Downscaled and bias corrected general circulation model data

Understanding the impact of climate variability and change is of great importance for developing adaptation and mitigation strategies. Coarse resolution data sets such as simulations of general circulation models (GCMs) are important for reconstructing historical climate and predicting the future. However, scale discrepancy and biases limit the coarse resolution data sets from being directly used for impact assessments and decision making. One solution for bridging this gap is to downscale and bias correct coarse resolution data to the local scale.

These results will be used as forcings for the watershed modeling and physical/biogeochemical modeling around the bay area to explore adaptation and mitigation strategies.

Click the Binder button below to follow the steps of our analysis:

• We downscaled and bias corrected 5 GCMs for 9 variables in the study area
• Section 1 shows the spatial distribution of long term mean and annual averaged time series for maximum temperature
• Section 2 shows the same statistics for daily precipitation.

• Fang Wang and Di Tian of the Hydroclimate Research Group, Department of Crop, Soil and Environmental Sciences at Auburn University

This work is funded by the National Oceanic and Atmospheric Administration's RESTORE Science Program under award NA19NOS4510194.

The modeling outputs are being hosted on Open Storage Network (OSN), through allocation EES210015 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.