Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection
Central Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation dis...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-02-01
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| Series: | Advances in Climate Change Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674927823000138 |
| _version_ | 1827987238130024448 |
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| author | Li-Jun Fan Zhong-Wei Yan Deliang Chen Zhen Li |
| author_facet | Li-Jun Fan Zhong-Wei Yan Deliang Chen Zhen Li |
| author_sort | Li-Jun Fan |
| collection | DOAJ |
| description | Central Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes, especially in areas with complex terrain. In this study, we proposed a statistical downscaling (SD) model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models (GCMs) for present and future (2081–2100) periods under two shared socioeconomic pathways (SSP245 and SSP585). The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity (SDII) and maximum 1-day precipitation (RX1DAY) and overestimate the number of wet days (R1MM) and maximum consecutive wet days (CWD) at stations across CA. However, the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations. Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA. In addition, it is skilled in capturing the spatial patterns of the observed precipitation indices. Obviously, SDII and RX1DAY are improved by the SD model, especially in the southeastern mountainous area. Under the intermediate scenario (SSP245), our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation (R95PTOT) of 0.5 mm d−1 and 19.7 mm, respectively, over CA at the end of the 21st century (2081–2100) compared to the present values (1995–2014). More pronounced increases in indices R95PTOT, SDII, number of very wet days (R10MM), and RX1DAY are projected under the higher emission scenario (SSP585), particularly in the mountainous southeastern region. The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081–2100. The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA. |
| first_indexed | 2024-04-09T23:43:46Z |
| format | Article |
| id | doaj.art-6eda02c0bfa649d4a92bff878cff5115 |
| institution | Directory Open Access Journal |
| issn | 1674-9278 |
| language | English |
| last_indexed | 2024-04-09T23:43:46Z |
| publishDate | 2023-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Advances in Climate Change Research |
| spelling | doaj.art-6eda02c0bfa649d4a92bff878cff51152023-03-18T04:40:44ZengKeAi Communications Co., Ltd.Advances in Climate Change Research1674-92782023-02-011416276Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projectionLi-Jun Fan0Zhong-Wei Yan1Deliang Chen2Zhen Li3Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029, China; Corresponding author.Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, ChinaRegional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, 40530, SwedenKey Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Science, Beijing 100029, ChinaCentral Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes, especially in areas with complex terrain. In this study, we proposed a statistical downscaling (SD) model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models (GCMs) for present and future (2081–2100) periods under two shared socioeconomic pathways (SSP245 and SSP585). The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity (SDII) and maximum 1-day precipitation (RX1DAY) and overestimate the number of wet days (R1MM) and maximum consecutive wet days (CWD) at stations across CA. However, the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations. Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA. In addition, it is skilled in capturing the spatial patterns of the observed precipitation indices. Obviously, SDII and RX1DAY are improved by the SD model, especially in the southeastern mountainous area. Under the intermediate scenario (SSP245), our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation (R95PTOT) of 0.5 mm d−1 and 19.7 mm, respectively, over CA at the end of the 21st century (2081–2100) compared to the present values (1995–2014). More pronounced increases in indices R95PTOT, SDII, number of very wet days (R10MM), and RX1DAY are projected under the higher emission scenario (SSP585), particularly in the mountainous southeastern region. The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081–2100. The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.http://www.sciencedirect.com/science/article/pii/S1674927823000138Local precipitation extremesStatistical downscalingMulti-model ensemble projectionRobustness and uncertaintyCentral Asia |
| spellingShingle | Li-Jun Fan Zhong-Wei Yan Deliang Chen Zhen Li Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection Advances in Climate Change Research Local precipitation extremes Statistical downscaling Multi-model ensemble projection Robustness and uncertainty Central Asia |
| title | Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection |
| title_full | Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection |
| title_fullStr | Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection |
| title_full_unstemmed | Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection |
| title_short | Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection |
| title_sort | assessment of total and extreme precipitation over central asia via statistical downscaling added value and multi model ensemble projection |
| topic | Local precipitation extremes Statistical downscaling Multi-model ensemble projection Robustness and uncertainty Central Asia |
| url | http://www.sciencedirect.com/science/article/pii/S1674927823000138 |
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