The Paris Agreement aims to limit global warming to well below 2.00℃and pursue efforts to limit the temperature increase to 1.50℃.However,the response of climate change to unbalanced global warming is affected by sp...The Paris Agreement aims to limit global warming to well below 2.00℃and pursue efforts to limit the temperature increase to 1.50℃.However,the response of climate change to unbalanced global warming is affected by spatial and temporal sensitivities.To better understand the regional warming response to global warming at 1.50℃and 2.00℃,we detected the 1.50℃and 2.00℃warming threshold-crossing time(WTT)above pre-industrial levels globally using the Coupled Model Intercomparison Project phase 6(CMIP6)models.Our findings indicate that the 1.50℃or 2.00℃WTT differs substantially worldwide.The warming rate of land would be approximately 1.35–1.46 times that of the ocean between 60°N–60°S in 2015–2100.Consequently,the land would experience a 1.50℃(2.00℃)warming at least 10–20 yr earlier than the time when the global mean near-surface air temperature reaches 1.50℃(2.00℃)WTT.Meanwhile,the Southern Ocean between 0°and 60°S considerably slows down the global 1.50℃and 2.00℃WTT.In 2040–2060,over 98.70%(77.50%),99.70%(89.30%),99.80%(93.40%),and 100.00%(98.00%)of the land will have warmed by over 1.50℃(2.00℃)under SSP(Shared Socioeconomic Pathway)1–2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5,respectively.We conclude that regional 1.50℃(2.00℃)WTT should be fully considered,especially in vulnerable high-latitude and high-altitude regions.展开更多
Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the...Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the latest Coupled Model Intercomparison Project Phase 6(CMIP6),this study assesses future EHT changes across China at five specific global warming thresholds(1.5℃-5℃).The results indicate that global mean temperature will increase by 1.5℃/2℃ before 2030/2050 relative to pre-industrial levels(1861-1900)under three future scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5),and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5.Under SSP5-8.5,global warming will eventually exceed 5℃ by 2100,while under SSP1-2.6,it will stabilize around 2℃ after 2050.In China,most of the areas where warming exceeds global average levels will be located in Tibet and northern China(Northwest China,North China and Northeast China),covering 50%-70%of the country.Furthermore,about 0.19-0.44 billion people(accounting for 16%-41%of the national population)will experience warming above the global average.Compared to present-day(1995-2014),the warmest day(TXx)will increase most notably in northern China,while the number of warm days(TX90p)and warm spell duration indicator(WSDI)will increase most profoundly in southern China.For example,relative to the present-day,TXx will increase by 1℃-5℃ in northern China,and TX90p(WSDI)will increase by 25-150(10-80)days in southern China at 1.5℃-5℃ global warming.Compared to 2℃-5℃,limiting global warming to 1.5℃ will help avoid about 36%-87%of the EHT increases in China.展开更多
Future changes of heating degree days （HDD） and cooling degree days （CDD） in the 21st century with and without considering populationfactor are investigated based on four sets of climate change simulations over Ea...Future changes of heating degree days （HDD） and cooling degree days （CDD） in the 21st century with and without considering populationfactor are investigated based on four sets of climate change simulations over East Asia using the regional climate model version 4.4 （RegCM4.4）driven by the global models of CSIRO-Mk3-6-0, EC-EARTH, HadGEM2-ES, and MPI-ESM-MR. Under global warming of 1.5℃, 2℃, 3℃,and 4℃, significant decrease of HDD can be found over China without considering population factor, with greater decrease over high elevationand high latitude regions, including the Tibetan Plateau, the northern part of Northeast China, and Northwest China; while population-weightedHDD increased in areas where population will increase in the future, such as Beijing, Tianjin, parts of southern Hebei, northern Shandong andHenan provinces. Similarly, the CDD projections with and without considering population factor are largely different. Specifically, withoutconsidering population, increase of CDD were observed over most parts of China except the Tibetan Plateau where the CDD remained zerobecause of the cold climate even under global warming; while considering population factor, the future CDD decreases in South China andincreases in North China, the Sichuan Basin, and the southeastern coastal areas, which is directly related to the population changes. The differentfuture changes of HDD and CDD when considering and disregarding the effects of population show that population distribution plays animportant role in energy consumption, which should be considered in future research.展开更多
基于耦合模式比较计划第6阶段(CMIP6)中的全球气候模式的模拟结果,采用考虑模式性能和独立性结合(Climate model Weighting by Independence and Performance,ClimWIP)的加权方案进行中国区域气候的多模式集合预估及不确定性研究。结果...基于耦合模式比较计划第6阶段(CMIP6)中的全球气候模式的模拟结果,采用考虑模式性能和独立性结合(Climate model Weighting by Independence and Performance,ClimWIP)的加权方案进行中国区域气候的多模式集合预估及不确定性研究。结果表明,ClimWIP方案在历史阶段的模拟优于等权重方案,降低了多模式模拟的气候态偏差。温度指数的未来预估不确定性较大的区域主要集中在中国北方和青藏高原,而降水指数主要集中在华北和西北地区。ClimWIP方案的预估不确定性与等权重方案相比有所降低。ClimWIP方案预估的温度指数的增温大值区主要集中在中国北方和青藏高原;降水指数在西北和青藏高原增加最为显著。全球额外0.5℃增暖时,中国区域平均的温度指数变化更强,平均高于全球0.2℃,最低温在东北部分地区的额外增温甚至是全球平均的3倍;总降水额外增加5.2%;强降水额外增加10.5%。全球增暖2℃下,中国大部分区域温度指数较当前气候态增加可能超过1.5℃(概率>50%),在中国北方和青藏高原的部分地区增温超过1.5℃的可能性更大(概率>90%);总降水,强降水和连续干日在西北和华北增加幅度有可能超过10%、25%和-5 d(概率>50%)。展开更多
In this study, the period that corresponds to the threshold of a 1.5℃ rise (relative to 1861e1880) in surface temperature is validated using a multi-model ensemble mean from 17 global climate models in the Coupled Mo...In this study, the period that corresponds to the threshold of a 1.5℃ rise (relative to 1861e1880) in surface temperature is validated using a multi-model ensemble mean from 17 global climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). On this basis, the changes in permafrost and snow cover in the Northern Hemisphere are investigated under a scenario in which the global surface temperature has risen by 1.5℃, and the uncertainties of the results are further discussed. The results show that the threshold of 1.5℃ warming will be reached in 2027, 2026, and 2023 under RCP2.6, RCP4.5, RCP8.5, respectively. When the global average surface temperature rises by 1.5℃, the southern boundary of the permafrost will move 1e3.5 northward (relative to 1986e2005), particularly in the southern Central Siberian Plateau. The permafrost area will be reduced by 3.43x106 km2 (21.12%), 3.91x106 km2 (24.1%) and 4.15x106 km2 (25.55%) relative to 1986e2005 in RCP2.6, RCP4.5 and RCP8.5, respectively. The snow water equivalent will decrease in over half of the regions in the Northern Hemisphere but increase only slightly in the Central Siberian Plateau. The snow water equivalent will decrease significantly (more than 40% relative to 1986e2005) in central North America, western Europe, and northwestern Russia. The permafrost area in the QinghaieTibet Plateau will decrease by 0.15x106 km2 (7.28%), 0.18x 106 km2 (8.74%), and 0.17x106 km2 (8.25%), respectively, in RCP2.6, RCP4.5, RCP8.5. The snow water equivalent in winter (DJF) and spring (MAM) over the QinghaieTibet Plateau will decrease by 14.9% and 13.8%, respectively.展开更多
Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4？C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse...Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4？C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse gas concentration scenario. Results show that, according to the 39 models, the median year in which 4？C global warming will occur is 2084.Based on the median results of models that project a 4？C global warming by 2100, land areas will generally exhibit stronger warming than the oceans annually and seasonally, and the strongest enhancement occurs in the Arctic, with the exception of the summer season. Change signals for temperature go outside its natural internal variabilities globally, and the signal-tonoise ratio averages 9.6 for the annual mean and ranges from 6.3 to 7.2 for the seasonal mean over the globe, with the greatest values appearing at low latitudes because of low noise. Decreased precipitation generally occurs in the subtropics, whilst increased precipitation mainly appears at high latitudes. The precipitation changes in most of the high latitudes are greater than the background variability, and the global mean signal-to-noise ratio is 0.5 and ranges from 0.2 to 0.4 for the annual and seasonal means, respectively. Attention should be paid to limiting global warming to 1.5？C, in which case temperature and precipitation will experience a far more moderate change than the natural internal variability. Large inter-model disagreement appears at high latitudes for temperature changes and at mid and low latitudes for precipitation changes. Overall, the intermodel consistency is better for temperature than for precipitation.展开更多
基金Under the auspices of the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK020104)the National Natural Science Foundation of China(No.41571062,42101122)+2 种基金the Fundamental Research Funds for the Central Universities(No.2020TS100)the Natural Science Foundation of Shaanxi Province,China(No.2023-JC-YB-259)the China Postdoctoral Science Foundation(No.2017M610622)。
文摘The Paris Agreement aims to limit global warming to well below 2.00℃and pursue efforts to limit the temperature increase to 1.50℃.However,the response of climate change to unbalanced global warming is affected by spatial and temporal sensitivities.To better understand the regional warming response to global warming at 1.50℃and 2.00℃,we detected the 1.50℃and 2.00℃warming threshold-crossing time(WTT)above pre-industrial levels globally using the Coupled Model Intercomparison Project phase 6(CMIP6)models.Our findings indicate that the 1.50℃or 2.00℃WTT differs substantially worldwide.The warming rate of land would be approximately 1.35–1.46 times that of the ocean between 60°N–60°S in 2015–2100.Consequently,the land would experience a 1.50℃(2.00℃)warming at least 10–20 yr earlier than the time when the global mean near-surface air temperature reaches 1.50℃(2.00℃)WTT.Meanwhile,the Southern Ocean between 0°and 60°S considerably slows down the global 1.50℃and 2.00℃WTT.In 2040–2060,over 98.70%(77.50%),99.70%(89.30%),99.80%(93.40%),and 100.00%(98.00%)of the land will have warmed by over 1.50℃(2.00℃)under SSP(Shared Socioeconomic Pathway)1–2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5,respectively.We conclude that regional 1.50℃(2.00℃)WTT should be fully considered,especially in vulnerable high-latitude and high-altitude regions.
基金supported by the National Key Research and Development Program of China(2017YFA0603804)the National Natural Science Foundation of China(41831174 and 41430528)+1 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX19_1026)Guwei ZHANG was supported by the China Scholarship Council(NO.201908320503)。
文摘Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the latest Coupled Model Intercomparison Project Phase 6(CMIP6),this study assesses future EHT changes across China at five specific global warming thresholds(1.5℃-5℃).The results indicate that global mean temperature will increase by 1.5℃/2℃ before 2030/2050 relative to pre-industrial levels(1861-1900)under three future scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5),and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5.Under SSP5-8.5,global warming will eventually exceed 5℃ by 2100,while under SSP1-2.6,it will stabilize around 2℃ after 2050.In China,most of the areas where warming exceeds global average levels will be located in Tibet and northern China(Northwest China,North China and Northeast China),covering 50%-70%of the country.Furthermore,about 0.19-0.44 billion people(accounting for 16%-41%of the national population)will experience warming above the global average.Compared to present-day(1995-2014),the warmest day(TXx)will increase most notably in northern China,while the number of warm days(TX90p)and warm spell duration indicator(WSDI)will increase most profoundly in southern China.For example,relative to the present-day,TXx will increase by 1℃-5℃ in northern China,and TX90p(WSDI)will increase by 25-150(10-80)days in southern China at 1.5℃-5℃ global warming.Compared to 2℃-5℃,limiting global warming to 1.5℃ will help avoid about 36%-87%of the EHT increases in China.
文摘Future changes of heating degree days （HDD） and cooling degree days （CDD） in the 21st century with and without considering populationfactor are investigated based on four sets of climate change simulations over East Asia using the regional climate model version 4.4 （RegCM4.4）driven by the global models of CSIRO-Mk3-6-0, EC-EARTH, HadGEM2-ES, and MPI-ESM-MR. Under global warming of 1.5℃, 2℃, 3℃,and 4℃, significant decrease of HDD can be found over China without considering population factor, with greater decrease over high elevationand high latitude regions, including the Tibetan Plateau, the northern part of Northeast China, and Northwest China; while population-weightedHDD increased in areas where population will increase in the future, such as Beijing, Tianjin, parts of southern Hebei, northern Shandong andHenan provinces. Similarly, the CDD projections with and without considering population factor are largely different. Specifically, withoutconsidering population, increase of CDD were observed over most parts of China except the Tibetan Plateau where the CDD remained zerobecause of the cold climate even under global warming; while considering population factor, the future CDD decreases in South China andincreases in North China, the Sichuan Basin, and the southeastern coastal areas, which is directly related to the population changes. The differentfuture changes of HDD and CDD when considering and disregarding the effects of population show that population distribution plays animportant role in energy consumption, which should be considered in future research.
基金We acknowledge the World Climate Research Programme＇s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups （listed in Table l） for producing and making available their model output. This research is supported by the National Key Research and Development Program of China （2017YFA0603804） and the State Key Program of National Natural Science Foundation of China （41230528）.
基金This research was supported by the National Key R＆D Program of China （2017YFA0603802）, the National Natural Science Foundation of China （41661144005 and 41320104007）, and the CAS-PKU Joint Research Program. We would like to thanks the IPCC for providing the CMIP5 datasets （http：//www.ipccdata.org/sim/gcm_monthly/AR5/Reference-Archive.html）.
文摘基于耦合模式比较计划第6阶段(CMIP6)中的全球气候模式的模拟结果,采用考虑模式性能和独立性结合(Climate model Weighting by Independence and Performance,ClimWIP)的加权方案进行中国区域气候的多模式集合预估及不确定性研究。结果表明,ClimWIP方案在历史阶段的模拟优于等权重方案,降低了多模式模拟的气候态偏差。温度指数的未来预估不确定性较大的区域主要集中在中国北方和青藏高原,而降水指数主要集中在华北和西北地区。ClimWIP方案的预估不确定性与等权重方案相比有所降低。ClimWIP方案预估的温度指数的增温大值区主要集中在中国北方和青藏高原;降水指数在西北和青藏高原增加最为显著。全球额外0.5℃增暖时,中国区域平均的温度指数变化更强,平均高于全球0.2℃,最低温在东北部分地区的额外增温甚至是全球平均的3倍;总降水额外增加5.2%;强降水额外增加10.5%。全球增暖2℃下,中国大部分区域温度指数较当前气候态增加可能超过1.5℃(概率>50%),在中国北方和青藏高原的部分地区增温超过1.5℃的可能性更大(概率>90%);总降水,强降水和连续干日在西北和华北增加幅度有可能超过10%、25%和-5 d(概率>50%)。
基金This work was supported by the China National Basic Research Program (2013CBA01808), the National Science Foundation of China (91437217, 41275061, 41471034,41661144017) and the Fundamental Research Funds for the Central Universities (lzujbky-2015-k03).
文摘In this study, the period that corresponds to the threshold of a 1.5℃ rise (relative to 1861e1880) in surface temperature is validated using a multi-model ensemble mean from 17 global climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). On this basis, the changes in permafrost and snow cover in the Northern Hemisphere are investigated under a scenario in which the global surface temperature has risen by 1.5℃, and the uncertainties of the results are further discussed. The results show that the threshold of 1.5℃ warming will be reached in 2027, 2026, and 2023 under RCP2.6, RCP4.5, RCP8.5, respectively. When the global average surface temperature rises by 1.5℃, the southern boundary of the permafrost will move 1e3.5 northward (relative to 1986e2005), particularly in the southern Central Siberian Plateau. The permafrost area will be reduced by 3.43x106 km2 (21.12%), 3.91x106 km2 (24.1%) and 4.15x106 km2 (25.55%) relative to 1986e2005 in RCP2.6, RCP4.5 and RCP8.5, respectively. The snow water equivalent will decrease in over half of the regions in the Northern Hemisphere but increase only slightly in the Central Siberian Plateau. The snow water equivalent will decrease significantly (more than 40% relative to 1986e2005) in central North America, western Europe, and northwestern Russia. The permafrost area in the QinghaieTibet Plateau will decrease by 0.15x106 km2 (7.28%), 0.18x 106 km2 (8.74%), and 0.17x106 km2 (8.25%), respectively, in RCP2.6, RCP4.5, RCP8.5. The snow water equivalent in winter (DJF) and spring (MAM) over the QinghaieTibet Plateau will decrease by 14.9% and 13.8%, respectively.
基金supported by the National Basic Research Program of China (Grant No. 2016YFA0602401)the National Natural Science Foundation of China (Grant No. 41421004)
文摘Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4？C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse gas concentration scenario. Results show that, according to the 39 models, the median year in which 4？C global warming will occur is 2084.Based on the median results of models that project a 4？C global warming by 2100, land areas will generally exhibit stronger warming than the oceans annually and seasonally, and the strongest enhancement occurs in the Arctic, with the exception of the summer season. Change signals for temperature go outside its natural internal variabilities globally, and the signal-tonoise ratio averages 9.6 for the annual mean and ranges from 6.3 to 7.2 for the seasonal mean over the globe, with the greatest values appearing at low latitudes because of low noise. Decreased precipitation generally occurs in the subtropics, whilst increased precipitation mainly appears at high latitudes. The precipitation changes in most of the high latitudes are greater than the background variability, and the global mean signal-to-noise ratio is 0.5 and ranges from 0.2 to 0.4 for the annual and seasonal means, respectively. Attention should be paid to limiting global warming to 1.5？C, in which case temperature and precipitation will experience a far more moderate change than the natural internal variability. Large inter-model disagreement appears at high latitudes for temperature changes and at mid and low latitudes for precipitation changes. Overall, the intermodel consistency is better for temperature than for precipitation.
基金jointly supported by the National Key R&D Program of China [grant number 2017YFA0603802]the National Natural Science Foundation of China [grant numbers 41421004,41325018,and 41805062]
基金This research was jointly supported by the National Key R&D Program of China[grant number 2017YFA0603802]the National Natural Science Foundation of China[grant numbers 41730964 and 41991283]the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai).
