Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective
This study examines the changes in the intensity and frequency of precipitation in China from a multi‐model perspective on 20 statistically downscaled fine-scale climate projections and categorizes them into four distinct patterns in response to globally targeted warming (1.5 °C and 3 °C). In a mult...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2020-01-01
|
| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/abc8bb |
| _version_ | 1797747755971510272 |
|---|---|
| author | Liying Qiu Eun-Soon Im Hyun-Han Kwon |
| author_facet | Liying Qiu Eun-Soon Im Hyun-Han Kwon |
| author_sort | Liying Qiu |
| collection | DOAJ |
| description | This study examines the changes in the intensity and frequency of precipitation in China from a multi‐model perspective on 20 statistically downscaled fine-scale climate projections and categorizes them into four distinct patterns in response to globally targeted warming (1.5 °C and 3 °C). In a multivariate setting, the asymmetric responses of frequency and intensity to different levels of warming can be considered jointly. This study focuses on relatively moderate precipitation to determine if the ensemble of a subset of climate models, which are selected based on the categorization, can provide a better interpretation of the changing patterns compared to that from the conventional unweighted ensemble mean. The results show that the spatial distribution of the predominant category and inter-model agreement are dependent mainly on the degree of warming. As warming becomes more extensive, the projected change in precipitation tends to converge to the category that indicates an increase in both the intensity and frequency of precipitation, from the mixed-mode and even decreasing pattern. The use of subsampling to produce an ensemble of joint probability (or return period) has potential benefits in detecting asymmetric changes in the intensity and frequency of precipitation that is seen in the majority of models but hidden by the unweighted ensemble average particularly for regions where different models show mixed signals. A substantial portion of the region in China is likely to experience a transition of changes in precipitation frequency and (or) intensity under continuous warming, which would not be revealed clearly by univariate analysis. |
| first_indexed | 2024-03-12T15:55:11Z |
| format | Article |
| id | doaj.art-db320f3422ae44a89b842ec1b497d534 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:55:11Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-db320f3422ae44a89b842ec1b497d5342023-08-09T14:57:57ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-01151212404310.1088/1748-9326/abc8bbCategorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspectiveLiying Qiu0https://orcid.org/0000-0001-9944-4311Eun-Soon Im1https://orcid.org/0000-0002-8953-7538Hyun-Han Kwon2https://orcid.org/0000-0003-4465-2708Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology , Kowloon, Hong Kong, People’s Republic of ChinaDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and Technology , Kowloon, Hong Kong, People’s Republic of China; Division of Environment and Sustainability, The Hong Kong University of Science and Technology , Kowloon, Hong Kong, People’s Republic of ChinaDepartment of Civil and Environmental Engineering, Sejong University , Seoul, Republic of KoreaThis study examines the changes in the intensity and frequency of precipitation in China from a multi‐model perspective on 20 statistically downscaled fine-scale climate projections and categorizes them into four distinct patterns in response to globally targeted warming (1.5 °C and 3 °C). In a multivariate setting, the asymmetric responses of frequency and intensity to different levels of warming can be considered jointly. This study focuses on relatively moderate precipitation to determine if the ensemble of a subset of climate models, which are selected based on the categorization, can provide a better interpretation of the changing patterns compared to that from the conventional unweighted ensemble mean. The results show that the spatial distribution of the predominant category and inter-model agreement are dependent mainly on the degree of warming. As warming becomes more extensive, the projected change in precipitation tends to converge to the category that indicates an increase in both the intensity and frequency of precipitation, from the mixed-mode and even decreasing pattern. The use of subsampling to produce an ensemble of joint probability (or return period) has potential benefits in detecting asymmetric changes in the intensity and frequency of precipitation that is seen in the majority of models but hidden by the unweighted ensemble average particularly for regions where different models show mixed signals. A substantial portion of the region in China is likely to experience a transition of changes in precipitation frequency and (or) intensity under continuous warming, which would not be revealed clearly by univariate analysis.https://doi.org/10.1088/1748-9326/abc8bbcategorization of precipitation changesubsampling ensemblebivariate joint distribution |
| spellingShingle | Liying Qiu Eun-Soon Im Hyun-Han Kwon Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective Environmental Research Letters categorization of precipitation change subsampling ensemble bivariate joint distribution |
| title | Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective |
| title_full | Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective |
| title_fullStr | Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective |
| title_full_unstemmed | Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective |
| title_short | Categorization of precipitation changes in China under 1.5 °C and 3 °C global warming using the bivariate joint distribution from a multi-model perspective |
| title_sort | categorization of precipitation changes in china under 1 5 °c and 3 °c global warming using the bivariate joint distribution from a multi model perspective |
| topic | categorization of precipitation change subsampling ensemble bivariate joint distribution |
| url | https://doi.org/10.1088/1748-9326/abc8bb |
| work_keys_str_mv | AT liyingqiu categorizationofprecipitationchangesinchinaunder15cand3cglobalwarmingusingthebivariatejointdistributionfromamultimodelperspective AT eunsoonim categorizationofprecipitationchangesinchinaunder15cand3cglobalwarmingusingthebivariatejointdistributionfromamultimodelperspective AT hyunhankwon categorizationofprecipitationchangesinchinaunder15cand3cglobalwarmingusingthebivariatejointdistributionfromamultimodelperspective |