分位数映射法在RegCM4中国气温模拟订正中的应用

doi:10.12006/j.issn.1673-1719.2017.156

摘要/Abstract

摘要：

将一种分位数映射法RQUANT,应用到一个区域气候模式（RegCM4）所模拟中国气温的误差订正中。从气候平均态、年际变率、极端气候及农业气候等多方面,评估了该方法对日平均气温、日最高气温和日最低气温模拟的订正效果。结果表明,该订正方法对模式模拟的日平均、日最高和最低气温气候平均态的订正效果都非常明显,中国大部分地区的订正结果与观测的偏差在±0.5℃之间。在降低极端气温指数和农业气候相关指数的模拟误差方面也有显著的效果,但对气温年际变率的订正效果有限。结合以往对降水订正的评估分析,该方法对模式模拟结果有较好的订正效果,可以应用于区域气候模式的气候变化模拟预估中,为气候变化及相关影响评估研究提供更适用和可靠的数据。

关键词: 分位数映射, 误差订正, 区域气候模式, 气温

Abstract:

A quantile mapping method called RQUANT was applied to the bias correction on the temperature (including daily mean temperature, daily maximum temperature, and daily minimum temperature) simulation of a regional climate model (RegCM4). Assessment on the correction effect was carried out, focusing on the simulation of climatological mean, interannual variation, extreme temperature, and agro-climatic conditions. Results show that this correction method is capable of improving the simulation of mean state of temperature. The biases of the correction results on mean temperature are within ±0.5℃. It can also significantly improve the simulation of both extreme temperature and agro-climatic conditions. However, it has little correction effect on interannual variation. Considering the good performance of this method on correcting precipitation simulation in previous study, it implies that the RQUANT method can be used on climate change projection simulations, and these bias corrected simulations would be more useful and reliable for climate change and climate change impact studies.

Key words: Quantile mapping, Bias correction, Regional climate model, Temperature

韩振宇,童尧,高学杰,徐影. 分位数映射法在RegCM4中国气温模拟订正中的应用[J]. 气候变化研究进展, 2018, 14(4): 331-340.

Zhen-Yu HAN,Yao TONG,Xue-Jie GAO,Ying XU. Correction based on quantile mapping for temperature simulated by the RegCM4[J]. Climate Change Research, 2018, 14(4): 331-340.

图/表 9

表1 本文使用的极端气温事件指数和农业气候指数

Table 1 The extreme temperature indices and agro-climatic indices employed in the study

图1 验证时段冬季和夏季日平均气温气候均值的模式模拟和订正结果与观测的差值.

Fig. 1 Biases of mean temperature from simulations before and after bias correction during the validation period in.

表2 各个季节日平均、日最高和日最低气温的气候平均态模拟结果,在订正前后与观测的SCOR和RMSE

Table 2 The SCORs and RMSEs in the simulated and bias corrected daily mean temperature, daily maximum temperature, and daily minimum temperature compared to observations in different seasons

图2 气候平均态(a)和年际变率(b)模拟能力的泰勒图注：图中横纵轴都是标准化的标准差,以“0”为圆心的角度是SCOR,以“REF”为中心点的距离是标准化的RMSE。

Fig. 2 Taylor diagram for the mean temperature (a) and interannual temperature variation (b) of simulations and bias corrected simulations.

图3 验证时段冬季和夏季日平均气温年际变率的观测、模式模拟和订正结果.

Fig. 3 The observations, simulations, and bias corrected simulations of interannual variations of daily mean temperature in different seasons during the validation period in DJF and JJA.

表3 各个季节日平均、日最高和日最低气温的年际变率模拟结果,在订正前后与观测的SCOR和RMSE

Table 3 The SCORs and RMSEs in the simulated and bias corrected interannual variations of daily mean temperature, daily maximum temperature, and daily minimum temperature compared to observations in different seasons

图4 验证时段TXx、TNn、GSL和TS5的模式模拟和订正结果与观测的差值.

Fig. 4 Biases of TXx, TNn, GSL, and TS5 from simulations and bias corrected simulations during the validation period.

图5 极端气温指数和农业气候指数模拟能力的泰勒图.

Fig. 5 Taylor diagram for the extreme temperature indices and agro-climatic indices of simulations and bias corrected simulations.

图6 验证时段冬季日平均气温的日尺度变率和年际尺度变率的模式模拟和订正结果与观测的差值.

Fig. 6 Biases of daily variations and interannual variations of daily mean temperature from simulations and bias corrected simulations during the validation period in DJF.

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