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The maps above, featured in our January 9, 2016 Image of the Day, show soil composition across the United States (bottom) and the space available for water to reside within those soil types (top). Douglas Miller—a soil, informatics, and remote sensing expert at Penn State—compiled the dataset on which the map is based (soil characteristics for the conterminous United States, or CONUS-Soil.) By combining information about soil type with current, satellite-derived estimates of soil moisture, scientists can better predict events such as flooding, drought, and severe storms. Miller answered some of questions about soil composition, water storage, and why such things matter via email.

We have all heard about soil since we were kids, but what is it actually made of?
Soil contains many different things, but the most basic elements that soil scientists would talk about include various particle sizes (sand, silt, and clay), rock fragments, open pores, roots and live organisms, water, and air. Depending upon the exact combination of all of these things, there can be more (or less) space available for water to reside. The image below shows a soil texture triangle that’s very colorful and is a handy way of thinking about soil particle composition.

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Image courtesy Douglas Miller, from the CONUS-Soil web site.

Soils that have more sand in them will not tend to hold water for a very long time. Think of what happened when you were a kid at the beach with your bucket and you tried to keep water in the castle’s moat! Soils that are heavy with clay will tend to hold water longer and not drain as quickly. Soils that have more silt in them will tend to be intermediate in drainage properties. All told, the ideal soil would have nearly equal amounts of the three major textures (somewhere in the middle of the soil triangle).

Why does soil composition matter?
Farmers, gardeners–essentially anyone interested in growing plants in soil–would be interested in knowing soil composition. Thinking back to the soil triangle mentioned above, one would ideally love to have a medium textured soil from near the middle of the soil triangle. By being aware of the soil texture that you have and the capacity of that soil to hold water (along with the water requirements of the plants that you wish to grow), you can manage your landscape. If I have too much clay in my soil, I would want to work in materials (like leaves, peat moss, etc.) to moderate the texture and open more space in the soil profile for water. Years ago, my back yard garden was mostly clay soil. For three years I chopped up all of my leaves and put them in the garden. This helped to add organic matter and nutrients, but also made the soil texture closer to middle of the triangle.

Can knowledge about soil composition and soil moisture tell you something that wouldn’t be known by looking at just one or the other?
Yes! The interesting thing about soils is that they’re closely connected to weather through soil moisture. Satellites like SMAP and SMOS, flying overhead, give us near-real time estimates of soil moisture. When combined with soil properties, we can improve our ability to predict things like flooding, drought relief, and even severe storm generation. There’s a strong connection between soil moisture at the land surface and severe storms (thunderstorms, tornados, derechos, etc.). Soil moisture near the surface is available to be easily evaporated in to the atmosphere. With the proper atmospheric conditions, rapid evaporation can lead to strong storm development. Using a combination of weather data, SMOS/SMAP data, and land surface properties (soils, vegetation, and topography), we can develop improved models that more accurately predict when and where storms and consequent flooding, damage, etc. will occur.

What have been the developments in this area of research since the dataset was compiled?
Since we compiled CONUS-Soil from the USDA National Resources Conservation Service database in the mid-1990s, USDA has now completed SSURGO–detailed soil surveys that are conducted on a county-level basis for the entire continental U.S. As compared to CONUS-Soil (1 kilometer resolution grid cells), SSURGO can be gridded at 10 meters in most places. This provides a tremendous amount of detail. I believe the entire U.S. dataset for SSURGO gridded at 10 meters is about 16GB. It’s a huge dataset.

However, a real challenge still exists in creating a standardized dataset (like CONUS-Soil) that has the same number of layers for each grid cell, anywhere in the U.S. What makes our product still unique, after all these years, are the standardized layers that a climate or hydrology model can count on being the same, from cell-to-cell. The monthly downloads that we still get for CONUS-Soil indicate that its 1-kilometer resolution is still valuable for regional climate and hydrology models. We are investigating what it will take to create a new CONUS-Soil from SSURGO (with standard layers). We believe that will require the use of a significantly sized supercomputer!

Read more in our Image of the Day, Soil Composition Across the U.S., and in our feature story, A Little Bit of Water, A Lot of Impact.

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Our July 16 Image of the Day—Changing Forest Cover Since the Soviet Era—features a Landsat-derived map showing how forests have changed in Eastern Europe since 1985. After exploring the three areas we highlighted, I highly recommend browsing the map at full resolution using either Google Earth or GigaPan. The amount of detail you will find is extraordinary. There are dozens of other interesting forest loss and gain hot spots that we could have highlighted. In fact, we may publish additional stories using these data, so please let us know if you are aware of local stories of forest change in eastern Europe that deserve more attention.

While the satellite maps offer invaluable “big picture” perspective, ground photographs really bring the changes to life. Peter Potapov, the University of Maryland scientist who led the mapping effort, passed along a few photographs taken during his field research in Russia. It is one thing to know that a brown pixel in the maps indicate forest loss and the a green pixel indicates gain. It becomes real when you can actually see charred trunks after a forest fire or stands of saplings springing up in abandoned Soviet farm fields.

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Logging site in the Vladimir region of Russia. Photo Credit: Peter Potapov.

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Spruce trees killed by bark beetle in the Vladimir region of Russia. Photo Credit: Peter Potapov.

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Charred trunks caused by a forest fire in the Vladimer region of Russia. Photo credit: Peter Potapov

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Pine forests in an abandoned pasture in the Vladimir region of Russia. The pine trees are about ten years old. Photo Credit: Peter Potapov.

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Birch forest growing on abandoned farmland in the Nizhny Novgorod region of Russia. Photo Credit: Peter Potopov

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Early stages of forest recovery in abandoned farmland in the Kirov region of Russia. Photo Credit: Peter Potapov

Fine Particulate Maps With and Without Dust

June 23rd, 2015 by Adam Voiland
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Fine particulate matter (PM2.5) for 2010-2012 with dust and sea salt included. Visualization by Josh Stevens. Data from van Donkelaar et al. 

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Fine particulate matter (2.5) concentration for 2010-2012 without dust and sea salt included. Data from van Donkelaar et al.

If you saw our June 22 Image of the Day with global maps of fine particulate matter (PM2.5), you may have noticed large concentrations over the Sahara Desert and the Arabian Peninsula. With vast deserts in these areas, it’s not a surprise that the satellites detected so many particulates. Winds regularly send plumes of dust blowing over the region and even to Europe and the Americas.

However, it isn’t clear how damaging dust particles are to human health in comparison to other types of fine aerosol particles (such as those produced by burning fossil fuels or biomass burning). Several teams of epidemiologists have looked for associations between outbreaks of Saharan dust and health problems, but the results have been mixed. A literature review published in 2012 summarized the state of the science this way: “The association of fine particles PM2.5, with total or cause-specific mortality is not significant during Saharan dust intrusions. However, regarding coarser fractions PM10 and PM2.5-10, an explicit answer cannot be given. Some of the published studies state that they increase mortality during Sahara dust days while other studies find no association between mortality and PM10 or PM2.5-10. The main conclusion of this review is that health impacts of Saharan dust outbreaks needs to be further explored.”

Since dust is natural and may not have significant effects on human health, the team of Dalhousie University scientists who developed the global PM2.5 exposure maps prepared two versions of their data. One shows total PM2.5 concentration (top map above) globally; the other shows PM2.5 excluding contributions from dust and sea salt (bottom map). Notice how much less PM2.5 appears in northern Africa when dust is excluded.

To get a sense of how PM2.5 concentration (excluding dust and sea salt) has changed between 2000 and 2010, see the map below. Notice that while PM2.5 has decreased over North America and Europe, it has increased over Asia. To read more about what is driving these trends, read this story.  To learn more about the data used to create these maps, visit this website. 

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Areas where PM2.5 concentration has increased between 1998 and 2012 are shown with shades of red. Decreases are shown with shades of blue. Data from van Donkelaar et al.

 

See One of the First Climate Models

May 21st, 2015 by Adam Voiland
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Columbia University climate scientist Kátia Fernandes appeared on the cover of the 2014 Climate Models wall calendar. The calendar, dreamed up by two science writers at Columbia University, offered a fresh look on the meaning of the term ‘climate model.” Read more about the calendar from AGU’s Plainspoken Scientist blog. Image credit: Charlie Naebeck.

Based on email and social media comments we receive, climate models are one of the least understood and most maligned tools used by Earth scientists.

What is a climate model? Putting aside the scientists from the Lamont-Doherty Earth Observatory who posed for a climate model calendar in 2014 (cover above), climate models are simply mathematical representations of Earth’s climate that are based on fundamental physical, biological, and chemical laws and theories. As NOAA explained in a story about the first general circulation model to include both the ocean and atmosphere, scientists divide the planet into a three-dimensional grid, use computers to solve the equations, and then evaluate the results when they “run” a climate model. As the story noted: “Models calculate winds, heat transfer, radiation, relative humidity, and surface hydrology within each grid and evaluate interactions with neighboring points.” The illustration below should help you visualize how the grids are laid out and some of the physical processes models include.

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Image credit: NOAA

One of the first general circulation models was developed at Lawrence Livermore National Laboratory by Cecil “Chuck” Leith in the early 1960s. Unlike the NOAA model mentioned above, Leith’s model only simulated the atmosphere. What make Leith’s work so remarkable was that he was the first to produce a computer animation based on the model output. Watch the video below to see how these wobbling yet compelling animations looked.

From the very beginning, Leith’s animations attracted attention. “The one that I have is essentially a polar projection of the Northern Hemisphere, and you can see the patterns moving in mid-latitudes,” Leith explained during an oral history interview conducted by the American Institute of Physics. “I did it just because I knew we could do it, it would be interesting to look at, but it was almost too interesting. Whenever I’d go anywhere and give a talk about what I was doing, I would show the film and everybody was fascinated by the film, and they didn’t care what I said about the technical aspects of the model, as far as I could tell. And, in fact, Smagorinsky (another pioneer of climate modeling and the first director of NOAA) used to chide me about it a little bit. He says: ‘That’s just big plan showmanship. There’s no science there.’ But they started making movies too.” You can read more about Leith’s animation from Climate Central.

If the animation makes you curious about the history of climate modeling, try this chapter of Spencer Weart’s excellent book “The Discovery of Global Warming,” as well as this excerpt from Warren Washington’s autobiography “Odyssey in Climate Modeling, Global Warming, and Advising Five Presidents.” And if you’re looking for a more current take on climate models, how they work, and how they can be useful, see Motherboard’s new story and video profile (below) of NASA’s Goddard Institute for Space Studies (GISS). Finally, the TED talk by GISS Director Gavin Schmidt about models and the emergent patterns of climate change is well worth the twelve minutes.

Elusive Earthquake Imagery

April 29th, 2015 by Adam Voiland
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Clouds obscuring the Operational Land Imager’s view of Kathmandu, Nepal.

Several readers have asked us to post satellite imagery related to the earthquake that struck Nepal on April 25, 2015. While we regularly post imagery of natural hazards, the weather and the satellites haven’t cooperated in this case.

Some people assume NASA’s satellite fleet can collect images of virtually any part of the world in near-real time, but the reality is more complicated. The orbital track of the satellites and the specific capabilities of the sensors on board determine whether we have imagery to share. In the case of Nepal, things haven’t lined up in our favor.

NASA did acquire imagery of Nepal soon after the earthquake. The Aqua and Terra satellites capture images of Nepal every day with their identical Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Note, however, the words “moderate resolution” in the name. Each MODIS pixel corresponds to 250 meters of the Earth — not 1 meter or less like you will find if you zoom all the way in on Google maps. MODIS does a fantastic job of showing a broad area, but if you compare an April 22 MODIS view of Nepal with an April 27 view, you’ll see the sensor doesn’t have enough spatial resolution to see changes caused by the earthquake. What’s more, it has been rather cloudy since the earthquake anyway.

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Mount Everest before and after the earthquake. Not much change is visible because of a fresh coat of snow and cloud cover. The April 23 image was acquired by the Operational Land Imager on Landsat 8. The April 28 image was acquired by the Advanced Land Imager on Earth Observing-1.

Other sensors like the Advanced Land Imager (ALI) on Earth Observing-1, the Operational Land Imager (OLI) on Landsat 8, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on Terra have much higher spatial resolution (10, 15, and 15 meters per pixel respectively…good enough to see individual buildings). But each satellite passes over Nepal much less frequently. OLI, for instance, captured imagery of Nepal on April 23, but it isn’t due for another pass until May 9. ALI did get an image of Mount Everest on April 28, but as shown in the images above, there’s no noticeable sign of the earthquake and avalanche due to a fresh coating of snow and some cloud cover. ASTER also was clouded out.

It’s also possible for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi NPP satellite to detect the widespread blackouts that have occurred since the earthquake but, again, the weather has not cooperated. As you can see in the images (below) tweeted by NASA researcher Miquel Roman, clouds blocked the satellite’s view on April 25 (below), 26, and 27.

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Why doesn’t NASA have sensors with extremely high spatial resolution (less than one meter per pixel) that like some commercial satellite companies do? (Some of those satellites have glimpsed damage to individual structures and shown groups of people congregating in streets.) That’s a complicated subject that would need a much longer blog post to explore properly, but the short answer is that NASA’s emphasis is on the broad view—using medium- and low-resolution imagers to understand macro scale processes on Earth.

NASA sensors are sometimes useful for disaster response and often provide a unique and memorable view of an event like a landslide or wildfire. Yet the strength of satellites like Terra, Aqua, Aura, Landsat, CALIPSO, Cloudsat, GPM, OCO-2, Aquarius, and GRACE is that they drive cutting-edge science by providing global perspective. Want a global map  of the world’s fires? Or global view of sea surface temperatures? A map of ground water? A record of how Arctic ice has changed over decades? A view through a smoke plume as it drifts from Asia to North America? A three-dimensional perspective on the world’s forests?  That’s where the NASA satellite fleet shines. For high-resolution imagery of specific events…well, there are plenty of other organizations that specialize in that.

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Tournament Earth 2015 Champion

April 10th, 2015 by Adam Voiland

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Tournament Earth 2015 has come to a dramatic end. Despite some tough match ups, the colorful faults of Xinjiang fought off a bolt of lightning (as seen from the International Space Station), taking the #2 seed from the art division all the way to the championship.

This year’s victory was a first for an image from a Landsat satellite. In 2014, the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra captured the winning shot. In 2013, it was the Advanced Land Imager (ALI) on the Earth Observing-1 (EO-1) satellite. This was also the first year that an image not associated with the Canary Islands won the tournament.

As noted in our original Image of the Day, Piqiang Fault is a northwest trending strike-slip fault that runs roughly perpendicular to a series of thrust faults. The thrust faults are marked by the colorful southeast-to-northeast running ridges. The ridges are offset by about 3 kilometers (2 miles) due to the strike-slip fault. For another perspective on the faults, see how they look in the near infrared and shortwave infrared (below).  In the near infrared, variations in mineral content, vegetation, and water cause the patterns of light and dark. Below that, comparing the differences between 3 shortwave infrared bands highlights the mineral geology surrounding the fault.

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Though obvious from above, the Piqiang Fault can be a challenge to see from the ground. “You can’t actually see the fault unless you hike into the mountains,” explained Sebastian Turner, a geologist who has conducted studies in the area. If you would like to learn more about the geology of this area, I would recommend looking through Turner’s study or this one by Mark Allen.

Thank you for voting!

February Puzzler

February 24th, 2015 by Kathryn Hansen

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Every month on Earth Matters, we offer a puzzling satellite image. The February 2015 puzzler is above. Your challenge is to use the comments section to tell us what part of the world we are looking at, when the image was acquired, what the image shows, and why the scene is interesting.

How to answer. Your answer can be a few words or several paragraphs. (Try to keep it shorter than 200 words). You might simply tell us what part of the world an image shows. Or you can dig deeper and explain what satellite and instrument produced the image, what spectral bands were used to create it, or what is compelling about some obscure speck in the far corner of an image. If you think something is interesting or noteworthy, tell us about it.

The prize. We can’t offer prize money, but, we can promise you credit and glory (well, maybe just credit). Roughly one week after a puzzler image appears on this blog, we will post an annotated and captioned version as our Image of the Day. In the credits, we’ll acknowledge the person who was first to correctly ID the image. We’ll also recognize people who offer the most interesting tidbits of information about the geological, meteorological, or human processes that have played a role in molding the landscape. Please include your preferred name or alias with your comment. If you work for or attend an institution that you want us to recognize, please mention that as well.

Recent winners. If you’ve won the puzzler in the last few months or work in geospatial imaging, please sit on your hands for at least a  day to give others a chance to play.

Releasing Comments. Savvy readers have solved some of our puzzlers after only a few minutes or hours. To give more people a chance to play, we may wait between 24-48 hours before posting the answers we receive in the comment thread.

Good luck!

January Puzzler

January 26th, 2015 by Kathryn Hansen

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Every month on Earth Matters, we offer a puzzling satellite image. The January 2015 puzzler is above. Your challenge is to use the comments section to tell us what part of the world we are looking at, when the image was acquired, what the image shows, and why the scene is interesting.

How to answer. Your answer can be a few words or several paragraphs. (Try to keep it shorter than 200 words). You might simply tell us what part of the world an image shows. Or you can dig deeper and explain what satellite and instrument produced the image, what spectral bands were used to create it, or what is compelling about some obscure speck in the far corner of an image. If you think something is interesting or noteworthy, tell us about it.

The prize. We can’t offer prize money, but, we can promise you credit and glory (well, maybe just credit). Roughly one week after a puzzler image appears on this blog, we will post an annotated and captioned version as our Image of the Day. In the credits, we’ll acknowledge the person who was first to correctly ID the image. We’ll also recognize people who offer the most interesting tidbits of information about the geological, meteorological, or human processes that have played a role in molding the landscape. Please include your preferred name or alias with your comment. If you work for or attend an institution that you want us to recognize, please mention that as well.

Recent winners. If you’ve won the puzzler in the last few months or work in geospatial imaging, please sit on your hands for at least a  day to give others a chance to play.

Releasing Comments. Savvy readers have solved some of our puzzlers after only a few minutes or hours. To give more people a chance to play, we may wait between 24-48 hours before posting the answers we receive in the comment thread.

Good luck!

Why So Many Global Temperature Records?

January 21st, 2015 by Adam Voiland

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If you follow Earth and climate science closely, you may have noticed that the media is abuzz every December and January with stories about how the past year ranked in terms of global temperatures. Was this the hottest year on record? In fact, it was. The Japanese Meteorological Agency released data on January 5, 2015, that showed 2014 was the warmest year on its record. NASA and NOAA made a similar announcement on January 16. The UK Met Office, which maintains the fourth major global temperature record, ranked 2014 as tied with 2010 for being the hottest year on record on January 26.

Astute readers then ask a deeper question: why do different institutions come up with slightly different numbers for the same planet? Although all four science institutions have strong similarities in how they track and analyze temperatures, there are subtle differences. As shown in the chart above, the NASA record tends to run slightly higher than the Japanese record, while the Met Office and NOAA records are usually in the middle.

There are good reasons for these differences, small as they are. Getting an accurate measurement of air temperature across the entire planet is not simple. Ideally, scientists would like to have thousands of standardized weather stations spaced evenly all around Earth’s surface. The trouble is that while there are plenty of weather stations on land, there are some pretty big gaps over the oceans, the polar regions, and even parts of Africa and South America.

The four research groups mentioned above deal with those gaps in slightly different ways. The Japanese group leaves areas without plenty of temperature stations out of their analysis, so its analysis covers about 85 percent of the globe. The Met Office makes similar choices, meaning its record covers about 86 percent of Earth’s surface. NOAA takes a different approach to the gaps, using nearby stations to interpolate temperatures in some areas that lack stations, giving the NOAA analysis 93 percent coverage of the globe. The group at NASA interpolates even more aggressively—areas with gaps are interpolated from the nearest station up to 1,200 kilometers away—and offers 99 percent coverage.

See the chart below to get a sense of where the gaps are in the various records. Areas not included in the analysis are shown in gray. JMA is Japan Meteorological Agency data, GISTEMP is the NASA Goddard Institute for Space Studies data, HadCrut4 is the Met Office data, UAH is a satellite-based record maintained by the University of Alabama Huntsville (more on that below), NCDC is the NOAA data, and NCEP/NCAR is a reanalysis of weather model data from the National Center for Atmospheric Research.

If you’re a real data hound, you may have heard about other institutions that maintain global temperature records. In the last few years, a group at UC Berkeley — a group that was initially skeptical of the findings of the other groups — developed yet another approach that involved using data from even more temperature stations (37,000 stations as opposed to the 5,000-7,000 used by the other groups). For 2014, the Berkeley group came to the same conclusion: the past year was the warmest year on record, though their analysis has 2014 in a virtual tie with 2005 and 2010.

Rather than coming up with a way to fill the gaps, a few other groups have tried using a completely different approach. A group from the University of Alabama-Huntsville maintains a record of temperatures based on microwave sounders on satellites. The satellite-based record dates back 36 years, and the University of Alabama group has ranked 2014 as the third warmest on that record, though only by a very small margin. Another research group from Remote Sensing Systems maintains a similar record based on microwave sounders on satellites, although there are a few differences in the way the Remote Sensing Systems and University of Alabama teams handle gaps in the record and correct for differences between sensors. Remote Sensing Systems has 2014 as the 6th warmest on its record.

But let’s get back to the original question: why are there so many temperature records? One of the hallmarks of good science is that observations should be independently confirmed by separate research groups using separate methods when possible. And in the case of global temperatures, that’s exactly what is happening. Despite some differences in the year-to-year rankings, the trends observed by all the groups are roughly the same. They all show warming. They all find the most recent decade to be warmer than previous decades.

You may observe some hand-wringing and contrarian arguments about how one group’s ranking is slightly different than another and about how scientists cannot agree on the “hottest year” or the temperature trend. Before you get caught up in that, know this: the difference between the hottest and the second hottest or the 10th hottest and 11th hottest year on any of these records is vanishingly small. The more carefully you look at graph at the top of this page, the more you’ll start to appreciate that the individual ranking of a given year hardly even matters. It’s the longer term trends that matter. And, as you can see in that chart, all of the records are in remarkably good agreement.

That said, if you are still interested in the minutia of how these these data sets are compiled and analyzed, as well as how they compare to one other, wade through the links below. Some of the sites will even explain how you can access the data and calculate the trends yourself.

+ UCAR’s Global Temperature Data Sets Overview and Comparison page.

+ NASA GISS’s GISTEMP and FAQ page.

+ NOAA’s Global Temperature Analysis page.

+ Met Office’s Hadley Center Temperature page.

+ Japan Meteorological Agency’s Global Average Surface Temperature Anomalies page.

+ University of Alabama Huntsville Temperature page.

+Remote Sensing Systems Climate Analysis, Upper Air Temperature, and The Recent Slowing in the Rise of Global Temperatures pages.

+ Berkley Earth’s Data Overview page.

+ Moyhu’s list of climate data portals.

+ Skeptical Science’s Comparing All the Temperature Records, The Japan Meteorological Agency Temperature Records, Satellite Measurements of the Warming in the Troposphere, GISTEMP: Cool or Uncool, and Temperature Trend Calculator posts.

+ Tamino’s comparing Temperature Data Sets post.

+NOAA/NASA 2014 Warmest Year on Record powerpoint.

+James Hansen’s Global Temperature in 2014 and 2015 update posted on his Columbia University page.

+The Carbon Brief’s How Do Scientists Measure Global Temperature?

+Yale’s A Deeper Look: 2014’s Warming Record and the Continued Trend Upwards post.

2014 Temperatures From A Regional Perspective

January 16th, 2015 by Adam Voiland

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NASA and NOAA announced today that 2014 brought the warmest global temperatures in the modern instrumental record. But what did the year look like on a more regional scale?

According to the Met Office, the United Kingdom experienced it warmest year since 1659. Despite the record-breaking temperatures, however, no month was extremely warm. Instead, each month (with the exception of August) was consistently warm. The UK was not alone. Eighteen other countries in Europe experienced their hottest year on record, according to Vox.

The contiguous United States, meanwhile, only experienced the 34th warmest year since 1895, according to a NOAA analysis. The Midwest and the Mississippi Valley were particularly cool, while unusually warm conditions gripped the West. California, for instance, went through its hottest year on record. Meanwhile, temperatures in Alaska were unusually warm; in Anchorage, temperatures never dropped below 0 degrees Fahrenheit. 

James Hansen, a retired NASA scientist, underscored this point in an update on his Columbia University website: “Residents of the eastern two-thirds of the United States and Canada might be surprised that 2014 was the warmest year, as they happened to reside in an area with the largest negative temperature anomaly on the planet, except for a region in Antarctica.”

According to Australia’s Bureau of Meteorology, 2014 was the third warmest year on record in that country. “Much of Australia experienced temperatures very much above average in 2014, with mean temperatures 0.91°C above the long-term average,” said the bureau’s assistant director of climate information services.

The map at the top of this page depicts global temperature anomalies in 2014. It does not show absolute temperatures, but instead shows how much warmer or cooler the Earth was compared to a baseline average from 1951 to 1980.  Areas that experienced unusually warm temperatures are shown in red; unusually cool temperatures are shown in blue.