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Journal of Geo-information Science >

2017 , Vol. 19 >Issue 2: 197 - 204

DOI: https://doi.org/10.3724/SP.J.1047.2017.00197

Orginal Article

Simulation and Analysis of Carbon Dioxide Concentration in the Surface Layer

  • LIU Yu , 1, 2 ,
  • GUO Jianhong , 1, * ,
  • YUE Tianxiang 1 ,
  • ZHAO Na 1
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  • 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Corresponding author: GUO Jianhong, E-mail:

Received date: 2015-12-30

  Request revised date: 2016-05-11

  Online published: 2017-02-17

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《地球信息科学学报》编辑部 所有
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Abstract

As an important cause of global warming, carbon dioxide concentration and its change has aroused worldwide concern. How to have an explicit understanding of the spatial and temporal distribution of carbon dioxide concentration is a crucial technical challenge for climate change research. In this paper, based on the in situ observation data set collected in the TanSat flight test area, the correlations between the carbon dioxide concentrations and the environmental variables are analyzed, and suitable environment variables can be selected to establish a regression equation, through which we obtain a preliminary trend of surface carbon dioxide concentrations. Then combining the multiple linear regression model and High Accuracy Surface Modelling (HASM), the carbon dioxide concentrations with a high accuracy in the entire test area are produced. The results indicate that the spatial distributions of the carbon dioxide concentrations in the study area are significantly different between three periods, and the short-wave radiation is an important factor for the regression equation. Because of the high temperature and drought condition, the highest concentration appears in the first period especially in the western area. The second period has a different distribution on the carbon dioxide concentration comparing with the previous period, as in this period the high value region moves eastward, and making the concentration high in the eastern area but low in the western area. Both of the second and third periods have similar characteristics except that the high value region in the eastern area is reduced in third period. Moreover, statistical analyses show that the mean absolute error and the mean relative error of the predicted value of the HASM model are 9.8 ppm and 2.48% respectively, which are both lower than the errors produced using the Kriging method, therefore the HASM model remains to have higher simulation accuracy in a condition of few sampling points and low sampling density. Therefore a combined method of multiple linear regression model and HASM model can be used as an effective method for simulating the spatial and temporal distribution of carbon dioxide concentration in the surface layer.

Key words： HASM method; carbon dioxide concentration; spatial simulation; WRF

Cite this article

LIU Yu , GUO Jianhong , YUE Tianxiang , ZHAO Na . Simulation and Analysis of Carbon Dioxide Concentration in the Surface Layer[J]. Journal of Geo-information Science, 2017 , 19(2) : 197 -204 . DOI: 10.3724/SP.J.1047.2017.00197

1 引言

全球气候变暖已在国际上引起广泛关注[1-3]。CO2作为重要的温室气体,其在大气中含量的变化能够改变未来气候的发展趋势[4-6],进而影响到国家经济的可持续发展与国家安全。研究标明,与工业革命时期相比,全球大气中CO2浓度已从最初的280 μmol mol 上升到约400 μmol mol [7]。因此,为了控制CO2气体的排放,减少人为因素对气候变暖的促进作用,需要尽可能的了解和认识大气中CO2浓度的时空分布。
CO2观测卫星（如GOSAT[8-9]、OCO-2[10-11]、SCIAMACHY[12]等）具有能够获取大范围观测数据的优势,但对于同一地点进行观测的时间间隔较长。地面观测（如TCCON[13]）可获得局地高时间分辨率的观测数据,但难以大范围密集布网。因此根据观测数据,结合一些模拟方法,是得到高时空分辨率CO2浓度的一个有效手段。国内外通过对观测数据的插值和模拟进行研究,发展出多种点转换面的算法。以反距离权重插值（IDW）[14]、克里金插值（Kriging）[15]和样条插值（Spling）[16]为代表的模型称为空间自相关模型。这种模型根据离采样点距离的远近,获取区域内所有点的插值信息,因此通常需要大量的样本数据以保证更精确的插值结果。多元回归[17]和GIDS[18]属于空间异相关模型,通过引入其他环境变量,根据目标信息与环境变量之间的关系推测插值信息的空间分布。受局地排放和吸收的影响,CO2浓度空间分布可以看作是多维空间上的一个曲面,因此可使用上述插值方法结合地面观测数据获得CO2浓度的分布特征。
基于曲面论的高精度曲面建模方法HASM（High Accuracy Surface Model）是近年来发展起来的一种空间自相关模型[19-21],该方法将问题转化为等式约束的最小二乘问题,把观测值作为目标,通过迭代算法使趋势面逼近真实的曲面。HASM方法已应用于模拟人口分布、土壤信息、气温及降水等方面[22-24],并表现出良好的性能。
目前,受研究区域及观测数据等的限制,利用插值方法模拟CO2浓度时空分布的研究少见。常见的研究手段集中在使用大气传输模型[25-26],但由于模型所需的相关碳排放数据的滞后性,研究工作通常要半年甚至一年以后才能开展。本文将空间异相关模型与空间自相关模型结合,首先采用多种地表气象要素作为解释变量,建立CO2与气象要素之间的多元线性回归方程,然后使用HASM方法对趋势面的残差数据进行插值处理,最后获得高精度高分辨率的CO2浓度时空分布图。

2 数据与方法

2.1 观测数据

中国自行研制的CO2观测卫星TanSat于2015年9月在吉林进行了航飞试验。为配合此次航飞试验,在航飞区中架设了11台自动气象观测站,提供地表气象要素及CO2浓度的监测数据。图1是自动气象观测站的分布情况：航飞区东部松原市区的镜湖公园及市郊的龙安寺各安装1台观测站;松原市东北侧的长山镇有2台观测站,1台位于农田中,另1台邻近长山发电厂;航飞区中部安装有3台观测站,分别位于乾安县、鸿兴镇和黑水镇;航飞区西部的向海渔村和向海鹤岛湿地各装有1台;最后1台安装在向海西南侧的利民草地。各个自动气象站所处的环境及下垫面信息如表1所示。
Fig.1 Location of automatic weather stations

图1 自动气象站安置点分布图

Tab.1 Surrounding environment of automatic weather stations

表1 自动气象站周边环境

气象站名称 植被状况 下垫面类型
镜湖公园 位于城区内,周围是附近居民开辟的小片菜地,植被高度较低,不远处就是成片的建筑物 城市
龙华寺 在一片果园中,植被高度较高约2 m。果树间隙种植了一些玉米 果园
长山农田 在大片农田中,植被覆盖度较高,植被高度约40 cm左右 农田
长山电厂 位于发电厂东侧约600 m,植被覆盖少,地表以裸土、石块、砖块为主。自动站北侧40 m外有大片农田,东侧有些闲置的砖堆 发电厂+农田
查干湖 距离查干湖约100 m,地表植被以杂草为主,植被高度很低 水体附近
乾安 位于一沙场内,自动站一侧是植被覆盖少的沙场,另一侧是植被覆盖多的杂草,杂草高度约40 cm左右 沙地+杂草
鸿兴镇 位于菜地中,自动站南侧主要是玉米等作物,其他区域为葱等高度较低的农作物 农田
黑水镇 位于荒地中,植被很少,主要是高度很矮的小丛的杂草,地表以裸土为主 裸土
鹤岛湿地 位于湿地,植被覆盖度较高,植被高度约1 m左右 湿地+草地
利民草地 位于大片草地中,植被覆盖度很高,植被高度约30 cm左右 草地
向海渔村 位于向海边的向日葵田中,向日葵约2 m 水体附近
自动气象观测站提供CO2浓度（ppm）、地表风速（m/s）及风向（°）、2 m温度（℃）、2 m湿度（%）、土壤温度（℃）、土壤湿度（%）、降水量（mm/min）、向下短波辐射（W/m2）、向上短波辐射（W/m2）和地表气压（hPa）共11种气象要素。本文选取的观测时段为8月19日-9月17日共30 d,对每10 d做平均计算,分别得到8月19日-8月28日、8月29日-9月7日、 9月8日-9月17日共3个时段气象特征的观测数据平均值。

2.2 气象要素场模拟

WRF（Weather Research and Forecasting Model）模式是由美国国家大气中心（NCAR）、美国国家环境预报中心（NCEP）等多个部门联合开发的新一代中尺度天气数值预报模式。该模式属于完全可压缩非静力平衡模式,包含丰富的物理参数化方案选项。因预报准确度高、可移植性好、运算速度快及易于维护等特点,使得该模式广泛应用于气象科研及业务工作中[27-28]。
本文使用WRFV3.5版本,对8月19日-9月17日进行模拟。每天20时（北京时）起报,每次积分30 h,初始数据来自于美国国家环境预报中心（NCEP）的FNL1°×1°再分析资料,每6 h更新边界条件。图1中黑框区域是模式设置的模拟区域,空间分辨率为1 km×1 km。物理参数化方案分别选择：YSU边界层方案、Monin-Obukhov近地层方案、WSM6微物理方案、RRTM长波辐射方案、Goddard短波辐射方案、Noah陆面过程、KFETA积云方案。参照自动气象观测站提供的气象要素类型,调整WRF模式的输出数据类型,每半小时输出一次模拟数据,并按照8月19日-8月28日、8月29日-9月7日、9月8日-9月17日做10 d平均计算,与观测数据保持一致。

2.3 模拟方案

CO2浓度的模拟方案如图2所示。具体流程为：
（1）对地表CO2浓度及环境变量进行回归分析,选取与CO2浓度显著相关的环境变量,使用普通最小二乘回归建立CO2浓度与相关环境变量之间的回归方程;
（2）根据步骤（1）选出的相关环境变量,使用WRF中尺度数值预报模式输出该区域相应的模拟数据;
（3）回归方程与模拟数据想结合,建立初步的地表CO2浓度空间分布趋势面;
（4）将CO2浓度观测值与步骤（3）中的趋势面相减,得到观测点处CO2浓度残差值。用空间分辨率1 km×1 km的栅格组成的矩阵覆盖航飞区,将每个观测点处的残差值输入HASM,计算得出这个矩形计算域中每个栅格处的残差,即获得一个残差面;
（5）将步骤（3）的CO2浓度趋势面与步骤（4）中HASM输出的残差面相加,最终得到高精度CO2浓度分布图。
上述模拟过程可用式（1）所示的方程来表示。
C O 2 = θ ols ∙ X T + HASM O k - θ ols ∙ X T （1）
式中：CO2是CO2浓度空间分布最终结果; θ ols 是步骤（1）中得到的最小二乘回归系数; X T 是步骤（2）中WRF模式输出的气象要素场; θ ols ∙ X T 表示 步骤（3）中初步的地表CO2浓度趋势面; O k 为观测点k处观测的CO2浓度值。 HASM O k - θ ols ∙ X T 表示步骤（4）HASM输出的残差面。
Fig.2 The flowchart of simulation

图2 模拟流程图

3 模拟结果及分析

3.1 相关分析及多元线性回归分析

本次试验观测数据中的环境变量共有13种,其中包括自动气象观测站提供的10种气象要素,以及观测站所在的经度、纬度和海拔高度。自动气象观测站在观测过程中,风速仪和风向标距离地表的高度约3 m,与WRF输出的地表10 m高度风速风向不符,因此下述分析中不考虑风速及风向。根据采样结果,第1时段可用的采样点数为11个,第2时段10个,第3时段9个。
根据3个时段CO2浓度与环境变量之间的相关性分析,确定能够显著影响CO2浓度的变量。由表2可看出,每个时段均能确定至少一种与CO2浓度相关性达到较显著或显著的变量。第1时段中向下短波辐射为显著相关的解释变量。第2时段有经度（显著）、大气温度（较显著）、向下短波辐射（较显著）和向上短波辐射（显著）作为备选解释变量。第3时段能够确定4种备选解释变量,分别为经度、大气温度、向上短波辐射、地表气压,相关性均较显著。
Tab.2 Correlation analysis between CO2 concentration and environmental variables

表2 各时段CO2浓度与环境变量相关性分析

时段
8月19日-
8月28日		 8月29日-
9月7日		 9月8日-
9月17日
采样点数/个 11 10 9
纬度 0.1494 -0.0185 0.1484
经度 -0.2443 0.6686** 0.5998*
海拔 0.1884 -0.4430 -0.3562
雨量 0.0152 0.0247 0.0653
大气温度 -0.1553 -0.5578* -0.4740*
土壤温度 -0.5031 0.0370 0.0678
向下短波辐射 0.6114** -0.5739* -0.1991
向上短波辐射 0.0322 -0.6928** -0.4077*
土壤湿度 0.1407 -0.2775 -0.2722
大气湿度 0.2785 0.3789 0.2644
地表气压 -0.1090 0.4328 0.4431*
利用CO2浓度与环境变量相关性分析的结果,选择较显著相关以上的环境变量作为备选解释变量,建立各时段内CO2浓度与解释变量之间的多元线性回归方程,同时检查环境变量之间的多重共线性,并进行T检验,从中选择校正R2最大的方程,作为该时段内CO2浓度的回归方程。该部分使用 ArcGIS软件中的“探索性回归”工具进行计算。
表3列出了回归分析的结果。由于变量间的多重共线性,第2时段的经度、向下短波辐射和第3时段的经度被排除。经过回归分析之后,得到的3组回归方程均包含了向下短波辐射或向上短波辐射,说明在该观测时段内,短波辐射是影响CO2浓度空间分布的一个重要因素。第1时段和第2时段建立的回归模型达到显著水平（p≤0.05）,第3时段较显著（p≤0.1）。3个时段回归方程的校正R2在0.3-0.6之间,其中9月19日-9月28日最低为0.304, 9月29日-10月7日最高为0.568,说明回归方程能够部分代表CO2浓度的变化。考虑到WRF中尺度模式输出的气象要素场相对于真实大气必然存在偏差,当使用回归方程和模拟气象要素场得到初步的CO2浓度趋势面时,二者偏差的叠加会进一步影响到趋势面的准确度。在此基础上,需要使用HASM对CO2浓度进行残差修正。
Tab.3 Regression models of CO2 concentration

表3 CO2浓度回归方程

时段 回归方程 校正R2 P
8月19日-8月28日 CO2= 327.995752+ 0.439485×向下短波辐射 0.304 0.046
8月29日-9月7日 CO2= 682.554308-12.271210×2 m温度-0.833343×向上短波辐射 0.568 0.022
9月8日-9月17日 CO2=-2714.532387-6.129403×2 m温度-0.510322×向上短波辐射+3.258770×地表气压 0.506 0.095

3.2 模拟结果分析

依据式（1）,使用3.1中的回归曲面与HASM优化的残差曲面进行叠加,分别获得3个时段航飞区高分辨率高精度的CO2浓度空间分布图（图3-5）,图中的绿色点代表该时段可用的观测点。
Fig.3 Spatial distribution of CO2 concentration from 19 August to 28 August

图3 8月19日-8月28日CO2浓度空间分布模拟图

由第1时段CO2浓度空间分布模拟图（图3）可以看出,该时段内航飞区CO2浓度大体呈现西部高东部低的特点。以123.5°E为界,航飞区西侧大片区域CO2浓度均超过405 ppm,特别是在该区域的北侧和向海以南的部分地区浓度超过410 ppm。航飞区东侧CO2浓度整体比西部偏低,其中乾安县及松原城区附近的CO2浓度在390~400 ppm之间,长山镇附近存在小片的高值区,CO2浓度超过405 ppm。
图4为第2时段CO2浓度空间分布模拟图。图中显示,与第1时段相比,该时段CO2浓度高值区东移,浓度整体分布特征与第1时段相反,呈现出西部低东部高的特点。该时段内高值区主要在松原市及以东地区,部分区域浓度超过405 ppm。从查干湖地区到松原市区能够看到明显的CO2浓度梯度带,长山镇及乾安县位于该梯度带上,CO2浓度约390~395 ppm。从查干湖地区到西部的向海一带均是低值区,浓度在385 ppm以下。
Fig.4 Spatial distribution of CO2 concentration from 29 August to 7 September

图4 8月29日-9月7日CO2浓度空间分布模拟图

图5为第3时段CO2浓度空间分布模拟图。该时段的CO2浓度分布特点与上一时段类似,整体上航飞区西部浓度偏低,东部偏高。东部400 ppm以上的高值区的范围明显缩小,高值区主要位于松原市、长山镇及东北方向的部分区域,查干湖、乾安县一带地区在395 ppm以下。乾安县西南侧存在一些零散的高浓度区,其浓度值在400 ppm以上。航飞区西部大部分地区CO2浓度小于395 ppm,仅在北侧部分区域超过395 ppm。
Fig.5 Spatial distribution of CO2 concentration from 8 September to 17 September

图5 9月8日-9月17日CO2浓度空间分布模拟图

在第3时段内,航飞区西北和东南区域存在CO2浓度显著偏小的区域,这与该时段回归方程的解释变量中包含了地表气压（PSFC）项有关。由于自动气象站的架设地点位于松嫩平原,各站点间的海拔高度差距不大,获取的地表气压数据主要代表平原地区的特点。本次试验选取的模拟区域中,西北侧为内蒙古高原的边缘,海拔超过350 m;东南为前郭尔罗斯蒙古族自治县海拔较高的洪泉乡,海拔超过250 m。与松嫩平原相比,高海拔地区模拟的地表气压会偏低,且考虑到回归方程中地表气压（PSFC）是正贡献,从而使得CO2浓度异常偏低。
综合以上分析,航飞区地表CO2浓度逐渐由西高东低转为西低东高,转折点大概在8月底至9月初。试验区西部（鸿兴镇、黑水镇至向海一带）处于温带大陆性季风气候区的半干旱草原地带[29],以低矮植被覆盖为主,人口稀少,经济欠发达,人为因素对CO2浓度的影响较小。8月该地区气温较高,长时间没有降水,且当地没有发达的灌溉系统,因此高温干旱导致该地区植被的光合作用被抑制,CO2吸收量减少。这是第1时段内地表CO2浓度偏高的可能原因。第2、3时段气温有所下降,且有少量降雨产生,植物光合作用相对第一时段旺盛,因此地表CO2浓度下降。航飞区东部有经济发展排名吉林省第二的松原市,人口相对集中,城区周边以种植水稻、玉米等庄稼为主,8月农作物在生长期,9月逐渐成熟,因此CO2浓度先低后高。

3.3 模拟精度对比

Kriging插值方法又称空间自协方差最佳插值法,是一种被广泛应用的经典插值方法。将第1时段航飞区的11个观测站进行划分,抽取1个站点为验证点,剩余10个为采样点,分别利用HASM及Kriging方法进行插值试验,试验重复11次,计算 2种方法的模拟误差。第2、3时段依照可用的观测站数量进行相同的处理和计算。绝对误差MAE和相对误差MRE的表达式如式（2）、（3）所示。
MAE = 1 n ∑ k = 1 n o k - s k （2）
MRE = 1 n ∑ k = 1 n o k - s k / o k × 100 % （3）
式中： n 是验证点总数; o k 是第 k 个观测站的观测值; s k 是第 k 个观测站所在位置的模拟值。
模拟结果误差分析见表4。结果为：① Kriging方法绝对误差平均值为10.3 ppm,HASM模型模拟结果为9.8 ppm,比Kriging方法小0.5 ppm; ② HASM模型相对误差平均值为2.48%,比Kriging方法低0.12%。前人的研究结论中已经指出,在采样点数量较多,采样密度较大时,HASM模拟结果比经典插值方案有明显提高。本文实验显示在采样点较少及采样密度较低的情况下,与Kriging方法相比,HASM模型的模拟精度也有所提升。
Tab.4 Absolute error and relative error between Kriging and HASM

表4 Kriging与HASM误差分析

时段 Kringing HASM
MAE/ppm MRE/% MAE/ppm MRE/%
8月19日-8月28日 10.6 2.63 10.1 2.52
8月29日-9月7日 11.3 2.89 10.7 2.74
9月8日-9月17日 9.1 2.28 8.7 2.19
平均 10.3 2.60 9.8 2.48

4 结论

本文根据吉林省航飞区内11个自动气象观测站获取的CO2浓度（ppm）、地表风速（m/s）、风向（°）、2 m温度（℃）、2 m湿度（%）、土壤温度（℃）、土壤湿度（%）、降水量（mm/min）、向下短波辐射（W/m2）、向上短波辐射（W/m2）、地表气压（hPa）共11种气象要素及观测站经纬度、海拔高度等数据,运用多元线性回归与HASM相结的方法,对航飞区CO2浓度的空间分布进行了3个时段的模拟。结论如下：
（1）每个时段回归方程中保留的解释变量不完全相同,说明10 d尺度的CO2浓度空间分布受气象条件的影响较大,同时也体现出短波辐射是CO2浓度分布计算的重要因素。
（2）各时段CO2浓度空间分布差异明显,第1时段整体浓度最高,航飞区西部超过405 ppm。第2时段CO2浓度高值区东移,呈现西低东高的分布特点,低值区小于385 ppm,高值区在405 ppm以上。第3时段浓度空间分布与第2时段有类似的特征,但细节存在差异,405 ppm以上的高值区范围缩小。
（3）对HASM方法和Kriging方法模拟结果的误差分析表明,HASM方法的模拟误差小于Kriging方法,即在采样点较少及采样密度不大的情况下,HASM模型的模拟精度与Kriging方法相比有所提升。
航飞区地表CO2浓度时空分布的变化是由多种原因引起。前期高温干旱使得试验区西部植被光合作用减弱,CO2的吸收量减少,故第1时段该区域地表CO2浓度偏高;后期降温及降水有利于植被光合作用对CO2的吸收,故CO2浓度出现由高到低的变化趋势。而在东部松原市及周边地区,虽然前期CO2浓度整体低于西部地区,但其密集的人口、发达的经济及农作物的成熟是显著的碳源,是使CO2浓度升高的原因。与CO2吸收排放有直接关联的植被覆盖度、叶面积指数、人口密度及光合作用等要素由于缺少数据,本文没有涉及,因此后续研究中若能够获取到这些数据,对提高模拟结果及进一步的分析会有很大帮助。
本文使用回归方程的方法适用于少量时段模拟。若对大量时段进行模拟,每10 d天需要更换一个回归方程的方法则过于繁琐,可考虑使用大气传输模型代替回归方程,产生初步的CO2浓度趋势面。国内外CO2地面观测点数量较少且分布稀疏,即使本文中模拟区域的范围不大,但11个观测点仍然偏少,模拟结果的准确度仍需进一步提高。

The authors have declared that no competing interests exist.

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岳天祥,王英安,张倩,等.北京市人口空间分布的未来情景模拟分析[J].地球信息科学,2008,10(4):479-488.

[ Yue T X, Wang Y A, Zhang Q, et al.YUE-SMPD scenarios of Beijing population distribution[J]. Journal of Geo-Information Science, 2008,10(4):479-488. ]

[23]
李启权,岳天祥,范泽孟,等.中国表层土壤全氮的空间模拟分析[J].地理研究,2010,29(11):1981-1992.基于第二次全国土壤普查5336个典型土壤剖面数据,分析表土全 氮(A层)与环境因素的相关关系,利用多元回归模型和HASM模型结合的方法模拟中国国家尺度上表层土壤全氮的空间分布格局.结果表明:对350个检验点 模拟结果的平均绝对误差和平均相对误差为0.67g·kg-1 和61.06%,与普通克里格法相比分别降低了0.05g·kg-1 和17.53%;对样点分布较少的西北地区的模拟结果也更符合实际情况.多元回归模型和HASM模型结合的方法考虑了环境因素的影响,可作为目前模拟大尺 度土壤性质空间分布相对有效的方法.

DOI

[ Li Q Q, Yue T X, Fan Z M, et al.Spatial simulation of topsoil TN at the national scale in China[J]. Geographical Research, 2010,29(11):1981-1992. ]

[24]
Yue T X, Du Z P, Song D J, et al.A new method of surface modeling and its application to DEM construction. Geomorphology, 2007,91(1):161-1721.A new method of surface modelling based on the fundamental theorem of surfaces (SMTS) is presented. Eight different test surfaces are employed to comparatively analyze the simulation errors of SMTS and the classical methods of surface modeling in GIS, including TLI (triangulated irregular network with linear interpolation), SPLINE, IDW (inverse distance weighted) and KRIGING. Numerical tests show that SMTS is much more accurate than the classical methods. SMTS theoretically gives a solution to the error problem that has long troubled DEM construction. As a real-world example, SMTS is used to construct a DEM of the Da-Fo-Si coal mine in Shaan-Xi Province, China. Its root mean square error (RMSE) is compared with those of DEMs constructed by the four classical methods. The results show that although SMTS also has a higher accuracy in the real-world example, the improvement of accuracy is less distinct than that expected from the numerical tests. The accuracy loss seems to be caused by location differences between sampling points and the central points of lattices of the simulated surfaces. Two alternative ways are proposed to solve this problem.

DOI

[25]
雷莉萍,关贤华,曾招城,等.基于GOSAT卫星观测的大气CO2浓度与模型模拟的比较[J].中国科学:地球科学,2014,44(1):61-71.卫星从空间对大气CO2的实时观测可以客观地获取全球和区域大气CO2浓度的变化信息;另一方面,利用全球大气输送模型的数值模式模拟得到时空连续的全球大气CO2浓度是目前科学家们定性和定量地研究大气CO2全球输送过程及时空变化规律的主要途径之一.卫星观测和模型模拟以两种不同的方式为我们提供大气CO2浓度信息,但对于这两种方式所揭示的全球以及区域大气CO2浓度特征的差异还没有一个综合的对比分析与评价.本文收集2009年6月到2010年5月的GOSAT卫星观测数据,利用GEOS-Chem模型模拟了同时期全球大气CO2浓度,对比分析两种方式揭示的大气CO2时空变化特征差异,通过比较中国陆地与同纬度美国陆地区域的差异,评价分析卫星观测和模型模拟各自的合理性和不确定性.结果指出卫星GOSAT观测反演的大气CO2浓度总体低于模型模拟2 ppm左右,与地面观测验证的结果相近.但是两者的差异在不同的区域上明显不同,在中国陆地区域显示了从0.6~5.6 ppm很大的差值变化,而在全球陆地区域为1.6~3.7 ppm、美国陆地区域为1.4~2.7 ppm.卫星GOSAT观测与模型模拟在美国陆地显示了0.81的拟合优度,高于全球陆地区域的0.67和中国区域的0.68.综合分析结果指出在中国区域卫星观测与模型模拟的不一致性高于美国和全球,其原因与卫星观测反演算法中输入参数的不整合所引起的CO2浓度反演误差以及模型模拟中驱动参数数据的准确性有关.

DOI

[ Lei L P, Guan X H, Zeng Z C, et al.A comparison of atmospheric CO2 concentration GOSAT-based observations and model simulations[J]. Science China: Earth Sciences, 2014,44(1):61-71. ]

[26]
麦博儒,邓雪娇,安兴琴,等.基于碳源汇模式系统Carbon Tracker的广东省近地层典型CO2过程模拟研究[J].环境科学学报,2014,34(7):1833-1844.利用瓦里关全球本底站和番禺气象局站地面观测的CO2浓度资料对改进的CarbonTracker-2010（CT-2010）模式系统进行了验证.结果显示，CT-2010能较好地反映近地层CO2浓度的分布状况，在瓦里关地区，模拟值与观测值的决定系数（R2）为0.584，残差为4.49μmol·mol-1，相对误差为1.18%；在珠三角地区，上述3个参数值分别为0.430、13.89μmol·mol-1和3.63%.利用CT-2010模式对广东地区近地层典型CO2过程及其影响因素进行了模拟和分析研究.结果表明：在典型高、低浓度CO2过程中，以广州为中心的珠三角区域始终为CO2浓度高值区，从东北至西南方向的梅州、河源、广州、肇庆和云浮等区域存在明显的CO2聚集带.在典型高浓度CO2过程中，珠三角和粤北区域的CO2浓度上升最明显，而粤东和粤西地区的CO2浓度变化较小；在典型低浓度过程中，珠三角、粤北及粤东的CO2浓度波动明显小于过程前和过程后，而粤西地区的CO2浓度波动较大.这些变化主要是受到了风场、下垫面植被、相对湿度及气温等因子的显著影响.

DOI

[ Mai B R, Deng X J, An X Q, et al.Simulation of typical surface CO2 cases over Guangdong region base on Carbon Tracker numerical model[J]. Acta Scientiae Circumstantiae, 2014,34(7):1833-1844. ]

[27]
Hong S Y, Noh Y.A new vertical diffusion package with an explicit treatment of entrainment processes[J]. Monthly Weather Review, 2006,134:2318-2341.This paper proposes a revised vertical diffusion package with a nonlocal turbulent mixing coefficient in the planetary boundary layer (PBL). Based on the study of Noh et al. and accumulated results of the behavior of the Hong and Pan algorithm, a revised vertical diffusion algorithm that is suitable for weather forecasting and climate prediction models is developed. The major ingredient of the revision is the inclusion of an explicit treatment of entrainment processes at the top of the PBL. The new diffusion package is called the Yonsei University PBL (YSU PBL). In a one-dimensional offline test framework, the revised scheme is found to improve several features compared with the Hong and Pan implementation. The YSU PBL increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime, which alleviates the well-known problems in the Medium-Range Forecast (MRF) PBL. Excessive mixing in the mixed layer in the presence of strong winds is resolved. Overly rapid growth of the PBL in the case of the Hong and Pan is also rectified. The scheme has been successfully implemented in the Weather Research and Forecast model producing a more realistic structure of the PBL and its development. In a case study of a frontal tornado outbreak, it is found that some systematic biases of the large-scale features such as an afternoon cold bias at 850 hPa in the MRF PBL are resolved. Consequently, the new scheme does a better job in reproducing the convective inhibition. Because the convective inhibition is accurately predicted, widespread light precipitation ahead of a front, in the case of the MRF PBL, is reduced. In the frontal region, the YSU PBL scheme improves some characteristics, such as a double line of intense convection. This is because the boundary layer from the YSU PBL scheme remains less diluted by entrainment leaving more fuel for severe convection when the front triggers it.

DOI

[28]
于灵雪,张树文,刘廷祥,等.基于WRF和统计方法的气温空间尺度分析[J].地球信息科学学报,2013,15(4):546-553.WRF模式作为一个中尺度气候模式,其分辨率从几米到几千公里,其自身的双向嵌套特征也为进行动力尺度下推提供了有力条件。本文利用WRF模式和传统的统计方法对研究区的气温进行尺度下推。首先,通过动力下推得到不同分辨率下的气温空间分布,并选取15个气象站点进行点对点验证,为了更明显观察不同尺度间的差异,对不同尺度的输出与ANUSPLIN插值结果进行比对,结果显示动力尺度下推中,分辨率越高模拟效果越好。其次,我们采用传统的统计下推方法,从27km下推到3km分辨率,并与WRF和ANUSPLIN插值在该尺度的结果进行对比分析,结果显示统计下推结果的趋势与动力下推的插值结果是一致的,但具有明显的马赛克效果,通过分析认为,这与统计方法的尺度下推只考虑高程信息的变化对气温的影响,而未考虑其他因素有关,如若在下推时加入更多的变量,如对温度有较大影响的坡度、坡向、土地覆被类型等因素,综合分析不同尺度之间的关系,会使下推结果有所改善。

DOI

[ Yu L X, Zhang S W, Liu T X, et al.Spatial scale analysis of temperature based on WRF and statistical methods[J]. Journal of Geo-Information Science, 2013,15(4):546-553. ]

[29]
白军红,丁秋祎,高海峰,等.向海湿地不同植被群落下土壤氮素的分布特征[J].地理科学,2009,29(3):381-384.对比分析向海湿地8种植被群落下表层和亚表层土壤氮形态和全氮含量差异，研究不同植被群落下土壤氮素分布特征。结果表明，各植被群落下表层和亚表层土壤全氮主要以有机氮的形态存在；沼柳群落下表层土壤全氮含量最高，三棱群落最低；而香蒲群落下亚表层土壤全氮含量最高，碱蓬群落最低；根系分布较深的植物群落下表层和亚表层土壤氮素一般高于根系分布较浅的植物群落；除辣蓼群落下土壤无机氮以硝态氮为主外，其它各群落下土壤无机氮均以铵态氮为主。相关分析表明，土壤全氮和有机氮含量与土壤有机质含量显著正相关，与土壤pH值呈现显著负相关关系。

DOI

[ Bai J H, Ding Q Y, Gao H F, et al. Spatial distribution of nitrogen in marsh soils with different plant communities in Xianghai wetland[J]. Scientia Geographica Sinica, 2009,29(3):381-384. ]

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