A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
Abstract Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical pr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-025-01157-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387862005383168 |
|---|---|
| author | Jonathan Schmidt Luca Schmidt Felix M. Strnad Nicole Ludwig Philipp Hennig |
| author_facet | Jonathan Schmidt Luca Schmidt Felix M. Strnad Nicole Ludwig Philipp Hennig |
| author_sort | Jonathan Schmidt |
| collection | DOAJ |
| description | Abstract Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-temporally coherent high-resolution weather dynamics for multiple variables across long time horizons is crucial. We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics. After training, we condition on coarse climate model data to generate weather patterns consistent with the aggregate information. As this predictive task is inherently uncertain, we leverage the probabilistic nature of diffusion models and sample multiple trajectories. We evaluate our approach with high-resolution reanalysis information before applying it to the climate model downscaling task. We then demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output. |
| format | Article |
| id | doaj-art-a145346239dc4c41b48cd037107b7ccf |
| institution | Kabale University |
| issn | 2397-3722 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Climate and Atmospheric Science |
| spelling | doaj-art-a145346239dc4c41b48cd037107b7ccf2025-08-20T03:42:27ZengNature Portfolionpj Climate and Atmospheric Science2397-37222025-07-018111210.1038/s41612-025-01157-yA Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate SimulationJonathan Schmidt0Luca Schmidt1Felix M. Strnad2Nicole Ludwig3Philipp Hennig4University of TübingenUniversity of TübingenUniversity of TübingenUniversity of TübingenUniversity of TübingenAbstract Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical downscaling methods infer these phenomena as temporally decoupled spatial patches. However, to preserve physical properties, estimating spatio-temporally coherent high-resolution weather dynamics for multiple variables across long time horizons is crucial. We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics. After training, we condition on coarse climate model data to generate weather patterns consistent with the aggregate information. As this predictive task is inherently uncertain, we leverage the probabilistic nature of diffusion models and sample multiple trajectories. We evaluate our approach with high-resolution reanalysis information before applying it to the climate model downscaling task. We then demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output.https://doi.org/10.1038/s41612-025-01157-y |
| spellingShingle | Jonathan Schmidt Luca Schmidt Felix M. Strnad Nicole Ludwig Philipp Hennig A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation npj Climate and Atmospheric Science |
| title | A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation |
| title_full | A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation |
| title_fullStr | A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation |
| title_full_unstemmed | A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation |
| title_short | A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation |
| title_sort | generative framework for probabilistic spatiotemporally coherent downscaling of climate simulation |
| url | https://doi.org/10.1038/s41612-025-01157-y |
| work_keys_str_mv | AT jonathanschmidt agenerativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT lucaschmidt agenerativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT felixmstrnad agenerativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT nicoleludwig agenerativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT philipphennig agenerativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT jonathanschmidt generativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT lucaschmidt generativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT felixmstrnad generativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT nicoleludwig generativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation AT philipphennig generativeframeworkforprobabilisticspatiotemporallycoherentdownscalingofclimatesimulation |