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The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications
Regional climate downscaling has arrived at an important juncture. Some in the research community favour continued refinement and evaluation of downscaling techniques within a broader framework of uncertainty characterisation and reduction. Others are calling for smarter use of downscaling tools, ac...
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| Format: | Default Article |
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2014
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| Online Access: | https://hdl.handle.net/2134/17684 |
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| _version_ | 1818172900636622848 |
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| author | Robert Wilby Christian Dawson Conor Murphy P. O'Connor E. Hawkins |
| author_facet | Robert Wilby Christian Dawson Conor Murphy P. O'Connor E. Hawkins |
| author_sort | Robert Wilby (1255929) |
| collection | Figshare |
| description | Regional climate downscaling has arrived at an important juncture. Some in the research community favour continued refinement and evaluation of downscaling techniques within a broader framework of uncertainty characterisation and reduction. Others are calling for smarter use of downscaling tools, accepting that conventional, scenario-led strategies for adaptation planning have limited utility in practice. This paper sets out the rationale and new functionality of the Decision Centric (DC) version of the Statistical DownScaling Model (SDSM-DC). This tool enables synthesis of plausible daily weather series, exotic variables (such as tidal surge), and climate change scenarios guided, not determined, by climate model output. Two worked examples are presented. The first shows how SDSM-DC can be used to reconstruct and in-fill missing records based on calibrated predictor-predictand relationships. Daily temperature and precipitation series from sites in Africa, Asia and North America are deliberately degraded to show that SDSM-DC can reconstitute lost data. The second demonstrates the application of the new scenario generator for stress testing a specific adaptation decision. SDSM-DC is used to generate daily precipitation scenarios to simulate winter flooding in the Boyne catchment, Ireland. This sensitivity analysis reveals the conditions under which existing precautionary allowances for climate change might be insufficient. We conclude by discussing the wider implications of the proposed approach and research opportunities presented by the new tool. |
| format | Default Article |
| id | rr-article-9481445 |
| institution | Loughborough University |
| publishDate | 2014 |
| record_format | Figshare |
| spelling | rr-article-94814452014-01-01T00:00:00Z The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications Robert Wilby (1255929) Christian Dawson (1257441) Conor Murphy (7190264) P. O'Connor (7190636) E. Hawkins (7190639) Atmospheric sciences not elsewhere classified Other earth sciences not elsewhere classified Downscaling Climate scenario Weather generator Stress test Data reconstruction Adaptation Earth Sciences not elsewhere classified Atmospheric Sciences Regional climate downscaling has arrived at an important juncture. Some in the research community favour continued refinement and evaluation of downscaling techniques within a broader framework of uncertainty characterisation and reduction. Others are calling for smarter use of downscaling tools, accepting that conventional, scenario-led strategies for adaptation planning have limited utility in practice. This paper sets out the rationale and new functionality of the Decision Centric (DC) version of the Statistical DownScaling Model (SDSM-DC). This tool enables synthesis of plausible daily weather series, exotic variables (such as tidal surge), and climate change scenarios guided, not determined, by climate model output. Two worked examples are presented. The first shows how SDSM-DC can be used to reconstruct and in-fill missing records based on calibrated predictor-predictand relationships. Daily temperature and precipitation series from sites in Africa, Asia and North America are deliberately degraded to show that SDSM-DC can reconstitute lost data. The second demonstrates the application of the new scenario generator for stress testing a specific adaptation decision. SDSM-DC is used to generate daily precipitation scenarios to simulate winter flooding in the Boyne catchment, Ireland. This sensitivity analysis reveals the conditions under which existing precautionary allowances for climate change might be insufficient. We conclude by discussing the wider implications of the proposed approach and research opportunities presented by the new tool. 2014-01-01T00:00:00Z Text Journal contribution 2134/17684 https://figshare.com/articles/journal_contribution/The_Statistical_Downscaling_Model_-_Decision_Centric_SDSM-DC_conceptual_basis_and_applications/9481445 CC BY-NC-ND 4.0 |
| spellingShingle | Atmospheric sciences not elsewhere classified Other earth sciences not elsewhere classified Downscaling Climate scenario Weather generator Stress test Data reconstruction Adaptation Earth Sciences not elsewhere classified Atmospheric Sciences Robert Wilby Christian Dawson Conor Murphy P. O'Connor E. Hawkins The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications |
| title | The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications |
| title_full | The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications |
| title_fullStr | The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications |
| title_full_unstemmed | The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications |
| title_short | The Statistical Downscaling Model - Decision Centric (SDSM-DC): conceptual basis and applications |
| title_sort | statistical downscaling model - decision centric (sdsm-dc): conceptual basis and applications |
| topic | Atmospheric sciences not elsewhere classified Other earth sciences not elsewhere classified Downscaling Climate scenario Weather generator Stress test Data reconstruction Adaptation Earth Sciences not elsewhere classified Atmospheric Sciences |
| url | https://hdl.handle.net/2134/17684 |