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Highlights

Since 1989, the U.S. Global Change Research Program (USGCRP) has submitted annual reports to Congress called Our Changing Planet. The reports describe the status of USGCRP research activities, provide progress updates, and document recent accomplishments.

In particular, Our Changing Planet highlights progress and accomplishments in interagency activities. These highlights represent the broad spectrum of USGCRP activities that extend from Earth system observations, modeling, and fundamental research through synthesis and assessment, decision support, education, and public engagement.

Modeling

Observations to Improve Climate Models

The 2012–2021 Strategic Plan emphasizes the need for synergy between Earth observations and Earth system modeling—two cor- nerstones of global change research. Obs4MIPs (or Observations for Model Intercomparison Projects) is an emerging activity that uses observations to better support the Coupled Model Intercomparison Project (CMIP), an international effort under the

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Modeling

Building Synergy in the U.S. Modeling Community

The U.S. Climate Modeling Summit brought together representatives from the Nation’s major experimental and operational climate modeling programs. Above, Summit participants gather at the NOAA Center for Weather and Climate Prediction. (Source: NOAA)

Modeling Earth’s climate furthers priorities of national interest, from experimental research to understand the Earth system to operational forecasts and projections that inform decisions. Coordination among the Nation’s premier modeling centers—particularly between experimental and operational programs (see also Highlight 31)—has the potential to advance forecasting capabilities, yield more robust predictions, and bridge models of near-term weather and longer-term climate that currently are separated by high-uncertainty gaps in coverage.

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Modeling

Seasonal Prediction Systems: From Research to Operations

Warm colors on this map show where output from NOAA's GFDL-FLOR model correlates positively (yellow = strongest correlation) with seasonal observations of tropical cyclone activity, demonstrating the model's capacity for predicting such phenomena. (Source

Predicting climate on a seasonal basis can benefit decision makers in key sectors like energy, water resources, and agriculture, among others. A number of USGCRP agencies are working to improve the Nation’s seasonal forecasting capacity through major investments in innovative climate models that can bridge the needs of atmospheric research and operational forecasts. As one example, a new model developed by NOAA’s Geophysical Fluid Dynamics Laboratory, known as

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Observations, Modeling, Water Resources, Land Use & Land Cover, Agriculture & Food, Extreme Events

Mapping Fallowed Farmland During Drought

The greenness of croplands in January is shown relative to the 13-year average from NASA MODIS records. Satellite imagery can be a powerful tool for understanding the impacts of drought on agricultural lands. (Source: NIDIS Newsletter, April 2014)

The severe, sustained drought affecting the Central Valley of California has caused a shortage of water for irrigation and crop production. The effect of this shortage is most immediately evident as an increase in the extent of fallowed farmland (or land taken out of agricultural production), which in turn serves as a proxy for socioeconomic impacts. Decision makers can use information about fallowed land to better understand the severity of drought impacts and to support requests for USDA drought disaster designations or emergency proclamations. USDA

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Modeling

Data on Scales Needed by Resource Managers

Apart from serving scientists studying global change, output from the Coupled Model Intercomparison Project (CMIP; see related Highlight 7) can be useful to decision makers confronting regional and local climate impacts. A number of USGCRP agencies have supported the “downscaling” of CMIP output to provide climate information on scales of space and time that are relevant to decisions facing resource managers and planners. Downscaled data permit a range of analyses, such as evaluation of uncertainty in

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