The 2 ℃ warming target has been used widely in global and regional climate change research. Previous studies have shown large uncertainties in the time when surface air temperature （SAT） change over China will reac...The 2 ℃ warming target has been used widely in global and regional climate change research. Previous studies have shown large uncertainties in the time when surface air temperature （SAT） change over China will reach 2℃ rela- tive to the pre-industrial era. To understand the uncertainties, we analyzed the projected SAT in the twenty-first century using 40 state-of-the-art climate models under two Repre- sentative Concentration Pathways （RCP4.5 and RCPS.5） from the Coupled Model Intercomparison Project Phase 5. The 2℃ threshold-crossing time （TCT） of SAT averaged across China was around 2033 and 2029 for RCP4.5 and RCP8.5, respectively. Considering a 4-1o- range of inter- model SAT change, the upper and lower bounds of the 2 ℃ TCT could differ by about 25 years or even more. Uncer- tainty in the projected SAT and the warming rate around the TCT are the two main factors responsible for the TCT uncertainty. The former is determined by the climate sensi- tivity represented by the global mean surface temperature response. About 45 % of the intermodel variance of the projected 2 ~C TCT for averaged SAT over China can be explained by climate sensitivity across the models, which is contributed mainly by central and southern China. In a cli- mate more sensitive to CO2 forcing, stronger greenhouse effect, less stratus cloud over the East Asian monsoon region, and less snow cover on the Tibetan Plateau result in increased downward longwave radiation, increased shortwave radia- tion, and decreased shortwave radiation reflected by the surface, respectively, all of which may advance the TCT.展开更多
Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literat...Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literature employ a single or narrow range of soil databases, which largely overlooks the impact of utilizing multiple mapping scales in estimating soil TP, especially in hilly topographies. In this study, Fujian Province, a subtropical hilly region along China’s southeast coast covered by a complex topographic environment, was taken as a case study. The influence of the mapping scale on soil TP storage (TPS)estimation was analyzed using six digital soil databases that were derived from 3 082 unique soil profiles at different mapping scales, i.e., 1:50 000 (S5),1:200 000 (S20), 1:500 000 (S50), 1:1 000 000 (S100), 1:4 000 000 (S400), and 1:10 000 000 (S1000). The regional TPS in the surface soil (0–20 cm) based on the S5, S20, S50, S100, S400, and S1000 soil maps was 20.72, 22.17, 23.06, 23.05, 22.04, and 23.48 Tg, respectively, and the corresponding TPS at0–100 cm soil depth was 80.98, 80.71, 85.00, 84.03, 82.96, and 86.72 Tg, respectively. By comparing soil TPS in the S20 to S1000 maps to that in the S5map, the relative deviations were 6.37%–13.32%for 0–20 cm and 0.33%–7.09%for 0–100 cm. Moreover, since the S20 map had the lowest relative deviation among different mapping scales as compared to S5, it could provide additional soil information and a richer soil environment than other smaller mapping scales. Our results also revealed that many uncertainties in soil TPS estimation originated from the lack of detailed soil information, i.e., representation and spatial variations among different soil types. From the time and labor perspectives, our work provides useful guidelines to identify the appropriate mapping scale for estimating regional soil TPS in areas like Fujian Province in subtropical China or other places with similar complex topographies. Moreover, it is of tremendous importance to 展开更多
We study the uncertainties of quantum mechanical observables, quantified by the standard deviation(square root of variance) in Haar-distributed random pure states. We derive analytically the probability density functi...We study the uncertainties of quantum mechanical observables, quantified by the standard deviation(square root of variance) in Haar-distributed random pure states. We derive analytically the probability density functions(PDFs) of the uncertainties of arbitrary qubit observables.Based on these PDFs, the uncertainty regions of the observables are characterized by the support of the PDFs. The state-independent uncertainty relations are then transformed into the optimization problems over uncertainty regions, which opens a new vista for studying stateindependent uncertainty relations. Our results may be generalized to multiple observable cases in higher dimensional spaces.展开更多
基金supported jointly by the ‘‘Strategic Priority Research Program–Climate Change: Carbon Budget and Related Issues’’ of the Chinese Academy of Sciences (XDA05110300)the Research Fund for Commonwealth Trades (Meteorology) (GYHY201506012)+1 种基金the National Natural Science Foundation of China (41420104006)the China Postdoctoral Science Foundation (2015M581152)
文摘The 2 ℃ warming target has been used widely in global and regional climate change research. Previous studies have shown large uncertainties in the time when surface air temperature （SAT） change over China will reach 2℃ rela- tive to the pre-industrial era. To understand the uncertainties, we analyzed the projected SAT in the twenty-first century using 40 state-of-the-art climate models under two Repre- sentative Concentration Pathways （RCP4.5 and RCPS.5） from the Coupled Model Intercomparison Project Phase 5. The 2℃ threshold-crossing time （TCT） of SAT averaged across China was around 2033 and 2029 for RCP4.5 and RCP8.5, respectively. Considering a 4-1o- range of inter- model SAT change, the upper and lower bounds of the 2 ℃ TCT could differ by about 25 years or even more. Uncer- tainty in the projected SAT and the warming rate around the TCT are the two main factors responsible for the TCT uncertainty. The former is determined by the climate sensi- tivity represented by the global mean surface temperature response. About 45 % of the intermodel variance of the projected 2 ~C TCT for averaged SAT over China can be explained by climate sensitivity across the models, which is contributed mainly by central and southern China. In a cli- mate more sensitive to CO2 forcing, stronger greenhouse effect, less stratus cloud over the East Asian monsoon region, and less snow cover on the Tibetan Plateau result in increased downward longwave radiation, increased shortwave radia- tion, and decreased shortwave radiation reflected by the surface, respectively, all of which may advance the TCT.
基金Supported by National High Technology Research and Development Program of China （863 Program） （2007AA809502C） National Natural Science Foundation of China （50979093） Program for New Century Excellent Talents in University （NCET-06-0877）
基金supported by the National Natural Science Foundation of China(Nos.41971050 and 42207271)the Provincial Natural Science Foundation of Fujian,China(No.2022J05036)the Open Project Program of the State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics,Chinese Academy of Sciences(No.LAPC-KF-2022-08)。
文摘Soil phosphorus (P) plays a vital role in both ecological and agricultural ecosystems, where total P (TP) in soil serves as a crucial indicator of soil fertility and quality. Most of the studies covered in the literature employ a single or narrow range of soil databases, which largely overlooks the impact of utilizing multiple mapping scales in estimating soil TP, especially in hilly topographies. In this study, Fujian Province, a subtropical hilly region along China’s southeast coast covered by a complex topographic environment, was taken as a case study. The influence of the mapping scale on soil TP storage (TPS)estimation was analyzed using six digital soil databases that were derived from 3 082 unique soil profiles at different mapping scales, i.e., 1:50 000 (S5),1:200 000 (S20), 1:500 000 (S50), 1:1 000 000 (S100), 1:4 000 000 (S400), and 1:10 000 000 (S1000). The regional TPS in the surface soil (0–20 cm) based on the S5, S20, S50, S100, S400, and S1000 soil maps was 20.72, 22.17, 23.06, 23.05, 22.04, and 23.48 Tg, respectively, and the corresponding TPS at0–100 cm soil depth was 80.98, 80.71, 85.00, 84.03, 82.96, and 86.72 Tg, respectively. By comparing soil TPS in the S20 to S1000 maps to that in the S5map, the relative deviations were 6.37%–13.32%for 0–20 cm and 0.33%–7.09%for 0–100 cm. Moreover, since the S20 map had the lowest relative deviation among different mapping scales as compared to S5, it could provide additional soil information and a richer soil environment than other smaller mapping scales. Our results also revealed that many uncertainties in soil TPS estimation originated from the lack of detailed soil information, i.e., representation and spatial variations among different soil types. From the time and labor perspectives, our work provides useful guidelines to identify the appropriate mapping scale for estimating regional soil TPS in areas like Fujian Province in subtropical China or other places with similar complex topographies. Moreover, it is of tremendous importance to
基金supported by the NSF of China under Grant Nos.11971140,12075159,and 12171044Beijing Natural Science Foundation(Z190005)+1 种基金the Academician Innovation Platform of Hainan Province,and Academy for Multidisciplinary Studies,Capital Normal Universityfunded by Natural Science Foundations of Hubei Province Grant No.2020CFB538。
文摘We study the uncertainties of quantum mechanical observables, quantified by the standard deviation(square root of variance) in Haar-distributed random pure states. We derive analytically the probability density functions(PDFs) of the uncertainties of arbitrary qubit observables.Based on these PDFs, the uncertainty regions of the observables are characterized by the support of the PDFs. The state-independent uncertainty relations are then transformed into the optimization problems over uncertainty regions, which opens a new vista for studying stateindependent uncertainty relations. Our results may be generalized to multiple observable cases in higher dimensional spaces.
