Introducing Our New Global Downscaled Projections for Climate Impacts Research
The Lab’s open and freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the U.N. Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those CMIP6 simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
To produce GDPCIR, the Lab’s team of climate scientists, economists, and computational experts developed methods tailored to meet the needs of climate impacts researchers. The Lab is well-positioned to serve this community, with decades of combined analytical experience quantifying the costs of climate change across fields spanning from human health to energy to agriculture. Together, the Lab’s team is leveraging a first-of-its-kind, evidence-based, data-driven approach to quantifying the impacts and costs of climate change, sector-by-sector, and community-by-community around the world.
Drawing on this expertise, GDPCIR preserves trends in climate extremes projected by the CMIP6 models to help researchers study the way high temperatures or heavy precipitation manifest in future climate impacts. Additionally, GDPCIR provides projections under four future emissions pathways, as well as a historical reference dataset, giving researchers the ability to analyze a range of possible climate futures at a finer spatial scale with past conditions for context. These optimized daily projections for 25 global climate models comprise the most comprehensive and high-resolution dataset that exists for this application.
The availability of GDPCIR via Microsoft’s Planetary Computer is a game-changer for many climate impacts research applications, considering its global coverage, ease of use, and transparency. For instance, researchers can search, discover, and interact with GDPCIR on the cloud, as well as download the data, facilitating its use as an input to climate impacts models. The pipeline for the project was designed to be fully reproducible and run on all cloud operating systems to ensure others could replicate the methods. GDPCIR is also provided with SpatioTemporal Asset Catalog (STAC) specification, a common language to describe a range of geospatial information, so it can more easily be indexed and discovered.
Documentation and sample notebooks are available on the Planetary Computer, which was developed by Microsoft AI for Earth, and includes petabytes of environmental monitoring data, in consistent, analysis-ready formats. A publication providing additional detail is in the works.
This research has been supported by The Rockefeller Foundation and the Microsoft AI for Earth Initiative.