Combining a Multi‐Lake Model Ensemble and a Multi‐Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi-scenario, and multi GCM- RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature (Formula presented.) and substantial bottom warming (Formula presented.), longer stratification periods (+55 days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems.
Details
Original language | English |
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Article number | e2023WR036511 |
Journal | Water resources research |
Volume | 60 |
Issue number | 11 |
Publication status | Published - Nov 2024 |
Peer-reviewed | Yes |
External IDs
Scopus | 85210103242 |
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