Statistical Downscaling of Precipitation in the South and Southeast of Mexico
The advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Climate |
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| Online Access: | https://www.mdpi.com/2225-1154/11/9/186 |
| _version_ | 1797580746369531904 |
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| author | Mercedes Andrade-Velázquez Martín José Montero-Martínez |
| author_facet | Mercedes Andrade-Velázquez Martín José Montero-Martínez |
| author_sort | Mercedes Andrade-Velázquez |
| collection | DOAJ |
| description | The advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods, remains evident, particularly in the context of precipitation variability. This study centered on the systematic application of a bias-correction technique (quantile mapping) to four designated CMIP6 models: CNRM-ESM2-6A, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0. The selection of these models was informed by a methodical approach grounded in previous research conducted within the southern–southeastern region of Mexico. Diverse performance evaluation metrics were employed, including root-mean-square difference (<i>rmsd</i>), normalized standard deviation (<i>NSD</i>), bias, and Pearson’s correlation (illustrated by Taylor diagrams). The study area was divided into two distinct domains: southern Mexico and the southeast region covering Tabasco and Chiapas, and the Yucatan Peninsula. The findings underscored the substantial improvement in model performance achieved through bias correction across the entire study area. The outcomes of <i>rmsd</i> and <i>NSD</i> not only exhibited variations among different climate models but also manifested sensitivity to the specific geographical region under examination. In the southern region, CNRM-ESM2-1 emerged as the most adept model following bias correction. In the southeastern domain, including only Tabasco and Chiapas, the optimal model was again CNRM-ESM2-1 after bias-correction. However, for the Yucatan Peninsula, the IPSL-CM6A-LR model yielded the most favorable results. This study emphasizes the significance of tailored bias-correction techniques in refining the performance of climate models and highlights the spatially nuanced responses of different models within the study area’s distinct geographical regions. |
| first_indexed | 2024-03-10T22:54:15Z |
| format | Article |
| id | doaj.art-714443d1d3cd4ab58dd5b6c27f979825 |
| institution | Directory Open Access Journal |
| issn | 2225-1154 |
| language | English |
| last_indexed | 2024-03-10T22:54:15Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Climate |
| spelling | doaj.art-714443d1d3cd4ab58dd5b6c27f9798252023-11-19T10:05:55ZengMDPI AGClimate2225-11542023-09-0111918610.3390/cli11090186Statistical Downscaling of Precipitation in the South and Southeast of MexicoMercedes Andrade-Velázquez0Martín José Montero-Martínez1CONAHCYT—Centro del Cambio Global y la Sustentabilidad (CCGS), Calle Centenario del Instituto Juárez S/N, Colonia Reforma, Villahermosa C.P. 86080, Tabasco, MexicoInstituto Mexicano de Tecnología del Agua, Subcoordinación de Eventos Extremos y Cambio Climático, Paseo Cuauhnáhuac 8532, Colonia Progreso, Jiutepec C.P. 62550, Morelos, MexicoThe advancements in global climate modeling achieved within the CMIP6 framework have led to notable enhancements in model performance, particularly with regard to spatial resolution. However, the persistent requirement for refined techniques, such as dynamically or statistically downscaled methods, remains evident, particularly in the context of precipitation variability. This study centered on the systematic application of a bias-correction technique (quantile mapping) to four designated CMIP6 models: CNRM-ESM2-6A, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0. The selection of these models was informed by a methodical approach grounded in previous research conducted within the southern–southeastern region of Mexico. Diverse performance evaluation metrics were employed, including root-mean-square difference (<i>rmsd</i>), normalized standard deviation (<i>NSD</i>), bias, and Pearson’s correlation (illustrated by Taylor diagrams). The study area was divided into two distinct domains: southern Mexico and the southeast region covering Tabasco and Chiapas, and the Yucatan Peninsula. The findings underscored the substantial improvement in model performance achieved through bias correction across the entire study area. The outcomes of <i>rmsd</i> and <i>NSD</i> not only exhibited variations among different climate models but also manifested sensitivity to the specific geographical region under examination. In the southern region, CNRM-ESM2-1 emerged as the most adept model following bias correction. In the southeastern domain, including only Tabasco and Chiapas, the optimal model was again CNRM-ESM2-1 after bias-correction. However, for the Yucatan Peninsula, the IPSL-CM6A-LR model yielded the most favorable results. This study emphasizes the significance of tailored bias-correction techniques in refining the performance of climate models and highlights the spatially nuanced responses of different models within the study area’s distinct geographical regions.https://www.mdpi.com/2225-1154/11/9/186climate changesouthern–southeastern MexicoCMIP6precipitationbias correction |
| spellingShingle | Mercedes Andrade-Velázquez Martín José Montero-Martínez Statistical Downscaling of Precipitation in the South and Southeast of Mexico Climate climate change southern–southeastern Mexico CMIP6 precipitation bias correction |
| title | Statistical Downscaling of Precipitation in the South and Southeast of Mexico |
| title_full | Statistical Downscaling of Precipitation in the South and Southeast of Mexico |
| title_fullStr | Statistical Downscaling of Precipitation in the South and Southeast of Mexico |
| title_full_unstemmed | Statistical Downscaling of Precipitation in the South and Southeast of Mexico |
| title_short | Statistical Downscaling of Precipitation in the South and Southeast of Mexico |
| title_sort | statistical downscaling of precipitation in the south and southeast of mexico |
| topic | climate change southern–southeastern Mexico CMIP6 precipitation bias correction |
| url | https://www.mdpi.com/2225-1154/11/9/186 |
| work_keys_str_mv | AT mercedesandradevelazquez statisticaldownscalingofprecipitationinthesouthandsoutheastofmexico AT martinjosemonteromartinez statisticaldownscalingofprecipitationinthesouthandsoutheastofmexico |