The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections
Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their abilit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rohith, A. N. [verfasserIn] Mejia, Alfonso [verfasserIn] Cibin, Raj [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2024 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| Übergeordnetes Werk: |
Enthalten in: Earth systems and environment - Springer International Publishing, 2017, 8(2024), 3 vom: 10. Juni, Seite 693-703 |
|---|---|
| Übergeordnetes Werk: |
volume:8 ; year:2024 ; number:3 ; day:10 ; month:06 ; pages:693-703 |
| Links: |
|---|
| DOI / URN: |
10.1007/s41748-024-00410-3 |
|---|
| Katalog-ID: |
SPR057375925 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR057375925 | ||
| 003 | DE-627 | ||
| 005 | 20240919064750.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240919s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s41748-024-00410-3 |2 doi | |
| 035 | |a (DE-627)SPR057375925 | ||
| 035 | |a (SPR)s41748-024-00410-3-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 550 |q VZ |
| 082 | 0 | 4 | |a 550 |q VZ |
| 100 | 1 | |a Rohith, A. N. |e verfasserin |0 (orcid)0000-0001-6522-7861 |4 aut | |
| 245 | 1 | 0 | |a The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. | ||
| 520 | |a Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. | ||
| 650 | 4 | |a GCM selection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Climate change |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bias correction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extreme precipitation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Reliability ensemble averaging |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mejia, Alfonso |e verfasserin |4 aut | |
| 700 | 1 | |a Cibin, Raj |e verfasserin |0 (orcid)0000-0001-5374-8504 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Earth systems and environment |d Springer International Publishing, 2017 |g 8(2024), 3 vom: 10. Juni, Seite 693-703 |w (DE-627)884895904 |w (DE-600)2892530-0 |x 2509-9434 |7 nnns |
| 773 | 1 | 8 | |g volume:8 |g year:2024 |g number:3 |g day:10 |g month:06 |g pages:693-703 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s41748-024-00410-3 |m X:SPRINGER |x Resolving-System |z lizenzpflichtig |3 Volltext |
| 912 | |a SYSFLAG_0 | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a SSG-OPC-GGO | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_90 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_120 | ||
| 912 | |a GBV_ILN_138 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_152 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_171 | ||
| 912 | |a GBV_ILN_187 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_224 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_250 | ||
| 912 | |a GBV_ILN_266 | ||
| 912 | |a GBV_ILN_281 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_636 | ||
| 912 | |a GBV_ILN_702 | ||
| 912 | |a GBV_ILN_2001 | ||
| 912 | |a GBV_ILN_2003 | ||
| 912 | |a GBV_ILN_2004 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2007 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2010 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_2021 | ||
| 912 | |a GBV_ILN_2025 | ||
| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2031 | ||
| 912 | |a GBV_ILN_2034 | ||
| 912 | |a GBV_ILN_2037 | ||
| 912 | |a GBV_ILN_2038 | ||
| 912 | |a GBV_ILN_2039 | ||
| 912 | |a GBV_ILN_2044 | ||
| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2093 | ||
| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2107 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2122 | ||
| 912 | |a GBV_ILN_2129 | ||
| 912 | |a GBV_ILN_2143 | ||
| 912 | |a GBV_ILN_2144 | ||
| 912 | |a GBV_ILN_2147 | ||
| 912 | |a GBV_ILN_2148 | ||
| 912 | |a GBV_ILN_2152 | ||
| 912 | |a GBV_ILN_2153 | ||
| 912 | |a GBV_ILN_2188 | ||
| 912 | |a GBV_ILN_2190 | ||
| 912 | |a GBV_ILN_2232 | ||
| 912 | |a GBV_ILN_2336 | ||
| 912 | |a GBV_ILN_2446 | ||
| 912 | |a GBV_ILN_2470 | ||
| 912 | |a GBV_ILN_2472 | ||
| 912 | |a GBV_ILN_2507 | ||
| 912 | |a GBV_ILN_2522 | ||
| 912 | |a GBV_ILN_2548 | ||
| 912 | |a GBV_ILN_4035 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4246 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4328 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 8 |j 2024 |e 3 |b 10 |c 06 |h 693-703 | ||
| author_variant |
a n r an anr a m am r c rc |
|---|---|
| matchkey_str |
article:25099434:2024----::hslcinflbllmtmdlfreinlmattdesolcnieifrainrmitr |
| hierarchy_sort_str |
2024 |
| publishDate |
2024 |
| allfields |
10.1007/s41748-024-00410-3 doi (DE-627)SPR057375925 (SPR)s41748-024-00410-3-e DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ Rohith, A. N. verfasserin (orcid)0000-0001-6522-7861 aut The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. GCM selection (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Bias correction (dpeaa)DE-He213 Extreme precipitation (dpeaa)DE-He213 Reliability ensemble averaging (dpeaa)DE-He213 Mejia, Alfonso verfasserin aut Cibin, Raj verfasserin (orcid)0000-0001-5374-8504 aut Enthalten in Earth systems and environment Springer International Publishing, 2017 8(2024), 3 vom: 10. Juni, Seite 693-703 (DE-627)884895904 (DE-600)2892530-0 2509-9434 nnns volume:8 year:2024 number:3 day:10 month:06 pages:693-703 https://dx.doi.org/10.1007/s41748-024-00410-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2024 3 10 06 693-703 |
| spelling |
10.1007/s41748-024-00410-3 doi (DE-627)SPR057375925 (SPR)s41748-024-00410-3-e DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ Rohith, A. N. verfasserin (orcid)0000-0001-6522-7861 aut The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. GCM selection (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Bias correction (dpeaa)DE-He213 Extreme precipitation (dpeaa)DE-He213 Reliability ensemble averaging (dpeaa)DE-He213 Mejia, Alfonso verfasserin aut Cibin, Raj verfasserin (orcid)0000-0001-5374-8504 aut Enthalten in Earth systems and environment Springer International Publishing, 2017 8(2024), 3 vom: 10. Juni, Seite 693-703 (DE-627)884895904 (DE-600)2892530-0 2509-9434 nnns volume:8 year:2024 number:3 day:10 month:06 pages:693-703 https://dx.doi.org/10.1007/s41748-024-00410-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2024 3 10 06 693-703 |
| allfields_unstemmed |
10.1007/s41748-024-00410-3 doi (DE-627)SPR057375925 (SPR)s41748-024-00410-3-e DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ Rohith, A. N. verfasserin (orcid)0000-0001-6522-7861 aut The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. GCM selection (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Bias correction (dpeaa)DE-He213 Extreme precipitation (dpeaa)DE-He213 Reliability ensemble averaging (dpeaa)DE-He213 Mejia, Alfonso verfasserin aut Cibin, Raj verfasserin (orcid)0000-0001-5374-8504 aut Enthalten in Earth systems and environment Springer International Publishing, 2017 8(2024), 3 vom: 10. Juni, Seite 693-703 (DE-627)884895904 (DE-600)2892530-0 2509-9434 nnns volume:8 year:2024 number:3 day:10 month:06 pages:693-703 https://dx.doi.org/10.1007/s41748-024-00410-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2024 3 10 06 693-703 |
| allfieldsGer |
10.1007/s41748-024-00410-3 doi (DE-627)SPR057375925 (SPR)s41748-024-00410-3-e DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ Rohith, A. N. verfasserin (orcid)0000-0001-6522-7861 aut The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. GCM selection (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Bias correction (dpeaa)DE-He213 Extreme precipitation (dpeaa)DE-He213 Reliability ensemble averaging (dpeaa)DE-He213 Mejia, Alfonso verfasserin aut Cibin, Raj verfasserin (orcid)0000-0001-5374-8504 aut Enthalten in Earth systems and environment Springer International Publishing, 2017 8(2024), 3 vom: 10. Juni, Seite 693-703 (DE-627)884895904 (DE-600)2892530-0 2509-9434 nnns volume:8 year:2024 number:3 day:10 month:06 pages:693-703 https://dx.doi.org/10.1007/s41748-024-00410-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2024 3 10 06 693-703 |
| allfieldsSound |
10.1007/s41748-024-00410-3 doi (DE-627)SPR057375925 (SPR)s41748-024-00410-3-e DE-627 ger DE-627 rakwb eng 550 VZ 550 VZ Rohith, A. N. verfasserin (orcid)0000-0001-6522-7861 aut The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. GCM selection (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Bias correction (dpeaa)DE-He213 Extreme precipitation (dpeaa)DE-He213 Reliability ensemble averaging (dpeaa)DE-He213 Mejia, Alfonso verfasserin aut Cibin, Raj verfasserin (orcid)0000-0001-5374-8504 aut Enthalten in Earth systems and environment Springer International Publishing, 2017 8(2024), 3 vom: 10. Juni, Seite 693-703 (DE-627)884895904 (DE-600)2892530-0 2509-9434 nnns volume:8 year:2024 number:3 day:10 month:06 pages:693-703 https://dx.doi.org/10.1007/s41748-024-00410-3 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 8 2024 3 10 06 693-703 |
| language |
English |
| source |
Enthalten in Earth systems and environment 8(2024), 3 vom: 10. Juni, Seite 693-703 volume:8 year:2024 number:3 day:10 month:06 pages:693-703 |
| sourceStr |
Enthalten in Earth systems and environment 8(2024), 3 vom: 10. Juni, Seite 693-703 volume:8 year:2024 number:3 day:10 month:06 pages:693-703 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
GCM selection Climate change Bias correction Extreme precipitation Reliability ensemble averaging |
| dewey-raw |
550 |
| isfreeaccess_bool |
false |
| container_title |
Earth systems and environment |
| authorswithroles_txt_mv |
Rohith, A. N. @@aut@@ Mejia, Alfonso @@aut@@ Cibin, Raj @@aut@@ |
| publishDateDaySort_date |
2024-06-10T00:00:00Z |
| hierarchy_top_id |
884895904 |
| dewey-sort |
3550 |
| id |
SPR057375925 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057375925</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240919064750.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240919s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41748-024-00410-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057375925</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41748-024-00410-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rohith, A. N.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6522-7861</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GCM selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate change</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bias correction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extreme precipitation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reliability ensemble averaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mejia, Alfonso</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cibin, Raj</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-5374-8504</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Earth systems and environment</subfield><subfield code="d">Springer International Publishing, 2017</subfield><subfield code="g">8(2024), 3 vom: 10. Juni, Seite 693-703</subfield><subfield code="w">(DE-627)884895904</subfield><subfield code="w">(DE-600)2892530-0</subfield><subfield code="x">2509-9434</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:3</subfield><subfield code="g">day:10</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:693-703</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s41748-024-00410-3</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_266</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2024</subfield><subfield code="e">3</subfield><subfield code="b">10</subfield><subfield code="c">06</subfield><subfield code="h">693-703</subfield></datafield></record></collection>
|
| author |
Rohith, A. N. |
| spellingShingle |
Rohith, A. N. ddc 550 misc GCM selection misc Climate change misc Bias correction misc Extreme precipitation misc Reliability ensemble averaging The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections |
| authorStr |
Rohith, A. N. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)884895904 |
| format |
electronic Article |
| dewey-ones |
550 - Earth sciences |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
2509-9434 |
| topic_title |
550 VZ The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections GCM selection (dpeaa)DE-He213 Climate change (dpeaa)DE-He213 Bias correction (dpeaa)DE-He213 Extreme precipitation (dpeaa)DE-He213 Reliability ensemble averaging (dpeaa)DE-He213 |
| topic |
ddc 550 misc GCM selection misc Climate change misc Bias correction misc Extreme precipitation misc Reliability ensemble averaging |
| topic_unstemmed |
ddc 550 misc GCM selection misc Climate change misc Bias correction misc Extreme precipitation misc Reliability ensemble averaging |
| topic_browse |
ddc 550 misc GCM selection misc Climate change misc Bias correction misc Extreme precipitation misc Reliability ensemble averaging |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Earth systems and environment |
| hierarchy_parent_id |
884895904 |
| dewey-tens |
550 - Earth sciences & geology |
| hierarchy_top_title |
Earth systems and environment |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)884895904 (DE-600)2892530-0 |
| title |
The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections |
| ctrlnum |
(DE-627)SPR057375925 (SPR)s41748-024-00410-3-e |
| title_full |
The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections |
| author_sort |
Rohith, A. N. |
| journal |
Earth systems and environment |
| journalStr |
Earth systems and environment |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2024 |
| contenttype_str_mv |
txt |
| container_start_page |
693 |
| author_browse |
Rohith, A. N. Mejia, Alfonso Cibin, Raj |
| container_volume |
8 |
| class |
550 VZ |
| format_se |
Elektronische Aufsätze |
| author-letter |
Rohith, A. N. |
| doi_str_mv |
10.1007/s41748-024-00410-3 |
| normlink |
(ORCID)0000-0001-6522-7861 (ORCID)0000-0001-5374-8504 |
| normlink_prefix_str_mv |
(orcid)0000-0001-6522-7861 (orcid)0000-0001-5374-8504 |
| dewey-full |
550 |
| author2-role |
verfasserin |
| title_sort |
the selection of global climate models for regional impact studies should consider information from historical simulations and future projections |
| title_auth |
The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections |
| abstract |
Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies. Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed. © King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| collection_details |
SYSFLAG_0 GBV_SPRINGER SSG-OPC-GGO GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 |
| container_issue |
3 |
| title_short |
The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections |
| url |
https://dx.doi.org/10.1007/s41748-024-00410-3 |
| remote_bool |
true |
| author2 |
Mejia, Alfonso Cibin, Raj |
| author2Str |
Mejia, Alfonso Cibin, Raj |
| ppnlink |
884895904 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s41748-024-00410-3 |
| up_date |
2024-09-19T04:49:51.637Z |
| _version_ |
1810598609958207488 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057375925</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240919064750.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240919s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41748-024-00410-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057375925</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41748-024-00410-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rohith, A. N.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6522-7861</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© King Abdulaziz University and Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The selection of Global Climate Models (GCMs) based on their ability to represent precipitation patterns of a region is required for hydrological climate change impact studies to address time and computational constraints. Generally, the selection of GCMs is determined based on their ability to reproduce observed climate statistics in historical simulations, assuming they will continue to perform well in the future. However, the performance of GCMs varies over time in ways that are not sensitive to their historical performance, indicating that GCMs’ selection needs to consider historical simulation and future projection information. We propose a framework to account for future GCM projection convergence to and divergence from the ensemble mean, along with historical performance, to select the GCMs that are applicable to a particular regional climate impact study. The framework uses Reliability Ensemble Averaging (REA) with 30 Coupled Model Intercomparison Project-6 (CMIP6) GCMs to select GCMs based on the ensemble mean and variability of projections. We demonstrate the framework using three climate indices (annual maximum precipitation, annual total precipitation, and wet day precipitation intensity) in the Chesapeake Bay watershed of the United States. Our analysis shows that using only the GCM performance during the historical period could result in the selection of GCMs that are extreme outliers due to an inherent underprediction of precipitation extremes by all GCMs and requires an efficient bias correction before selection. There was also no significant correlation between the historical period performance of GCMs and future GCM convergence for more than 95% of the cases in the study region. This highlights the need to consider convergence and divergence information from climate projections when selecting GCMs for practical and computationally intensive applications. The proposed framework can be adapted to any study region and can help identify GCMs for computationally intensive climate change impact studies.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Highlights Both historical performance and future projections need accounting in selecting GCMs. The future convergence of GCMs is not sensitive to their historical performance. Efficient bias correction can benefit the identification of GCMs. A subset of GCMs were identified for impact studies in the Chesapeake Bay watershed.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">GCM selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate change</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bias correction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extreme precipitation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reliability ensemble averaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mejia, Alfonso</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cibin, Raj</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-5374-8504</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Earth systems and environment</subfield><subfield code="d">Springer International Publishing, 2017</subfield><subfield code="g">8(2024), 3 vom: 10. Juni, Seite 693-703</subfield><subfield code="w">(DE-627)884895904</subfield><subfield code="w">(DE-600)2892530-0</subfield><subfield code="x">2509-9434</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:3</subfield><subfield code="g">day:10</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:693-703</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s41748-024-00410-3</subfield><subfield code="m">X:SPRINGER</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_138</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_171</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_250</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_266</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_281</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_636</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2031</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2039</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2093</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2107</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2144</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2188</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2446</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2472</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2548</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2024</subfield><subfield code="e">3</subfield><subfield code="b">10</subfield><subfield code="c">06</subfield><subfield code="h">693-703</subfield></datafield></record></collection>
|
| score |
7.401947 |