A Large Ensemble Global Dataset for Climate Impact Assessments

Abstract: We present a self-consistent, large ensemble, high-resolution global dataset of long‐term future climate developed by integrating a spatial disaggregation (SD) pattern-scaling technique and a bias-correction (BC) delta method. The delta method adds the anomalies or deltas (future climate trends) onto a historical, detrended climate that is based on the third phase of the Global Soil Wetness Project (GSWP3). The anomalies or deltas are derived by spatially disaggregating zonal climate projections from the MIT Integrated Global System Modeling (IGSM) framework based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models.

Four emission scenarios are considered to represent the existing energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2°C and Paris 1.5°C. For each emission scenario, a distribution of plausible trajectories is provided by a 50-member ensemble to represent the uncertainty in the Earth system (e.g., the climate sensitivity, rate of heat uptake by the ocean, uncertainty in carbon cycle), allowing for constructing a 900-member ensemble of regional climate outcomes. This global dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity.

Quantitative assessments clearly indicate the ability of the dataset to represent the expected large-scale climate features across various regions of the globe. This large‐ensemble, high-resolution dataset can be used for assessing impacts of climate change from a risk-based perspective across different applications, including hydropower, water resources, wind power resources to name a few.