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wdc-climate.de:3964631 eng; USA collection MITJP-MSD_LE Xiang Gao Massachusetts Institute of Technology not filled pointOfContact 2022-11-29T15:01:50 ISO 19115 ISO 19115-2:2009 MITJP-MSD: Large-ensemble 21st century monthly hydro-climatological forcing dataset 2023-02-07 publication 1 MITJP-MSD_LE doi:10.26050/WDCC/MITJP-MSD_LE Xiang Gao Massachusetts Institute of Technology not filled Email: xgao304@mit.edu principalInvestigator Xiang Gao Massachusetts Institute of Technology not filled Email: xgao304@mit.edu author Andrei Sokolov Massachusetts Institute of Technology not filled Email: sokolov@mit.edu author Adam Schlosser Massachusetts Institute of Technology not filled Email: casch@mit.edu author WDC Climate at DKRZ publisher 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/). The anomalies or deltas are derived by spatially disaggregating the IGSM zonal climate projections based on regional hydroclimate change patterns from the 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. 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. 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°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. A technical evaluation indicates the dataset well represents the expected large-scale climate features across various regions of the globe and can meet various needs associated with climate impact assessments, including uncertainty analyses, risk quantification, climate policy mitigation, and driving climate impact models which require monthly data inputs, on both global and regional scales. There is no model version. But all the developed models are available online (https://globalchange.mit.edu/research/research-tools/earth-system-model) and have relevant licenses. On the website you could find the following information: The source code of the MESM is publicly available for non-commercial research and educational purposes via github (i.e. github.com:mit-jp/igsm.git). Under this open source protocol, we have also established a software license through the MIT Technology Licensing Office. As the MESM has embedded models developed at three other institutions, appropriate copyright clearances for the third-party code are required. Xiang Gao Massachusetts Institute of Technology not filled pointOfContact NetCDF CMIP6 bias-correction climate impact earth system model forcing dataset large ensemble spatial disaggregation statistical reconstruction CC-BY-4.0: Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ eng; USA -179.75 179.75 -89.75 89.75 2021-01-01 2100-12-31 air_temperature_trend-atNearSurface (maximum) air_temperature_trend-atNearSurface (maximum); unit: K surface_downwelling_longwave_flux_in_air (trend) surface_downwelling_longwave_flux_in_air_trend [CF-Standard Name (cell_method:time)]; unit: W m-2 air_temperature-atNearSurface Air temperature is the bulk temperature of the air, not the surface (skin) temperature. [CF-Standard Name-vertical_coordinate]; unit: K precipitation_flux_trend precipitation_flux_trend; unit: mm d-1 air_temperature-atNearSurface (minimum) Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (minimum) [CF-Standard Name-vertical_coordinate-cell_method]; unit: K surface_downwelling_shortwave_flux_in_air (trend) surface_downwelling_shortwave_flux_in_air_trend [CF-Standard Name (cell_method:time)]; unit: W m-2 wind_speed-atNearSurface wind_speed-atNearSurface [CF-Standard Name-vertical_coordinate]; unit: m s-1 air_temperature_trend-atNearSurface (minimum) air_temperature_trend-atNearSurface (minimum); unit: K wind_speed_trend-atNearSurface wind_speed_trend-atNearSurface; unit: m s-1 surface_specific_humidity_trend surface_specific_humidity_trend; unit: kg kg-1 precipitation_flux In accordance with common usage in geophysical disciplines, "flux" implies per unit area, called "flux density" in physics. [CF-Standard Name]; unit: mm d-1 air_temperature_trend-atNearSurface air_temperature_trend-atNearSurface; unit: K relative_humidity_trend relative_humidity_trend; unit: None air_temperature-atNearSurface (maximum) Air temperature is the bulk temperature of the air, not the surface (skin) temperature. (maximum) [CF-Standard Name-vertical_coordinate-cell_method]; unit: K relative_humidity relative_humidity [CF-Standard Name]; unit: None surface_downwelling_shortwave_flux_in_air surface_downwelling_shortwave_flux_in_air [CF-Standard Name]; unit: W m-2 surface_specific_humidity The surface called "surface" means the lower boundary of the atmosphere. "specific" means per unit mass. Specific humidity is the mass fraction of water vapor in (moist) air. [CF-Standard Name]; unit: kg kg-1 surface_downwelling_longwave_flux_in_air surface_downwelling_longwave_flux_in_air [CF-Standard Name]; unit: W m-2 NetCDF 41573138 https://www.wdc-climate.de/ui/entry?acronym=MITJP-MSD_LE information collection n/a None n/a None