【摘要】：全球和區(qū)域尺度的氣候模式,是進(jìn)行氣候變化模擬和預(yù)估研究的首要工具,但受氣候系統(tǒng)復(fù)雜性和科學(xué)發(fā)展水平的制約,它們對(duì)當(dāng)代氣候的模擬與觀測(cè)相比,也總是存在或多或少的偏差,包括在平均態(tài)和概率密度分布等多方面。若將氣候模式結(jié)果直接應(yīng)用于驅(qū)動(dòng)如水文和農(nóng)業(yè)等影響評(píng)估模式時(shí),其偏差則會(huì)對(duì)模擬產(chǎn)生很大影響,因此需要進(jìn)行誤差訂正工作。本研究主要通過分位數(shù)映射的方法,對(duì)再分析資料ERA-interm驅(qū)動(dòng)下,RegCM4.4區(qū)域氣候模式所模擬的當(dāng)代日降水量、日平均氣溫、日最高氣溫和日最低氣溫進(jìn)行訂正。研究中,將全年劃分為冬(12~2月)、春(3~5月)、夏(6~8月)和秋(9~11月)四個(gè)季節(jié)。首先以對(duì)逐日降水量進(jìn)行訂正試驗(yàn),以模擬時(shí)段1991~2010年中的前半段(1991~2000年)作為參照時(shí)段,建立傳遞函數(shù),對(duì)后一時(shí)段(2001~2010)進(jìn)行訂正并檢驗(yàn)其效果。對(duì)使用參數(shù)和非參數(shù)所建立的6種不同傳遞函數(shù)方法進(jìn)行對(duì)比,發(fā)現(xiàn)6種方法均可明顯減少降水模擬的誤差,其中RQUANT相對(duì)表現(xiàn)更為突出一些,即被選擇做為主要訂正方法,用于RegCM4所模擬降水和氣溫結(jié)果的訂正。將RQUANT方法應(yīng)用到對(duì)日降水量的訂正上,發(fā)現(xiàn)經(jīng)過誤差訂正后的結(jié)果可以大大減少模式的偏差,使中國(guó)大部分地區(qū)的降水量與觀測(cè)的相對(duì)誤差集中在-25%~25%之內(nèi);可以大幅度提升與觀測(cè)的空間相關(guān)系數(shù)并減少誤差標(biāo)準(zhǔn)差,使降水量無論從分布形態(tài)還是數(shù)值上都更接近觀測(cè)數(shù)據(jù);而對(duì)降水的年際變率也同樣有一定的改善作用。由于模式本身對(duì)于氣溫的模擬效果要好于降水,因此誤差訂正對(duì)氣溫的訂正結(jié)果與觀測(cè)更接近,空間相關(guān)系數(shù)達(dá)到0.99以上。RQUANT方法對(duì)模式模擬的平均氣溫、日最高氣溫和日最低氣溫的訂正效果都非常明顯,中國(guó)大部分地區(qū)的訂正結(jié)果與觀測(cè)的偏差在±1°C之間;提高了空間相關(guān)系數(shù),減少誤差標(biāo)準(zhǔn)差;對(duì)氣溫年際變率的訂正效果不太明顯,但春季的空間相關(guān)系數(shù)提升明顯,而且減少了年際變率的誤差標(biāo)準(zhǔn)差。RQUANT方法對(duì)極端事件同樣有著很好的訂正效果,減少了模式對(duì)4個(gè)極端事件指數(shù)CDD(連續(xù)干旱日數(shù))、SDII(強(qiáng)降水指數(shù))、TXx(年極端最高氣溫)和TNn(年極端最低氣溫)的模擬偏差,提高了空間相關(guān)系數(shù),減少了誤差標(biāo)準(zhǔn)差,使訂正結(jié)果更加符合觀測(cè)數(shù)據(jù)。
[Abstract]:Climate models at the global and regional levels are the primary tools for modelling and predicting climate change. However, due to the complexity of the climate system and the level of scientific development, they are compared to contemporary climate modelling and observation. There are always more or less deviations, including average state and probability density distribution. If the climate model results are directly applied to driving the impact assessment models such as hydrology and agriculture, the deviation will have a great impact on the simulation, so it is necessary to carry out error correction work. In this study, the daily precipitation, daily mean temperature, daily maximum temperature and daily minimum temperature simulated by the RegCM4.4 regional climate model were revised by quantile mapping method driven by ERA-interm reanalysis data. In the study, the whole year is divided into four seasons: winter (December ~ February), spring (March ~ May), summer (June ~ August) and autumn (September ~ November). Firstly, the daily precipitation was revised, and the first half of the period from 1991 to 2010 (1991 ~ 2000) was used as the reference period. The transfer function was established, the later period (2001 ~ 2010) was revised and its effect was tested. By comparing the six different transfer function methods established by using parameter and non-parameter, it is found that the six methods can obviously reduce the error of precipitation simulation, and the relative performance of RQUANT is more prominent, that is, it is chosen as the main correction method. It is used to revise the simulated precipitation and temperature results of RegCM4. Applying the RQUANT method to the correction of daily precipitation, it is found that the deviation of the model can be greatly reduced after the error correction, and the relative error between precipitation and observation in most areas of China is concentrated within -25%. The spatial correlation coefficient can be greatly increased and the error standard deviation can be reduced, so that the precipitation is closer to the observed data in terms of distribution and numerical value, and the interannual variability of precipitation can also be improved to a certain extent. Because the effect of the model itself on temperature simulation is better than that of precipitation, the correction result of error correction is closer to that of observation, and the spatial correlation coefficient is more than 0.99. The average temperature simulated by RQUANT method is better than that of the model. The correction effect of daily maximum temperature and daily minimum temperature is very obvious, the deviation between the results of correction and observation in most areas of China is 鹵1 擄C, the spatial correlation coefficient is increased, and the error standard deviation is reduced. The effect on the correction of temperature interannual variability is not obvious, but the spatial correlation coefficient in spring is obviously increased, and the error standard deviation of interannual variability is reduced. The RQUANT method also has a good correction effect for extreme events. The simulation deviation of four extreme event indices (CDD (continuous drought days), SDII (heavy precipitation index (), TXx () and TNn (annual extreme minimum temperature) is reduced. The spatial correlation coefficient is increased and the error standard deviation is reduced. The revised results are more in line with the observed data.
【學(xué)位授予單位】：中國(guó)氣象科學(xué)研究院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P435

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