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Contributors: Swapan Mallick, Harald Schyberg, Jelena Bojarova

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This framework generates spatially coherent uncertainty fields that effectively capture variability related to atmospheric flow and orographic features. By harnessing the probabilistic capabilities inherent in diffusion models, DDPM-ML is able to learn intricate uncertainty structures from ensemble data and reconstruct the cross-scale uncertainty relationships between the coarser-resolution ERA5 ensemble data assimilation (ERA-EDA) and the finer-resolution CARRA2  dataset. Comprehensive details regarding the methodology, input data, and uncertainty quantification procedures are provided in this documentation of the method:  Copernicus pan-Arctic Regional Reanalysis (CARRA2): Uncertainty estimation method

Upon learning the uncertainty structures via the DDPM-ML framework, uncertainty estimates can be generated for the deterministic CARRA2 reanalysis. The trained model produces flow-dependent uncertainty fields by downscaling ERA5 ensemble information onto the high-resolution CARRA2 grid and its associated orography. Although applying this approach to the entire 40-year CARRA2 record presents significant computational challenges, we demonstrate the methodology using data for 2019. In the absence of an intrinsic ensemble prediction system for CARRA2, this dataset represents a pragmatic advancement toward providing uncertainty information to the CARRA2 user community.

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Uncertainty estimates for 2-meter temperature were produced at 00, 06, 12, and 18 UTC throughout the entirety of 2019 across the CARRA2 domain. This effort resulted in a dataset comprising 1,460 NetCDF files accompanied by corresponding visualizations in image format. All files are accessible via the CARRA2 project’s GitHub repository https://github.com/CARRA2/Uncertainty_Quantification/tree/Sep_2025/UQ_OUTPUT, where they are systematically organized into distinct directories for NetCDF data and image outputs. Each directory is further subdivided into two segments: 2019_PART1 (covering January to June) and 2019_PART2 (covering July to December).

Figure 1: Illustration of uncertainty quantification for the specific date of January 1, 2019, at 00:00 UTC.

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