Leading time length is an important issue for modeling seasonal forecasts. In this study, a comparison of the interannual predictability of the Western North Pacific (WNP) summer monsoon between different leading mont...Leading time length is an important issue for modeling seasonal forecasts. In this study, a comparison of the interannual predictability of the Western North Pacific (WNP) summer monsoon between different leading months was performed by using one-, four-, and sevenmonth lead retrospective forecasts (hindcasts) of four coupled models from Ensembles-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) for the period of 1960 2005. It is found that the WNP summer anomalies, including lower-tropospheric circulation and precipitation anomalies, can be well predicted for all these leading months. The accuracy of the four-month lead prediction is only slightly weaker than that of the one-month lead prediction, although the skill decreases with the increase of leading months.展开更多
A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low f...A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).展开更多
Previous studies have shown that year-to-year incremental prediction （YIP） can obtain considerable skill in seasonal forecasts. This study analyzes the mathematical deRnition of YiP and derives its formula in the no...Previous studies have shown that year-to-year incremental prediction （YIP） can obtain considerable skill in seasonal forecasts. This study analyzes the mathematical deRnition of YiP and derives its formula in the nonlinear time series prediction （NP） method, it is shown that the two methods are equivalent when the prediction time series is embedded in one-dimensional phase space. Compared to previous NP models, the new one introduces multiple external forcings in the form of year-to-year increments. The year-to-year increments have physical meaning, which is better than the NP model with empirically chosen parameters. The summer rainfall over the middle to lower reaches of the Yangtze River is analyzed to examine the prediction skill of the NP models. Results show that the NP model with year-to-year increments can reach a similar skill as the YiP model. When the embedded number of dimensions is increased to two, more accurate prediction can be obtained. Besides similar results, the NP method has more dynamical meaning, as it is based on the classical reconstruction theory. Moreover, by choosing different embedded dimensions, the NP model can reconstruct the dynamical curve into phase space with more than one dimension, which is an advantage of the NP model. The present study suggests that YIP has a robust dynamical foundation, besides its physical mechanism, and the modified NP model has the potential to increase the operationaJ skill in short- term climate prediction.展开更多
在全球气候变化的背景下,干旱半干旱区草地作为陆地生态系统中重要且非常脆弱的组分之一,显现出一系列生态问题。探究气候变化及人类活动对于该区草地生态系统净初级生产力(NPP)的影响,对于合理利用自然资源,保持农牧业可持续发展具有...在全球气候变化的背景下,干旱半干旱区草地作为陆地生态系统中重要且非常脆弱的组分之一,显现出一系列生态问题。探究气候变化及人类活动对于该区草地生态系统净初级生产力(NPP)的影响,对于合理利用自然资源,保持农牧业可持续发展具有重要的意义。施肥作为促进作物生长的一种方式,合理施肥也可以提高退化草地的NPP。基于此,本研究拟以天山北坡沿海拔梯度分布的4种草地类型:高山草甸(AM)、中山森林草甸(MMFM)、低山干草原(LMDG)和平原荒漠草原(PDG)为研究对象,基于反硝化-分解模型(DNDC)分析该区典型草地生态系统净初级生产力对施加不同氮肥的响应,并揭示施肥阈值及最优施肥方式。结果表明:1)适度氮肥添加促进了各个类型草地生态系统NPP的增长,但草地NPP对施肥量的响应存在阈值,且不存在适用于4种草地类型的统一最优施肥方式。LMDG草地生态系统对施氮肥的响应最敏感。2)PDG草地NPP达到最大的施肥方式为一年分两次施加100 kg·hm^(-2)硝酸盐,NPP的最大值为68.72 g C·m^(-2)·a^(-1)。LMDG草地NPP最大的施肥方式为一年分两次施加尿素260 kg·hm^(-2),NPP的最大值为263.28 g C·m^(-2)·a^(-1)。MMFM草地生态系统达到NPP最大的施肥方式为一年一次施尿素80 kg·hm^(-2),NPP的最大值为171.22 g C·m^(-2)·a^(-1)。无水氨作为在AM草地中反应最好的氮肥,以最小的施肥量(60 kg·hm^(-2))达到了NPP的最大值(114.62 g C·m^(-2)·a^(-1))。3)通过蒙特卡洛不确定分析的结果显示,施肥时间对PDG和LMDG的影响更为明显,施肥量波动对LMDG和MMFM的影响较其他两种草地更为明显。展开更多
The interannual variability of the east asian upper-tropospheric westerly jet（EAJ） in summer is characterized by the meridional displacement of its axis, or a seesaw pattern of zonal wind anomalies between the north...The interannual variability of the east asian upper-tropospheric westerly jet（EAJ） in summer is characterized by the meridional displacement of its axis, or a seesaw pattern of zonal wind anomalies between the northern and southern flanks of the EAJ. This study reveals a close relationship between the surface air temperature in the russian far east and the northern flank of the EAJ. Related to a warmer surface in the russian far east, the westerly decelerates in the northern flank of the EAJ. The relationship can be explained by a positive feedback mechanism between the surface air temperature in the russian far east and the overhead circulation： the anticyclonic circulation anomaly related to a weakened westerly in the northern flank of the EAJ induces surface warming in the russian far east and the warmer surface can in turn act as a heat source and induces a local anticyclonic circulation anomaly in the upper troposphere, therefore decelerating the westerly in the northern flank of the EAJ. The result implies that a better description of the summer surface condition in the russian far east may benefit seasonal forecasts of the EAJ and, subsequently, east asian summer climate.展开更多
基金supported by the Special Scientific Research Project for Public Interest (Grant No.GYHY201006021)supported by the U.K. National Centre for Atmospheric Science-Climate (NCAS-Climate) at the University of Reading
文摘Leading time length is an important issue for modeling seasonal forecasts. In this study, a comparison of the interannual predictability of the Western North Pacific (WNP) summer monsoon between different leading months was performed by using one-, four-, and sevenmonth lead retrospective forecasts (hindcasts) of four coupled models from Ensembles-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) for the period of 1960 2005. It is found that the WNP summer anomalies, including lower-tropospheric circulation and precipitation anomalies, can be well predicted for all these leading months. The accuracy of the four-month lead prediction is only slightly weaker than that of the one-month lead prediction, although the skill decreases with the increase of leading months.
基金supported by the Knowledge Innovation Key Project of Chinese Academy of Sciences (CAS) under Grant No.KZCX2-YW-217Doctor Research Startup Project at the Institute of Atmospheric Physics,the CAS under Grant No.7-098300
文摘A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).
基金supported by the National Natural Sciences Foundation of China[41375112],[41530426],[41575058]the Key Technology Talent Program of the Chinese Academy of Sciencesthe Public Science and Technology Research Funds Projects of Ocean[201505013]
文摘Previous studies have shown that year-to-year incremental prediction （YIP） can obtain considerable skill in seasonal forecasts. This study analyzes the mathematical deRnition of YiP and derives its formula in the nonlinear time series prediction （NP） method, it is shown that the two methods are equivalent when the prediction time series is embedded in one-dimensional phase space. Compared to previous NP models, the new one introduces multiple external forcings in the form of year-to-year increments. The year-to-year increments have physical meaning, which is better than the NP model with empirically chosen parameters. The summer rainfall over the middle to lower reaches of the Yangtze River is analyzed to examine the prediction skill of the NP models. Results show that the NP model with year-to-year increments can reach a similar skill as the YiP model. When the embedded number of dimensions is increased to two, more accurate prediction can be obtained. Besides similar results, the NP method has more dynamical meaning, as it is based on the classical reconstruction theory. Moreover, by choosing different embedded dimensions, the NP model can reconstruct the dynamical curve into phase space with more than one dimension, which is an advantage of the NP model. The present study suggests that YIP has a robust dynamical foundation, besides its physical mechanism, and the modified NP model has the potential to increase the operationaJ skill in short- term climate prediction.
文摘在全球气候变化的背景下,干旱半干旱区草地作为陆地生态系统中重要且非常脆弱的组分之一,显现出一系列生态问题。探究气候变化及人类活动对于该区草地生态系统净初级生产力(NPP)的影响,对于合理利用自然资源,保持农牧业可持续发展具有重要的意义。施肥作为促进作物生长的一种方式,合理施肥也可以提高退化草地的NPP。基于此,本研究拟以天山北坡沿海拔梯度分布的4种草地类型:高山草甸(AM)、中山森林草甸(MMFM)、低山干草原(LMDG)和平原荒漠草原(PDG)为研究对象,基于反硝化-分解模型(DNDC)分析该区典型草地生态系统净初级生产力对施加不同氮肥的响应,并揭示施肥阈值及最优施肥方式。结果表明:1)适度氮肥添加促进了各个类型草地生态系统NPP的增长,但草地NPP对施肥量的响应存在阈值,且不存在适用于4种草地类型的统一最优施肥方式。LMDG草地生态系统对施氮肥的响应最敏感。2)PDG草地NPP达到最大的施肥方式为一年分两次施加100 kg·hm^(-2)硝酸盐,NPP的最大值为68.72 g C·m^(-2)·a^(-1)。LMDG草地NPP最大的施肥方式为一年分两次施加尿素260 kg·hm^(-2),NPP的最大值为263.28 g C·m^(-2)·a^(-1)。MMFM草地生态系统达到NPP最大的施肥方式为一年一次施尿素80 kg·hm^(-2),NPP的最大值为171.22 g C·m^(-2)·a^(-1)。无水氨作为在AM草地中反应最好的氮肥,以最小的施肥量(60 kg·hm^(-2))达到了NPP的最大值(114.62 g C·m^(-2)·a^(-1))。3)通过蒙特卡洛不确定分析的结果显示,施肥时间对PDG和LMDG的影响更为明显,施肥量波动对LMDG和MMFM的影响较其他两种草地更为明显。
基金supported by the National Natural Science Foundation of China(Grant Nos.41320104007,41775062,41375086,U1502233,and 41775083)
文摘The interannual variability of the east asian upper-tropospheric westerly jet（EAJ） in summer is characterized by the meridional displacement of its axis, or a seesaw pattern of zonal wind anomalies between the northern and southern flanks of the EAJ. This study reveals a close relationship between the surface air temperature in the russian far east and the northern flank of the EAJ. Related to a warmer surface in the russian far east, the westerly decelerates in the northern flank of the EAJ. The relationship can be explained by a positive feedback mechanism between the surface air temperature in the russian far east and the overhead circulation： the anticyclonic circulation anomaly related to a weakened westerly in the northern flank of the EAJ induces surface warming in the russian far east and the warmer surface can in turn act as a heat source and induces a local anticyclonic circulation anomaly in the upper troposphere, therefore decelerating the westerly in the northern flank of the EAJ. The result implies that a better description of the summer surface condition in the russian far east may benefit seasonal forecasts of the EAJ and, subsequently, east asian summer climate.
