Long-term memory(LTM)in the climate system has been well recognized and applied in different research fields,but the origins of this property are still not clear.In this work,the authors contribute to this issue by st...Long-term memory(LTM)in the climate system has been well recognized and applied in different research fields,but the origins of this property are still not clear.In this work,the authors contribute to this issue by studying model simulations under different scenarios.The global mean temperatures from pre-industrial control runs(pi Control),historical(all forcings)simulations,natural forcing only simulations(Historical Nat),greenhouse gas forcing only simulations(Historical GHG),etc.,are analyzed using the detrended fluctuation analysis.The authors find that the LTM already exists in the pi Control simulations,indicating the important roles of internal natural variability in producing the LTM.By comparing the results among different scenarios,the LTM from the piControl runs is further found to be strengthened by adding natural forcings such as the volcanic forcing and the solar forcing.Accordingly,the observed LTM in the climate system is suggested to be mainly controlled by both the‘internal’natural variability and the‘external’natural forcings.The anthropogenic forcings,however,may weaken the LTM.In the projections from RCP2.6 to RCP8.5,a weakening trend of the LTM strength is found.In view of the close relations between the climate memory and the climate predictability,a reduced predictability may be expected in a warming climate.展开更多
The Coupled Model Inter-comparison Project Phase 5 （CMIP5） contains a group of state-of-the-art climate models and represents the highest level of climate simulation thus far. However, these models significantly ove...The Coupled Model Inter-comparison Project Phase 5 （CMIP5） contains a group of state-of-the-art climate models and represents the highest level of climate simulation thus far. However, these models significantly overestimated global mean surface temperature （GMST） during 2006-2014. Based on the ensemble empirical mode decomposition （EEMD） method, the long term change of the observed GMST time series of HadCRUT4 records during 1850-2014 was analyzed, then the simulated GMST by 33 CMIP5 climate models was assessed. The possible reason that climate models failed to project the recent global warming hiatus was revealed. Results show that during 1850-2014 the GMST on a centennial timescale rose with fluctuation, dominated by the secular trend and the multi-decadal variability （MDV）. The secular trend was relatively steady beginning in the early 20th century, with an average warming rate of 0.0883℃/decade over the last 50 years. While the MDV （with a -65-year cycle） showed 2.5 multi-decadal waves during 1850-2014, which deepened and steepened with time, the alarming warming over the last quarter of the 20th century was a result of the concurrence of the secular wanning trend and the warming phase of the MDV, both of which accounted one third of the temperature increase during 1975-1998. Recently the slowdown of global warming emerged as the MDV approached its third peak, leading to a reduction in the warming rate. A comparative analysis between the GMST time series derived from HadCRUT4 records and 33 CMIP5 model outputs reveals that the GMSTs during the historical simulation period of 1850-2005 can be reproduced well by models, especially on the accelerated global warming over the last quarter of 20th century. However, the projected GMSTs and their linear trends during 2006-2014 under the RCP4.5 scenario were significantly higher than observed. This is because the CMIP5 models confused the MDV with secular trend underlying the GMST time series, which results in a fast secular trend and an improper MDV with irregular phases and small amplitudes. This implies that the role of atmospheric CO2 in global warming may be overestimated, while the MDV which is an interior oscillation of the climate system may be underestimated, which should be related to insufficient understanding of key climatic internal dynamic processes. Our study puts forward an important criterion for the new generation of climate models： they should be able to simulate both the secular trend and the MDV of GMST.展开更多
基金supported by the National Natural Science Foundation of China grant number 41675088the CAS Pioneer Hundred Talents Program。
文摘Long-term memory(LTM)in the climate system has been well recognized and applied in different research fields,but the origins of this property are still not clear.In this work,the authors contribute to this issue by studying model simulations under different scenarios.The global mean temperatures from pre-industrial control runs(pi Control),historical(all forcings)simulations,natural forcing only simulations(Historical Nat),greenhouse gas forcing only simulations(Historical GHG),etc.,are analyzed using the detrended fluctuation analysis.The authors find that the LTM already exists in the pi Control simulations,indicating the important roles of internal natural variability in producing the LTM.By comparing the results among different scenarios,the LTM from the piControl runs is further found to be strengthened by adding natural forcings such as the volcanic forcing and the solar forcing.Accordingly,the observed LTM in the climate system is suggested to be mainly controlled by both the‘internal’natural variability and the‘external’natural forcings.The anthropogenic forcings,however,may weaken the LTM.In the projections from RCP2.6 to RCP8.5,a weakening trend of the LTM strength is found.In view of the close relations between the climate memory and the climate predictability,a reduced predictability may be expected in a warming climate.
基金supported by the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers(Grant No.U1406404)the Transparent Ocean Project (Grant No.2015ASKJ01)the corresponding author is also supported by Ao-Shan Talent Program
文摘The Coupled Model Inter-comparison Project Phase 5 （CMIP5） contains a group of state-of-the-art climate models and represents the highest level of climate simulation thus far. However, these models significantly overestimated global mean surface temperature （GMST） during 2006-2014. Based on the ensemble empirical mode decomposition （EEMD） method, the long term change of the observed GMST time series of HadCRUT4 records during 1850-2014 was analyzed, then the simulated GMST by 33 CMIP5 climate models was assessed. The possible reason that climate models failed to project the recent global warming hiatus was revealed. Results show that during 1850-2014 the GMST on a centennial timescale rose with fluctuation, dominated by the secular trend and the multi-decadal variability （MDV）. The secular trend was relatively steady beginning in the early 20th century, with an average warming rate of 0.0883℃/decade over the last 50 years. While the MDV （with a -65-year cycle） showed 2.5 multi-decadal waves during 1850-2014, which deepened and steepened with time, the alarming warming over the last quarter of the 20th century was a result of the concurrence of the secular wanning trend and the warming phase of the MDV, both of which accounted one third of the temperature increase during 1975-1998. Recently the slowdown of global warming emerged as the MDV approached its third peak, leading to a reduction in the warming rate. A comparative analysis between the GMST time series derived from HadCRUT4 records and 33 CMIP5 model outputs reveals that the GMSTs during the historical simulation period of 1850-2005 can be reproduced well by models, especially on the accelerated global warming over the last quarter of 20th century. However, the projected GMSTs and their linear trends during 2006-2014 under the RCP4.5 scenario were significantly higher than observed. This is because the CMIP5 models confused the MDV with secular trend underlying the GMST time series, which results in a fast secular trend and an improper MDV with irregular phases and small amplitudes. This implies that the role of atmospheric CO2 in global warming may be overestimated, while the MDV which is an interior oscillation of the climate system may be underestimated, which should be related to insufficient understanding of key climatic internal dynamic processes. Our study puts forward an important criterion for the new generation of climate models： they should be able to simulate both the secular trend and the MDV of GMST.
