基于BCC_CSM模式的中国东部夏季降水预测检验及订正

郭渠; 刘向文; 吴统文; 程炳岩; 李瑞; 魏鳞骁

doi:10.3878/j.issn.1006-9895.1602.15280

基于BCC_CSM模式的中国东部夏季降水预测检验及订正

doi: 10.3878/j.issn.1006-9895.1602.15280

1.
重庆市气候中心, 重庆 401147
2.
国家气候中心, 北京 100081
3.
济南市气象局, 济南 250002

基金项目:

国家自然科学基金项目 Grant 41305057

重庆市前沿与应用基础研究项目 Grant cstc2015jcyjA0017

中国气象局关键技术集成与应用面上项目 Grant CMAGJ2015M47

详细信息

作者简介:
郭渠,男,1978年出生,高级工程师,主要从事气候预测研究。E-mail:guoqu510@163.com

通讯作者:
刘向文,E-mail:xwliu@cma.gov.cn

中图分类号: P467
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出版历程
- 收稿日期: 2015-09-29
- 网络出版日期: 2016-02-01
- 刊出日期: 2017-01-15

Verification and Correction of East China Summer Rainfall Prediction Based on BCC_CSM Model

1.
Chongqing Climate Center, Chongqing 401147
2.
National Climate Center, Beijing 100081
3.
Jinan Meteorological Bureau, Jinan 250002

Funds:

National Natural Science Foundation of China Grant 41305057

Advanced and Applied Basic Research Project of Chongqing Grant cstc2015jcyjA0017

Key Technology Integration and Application Project of China Meteorological Administration Grant CMAGJ2015M47

摘要

摘要: 基于国家气候中心第二代季节预测模式的历史回报试验数据，检验了模式对我国东部夏季降水的预测能力，探讨了预测误差形成的可能原因，并应用降尺度方法提高了模式的降水预测技巧。分析表明：（1）模式能在一定程度上把握我国东部夏季降水时空变率的两个主要模态（偶极子型模态和全区一致型模态），但是不同超前时间的预测在刻画模态方差贡献、异常空间分布特征、时间系数的年际变化等方面存在明显误差；（2）模式能够合理预测大尺度环流和海表温度（SST）的变化特征，但是对中国东部夏季降水的总体预测技巧有限，这与模式不能准确刻画西太平洋副热带高压、大陆高压、中高纬阻塞高压等环流系统以及热带太平洋、印度洋SST变率对中国东部降水模态的影响有关；（3）针对1991~2003年回报试验数据中的500 hPa位势高度、850 hPa纬向风和经向风、SST变量，在全球范围内寻找并定位与中国东部站点降水关系最密切的预报因子，进而建立针对降水预测的单因子线性回归、多因子逐步和多元回归模型。采用2004~2013年回报试验对所建立的降水预测模型进行了独立检验，结果表明：所建立的降尺度预测模型能显著提高中国东部地区夏季降水的预报技巧。以6月1日起报试验为例，预测的第一模态（第二模态）与观测的空间相关系数由原始的0.12（0.48）提高到了0.58（0.80），时间相关系数则从0.47（0.15）提高到0.80（0.67）；其它超前时间的预测试验中，降尺度预测模型的降水预测技巧相比模式原始预测技巧也同样明显提高。
- BCC_CSM模式 /
- 我国东部夏季降水 /
- 模态 /
- 预报超前时间 /
- 降尺度
Abstract: Based on the re-forecast data from the second-generation seasonal prediction model of National Climate Center, the model's capability to predict summer rainfall over East China and possible reasons for the forecast errors are investigated. Furthermore, the rainfall forecast skill is improved by the application of downscaling approaches. Results indicate that the model is able to capture the two major modes of spatiotemporal variability of summer rainfall over East China to some extent (i.e. the dipole mode and the uniform-distribution mode). However, forecasts at various lead times show obvious errors in variance contributions of these modes and spatial distributions of anomalies and interannual variations of time coefficients, etc. In addition, although the model can reasonably reproduce variations of large-scale circulation and sea surface temperature (SST), it shows limited skills in forecasting summer rainfall over East China. This is partially due to the model's inability to realistically depict the impacts of circulation systems such as the West Pacific subtropical high, the continental high and the middle-high-latitude blocking high. Influences of SST in the tropical Pacific and Indian Ocean on major rainfall modes over East China are also not well described in the model. Furthermore, in terms of the 500-hPa geopotential height, 850-hPa zonal and meridional winds, and SST in reforecasts for 1991-2003, predictors with the closest relationship with East China rainfall are identified on global scale and used to establish the single-factor linear regression, multi-factor stepwise regression, and multiple regression downscaling models for rainfall prediction. These downscaling rainfall prediction models are tested independently using reforecasts for 2004-2013, and significant improvements in the forecast of East China summer rainfall are obtained. For the forecast initialized on June 1, for example, the spatial correlation coefficient between predicted and observed EOF1 (EOF2) modes increases from 0.12 (0.48) for the original prediction to 0.58 (0.80) for the downscale prediction, and the corresponding temporal correlation coefficient rises from 0.47 (0.15) to 0.80 (0.67). Compared to the original forecasts by the model at other lead times, the downscaling forecast models also significantly enhance the prediction skill of rainfall.
- BCC_CSM model /
- East China summer rainfall /
- Mode /
- Lead time /
- Downscaling

HTML全文

图 1 观测（OBS；左列）与模式预测（右列）的1991～2013年夏季降水量（a、b）气候态及（c、d）均方差。单位: mm d^-1

Figure 1. Summer rainfall (a, b) climatology and (c, d) standard deviations from observations (OBS, left column) and predictions (right column) during 1991-2013. Units: mm d^-1

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图 2 （a、b）观测与（c-j）不同超前时间的模式预测的1991～2013年夏季降水EOF1（左列）、EOF2（右列）模态的空间分布（PCC：空间相关系数）

Figure 2. Spatial distributions of the first (EOF1, left column) and second (EOF2, right column) EOF modes of summer rainfall from (a, b) observations and (c-j) predictions at different lead times during 1991-2013 (PCC: spatial correlation coefficient)

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图 3 观测与不同超前时间预测的夏季降水EOF（a）第一（PC1）、（b）第二（PC2）模态时间系数的年际变化

Figure 3. Interannual variations of time coefficients of (a) the first (PC1) and (b) second (PC2) EOF mode of summer rainfall from observations and predictions at different lead times (TCC: time correlation coefficient)

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图 4 （a、b）观测和（c-j）不同超前时间模式预测的夏季降水PC1（左列）、PC2（右列）与500 hPa位势高度场的相关分布。填色区通过95%的信度检验

Figure 4. Correlation of PC1 (left column) and PC2 (right column) of summer rainfall from (a, b) observations and (c-j) predictions at different lead times with 500-hPa geopotential height. The shaded areas indicate the correlation is statistically significant above the 95% confidence level

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图 5 （a、b）观测、（c-j）不同超前时间预测的夏季降水PC1（左列）、PC2（右列）与850 hPa风场的回归分布。回归系数小于3 m s-1的量值未给出，填色区通过95%的信度检验

Figure 5. Regressions of 850-hPa winds on summer rainfall PC1 (left column) and PC2 (right column) from (a, b) observations and (c-j) predictions at different lead times. Regression coefficients less than 3 m s-1 are not shown. The shaded areas indicate correlations statistically significant above the 95% confidence level

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图 6 同图 4，但为降水PC1(左列)、PC2(右列)与海表温度场的相关分布

Figure 6. Same as Fig. 4，but for correlations of rainfall PC1(left column)and PC2(right column)with SST

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图 7 1991～2013年（a）南京站、（b）南宁站降水观测值与所选预报因子的10年滑动相关

Figure 7. 10-year moving correlations of rainfall observations at (a) Nanjing station and (b) Nanning station during 1991-2013 with selected predictors

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图 8 2004～2013年中国东部夏季降水EOF1模态的空间分布：（a）观测；（b）LM0原始预测；（c-f）单因子线性回归模型预测[500 hPa位势高度（H500）、850 hPa纬向风（U850）、850 hPa经向风（V850）、SST]；（g）多因子逐步回归模型预测；（h）多因子多元回归模型预测

Figure 8. Spatial distributions of the EOF1 mode of East China summer rainfall during 2004-2013 from (a) observations, (b) the LM0 original prediction, (c-f) single-factor linear regression predictions [500 hPa geopotential height (H500), 850 hPa zonal winds (U850), 850 hPa meridional winds (V850), SST], (g) multi-factor stepwise regression prediction, and (h) multi-factor multiple regression prediction

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图 9 同图 8，但为EOF2

Figure 9. Same as Fig. 8，but for the EOF2

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图 10 观测、LM0 原始预测及多元回归模型预测的夏季降水（a）PC1、（b）PC2 的年际变化

Figure 10. Interannual variations of (a) PC1 and(b) PC2 of the summer rainfall from observations, original prediction, and multiple regression prediction at 0-month lead time

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图 11 不同超前时间（a、c、e、g）原始预测、（b、d、f、h）多元回归降尺度模型预测的2004～2013年中国东部夏季降水与观测的时间相关系数分布；（i）原始预测和多元回归降尺度模型预测降水与观测的空间相关系数的年际变化

Figure 11. Time correlation coefficients of East China summer rainfalls between observations with (a, c, e, g) original predictions and (b, d, f, h) multiple regression downscaling predictions at different lead times during 2004-2013. (i) Interannual variations of spatial correlation coefficients of the rainfalls between observations with original predictions and with multiple regression downscaling results

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表 1 观测、不同超前时间预测的夏季降水PC1、PC2和各项指数（西太平洋副高指数、大陆高压指数、乌拉尔山阻高指数、鄂霍次克海阻高指数、Niño3.4 SST指数、太平洋SST主模态时间系数和印度洋SST主模态时间系数）的年际相关

Table 1. Interannual correlations between PC1/PC2 of the summer rainfall and various indices (the western Pacific subtropical high, the continental high, the Ural blocking high, the Okhotsk blocking high, Niño3.4 SST, time coefficients of the major Pacific SST mode, and time coefficients of the major Indian Ocean SST mode) for observations and predictions at different lead times

		夏季降水PC1、PC2和各项指数的相关
		西太副高强度指数	大陆高压指数	乌拉尔山阻高指数	鄂霍次克海阻高指数	Niño3.4 SST指数	太平洋SST主模态时间系数	印度洋SST主模态时间系数
OBS	PC1	-0.25	0.08	-0.54^*	0.02	0.28	0.32	-0.46^*
	PC2	0.06	0.10	0.00	0.00	-0.29		-0.23
LM0	PC1	-0.12	0.52^*	-0.01	0.25	0.19	0.40	0.03
	PC2	0.53^*	0.07	-0.11	0.24	-0.50^*	-0.44^*	0.23
LM1	PC1	-0.31	0.58^*	-0.38	0.35	0.37	0.52^*	-0.37
	PC2	0.52^*	-0.18	-0.41	-0.53^*	-0.64^*	-0.42^*	0.13
LM2	PC1	0.15	0.32	-0.45^*	-0.16	0.48^*	0.52^*	0.04
	PC2	0.64^*	0.18	-0.22	-0.03	-0.47^*	-0.42^*	0.54^*
LM3	PC1	0.13	0.48^*	0.35	0.08	0.00		0.04
	PC2	0.62^*	0.32	-0.29	-0.23	-0.75^*	-0.69^*	0.49^*
注：加粗字体并有“*”表示通过95%的信度检验。

下载: 导出CSV

表 2 2004～2013年不同超前时间原始预测（ORI）、多元回归（M-Reg）降尺度模型预测的降水EOF空间模态（时间系数）与观测的空间（时间）相关

Table 2. Spatial (temporal) correlations between EOF spatial modes (time coefficients) of summer rainfall from observations and predictions at various lead times during 2004-2013. The original predictions (ORI) and multiple regression（M-Reg） downscaling predictions at different lead times are shown in turn

		预测的夏季降水EOF空间模态（时间系数）与观测的空间（时间）相关
		EOF1(PCC)	EOF2(PCC)	PC1(TCC)	PC2(TCC)
LM0	ORI	0.12	0.48	0.47	0.15
	M-Reg	0.58	0.80	0.80	0.67
LM1	ORI	0.22	0.39	0.35	0.19
	M-Reg	0.60	0.77	0.81	0.78
LM2	ORI	0.40	0.42	0.58	0.17
	M-Reg	0.58	0.65	0.73	0.58
LM3	ORI	0.16	0.58	0.60	0.34
	M-Reg	0.60	0.89	0.95	0.77

下载: 导出CSV

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