Assessing the Impacts of Initial Snow Conditions over the Tibetan Plateau on China Precipitation Prediction Using a Global Climate Model

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CHEN Hong. Assessing the Impacts of Initial Snow Conditions over the Tibetan Plateau on China Precipitation Prediction Using a Global Climate Model. Atmospheric and Oceanic Science Letters, 2014, 7(2): 81-86 Copy to clipboard

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Assessing the Impacts of Initial Snow Conditions over the Tibetan Plateau on China Precipitation Prediction Using a Global Climate Model

CHEN Hong

International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Corresponding author: CHEN Hong,chh@mail.iap.ac.cn

Abstract

Two ensemble experiments were conducted using a general atmospheric circulation model. These experiments were used to investigate the impacts of initial snow anomalies over the Tibetan Plateau (TP) on China precipitation prediction. In one of the experiments, the initial snow conditions over the TP were climatological values; while in the other experiment, the initial snow anomalies were snow depth estimates derived from the passive microwave remote-sensing data. In the current study, the difference between these two experiments was assessed to evaluate the impact of initial snow anomalies over the TP on simulated precipitation. The results indicated that the model simulation for precipitation over eastern China had certain improvements while applying a more realistic initial snow anomaly, especially for spring precipitation over Northeast China and North China and for summer precipitation over North China and Southeast China. The results suggest that seasonal prediction could be enhanced by using more realistic initial snow conditions over TP, and microwave remote-sensing snow data could be used to initialize climate models and improve the simulation of eastern China precipitation during spring and summer.

Keyword: Tibetan Plateau; initial snow anomalies; predictive skill; precipitation

Show Figures

Further analyses showed that higher snow anomalies over TP cooled the surface, resulting in lower near- surface air temperature over the TP in spring and summer. The surface cooling over TP weakened the Asian summer monsoon and brought more precipitation in South China in spring and more precipitation to Southeast China during summer.

1 Introduction

The impacts of snow on local weather and climate have been extensively studied and well understood. In the North hemisphere, snow cover in the Tibetan Plateau (TP) pla an important role in climate variations, especially in the Asian monsoon systems. Numerous studies have been conducted to understand the impact of snow conditions over the Tibetan Plateau in climate anomalies. It has been proposed by numerous studies that the TP snow cover anomalies in winter and spring have considerable effects on the variability of East Asian summer monsoon (EASM), which lead to the anomalous precipitation in eastern China ( Chen and Wu, 2000; Chen and Song, 2000; Dong and Yu, 1997; Qian et al., 2003; Ose, 1996; Wu and Qian, 2003).

Therefore, having accurate initial conditions of winter and spring snow over TP would be important for seasonal prediction of EASM and its precipitation by models. Despite its importance, the utilization of snow information over TP for seasonal forecast is limited. The lack of snow observations have been the major obstacle for its application. Due to the high elevation at the TP, there are few conventional surface observations, especially for the western sections. Fortunately, remote sensing from satellite could provide useful snow information. Despite its uncertainty, microwave remote-sensing snow depth data could be used to improve the initial snow anomalies for dynamic seasonal prediction models since it includes the information of the spatial distribution of snow amount ( Li, 1993). The objectives of this study were to introduce the microwave remote-sensing snow depth to initialize a global climate model (GCM), to investigate its effect on China precipitation predictability in the simulation of EASM, and to demonstrate the importance of realistic initial snow anomalies to the seasonal prediction of EASM precipitation.

2 Model, data, and numerical experiments

The GCM used in the present study was an Institute of Atmospheric Physic (IAP) nine-level atmospheric general circulation model coupled with Common Land Model ( IAP9L_CoLM; Liu, 2007). IAP9L GCM was developed by IAP (China) in the 1980s, and has 4° × 5° horizontal resolution ( Zhang, 1990). The GCM has been used in seasonal climate predictions ( Lang et al., 2003) since 2002. CoLM was developed by Dai et al. (2003).

The snow data used in this study was snow depth data of China derived from passive microwave remote-sensing from 1979 to 2002 ( Che et al., 2008). Li (1993) proposed that microwave remote-sensing snow depth data was similar to conventional surface observation. Due to the absence of station data in the western part of TP, the mi- crowave remote-sensing snow depth data was used for the ensemble experiment. The station data used to assess the model skill was monthly mean precipitation collected from 160 gauge stations over China, which was obtained by the National Climate Center of the China Meteorological Administration.

Two ensemble simulation experiments, including a control experiment (CTE) and an initial snow anomalies experiment (SAE), were performed using IAP9L_CoLM to investigate the impact of initial snow conditions over TP on model skill. In both these experiments, ten initial atmospheric conditions were obtained 0000 UTC and 0012 UTC National Centers for Environmental Prediction (NCEP) global reanalyses on the last five days of February. Monthly observed SSTs of Hadley Center ( Rayner et al., 2003) were used as a source of anomalous boundary-forced response. The simulations were run from 1980 to 2002, and the integration time was from 15 February to the end of August. The results were expressed as the ensemble mean of ten integrations with identical weights.

Since the initial snow states can only be obtained for the real-time seasonal prediction, in the snow anomalies experiment, the impact of snow anomalies on climate was investigated by changing the initial snow states to get the distribution of snow anomalies. In the CTE, the initial snow conditions were climatological values in February based on the output of multi-year simulations of IAP9L_ CoLM using climatological SSTs. In the SAE, the realistic snow depth anomalies in February over the TP were introduced in initial time, while in the other regions, the initial snow depth was climatological values. The domain of TP was 25-40°N, 70-105°E.

Due to climate biases in the climate model relative to observation, the observed snow anomalies could not be added to the climatological snow value of the model directly. Standard normal variable were used to the first order. was the average value of observed snow depth X_obs for 23 yr, and corresponds to model climatological statistics, the value X_mod used in the initial time is the one that satisfies the equation: .

Using the above equation ensures that a relatively high snow state from observation translates to a corresponding high snow state in the model.

3 Results

3.1 Impact of initial snow anomalies on predictive skill of precipitation

Figures 1a-b showed the temporal correlation coefficients (TCC) for precipitation in spring (seasonal mean of March-April-May). In the CTE (Fig. 1a), positive correlations were observed to the east of 110°E. A better predictive skill was found in the eastern part of Inner Mongolia

and the area to the south of Yangtze River Valley, with TCC value exceeding the 90% confidence level in some parts. The predictive skill increased when the realistic snow anomalies over TP in February were considered. This showed positive correlations for most part of eastern China (Fig. 1b). In addition, statistically significant correlations were obtained in Hetao area, the western part of Northeast China, and Huaihe River Valley. Figure 2a shows the interannual variation of anomaly correlation coefficient (ACC, i.e., spatial correlation coefficient) between observed and simulated spring precipitation in eastern China. The ACC increased in many years when the realistic snow anomalies were considered, the total years with positive ACC increased from 8 to 15 years. The 23-year average ACC increased from -0.04 in the CTE to 0.05 in the SAE.

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	Figure 1 The temporal correlation coefficients (TCC) for precipitation between observation and simulation: (a) Control experiment (CTE) in spring, (b) Snow anomalies experiment (SAE) in spring, (c) CTE in summer, and (d) SAE in summer. The light shaded areas represent positive correlations and dark shaded areas represent correlations with a significance at 0.1 level using Student t-test.

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	Figure 2 The time series of predictive skill for percentage precipitation anomalies: (a) for ACC between observed and simulated spring results in eastern China; (b) and (c) for RSSA between observed and simulated summer results in North China and Yangtze River, respectively.

Eastern China was divided as follows: Northeast China (north of 42°N, east of 110°E), North China (34-42°N, east of 105°E), Yangtze River Valley (26-34°N, east of 105°E), and South China (south of 26°N, east of 105°E). The parameter of ACC and the rate with the same sign of anomaly (RSSA) between the prediction and observations were used to assess the predictive skill of the model ( Chen and Zhao, 1998).

For the predictive skill in sub-region of eastern China (Table 1), 23-year average ACC and RSSA increased in Northeast China, North China, and South China, with ACC improvement from negative value to positive value for North China and South China. In the case of the interannual variation and spatial distribution, the predictive skill for the spring precipitation in eastern China, especially in Northeast China and North China, could be improved by applying realistic TP snow anomalies in February.

Table 1 23-year average anomaly correlation coefficient (ACC) and the rate with the same sign of anomaly (RSSA) between the observed and simulated percentage precipitation anomalies in spring for an Institute of Atmospheric Physic (IAP) nine-level atmospheric general circulation model coupled with Common Land Model (IAP9L_CoLM) with CTE and SAE respectively.

Figure 1c showed that the predictive skill of model for summer (seasonal mean of June-July-August) precipitation was low in eastern China. In CTE, positive correlations were observed only in northern China, such as Northeast China, and the eastern parts of Inner Mongolia. When the realistic snow anomalies over TP in February were applied, the predictive skill improved, and positive correlations were observed in North China, Northeast China, western part of Yangtze-Huaihe River Valley, and a small part of South China (Fig. 1d). In addition, statistically significant correlations were observed over North China.

Table 2 showed the 23-year average ACC and RSSA between the simulated and observed percentage precipitation anomaly in summer for two experiments. The 23- year average predictive skill increased over eastern China. In the sub-region of eastern China, except Northeast China, in the other three regions of eastern China, the 23-year average ACC and RSSA increased with ACC improvement from negative value to positive value. To confirm the results from the correlation calculations, each of the 23 years were examined year by year. For most years, summer precipitation anomalies simulated by SAE agreed with observations better than the CTE, especially in North China and Southeast China (Figs. 2b-c). The predictive skill for summer precipitation in Northeast China had no improvement, which maybe due to TP snow anomalies, which can influence the variation of summer monsoon by changing the sea-land heat contrast, which then impacts the Asian climate ( Dong and Yu, 1997; Zhang and Tao, 2001). But the summer precipitation in Northeast China was more influenced by a climate system in middle-high latitude, the impact of summer monsoon system to it is low, so the predictive skill had no improvement when TP snow anomalies were considered.

Table 2 23-year average ACC and RSSA between the observed and simulate percentage precipitation anomalies in summer for IAP9L_CoLM with CTE and SAE respectively.

In general, the predictive skill of IAP9L_CoLM for summer precipitation in eastern China improved when realistic TP snow anomalies in February were considered, especially in Southeast China and North China. However, the predictive skill of the model for summer precipitation was still low. Hence, to improve the predictive skill of summer precipitation anomalies, statistical-dynamical approaches must be applied to correct the ensemble forecasts.

3.2 Asian summer monsoon associated with TP snow anomalies in February

To understand how anomalous snow is related to precipitation variability, the circulation anomalies associated with anomalous snow conditions were analyzed. Based on snow depth for February averaged over the TP (figure ignored), six heavy snow years (1986,1992, 1993, 1996, 1998, and 2000) were selected to conduct a composite analysis.

Figures 3a-b showed the composite difference in near surface temperature between the SAE and CTE in spring and summer, respectively. The higher snow anomalies in SAE cooled the surface, resulting in lower near-surface air temperature over the TP than in CTE during both periods.

Figure 3c showed the difference in the mean geopotential height and winds at 850 hPa in spring between SAE and CTE. The surface cooling in SAE resulted in positive anomalies in geopotential height at 850 hPa over TP. In addition, there was an anti-cyclonic wind anomaly over the TP. From Fig. 3c, it can be seen that there are easterlies anomalies on the south side of TP. These anomalies suppressed the northward extension of the southwesterly monsoon flow and the northward propagation of its associated precipitation belt. This increased the precipitation in South China and reduced the precipitation in other parts of the monsoon regions over eastern China (Fig. 4c); the difference between SAE and CTE showed similar patterns as observed (Fig. 4a), which suggests improved spring precipitation predictions in SAE.

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	Figure 3 Composite differences in near surface temperature between the SAE and CTE in (a) spring and (b) summer, respectively; difference in mean geopotential height and wind vector at 850 hPa between SAE and CTE in (c) spring and (d) summer.

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	Figure 4 The composite observed percentage precipitation anomalies in (a) spring and (b) summer; the composite difference in seasonal mean percentage precipitation anomalies in (c) spring and (d) summer between SAE and CTE over eastern China.

Figure 3d showed the difference in the mean geopotential height and winds at 850hPa in summer between SAE and CTE. There was an anti-cyclonic wind anomaly over the Indian subcontinent and easterlies anomalies over the Bay of Bengal. In addition, northerly wind anomalies were found in Somali, all of these suggesting that the Asian summer monsoon is weaker when TP snow is above normal in February. Figure 3d also showed that there was a cyclonic wind anomaly over western tropical Pacific, suggesting that summer western tropical Pacific high in high TP snow year was weak and southward than in normal year. Low-level northerlies wind anomalies can be found in Southeast China, and anti-cyclonic wind anomaly can be found in North China. These anomalies resulted in the increased precipitation in southeast China and decreased precipitation in North China (Fig. 4d), which was similar to the observation (Fig. 4b). Thus, with observed initial TP snow anomaly, the potential predictability for summer precipitation also increased in these two regions.

4 Conclusion and discussion

The impact of winter snow anomalies on China precipitation prediction over TP was investigated on the basis of two ensemble experiments with IAP9L_CoLM. For one of the experiments, the initial snow states over the TP were the climatological values; for the other experiment, the initial snow anomalies were considered from February microwave remote-sensing snow depth estimates. The results indicated that the prescribed snow anomalies in February in GCM played an effective role in capturing the observed spatial/temporal patterns of interannual variations of precipitation over eastern China. The predictive skill for spring precipitation increased in many part of eastern China, especially in Northeast China and North China. Although the predictive skill for summer precipitation was low, the potential predictability for Southeast China and North China increased with realistic TP snow anomalies in February.

Composite analyses showed that the higher snow anomalies in TP cooled the surface, resulting in lower near- surface air temperature over TP. The surface cooling over TP weakened the Asian monsoon and brought more precipitation in South China in spring and more precipitation to Southeast China in summer, which were close to the observations. Therefore, the results demonstrated that the microwave remote-sensing snow data could be used to initialize climate models and improve the simulation of East Asian precipitation during spring and summer.

In the current paper, the impact of winter snow anomalies over TP on predictive skill of Chinese climate was investigated. The mechanism of snow’s influence on Chinese weather and climate needs to be further researched. Since the implementation of the microwave remote- sensing snow conditions were imperfect, the assessment of the snow impact on the GCM precipitation could be underestimated. It should be noted that the predictive skill of IAP9L_CoLM for eastern China climate improved when initial snow condition in February were considered. Hence, to improve the predictive skill of China climate, it is crucial to explore suitable snow initialization scheme for the climate models. The analysis presents results from IAP9L_CoLM, and the quantitative assessment of impact of snow anomalies on China climate were considerably model dependent, so other models should be taken into account in future studies.

Acknowledgements. This work was jointly supported by the National Basic Research Program of China (Grant No. 2009CB421407), the Special Fund for Public Welfare (Meteorology) (Grant No. GYHY200906018), "Strategic Priority Research Program—Climate Change: Carbon Budget and Related Issues" of the Chinese Academy of Sciences (Grant No. XDA05110201), and the National Key Technologies R&D Program of China (Grant No. 2007BAC29B03).

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2008

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... The snow data used in this study was snow depth data of China derived from passive microwave remote-sensing from 1979 to 2002 (<xref ref-type="bibr" rid="R1">Che et al ...

1998

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... The parameter of ACC and the rate with the same sign of anomaly (RSSA) between the prediction and observations were used to assess the predictive skill of the model (<xref ref-type="bibr" rid="R2">Chen and Zhao, 1998</xref>) ...

2000

1.338

0.9244

Adv Atmos Sci. 2000, 17(1):18 - 30 DOI:10.1007/s00376-000-0040-7

Interannual and Decadal Variations of Snow Cover over Qinghai-Xizang Plateau and Their Relationships to Summer Monsoon Rainfall in China

Chen Lieting,Wu Renguang

Institute of Atmospheric Physics; Chinese Academy of Sciences; Beijing 100029,Department of Meteorology; University of Hawall at Manoa; Honolulu; HI96822; USA

Interannual and decadal variations of winter snow cover over the Qinghai-Xizang Plateau (QXP) are analyzed by using monthly mean snow depth data set of 60 stations over QXP for the period of 1958 through 1992. It is found that the winter snow cover over QXP bears a pronounced quasi-biennial oscillation, and it underwent an obvious decadal transition from a poor snow cover period to a rich snow cover period in the late 1970��s during the last 40 years.It is shown that the summer rainfall in the eastern China is closely associated with the winter snow cover over QXP not only in the interannual variation but also in the decadal variation. A clear relationship exists in the quasi-biennial oscillation between the summer rainfall in the northern part of North China and the southern China and the winter snow cover over QXP. Furthermore, the summer rainfall in the four climate divisions of Qinling-Daba Mountains, the Yangtze-Huaihe River Plain, the upper and lower reaches of the Yangtze River showed a remarkable transition from drought period to rainy period in the end of 1970��s, in good correspondence with the decadal transition of the winter snow cover over QXP.

... It has been proposed by numerous studies that the TP snow cover anomalies in winter and spring have considerable effects on the variability of East Asian summer monsoon (EASM), which lead to the anomalous precipitation in eastern China (<xref ref-type="bibr" rid="R3">Chen and Wu, 2000</xref> ...

2000

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... <xref ref-type="bibr" rid="R4">Chen and Song, 2000</xref> ...

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1997

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... <xref ref-type="bibr" rid="R6">Dong and Yu, 1997</xref> ...

... The predictive skill for summer precipitation in Northeast China had no improvement, which maybe due to TP snow anomalies, which can influence the variation of summer monsoon by changing the sea-land heat contrast, which then impacts the Asian climate (<xref ref-type="bibr" rid="R6">Dong and Yu, 1997</xref> ...

2003

1.319

0.95

Chin Sci Bull. 2003, 48(19):2121 - 2125 DOI:10.1360/03wd0171

Extraseasonal ensemble numerical predictions of winter climate over China

Lang X. M. , H. J. Wang , D. B. Jiang , 2003 : Extraseasonal ensemble numerical prediction of winter climate over China , Chinese Sci. Bull. , 48 ( 19 ), 2121 - 2125 .

LANG Xianmei;WANG Huijun;JIANG Dabang

LANG Xianmei, WANG Huijun & JIANG Dabang Nansen-Zhu International Research Center and LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

<Abstract>Using the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM) under the Chinese Academy of Sciences, 30-year extraseasonal short-term ensemble hincast of winter climate is performed, with integrations starting from annual autumn during 1969��1998. Winter climate predictability over China is then evaluated for the first time. It follows that the predictability is higher in tropics than in extratropics. Also, it is higher over ocean compared with land, especially for surface air temperature. With height increasing in troposphere, the predictability of geopotential height slightly changes zonally, but for weakening of band-ship distribution and dropping near the date line. Of all analyzed variables, the prediction skill of air temperature and geopotential height (precipitation) is the highest (smallest). In addition, the predictability of winter climate over China and even East Asia enhances obviously during ENSO cycle, especially during La Nia phase. Simulation comparison against verifying analysis for surface temperature anomaly exhibits the model�s skill in predicting surface temperature�s interannual variation trend in winter.

Using the nine-level Atmospheric General Circulation Model developed at the Institute of Atmospheric Physics (IAP9L-AGCM) under the Chinese Academy of Sciences, 30-year extraseasonal short-term ensemble hincast of winter climate is performed, with integrations starting from annual autumn during 1969��1998. Winter climate predictability over China is then evaluated for the first time. It follows that the predictability is higher in tropics than in extratropics. Also, it is higher over ocean compared with land, especially for surface air temperature. With height increasing in troposphere, the predictability of geopotential height slightly changes zonally, but for weakening of band-ship distribution and dropping near the date line. Of all analyzed variables, the prediction skill of air temperature and geopotential height (precipitation) is the highest (smallest). In addition, the predictability of winter climate over China and even East Asia enhances obviously during ENSO cycle, especially during La Niña phase. Simulation comparison against verifying analysis for surface temperature anomaly exhibits the model��s skill in predicting surface temperature��s interannual variation trend in winter.

... The GCM has been used in seasonal climate predictions (<xref ref-type="bibr" rid="R7">Lang et al ...

1993

0.0

2.937

Acta Geogr Sin. 1993, 48(6):505 - 515

DYNAMIC CHARACTERISTIC OF SNOW COVER IN WESTERN CHINA

�й��ѩ�仯��

Li Peiji

��

Lanzhou Institute of Glaciology and Geoeryology, Chinese Academy of Sciences

The dynamic characteristics of spatial distribution��annual cycle and secular variation of seasonal snow cover in western China were investigated by using the best information concerning microwave derived snow depth charts produced by NASA data from SMMR(1978һ1987)��operational NOAA digitized weekly snow cover extent charts(1973һ1987),short wave DMSP imagery(1978һ1987)��in conjuction with daily snow depth records in 175 ground climate stations covering the period from 1957 through 1987. Of all seasons��winter(dec. ,Jan. and Feb. )has the greatest snow volume and areal extent. It is the most variable season too. Yearһtoһyear variability of snow volume is more prominent than that of snow area. The increase in snow cover area is succeeded by the increase in snow volume during growing progression of snow cover .whereas in decaying phase��the decrease in snow volume is followed by the decrease in snow cover area. The large interannual snow cover anomalies over the Tibetan Plateau are the most ,striking feature. The seasonal progress of snow covet in the Tibetan Plateau is opposite to that in the lowlands. It is characterized by a rapid grow and a slow decline. All generated time series of snow volume��snow cover area��and number of snow covered days by using SMMR snow charts��NOAA snow charts ,and ground station data demostrated that the periods with above normal snow cover have included the late I970s and mid 1980s, while the mid 1970s and early 1980s were on the low side. The years with greatest snow volume and snow cover extent in western China were 1976/1977��1977/1978 and 1985/1986��while winters with the least snows were 1973/1974��1984/1985. There is a clear indication of long term trend in 30һyear records over the Tibetan Plateau .increasing with the global warming. Time evolution for the recent 30-year events suggests that both excessive Tibetan snow cover and poor Indian summer monsoon rainfall are mainly result from the global anomalies of oceanic and atmospheric pattern referred as the ElһNinoһSouthern Oscillation. The eastward excursion of the West Pacific warm pool leads to a poor Indian summer monsoons. The wintertime atmospheric anomalies and the northern hemisphere teleconnection with ENSO events bring more snowfall on the Tibetan Plateau. Empirical and modeling studies indicate that snow cover over the Tibetan Plateau plays a significant role in seasonal atmospheric conditions. Our study fails to confirm the widely-accepted view. Perhaps��the fact is that the dearth of snow cover limits the snow-albedo effect and snow-hydrological effect on climate. The implication of these results for the Ice Age issues are also briefly discussed.

�ۺ��й��175��̨վ1957��1987��ջ�ѩ��ȡ��ܶȺ��»�ѩ��,1978��-1987��SMMR�ܻ�ѩ��,1973��1987��NOAA�ܻ�ѩ��,�Լ�50��DMSPӰ��ͼ,��Ĳ��й��ѩ�ռ�ֲ��ڱ仯��ʲ��,��й��ѩ��߶��ЧӦ��ظ�ԭ��ļͱ��˳��ۡ�

... Despite its uncertainty, microwave remote-sensing snow depth data could be used to improve the initial snow anomalies for dynamic seasonal prediction models since it includes the information of the spatial distribution of snow amount (<xref ref-type="bibr" rid="R8">Li, 1993</xref>) ...

2007

0.0

... 2 Model, data, and numerical experimentsThe GCM used in the present study was an Institute of Atmospheric Physic (IAP) nine-level atmospheric general circulation model coupled with Common Land Model (<xref ref-type="bibr" rid="R9">IAP9L_CoLM ...

1996

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... <xref ref-type="bibr" rid="R10">Ose, 1996</xref> ...

2003

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... <xref ref-type="bibr" rid="R11">Qian et al ...

2003

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... Monthly observed SSTs of Hadley Center (<xref ref-type="bibr" rid="R12">Rayner et al ...

2003

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... <xref ref-type="bibr" rid="R13">Wu and Qian, 2003</xref>) ...

2001

0.0

1.948

Chin J Atmosph Sci. 2001, 25(3):372 - 390

The Influences of Snow Cover over the Tibetan Plateau on Asian Summer Monsoon

��ظ�ԭ��ѩ��ļ��Ӱ��ϼ��ֵ�о�

ZhangShunli ;TaoShiyan

��˳��;��ʫ��

ͨ��ظ�ԭ�ࡢ��ѩ��ĺϳɷ��ֵ��飬�о��ظ�ԭ��ѩ��ļ��ҹ��쳣��Ӱ�졣��ظ�ԭ��ѩ��޴��ϴ��ʱ仯��ԭ��ѩ�ı��˸�ԭ½�洺��ļ��״��ʹ��ļ��籬��Ƴ�20��ҡ��ԭ��ѩͨ��Ӱ��ļ��ҹ��򣺸�ԭ��ѩ��(��)��ԭ��ļ��ĸ��(ǿ)��ȼ��˶��(ǿ)��ԭǿ(��)��糡��(��)�ڸ�ԭ��ͨ��͡��ԭ�Ͽն��(ǿ)��ԭ��¶ȵ�(��)��ԭ�ϲ��¶ȶԱ��(ǿ)��ļ��(ǿ)��ҹ��(��)��<dt><t>Abstract��</t></dt><dd>The influences of snow cover over the Tibetan Plateau on Asian summer monsoon are stu died by composed analyses and conducting numerical experiments. The main conclusions are as follows:the snow cover over the Tibetan Plateau changes the characteristics of surface heating and directly in fluences on the Asian summer monsoon and climate of the eastern China. The possible mechanism link ing snow cover over the Tibetan Plateau and anomalous Asian summer monsoon is that more (less)snow cover over the Tibetan Plateau��weak (strong) surface heating in spring and summer��weak (strong) upward motion and strong (weak) environmental wind��unfavorable (favorable) condition for transporting surface sensible heating from near-surface layers to upper layers��weak (strong)tropospheric heating��low (high) tropospheric temperature surrounding the Tibetan Plateau��weak (strong) tropospheric temperature contrast south of the Tibetan Plateau in spring and summer��weak (strong) Asian summer monsoon.

... <xref ref-type="bibr" rid="R14">Zhang and Tao, 2001</xref>) ...

1990

0.0

... horizontal resolution (<xref ref-type="bibr" rid="R15">Zhang, 1990</xref>) ...