Global Paper Term Warming

Global Paper Term Warming-38
Going forward, this assessment will give scientists the tools to explain their results with greater confidence.GISTEMP is a widely used index of global mean surface temperature anomaly — it shows how much warmer or cooler than normal Earth’s surface is in a given year.“We’re testing the robustness of the method itself, the robustness of the assumptions, and of the final result against a totally independent data set.” In all cases, he said, the resulting trends are more robust than what can be accounted for by any uncertainty in the data or methods.

Going forward, this assessment will give scientists the tools to explain their results with greater confidence.GISTEMP is a widely used index of global mean surface temperature anomaly — it shows how much warmer or cooler than normal Earth’s surface is in a given year.“We’re testing the robustness of the method itself, the robustness of the assumptions, and of the final result against a totally independent data set.” In all cases, he said, the resulting trends are more robust than what can be accounted for by any uncertainty in the data or methods.

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“The Arctic is one of the places we already detected was warming the most.

The AIRS data suggests that it’s warming even faster than we thought,” said Schmidt, who was also a co-author on the Susskind paper.

"Normal" is defined as the average during a baseline period of 1951-80.

NASA uses GISTEMP in its annual global temperature update, in partnership with the National Oceanic and Atmospheric Administration.

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“We don’t have to restate any conclusions based on this analysis.” Another recent study evaluated GISTEMP in a different way that also added confidence to its estimate of long-term warming.

A paper published in March 2019, led by Joel Susskind of NASA's Goddard Space Flight Center, compared GISTEMP data with that of the Atmospheric Infrared Sounder (AIRS), onboard NASA's Aqua satellite.

Like other surface temperature records, GISTEMP estimates the temperatures between weather stations using data from the closest stations, a process called interpolation.

Quantifying the statistical uncertainty present in those estimates helped researchers to be confident that the interpolation was accurate.

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