BCC二代气候系统模式的季节预测评估和可预报性分析

吴捷; 任宏利; 张帅; 刘颖; 刘向文

doi:10.3878/j.issn.1006-9895.1703.16256

BCC二代气候系统模式的季节预测评估和可预报性分析

doi: 10.3878/j.issn.1006-9895.1703.16256

吴捷^{1, 2, 4,},
任宏利^{1, 4, ,},
张帅³,
刘颖¹,
刘向文¹

1.
中国气象局国家气候中心气候研究开放实验室, 北京 100081
2.
南京信息工程大学大气科学学院, 南京 210044
3.
南京信息工程大学地理与遥感学院, 南京 210044
4.
南京大学大气科学学院/中国气象局-南京大学气候预测研究联合实验室, 南京 210023

基金项目:

公益性行业（气象）科研专项 GYHY201506013

国家重点研发计划项目 2016YFA0602100

国家自然科学基金项目 41405080

气象预报业务关键技术发展专项 YBGJXM(2017)05

气象预报业务关键技术发展专项 7B

详细信息

作者简介:
吴捷, 男, 1988年生, 博士研究生, 工程师, 主要从事短期气候预测研究。E-mail:wujie@cma.gov.cn

通讯作者:
任宏利, E-mail:renhl@cma.gov.cn

中图分类号: P435+.2
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出版历程
- 收稿日期: 2016-11-02
- 网络出版日期: 2017-03-31
- 刊出日期: 2017-11-15

Evaluation and Predictability Analysis of Seasonal Prediction by BCC Second-Generation Climate System Model

Jie WU^{1, 2, 4
,},
Hongli REN^{1, 4
, ,},
Shuai ZHANG³,
Ying LIU¹,
Xiangwen LIU¹

1.
Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081
2.
College of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044
3.
School of Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044
4.
China Meteorological Administration-Nanjing University(CMA-NJU) Joint Laboratory for Climate Prediction Studies, Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing 210023

Funds:

China Meteorological Administration Special Public Welfare Research Fund GYHY201506013

National Basic Research Program of China 2016YFA0602100

National Natural Science Foundation of China 41405080

Project for Development of Key Techniques in Meteorological Forecasting Operation YBGJXM(2017)05

Project for Development of Key Techniques in Meteorological Forecasting Operation 7B

摘要

摘要: 本文利用国家气候中心（BCC）第二代季节预测模式系统历史回报数据，从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能，并与BCC一代气候预测模式的结果进行了对比，重点分析了二代模式的季节可预报性问题。结果显示，BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式，特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进，可以通过激发太平洋—北美型（PNA）、东亚—太平洋型（EAP）等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明，厄尔尼诺和南方涛动（ENSO）信号在热带和热带外地区均是模式季节可预报性的重要来源，BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征，从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看，BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力，特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此，进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。
- 季节预测 /
- 确定性预报 /
- 概率预报 /
- 可预报性 /
- BCC二代模式
Abstract: Based on the hindcast data of Beijing Climate Center (BCC) second-generation seasonal prediction model system (BCCv2), the seasonal prediction performance of 2-m temperature, precipitation and circulation was evaluated by employing deterministic and probabilistic forecast verification methods. BCCv2 simulations were compared with that of BCC first-generation prediction system (BCCv1) to further analyze the seasonal climate predictability. The results show that the performance of the BCCv2 is significantly improved compared to that of the BCCv1 especially in the tropical eastern Pacific Ocean, the Indian Ocean and the Maritime Continent areas. The improvements of precipitation prediction in the tropics are the major reason for the improvements of the forecast skill in the mid-high latitudes through realistic description of the atmospheric teleconnection patterns, such as the Pacific-North American (PNA) and East Asian -Pacific (EAP) patterns. The El Niño and South Oscillation (ENSO) signal is the dominant source of predictability for both the tropical and extra-tropical regions. The global atmosphere circulation in response to ENSO signal is accurately described in BCCv2, which improves its overall prediction performance by advancing ENSO prediction skill. From the perspective of probabilistic prediction, the BCCv2 showed useful prediction skills for the prediction of China surface air temperature anomalies in winter and precipitation anomalies in summer especially for the above normal (AN) and below normal (BN) events of winter temperature in eastern China with relatively high reliability and resolution. Therefore, further improvements of the capability of the BCCv2 in predicting tropical large-scale anomalies and primary climate variability modes and the application of probabilistic prediction products of this model are two key issues for improving seasonal climate prediction in China.
- Seasonal prediction /
- Deterministic forecast /
- Probabilistic forecast /
- Predictability /
- BCC Second-Generation Climate System Model

HTML全文

图 1 BCC二代模式在LM1时对（a）夏季和（b）冬季2 m气温异常预报技巧TCC的空间分布，打点区域代表相关系数通过0.05显著性检验，（c）和（d）为BCC二代与一代模式技巧TCC差值图，（d）中粗蓝实线为遥相关型示意曲线

Figure 1. TCC (Temporal Correlation Coefficient) skills for 2-m air temperature anomalies based on observations and lead 1 month (LM1) seasonal prediction by BCC_CSM1.1m in (a) JJA (June-July-August) and (b) DJF (December-January-February), respectively. The stipple areas represent the correlation coefficients are statistically significant at 0.05 significance level. The lower panels indicate the skill differences between BCC_CSM1.1m model and BCC_CM1.0 model; the bluehick line in (d) is the teleconnection pattern sketch curve

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图 2 同图 1，但为对降水异常的预报

Figure 2. Same as Fig. 1, but for precipitation anomaly

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图 3 同图 1，但为对500 hPa位势高度场异常的预报

Figure 3. Same as Fig. 1, but for 500-hPa geopotential height anomaly

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图 4 BCC二代模式在不同LM（lead time）时对（a、b）2 m气温、（c、d）降水和（e、f）500 hPa位势高度场预报技巧TCC在不同地区的平均值：夏季（左列）；冬季（右列）

Figure 4. Regional-average TCC skills for (a, b) 2-m air temperature, (c, d) precipitation, and 500-hPa geopotential height simulation with BCC_CSM1.1m at different leading time, GL is global, TP is tropics, NET is Northern extra-tropics, SET is Southern extra-tropics, EA is East Asia, SA is South Asia: JJA (left column); DJF (right column)

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图 5 BCCv1和BCCv2式在热带地区对（a、b）2 m气温和（c、d）降水预报的历年ACC与Niño3.4指数绝对值的散点图及其相关系数：夏季（左列）；冬季（右列）。图中实心圆点代表Niño3.4指数大于0，空心圆圈代表Niño3.4指数小于0

Figure 5. Scatter plots of ACC skills in the tropics for BCC_CSM1.1m model and BCC_CM1.0 model against the absolute value of Niño3.4 index: (a, b) 2-m air temperature; (c, d) precipitation. (a, c) are for JJA and (b, d) for DJF, the solid circles represent the Niño3.4 index above 0 and the hollow circles represent Niño3.4 index below 0

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图 6 BCCv1和BCCv2在北半球热带外地区对降水预报的历年ACC与Niño3.4指数绝对值的散点图和相关系数:（a）春季；（b）夏季；（c）秋季；（d）冬季。图中实心圆点代表Niño3.4指数大于0，空心圆圈代表Niño3.4指数小于0

Figure 6. Scatterplots of ACC skills for precipitation in the extra-tropics of Northern Hemisphere simulated by BCC_CSM1.1m model and BCC_CM1.0 model against the absolute value of Niño3.4 index: (a) MAM (March-April-May), (b) JJA, (c) SON (September-October-November), (d) DJF

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图 7 冬季（a、b）2 m气温（单位：℃）、（b、d）降水（单位：mm d^-1）和（e、f）500 hPa位势高度（单位：gpm）与Niño3.4指数的回归系数分布：观测（左列）；BCCv2模拟（LM1；右列）。打点区域代表相关系数通过0.05显著性检验，右上角数字为模式与观测的空间相关系数

Figure 7. Regression coefficient distributions between (a, b) 2-m temperature (units: ℃), (c, d) precipitation (units: mm d^-1), (e, f) 500-hPa geopotential height (units: gpm) and Niño3.4 index in winter. Stippled areas represent correlation coefficients are significant at 0.05 significance level. (a, c, e) are for the observations and (b, d, f) for the BCC_CSM1.1m model at LM1; the number at the up-right corner means the spatial correlation coefficient between the observations and model simulation

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图 8 BCCv2在LM3时对我国台站冬季2 m气温（左列）和夏季降水（右列）概率预报的ROCSS空间分布：（a、b）AN；（c、d）NN；（e、f）BN。蓝色实线代表长江和黄河

Figure 8. Spatial distribution of the Relative Operating Characteristics Score Skill (ROCSS) for three categorical probabilistic forecast at weather stations in China at LM3 from BCC_CSM1.1m: 2 m air temperature in DJF (left column), precipitation in JJA (right column). (a, b) Above Normal (AN); (c, d) Near Normal (NN); (e, f) Below Normal (BN). Blue solid lines represent the Yangtze River and the Yellow River

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图 9 BCC二代模式在LM3时对我国冬季2 m气温概率预报的（a）ROC曲线、（b）可靠性曲线（右侧小图为对AN、NN和BN事件预报概率的相对频率直方图）和（c）Brier技巧评分（BSS）

Figure 9. (a) ROC Curve, (b) reliability diagram and relative frequency of forecast sharpness for AN, BN, and NN events in the right column, (c) Brier skill score for three categorical probabilistic forecast of 2-m air temperature in DJF at weather stations in China at LM3 from BCC_CSM1.1m

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表 1 概率预报的ROC列联表

Table 1. General ROC (Relative Operating Characteristic) contingency table for probabilistic forecasts of binary events

仓数 (Bin number)	集合成员分布 (Member distribution)	观测发生次数 (Observed occurrences)	观测未发生次数 (Observed non-occurrences)
1	F=0, NF=N	O₁	NO₁
2	F=1, NF=N-1	O₂	NO₂
3	F=2, NF=N-2	O₃	NO₃
…	…	…	…
n	F=n-1, F =N-n+1	O_n	NO_n
…	…	…	…
N +1	F=N, NF=0	O_N₊₁	NO_N₊₁
注：N为集合成员数，F为预报事件发生的成员数，NF为预报事件不发生的成员数。

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表 2 评估所用区域范围和简称

Table 2. The evaluation area rang and abbreviation

区域	范围	备注
全球（GL）	90°S~90°N，0°~360°E
热带（TP）	20°S~20°N，0°~360°E	WMO推荐
北半球热带外（NET）	20°N~90°N，0°~360°E	WMO推荐
南半球热带外（SET）	20°S~90°S，0°~360°E	WMO推荐
东亚（EA）	20°N~50°N，90°~150°E
南亚（SA）	10°S~30°N，60°~130°E

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