Visualizations for Educators

Phenomena are observable events that occur in nature. Data visualizations can offer new ways for students to experience and explore Earth and space phenomena that happen over large scales of time and at great distances. This gallery includes visualizations of phenomena that support topics that are taught in middle and high school and are aligned with select Next Generation Science Standards.

This gallery was curated by Anne Arundle County Science Teachers Margaret Graham and Jeremy Milligan with support from Dr. Rachel Connolly during the summer of 2022. A video showing how Jeremy Milligan uses SVS resources to develop a phenomena-based lesson is also available.

Content Contact:

ESS1.A The Universe and its Stars

The Earth’s Place in the Universe collection contains visuals that support phenomena based learning with a focus on the NGSS standards. The middle school standards focus on patterns of motion including rotation, seasons, eclipses, moon phases, orbits of planets, and the motions of galaxies. At the high school level topics include the life cycle of the Sun, Big Bang theory, and orbital motion.

  • Electromagnetic Spectrum
    2008.07.14
    This animation shows a graphical representation of the electromagnetic spectrum and includes - Radio Waves, Infrared, Visible, Ultraviolet, X-Rays and Gamma Rays
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    The Electromagnetic Spectrum components
    2016.09.20
    The electromagnetic (EM) spectrum is the range of all types of EM radiation. Radiation is energy that travels and spreads out as it goes – the visible light that comes from a lamp in your house and the radio waves that come from a radio station are two types of electromagnetic radiation. The other types of EM radiation that make up the electromagnetic spectrum are microwaves, infrared light, ultraviolet light, X-rays and gamma-rays.
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    Big Bang Animation--5k Resolution
    2017.12.22
    Artist's interpretation of the Big Bang, with representations of the early universe and its expansion.
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    Zoom In on Galaxy M87
    2019.09.24
    This movie zooms into galaxy M87 using real visible light, X-ray and radio pictures of the galaxy, its jet of high-speed particles, and the shadow of its central black hole.
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    An Orrery of Black Holes and Their Companions
    2022.05.02
    This visualization shows 22 X-ray binaries in our Milky Way galaxy and its nearest neighbor, the Large Magellanic Cloud, that host confirmed stellar-mass black holes. The systems are shown at the same physical scale, and their orbital motion is sped up by nearly 22,000 times. All of the binaries are angled to replicate our view of them from Earth. The star colors range from blue-white to reddish, representing temperatures from 5 times hotter to 45% cooler than our Sun. Because the accretion disks reach even higher temperatures, they use a different color scheme. While the black holes appear on a scale reflecting their masses, all are depicted using spheres much larger than actual size. Cygnus X-1’s black hole, the first one ever confirmed, weighs about 21 times more than the Sun, but its surface – called its event horizon – spans only about 77 miles (124 kilometers). The enlarged spheres also cover up visible distortions produced by the black holes’ gravitational effects. In most of these systems, a stream of gas often flows directly from the star toward the black hole, forming around it a broad, flattened structure called an accretion disk. In others, like Cygnus X-1, a massive star produces a thick outflow called a stellar wind, some of which becomes swept up by the black hole’s intense gravity. Gas in the accretion disk heats up as the material slowly spirals inward, glowing in visible, ultraviolet, and finally X-ray light.

ESS1.B: Earth and the Solar System

Earth and the Solar System (ESS1.B)
  • NASA Eyes
    External Resource
    Welcome to NASA's Eyes, a way for you to learn about your home planet, our solar system, the universe beyond and the spacecraft exploring them.
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    The Moon's Rotation
    2017.10.06
    An enduring myth about the Moon is that it doesn't rotate. While it's true that the Moon keeps the same face to us, this only happens because the Moon rotates at the same rate as its orbital motion, a special case of tidal locking called synchronous rotation. The animation shows both the orbit and the rotation of the Moon. The yellow circle with the arrow and radial line have been added to make the rotation more apparent. The arrow indicates the direction of rotation. The radial line points to the center of the visible disk of the Moon at 0°N 0°E.
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    Rotating Earth at Night
    2013.10.04
    This new space-based view of Earth’s city lights is a composite assembled from data acquired by the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The data was acquired over nine days in April 2012 and thirteen days in October 2012. It took the satellite 312 orbits and 2.5 terabytes of data to get a clear shot of every parcel of Earth’s land surface and islands. This new data was then mapped over existing MODIS Blue Marble imagery to provide a realistic view of the planet. The view was made possible by the “day-night band” of Suomi NPP’s Visible Infrared Imaging Radiometer Suite. VIIRS detects light in a range of wavelengths from green to near-infrared and uses “smart” light sensors to observe dim signals such as city lights, auroras, wildfires, and reflected moonlight. This low-light sensor can distinguish night lights tens to hundreds of times better than previous satellites.
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    Great Zoom into Salt Lake City, UT: Rice-Eccles Olympic Stadium (with Spin and Night Lights)
    2002.02.08
    Using data from different spacecraft and some powerful computer technology, visualizers at the Goddard Space Flight Center present you with a collection of American cities in a way you have never seen them before. Starting with our camera high above the Earth, we rush in towards the surface at what would be an impossible speed for any known vehicle. Passing though layers of atmosphere, the colors of our destinations shimmer with their own unique characteristics, and suddenly we find ourselves floating in virtual space just above the ground.
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    Black Marble 2016 (Rotating Globe)
    2017.04.25
    Satellite images of Earth at night—often referred to as "night lights"—have been a gee-whiz curiosity for the public and a tool for fundamental research for nearly 25 years. They have provided a broad, beautiful picture, showing how humans have shaped the planet and lit up the darkness. Produced every decade or so, such maps have spawned hundreds of pop-culture uses and dozens of economic, social science, and environmental research projects.

    This image of Earth at night in 2016 was created with data from the Suomi National Polar-orbiting Partnership (NPP) satellite launched in October 2011 by NASA, the National Oceanic and Atmospheric Administration, and the U.S. Department of Defense. Each pixel shows roughly 0.46 miles (742 meters) across.

    Scientists use the Suomi NPP night-lights dataset in many ways. Some applications include: forecasting a city’s energy use and carbon emissions; eradicating energy poverty and fostering sustainable energy development; providing immediate information when disasters strike; and monitoring the effects of conflict and population displacement. Scientists at NASA are working to automate nighttime VIIRS data processing so that data users are able to view nighttime imagery within hours of acquisition, which could lead to other potential uses by research, meteorological, and civic groups.

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    NASA On Air: A Reason For The Seasons (9/22/2014)
    2014.09.22
    LEAD: The fall or autumnal equinox, officially arrives at 10:29 PM, EDT Monday evening. Equinox means equal night and day.

    1. Looking at the Northern Hemisphere, night is on the left and day is on the right.

    2. Advancing to December, night on the left becomes longer and days become shorter.

    3. It is the relative tilt of Earth, as it goes around the sun, that causes our seasons.

    4. Fewer hours of sunshine result in our colder winter temperatures.

    TAG: By December 21 Earth’s North Pole will be tilted 23.5 degrees away from the sun which decreases temperatures. When the Northern Hemisphere is tilted towards the sun temperatures can double.

    NOTE

    This video is also playable via NASAviz (iPad App): “Follow the Line” Oct 31, 2013.

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    Normalized Difference Vegetation Index (NDVI) Seasonal Cycles
    2020.04.20
    This visualization shows the Normalized Difference Vegetation Index (NDVI) over seaveral seasonal cycles. This NDVI dataset is part of the Next Generation Blue Marble product. This visualization was created in part to support Earth Day 2020 media releases.
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    Seasonal sea ice and snow cover visualizations
    2017.01.04
    Seasonal snow cover and sea ice across the globe from September 2010 to August 2011
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    Seasonal Glow from plants
    2014.01.09
    Scientists map the light emitted by Earth’s land plants.
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    20 Years of Global Biosphere (updated)
    2017.11.14
    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.
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    Net Top of Atmosphere Radiation
    2020.04.17
    This visualization shows top-of-atmosphere (TOA) net radiation for the Earth, as measured from space by the CERES instrument, for the period of August 2005 to July 2014 (this period was chosen for convenience rather than for scientific significance). The net radiation is the difference between absorbed solar radiation and outgoing longwave radiation. This visualization was created in part to support Earth Day 2020 media releases.
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    LRO and the Lunar Eclipse of April 15, 2014: Shadow View
    2014.04.07
    In the early morning hours of April 15, 2014, the Moon enters the Earth’s shadow, creating a total lunar eclipse, the first of four that are visible in the Western Hemisphere in the next two years. This animation shows the changing appearance of the Moon as it travels into and out of the Earth’s shadow, along with the times at various stages. Versions of the animation have been created for each of the four time zones of the contiguous United States. All of North and South America will see this eclipse, and you won’t need special equipment to see it. Just stay up late, go outside and look up! The penumbra is the part of the Earth’s shadow where the Sun is only partially covered by the Earth. The umbra is where the Sun is completely hidden. The animation includes the position of the Lunar Reconnaissance Orbiter spacecraft. LRO is powered by sunlight, but during the eclipse, it will have to rely on its battery for almost three hours.
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    Lunar Eclipses and the Moon's Orbit
    2014.04.10
    The animations on this page illustrate the Moon’s orbit and its role in lunar and solar eclipses. A solar eclipse happens when the Moon’s shadow falls on the Earth, while a lunar eclipse happens when the Earth’s shadow falls on the Moon. Eclipses can only happen at New and Full Moon, when the Earth, Moon, and Sun are all in a straight line. But they don’t happen every New and Full Moon, because the Moon’s orbit is tilted by about 5 degrees. As the Earth and Moon travel around the Sun, the tilt of the Moon’s orbit changes direction relative to the Sun. This is analogous to the way the tilt of the Earth causes seasons. Just like winter and summer happen every six months, eclipses tend to occur on a roughly six-month cycle. Unlike most eclipse shadow diagrams, the first three animations here don’t greatly exaggerate the scale of the Earth and Moon. They are only 2x their true scale. The view is exactly perpendicular to the Earth-Sun line. The angle of the Moon’s orbital tilt and the “tapering” of the shadows are both accurate. The orbit happens to be calculated for the months preceding the April 15, 2014 total lunar eclipse.
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    Lunar Eclipse of April 15, 2014 As Viewed from the Moon
    2014.04.10
    In the early morning hours of April 15, 2014, the Moon enters the Earth’s shadow, creating a total lunar eclipse. When viewed from the Moon, as in this animation, the Earth hides the Sun. A red ring, the sum of all Earth’s sunrises and sunsets, lines the Earth’s limb and casts a ruddy light on the lunar landscape. With the darkness of the eclipse, the stars come out. The city lights of North and South America are visible on the night side of the Earth. The part of the Earth visible in this animation is the part where the lunar eclipse can be seen.
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    Lunar Eclipse Essentials
    2011.06.08
    When the moon passes through the Earth's shadow, it causes the moon to look very unusual for a short period of time. This event is called a lunar eclipse, and it occurs roughly twice a year. Learn more about how lunar eclipses work in this video!

    These videos and animations are available in both standard formats as well as stereoscopic 3D for those who can view it. We've included left and right eye clips, a side-by-side version, and an anaglyph (red/blue) version of the narrated video, and left and right eye clips for each of the animations. The labels next to each link will help you pick!

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    An EPIC View of the 2017 Total Solar Eclipse
    2017.08.22
    NASA's Earth Polychromatic Imaging Camera (EPIC) tracked the path of the total solar eclipse across North America on Aug. 21, 2017. On board NOAA's Deep Space Climate Observatory (DSCOVR), EPIC collected these natural color images. Scientists set the instrument to gather images more frequently than usual to study this eclipse. Learn more about how EPIC contributed to research conducted during the 2017 total solar eclipse.
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    Moon Phase and Libration, 2022
    2021.11.18

    Dial-A-Moon

    Month: Day: UT Hour:



    Click on the image to download a high-resolution version with feature labels and additional graphics. Hover over the image to reveal the animation frame number, which can be used to locate and download the corresponding frame from any of the animations on this page, including unlabeled high-resolution Moon images. The data in the table for the entire year can be downloaded as a JSON file or as a text file. The animation archived on this page shows the geocentric phase, libration, position angle of the axis, and apparent diameter of the Moon throughout the year 2022, at hourly intervals. Until the end of 2022, the initial Dial-A-Moon image will be the frame from this animation for the current hour.

    More in this series:
    Moon Phase and Libration Gallery

    Lunar Reconnaissance Orbiter (LRO) has been in orbit around the Moon since the summer of 2009. Its laser altimeter (LOLA) and camera (LROC) are recording the rugged, airless lunar terrain in exceptional detail, making it possible to visualize the Moon with unprecedented fidelity. This is especially evident in the long shadows cast near the terminator, or day-night line. The pummeled, craggy landscape thrown into high relief at the terminator would be impossible to recreate in the computer without global terrain maps like those from LRO. The Moon always keeps the same face to us, but not exactly the same face. Because of the tilt and shape of its orbit, we see the Moon from slightly different angles over the course of a month. When a month is compressed into 24 seconds, as it is in this animation, our changing view of the Moon makes it look like it's wobbling. This wobble is called libration. The word comes from the Latin for "balance scale" (as does the name of the zodiac constellation Libra) and refers to the way such a scale tips up and down on alternating sides. The sub-Earth point gives the amount of libration in longitude and latitude. The sub-Earth point is also the apparent center of the Moon's disk and the location on the Moon where the Earth is directly overhead. The Moon is subject to other motions as well. It appears to roll back and forth around the sub-Earth point. The roll angle is given by the position angle of the axis, which is the angle of the Moon's north pole relative to celestial north. The Moon also approaches and recedes from us, appearing to grow and shrink. The two extremes, called perigee (near) and apogee (far), differ by as much as 14%. The most noticed monthly variation in the Moon's appearance is the cycle of phases, caused by the changing angle of the Sun as the Moon orbits the Earth. The cycle begins with the waxing (growing) crescent Moon visible in the west just after sunset. By first quarter, the Moon is high in the sky at sunset and sets around midnight. The full Moon rises at sunset and is high in the sky at midnight. The third quarter Moon is often surprisingly conspicuous in the daylit western sky long after sunrise. Celestial north is up in these images, corresponding to the view from the northern hemisphere. The descriptions of the print resolution stills also assume a northern hemisphere orientation. (There is also a south-up version of this page.)

    The Moon's Orbit

    From this birdseye view, it's somewhat easier to see that the phases of the Moon are an effect of the changing angles of the Sun, Moon and Earth. The Moon is full when its orbit places it in the middle of the night side of the Earth. First and Third Quarter Moon occur when the Moon is along the day-night line on the Earth. The First Point of Aries is at the 3 o'clock position in the image. The Sun is in this direction at the March equinox. You can check this by freezing the animation at around the 1:03 mark, or by freezing the full animation with the time stamp near March 20. This direction serves as the zero point for both ecliptic longitude and right ascension. The north pole of the Earth is tilted 23.5 degrees toward the 12 o'clock position at the top of the image. The tilt of the Earth is important for understanding why the north pole of the Moon seems to swing back and forth. In the full animation, watch both the orbit and the "gyroscope" Moon in the lower left. The widest swings happen when the Moon is at the 3 o'clock and 9 o'clock positions. When the Moon is at the 3 o'clock position, the ground we're standing on is tilted to the left when we look at the Moon. At the 9 o'clock position, it's tilted to the right. The tilt itself doesn't change. We're just turned around, looking in the opposite direction. The subsolar and sub-Earth points are the locations on the Moon's surface where the Sun or the Earth are directly overhead, at the zenith. A line pointing straight up at one of these points will be pointing toward the Sun or the Earth. The sub-Earth point is also the apparent center of the Moon's disk as observed from the Earth. In the animation, the blue dot is the sub-Earth point, and the yellow cone is the subsolar point. The lunar latitude and longitude of the sub-Earth point is a measure of the Moon's libration. For example, when the blue dot moves to the left of the meridian (the line at 0 degrees longitude), an extra bit of the Moon's western limb is rotating into view, and when it moves above the equator, a bit of the far side beyond the north pole becomes visible. At any given time, half of the Moon is in sunlight, and the subsolar point is in the center of the lit half. Full Moon occurs when the subsolar point is near the center of the Moon's disk. When the subsolar point is somewhere on the far side of the Moon, observers on Earth see a crescent phase. The Moon's orbit around the Earth isn't a perfect circle. The orbit is slightly elliptical, and because of that, the Moon's distance from the Earth varies between 28 and 32 Earth diameters, or about 356,400 and 406,700 kilometers. In each orbit, the smallest distance is called perigee, from Greek words meaning "near earth," while the greatest distance is called apogee. The Moon looks largest at perigee because that's when it's closest to us. The animation follows the imaginary line connecting the Earth and the Moon as it sweeps around the Moon's orbit. From this vantage point, it's easy to see the variation in the Moon's distance. Both the distance and the sizes of the Earth and Moon are to scale in this view. In the HD-resolution frames, the Earth is 50 pixels wide, the Moon is 14 pixels wide, and the distance between them is about 1500 pixels, on average. Note too that the Earth appears to go through phases just like the Moon does. For someone standing on the surface of the Moon, the Sun and the stars rise and set, but the Earth doesn't move in the sky. It goes through a monthly sequence of phases as the Sun angle changes. The phases are the opposite of the Moon's. During New Moon here, the Earth is full as viewed from the Moon.

    The Named Phases

    The following is a gallery containing examples of each of the Moon phases that have names in English. New, full, and quarter phases occur on specific days, while crescent and gibbous phases are the transitions between these points and span multiple days. The quarters are so named because they occur when the Moon is one fourth or three fourths of the way through its cycle of phases. Many people find this confusing, though, since visually they are half moons. It might be helpful to remember that the visible half of the Moon's disk is really only one quarter of its spherical surface.

ESS1.B: Weather and Climate

The Earth’s Systems collection contains visuals showing weather and climate. These standards also focus on systems like the hydrologic cycle, ocean currents, glacial ice melt, and the carbon cycle.

Weather and Climate(ESS1.B)
  • Near Real-Time Global Precipitation from the Global Precipitation Measurement Constellation
    2015.03.31
    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.
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    Global Tour of Precipitation from NASA
    2016.05.16
    Precipitation (falling rain and snow) is our fresh water reservoir in the sky and is fundamental to life on Earth. A Global Tour of Precipitation from NASA shows how rain and snowfall moves around the world from the vantage of space using measurements from the Global Precipitation Measurement Core Observatory, or GPM. This is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA) and offers the most detailed and worldwide view of rain and snowfall ever created.

    This narrated movie is created for Science On a Sphere, a platform designed by NOAA that displays movies on a spherical screen. Audiences can view the movie from any side of the sphere and can see any part of Earth. During this show viewers will be guided through a variety of precipitation patterns and display features such as the persistent band of the heaviest rainfall around the equator and tight swirls of tropical storms in the Northern Hemisphere. At subtropical latitudes in both hemispheres there are persistent dry areas and this is where most of the major deserts reside. Sea surface temperature and winds are also shown to highlight the interconnectedness of the Earth system. The movie concludes with near real-time global precipitation data from GPM, which is provided to Science On a Sphere roughly six hours after the observation. To download this movie formatted for a spherical screen, visit NOAA's official Science On a Sphere website below: ‌• A Global Tour of Precipitation from NASA ‌• Near Real-Time Global Precipitation Data

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    IMERG Global Precipitation Rates
    2015.10.02
    NASA's Global Precipitation Measurement mission has produced its first global map of rainfall and snowfall. The GPM Core Observatory launched one year ago on Feb. 27, 2014 as a collaboration between NASA and the Japan Aerospace Exploration Agency and acts as the standard to unify precipitation measurements from a network of 12 satellites. The result is NASA's Integrated Multi-satellitE Retrievals for GPM data product, called IMERG, which combines data from all 12 satellites into a single, seamless map. The map covers more of the globe than any previous precipitation data set and is updated every half hour, allowing scientists to see how rain and snow storms move around nearly the entire 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 the scientific community comprehensive and consistent measurements of precipitation.
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    Earth: A System of Systems
    2020.05.08
    In order to study the Earth as a whole system and understand how it is changing, NASA develops and supports a large number of Earth-observing missions. These missions provide Earth science researchers the necessary data to address key questions about global climate change. This visualization reveals that the Earth system, like the human body, comprises diverse components that interact in complex ways. Shown first, the Multi-Scale Ultra-High Resolution (MUR) sea surface temperature (SST) dataset combines data from the Advanced Very High-Resolution Radiometer (AVHRR), Moderate Imaging Spectroradiometer (MODIS) Terra and Aqua, and Advanced Microwave Spectroradiometer-EOS (AMSR-E) instruments. Constantly released into the Earth’s atmosphere, heat and moisture from the ocean and land influence Earth’s weather patterns—represented here as wind speeds from the Modern-Era Retrospective analysis for Research and Applications (MERRA) dataset. Moisture in the atmosphere—represented as water vapor (also from MERRA)—forms clouds (shown here using cloud layer data from the NOAA Climate Prediction Center) and precipitation. Precipitation (data from GPM IMERG) significantly impacts water availability, which influences soil moisture (data from NASA-USDA-FA) and ocean salinity. While scientists learn a great deal from studying each of these components individually, improved observational and computational capabilities increasingly allow them to study the interactions between these interrelated geophysical and biological parameters, leading to unprecedented insight into how the Earth system works—and how it might change in the future.
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    El Niño Watch 2015
    2015.12.27
    Interest in the 2015-16 El Niño is high, as the strength of the event will influence how much rain and snowfall states like California receive this winter. Strong El Niño conditions typically result in wetter weather patterns along the United States west coast, which could help to ameliorate the ongoing drought. A statement issued by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center on December 10, 2015, states that "El Niño is expected to remain strong through the Northern Hemisphere winter 2015-16, with a transition to ENSO-neutral anticipated during late spring or early summer 2016." El Niño is characterized by unusually warm ocean temperatures in the eastern equatorial Pacific. Conditions in 2015 bear some similarities to those of 1997, a year that brought one of the most potent El Niño events of the twentieth century. This visualization provides side-by-side comparisons of Pacific Ocean sea surface height anomalies (SSHA) in 2015 [right] with SSHA during the famous 1997 El Niño [left]. Red shades indicate where the ocean stood above normal sea level because warmer water expands to fill more volume (thermal expansion). Blue shades indicate where sea level and temperatures were lower than average (thermal contraction). Normal sea-level conditions appear yellow. The maps—based on altimetry data collected by the TOPEX/Poseidon (1997) and OSTM/Jason-2 (2015) satellites—have been processed to highlight the interannual signal of SSH, i.e., the mean signal, seasonal signal, and the trend have been removed.
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    El Niño: GMAO Daily Sea Surface Temperature Anomaly from 1997/1998 and 2015/2016
    2016.02.25
    Every two to seven years, an unusually warm pool of water -- sometimes two to three degrees Celsius higher than normal -- develops across the eastern tropical Pacific Ocean to create a natural short-term climate change event. This warm condition, known as El Niño, affects the local aquatic environment, but also spurs extreme weather patterns around the world, from flooding in California to droughts in Australia. In 2015 El Niño unfolded in the Pacific Ocean, as sea surface temperatures create different patterns than seen in the 1997-1998 El Niño. Computer models are just one tool that NASA scientists are using to study this large El Nino event, and compare it to other events in the past."The start of an El Niño is important," said Robin Kovach, a research scientist with the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The visualization shows how the 1997 event started from colder-than-average sea surface temperatures – but the 2015 event started with warmer-than-average temperatures not only in the Pacific but also in in the Atlantic and Indian Oceans."The '97 El Niño was much stronger in the Eastern Pacific, with much warmer water up to the coast of South America," Kovach said. In 2015, the warmest waters are instead in the Central Pacific and extend west of the International Date Line. The water temperature variations typical of El Niño are not only at the surface of the equatorial Pacific, but below the surface as well. And these variations were also different in 2015, compared to 1997. At the height of the El Niño in November, colder-than-average temperatures in the Western Pacific and warmer-than-average temperatures in the Eastern Pacific were stronger and extended deeper in 1997 than in 2015. Goddard’s computer models, with input from ocean buoys, atmospheric models, satellite data and other sources, can also simulate what ocean water temperatures could do in the coming months. The GMAO seasonal forecast, which takes 18 hours to complete, and creates more than nine terabytes of data, shows that this 2015 El Niño could be different until the end. "In the past, very strong El Niño events typically transition to neutral conditions and then a La Niña event," said Kovach. February computer model runs forecast a return to normal sea surface temperatures by June. The latest Feb 5, 2016 forecast does not yet predict below normal sea surface temperatures that would result in a large La Niña. As of Feb. 14, 2016, the latest ocean computer model shows colder-than-average water temperatures off the South American coast from Ecuador to Panama. "This current El Niño has been different so it will be interesting to see what happens in the next forecast and the coming months."
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    Monthly Water Vapor (Terra/MODIS)
    2013.10.24
    Water vapor is the most abundant greenhouse gas in the atmosphere as it traps heat near the surface of the Earth making our planet warm enough to support life. Scientists monitor water vapor in the atmosphere because it influences Earth's weather patterns, and because it is a very important component of Earth's climate system. These maps show a monthly water vapor product from January 2005 to the present, derived using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA’s Terra satellite. The water vapor product reveals the total amount of water vapor in a 1-kilometer by 1-kilometer column of the atmosphere. Dark blue shades indicate areas with high water vapor content, while light yellow shades indicate areas with little or no water vapor content.
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    Precipitation Diurnal Cycles
    2019.10.15
    The daily cycle of weather, also known as the diurnal cycle, shapes how and when our weather develops and is fundamental to regulating our climate. These animations show the most detailed view of the diurnal cycles over the United States. They were created using NASA's newest extended precipitation record known as the Integrated Multi-satellitE Retrievals for GPM, or IMERG analysis. The IMERG analysis combines almost 20 years of rain and snow data from the Tropical Rainfall Measuring Mission (TRMM) and the joint NASA-JAXA Global Precipitation Measurement mission (GPM). Learn more at NASA.gov.
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    Complete 2021 Hurricane Season
    2022.04.21
    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.
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    GEOS-5 Aerosols Simulation
    2014.12.10
    This animation was shown on the hyperwall at the Supercomputing conference in 2014 (SC 2014). This animation covers the period March 1, 2007 - June 16, 2007.
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    A 3D View of an Atmospheric River from an Earth System Model
    2022.01.25
    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.
  •
    Dust in the Wind
    2018.08.27
    Dust, salt and smoke swirling in the air tell a story of summer 2017.
  •
    GEOS-5 Modeled Clouds
    2010.06.18
    This visualization shows clouds from a simulation using the Goddard Earth Observing System Model, Verison 5 (GEOS5). The global atmospheric simulation ran at a resolution of 5-km per grid cell and covered a period from Feb 2, 2010 through Feb 22, 2010. The results of the simulation were written out at 30 minute intervals. This is a high-resolution non-hydrostatic global model.

    This visualization was created for display on the NASA Center for Climate Simulation (NCCS) hyperwall. This is a set of tiled high definition displays consisting of 5 displays across by 3 displays down. The full resolution of all combined displays is 6840 pixels accross by 2304 pixels down. This movie was rendered at this high resolution, then diced up into images to be displayed on each screen.

    A similar, lower resolution visualization is available in entry #3724. The lower resolution version is for comparison to current operational model resolution output. When displaying these visualizations on the hyperwall, we sometimes show them in a checkerboard pattern with alternating 5-km and quarter-degree tiles for easy comparison. We chose to stretch the image to fit the hyperwall aspect rather than cropping or adding black bars.

  •
    GLOBE Observer Clouds: Getting Started
    2019.04.04

    Clouds are a major component of the Earth’s system that reflect, absorb, and scatter sunlight and infrared emissions from Earth. This affects how energy passes through the atmosphere. Different types of clouds have different effects, and the amount of cloud cover is also important. Clouds can change rapidly, so frequent observations are useful to track these changes. Such observations are able to see change over time and help with interpretation of satellite cloud data.

    The cloud observation tool in the GLOBE Observer app allows you to photograph clouds and record sky observations and compare them with NASA satellite images. Our goal is to provide a step-by-step process that helps you learn about clouds and their classification through simple observations and photography.

    You are an important part of the puzzle, providing a new perspective of the clouds that our NASA satellites do not have, looking up. We are excited for you to start collecting data through this updated cloud protocol featuring NASA satellite comparison!

    Learn the basics of how to observe clouds with the GLOBE Observer App. Observations can be made anywhere and anytime. Open the app, select Clouds and follow the directions! Help scientists as a citizen scientist with your observations. For more information, go to https://observer.globe.gov/

  •
    Global Sea Surface Currents and Temperature
    2012.03.16
    This visualization shows sea surface current flows. The flows are colored by corresponding sea surface temperature data. This visualization is rendered for display on very high resolution devices like hyperwalls or for print media.

    This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization.

  •
    Gulf Stream Sea Surface Currents and Temperatures
    2012.02.15
    This visualization shows the Gulf Stream stretching from the Gulf of Mexico all the way over towards Western Europe. This visualization was designed for a very wide, high resolution display (e.g., a 5x3 hyperwall display).

    This visualization was produced using model output from the joint MIT/JPL project entitled Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2). ECCO2 uses the MIT general circulation model (MITgcm) to synthesize satellite and in-situ data of the global ocean and sea-ice at resolutions that begin to resolve ocean eddies and other narrow current systems, which transport heat and carbon in the oceans. The ECCO2 model simulates ocean flows at all depths, but only surface flows are used in this visualization. There are 2 versions provided: one with the flows colored with gray, the other with flows colored using sea surface temperature data. The sea surface temperature data is also from the ECCO2 model. The dark patterns under the ocean represent the undersea bathymetry. Topographic land exaggeration is 20x and bathymetric exaggeration is 40x.

  •
    Earth Day 2020: Gulf Stream ocean current pull out to Earth observing fleet
    2020.04.21
    This visualization was created to be one of the final shots of a video celebrating the 50th anniversary of Earth Day. The camera starts under water off the coast of the Eastern United States showing layers of ocean currents from a computational model called ECCO-2. The camera slowly pulls back revealing the Gulf Stream, one of the most powerful ocean currents on Earth. The camera continues to pull back revealing NASA's Earth observing fleet.

HESS3.C: Human Impacts on Earth Systems

The Earth and Human Activity collection focuses on how humans interact with and change the Earth. These standards include global climate change, environmental hazards and mitigation strategies, and the effects of population increases on natural resources.

  • Impact of Climate Change on Global Agricultural Yields
    2022.03.02
    Climate change will affect agricultural production worldwide. Average global crop yields for maize, or corn, may see a decrease of 24% by late century, if current climate change trends continue. Wheat, in contrast, may see an uptick in crop yields by about 17%. The change in yields is due to the projected increases in temperature, shifts in rainfall patterns and elevated surface carbon dioxide concentrations due to human-caused greenhouse gas emissions, making it more difficult to grow maize in the tropics and expanding wheat’s growing range. Maize is grown all over the world, and large quantities are produced in countries nearer the equator. North and Central America, West Africa, Central Asia, Brazil and China will potentially see their maize yields decline in the coming years and beyond as average temperatures rise across these breadbasket regions, putting more stress on the plants. Wheat, which grows best in temperate climates, may see a broader area where it can be grown in places such as the northern United States and Canada, North China Plains, Central Asia, southern Australia and East Africa as temperatures rise, but these gains may level off mid-century. Temperature alone is not the only factor the models consider when simulating future crop yields. Higher levels of carbon dioxide in the atmosphere have a positive effect on photosynthesis and water retention, more so for wheat than maize, which are accounted for better in the new generation of models. Rising global temperatures are linked with changes in rainfall patterns and the frequency and duration of heat waves and droughts. They also affect the length of growing seasons and accelerate crop maturity. To arrive at their projections, the research team used two sets of models. First, they used climate model simulations from the international Climate Model Intercomparison Project-Phase 6 (CMIP6). Each of the five climate models runs its own unique response of Earth’s atmosphere to greenhouse gas emission scenarios through 2100. Then the research team used the climate model simulations as inputs for 12 state-of-the-art global crop models that are part of the Agricultural Model Intercomparison Project (AgMIP), creating in total about 240 global climate-crop model simulations for each crop. By using multiple climate and crop models in various combinations, the researchers were able to be more confident in their results.
  •
    Ji-Paraná Land Use Data Over Time
    2021.04.19
    The Amazon has undergone major transformations throughout the past three decades (1985 - 2018). Working closely with their Brazilian counterparts, NASA scientists have mapped the entire country of Brazil to show different kinds of land use throughout the country. This data is based on the Landsat Data Continuity Mission (LDCM) through the MapBiomas project. As part of this research, we look at the general area around the town of Ji-Paraná and show how the land use changed from 1985 to 2018. 2019 fire data is also used to show controlled burns in order to clear cut forest areas for other use.
  •
    Amazon Deforestation
    2013.10.17
    The state of Rondônia in western Brazil has become one of the most deforested parts of the Amazon. This image series, created with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra satellite, shows the region from 2000 to 2010. By the year 2000, the frontier had reached the remote northwest corner of Rondônia. Intact forest is deep green, while cleared areas are tan (bare ground) or light green (crops, pastures). Deforestation follows a predictable pattern in these images. The first clearings appear in a fishbone pattern, arrayed along the edges of roads. Over time, the fishbones collapse into a mixture of forest remnants, cleared areas, and settlements. This pattern is common in the Amazon. Legal and illegal roads penetrate a remote part of the forest, and small farmers migrate to the area. They claim land along the road and clear some of it for crops. Within a few years, heavy rains and erosion deplete the soil, and crop yields fall. Farmers then convert the degraded land to cattle pasture, and clear more forest for crops.
  •
    Two Decades of Soil Moisture from Space
    2022.02.15
    These maps combine data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with other satellite and ground-based measurements to model the relative amount of water stored at three different depths: at the surface, at plant root level and underground. The wetness, or water content, of each layer is compared to the average between 1948 and 2012. The darkest red regions represent dry conditions that should occur only 2 percent of the time (about once every 50 years). All of the maps are experimental products funded by NASA’s Applied Sciences Program and developed by scientists at NASA’s Goddard Space Flight Center and the National Drought Mitigation Center. The maps do not attempt to represent human consumption of water; but rather, they show changes in water storage related to weather, climate, and seasonal patterns.
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    Drought in the Horn of Africa
    2022.08.17
    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.
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    Sea Surface Temperature Anomalies and Patterns of Global Disease Outbreaks: 2009-2018 (4K version)
    2020.01.09
    This webpage provides the 4K version of: Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (updated), released on January 6, 2020. Content has been created for 4K display systems that can handle finer resolution and details. It is recommended to use content from this version for HD (1920x1080) and lower resolutions.

    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 on 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 the 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 over 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 11 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, tularemia and zika virus/ 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:

    The impact of ENSO induced anomalous rainfall is 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: 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 ENSO 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.
    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).
    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.
    This rest of this webpage provides frames, movies, colorbar information and layers associated with the development of this data visualization.
  •
    Trends in Global Atmospheric Methane (CH₄)
    2022.08.11
    Data Sources:
    • Trends in Athmospheric Methane by NOAA. The visualizations featured on this page utilize the complete record from the Globally averaged marine surface monthly mean data for the period July 1983-March 2022 (accessed: August 4, 2022). Within the data record the globally averaged monthly mean values are centered on the middle of each month and are represented in the visualization as the jagged/wavy Average line. The continuous line shows the long-term Trend, where the average seasonal cycle has been removed.
      Citation: Ed Dlugokencky, NOAA/GML (https://gml.noaa.gov/ccgg/trends_ch4/)
      Citation: Dlugokencky, E. J., L. P. Steele, P. M. Lang, and K. A. Masarie (1994), The growth rate and distribution of atmospheric methane, J. Geophys. Res., 99, 17,021– 17,043, doi:10.1029/94JD01245.
  • Global Atmospheric Methane
    2020.04.21
    Methane is a powerful greenhouse gas that traps heat 28 times more effectively than carbon dioxide over a 100-year timescale. Concentrations of methane have increased by more than 150% since industrial activities and intensive agriculture began. After carbon dioxide, methane is responsible for about 20% of climate change in the twentieth century. Methane is produced under conditions where little to no oxygen is available. About 30% of methane emissions are produced by wetlands, including ponds, lakes and rivers. Another 20% is produced by agriculture, due to a combination of livestock, waste management and rice cultivation. Activities related to oil, gas, and coal extraction release an additional 30%. The remainder of methane emissions come from minor sources such as wildfire, biomass burning, permafrost, termites, dams, and the ocean. Scientists around the world are working to better understand the budget of methane with the ultimate goals of reducing greenhouse gas emissions and improving prediction of environmental change. For additional information, see the Global Methane Budget. The NASA SVS visualization presented here shows the complex patterns of methane emissions produced around the globe and throughout the year from the different sources described above. The visualization was created using output from the Global Modeling and Assimilation Office, GMAO, GEOS modeling system, developed and maintained by scientists at NASA. Wetland emissions were estimated by the LPJ-wsl dynamic global vegetation 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 while the bathymetry below sea level is exaggerated by 11.6-times. Outflow from different regions result from different sources. 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.
  •
    Nitrogen Dioxide Over the United States, 2005-2021
    2022.04.18
    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 2005-2021.
  •
    Increasingly Dangerous Climate for Agricultural Workers
    2022.03.09
    A warming climate will create challenges for agricultural workers as well as the crops which they grow. This visualization shows the increased number of days per year that are expected to have a NOAA Heat Index greater than 103 degrees Fahrenheit, a threshold that NOAA labels ‘dangerous’ given that people struggle to regulate their body temperatures at this level of heat and humidity. These results are from an ensemble of 22 global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6) bias-adjusted by the NASA Earth Exchange (NEX GDDP). Two projections are visualized, one for a moderate emissions climate scenerio (SSP2-4.5) and one for a high emmissions climate scenerio (SSP5-8.5).
  •
    NASA Studies How COVID-19 Shutdowns Affect Emissions
    2020.11.17
    Pandemic-related shutdowns have affected how people act, so scientists began monitoring how that’s affected the planet— specifically nitrogen dioxide emissions. How does COVID-19 pollution patterns play into NASA computer models? NASA’s GEOS atmospheric composition model shows us the answer.
  •
    Reductions in Pollution Associated with Decreased Fossil Fuel Use Resulting from COVID-19 Mitigation
    2020.04.24
    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.

HESS3.D: Human Impacts on Earth Systems

  •
    Impact of Climate Change on Crops
    2022.03.02
    Climate change will affect agricultural production worldwide. Average global crop yields for maize, or corn, may see a decrease of 24% by late century, if current climate change trends continue. Wheat, in contrast, may see an uptick in crop yields by about 17%. The change in yields is due to the projected increases in temperature, shifts in rainfall patterns and elevated surface carbon dioxide concentrations due to human-caused greenhouse gas emissions, making it more difficult to grow maize in the tropics and expanding wheat’s growing range. Maize is grown all over the world, and large quantities are produced in countries nearer the equator. North and Central America, West Africa, Central Asia, Brazil and China will potentially see their maize yields decline in the coming years and beyond as average temperatures rise across these breadbasket regions, putting more stress on the plants. Wheat, which grows best in temperate climates, may see a broader area where it can be grown in places such as the northern United States and Canada, North China Plains, Central Asia, southern Australia and East Africa as temperatures rise, but these gains may level off mid-century. Temperature alone is not the only factor the models consider when simulating future crop yields. Higher levels of carbon dioxide in the atmosphere have a positive effect on photosynthesis and water retention, more so for wheat than maize, which are accounted for better in the new generation of models. Rising global temperatures are linked with changes in rainfall patterns and the frequency and duration of heat waves and droughts. They also affect the length of growing seasons and accelerate crop maturity. To arrive at their projections, the research team used two sets of models. First, they used climate model simulations from the international Climate Model Intercomparison Project-Phase 6 (CMIP6). Each of the five climate models runs its own unique response of Earth’s atmosphere to greenhouse gas emission scenarios through 2100. Then the research team used the climate model simulations as inputs for 12 state-of-the-art global crop models that are part of the Agricultural Model Intercomparison Project (AgMIP), creating in total about 240 global climate-crop model simulations for each crop. By using multiple climate and crop models in various combinations, the researchers were able to be more confident in their results.
  •
    Ji-Paraná Land Use Data Over Time
    2021.04.19
    The Amazon has undergone major transformations throughout the past three decades (1985 - 2018). Working closely with their Brazilian counterparts, NASA scientists have mapped the entire country of Brazil to show different kinds of land use throughout the country. This data is based on the Landsat Data Continuity Mission (LDCM) through the MapBiomas project. As part of this research, we look at the general area around the town of Ji-Paraná and show how the land use changed from 1985 to 2018. 2019 fire data is also used to show controlled burns in order to clear cut forest areas for other use.
  •
    Amazon Deforestation
    2013.10.17
    The state of Rondônia in western Brazil has become one of the most deforested parts of the Amazon. This image series, created with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra satellite, shows the region from 2000 to 2010. By the year 2000, the frontier had reached the remote northwest corner of Rondônia. Intact forest is deep green, while cleared areas are tan (bare ground) or light green (crops, pastures). Deforestation follows a predictable pattern in these images. The first clearings appear in a fishbone pattern, arrayed along the edges of roads. Over time, the fishbones collapse into a mixture of forest remnants, cleared areas, and settlements. This pattern is common in the Amazon. Legal and illegal roads penetrate a remote part of the forest, and small farmers migrate to the area. They claim land along the road and clear some of it for crops. Within a few years, heavy rains and erosion deplete the soil, and crop yields fall. Farmers then convert the degraded land to cattle pasture, and clear more forest for crops.
  •
    Two Decades of Soil Moisture from Space
    2022.02.15
    These maps combine data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with other satellite and ground-based measurements to model the relative amount of water stored at three different depths: at the surface, at plant root level and underground. The wetness, or water content, of each layer is compared to the average between 1948 and 2012. The darkest red regions represent dry conditions that should occur only 2 percent of the time (about once every 50 years). All of the maps are experimental products funded by NASA’s Applied Sciences Program and developed by scientists at NASA’s Goddard Space Flight Center and the National Drought Mitigation Center. The maps do not attempt to represent human consumption of water; but rather, they show changes in water storage related to weather, climate, and seasonal patterns.
  •
    Sea Surface Temperature Anomalies and Patterns of Global Disease Outbreaks: 2009-2018 (4K version)
    2020.01.09
    This webpage provides the 4K version of: Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (updated), released on January 6, 2020. Content has been created for 4K display systems that can handle finer resolution and details. It is recommended to use content from this version for HD (1920x1080) and lower resolutions.

    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 on 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 the 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 over 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 11 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, tularemia and zika virus/ 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:

    The impact of ENSO induced anomalous rainfall is 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: 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 ENSO 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.
    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).
    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.
    This rest of this webpage provides frames, movies, colorbar information and layers associated with the development of this data visualization.
  •
    Trends in Global Atmospheric Methane (CH₄)
    2022.08.11
    Data Sources:
    • Trends in Athmospheric Methane by NOAA. The visualizations featured on this page utilize the complete record from the Globally averaged marine surface monthly mean data for the period July 1983-March 2022 (accessed: August 4, 2022). Within the data record the globally averaged monthly mean values are centered on the middle of each month and are represented in the visualization as the jagged/wavy Average line. The continuous line shows the long-term Trend, where the average seasonal cycle has been removed.
      Citation: Ed Dlugokencky, NOAA/GML (https://gml.noaa.gov/ccgg/trends_ch4/)
      Citation: Dlugokencky, E. J., L. P. Steele, P. M. Lang, and K. A. Masarie (1994), The growth rate and distribution of atmospheric methane, J. Geophys. Res., 99, 17,021– 17,043, doi:10.1029/94JD01245.
  • Nitrogen Dioxide Over the United States, 2005-2021
    2022.04.18
    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 2005-2021.
  •
    Increasingly Dangerous Climate for Agricultural Workers
    2022.03.09
    A warming climate will create challenges for agricultural workers as well as the crops which they grow. This visualization shows the increased number of days per year that are expected to have a NOAA Heat Index greater than 103 degrees Fahrenheit, a threshold that NOAA labels ‘dangerous’ given that people struggle to regulate their body temperatures at this level of heat and humidity. These results are from an ensemble of 22 global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6) bias-adjusted by the NASA Earth Exchange (NEX GDDP). Two projections are visualized, one for a moderate emissions climate scenerio (SSP2-4.5) and one for a high emmissions climate scenerio (SSP5-8.5).
  •
    NASA Studies How COVID-19 Shutdowns Affect Emissions
    2020.11.17
    Pandemic-related shutdowns have affected how people act, so scientists began monitoring how that’s affected the planet— specifically nitrogen dioxide emissions. How does COVID-19 pollution patterns play into NASA computer models? NASA’s GEOS atmospheric composition model shows us the answer.
  •
    Reductions in Pollution Associated with Decreased Fossil Fuel Use Resulting from COVID-19 Mitigation
    2020.04.24
    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.
  •
    Weekly Arctic Sea Ice Age with Graph of Ice Age By Area: 1984 - 2019
    2019.09.30
    One significant change in the Arctic region in recent years has been the rapid decline in perennial sea ice. Perennial sea ice, also known as multi-year ice, is the portion of the sea ice that survives the summer melt season. Perennial ice may have a life-span of nine years or more and represents the thickest component of the sea ice; perennial ice can grow up to four meters thick. By contrast, first year ice that grows during a single winter is generally at most two meters thick. Above is a visualization of the weekly sea ice age between 1984 and 2019. The animation shows the seasonal variability of the ice, growing in the Arctic winter and melting in the summer. In addition, this also shows the changes from year to year, depicting the age of the sea ice in different colors. Younger sea ice, or first-year ice, is shown in a dark shade of blue while the ice that is over four years old is shown as white. A color scale identifies the age of the intermediary years. Note that data for the sea ice age is not available along the coastlines. The region where data is not available is shown in a dark lavender color. A graph in the upper left corner the quantifies the change over time by showing the area covered by sea ice that is 4 years old or older in millions of square kilometers. This graph also includes a memory bar - the green line that indicates the maximum value seen thus far in the animation for the given week being displayed. For example, when viewing the sea ice age for the first week in September, the memory bar will display the maximum value seen for the first week of September in all prior years from the beginning of the animation (1984). In addition, a violet bar indicates the average area covered by sea ice greater than 4 years of age during the the 20-year time period from 1984 through 2003. Below are matching pairs of images showing identical weeks in two different years, where one year is late in the time series and the other is early. These matched images are labeled Pair 1A and Pair 1B. The caption below the image provides additional details. Each image is available with and without the graph.
  •
    Global Temperature Anomalies from 1880 to 2021
    2022.01.13
    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
  •
    2021 Tied for the Sixth Warmest Year on Record, NASA Finds
    2022.01.13
    2021 was tied for the sixth warmest year on NASA’s record, stretching more than a century. But, what is a temperature record? GISTEMP, NASA’s global temperature analysis, takes in millions of observations from instruments on weather stations, ships and ocean buoys, and Antarctic research stations, to determine how much warmer or cooler Earth is on average from year to year. Stretching back to 1880, NASA’s record shows a clear warming trend. However, individual weather events and La Niña — a pattern of cooler waters in the Pacific that was responsible for slightly cooling 2021’s average temperature — can affect individual years. Because the record is global, not every place on Earth experienced the sixth warmest year on record. Some places had record-high temperatures, and we saw record droughts, floods and fires around the globe.
  •
    NASA Earth Observatory Temp Anomaly Graph
    External Resource
    The years from 1880 to 1939 tend to be cooler, then level off by the 1950s. Decades within the base period (1951-1980) do not appear particularly warm or cold because they are the standard against which other years are measured.
  •
    NASA Climate Spiral
    2022.03.07
    The visualization presents monthly global temperature anomalies between the years 1880-2021. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. Anomalies are defined relative to a base period of 1951-1980. The data file used to create this visualization can be accessed here. The Goddard Institute of Space Studies (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. The 'climate spiral' is a visualization designed by climate scientist Ed Hawkins from the National Centre for Atmospheric Science, University of Reading. Climate spiral visualizations have been widely distributed, a version was even part of the opening ceremony of the Rio de Janeiro Olympics.
  •
    Zonal Climate Anomalies
    2022.03.07
    The visualization presents zonal temperature anomalies between the years 1880-2021. The visualization illustrates that the Arctic is warming much faster than other regions of the Earth. These temperatures are based on the GISS Surface Temperature Analysis (GISTEMP v4), an estimate of global surface temperature change. The latitude zones are 90N-64N, 64N-44N, 44N-24N, 24N-EQU, EQU-24S, 24S-44S, 44S-64S, 64S-90S. Anomalies are defined relative to a base period of 1951-1980. The data file used to create this visualization can be accessed here. The Goddard Institute of Space Studies (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.
  •
    Shifting Distribution of Land Temperature Anomalies, 1951-2020
    2021.04.23
    This visualization shows how the distribution of land temperature anomalies has varied over time. As the planet has warmed, we see the peak of the distribution shifting to the right. The distribution of temperatures broadens as well. This broadening is most likely due to differential regional warming rather than increased temperature variability at any given location. These distributions are calculated from the Goddard Institute of Space Studies GISTEMP surface temperature analysis. Distributions are determined for each year using a kernal density esitmator, and we morph between those distributions in the animation. NASA’s full surface temperature data set – and the complete methodology used to make the temperature calculation – are available at: https://data.giss.nasa.gov/gistemp 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. The python based Jupyter Notebook used to create these visualizations is available. Click here to download.
  •
    Climate Drivers
    2021.06.30
    Climate models simulate interactions of critical climate processes and drivers of change and aim to increase our understanding of Earth’s climate system. Measurements clearly demonstrate the changes to the Earth’s climate over the twentieth century up to the present day, and climate models are used to inform us about possible changes in the future climate. In the published articles titled GISS-E2.1: Configurations and climatology and CMIP6 historical simulations (1850–2014) with GISS-E2.1, the NASA GISS research team describe the computer climate model GISS-E.2.1 including its development over the last few years, summarize its main features, and compare it with previous versions and the observations. Notably, they describe how well the trends in multiple features of the climate are captured in the historical simulations from 1850 to 2014. The data visualizations featured on this page present a high-level summary of the contributions of the NASA GISS-E2.1-G model to the Coupled Model Intercomparison Project (Phase 6) (CMIP6), while showcasing the human and natural drivers of climate change, which were part of the Detection and Attribution Model Intercomparison Project (DAMIP), a sub-project of CMIP6. The visualization includes spatial maps paired with multiplots. Assuming a grid-like structure, the visualization comprises two major parts: Left: On the left we can see four separate maps in Wagner projection showing four distinct simulated datasets, displaying as anomalies. The anomalies are calculated using a baseline of 1951-1980. Top to bottom the datasets are: Mid-Stratosphere Temperature, Lower Stratosphere Temperature, Surface Temperature and Ocean Heat Content. The datasets present historical simulations with both natural and anthropogenic drivers for the period 1850-2014 and are paired with corresponding colormaps. Middle and Right: In the middle, synchronized multiplots for the same period track and visualize distinct drivers: natural (green), human (red), human and natural (blue), for each dataset. On the right, the Arctic Ice Area anomaly multiplot is followed by the visualization legends. As the timeline unfolds, we can see observations (in black for light background / in white for black background) starting to appear. These observations are used to evaluate the NASA GISS-E2.1-G model and while simulated data end in 2014, observations are plotted through 2018 (with the exception of Ocean Heat Content which is plotted through 2015). The historical timeline and the visualized maps show trends of stratospheric cooling, surface warming and significantly increased Arctic Ice Area loss, all of which are consistent with human forcing, especially rising greenhouse gas concentrations and ozone depletion. Therefore, it becomes apparent that the influence of human drivers is predominant in the climate system trends over the twentieth century and up to the present day. The NASA GISS-E2.1 contributions to CMIP6 and its evaluation with observations offer new knowledge and serve our society by improving our understanding of the processes that govern climate change and climate interactions with natural and human activities for the past and the future.
    Data Sources:
  • 20 Years of Global Biosphere (updated)
    2017.11.14
    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.
  •
    GRACE Groundwater Storage
    2020.04.20
    This visualization shows groundwater storage as measured by the Gravity Recovery and Climate Experiment (GRACE) between August 2005 and June 2014 (the date range for the visualization was chosen for convenience rather than scientific significance). This visualization was created in part to support Earth Day 2020 media releases.
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    Global Carbon Dioxide 2020-2021
    2021.11.02
    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.
    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.
    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.
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    20 years of AIRS Global Carbon Dioxide (CO2) measurements (2002-2022)
    2022.05.28
    This data visualization shows the global distribution and variation of the concentration of mid-tropospheric carbon dioxide observed by the Atmospheric Infrared Sounder (AIRS) on the NASA Aqua spacecraft over a 20 year timespan. One obvious feature that we see in the data is a continual increase in carbon dioxide with time, as seen in the shift in the color of the map from light yellow towards red as time progresses. Another feature is the seasonal variation of carbon dioxide in the northern hemisphere, which is governed by the growth cycle of plants. This can be seen as a pulsing in the colors, with a shift towards lighter colors starting in April/May each year and a shift towards red as the end of each growing season passes into winter. The seasonal cycle is more pronounced in the northern hemisphere than the southern hemisphere, since the majority of the land mass is in the north. The visualization includes a data-driven spatial map of global carbon dioxide and a timeline on the bottom. The timeline showcases the monthly timestep and is paired with the adjusted carbon dioxide value. Areas where the air pressure is less than 750mB (areas of high-altitude) have been marked in the visualization as low data quality (striped) areas. This entry offers two versions of low data quality (stiped) areas. One version includes striped regions as they are calculated on data values and the second version features striped regions below 60 South.
    Data Sources:
    • Carbon Dioxide (CO2) from the Sounder SIPS: AQUA AIRS IR-only Level 3 CLIMCAPS: Comprehensive Quality Control Gridded Monthly V2 (SNDRAQIL3CMCCP), which is a monthly product of global coverage and of spatial resolution 1x1 degrees. The visualizations included on this page, utilize the variable co2_vmr_uppertop from the CLIMCAPS product. Areas where the air pressure is less than 750mB (areas of high-altitude) and below 60 degrees South have been marked in the visualization as low data quality (striped areas). In addition, areas with data gaps and of high altitude less than 5% of the resolution of the product have been filled using the nearest neighbor algorithm. Citation: Chris Barnet (2019), Sounder SIPS: AQUA AIRS IR-only Level 3 CLIMCAPS: Comprehensive Quality Control Gridded Monthly V2, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [May 26, 2022], doi: 10.5067/ZPZ430KOPMIX
    • Trends in Atmospheric Carbon Dioxide by NOAA. The visualizations on this page feature de-seasonalized mean value measurements from the Mauna Loa CO2 monthly mean data for the period September 2002-March 2022, Accessed: [April 8, 2022]. Citation: Dr. Pieter Tans, NOAA/GML (gml.noaa.gov/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu). Citation: Keeling, Ralph F; Keeling, Charles D. (2017). Atmospheric Monthly In Situ CO2 Data - Mauna Loa Observatory, Hawaii (Archive 2021-09-07). In Scripps CO2 Program Data. UC San Diego Library Digital Collections. https://doi.org/10.6075/J08W3BHW
    • Continental and country outlines from the Scientific Visualization Studio, NASA/GSFC.
    The rest of this webpage offers custom versions for web, HD and 4K display systems.
    climate.nasa.gov This section contains assets designed for climate.nasa.gov
    HD content Additional visualization content in HD resolution.
    4K content
    Science On a Sphere (SOS) content The following section contains assets designed for Science On a Sphere and related displays. SOS playlist file: playlist.sos SOS label file: labels.txt
    Colormap The following section contains colormap information.
  • Concentration Increase of Atmospheric Carbon Dioxide (CO₂)
    2022.01.12
    Using the complete record of Mauna Loa CO2 monthly mean data, the timeplot featured on this page displays the ongoing Keeling’s research and observations: the monthly average of atmospheric CO2 concentration values, which show the seasonal cycle of CO2 (jagged/wavy red line) and the seasonally-adjusted mean values (adjusted/straight red line). The jagged/wavy red line visualizes natural oscillations caused by plant growth cycles, while the adjusted/straight red line demonstrates the steady increase over time that is caused by human activities, such as the burning of fossil fuels. To illustrate the significance of the steady increase of atmospheric CO2 since 1958 and to provide a visual understanding of the monthly average CO2 values as they are measured in parts per million (ppm):
    • Monthly CO2 values are plotted on the range of [0-500] ppm over the period of March 1958 to December 2021 (present).
    • The pre-industrial CO22 average of 278 ppm is marked as an orange block.
    • As time passes the monthly adjusted percent increase is calculated relative to the pre-industrial CO2 value of 278ppm and is shown next to a red arrow.
    As the timeline unfolds, we can see an increase growing from 13% in March 1958 to more than 50% in December 2021. In addition to highlighting the steady increase of CO2 in the Earth’s atmosphere, this timeplot underpins the historical contributions of the Keeling Curve to climate science, as it was designated a National Historic Chemical Landmark by the American Chemical Society in 2015. Continuous and precise observations across agencies and institutions are critical to help scientists and the public understand the linkages between increases in CO2 and human-caused climate change.
    The rest of this webpage offers a version of the timeplot with dark background, the Keeling Curve, as of December 2021 and data sources.
    Data Sources:
    • Trends in Atmospheric Carbon Dioxide by NOAA. The visualization featured on this page utilizes the complete record from the Mauna Loa CO2 monthly mean data for the period March 1958-December 2021 (accessed: January 7, 2022). Within the data record the continuous monthly average values are represented in the visualization as the jagged/wavy line that shows the seasonal cycle of CO2 and the monthly de-seasonalized mean values are represented in the visualization as the adjusted line. Citation: Dr. Pieter Tans, NOAA/GML (gml.noaa.gov/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/). Citation: Keeling, Ralph F; Keeling, Charles D. (2017). Atmospheric Monthly In Situ CO2 Data - Mauna Loa Observatory, Hawaii (Archive 2021-09-07). In Scripps CO2 Program Data. UC San Diego Library Digital Collections. https://doi.org/10.6075/J08W3BHW
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    Annual Arctic Sea Ice Minimum 1979-2021 with Area Graph
    2022.05.05
    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 2021. In 2021, the Arctic minimum sea ice covered an area of 4.13 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 2021 as derived from passive microwave data. A graph overlay shows the area in million square kilometers for each year's minimum day.
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    Climate Change in Yellowstone
    2014.12.02
    Yellowstone National Park is globally recognized for preserving the majority of the world’s geysers and is home to perhaps the most intact ecosystems in the lower United States. Coniferous forests dominate much of the park’s 900,000 hectares, providing habitats for many species including grizzly bears, black bears, wolves, mountain lions, wolverines, coyotes, bison, and elk. This visualization shows projected changes in late spring (May) temperatures for the U.S. from 1950-2100 using the NASA Earth Exchange Downscaled Climate Projections (NEX-DCP30) model. The results show how Yellowstone’s average May temperatures are expected to increase from below freezing to above freezing by 2100. These temperatures will impact the timing of snowmelt and summer soil moisture, which will subsequently influence the length of the fire season. The U.S. National Park Service is collaborating with scientists from NASA, Montana State University, Woods Hole Research Center, and Colorado State University to project the effect of climate change on dominant trees species and ecosystem functioning. Average temperatures in Yellowstone have already increased by at least 1.1 degrees Celsius over the past century. In fact, many trees in Yellowstone are already exhibiting signs of stress—e.g., browning and dying from infestations of forest insect pests and diseases.
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    Garbage Patch Visualization Experiment
    2015.08.10
    We wanted to see if we could visualize the so-called ocean garbage patches. We start with data from floating, scientific buoys that NOAA has been distributing in the oceans for the last 35-year represented here as white dots. Let's speed up time to see where the buoys go... Since new buoys are continually released, it's hard to tell where older buoys move to. Let's clear the map and add the starting locations of all the buoys... Interesting patterns appear all over the place. Lines of buoys are due to ships and planes that released buoys periodically. If we let all of the buoys go at the same time, we can observe buoy migration patterns. The number of buoys decreases because some buoys don't last as long as others. The buoys migrate to 5 known gyres also called ocean garbage patches. We can also see this in a computational model of ocean currents called ECCO-2. We release particles evenly around the world and let the modeled currents carry the particles. The particles from the model also migrate to the garbage patches. Even though the retimed buoys and modeled particles did not react to currents at the same times, the fact that the data tend to accumulate in the same regions show how robust the result is. The dataset used for the ocean buoy visualization is the Global Drifter Database from the GDP Drifter Data Assembly Center, part of the NOAA Atlantic Oceanographic & Meteorological Laboratory. The data covered the period February 1979 through September 2013. Although the actual dataset has a wealth of data, including surface temperatures, salinities, etc., only the buoy positions were used in the visualization. This visualization was accepted as one of the "Dailies" at SIGGRAPH 2015.