Earth System Model Output Research Articles

The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data

AbstractThe WFDEI meteorological forcing data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA‐Interim reanalysis data. We discuss the specifics of how changes in the reanalysis and processing have led to improvement over the WFD. We attribute improvements in precipitation and wind speed to the latest reanalysis basis data and improved downward shortwave fluxes to the changes in the aerosol corrections. Covering 1979–2012, the WFDEI will allow more thorough comparisons of hydrological and Earth System model outputs with hydrologically and phenologically relevant satellite products than using the WFD.

Trade-offs associated with different modeling approaches for assessment of fish and shellfish responses to climate change

Considerable progress has been made in integrating carbon, nutrient, phytoplankton and zooplankton dynamics into global-scale physical climate models. Scientists are exploring ways to extend the resolution of the biosphere within these Earth system models (ESMs) to include impacts on global distribution and abundance of commercially exploited fish and shellfish. This paper compares different methods for modeling fish and shellfish responses to climate change on global and regional scales. Several different modeling approaches are considered including: direct applications of ESM’s, use of ESM output for estimation of shifts in bioclimatic windows, using ESM outputs to force single- and multi-species stock projection models, and using ESM and physical climate model outputs to force regional bio-physical models of varying complexity and mechanistic resolution. We evaluate the utility of each of these modeling approaches in addressing nine key questions relevant to climate change impacts on living marine resources. No single modeling approach was capable of fully addressing each question. A blend of highly mechanistic and less computationally intensive methods is recommended to gain mechanistic insights and to identify model uncertainties.