<strong>Noah-MP</strong>模型中积雪模拟对参数化方案的敏感性评估

doi:10.11867/j.issn.1001-8166.2019.04.0356

地球科学进展 ›› 2019, Vol. 34 ›› Issue (4): 356 -365. doi: 10.11867/j.issn.1001-8166.2019.04.0356

Noah-MP模型中积雪模拟对参数化方案的敏感性评估

尤元红 ^1, ^2, ³(

),黄春林 ^1, ²(

),张莹 ^1, ²,侯金亮 ^1, ²

1. 中国科学院西北生态环境资源研究院, 甘肃省遥感重点实验室，甘肃兰州 730000
2. 中国科学院西北生态环境资源研究院, 黑河遥感试验研究站，甘肃兰州 730000
3. 中国科学院大学，北京 100049

收稿日期:2018-11-16 修回日期:2019-01-22 出版日期:2019-04-10
通讯作者: 黄春林 E-mail:youyuanhong@lzb.ac.cn;huangcl@lzb.ac.cn
基金资助:
国家自然科学基金项目“联合机器学习和多尺度集合卡尔曼滤波算法的积雪数据同化方法研究”（编号：41671375）和“基于贝叶斯模型平均和遗传粒子滤波的积雪数据同化方法研究”(编号：41871251)

Sensitivity Evaluation of Snow Simulation to Multi-parameterization Schemes in the Noah-MP Model

Yuanhong You ^1, ^2, ³( ),Chunlin Huang ^1, ²( ),Ying Zhang ^1, ²,Jinliang Hou ^1, ²

1. Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2. Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2018-11-16 Revised:2019-01-22 Online:2019-04-10 Published:2019-05-27
Contact: Chunlin Huang E-mail:youyuanhong@lzb.ac.cn;huangcl@lzb.ac.cn
About author: You Yuanhong (1990-), male, Susong County, Anhui Province, Ph.D student. Research areas include land surface simulation and data assimilation. E-mail: youyuanhong@lzb.ac.cn | You Yuanhong (1990-), male, Susong County, Anhui Province, Ph.D student. Research areas include land surface simulation and data assimilation. E-mail: youyuanhong@lzb.ac.cn
Supported by:
Project supported by the National Natural Science Foundation of China “Study of snow data assimilation based on machine leaning and multi-scale ensemble Kalman Filter” (No. 41671375) and “Study of snow data assimilation based on Bayesian Model averaging and genetic particle filter”(No. 41871251)

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针对Noah-MP模型多参数化方案、模拟结果不确定性范围难以确定的特点，选取北疆地区具有代表性的阿勒泰站气象资料作为模型驱动数据，探讨了积雪对多参数化方案的敏感性。在不考虑模型参数和驱动数据不确定性的条件下，设计了集合数为13 824的多参数化方案集合模拟试验。选用Natural selection方法对物理过程的敏感性进行分析，并在敏感性分析结果的基础上进一步讨论了模拟结果的不确定性。结果表明：积雪对地表热交换、雨雪分离、土壤温度底层边界条件和第一层积雪或土壤时间方案4个物理过程敏感；在不考虑驱动数据和模型参数不确定性的条件下，多参数化方案集合模拟试验中的不确定性主要来源于敏感物理过程。去除敏感物理过程中能够明显降低模拟性能的参数化方案后，集合模拟结果的不确定性大幅减小。最后，根据分析结果构建了该站雪深和雪水当量模拟的最优参数化方案组合。

关键词: 敏感性分析, 不确定性分析, 多参数化方案集合模拟

On account of the latest community Noah land surface model with multi-parameterization (Noah-MP) schemes and its uncertainty breadth in simulation results being difficult to be determined, this study assessed the sensitivity of snow to physics options using meteorological data from the Altay Station in northern Xinjiang. The Noah-MP physics ensemble simulation with the total number of 13 824 was designed without the consideration of the uncertainties of forcing data and parameters. The natural selection approach was used to analyze the sensitivity of physical processes. Based on the results of sensitivity analysis, the uncertainty of ensemble simulation results was further discussed. The results showed that snow was sensitive to the physical processes of surface-layer exchange coefficient, partitioning precipitation into rainfall and snowfall, lower boundary condition of soil temperature, and first-layer snow or soil temperature time scheme; Uncertainties in multi-parameterization ensemble simulation experiments were mainly from sensitive physical processes under the condition of disregarding uncertainties of forcing data and parameters. After removing the parameterization schemes that notably reduced simulation performance in sensitive physical processes, the uncertainty breadth in ensemble simulations decreased significantly. Finally, an optimal combination group of parameterization schemes for this station was configured.

Key words: Sensitivity analysis, Uncertainty analysis, Multi-parameterization ensemble simulation.

中图分类号:

P426.63+5

图1 北疆高程与阿勒泰站点位置

Fig.1 Terrain height of the Northern Xinjiang and the location of the Altay site

表1 Noah-MP模型中 11个物理过程对应的参数化方案

Table 1 The available options for eleven physical processes in Noah-MP

物理过程	参数化方案
冠层气孔阻力（Canopy Stomatal Resistance, CRS）	a: Ball-berry [Default], b: Jarvis
控制气孔阻力的土壤湿度参数（Soil moisture factor controlling stomatal Resistance, BTR）	a: Noah scheme [Default], b: CLM scheme, c: SSiB scheme
径流和地下水（Runoff and groundwater, RUN）	a: SIMGM [Default], b: SIMTOP， c: Schaake96， d: BATS
表面热交换系数（Surface exchange coefficient for heat, SFC）	a: M-O [Default], b: Original Noah (Chen97)
冻结土壤渗透（Frozen soil permeability, INF）	a: NY06 [Default], b: Koren99
冻结土壤中过冷液态水（Supercooled liquid water in frozen soil, FRZ）	a: NY06 [Default], b: Koren99
植被冠层辐射传输（Radiation transfer, RAD）	a: gap=F(3D,cosz) [Default], b: gap=0, c: gap=1-FVEG
雪表层反照率（Snow surface albedo, ALB）	a: BATS， b: CLASS [Default]
雨雪分离（Partitioning precipitation into rainfall and snowfall, PCP）	a: Jordan91 [Default], b: BATS, c: Noah
土壤温度下边界条件（Lower boundary condition of soil temperature, TBOT）	a: Zero-flux scheme, b: Noah [Default]
第一层积雪或土壤温度的时间方案（The first-layer snow or soil temperature time scheme, TEMP）	a: Semi-implicit [Default], b: Full implicit

表1 Noah-MP模型中 11个物理过程对应的参数化方案

Table 1 The available options for eleven physical processes in Noah-MP

物理过程	参数化方案
冠层气孔阻力（Canopy Stomatal Resistance, CRS）	a: Ball-berry [Default], b: Jarvis
控制气孔阻力的土壤湿度参数（Soil moisture factor controlling stomatal Resistance, BTR）	a: Noah scheme [Default], b: CLM scheme, c: SSiB scheme
径流和地下水（Runoff and groundwater, RUN）	a: SIMGM [Default], b: SIMTOP， c: Schaake96， d: BATS
表面热交换系数（Surface exchange coefficient for heat, SFC）	a: M-O [Default], b: Original Noah (Chen97)
冻结土壤渗透（Frozen soil permeability, INF）	a: NY06 [Default], b: Koren99
冻结土壤中过冷液态水（Supercooled liquid water in frozen soil, FRZ）	a: NY06 [Default], b: Koren99
植被冠层辐射传输（Radiation transfer, RAD）	a: gap=F(3D,cosz) [Default], b: gap=0, c: gap=1-FVEG
雪表层反照率（Snow surface albedo, ALB）	a: BATS， b: CLASS [Default]
雨雪分离（Partitioning precipitation into rainfall and snowfall, PCP）	a: Jordan91 [Default], b: BATS, c: Noah
土壤温度下边界条件（Lower boundary condition of soil temperature, TBOT）	a: Zero-flux scheme, b: Noah [Default]
第一层积雪或土壤温度的时间方案（The first-layer snow or soil temperature time scheme, TEMP）	a: Semi-implicit [Default], b: Full implicit

表2 数值试验设计

Table 2 Numerical experiments setup

试验	试验描述	集合数
Def	2014年10月1日至2015年9月30日站点观测气象数据作为模型驱动，采用默认参数化方案组合	1
Ens1	驱动同上，11个物理过程的参数化方案任意组合	13 824
Ens2	驱动同上，4个敏感物理过程的参数化方案任意组合	24
Ens3	同Ens2, 其中SFC=1, TEMP=1	6
Ens4	同Ens2, 其中 SFC=1, TEMP=2	6
Ens5	同 Ens2, 其中 SFC=2, TEMP=1	6
Ens6	同 Ens2, 其中 SFC=2, TEMP=2	6
Opt	最优参数化方案组合	1

表2 数值试验设计

Table 2 Numerical experiments setup

试验	试验描述	集合数
Def	2014年10月1日至2015年9月30日站点观测气象数据作为模型驱动，采用默认参数化方案组合	1
Ens1	驱动同上，11个物理过程的参数化方案任意组合	13 824
Ens2	驱动同上，4个敏感物理过程的参数化方案任意组合	24
Ens3	同Ens2, 其中SFC=1, TEMP=1	6
Ens4	同Ens2, 其中 SFC=1, TEMP=2	6
Ens5	同 Ens2, 其中 SFC=2, TEMP=1	6
Ens6	同 Ens2, 其中 SFC=2, TEMP=2	6
Opt	最优参数化方案组合	1

图2 默认参数化方案组合雪深和雪水当量模拟结果与观测比较

Fig.2 Variation of snow depth and snow water equivalent, observed and simulated, by default parameterization scheme combination

图3 雪深和雪水当量集合模拟试验中同一物理过程的不同参数化方案在best members(0~1）和worst members(-1~0)中的选择频率

Fig.3 The selected frequency of different schemes of each physical process for snow depth and snow water equivalent, respectively, in the best members (0~1) or worst members (-1~0) in ensemble experiments

图4 全部参数化方案组合和敏感物理过程参数化方案组合的不确定范围比较

Fig.4 Comparison of uncertainty breadth in total parameterization combination to sensitivity parameterization combination

图5 最敏感参数化方案组合的不确定性范围比较

Fig.5 Comparison the uncertainty breadth of the most sensitivity parameterization combination

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