Dynamic downscaling simulation of temperature and precipitation in the Qilian Mountains and its surrounding areas

doi:10.7522/j.issn.1000-694X.2023.00068

Journal of Desert Research ›› 2024, Vol. 44 ›› Issue (1): 61-74.DOI: 10.7522/j.issn.1000-694X.2023.00068

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Dynamic downscaling simulation of temperature and precipitation in the Qilian Mountains and its surrounding areas

Xia Li¹^,²(), Bao Yang¹()

^1.Key Laboratory of Desert and Desertification，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China
^2.University of Chinese Academy of Sciences，Beijing 100049，China

Received:2023-03-12 Revised:2023-05-16 Online:2024-01-20 Published:2023-12-26
Contact: Bao Yang

Abstract

Abstract:

The study region is located in the Qilian Mountains and its surrounding areas， which are sensitive zone for monsoon-westerly interaction， and have complex climate change mechanisms. In this paper， a one-year sensitivity test was conducted to select the optimum parameterization scheme combination from five sets of schemes. With the optimum parameterization setting， a ten-year dynamic downscaling simulation of the study region was carried out over the period 2005-2014 using the regional climate model WRF driven by bias-corrected CMIP6 data. The results show that：（1） The WRF model is capable to simulate the air temperature well； different Parameterization scheme combinations perform weak effect on the simulation of temperature whilst the simulation of precipitation by the WRF model is more influenced by the parametric scheme combinations； and the simulation accuracy of precipitation is generally poorer than that of temperature. Sensitivity tests on the Parameterization scheme combinations show that the parametric scheme combination of the Thompson cloud microphysics scheme， Grell-D cumulus convection scheme， RRTM-Dudhia radiation physics scheme， and Noah land surface process scheme is the most suitable for the Qilian Mountains and surrounding areas. The results of the sensitivity tests on topographic data show no significant improvement in the simulation of temperature and precipitation. （2） The spatial distribution characteristics of simulated temperature and precipitation are generally able to reproduce the observed datasets. The spatial distribution of temperature and precipitation is greatly influenced by the altitude， with lower temperature but more precipitation at the higher altitudes than the surrounding lower altitudes； the correlation coefficients between the simulated and observed temperature is much significant than that on precipitation. Simulation biases are mainly identified in the underestimation of temperature but overestimation of precipitation. At the station sites， both simulated temperature and precipitation have almost identically normal distribution patterns for observed temperature and precipitation， specifically with deviations in winter temperature and summer precipitation simulations at each station.

Key words: WRF, the Qilian Mountains, parametric scheme evaluation, dynamic downscaling

CLC Number:

P413

Xia Li, Bao Yang. Dynamic downscaling simulation of temperature and precipitation in the Qilian Mountains and its surrounding areas[J]. Journal of Desert Research, 2024, 44(1): 61-74.

Figures/Tables 11

Fig.1 WRF simulation of nested areas， altitude and weather station distribution

Table 1 Parametric programme combinations

方案	云微物理过程	积云对流	长波辐射	短波辐射	边界层	近地层	陆面过程
Case A	WSM6	Kain-Fritsch	CAM	CAM	YSU	MM5	Noah
Case B	WSM6	Kain-Fritsch	RRTM	Dudhia	YSU	MM5	Noah
Case C	WSM6	Grell-Devenyi	RRTM	Dudhia	YSU	MM5	Noah
Case D	Thompson	Grell-Devenyi	RRTM	Dudhia	YSU	MM5	Noah
Case E	Thompson	Kain-Fritsch	RRTM	Dudhia	YSU	MM5	Noah

Fig.2 Average daily temperatures averaged over the four seasons of winter， spring， summer and autumn Taylor diagram

Fig.3 Average daily precipitation for the four seasons of winter， spring， summer and autumn Taylor diagram

Table 2 Results of simulated temperature assessment for two sets of topographic data

TM	地形资料	冬季	春季	夏季	秋季
R	默认	0.8514	0.9834	0.9811	0.9694
R	SRTM3	0.8456	0.9848	0.9800	0.9700
RMSE	默认	2.1529	0.9702	1.2508	1.0850
RMSE	SRTM3	2.1583	0.9122	1.3001	1.0944
σ	默认	1.4450	1.0548	1.1808	1.0627
σ	SRTM3	1.4336	1.0444	1.1896	1.0751
PB	默认	23.13%	-43.57%	-4.77%	10.32%
PB	SRTM3	22.61%	-43.27%	-4.49%	4.99%

Table 3 Results of simulated precipitation assessment for two sets of topographic data

PR	地形资料	冬季	春季	夏季	秋季
R	默认	0.5600	0.7439	0.7488	0.8537
R	SRTM3	0.5635	0.7671	0.7610	0.8501
RMSE	默认	0.1220	0.5701	1.9509	0.5111
RMSE	SRTM3	0.1281	0.5527	2.5444	0.5176
σ	默认	2.6045	1.8488	2.3639	1.9915
σ	SRTM3	2.7283	1.8421	2.9436	2.0027
PB	默认	278.60%	79.12%	89.90%	62.41%
PB	SRTM3	283.58%	85.68%	114.29%	67.88%

Fig.4 Spatial distribution of observed （A） and modelled （B） annual mean temperatures and spatial distribution of the difference between the two （C） for 2005-2014

Fig.5 Spatial distribution of daily（A， B， C） and monthly （D， E， F） scale temperature spatial correlation coefficients， root mean square errors and absolute deviations

Fig.6 Spatial distribution of observed （A） and simulated （B） mean annual precipitation and the difference between the two （C） for 2005-2014

Fig.7 Spatial distribution of daily （A， B， C） and monthly （D， E， F） scale precipitation spatial correlation coefficients， root mean square errors and absolute deviations

Fig.8 Quantile-Quantile plots of temperature （left） and precipitation （right） at five sites （Gray shaded region indicates 90% confidence intervals）

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Dynamic downscaling simulation of temperature and precipitation in the Qilian Mountains and its surrounding areas

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