parts-per-millon (ppm) and 405 ppm in the atmosphere making them transparent. These lower values tend to be higher up in the atmosphere. By doing this, the higher CO₂ concentrations, which are closer to the ground, are highlighted revealing the seasonal movement of high CO₂ at a global scale. During the months of June-September (summer months for northern hemisphere), global CO2 concentrations tend to be lowest because northern hemisphere plants actively absorb CO₂ from the atmosphere via photosynthesis. During northern hemisphere fall and winter months, much of this CO₂ is re-released to the atmosphere due to respiration and can be seen building up. By June and July 2021, plants again draw CO₂ out of the atmosphere, but notably higher concentrations remain in contrast to the nearly transparent colors of the previous year. The diurnal rhythm of CO₂ is apparent over our planet's largest forests, such as the Amazon rainforest in South America and the Congo rainforest in Central Africa. The fast-paced pulse in those rainforests is due to the day-night cycle; plants absorb CO₂ during the day via photosynthesis when the sun is out, then stop absorbing CO₂ at night. In addition to highlighting the buildup of atmospheric CO₂, this visualization shows how interconnected the world’s greenhouse gas problem is. NASA’s unique combination of observations and models plays a critical role in helping scientists track increases in CO₂ as they happen to better understand their climate impact.

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This visualization was created specifically to support a series of talks from NASA scientists for the 2021 United Nations Climate Change Conference (COP26), Glasgow, UK, 31 October-12 November 2021.

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Data Sources:

• Volumetric Carbon Dioxide extracted from NASA's Goddard Earth Observing System (GEOS) model, which is produced by the Global Modeling and Assimilation Office. The visualization featured on this page utilizes 3-hourly data for the period June 1, 2020-July 31, 2021.
• Blue Marble: Next Generation was produced by Reto Stöckli, NASA Earth Observatory (NASA Goddard Space Flight Center). Citation: Reto Stöckli, Eric Vermote, Nazmi Saleous, Robert Simmon and David Herring. The Blue Marble Next Generation – A true color earth dataset including seasonal dynamics from MODIS, October 17, 2005. The visualization on this page utilizes monthly Blue Marble data to map the water and land bodies around the globe and show seasonal changes.
• Sea ice for the Arctic and Antarctic regions, provided by the Japan Aerospace Exploration Agency (JAXA), by utilizing GCOMP-W/AMSR2 10 km Level 3 daily Sea Ice Concentration (SIC) and GCOMP-W/AMSR2 10 km Level 3 daily 89 GHz Brightness Temperature (BT) data for the period June 1, 2020-July 31, 2021.
• Global 30 Arc-Second Elevation (GTOPO 30) from U.S. Geological Survey (USGS). GTOPO30 is a global raster digital elevation model (DEM) with a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). GTOPO30 was derived from several raster and vector sources of topographic information. The data-driven visualization featured on this page utilizes the GTOPO30 model to represent the three-dimensional features of over land terrain and submarine topography world-wide. doi: 10.5066/F7DF6PQS.

The mass of the Polar ice sheets have changed over the last decades. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2020, Antarctica shed approximately 150 gigatons of ice per year, causing global sea level to rise by 0.4 millimeters per year; and Greenland shed approximately 280 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year. These images, created from GRACE and GRACE-FO data, show changes in polar land ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002. The average flow lines (grey; created from satellite radar interferometry) of the icesheets converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss. This supports other observations that warming ocean waters near polar icesheets play a key role in contemporary ice mass loss.

The mass of the Greenland ice sheet has rapidly declined in the last several years due to surface melting and iceberg calving. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2020, Greenland shed approximately 280 gigatons of ice per year, causing global sea level to rise by 0.03 inches (0.8 millimeters) per year. These images, created from GRACE and GRACE-FO data, show changes in Greenland ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002. In general, higher-elevation areas near the center of Greenland experienced little to no change, while lower-elevation and coastal areas experienced over 16.4 feet (5 meters) of ice mass loss (expressed in equivalent-water-height; dark red) over this 19-year period. The largest mass decreases occurred along the West Greenland coast. The average flow lines (grey; created from satellite radar interferometry) of Greenland’s ice converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss. This supports other observations that warming ocean waters around Greenland play a key role in contemporary ice mass loss.

The mass of the Antarctic ice sheet has changed over the last decades. Research based on observations from the Gravity Recovery and Climate Experiment (GRACE) satellites (2002-2017) and GRACE Follow-On (since 2018 - ) indicates that between 2002 and 2020, Antarctica shed approximately 150 gigatons of ice per year, causing global sea level to rise by 0.4 millimeters per year. These images, created from GRACE and GRACE-FO data, show changes in Antarctic ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002. Areas in East Antarctica experienced modest amounts of mass gain due to increased snow accumulation. However, this gain is more than offset by significant ice mass loss on the West Antarctic Ice Sheet (dark red) over the 19-year period. Floating ice shelves whose mass change GRACE & GRACE-FO do not measure are colored gray. The average flow lines (grey; created from satellite radar interferometry) of Antarctica’s ice converge into the locations of prominent outlet glaciers, and coincide with areas of highest mass loss (i.e., Pine Island and Thwaites glaciers in West-Antarctica). This supports other observations that warming ocean waters around Antarctica play a key role in contemporary ice mass loss.

This visualization shows active fires as observed by the Visible Infrared Imaging Radiometer Suite, or VIIRS, between January 1 and September 24 2021. The VIIRS instrument flies on the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 polar-orbiting satellites. Instruments on polar orbiting satellites typically observe a wildfire at a given location a few times a day as they orbit the Earth from pole to pole. VIIRS detects hot spots at a resolution of 375 meters per pixel, which means it can detect smaller, lower temperature fires than other fire-observing satellites. Its observations are about three times more detailed than those from the MODIS instrument, for example. VIIRS also provides nighttime fire detection capabilities through its Day-Night Band, which can measure low-intensity visible light emitted by small and fledgling fires. This visualization uses data from the Suomi-NPP VIIRS instrument, and will be updated periodically until the end of 2021.

Hurricane Ida struck southeast Louisiana as a powerful Category 4 storm on Sunday, Aug. 29, 2021- the 16th anniversary of Hurricane Katrina’s landfall in 2005. Ida brought destructive storm surge, high winds, and heavy rainfall to the region, and left over 1 million homes and businesses without power, including the entire city of New Orleans. The NASA / JAXA GPM Core Observatory satellite flew over the eye of Ida shortly before landfall at 10:13 a.m. CDT (1513 UTC), capturing data on the structure and intensity of precipitation within the storm. This animation shows NASA's IMERG multi-satellite precipitation estimates and NOAA GOES-E satellite cloud data, followed by 3D data from the GPM Core satellite. NASA processed these observations in near real-time and made them available to a wide range of users including weather agencies and researchers. After Ida passed over Cuba as a Category 1 storm, it intensified rapidly to reach Category 4 strength near its Louisiana landfall. According to the National Hurricane Center (NHC), Ida's central pressure reached a minimum of 929 hPa with a 15 nautical mile (17 statute mile) wide eye. At the time, Ida had its lifetime-maximum wind speed of 130 kt (150 mph) in the eyewall shortly before 10 a.m. CDT on Aug. 29. The 3D Dual-frequency Precipitation Radar (DPR) data collected by the GPM Core satellite shows a healthy hurricane inner core in Ida. The small 17-mile-diameter eyewall is surrounded by a nearly complete outer ring of precipitation approximately 85 miles in diameter. Beyond this central structure, an arc of precipitation exists another 40 miles further from the eye to the southeast. The eye hosts many clouds extending well above 6 miles (10 km), which indicates that Ida was still actively growing at the time of this overpass. NASA continues to monitor Ida as it moves north over the southeastern U.S., providing Earth-observing satellite data, maps and analysis to stakeholders to aid response and recovery efforts. Get the latest updates on Hurricane Ida from the National Hurricane Center (NHC). Learn more about how NASA monitors hurricanes. GPM data is archived at https://pps.gsfc.nasa.gov/

The Arctic Animal Movement Archive (AAMA) is a new and growing collection of studies describing movements of animals in and near the Arctic. The AAMA includes millions of locations of thousands of animals over more than three decades, recorded by hundreds of scientists and institutions. By compiling these data, the AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. We have used the AAMA to document climatic influences on the migration phenology of golden eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, species-specific changes in terrestrial mammal movement rates in response to increasing temperature, and the utility of animal-borne sensors as proxies for ambient air temperature. The AAMA is a living archive that can be used to uncover other such changes, investigate their causes and consequences, and recognize larger ecosystem changes taking place in the Arctic. These visualizations show AAMA animal location data. Some of the visualizations collpase the years down as if all of the data were from the same year; others show the data with the years passing. Several different groupings of animals are shown: marine mammals, raptors, seabirds, shorebirds, terrestrial mammals, and waterbirds. Snow and sea ice are also shown for context as they correlate to animal movements. Citation: Ecological insights from three decades of animal movement tracking across a changing Arctic. S.C. Davidson, et al. Science 06 Nov 2020: Vol. 370, Issue 6517, pp. 712-715 DOI: 10.1126/science.abb7080 Data citation: The Arctic Animal Movement Archive (AAMA) is a collection of studies that contain animal movement and other animal-borne sensor data from the Arctic and Subarctic, owned by hundreds of participating experts and organizations. As of November 2020, this collection includes 214 studies that contain over 43 million locations of over 12,000 animals recorded from 1988 to the present. Initial development of the AAMA was funded by NASA's Arctic-Boreal Vulnerability Experiment. The AAMA is hosted on Movebank, a free, global research platform for animal movement and animal-borne sensor data. Long-term support for the storage and curation of the AAMA in Movebank comes from the Max Planck Institute of Animal Behavior. Visit the archive to learn more and find out how to participate. NASA Media: https://www.nasa.gov/feature/goddard/2020/arctic-animals-movement-patterns-are-shifting-in-different-ways-as-the-climate-changes AGU iPoster: https://agu2020fallmeeting-agu.ipostersessions.com/Default.aspx?s=1C-9F-40-11-B3-77-C2-50-6F-F3-B1-3B-60-B4-93-5E# AGU Hyperwall Talk: [placeholder]

This visualization shows the hurricanes and tropical storms of 2021 as seen by NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) - a data product combining precipitation observations from infrared and microwave satellite sensors united by the GPM Core Observatory. IMERG provides near real-time half-hourly precipitation estimates at ~10km resolution for the entire globe, helping researchers better understand Earth’s water cycle and extreme weather events, with applications for disaster management, tracking disease, resource management, energy production and food security. IMERG rain rates (in mm/hr) are laid under infrared cloud data from the NOAA Climate Prediction Center (CPC) Cloud Composite dataset together with storm tracks from the NOAA National Hurricane Center (NHC) Automated Tropical Cyclone Forecasting (ATCF) model. Sea surface temperatures (SST) are also shown over the oceans, derived from the NASA Multi-sensor Ultra-high Resolution (MUR) dataset, which combines data from multiple geostationary and orbiting satellites. Sea surface temperatures play an important role in hurricane formation and development, with warmer temperatures linked to more intense storms. This data visualization was done for the United Nations Climate Change Conference - Conference of the Parties (COP) 26. The 2021 hurricane season officially ends November 30th. This data visualization will be periodically updated until that date.

This visualization shows total sea level change between 1992 and 2019, based on data collected from the TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3 satellites. Blue regions are where sea level has gone down, and orange/red regions are where sea level has gone up. Since 1992, seas around the world have risen an average of nearly 6 inches.
The color range for this visualization is -15 cm to +15 cm (-5.9 inches to +5.9 inches), though measured data extends above and below 15 cm (5.9 inches). This particular range was chosen to highlight variations in sea level change.

Earth’s seas are rising, a direct result of a changing climate. Ocean temperatures are increasing, leading to ocean expansion. And as ice sheets and glaciers melt, they add more water. A fleet of increasingly sophisticated instruments deployed by NASA across the oceans, on polar ice and in orbit, reveals significant changes among globally interlocking factors that are driving sea levels higher.

NASA, ESA (European Space Agency) and JAXA (Japan Aerospace Exploration Agency) have created a dashboard of satellite data showing impacts on the environment and socioeconomic activity caused by the global response to the coronavirus (COVID-19) pandemic.

The Greenland Ice Sheet holds enough water to raise the world’s sea level by over 7 meters (23 feet). Rising atmosphere and ocean temperatures have led to an ice loss equivalent to over a centimeter increase in global mean sea-level between 1991 and 2015. Large outlet glaciers, rivers of ice moving to the sea, drain the ice from the interior of Greenland and cause the outer margins of the ice sheet to recede. Improvements in measuring the ice thickness in ice sheets is enabling better simulation of the flow in outlet glaciers, which is key to predicting the retreat of ice sheets into the future. Recently, a simulation of the effects of outlet glacier flow on ice sheet thickness coupled with improved data and comprehensive climate modeling for differing future climate scenarios has been used to estimate Greenland’s contribution to sea-level over the next millennium. Greenland could contribute 5–34 cm (2-13 inches) to sea-level by 2100 and 11–162 cm (4-64 inches) by 2200, with outlet glaciers contributing 19–40 % of the total mass loss. The analysis shows that uncertainties in projecting mass loss are dominated by uncertainties in climate scenarios and surface processes, followed by ice dynamics. Uncertainties in ocean conditions play a minor role, particularly in the long term. Greenland will very likely become ice-free within a millennium without significant reductions in greenhouse gas emissions. This movie shows the evolution of several regions of the Greenland Ice Sheet between 2008 and 2300 based on three different climate scenarios. Each scenario reflects a potential future climate outcome based on current and future greenhouse gas emmisions. The scenario labelled "LOW" here is based on the Representative Concentration Pathway (RCP) 2.6 climate scenario while the one labelled "MEDIUM" is based on RPC 4.6. The visualization labelled "HIGH" is based on RPC 8.5 and reflects the current trajectory of emissions in the 21st century. The regions shown in a violet color are exposed areas of the Greenland bed that were covered by the ice sheet in 2008. The data sets used for these animations are the control (“CTRL”) simulations and were produced with the open-source Parallel Ice Sheet Model . All data sets for this study are publicly available at the NSF Arctic Data Center

After regaining hurricane intensity over the Gulf Stream, Hurricane Isaias made landfall on the south coast of North Carolina on Monday August 3rd at 11:10 pm EDT near Ocean Isle Beach. This data visualization shows Isaias as is makes its way northward from the Bahamas to the coast of North Carolina using NASA’s IMERG rainfall product. With IMERG, precipitation estimates from the GPM core satellite are used to calibrate precipitation estimates from microwave and IR sensors on other satellites to produce half-hourly precipitation maps at 0.1 degrees horizontal resolution. After making landfall, Isaias continued tracking northward over eastern North Carolina in response to a large upper-level trough located over the eastern half of the US. It was at this time that Isaias was again overflown by the GPM core satellite at 8:51 UTC (4:51 am EDT) on the morning of Tuesday August 4th, as shown in the second part of the data visualization. Here rainfall rates derived directly from the GPM Microwave Instrument (or GMI) and Dual-Polarization Radar (or DPR) provide a detailed look at Isaias, which at the time was still a strong tropical storm with sustained winds reported at 70 mph by the National Hurricane Center. GPM clearly shows the center of circulation over northeastern North Carolina, which at the time was just southeast of Roanoke Rapids, NC, with a large eye that is open on the southern side. Amazingly, despite the center being located down in North Carolina, GPM shows a large rain shield extending from North Carolina all the way into New England to the Canadian border as a result of the storm’s counterclockwise circulation drawing abundant moisture off the Atlantic and over land where the combination of an old frontal boundary and the Appalachian terrain squeeze out this moisture to form large amounts of precipitation ahead of the storm, which is then drawn further northward by southerly flow aloft from the upper-level trough. GPM data is archived at https://pps.gsfc.nasa.gov/

One of the big challenges in polar science is measuring the thickness of the floating sea ice that blankets the Arctic and Southern Oceans. Newly formed sea ice might be only a few inches thick, whereas sea ice that survives several winter seasons can grow to several feet in thickness (over ten feet in some places). Sea ice thickness is typically estimated by first measuring sea ice freeboard - how much of the floating ice can be observed above sea level. Sea ice floats slightly above sea level because it is less dense than water. An additional complexity is that snow fall on sea ice pushes the floating ice downward and has a lower density than the sea ice. In order to estimate the sea ice thickness, some accommodation for the overlying snow must be made. NASA’s ICESat-2 satellite measures the Earth’s surface height by firing green laser pulses towards Earth and timing how long it takes for those laser pulses to reflect back to the satellite. The laser light reflects off the top of the snow layer on top of the sea ice. In contrast, the European Space Agency’s CryoSat-2 mission uses radar waves to measure height. These radar waves penetrate the overlying snow and are reflected off the sea ice, rather than the overlying snow. In July 2020, ESA elected to slightly perturb the orbit of CryoSat-2 to increase the overlap with ICESat-2. Given their different orbit altitudes, the result is a ~3000km stretch of sea ice that is measured by both ICESat-2 and CryoSat-2. By combining data from these two sensors, scientists can measure the snow layer thickness, and produce substantially improved sea ice thickness estimates.

The Joint Polar Satellite System (JPSS) is the nation’s advanced series of polar-orbiting environmental satellites. JPSS satellites circle the Earth from pole-to-pole and cross the equator 14 times daily in the afternoon orbit—providing full global coverage twice a day. Polar satellites are considered the backbone of the global observing system. The operational JPSS constellation currently consists of the NASA-NOAA Suomi National Polar-Orbiting Partnership satellite, the technology pathfinder mission for JPSS launched in 2011, and NOAA-20, previously called JPSS-1 and launched in 2017. The next satellite in the series, JPSS-2, is scheduled to launch in the first quarter of 2022. Once it is accepted into the constellation post-launch, JPSS-2 will be renamed NOAA-21 and replace Suomi-NPP. JPSS represents significant technological and scientific advancements in observations used for severe weather prediction and environmental monitoring. These data are critical to the timeliness and accuracy of forecasts three to seven days in advance of a severe weather event. JPSS is a collaborative effort between NOAA and NASA.

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 newly updated data visualization of the Earth's Biosphere was unveiled at the 2022 United Nations Climate Change Conference (COP 27). By monitoring the color of reflected light via satellite, scientists can determine how successfully plant life is photosynthesizing. A measurement of photosynthesis is essentially a measurement of successful growth, and growth means successful use of ambient carbon. This data visualization represents five years' worth of data taken primarily by Suomi NPP/VIIRS satellite sensors, showing the abundance of life both on land and in the sea. In the ocean, dark blue represents warmer areas where there is little life due to lack of nutrients, where yellow and orange represent cooler nutrient-rich areas. The nutrient-rich areas include coastal regions where cold water rises from the sea floor bringing nutrients along and areas at the mouths of rivers where the rivers have brought nutrients into the ocean from the land. On land, green represents areas of abundant plant life, such as forests and grasslands, while tan and white represent areas where plant life is sparse or non-existent, such as the deserts in Africa and the Middle East and snow-cover and ice at the poles.

By monitoring the color of reflected light via satellite, scientists can determine how successfully plant life is photosynthesizing. A measurement of photosynthesis is essentially a measurement of successful growth, and growth means successful use of ambient carbon. This data visualization represents twenty years' worth of data taken primarily by SeaStar/SeaWiFS, Aqua/MODIS, and Suomi NPP/VIIRS satellite sensors, showing the abundance of life both on land and in the sea. In the ocean, dark blue to violet represents warmer areas where there is little life due to lack of nutrients, and greens and reds represent cooler nutrient-rich areas. The nutrient-rich areas include coastal regions where cold water rises from the sea floor bringing nutrients along and areas at the mouths of rivers where the rivers have brought nutrients into the ocean from the land. On land, green represents areas of abundant plant life, such as forests and grasslands, while tan and white represent areas where plant life is sparse or non-existent, such as the deserts in Africa and the Middle East and snow-cover and ice at the poles.

The El Niño-Southern Oscillation (ENSO) phenomenon is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of Earth. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in rainfall, temperature and environmental anomaly conditions worldwide that directly favor outbreaks of infectious diseases of public health concern. During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying interannual climate variability patterns associated with El Niño by monitoring various climate datasets, among them land surface temperature and vegetation data from the Advanced High Resolution Radiometer (AVHRR) on board NOAA POES satelittes, the Moderate Resolution Imaging Spectroradiometer aboard NASA's Terra and Aqua satellites, and Sea Surface Temperature and precipitation anomaly datasets from NASA and the National Oceanic and Atmospheric Administration (NOAA). At the same time, the science team has been collecting, cataloguing and analyzing patterns of disease outbreaks worldwide. Dr. Anyamba and colleagues conducted a scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - that was published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event and is open access available. According to this study, the 2015-2016 El Niño event brought weather conditions that triggered disease outbreaks in ENSO teleconnected regions throughout the world. The visualization showcases a global flat map with monthly Sea Surface Temperature (SST) anomaly data on the water, the locations of Global Disease Outbreaks of ten infectious diseases on land, along with a timeline plot of the ENSO Index (Niño 3.4 Index region SST anomaly) for the period 2009-2018 on the bottom. The Nino 3.4 Index region SST with extents (5N-5S, 120W-170W) is the box region, highlighted on the Pacific Ocean. During ENSO events, SST anomalies influence the nature and patterns of rainfall, vegetation and land surface temperatures on the land surface, which in turn influence the disease outbreaks that are mapped on a global scale. The 10 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, and tularemia. During the 2015-2016 El Nino event, which is manifested in the visualization with increased sea surface temperature anomaly (reds in Niño 3.4 Index Region), changes in precipitation, land surface temperatures and vegetation created and facilitated conditions for transmission of diseases, resulting in an uptick in reported cases for plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015 and 2016), and dengue fever in Brazil and Southeast Asia (during 2015), among others. According to the study, El Niño-driven increase in rainfall and milder temperatures over the American Southwest, spurred vegetative growth, providing more food for rodents that carry hantavirus. A resulting rodent population explosion put them in more frequent contact with humans, who contract the potentially fatal disease mostly through rodent fecal or urine contamination. As their rodent hosts proliferated, so did plague-carrying fleas. Regarding dengue outbreaks, the strong El Niño period produced higher than normal land surface temperatures and therefore drier habitats, which drew mosquitoes into populated, urban areas where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher the normal temperatures increase the maturation time of larvae to adult mosquitos and also induce frequent blood feeding/biting by mosquito vectors resulting in increased number of disease cases. The following 3 data driven visualizations demonstrate the complex relationships between the El Niño event in 2015-2016 and disease outbreaks of dengue in the South East Asia region:

• Teleconnections in South East Asia for the period of 2015-2016
• Precipitation Anomaly and Dengue Outbreaks in South East Asia: 2015-2016
• Land Surface Temperature Anomaly and Dengue Outbreaks in South East Asia Region: 2015-2016

The effects of ENSO induced anomalous rainfall are clearly illustrated by outbreak patterns of Rift Valley fever (RVF) in East and South Africa. During ENSO events, Eastern Africa (El Niño) and South Africa (La Niña) receive persistent and above normal rainfall, which floods habitats of RVF mosquito vectors triggering hatching of RVF virus infected eggs. The above-normal rainfall is followed by an increase in vegetation creating appropriate habitats for the mosquito vectors setting the stage for RVF outbreak activity, which in simple terms means an uptick in mosquito populations that cause infections of domestic livestock and human populations. The results is the sea-saw pattern exhibited by the ENSO events drives patterns of disease outbreaks in different regions around the world. To learn more about the relationship between ENSO and Rift Valley fever outbreaks in the region of South Africa, please refer to:

• ENSO Teleconnections and Rift Valley fever (RVF) Outbreaks
• Precipitation Anomaly and Rift Valley fever (RVF) outbreaks in South Africa: 2008-2011
• Vegetation Index Anomalies and Rift Valley fever (RVF) outbreaks in South Africa region: 2008-2011

The strong relationship between ENSO events (i.e El Niño, La Niña) and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study suggest that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.

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Data Sources:

• Sea Surface Temperature (SST) data: The SST known as the NOAA OI.v2 SST monthly fields are derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). The SST dataset is available here.
• Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED), the Pan-American Health Organization (PAHO) online country reports, weekly summaries of disease outbreaks reported by the Department of Defense Armed Forces Health Surveillance Branch and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).
• SST ENSO index (Niño 3.4) for the period 2009-2018 is obtained from the NOAA National Center for Climate Prediction online archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5°C based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).

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This visualization was created to support the AGU 2019 conference presentation, titled El Niño-Southern Oscillation Teleconnections and Global Patterns of Disease Outbreaks (December 11 2019, Moscone Conference Center, San Francisco, CA) Supported with funding from the Defense Threat Reduction Agency's (DTRA) Joint Science and Technology Office for Chemical and Biological Defense (JSTO-CBD) Biosurveillance Ecosystem (BSVE) Program (HDTRA1-16-C-0045) and the Defense Health Agency-Armed Forces Health Surveillance Branch (AFHSB) Global Emerging Infections Surveillance and Response System (GEIS) under Project # P0072_19_NS.

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Below, you can find frames, alternate movies, colorbar information and layers associated with the development of this data-driven visualization.

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.

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.

VIDEO: "Witness the Breathtaking Beauty of Earth’s Polar Regions"

Operation IceBridge recorded the diversity and fragility of our rapidly changing polar regions. These areas are some of the most inhospitable, but breathtaking places on Earth. Sit back and witness the polar regions, from western Greenland to Antarctica. Notable features include the Pine Island Glacier, Larsen C ice shelf, and rapid summer melt on the western Greenland Ice Sheet.

Learn more: Operation IceBridge

Music Provided by Universal Production Music: "Arabesque No.1" by Claude Debussy [PD]

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If it feels like spring came early this year, it’s not your imagination. Thanks to the leap year, this is the earliest spring equinox since 1896 — more than 120 years ago. NOAA satellites, launched by NASA, can see signs of spring everywhere from the unique vantage point of space. From plants greening up to changes in our weather, NOAA satellites have you covered by continuously monitoring instant and long-term change. For 50 years, NOAA’s weather satellites have provided observations and imagery of storm systems, which helps forecasters monitor and assess a storm’s evolution. Orbiting Earth at different heights and paths, NOAA’s fleet of satellites gives us an important, comprehensive view of our planet. The Geostationary Operational Environmental Satellite (GOES) system is parked in an orbit over the equator and continuously tracks the same area. Meanwhile, the Joint Polar Satellite System (JPSS) is in a lower orbit, flying over the north and south poles to give us a constantly shifting global perspective. These satellites work in concert to provide imagery for monitoring a storm, and temperature and moisture data to be fed into the weather forecast models meteorologists use to develop the weather forecast you rely on every day.

Satellite data offers a broad, global view of land surface changes, but cloud cover interferes with collecting data. Landsat satellites provide observations every 16 days, and having two satellites reduces that to every 8 days. The European Space Agency Sentinel-2 satellites collect data in similar wavelengths and at a similar spatial resolution, enabling the data to be combined for even more observations. When harmonized into one data set, the result is global observations every two or three days at 30-meter resolution. Any application looking at very dynamic phenomena, where changes occur on the timescales of a few days or weeks, will benefit from the harmonized Landsat/Sentinel dataset. For example, crop condition and area, burned area, or surface water extent. Also, this will benefit any application where short-term environmental conditions (like drought) have a rapid impact on ecosystems.

The Clouds and the Earth’s Energy Radiant System (CERES) instrument is a key component of NASA’s Earth Observing System, with six active CERES instruments on satellites orbiting Earth and taking data.   For Earth’s temperature to be stable over long periods of time, absorbed solar and emitted thermal radiation must be equal. Increases in greenhouse gases, like carbon dioxide and methane, trap emitted thermal radiation from the surface and reduce how much is lost to space, resulting in a net surplus of energy into the Earth system. Most of the extra energy ends up being stored as heat in the ocean and the remainder warms the atmosphere and land, and melts snow and ice. As a consequence, global mean surface temperature increases and sea levels rise. Much like a pulse or heartbeat, CERES monitors reflected solar and emitted thermal infrared radiation, which together with solar irradiance measurements is one of Earth’s ‘vital signs.’ Better understanding Earth’s energy balance enables us to be informed and adapt to a changing world.

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 GEOS modeling system with atmospheric chemistry. Shown is a sequence of modeled surface concentrations of 96 chemical species during the time period July 22, 2018 to October 2, 2018. These chemicals undergo rapid changes as they are being emitted by natural and anthropogenic activities, transported by prevailing winds and vertical lifting motions, deposited to the surface, or chemically transformed. The visualization starts with a global map of model predicted concentrations of surface ozone, a potent air pollutant that is chemically produced from hydrocarbons and nitrogen oxides under the presence of sunlight. Consequently, the highest concentrations of surface ozone can be found during daytime close to urban areas and in the vicinity of forest fires (e.g., Africa). At night, ozone is chemically destroyed in highly polluted environments, leading to very low nighttime concentrations over industrial areas such as Eastern China. These processes are captured in detail by NASA’s GEOS composition forecast (CF) system, which incorporates the latest scientific understanding of the physics and chemistry that guide the formation of ozone, along with measurements from satellites and other instrument platforms. A particular feature of the model system used here is its comprehensive representation of atmospheric chemistry using the GEOS-Chem chemistry model, capturing 240 gaseous species that react with each other via 725 chemical reactions. Directly or indirectly, all of these species impact the formation of ozone. The visualization shows snapshots of modeled concentrations of 96 of the most important chemical compounds, loosely grouped into seven ‘families’ based on their physical and chemical properties. Very tightly linked to ozone is the hydrogen oxides “HOx” family. It contains the highly reactive hydroxyl radical, OH, which plays a prominent role in atmospheric chemistry due to its role as a ‘cleansing agent’ of the atmosphere. The abundance of OH, which is subject to the availability of water vapor and sunlight, in turn directly impacts the atmospheric lifetime of hydrocarbons such as methane and carbon monoxide. Human activities constitute an important source for both of these gases (beside natural sources) and directly influence the long-term concentration trends of these pollutants, as can be directly observed from NASA satellites. Another related group of chemicals are hydrocarbons from biogenic activity: “Isoprene oxidation”. Plants emit hundreds of (structurally similar) compounds, with isoprene being the most important one. These compounds rapidly react with each other through a complex cascade of reactions, which makes the chemistry over vegetation-rich areas such as rain forests or the Southeast US challenging to simulate. Biogenic compounds also play an important role for the formation of aerosols: tiny particles that can constitute a major health risk when inhaled. The abundance and composition of aerosol particles is highly variable and is influenced by anthropogenic activities (e.g., soot from biofuel burning) as well as natural events, such as wildfires, dust storms, volcanic eruptions, and sea spray. The ocean is also a source of another group of chemicals, the halogens. These species tend to be highly reactive and can effectively destroy ozone, especially over remote areas. The last chemical group depicted in the visualization is related to nitrogen. Nitrogen oxides are central to atmospheric chemistry in general and ozone formation in particular. At the surface, the most important source of nitrogen dioxide (NO2) is the combustion of fossil fuels. As a result, NO2 concentrations are highest over urban areas (e.g. highways, power plants) and along ship routes. The visualization ends back at the beginning with ozone, illustrating the connectiveness of the chemical system of the atmosphere. Given the complexity of atmospheric chemistry, computer simulations – such as those by the NASA GEOS composition forecasting system – are an essential tool to understanding the formation of air pollution and to help formulate effective mitigation strategies. Here's a list of each of the chemical species shown and their groupings (ppbV=pars per billion by volume):

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

The Earth Sciences Division at NASA's Goddard Space Flight Center plans, organizes, evaluates, and implements a broad program of research on our planet's natural systems and processes. Major focus areas include climate change, severe weather, the atmosphere, the oceans, sea ice and glaciers, and the land surface. To study the planet from the unique perspective of space, the Earth Science Division develops and operates remote-sensing satellites and instruments. We analyze observational data from these spacecraft and make it available to the world's scientists. Our Education and Public Outreach efforts raise public awareness of the Division's research and its benefits to society.

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 Goddard Space Flight Center has developed the Goddard Earth Observing System (GEOS), a family of mathematical models. Combined with data from 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

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.

The Reference Elevation Model of Antarctica (REMA) provides the first, high resolution (8-meter) terrain map of nearly the entire continent. REMA is constructed from hundreds of thousands of individual stereoscopic Digital Elevation Models (DEM) extracted from pairs of submeter (0.32 to 0.5 m) resolution DigitalGlobe satellite imagery, including data from WorldView-1, WorldView-2, and WorldView-3, and a small number from GeoEye-1, acquired between 2009 and 2017, with most collected in 2015 and 2016, over the austral summer seasons (mostly December to March). Each individual DEM was vertically registered to satellite altimetry measurements from Cryosat-2 and ICESat, resulting in absolute uncertainties of less than 1 m over most of its area, and relative uncertainties of decimeters. This visualization compares the spatial resolution of REMA with DEM data from RADARSAT.

NASA’s ICESat-2 satellite measures the elevation of Earth’s surfaces – and two data products from the mission map the height of Antarctic and Greenland ice sheets, as well as how those ice sheets change over time. The ICESat-2 ATL14 data product provides a reference ice sheet surface corresponding to the ice sheet elevation in April 2019, while ATL15 provides elevation changes to that surface through time. These products are re-generated every 91 days, which is how long it takes ICESat-2 to complete its 1,387 unique orbits and collect a complete grid of measurements. Every time ATL14 and 15 are regenerated, all of the data over the life of the mission is used to improve the April 2019 standard, and best represent how the ice sheets are changing. ATL14 is posted at 100m resolution, and ATL15 is posted at 1 km resolution at one month time resolution.

As cities and countries locked down during COVID-19, some changes were visible from space. NASA, ESA and JAXA have partnered to create a dashboard making those data available. Read more: https://www.nasa.gov/press-release/nasa-partner-space-agencies-amass-global-view-of-covid-19-impacts

NASA, ESA (European Space Agency) and JAXA (Japan Aerospace Exploration Agency) have created a dashboard of satellite data showing impacts on the environment and socioeconomic activity caused by the global response to the coronavirus (COVID-19) pandemic. The dashboard will be released on Thursday, June 25 during a tri-agency media briefing. The briefing speakers are: • Josef Aschbacher, director of ESA Earth Observation Programmes • Thomas Zurbuchen, associate administrator of NASA’s Science Mission Directorate • Koji Terada, vice president and director general for the Space Technology Directorate at JAXA • Shin-ichi Sobue, project manager for JAXA’s ALOS-2 mission • Ken Jucks, program scientist for NASA’s OCO-2 and Aura missions • Anca Anghelea, open data scientist, ESA Earth observation programmes

On June 1, the World Health Organization noted that Central and South American countries have become “the intense zones” for COVID-19 transmission. The Ozone Monitoring Instrument (OMI) on board NASA’s Aura satellite provides data that indicate that restrictions on human activity have led to about a 36% decrease in NO₂ levels in Rio de Janeiro, Brazil, relative to previous years. Other large cities in South America show similar decreases in NO₂: 36% in Santiago, Chile; 35% in São Paolo, Brazil; and 40% in Buenos Aires, Argentina. One notable exception is in Lima, Peru, showing a 69% decrease. The large decrease may partly be associated with natural variations in weather that can, for instance, disperse air pollution more quickly. Additional analysis is required to determine the amount of the decrease of NO₂ in Lima that is associated with a decrease in human activity. A notable increase in NO₂ occurred in northern South America, which is likely associated with increased agricultural burning in 2020 relative to previous years.

Over the past several weeks, the United States has seen significant reductions in air pollution over its major metropolitan areas. Similar reductions in air pollution have been observed in other regions of the world. These recent improvements in air quality have come at a high cost, as communities grapple with widespread lockdowns and shelter-in-place orders as a result of the spread of COVID-19. One air pollutant, nitrogen dioxide (NO2), is primarily emitted from burning fossil fuels (diesel, gasoline, coal), coming out of our tailpipes when driving cars and smokestacks when generating electricity. Therefore, changes in NO2 levels can be used as an indicator of changes in human activity. However, care must be taken when processing and interpreting satellite NO2 data as the quantity observed by the satellite is not exactly the same as the NO2 abundance at ground level. NO2 levels are influenced by dynamical and chemical processes in the atmosphere. For instance, atmospheric NO2 levels can vary day-to-day due to changes in the weather, which influences both the lifetime of NO2 molecules as well as the dispersal of the molecules by the wind. It is also important to note that satellites that observe NO2 cannot see through clouds, so all data shown is for days with low amounts of cloudiness. If processed and interpreted carefully, NO2 levels observed from space serve as an effective proxy for NO2 levels at Earth's surface. NASA's air quality group is also monitoring other air pollutants, such as sulfur dioxide (SO2). Major anthropogenic activities that emit SO2 include electricity generation, oil and gas extraction, and metal smelting. SO2 is emitted during electricity generation if the coal burned has sulfur impurities that are not removed (or not “scrubbed”) from the plant’s exhaust stacks. For more information on what pollutants NASA satellites observe, visit the NASA Air Quality website.

Preventative measures adopted to reduce the rate of spread of COVID-19 in the U.S. prompted an overall slowdown in economic activity and fewer vehicles on the roadways in the spring of 2020. To examine changes in air quality in California, NASA constructed weekly averaged nitrogen dioxide (NO2) maps for March and April 2020 at 0.05° grid spacing from high-quality, cloud-free retrievals provided by Tropospheric Monitoring Instrument (TROPOMI) level 2 data. During first weekday period (March 2-6, pre-shutdown) when COVID-19 measures were yet to be implemented, the largest tropospheric NO2 concentrations were observed in Los Angeles and bordering counties with a less prominent peak in NO2 around San Francisco. The TROPOMI scans also resolved areas of enhanced NO2 along the heavily trafficked corridor of State Route 99 (SR-99) in the Central Valley. As initial, soft COVID-19 measures were adopted by businesses in California during the second weekday period, March 9-13, TROPOMI observed strong reductions in tropospheric column NO2 around the large cities of Los Angeles and San Francisco along with noticeable decreases along SR-99. When California announced statewide “shelter-in-place” orders during the third weekday period, March 16-20, further decreases in NO2 were apparent throughout all populated areas in the state and along SR-99. Further weekly areages showed variable decreases in NO2 as decreased economic activity continued. Overall, these observed reductions in TROPOMI NO2 throughout the spring season are the result of decreased emissions on top of the seasonal changes in meteorological conditions.

These images show the impact the spread of the novel coronavirus (COVID-19) has had on reducing air pollution in the United States as widespread lockdowns and shelter-in-place orders have been put in place. The images show a reduction in the levels of nitrogen dioxide (NO2)—a noxious gas emitted by motor vehicles, power plants, and industrial facilities—as measured by the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite in March 2020. The “without stay-at-home orders” images show average monthly NO2 concentrations during March and April from 2015 through 2019, while the “during stay-at-home orders” images show average monthly concentrations in March and April 2020. These improvements in air quality have come at a high cost, as communities grapple with the impacts of COVID-19. The data indicate that the NO2 levels in March and April 2020 are much lower on average across the United States when compared to the mean of 2015 to 2019.