Pub Date : 2024-09-30DOI: 10.1038/s41612-024-00779-y
Fenying Cai, Caihong Liu, Dieter Gerten, Song Yang, Tuantuan Zhang, Shuheng Lin, Jürgen Kurths
Heatwaves are projected to substantially increase at a global scale, exacerbating worldwide heat-related risks in the future. However, understanding future heterogeneous heatwave changes and their origins remains challenging. By analyzing the output of various climate models from the Coupled Model Intercomparison Project Phase 6, we found pronounced spatial disparity of projected heatwave increases in the Northern Hemisphere, even outstretching seven-fold inter-regional differences in extreme heatwave occurrences, attributed primarily to future changes in heat-dome-like circulations and soil moisture–temperature coupling. Specifically, we found that by the end of the 21st century, the modulations of combined Pacific El Niño and positive Pacific Meridional Mode on magnified heat-dome-like circulations would be translated into summertime hotspots over western Asia and western North America. Amplified soil moisture–temperature couplings then further aggravate the heatwave intensity over these two hotspots. This study provides support for formulating impact-based mitigation strategies and efficiently addressing the potential future risks of heatwaves.
{"title":"Pronounced spatial disparity of projected heatwave changes linked to heat domes and land-atmosphere coupling","authors":"Fenying Cai, Caihong Liu, Dieter Gerten, Song Yang, Tuantuan Zhang, Shuheng Lin, Jürgen Kurths","doi":"10.1038/s41612-024-00779-y","DOIUrl":"10.1038/s41612-024-00779-y","url":null,"abstract":"Heatwaves are projected to substantially increase at a global scale, exacerbating worldwide heat-related risks in the future. However, understanding future heterogeneous heatwave changes and their origins remains challenging. By analyzing the output of various climate models from the Coupled Model Intercomparison Project Phase 6, we found pronounced spatial disparity of projected heatwave increases in the Northern Hemisphere, even outstretching seven-fold inter-regional differences in extreme heatwave occurrences, attributed primarily to future changes in heat-dome-like circulations and soil moisture–temperature coupling. Specifically, we found that by the end of the 21st century, the modulations of combined Pacific El Niño and positive Pacific Meridional Mode on magnified heat-dome-like circulations would be translated into summertime hotspots over western Asia and western North America. Amplified soil moisture–temperature couplings then further aggravate the heatwave intensity over these two hotspots. This study provides support for formulating impact-based mitigation strategies and efficiently addressing the potential future risks of heatwaves.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-9"},"PeriodicalIF":8.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00779-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems.
{"title":"Accurate initial field estimation for weather forecasting with a variational constrained neural network","authors":"Wuxin Wang, Jinrong Zhang, Qingguo Su, Xingyu Chai, Jingze Lu, Weicheng Ni, Boheng Duan, Kaijun Ren","doi":"10.1038/s41612-024-00776-1","DOIUrl":"10.1038/s41612-024-00776-1","url":null,"abstract":"Weather forecasting is crucial for scientific research and society. Recently, deep learning (DL) methods have achieved significant advancements in medium-range weather forecasting. However, they generally depend on the initial fields generated by the computationally expensive four-dimensional variational (4DVar) data assimilation (DA) technique, which limits their real-time applicability in multivariate three-dimensional (3D) weather forecasting. Here we propose 4DVarFormer by exploring the potential of integrating the 4DVar constraint into an attention-based neural network. 4DVarFormer eliminates the need for background error covariance statistics and the complex adjoint model development. It can generate multivariate 3D weather states within 0.37 s. Moreover, 4DVarFormer can capture inter-variable relationships, allowing the assimilation of observed variables to correct unobserved variables. Hence, medium-range forecasts initiated by 4DVarFormer outperform those of DL-based DA methods and achieve performance comparable to the forecasts initiated by ERA5 reanalyses. These promising findings contribute to future advancements in integrated end-to-end DL weather forecasting systems.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-17"},"PeriodicalIF":8.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00776-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1038/s41612-024-00772-5
Guojian Wang, Agus Santoso
Multi-year La Niña events cause prolonged climate disruptions worldwide, but a systematic understanding of the underlying mechanisms is not yet established. Here we show using observations and models from the sixth phase of Coupled Model Intercomparison Project that a greater frequency of consecutive La Niña events is tied to the upper equatorial Pacific Ocean when it favors more rapid heat discharge. The propensity for heat discharge is underscored by negative skewness in upper-ocean heat content, underpinned by southward tropical Pacific wind shift during austral summer. Models with stronger westerly anomalies south of the equator simulate steeper east-to-west upward tilt of the thermocline that is favorable for a greater discharge rate. This highlights the crucial role of the southward wind shift in the nonlinear system of the El Niño-Southern Oscillation. The large inter-model spread in multi-year La Niña processes underscores the need in constraining models for reliable climate prediction and projection.
{"title":"Multi-year La Niña frequency tied to southward tropical Pacific wind shift","authors":"Guojian Wang, Agus Santoso","doi":"10.1038/s41612-024-00772-5","DOIUrl":"10.1038/s41612-024-00772-5","url":null,"abstract":"Multi-year La Niña events cause prolonged climate disruptions worldwide, but a systematic understanding of the underlying mechanisms is not yet established. Here we show using observations and models from the sixth phase of Coupled Model Intercomparison Project that a greater frequency of consecutive La Niña events is tied to the upper equatorial Pacific Ocean when it favors more rapid heat discharge. The propensity for heat discharge is underscored by negative skewness in upper-ocean heat content, underpinned by southward tropical Pacific wind shift during austral summer. Models with stronger westerly anomalies south of the equator simulate steeper east-to-west upward tilt of the thermocline that is favorable for a greater discharge rate. This highlights the crucial role of the southward wind shift in the nonlinear system of the El Niño-Southern Oscillation. The large inter-model spread in multi-year La Niña processes underscores the need in constraining models for reliable climate prediction and projection.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-8"},"PeriodicalIF":8.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00772-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-28DOI: 10.1038/s41612-024-00778-z
Shizhou Ma, Irena F. Creed, Pascal Badiou
There is debate about the use of wetlands as natural climate solutions due to their ability to act as a “double-edged sword” with respect to climate impacts by both sequestering CO2 while emitting CH4. Here, we used a process-based greenhouse gas (GHG) perturbation model to simulate wetland radiative forcing and temperature change associated with wetland state conversion over 500 years based on empirical carbon flux measurements, and CO2-equivalent (CO2-e.q.) metrics to assess the net flux of GHGs from wetlands on a comparable basis. Three CO2-e.q. metrics were used to describe the relative radiative impact of CO2 and CH4—the conventional global warming potential (GWP) that looks at pulse GHG emissions over a fixed timeframe, the sustained-flux GWP (SGWP) that looks at sustained GHG emissions over a fixed timeframe, and GWP* that explicitly accounts for changes in the radiative forcing of CH4 over time (initially more potent but then diminishing after about a decade)—against model-derived mean temperature profiles. GWP* most closely estimated the mean temperature profiles associated with net wetland GHG emissions. Using the GWP*, intact wetlands serve as net CO2-e.q. carbon sinks and deliver net cooling effects on the climate. Prioritizing the conservation of intact wetlands is a cost-effective approach with immediate climate benefits that align with the Paris Agreement and the Intergovernmental Panel on Climate Change timeline of net-zero GHG emissions by 2050. Restoration of wetlands also has immediate climate benefits (reduced warming), but with the majority of climate benefits (cooling) occurring over longer timescales, making it an effective short and long-term natural climate solution with additional co-benefits.
{"title":"New perspectives on temperate inland wetlands as natural climate solutions under different CO2-equivalent metrics","authors":"Shizhou Ma, Irena F. Creed, Pascal Badiou","doi":"10.1038/s41612-024-00778-z","DOIUrl":"10.1038/s41612-024-00778-z","url":null,"abstract":"There is debate about the use of wetlands as natural climate solutions due to their ability to act as a “double-edged sword” with respect to climate impacts by both sequestering CO2 while emitting CH4. Here, we used a process-based greenhouse gas (GHG) perturbation model to simulate wetland radiative forcing and temperature change associated with wetland state conversion over 500 years based on empirical carbon flux measurements, and CO2-equivalent (CO2-e.q.) metrics to assess the net flux of GHGs from wetlands on a comparable basis. Three CO2-e.q. metrics were used to describe the relative radiative impact of CO2 and CH4—the conventional global warming potential (GWP) that looks at pulse GHG emissions over a fixed timeframe, the sustained-flux GWP (SGWP) that looks at sustained GHG emissions over a fixed timeframe, and GWP* that explicitly accounts for changes in the radiative forcing of CH4 over time (initially more potent but then diminishing after about a decade)—against model-derived mean temperature profiles. GWP* most closely estimated the mean temperature profiles associated with net wetland GHG emissions. Using the GWP*, intact wetlands serve as net CO2-e.q. carbon sinks and deliver net cooling effects on the climate. Prioritizing the conservation of intact wetlands is a cost-effective approach with immediate climate benefits that align with the Paris Agreement and the Intergovernmental Panel on Climate Change timeline of net-zero GHG emissions by 2050. Restoration of wetlands also has immediate climate benefits (reduced warming), but with the majority of climate benefits (cooling) occurring over longer timescales, making it an effective short and long-term natural climate solution with additional co-benefits.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-11"},"PeriodicalIF":8.5,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00778-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent development of artificial intelligence (AI) technology has resulted in the fruition of machine learning-based weather prediction (MLWP) systems. Five prominent global MLWP model, Pangu-Weather, FourCastNet v2 (FCN2), GraphCast, FuXi, and FengWu, emerged. This study conducts a homogeneous comparison of these models utilizing identical initial conditions from ERA5. The performance is evaluated in the Eastern Asia and Western Pacific from June to November 2023. The evaluation comprises Root Mean Square Error and Anomaly Correlation Coefficients within the designated region, typhoon track and intensity predictions, and a case study for Typhoon Haikui. Results indicate that FengWu emerges as the best-performing model, followed by FuXi and GraphCast, with FCN2 and Pangu-Weather ranking lower. A multi-model ensemble, constructed by averaging predictions from the five models, demonstrates superior performance, rivaling that of FengWu. For the 11 typhoons in 2023, FengWu demonstrates the most accurate track prediction; however, it also has the largest intensity errors.
{"title":"Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific","authors":"Cheng-Chin Liu, Kathryn Hsu, Melinda S. Peng, Der-Song Chen, Pao-Liang Chang, Ling-Feng Hsiao, Chin-Tzu Fong, Jing-Shan Hong, Chia-Ping Cheng, Kuo-Chen Lu, Chia-Rong Chen, Hung-Chi Kuo","doi":"10.1038/s41612-024-00769-0","DOIUrl":"10.1038/s41612-024-00769-0","url":null,"abstract":"Recent development of artificial intelligence (AI) technology has resulted in the fruition of machine learning-based weather prediction (MLWP) systems. Five prominent global MLWP model, Pangu-Weather, FourCastNet v2 (FCN2), GraphCast, FuXi, and FengWu, emerged. This study conducts a homogeneous comparison of these models utilizing identical initial conditions from ERA5. The performance is evaluated in the Eastern Asia and Western Pacific from June to November 2023. The evaluation comprises Root Mean Square Error and Anomaly Correlation Coefficients within the designated region, typhoon track and intensity predictions, and a case study for Typhoon Haikui. Results indicate that FengWu emerges as the best-performing model, followed by FuXi and GraphCast, with FCN2 and Pangu-Weather ranking lower. A multi-model ensemble, constructed by averaging predictions from the five models, demonstrates superior performance, rivaling that of FengWu. For the 11 typhoons in 2023, FengWu demonstrates the most accurate track prediction; however, it also has the largest intensity errors.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-12"},"PeriodicalIF":8.5,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00769-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1038/s41612-024-00773-4
Xuan Wang, Shixian Zhai, Lu Shen
Climate assessments have largely overlooked the radiative effect of anthropogenic coarse particulate matter (PMcoarse, with an aerodynamic diameter between 2.5 and 10 µm) in China. Despite its similar mass concentration to fine particulate matter (PM2.5), anthropogenic sources of PMcoarse in China have been much less studied and typically underrepresented in models. Here, we present a new model simulation for PMcoarse in China that incorporates various anthropogenic sources. The model successfully captures the magnitude and distribution of observed PMcoarse and recently available aerosol optical depth measurements at near-infrared wavelengths, which are substantially underestimated if anthropogenic PMcoarse is not included. We find that anthropogenic PMcoarse exerts a cooling effect of -0.11 Wm−2 (-0.03 to -0.42 Wm−2) in China by aerosol–radiation interaction, capable of completely offsetting the warming effect from black carbon by 2060 under Dynamic Projection model for Emissions in China (DPEC) 1.1 scenario. We conclude that the radiative effect due to anthropogenic PMcoarse will likely dampen the warming penalty caused by the emission reduction of other aerosols in China and should be incorporated into climate models.
{"title":"Cooling from aerosol–radiation interaction of anthropogenic coarse particles in China","authors":"Xuan Wang, Shixian Zhai, Lu Shen","doi":"10.1038/s41612-024-00773-4","DOIUrl":"10.1038/s41612-024-00773-4","url":null,"abstract":"Climate assessments have largely overlooked the radiative effect of anthropogenic coarse particulate matter (PMcoarse, with an aerodynamic diameter between 2.5 and 10 µm) in China. Despite its similar mass concentration to fine particulate matter (PM2.5), anthropogenic sources of PMcoarse in China have been much less studied and typically underrepresented in models. Here, we present a new model simulation for PMcoarse in China that incorporates various anthropogenic sources. The model successfully captures the magnitude and distribution of observed PMcoarse and recently available aerosol optical depth measurements at near-infrared wavelengths, which are substantially underestimated if anthropogenic PMcoarse is not included. We find that anthropogenic PMcoarse exerts a cooling effect of -0.11 Wm−2 (-0.03 to -0.42 Wm−2) in China by aerosol–radiation interaction, capable of completely offsetting the warming effect from black carbon by 2060 under Dynamic Projection model for Emissions in China (DPEC) 1.1 scenario. We conclude that the radiative effect due to anthropogenic PMcoarse will likely dampen the warming penalty caused by the emission reduction of other aerosols in China and should be incorporated into climate models.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-9"},"PeriodicalIF":8.5,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00773-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023–2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere’s mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980–2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.
{"title":"Advancing annual global mean surface temperature prediction to 2 months lead using physics based strategy","authors":"Ke-Xin Li, Fei Zheng, Jiang Zhu, Jin-Yi Yu, Noel Keenlyside","doi":"10.1038/s41612-024-00736-9","DOIUrl":"10.1038/s41612-024-00736-9","url":null,"abstract":"Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023–2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere’s mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980–2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-11"},"PeriodicalIF":8.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00736-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The 2022 and 2023 western Mediterranean summer temperatures exceeded millennial natural variability, reaching unprecedented anomalies of +3.6 °C and +2.9 °C respectively. We show that anthropogenic climate change may turn extreme heatwaves from a rarity of 1 in 10,000 years into events occurring every 4–75 years, depending on future scenarios. This shift underscores the urgency of implementing adaptive strategies as extreme climate events manifest sooner and more intensely than expected.
{"title":"Recent heatwaves as a prelude to climate extremes in the western Mediterranean region","authors":"Ernesto Tejedor, Gerardo Benito, Roberto Serrano-Notivoli, Fidel González-Rouco, Jan Esper, Ulf Büntgen","doi":"10.1038/s41612-024-00771-6","DOIUrl":"10.1038/s41612-024-00771-6","url":null,"abstract":"The 2022 and 2023 western Mediterranean summer temperatures exceeded millennial natural variability, reaching unprecedented anomalies of +3.6 °C and +2.9 °C respectively. We show that anthropogenic climate change may turn extreme heatwaves from a rarity of 1 in 10,000 years into events occurring every 4–75 years, depending on future scenarios. This shift underscores the urgency of implementing adaptive strategies as extreme climate events manifest sooner and more intensely than expected.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-7"},"PeriodicalIF":8.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00771-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142245313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.
{"title":"Sea-ice loss in Eurasian Arctic coast intensifies heavy Meiyu-Baiu rainfall associated with Indian Ocean warming","authors":"Xiaodan Chen, Zhiping Wen, Jiping Liu, Wei Mei, Ruonan Zhang, Sihua Huang, Yuanyuan Guo, Juncong Li","doi":"10.1038/s41612-024-00770-7","DOIUrl":"10.1038/s41612-024-00770-7","url":null,"abstract":"Heavy Meiyu-Baiu rainfall can pose threat to the dense population in East Asia by catastrophic flooding. Although previous studies have identified Indian Ocean (IO) warming as the major cause of heavy Meiyu-Baiu rainfall, it failed to predict the record-breaking rainfall in July 2020. Synthesizing observational analysis, large-ensemble climate simulations, and atmospheric simulations, we show that sea-ice loss in the Kara Sea in May can intensify the IO warming-induced heavy Meiyu-Baiu rainfall and well explains the record-breaking rainfall in July 2020. In the precondition of IO warming, sea-ice loss tends to prolong Meiyu-Baiu season and strengthen convective activity over the Meiyu-Baiu region, thereby enhancing the IO warming-induced heavy Meiyu-Baiu rainfall by ~50% and doubling the risk of extreme events comparable to or greater than the one in 2020. A statistical model is further constructed to demonstrate that taking Arctic sea ice into consideration can significantly improve the seasonal prediction of extreme Meiyu-Baiu rainfall.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-12"},"PeriodicalIF":8.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00770-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142236595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost.
频率和强度不断增加的极端事件正在对冻土环境产生重大影响。利用ERA5-Land再分析数据进行的分析表明，青藏高原中部的冻土区在2022年经历了夏季热浪。2022年，四个活动层站点出现了最大活动层厚度（平均：207.7厘米），比2000-2021年的平均活动层厚度（平均：175.9厘米）高出20%。2022 年观测到的年平均地面温度（MAGT）也是最高的，比前几年的平均值高出 10%。利用 Stefan 模型量化了热浪对活动层季节性解冻深度的贡献率，其范围为 6.6% 至 13.6%，最大贡献率出现在 2022 年。这些发现有助于更好地理解极端事件对活动层和冻土的影响过程。
{"title":"Summer heat wave in 2022 led to rapid warming of permafrost in the central Qinghai-Tibet Plateau","authors":"Xiaofan Zhu, Tonghua Wu, Jie Chen, Xiaodong Wu, Pengling Wang, Defu Zou, Guangyang Yue, Xuchun Yan, Xin Ma, Dong Wang, Peiqing Lou, Amin Wen, Chengpeng Shang, Weiying Liu","doi":"10.1038/s41612-024-00765-4","DOIUrl":"10.1038/s41612-024-00765-4","url":null,"abstract":"Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost.","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":" ","pages":"1-15"},"PeriodicalIF":8.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41612-024-00765-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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