Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch
Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there i...
| Autores: | , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/342167 |
| Acceso en línea: | https://hdl.handle.net/2117/342167 https://dx.doi.org/10.1002/asl.978 |
| Access Level: | acceso abierto |
| Palabra clave: | Climatic changes Climatology Climate variations Bias adjustment Climate change signal Downscaling Observational uncertainty Canvis climàtics Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
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| dc.title.none.fl_str_mv |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| title |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| spellingShingle |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch Casanueva, Ana Climatic changes Climatology Climate variations Bias adjustment Climate change signal Downscaling Observational uncertainty Canvis climàtics Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
| title_short |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| title_full |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| title_fullStr |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| title_full_unstemmed |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| title_sort |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch |
| dc.creator.none.fl_str_mv |
Casanueva, Ana Herrera, Sixto Iturbide, Maialen Lange, Stefan Jury, Martin Dosio, Alessandro Maraun, Douglas Gutiérrez, José M. |
| author |
Casanueva, Ana |
| author_facet |
Casanueva, Ana Herrera, Sixto Iturbide, Maialen Lange, Stefan Jury, Martin Dosio, Alessandro Maraun, Douglas Gutiérrez, José M. |
| author_role |
author |
| author2 |
Herrera, Sixto Iturbide, Maialen Lange, Stefan Jury, Martin Dosio, Alessandro Maraun, Douglas Gutiérrez, José M. |
| author2_role |
author author author author author author author |
| dc.subject.none.fl_str_mv |
Climatic changes Climatology Climate variations Bias adjustment Climate change signal Downscaling Observational uncertainty Canvis climàtics Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
| topic |
Climatic changes Climatology Climate variations Bias adjustment Climate change signal Downscaling Observational uncertainty Canvis climàtics Àrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia |
| description |
Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020 2020-04-01 2021 2021-03-22 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/342167 https://dx.doi.org/10.1002/asl.978 |
| url |
https://hdl.handle.net/2117/342167 https://dx.doi.org/10.1002/asl.978 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ Attribution 3.0 Spain https://creativecommons.org/licenses/by/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Wiley |
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Wiley |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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UPCommons. Portal del coneixement obert de la UPC |
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1869422908466528257 |
| spelling |
Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatchCasanueva, AnaHerrera, SixtoIturbide, MaialenLange, StefanJury, MartinDosio, AlessandroMaraun, DouglasGutiérrez, José M.Climatic changesClimatologyClimate variationsBias adjustmentClimate change signalDownscalingObservational uncertaintyCanvis climàticsÀrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologiaSystematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state‐of‐the‐art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend‐preservation) for a case study in the Iberian Peninsula. The quantile trend‐preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP‐ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high‐quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions.We acknowledge the E‐OBS dataset from the EU‐FP6 project UERRA (https://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://eca.knmi.nl). The authors are grateful to the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organisation for Earth System Science Portals (GO‐ESSP). This study contributes to the EURO‐CORDEX pillar on statistical downscaling, which is a follow‐up of the EU COST Action ES1102 VALUE (Validating and Integrating Downscaling Methods for Climate Change Research). Participation of S. Herrera and J.M. Gutiérrez was partially supported by the project AfriCultuReS (European Union's Horizon 2020 program, grant agreement no, 774652). S. Lange acknowledges funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 641816 (CRESCENDO). The authors are also grateful to three anonymous reviewers who helped to improve the original manuscript.Peer ReviewedWiley20202020-04-0120212021-03-22journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/342167https://dx.doi.org/10.1002/asl.978reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/Attribution 3.0 Spainhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3421672026-05-27T15:37:01Z |
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15,301629 |