﻿ 统计降尺度方法的研究进展与挑战 Progress and Challenge in Statistically Downscaling Climate Model Outputs

Journal of Water Resources Research
Vol.05 No.04(2016), Article ID:18190,15 pages
10.12677/JWRR.2016.54037

Progress and Challenge in Statistically Downscaling Climate Model Outputs

Jie Chen1, Chongyu Xu1,2, Shenglian Guo1, Hua Chen1

1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan Hubei

2Department of Geosciences, University of Oslo, Oslo, Norway

Received: Jul. 13th, 2016; accepted: Jul. 28th, 2016; published: Aug. 9th, 2016

ABSTRACT

Statistical downscaling is a process to build up statistical relationships between large-scale (usually 1˚-3˚ on latitude and longitude) climate model outputs and point/watershed-scale meteorological variables. It is an important technique to conduct climate change impact assessment for a specific site or a watershed. This paper systematically reviewed the recent advances in three fields related to statistical downscaling methods: perfect prognosis, model output statistics, and stochastic weather generator. Merits and drawbacks associated with each downscaling method were summarized. In addition, the challenges in progressing statistical downscaling methods were stated, as well as the potential solutions. The contribution of this review is aimed at pointing out the direction of developing statistical downscaling methods and providing clues for climate change impact studies.

Keywords:Statistical Downscaling, Climate Model, Bias Correction, Stochastic Weather Generator, Progress and Challenge

1武汉大学水资源与水电工程科学国家重点实验室，湖北 武汉

2挪威奥斯陆大学地学系，奥斯陆，挪威

1. 引言

2. 统计降尺度方法的分类

Xu (1999) [16] 在其综述论文中较早地将统计降尺度方法划分为转换函数法(Wilby等，2002；Chu等，2010；Goyal和Ojha，2011；陈华等，2008；侯雨坤等，2014) [11] [12] [17] - [19] 、环流分型法(Schoof和Pryor，2001；Chen等，2012a) [20] [21] 和随机天气发生器法(Wilks，2010；Zhang，2005；Qian等，2010；Kilsby等，2007) [22] - [25] 。Maraun等(2010a) [26] 在总结统计降尺度方法最新发展成果的基础上，将统计降尺度分为理想预报(PerfectPrognosis, PP)、模型输出统计(Model Output Statistics, MOS)和随机天气发生器(Stochastic Weather Generator, SWG)三种方法。其中PP包括传统的转换函数和环流分型两种方法；而WG与传统方法一致；MOS是气象预报中常用的方法，近年来被广泛用于统计降尺度。以下将基于Maraun等(2010) [26] 分类方法综述各统计降尺度方法的最新研究进展。

2.1. 理想预报(PP)

PP是应用最为广泛的统计降尺度方法，其原理是在历史时段建立网格尺度多个大气环流因子(预报因子，predictor，如：相对湿度和风速等)和站点或区域观测气象要素(预报变量，predictand，如：降水和气温)之间的线性或非线性关系，然后将这种关系应用于未来时段的网格尺度大气环流因子，从而获得站点或区域未来气候变化情景。PP方法一般基于以下三个假设：① GCMs能够很好的模拟网格尺度的大气环流因子；② 大气环流因子与区域或站点尺度气象要素之间具有显著的统计关系；③ 基于历史阶段数据所建立的函数关系可用于未来气候变化时段。预报因子的选择对PP方法的效果具有决定性的作用。选择预报因子是一般要求其具有丰富的预报信息和预报能力，常常通过预报因子与预报变量之间的相关性予以确定。Wilby和Wigley (2000) [27] 归纳了用于降水降尺度的常用预报因子，包括不同位势高度(如：1000，850和500 hPa)的相对湿度、绝对湿度、经向风速、纬向风速、涡度等。

2.2. 模型输出估计(MOS)

MOS最先被应用于对RCMs模拟的降水和气温进行偏差校正。当模拟变量与观测变量具有相同的空间尺度时，MOS方法仅具有偏差校正的功能；当模拟变量空间尺度大于观测变量空间尺度时，MOS方法还具有降尺度的功能。随着RCMs在气候变化影响评估中的广泛应用，MOS方法取得了快速的发展，一系列方法得以开发并应用于气候变化影响评估中，已逐渐成为使用RCMs评估径流对气候变化响应的标准方法(Teutschbein和Seibert, 2013) [45] ，近年来也逐步被用于对GCMs模拟变量进行降尺度与偏差校正(Chen等，2016) [46] 。

2.3. 随机天气发生器(SWG)

SWG是一种根据历史观测气象要素的统计参数采用统计方法产生与历史气象数据具有相同统计特征的随机模拟模型。与历史观测数据往往时段较短且具有缺失值等问题相比，SWG可以产生任意时长、完整的气象数据序列。通过对SWG参数进行空间内插，SWG还可以产生缺测站点或区域的气象要素。SWG最重要的功能是作为时空降尺度工具产生站点尺度或区域平均未来气候变化情景。SWG分为参数与非参数模型两大类，其中以WGEN (Richardson，1981；Richardson和Wright，1984) [52] [53] 为代表的参数模型应用最为广泛。随着SWG应用的深入，多种基于参数的SWG得以发展，如：CLIGEN (Nicks等，1995) [54] 、CLIMGEN (Stockle等，1999) [55] 和WeaGETS (Chen等，2012b) [56] 。这些SWG均采用马尔科夫链模拟降水发生的转移概率，采用概率分布函数模拟日降水量，使用正态分布模拟最高与最低气温；不同点在于基于马尔科夫链的阶数和概率密度函数有所不同。如：WGEN和CLIGEN均基于一阶马尔科夫链模拟降水发生的转移概率，而CLIMGEN基于二阶马尔科夫链，WeaGETS提供了一阶到三阶马尔科夫链三个选项；WGEN、CLIMGEN和CLIGEN分别使用伽玛分布、韦伯分布和偏正态分布模拟日降水量，WeaGETS综合了指数、伽玛、韦伯、偏正态和混合指数等多种分布方法。Chen和Brissette (2014a, 2014b) [28] [50] 综述了多种降水模型并比较了多种SWG对全球不同站点降水和气温模拟的效果，指出一阶马尔科夫链能较好的模拟降水发生，但高阶马尔科夫在模拟最大持续干旱时长时具有一定的优势；同时，指数、伽玛、韦伯分布均可以较好地模拟降水均值，但低估了降水极端事件，而偏正态分布和混合分布能较好的模拟极值降水。

3. 统计降尺度方法的评估与比较

4. 统计将尺度方法面临的问题与挑战

4.1. 预报因子与预报变量间相关性

4.2. 气候模型输出变量偏差非一致性

MOS偏差校正方法均基于气候模式输出变量偏差一致性的假设，即气候模式输出变量在历史和未来时段具有相同的偏差。但部分研究想当然的认为气候模式偏差具有一致性。仅有少量研究(Piani等，2010；Terink等，2010；Maraun，2012；Teutschbein和Seibert，2013) [41] [45] [65] [68] 通过评估偏差校正方法在气候变化情景生成中的表现，检验上述假设的合理性。如，Teutschbein 和Seibert (2013) [45] 通过将历史观测数据划分为具有明显差异的两个时段，在第一时段校准偏差校正方法后，通过观测偏差校正方法在第二时段的表现来判断气候模型输出偏差一致性假设，如果偏差校正方法在校准期表现较好，而在验证期表现明显降低，则说明气候模式偏差具有非一致性。该研究使用了6种不同的偏差校正方法，其结果表明，基于分布的方法其表现优于基于均值的方法，但所有方法在验证期的表现均明显下降。Maraun (2012) [65] 使用基于伪观测的方法验证了多个气候模型输出变量偏差一致性的假设性。该方法通过在历史时段选取一个气候模式作为参考模式(代表观测数据)，通过偏差校正方法校正其它气候模式输出变量的偏差，在未来时段通过检测偏差校正方法的效果以判断气候模式输出变量偏差在历史和未来两个时段的一致性。研究发现，气候模型输出变量偏差总体具有一致性，但仍然具有区域差异性。

4.3. 变量间与站点间相关性

4.4. 降尺度方法的不确定性

5. 结语

Progress and Challenge in Statistically Downscaling Climate Model Outputs[J]. 水资源研究, 2016, 05(04): 299-313. http://dx.doi.org/10.12677/JWRR.2016.54037

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