Application of surface energy balance equation in simulation of surface temperature in Xinjiang， China

doi:10.7522/j.issn.1000-694X.2021.00032

Journal of Desert Research ›› 2021, Vol. 41 ›› Issue (3): 137-146.DOI: 10.7522/j.issn.1000-694X.2021.00032

Application of surface energy balance equation in simulation of surface temperature in Xinjiang， China

Yulong Ren¹^,²(), Yi Li², Hong Li¹, Jing Wu³, Yuanpu Liu¹

^1.Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province / Key Laboratory of Arid Climate Change and Disaster Reduction of CMA，Institute of Arid Meteorology，China Meteorological Administration，Lanzhou 730020，China
^2.CAS Key Laboratory of Desert and Desertification，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^3.Lanzhou Center Meteorological Station，Lanzhou 730020，China

Received:2020-11-17 Revised:2021-03-19 Online:2021-05-26 Published:2021-05-26

Abstract

Abstract:

Land surface temperature （LST） is an important factor affecting the balance of surface energy budget and climate change， and is also the main index to evaluate the performance of land surface model. Xinjiang is the largest arid desert area in China， and its surface temperature changes have an important impact on the local and downstream ecological environment and climate changes. In order to find out the main factors affecting the effect of the third generation land surface model NCAR CLM4.5 to simulate the land surface temperature in Xinjiang， and promote the development of the model， this paper evaluates the effect of CLM 4.5 in simulating the surface temperature of Xinjiang for a long time based on the evaluation of ERA-5 reanalysis data. Then， using the surface energy change equation， the main factors causing the deviation of surface temperature simulation in Xinjiang are revealed. The results show that the spatial distribution characteristics， magnitude and temporal variation of ERA-5 surface temperature are very consistent with the field observation， which can be used to study the regional surface temperature change. The cold deviation value of CLM4.5 for Xinjiang surface temperature simulation is large， and the correlation is significant， and the error size is related to the climate background and underlying surface conditions. The lower sensible calorific value and higher latent heat value simulated by CLM4.5 are the main reasons for the low simulated surface temperature. The latent heat value simulated by CLM4.5 is 90% more than that of ERA-5 in most areas of Xinjiang. 0-10 cm soil moisture simulated by CLM4.5 in Xinjiang is relatively large， and the evapotranspiration is relatively strong， which leads to the relatively large latent heat flux. Therefore， improving the soil moisture calculation scheme is an effective way to improve the simulation level of the land surface model to the surface temperature in Xinjiang.

Key words: arid desert area, CLM4.5, land surface temperature, simulation deviation

CLC Number:

P423

Yulong Ren, Yi Li, Hong Li, Jing Wu, Yuanpu Liu. Application of surface energy balance equation in simulation of surface temperature in Xinjiang， China[J]. Journal of Desert Research, 2021, 41(3): 137-146.

Figures/Tables 12

Table 1 Comparison of ERA-Interim and ERA-5 data

参数	ERA-Interim	ERA-5
时间长度	1979年至今	1950年至今
空间分辨率	79 km，60层顶层0.1 hPa	31 km，137层顶层0.01 hPa
时间分辨率	6 h	1 h
同化系统	IFS Cycle 31r2 4D-Var	IFS Cycle 41r2 4D-Var
参数个数	约100个	超过240个
不确定性估计	无	10个成员集合同化进行不确定性估计

Fig.1 Simulation range

Table 2 Setting of main simulation parameters

动力结构	试验方案
动力框架	MM5非静力框架
大尺度降水方案	SUBEX（次网格显式水汽方案）
辐射传输方案	NCAR CCM3
海表通量方案	Zeng
气压梯度方案	流体静力递推

Fig.2 The observed (A. annual; C. Spring; E. Summer; G. Autumn; I. Winter) and ERA-5 (B. annual; D. Spring; F. Summer; H. Autumn; J. Winter) LST in Xinjiang (℃)

Table 3 Deviation and root mean square error between ERA-5 and observed LST in Xinjiang

时段	年	春季	夏季	秋季	冬季
偏差/℃	-2.6	-2.9	-3.1	-2.2	-2.2
均方根误差/℃	3.6	4.2	3.8	3.3	4.2

Fig.3 The scatter diagram of observed and ERA-5 average LST in Xingjiang in different periods

Fig.4 LST (isolines, ℃) simulated by CLM.5 and its error (shadow, ℃) to that of ERA-5

Table 4 Bias and RMSE of LST simulated by CLM4.5

时段	年	春季	夏季	秋季	冬季
误差/℃	-3.3	-4.5	-6.9	-2.7	1.0
均方根误差/℃	5.8	7.0	8.5	5.0	5.2

Fig.5 The correlation coefficient between LSTs simulated by CLM4.5 and ERA-5 LSTs of different stations

Fig.6 LSTs observed, ERA-5 and simulated by CLM4.5 of different stations in Xinjiang during 1988-2017

Table 5 Contribution values of various factors in equation (1) to surface temperature change

时段	$S W i n ? α s$	$(1 - α s) ? S W i n$	$? L W i n$	$? H$	$? L E$
年	-5.7	33.2	-19.4	-14.2	-12.6
春季	-4.8	58.6	4.8	-3.9	-22.1
夏季	-14.3	29.3	-39.4	-22.4	-12.8
秋季	-13.4	0.0	-45.1	-25.0	-2.5
冬季	0.1	37.9	2.0	-5.3	-13.0

Table 5 Contribution values of various factors in equation (1) to surface temperature change

时段	$S W i n ? α s$	$(1 - α s) ? S W i n$	$? L W i n$	$? H$	$? L E$
年	-5.7	33.2	-19.4	-14.2	-12.6
春季	-4.8	58.6	4.8	-3.9	-22.1
夏季	-14.3	29.3	-39.4	-22.4	-12.8
秋季	-13.4	0.0	-45.1	-25.0	-2.5
冬季	0.1	37.9	2.0	-5.3	-13.0

Fig.7 The deviation percentage of surface energy balance factor relative to ERA-5 (The point chart is the annual average, and the bar chart is the regional average of different periods)

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Application of surface energy balance equation in simulation of surface temperature in Xinjiang， China

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