Project: Large-ensemble global dataset for climate impact assessments - The datasets presented here are part of the Multi-Sector Dynamics (MSD) project sponsored by Department of Energy (DOE) and conducted by the MIT Joint Program on the Science and Policy of Global Change. This self-consistent, large ensemble (900-member), high-resolution global dataset of long‐term future climate is developed by applying an integrated spatial disaggregation (SD) bias-correction (BC) method to the ensemble of climate projections from the MIT Integrated Global System Modeling (IGSM) framework. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5° for four emission scenarios, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. The dataset can be used for assessing local-scale climate change impacts from a risk-based perspective across different applications. Summary: We develop a self-consistent, large ensemble, high-resolution, bias-corrected global dataset of future climates for a set of four possible 21st century scenarios, which is suitable for assessing local-scale climate change impacts and climate policy benefits from a risk-based perspective across different applications. Four emission scenarios 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. We employ the MIT Integrated Global System Modeling (IGSM) framework, which consists of the MIT Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model (EPPA). The EPPA characterizes detailed economic activities to track inter-sectoral and inter-regional links, while the MESM represents key physical, chemical, and biological components of the Earth system that are impacted by human activity. Such integrated framework ensures consistent treatment of interactions among population growth, economic development, energy and land system changes and physical climate responses, which can provide improved assessments of climate impacts and climate policy benefits across multiple sectors. The MESM contains a two-dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly-diffusing ocean model. This architecture allows for conducting a large ensemble of climate simulations for robust uncertainty analyses at significantly less computational cost than state-of-the-art climate models. In addition, we apply a combined spatial disaggregation (SD) – bias correction (BC) delta method with SD for achieving the high resolution and BC for correcting the biases inherent in the MESM future climate projections. 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, http://hydro.iis.u-tokyo.ac.jp/GSWP3/).
