Recent Progress in Studies of Climate Change in China

An overview of basic research on climate change in recent years in China is presented. In the past 100 years in China, average annual mean surface air temperature （SAT） has increased at a rate ranging from 0.03℃ （10 yr）-1 to 0.12℃ （10 yr）-1. This warming is more evident in northern China and is more significant in winter and spring. In the past 50 years in China, at least 27% of the average annual warming has been caused by urbanization. Overall, no significant trends have been detected in annual and/or summer precipitation in China on a whole for the past 100 years or 50 years. Both increases and decreases in frequencies of major extreme climate events have been observed for the past 50 years. The frequencies of extreme temperature events have generally displayed a consistent pattern of change across the country, while the frequencies of extreme precipitation events have shown only regionally and seasonally significant trends. The frequency of tropical cyclone landfall decreased slightly, but the frequency of sand/dust storms decreased significantly. Proxy records indicate that the annual mean SAT in the past a few decades is the highest in the past 400-500 years in China, but it may not have exceeded the highest level of the Medieval Warm Period （1000 1300 AD）. Proxy records also indicate that droughts and floods in eastern China have been characterized by continuously abnormal rainfall periods, with the frequencies of extreme droughts and floods in the 20th century most likely being near the average levels of the past 2000 years. The attribution studies suggest that increasing greenhouse gas （GHG） concentrations in the atmosphere are likely to be a main factor for the observed surface warming nationwide. The Yangtze River and Huaihe River basins underwent a cooling trend in summer over the past 50 years, which might have been caused by increased aerosol concentrations and cloud cover. However, natural climate variability might have been a main driver for the mean and extreme precipitation variations observed over the past century. Climate models generally perform well in simulating the variations of annual mean SAT in China. They have also been used to project future changes in SAT under varied GHG emission scenarios. Large uncertainties have remained in these model-based projections, however, especially for the projected trends of regional precipitation and extreme climate events.

A Modeling Study of the Effects of Direct Radiative Forcing Due to Carbonaceous Aerosol on the Climate in East Asia

The study investigated the effects of global direct radiative forcing due to carbonaceous aerosol on the climate in East Asia, using the CAM3 developed by NCAR. The results showed that carbonaceous aerosols cause negative forcing at the top of the atmosphere （TOA） and surface under clear sky conditions, but positive forcing at the TOA and weak negative forcing at the surface under all sky conditions. Hence, clouds could change the sign of the direct radiative forcing at the TOA, and weaken the forcing at the surface. Carbonaceous aerosols have distinct effects on the summer climate in East Asia. In southern China and India, it caused the surface temperature to increase, but the total cloud cover and precipitation to decrease. However, the opposite effects are caused for most of northern China and Bangladesh. Given the changes in temperature, vertical velocity, and surface streamflow caused by carbonaceous aerosol in this simulation, carbonaceous aerosol could also induce summer precipitation to decrease in southern China but increase in northern China.

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC) Part Ⅱ:The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.

Evaluation of the Tropical Variability from the Beijing Climate Center's Real-Time Operational Global Ocean Data Assimilation System

The second-generation Global Ocean Data Assimilation System of the Beijing Climate Center （BCC_GODAS2.0） has been run daily in a pre-operational mode. It spans the period 1990 to the present day. The goal of this paper is to introduce the main components and to evaluate BCC_GODAS2.0 for the user community. BCC_GODAS2.0 consists of an observational data preprocess, ocean data quality control system, a three-dimensional variational （3DVAR） data assimilation, and global ocean circulation model [Modular Ocean Model 4 （MOM4）]. MOM4 is driven by six-hourly fluxes from the National Centers for Environmental Prediction. Satellite altimetry data, SST, and in-situ temperature and salinity data are assimilated in real time. The monthly results from the BCC_GODAS2.0 reanalysis are compared and assessed with observations for 1990-201 I. The climatology of the mixed layer depth of BCC_GODAS2.0 is generally in agreement with that of World Ocean Atlas 2001. The modeled sea level variations in the tropical Pacific are consistent with observations from satellite altimetry on interannual to decadal time scales. Performances in predicting variations in the SST using BCC_GODAS2.0 are evaluated. The standard deviation of the SST in BCC_GODAS2.0 agrees well with observations in the tropical Pacific. BCC_GODAS2.0 is able to capture the main features of E1 Nifio Modoki I and Modoki II, which have different impacts on rainfall in southern China. In addition, the relationships between the Indian Ocean and the two types of E1 Nino Modoki are also reproduced.

Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models

This study assessed the regional climate models (RCMs) employed in the Coordinated Regional climate Downscaling Experiment (CORDEX) South Asia framework to investigate the qualitative aspects of future change in seasonal mean near surface air temperature and precipitation over the Hindu Kush Himalayan (HKH) region. These RCMs downscaled a subset of atmosphere ocean coupled global climate models (AOGCMs) in the Coupled Model Intercomparison Project phase 5 (CMIP5) to higher 50 km spatial resolution over a large domain covering South Asia for two representation concentration pathways (RCP4.5 and RCP8.5) future scenarios. The analysis specifically examined and evaluated multi-model and multi-scenario climate change projections over the hilly sub-regions within HKH for the near-future (2036e2065) and far-future (2066e2095) periods. The downscaled multi-RCMs provide relatively better confidence than their driving AOGCMs in projecting the magnitude of seasonal warming for the hilly sub-region within the Karakoram and northwestern Himalaya, with higher projected change of 5.4
C during winter than of 4.9
C during summer monsoon season by the end of 21st century under the high-end emissions (RCP8.5) scenario. There is less agreement among these RCMs on the magnitude of the projected warming over the other sub-regions within HKH for both seasons, particularly associated with higher RCM uncertainty for the hilly sub-region within the central Himalaya. The downscaled multi-RCMs show good consensus and low RCM uncertainty in projecting that the summer monsoon precipitation will intensify by about 22% in the hilly subregion within the southeastern Himalaya and Tibetan Plateau for the far-future period under the RCP8.5 scenario. There is low confidence in the projected changes in the summer monsoon and winter season precipitation over the central Himalaya and in the Karakoram and northwestern Himalaya due to poor consensus and moderate to high RCM uncertainty among the downscaled multi-RCMs. Finally, the RCM related uncertainty is found to be large for the projected changes in seasonal temperature and precipitation over the hilly sub-regions within HKH by the end of this century, suggesting that improving the regional processes and feedbacks in RCMs are essential for narrowing the uncertainty, and for providing more reliable regional climate change projections suitable for impact assessments in HKH region.

An overview of studies of observed climate change in the Hindu Kush Himalayan (HKH) region

The Hindu Kush Himalayan (HKH hereafter) region is characterized by mountainous environments and a variety of regional climatic conditions. High-altitude regions in the HKH have the recent warming amplifications, especially during the global warming hiatus period. The rapid warming cause solid state water (snow, ice, glacier, and permafrost) to shrink, leading to increase in meltwater and there have been found more frequent incidences of flash floods, landslides, livestock diseases, and other disasters in the HKH region. Increasing awareness of climate change over the HKH region is reached a consensus. Meanwhile, the HKH region is often referred to as the water towers of Asia as many highaltitude regions store its water in the form of snow and/or glacier, feeding ten major large rivers in Asia. Therefore, the impacts of climate change on water availability in these river basins have huge influences on the livelihood of large number of population, especially in downstream regions. However, the scarcity of basic hydro-meteorological observations particularly in high-altitude regions of HKH limits rigorous analysis of climate change. Most studies used reanalysis data and/or model-reconstructed products to explore the spatial and temporal characteristics of hydro-meteorological processes, especially for extreme events. In this study, we review recent climate change in the HKH region, and the scientific challenges and research recommendations are suggested for this high-altitude area.

The Soil Moisture of China in a High Resolution Climate-Vegetation Model

The spatial distribution of soil moisture, especially the temporal variation at seasonal and interannual scales, is difficult for many land surface models （LSMs） to capture partly due to the deficiencies of the LSMs and the highly spatial variability of soil moisture, which makes it problematic to simulate the moisture for climate studies. However the soil moisture plays an important role in influencing the energy and hydrological cycles between the land and air, so it should be considered in land surface models. In this paper, a soil moisture simulation in China with a T213 resolution （about 0.5625°× 0.5625°） is compared to the observational data, and its relationship to precipitation is explored. The soil moisture distribution agrees roughly with the observations, and the soil moisture pattern reflects the variation and intensity of the precipitation. In particular, for the 1998 summer catastrophic floods along the Yangtze River, the soil moisture remains high in this region from July to August and represents the flood well. The seasonal cycle of soil moisture is roughly consistent with the observed data, which is a good calibration for the ground simulation capacity of the Atmosphere-Vegetation Interaction Model （AVIM） with respect to this tough problem for land surface models.

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Climate change in Mt. Qomolangma region since 1971

Using monthly average, maximum, minimum air temperature and monthly precipitation data from 5 weather stations in Mt. Qomolangma region in China from 1971 to 2004, climatic linear trend, moving average, low-pass filter and accumulated variance analysis methods, the spatial and temporal patterns of the climatic change in this region were analyzed. The main findings can be summarized as follows： （1） There is obvious ascending tendency for the interannual change of air temperature in Mt. Qomolangma region and the ascending tendency of Tingri, the highest station, is the most significant. The rate of increasing air temperature is 0.234℃/decade in Mt. Qomolangma region, 0.302 ℃/decade in Tingxi. The air temperature increases more strongly in non-growing season. （2） Compared with China and the global average, the warming of Mt. Qomolangma region occurred early. The linear rates of temperature increase in Mt. Qomolangma region exceed those for China and the global average in the same period. This is attributed to the sensitivity of mountainous regions to climate change. （3） The southern and northern parts of Mt. Qomolangma region are quite different in precipitation changes. Stations in the northern part show increasing trends but are not statistically significant. Nyalam in the southern part shows a decreasing trend and the sudden decreasing of precipitation occurred in the early 1990s. （4） Compared with the previous studies, we find that the warming of Mt. Qomolangma high-elevation region is most significant in China in the same period. The highest automatic meteorological comprehensive observation station in the world set up at the base camp of Mt. Qomolangma with a height of 5032 m a.s.l will play an important role in monitoring the global climate change.

Urbanization as a major driver of urban climate change

1.Definition Urbanization is the process of urban development and is characterized by the expansion of built-up areas and the growth of population in cities and towns.In the contexts of global change research and climatic science,urbanization is usually regarded as a special manifestation of land use and land cover(LULC)change on a local scale.Urbanization affects the local land surface,altering the surface climate in urban areas.The urbanization-induced change in surface parameters is a fundamental reason for the formation and evolution of urban climates.The urbanizationinduced climate change(UICC)in urban areas becomes

Implementation of a Surface Runoff Model with Horton and Dunne Mechanisms into the Regional Climate Model RegCM_NCC

A surface runoff parameterization scheme that dynamically represents both Horton and Dunne runoff generation mechanisms within a model grid cell together with a consideration of the subgrid-scaie soil heterogeneity, is implemented into the National Climate Center regional climate model （RegCM_NCC）. The effects of the modified surface runoff scheme on RegCMANCC performance are tested with an abnormal heavy rainfall process which occurred in summer 1998. Simulated results show that the model with the original surface runoff scheme （noted as CTL） basically captures the spatial pattern of precipitation, circulation and land surface variables, but generally overestimates rainfall compared to observations. The model with the new surface runoff scheme （noted as NRM） reasonably reproduces the distribution pattern of various variables and effectively diminishes the excessive precipitation in the CTL. The processes involved in the improvement of NRM-simulated rainfall may be as follows： with the new surface runoff scheme, simulated surface runoff is larger, soil moisture and evaporation （latent heat flux） are decreased, the available water into the atmosphere is decreased; correspondingly, the atmosphere is drier and rainfall is decreased through various processes. Therefore, the implementation of the new runoff scheme into the RegCMANCC has a significant effect on results at not only the land surface, but also the overlying atmosphere.

Climate-Induced Variability of Sea Level in Stockholm: Influence of Air Temperature and Atmospheric Circulation

This study is focused on climate-induced variation of sea level in Stockholm during 1873-1995. After the effect of the land uplift, is removed, the residual is characterized and related to large-scale temperature and atmospheric circulation. The residual shows an overall upward trend, although this result depends on the uplift rate used. However, the seasonal distribution of the trend is uneven. There are even two months （June and August） that show a negative trend. The significant trend in August may be linked to fresh water input that is controlled by precipitation. The influence of the atmospheric conditions on the sea level is mainly manifested through zonal winds, vorticity and temperature. While the wind is important in the period January-May, the vorticity plays a main role during June and December. A successful linear multiple-regression model linking the climatic variables （zonal winds, vorticity and mean air temperature during the previous two months） and the sea level is established for each month. An independent verification of the model shows that it has considerable skill in simulating the variability.

Climate change in the Hindu Kush Himalaya

The Hindu Kush Himalaya is the highest mountainous andplateau system in the world, sitting on most of the world'shighest peaks over 8000 m in height (Fig. 1). This regionencompasses an area of more than 4.3 million km2 and is characterized by a diversity of physiographic landscapes, climate types and bio-systems, the largest cryosphere in the world beyond the two poles, and being the source of a number of highly important large rivers including the Brahmaputra, Ganges, Indus, Mekong, Yangtze, and Yellow Rivers. The HKH is populated by about 210 million people and an additional 1.3 billion people live in downstream basins of the ten large rivers originating from this region.

Preliminary assessment on the hindcast skill of the Arctic Oscillation with decadal experiment by the BCC_CSM1.1 climate model

The prediction skill of Arctic Oscillation (AO) in the decadal experiments with the Beijing Climate Center Climate System Model version 1.1 (BCC_CSM1.1) is assessed. As compared with the observations and historical experiments, the contribution of initialization for climate model to predict the seasonal scale AO and its interannual variations is estimated. Results show that the spatial correlation coefficient of AO mode simulated by the decadal experiment is higher than that in the historical experiment. The two groups of experiments reasonably reproduce the characteristics that AO indices are the strongest in winter and the weakest in summer. Compared with historical experiments, the correlation coefficient of the monthly and winter AO indices are higher in the decadal experiments. In particular, the correlation coefficient of monthly AO index between decadal hindcast and observation reached 0.1 significant level. Furthermore, the periodicity of the monthly and spring AO indices are achieved only in the decadal experiments. Therefore, the initial state of model is initialized by using sea temperature data may help to improve the prediction skill of AO in the decadal prediction experiments to some extent.

‘Initial’ Soil Moisture Effects on the Climate in China——A Regional Climate Model Study

In this study,the effects of ‘initial’ soil moisture(SM) in arid and semi-arid Northwestern China on subsequent climate were investigated with a regional climate model. Besides the control simulations(denoted as CTL),a series of sensitivity experiments were conducted,including the DRY and WET experiments,in which the simulated ‘initial’ SM over the region 30 –50°N,75 –105°E was only 5% and 50%,and up to 150% and 200% of the simulated value in the CTL,respectively. The results show that SM change can modify the subsequent climate in not only the SM-change region proper but also the far downstream regions in Eastern and even Northeastern China. The SM-change effects are generally more prominent in the WET than in the DRY experiments. After the SM is initially increased,the SM in the SM-change region is always higher than that in the CTL,the latent(sensible) heat flux there increases(decreases),and the surface air temperature decreases. Spatially,the most prominent changes in the WET experiments are surface air temperature decrease,geopotential height decrease and corresponding abnormal changes of cyclonic wind vectors at the mid-upper troposphere levels. Generally opposite effects exist in the DRY experiments but with much weaker intensity. In addition,the differences between the results obtained from the two sets of sensitivity experiments and those of the CTL are not always consistent with the variation of the initial SM. Being different from the variation of temperature,the rainfall modifications caused by initial SM change are not so distinct and in fact they show some common features in the WET and DRY experiments. This might imply that SM is only one of the factors that impact the subsequent climate,and its effect is involved in complex processes within the atmosphere,which needs further investigation.

IMPROVEMENT OF OCEAN DATA ASSIMILATION SYSTEM AND CLIMATE PREDICTION BY ASSIMILATING ARGO DATA

The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.

Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models

The improvement of the accuracy of simulated cloud-related variables,such as the cloud fraction,in global climate models(GCMs)is still a challenging problem in climate modeling.In this study,the influence of cloud microphysics schemes(one-moment versus two-moment schemes)and cloud overlap methods(observation-based versus a fixed vertical decorrelation length)on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero.Compared with the fixed decorrelation length method,the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions.The utilization of a two-moment cloud microphysics scheme,on the other hand,notably improved the simulated cloud fraction compared with the one-moment scheme;specifically,the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%.Furthermore,the total cloud fraction bias decreased by 6.6%in the boreal winter(DJF)and 1.64%in the boreal summer(JJA).Cloud radiative forcing globally and in the four regions improved by 0.3%−1.2% and 0.2%−2.0%,respectively.Thus,our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.

The Unprecedented Extreme Anticyclonic Anomaly over Northeast Asia in July 2021 and Its Climatic Impacts

This study investigates the evolution of an extreme anomalous anticyclone(AA)event over Northeast Asia,which was one of the dominant circulation systems responsible for the catastrophic extreme precipitation event in July 2021 in Henan,and further explores the significant impact of this AA on surface temperatures beneath it.The results indicate that this AA event over Northeast Asia was unprecedented in terms of intensity and duration.The AA was very persistent and extremely strong for 10 consecutive days from 13 to 22 July 2021.This long-lived and unprecedented AA led to the persistence of warmer surface temperatures beyond the temporal span of the pronounced 500-hPa anticyclonic signature as the surface air temperatures over land in Northeast Asia remained extremely warm through 29 July 2021.Moreover,the sea surface temperatures in the Sea of Japan/East Sea were extremely high for 30 consecutive days from 13 July to 11 August 2021,persisting well after the weakening or departure of this AA.These results emphasize the extreme nature of this AA over Northeast Asia in July 2021 and its role in multiple extreme climate events,even over remote regions.Furthermore,possible reasons for this long-lasting AA are explored,and it is suggested to be a byproduct of a teleconnection pattern over extratropical Eurasia during the first half of its life cycle,and of the Pacific-Japan teleconnection pattern during the latter half.

Multimodel Ensemble Forecasts for Precipitations in China in 1998

Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the “Climate of the 20th Century Experiment” （20C3M） for the Fourth IPCC Assessment Report. Climate Research Unit （CRU） data were chosen for the observation analysis field. Root mean square （RMS） error and correlation coeffi-cients （R） are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression （MLR） performs best. However, for bias-corrected data, the superensemble based on singular value decomposition （SVD） produces a lower RMS error and a higher R than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.

An Empirical Formula to Compute Snow Cover Fraction in GCMs

There exists great uncertainty in parameterizing snow cover fraction in most general circulation models (GCMs) using various empirical formulae, which has great influence on the performance of GCMs. This work reviews the commonly used relationships between region-averaged snow depth (or snow water equivalent) and snow cover extent (or fraction) and suggests a new empirical formula to compute snow cover fraction, which only depends on the domain-averaged snow depth, for GCMs with different horizontal resolution. The new empirical formula is deduced based on the 10-yr (1978-1987) 0.5°× 0.5° weekly snow depth data of the scanning multichannel microwave radiometer (SMMR) driven from the Nimbus-7 Satellite. Its validation to estimate snow cover for various GCM resolutions was tested using the climatology of NOAA satellite-observed snow cover.