A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simu...A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander （KLM） state using the conditional nonlinear optimal parameter perturbation （CNOP-P） method. Because of their relatively large uncertainties, three model parameters were considered： the interracial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 （SPG2） algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation （CNOP-I） method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.展开更多
Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ＂spring predictability barrier＂ （SPB） for El Nio events. First, sensit...Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ＂spring predictability barrier＂ （SPB） for El Nio events. First, sensitivity experiments were respectively performed to the air-sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation （CNOP） approach was used to study the effect of the optimal mode （CNOP-P） of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nio events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode （i.e. CNOP error） of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.展开更多
利用1981-2014a逐日总辐射资料和最高气温、最低气温、日照时数资料,确定我国逐日太阳总辐射DSRM-C模型（Daily solar radiation model-C）的时不变参数。利用该模型与气象资料,估算各站逐日总辐射,分析近34a总辐射的空间格局。并利用Ma...利用1981-2014a逐日总辐射资料和最高气温、最低气温、日照时数资料,确定我国逐日太阳总辐射DSRM-C模型（Daily solar radiation model-C）的时不变参数。利用该模型与气象资料,估算各站逐日总辐射,分析近34a总辐射的空间格局。并利用Mann-Kendall（M-K）方法检验辐射量年和季节的变化趋势。结果表明,我国1981-2014a太阳总辐射量总体下降。华北、东北和西南地区总辐射量显著下降,西北地区辐射量明显增加,华南地区则无明显变化。夏、秋、冬季辐射量呈下降趋势,且以夏季变化幅度最大;春季辐射量略有上升。总辐射量四季变化趋势空间分异明显。东部地区夏季和华北地区春、秋、冬季总辐射量明显下降,将影响夏粮和秋粮的生产。展开更多
基金provided by the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No. KZCX2-EW-201)the Basic Research Program of Science and Technology Projects of Qingdao (Grant No.11-1-4-95-jch)the National Natural Science Foundation of China (Grant No. 40821092)
文摘A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander （KLM） state using the conditional nonlinear optimal parameter perturbation （CNOP-P） method. Because of their relatively large uncertainties, three model parameters were considered： the interracial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 （SPG2） algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation （CNOP-I） method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.
基金sponsored by the Knowl-edge Innovation Program of the Chinese Academy of Sciences (No. KZCX2-YW-QN203)the National Basic Re-search Program of China (No. 2007CB411800)the GYHY200906009 of the China Meteorological Administra-tion
文摘Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ＂spring predictability barrier＂ （SPB） for El Nio events. First, sensitivity experiments were respectively performed to the air-sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation （CNOP） approach was used to study the effect of the optimal mode （CNOP-P） of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nio events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode （i.e. CNOP error） of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.
文摘利用1981-2014a逐日总辐射资料和最高气温、最低气温、日照时数资料,确定我国逐日太阳总辐射DSRM-C模型（Daily solar radiation model-C）的时不变参数。利用该模型与气象资料,估算各站逐日总辐射,分析近34a总辐射的空间格局。并利用Mann-Kendall（M-K）方法检验辐射量年和季节的变化趋势。结果表明,我国1981-2014a太阳总辐射量总体下降。华北、东北和西南地区总辐射量显著下降,西北地区辐射量明显增加,华南地区则无明显变化。夏、秋、冬季辐射量呈下降趋势,且以夏季变化幅度最大;春季辐射量略有上升。总辐射量四季变化趋势空间分异明显。东部地区夏季和华北地区春、秋、冬季总辐射量明显下降,将影响夏粮和秋粮的生产。
