Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Sep 2022
Development and technical paper |
| 23 Sep 2022
| 23 Sep 2022
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Chahan M. Kropf
CORRESPONDING AUTHOR
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Alessio Ciullo
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Laura Otth
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Simona Meiler
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Arun Rana
Frankfurt School of Finance and Management Gemeinnützige GmbH, Adickesallee 32–34, 60322 Frankfurt am Main, Germany
Emanuel Schmid
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Jamie W. McCaughey
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
David N. Bresch
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
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- Analyzing the relationship between meteorological changes and evapotranspiration trends in Gia Lai province, Central Highlands of Vietnam P. Ha et al. 10.2166/wcc.2023.485
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- A novel framework for the assessment of hydro-meteorological risks taking into account nature-based solutions L. Brogno et al. 10.1088/1748-9326/ad53e6
- Asset-level assessment of climate physical risk matters for adaptation finance G. Bressan et al. 10.1038/s41467-024-48820-1
- Uncertainties and sensitivities in the quantification of future tropical cyclone risk S. Meiler et al. 10.1038/s43247-023-00998-w
- OpenStreetMap for multi-faceted climate risk assessments E. Mühlhofer et al. 10.1088/2515-7620/ad15ab
- The effects of climate change and regional water supply capacity on integrated drought risk J. Kim et al. 10.1016/j.jher.2024.08.003
- Climate-resilient strategy planning using the SWOT methodology: A case study of the Japanese wind energy sector L. Bachmann et al. 10.1016/j.crm.2024.100665
- Intercomparison of regional loss estimates from global synthetic tropical cyclone models S. Meiler et al. 10.1038/s41467-022-33918-1
- Projections and uncertainties of winter windstorm damage in Europe in a changing climate L. Severino et al. 10.5194/nhess-24-1555-2024
- How to provide actionable information on weather and climate impacts?–A summary of strategic, methodological, and technical perspectives T. Geiger et al. 10.3389/fclim.2024.1343993
2 citations as recorded by crossref.
- Quantifying uncertainty and sensitivity in climate risk assessments: Varying hazard, exposure and vulnerability modelling choices L. Dawkins et al. 10.1016/j.crm.2023.100511
- Introducing uncertainties in composite indicators. The case of the Impact Chain risk assessment framework C. Melo-Aguilar et al. 10.3389/fclim.2022.1019888
Latest update: 20 Nov 2024
Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Mathematical models are approximations, and modellers need to understand and ideally quantify...
