In this paper,we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature(EASAT)and EASAT decadal prediction.The observational analysis shows that the wi...In this paper,we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature(EASAT)and EASAT decadal prediction.The observational analysis shows that the winter EASAT and East Asian minimum SAT(EAmSAT)display strong in-phase fluctuations and a significant 60-80-year multidecadal variability,apart from a long-term warming trend.The winter EASAT experienced a decreasing trend in the last two decades,which is consistent with the occurrence of extremely cold events in East Asia winters in recent years.The winter NAO leads the detrended winter EASAT by 12-18 years with the greatest significant positive correlation at the lead time of 15 years.Further analysis shows that ENSO may affect winter EASAT interannual variability,but does not affect the robust lead relationship between the winter NAO and EASAT.We present the coupled oceanic-atmospheric bridge(COAB)mechanism of the NAO influences on winter EASAT multidecadal variability through its accumulated delayed effect of~15 years on the Atlantic Multidecadal Oscillation(AMO)and Africa-Asia multidecadal teleconnection(AAMT)pattern.An NAO-based linear model for predicting winter decadal EASAT is constructed on the principle of the COAB mechanism,with good hindcast performance.The winter EASAT for 2020-34 is predicted to keep on fluctuating downward until~2025,implying a high probability of occurrence of extremely cold events in coming winters in East Asia,followed by a sudden turn towards sharp warming.The predicted 2020/21 winter EASAT is almost the same as the 2019/20 winter.展开更多
Southwest China(SWC)is one of the major grain-producing areas in China,and the surface air temperature(SAT)during autumn has a substantial influence on grain production and planting.It is therefore important to unders...Southwest China(SWC)is one of the major grain-producing areas in China,and the surface air temperature(SAT)during autumn has a substantial influence on grain production and planting.It is therefore important to understand temporal changes in the SAT over SWC(SWC-SAT).Our analysis of observational and reanalysis datasets shows that the autumn SWC-SAT exhibits significant multidecadal variability.A significantly strong positive correlation also exists between the autumn SWC-SAT and the Atlantic multidecadal oscillation(AMO)time series(correlation coefficient of 0.85).These results suggest that the AMO is a remote driver of multidecadal variability in the autumn SWC-SAT.Further analyses show that the North Atlantic sea surface temperature anomalies(SSTA)associated with the AMO modulate the multidecadal variability of the autumn SWC-SAT through triggering the Africa-Asia multidecadal teleconnection(AAMT)pattern.Specifically,the AAMT corresponds to geopotential height anomalies over SWC,which adjust the local thickness of the air column and thereby induce multidecadal variability of the autumn SWC-SAT.This potential mechanism,derived from observational and reanalysis datasets,was verified by using a linear barotropic model and the Community Atmosphere Model version 4.Our results from combining observations and numerical modeling simulations indicate that the North Atlantic SSTA may act as a key pacemaker for the multidecadal SAT variability over SWC.展开更多
Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in ...Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017-35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend(ST) and multidecadal variability(MDV) in the Coupled Model Intercomparison Project Phase 5(CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition(EEMD) filter, reconstructed via the Bayesian model averaging(BMA) method for the historical period 1901-2005, and validated for 2006-16. In the simulations of the "medium" representative concentration pathways scenario during 2017-35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44℃(90% uncertainty range from 0.30 to 0.58℃) for global land, 0.48℃(90% uncertainty range from 0.29 to 0.67℃) for the Northern Hemispheric land(NL), and 0.29℃(90% uncertainty range from 0.23 to 0.35℃) for the Southern Hemispheric land(SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect(46%) exists in central America. In contrast,the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect(220%) in Alaska.展开更多
Subject Code:D02With the support by the National Natural Science Foundation of China,a collaborative study by the research group led by Prof.Yang Bao(杨保)from the Key Laboratory of Desert and Desertification,Northwes...Subject Code:D02With the support by the National Natural Science Foundation of China,a collaborative study by the research group led by Prof.Yang Bao(杨保)from the Key Laboratory of Desert and Desertification,Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences,and展开更多
The Pacific Decadal Oscillation （PDO） and the Atlantic Multidecadal Variability （AMV） are the two dominant low-frequency modes in the climate system. This research focused on the response of these two modes under ...The Pacific Decadal Oscillation （PDO） and the Atlantic Multidecadal Variability （AMV） are the two dominant low-frequency modes in the climate system. This research focused on the response of these two modes under weak global warming. Observational data were derived from the Hadley Center Sea Ice and Sea Surface Temperature dataset （HadISST） and coupled model outputs from the Coupled Model Intercomparison Project Phase 5 （CMIP5）. Changes in PDO and AMV were examined using four models （bcc-csml-1, CCSM4, IPSL-CM5A-LR, and MPI- ESM-LR） with long weak global warming scenarios （RCP2.6）. These models captured the two low-frequency modes in both pre-industrial run and RCP2.6 run. Under weak global warming, the time scales of PDO and AMV significantly decreased while the amplitude only slightly decreased. Interestingly, the standard deviation of the North Pacific sea surface temperature anomaly （SSTA） decreased only in decadal time scale, and that of the North Atlantic SSTA decreased both in interannual and decadal time scales. The coupled system consists of a slow ocean component, which has a decadal time scale, and a fast atmospheric component, which is calculated by subtracting the decadal from the total. Results suggest that under global warming, PDO change is dominated by ocean dynamics, and AMV change is dominated by ocean dynamics and stochastic atmosphere forcing.展开更多
This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigoro...This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.展开更多
The temperature variability over multidecadal and longer timescales(e.g., the cold epochs in the late 15 th, 17 th, and early 19 th centuries) is significant and dominant in the millennium-long, large-scale reconstruc...The temperature variability over multidecadal and longer timescales(e.g., the cold epochs in the late 15 th, 17 th, and early 19 th centuries) is significant and dominant in the millennium-long, large-scale reconstructions and model simulations;however, their temporal patterns in the reconstructed and simulated temperature series are not well understood and require a detailed assessment and comparison. Here, we compare the reconstructed and simulated temperature series for the Northern Hemisphere(NH) at multidecadal and longer-term timescales(>30 years) by evaluating their covariance, climate sensitivity and amplitude of temperature changes. We found that covariances between different reconstructions or between reconstructions and simulations are generally high for the whole period of 850–1999 CE, due to their similar long-term temporal patterns. However,covariances between different reconstructions or between reconstructions and simulations steadily decline as time series extends further back in time, becoming particularly small during Medieval times. This is related to the large uncetainties in the reconstructions caused by the decreased number of proxy records and sample duplication during the pre-instrumental periods.Reconstructions based solely on tree-ring data show higher skill than multiproxy reconstructions in capturing the amplitude of volcanic cooling simulated by models. Meanwhile, climate models have a shorter recovery(i.e., lag) in response to the cooling caused by volcanic eruptions and solar activity minima, implying the lack of some important feedback mechanisms between external forcing and internal climate processes in climate models. Amplitudes of temperature variations in the latest published tree-ring reconstructions are comparable to those of the multiproxy reconstructions. We found that the temperature difference between the Medieval Climate Anomaly(950–1250 CE) and the Little Ice Age(1450–1850 CE) is generally larger in proxybased reconstructions than in model simulations, but the reason is unclear.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)Project(Grant No.41790474)Shandong Natural Science Foundation Project(Grant No.ZR2019ZD12)Fundamental Research Funds for the Central Universities(Grant No.201962009).
文摘In this paper,we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature(EASAT)and EASAT decadal prediction.The observational analysis shows that the winter EASAT and East Asian minimum SAT(EAmSAT)display strong in-phase fluctuations and a significant 60-80-year multidecadal variability,apart from a long-term warming trend.The winter EASAT experienced a decreasing trend in the last two decades,which is consistent with the occurrence of extremely cold events in East Asia winters in recent years.The winter NAO leads the detrended winter EASAT by 12-18 years with the greatest significant positive correlation at the lead time of 15 years.Further analysis shows that ENSO may affect winter EASAT interannual variability,but does not affect the robust lead relationship between the winter NAO and EASAT.We present the coupled oceanic-atmospheric bridge(COAB)mechanism of the NAO influences on winter EASAT multidecadal variability through its accumulated delayed effect of~15 years on the Atlantic Multidecadal Oscillation(AMO)and Africa-Asia multidecadal teleconnection(AAMT)pattern.An NAO-based linear model for predicting winter decadal EASAT is constructed on the principle of the COAB mechanism,with good hindcast performance.The winter EASAT for 2020-34 is predicted to keep on fluctuating downward until~2025,implying a high probability of occurrence of extremely cold events in coming winters in East Asia,followed by a sudden turn towards sharp warming.The predicted 2020/21 winter EASAT is almost the same as the 2019/20 winter.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2442210,42175042,and 42275059)the Natural Science Foundation of Sichuan Province(Grant No.2024NSFTD0017)+1 种基金the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0103)the Natural Science Foundation of Chongqing(Grant No.cstc2021jcyj-msxmX0698).
文摘Southwest China(SWC)is one of the major grain-producing areas in China,and the surface air temperature(SAT)during autumn has a substantial influence on grain production and planting.It is therefore important to understand temporal changes in the SAT over SWC(SWC-SAT).Our analysis of observational and reanalysis datasets shows that the autumn SWC-SAT exhibits significant multidecadal variability.A significantly strong positive correlation also exists between the autumn SWC-SAT and the Atlantic multidecadal oscillation(AMO)time series(correlation coefficient of 0.85).These results suggest that the AMO is a remote driver of multidecadal variability in the autumn SWC-SAT.Further analyses show that the North Atlantic sea surface temperature anomalies(SSTA)associated with the AMO modulate the multidecadal variability of the autumn SWC-SAT through triggering the Africa-Asia multidecadal teleconnection(AAMT)pattern.Specifically,the AAMT corresponds to geopotential height anomalies over SWC,which adjust the local thickness of the air column and thereby induce multidecadal variability of the autumn SWC-SAT.This potential mechanism,derived from observational and reanalysis datasets,was verified by using a linear barotropic model and the Community Atmosphere Model version 4.Our results from combining observations and numerical modeling simulations indicate that the North Atlantic SSTA may act as a key pacemaker for the multidecadal SAT variability over SWC.
基金Supported by the National Key Research and Development Program of China(2016YFA0600404)Youth Innovation Promotion Association of the Chinese Academy of Sciences(2016075)Jiangsu Collaborative Innovation Center for Climate Change
文摘Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017-35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend(ST) and multidecadal variability(MDV) in the Coupled Model Intercomparison Project Phase 5(CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition(EEMD) filter, reconstructed via the Bayesian model averaging(BMA) method for the historical period 1901-2005, and validated for 2006-16. In the simulations of the "medium" representative concentration pathways scenario during 2017-35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44℃(90% uncertainty range from 0.30 to 0.58℃) for global land, 0.48℃(90% uncertainty range from 0.29 to 0.67℃) for the Northern Hemispheric land(NL), and 0.29℃(90% uncertainty range from 0.23 to 0.35℃) for the Southern Hemispheric land(SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect(46%) exists in central America. In contrast,the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect(220%) in Alaska.
文摘Subject Code:D02With the support by the National Natural Science Foundation of China,a collaborative study by the research group led by Prof.Yang Bao(杨保)from the Key Laboratory of Desert and Desertification,Northwest Institute of Eco-Environment and Resources of the Chinese Academy of Sciences,and
文摘The Pacific Decadal Oscillation （PDO） and the Atlantic Multidecadal Variability （AMV） are the two dominant low-frequency modes in the climate system. This research focused on the response of these two modes under weak global warming. Observational data were derived from the Hadley Center Sea Ice and Sea Surface Temperature dataset （HadISST） and coupled model outputs from the Coupled Model Intercomparison Project Phase 5 （CMIP5）. Changes in PDO and AMV were examined using four models （bcc-csml-1, CCSM4, IPSL-CM5A-LR, and MPI- ESM-LR） with long weak global warming scenarios （RCP2.6）. These models captured the two low-frequency modes in both pre-industrial run and RCP2.6 run. Under weak global warming, the time scales of PDO and AMV significantly decreased while the amplitude only slightly decreased. Interestingly, the standard deviation of the North Pacific sea surface temperature anomaly （SSTA） decreased only in decadal time scale, and that of the North Atlantic SSTA decreased both in interannual and decadal time scales. The coupled system consists of a slow ocean component, which has a decadal time scale, and a fast atmospheric component, which is calculated by subtracting the decadal from the total. Results suggest that under global warming, PDO change is dominated by ocean dynamics, and AMV change is dominated by ocean dynamics and stochastic atmosphere forcing.
基金US National Science Foundation Grant(No.AGS-1139479)
文摘This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.
基金supported by the National Key R&D Program of China (Grant No. 2017YFA0603302)supported by the National Natural Science Foundation of China (Grant Nos. 41888101 & 41602192 & 41977383)+4 种基金the Belmont Forum and JPI-Climate, Collaborative Research Action “INTEGRATE” (Grant No. 41661144008)support by the Youth Innovation Promotion Association Foundation of the Chinese Academy of Sciences (Grant No. 2018471)supported by the National Natural Science Foundation (Grant No. 41901095)supported by the National Natural Science Foundation (Grant No. 41877440)supported by Opening Fund of Key Laboratory of Desert and Desertification, Chinese Academy of Sciences (Grant No. KLDD-2019-04)。
文摘The temperature variability over multidecadal and longer timescales(e.g., the cold epochs in the late 15 th, 17 th, and early 19 th centuries) is significant and dominant in the millennium-long, large-scale reconstructions and model simulations;however, their temporal patterns in the reconstructed and simulated temperature series are not well understood and require a detailed assessment and comparison. Here, we compare the reconstructed and simulated temperature series for the Northern Hemisphere(NH) at multidecadal and longer-term timescales(>30 years) by evaluating their covariance, climate sensitivity and amplitude of temperature changes. We found that covariances between different reconstructions or between reconstructions and simulations are generally high for the whole period of 850–1999 CE, due to their similar long-term temporal patterns. However,covariances between different reconstructions or between reconstructions and simulations steadily decline as time series extends further back in time, becoming particularly small during Medieval times. This is related to the large uncetainties in the reconstructions caused by the decreased number of proxy records and sample duplication during the pre-instrumental periods.Reconstructions based solely on tree-ring data show higher skill than multiproxy reconstructions in capturing the amplitude of volcanic cooling simulated by models. Meanwhile, climate models have a shorter recovery(i.e., lag) in response to the cooling caused by volcanic eruptions and solar activity minima, implying the lack of some important feedback mechanisms between external forcing and internal climate processes in climate models. Amplitudes of temperature variations in the latest published tree-ring reconstructions are comparable to those of the multiproxy reconstructions. We found that the temperature difference between the Medieval Climate Anomaly(950–1250 CE) and the Little Ice Age(1450–1850 CE) is generally larger in proxybased reconstructions than in model simulations, but the reason is unclear.
