Snow cover variability across central Canada (1978–2002) derived from satellite passive microwave data

Twenty-four winter seasons (1978–2002) of mean February snow water equivalent (SWE) values were analyzed in an exploration of the spatial pattern of temporal variability in snow cover across the non-mountainous interior of Canada. The SWE data were derived from space-borne passive microwave brightness temperatures processed with a land cover-sensitive suite of algorithms. Spatial patterns in the frequency and amount of variability were investigated on an annual basis through comparisons with average trends over all 24 years. Changes in temporal variability through time were also investigated by comparing three eight year time periods to general trends. Analyses were synthesized at the ecozone scale in order to link results both to potential land cover influences on algorithm performance and climatological variability in SWE. Prairie and northern ecozones were typically found to be the most variable in terms of SWE magnitude. Analyses indicate that non-treed land cover classes are generally more variable than treed classes. The results also indicate that extreme weather events appear to be occurring with increasing consistency in the Prairie and Arctic regions. Discerning climatologically significant variability in the time series, compared to algorithm-related issues can be a challenge, but in an era of eroding surface observing networks the passive microwave time series represents an important resource for monitoring and detecting trends and variability in terrestrial snow cover.     

Interpreting observed northern hemisphere snow trends with large ensembles of climate simulations

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.     

Modeling soil thermal and hydrological dynamics and changes of growing season in Alaskan terrestrial ecosystems

Abundant evidence indicates the growing season has been changed in the Alaskan terrestrial ecosystems in the last century as climate warms. Reasonable simulations of growing season length, onset, and ending are critical to a better understanding of carbon dynamics in these ecosystems. Recent ecosystem modeling studies have been slow to consider the interactive effects of soil thermal and hydrological dynamics on growing season changes in northern high latitudes. Here, we develop a coupled framework to model these dynamics and their effects on plant growing season at a daily time step. In this framework, we (1) incorporate a daily time step snow model into our existing hydrological and soil thermal models and (2) explicitly model the moisture effects on soil thermal conductivity and heat capacity and the effects of active layer depth and soil temperature on hydrological dynamics. The new framework is able to well simulate snow depth and soil temperature profiles for both boreal forest and tundra ecosystems at the site level. The framework is then applied to Alaskan boreal forest and tundra ecosystems for the period 1923–2099. Regional simulations show that (1) for the historical period, the growing season length, onset, and ending, estimated based on the mean soil temperature of the top 20 cm soils, and the annual cycle of snow dynamics, agree well with estimates based on satellite data and other approaches and (2) for the projected period, the plant growing season length shows an increasing trend in both tundra and boreal forest ecosystems. In response to the projected warming, by year 2099, (1) the snow-free days will be increased by 41.0 and 27.5 days, respectively, in boreal forest and tundra ecosystems and (2) the growing season lengths will be more than 28 and 13 days longer in boreal forest and tundra ecosystems, respectively, compared to 2010. Comparing two sets of simulations with and without considering feedbacks between soil thermal and hydrological dynamics, our analyses suggest coupling hydrological and soil thermal dynamics in Alaskan terrestrial ecosystems is important to model ecosystem dynamics, including growing season changes.     

Coupled atmosphere-ocean-vegetation simulations for modern and mid-Holocene climates: role of extratropical vegetation cover feedbacks

A full global atmosphere-ocean-land vegetation model is used to examine the coupled climate/vegetation changes in the extratropics between modern and mid-Holocene (6,000 year BP) times and to assess the feedback of vegetation cover changes on the climate response. The model produces a relatively realistic natural vegetation cover and a climate sensitivity comparable to that realized in previous studies. The simulated mid-Holocene climate led to an expansion of boreal forest cover into polar tundra areas (mainly due to increased summer/fall warmth) and an expansion of middle latitude grass cover (due to a combination of enhanced temperature seasonality with cold winters and interior drying of the continents). The simulated poleward expansion of boreal forest and middle latitude expansion of grass cover are consistent with previous modeling studies. The feedback effect of expanding boreal forest in polar latitudes induced a significant spring warming and reduced snow cover that partially countered the response produced by the orbitally induced changes in radiative forcing. The expansion of grass cover in middle latitudes worked to reinforce the orbital forcing by contributing a spring cooling, enhanced snow cover, and a delayed soil water input by snow melt. Locally, summer rains tended to increase (decrease) in areas with greatest tree cover increases (decreases); however, for the broad-scale polar and middle latitude domains the climate responses produced by the changes in vegetation are relatively much smaller in summer/fall than found in previous studies. This study highlights the need to develop a more comprehensive strategy for investigating vegetation feedbacks.     

中国西部积雪类型划分

利用中国105°E以西地区189个地面气象台站1960-2004年积雪日资料和1981-2004年SMMR、SSM/Ⅰ反演的逐日雪深资料,使用积雪年际变率方法划分中国西部积雪类型,并与积雪日数方法的划分结果进行比较.在此基础上,尝试建立了结合以上两种要素的综合分类指标.利用积雪年际变率方法和台站资料,将中国西部积雪划分为3类.其中,稳定积雪区主要包括北疆、天山和青藏高原东部高海拔山区;年周期性不稳定积雪区包括南疆和东疆盆地周边、河西走廊、青海北部、青藏高原中西部、藏南谷地以及青藏高原东南缘;其他积雪区均为非年周期性不稳定积雪区.气候突变后,积雪日数方法划分的积雪类型变化反映出沙漠和低纬度地区积雪变幅增大,在积雪年际变率方法的结果中体现出青藏高原东部地区趋于稳定的积雪面积在增加.在没有台站记录地区,卫星遥感资料很大程度上弥补了台站观测的缺陷,使用这种资料划分积雪类型时,积雪年际变率方法比积雪日数方法的结果更符合西部积雪的分布特点,反映出积雪分布与地形的密切关系.利用综合分类指标划分西部积雪类型的结果表明,台站资料的划分结果很大程度上受积雪持续时间的影响,而在卫星遥感结果中,积雪年际变率则是影响类型划分的主要因素.     

Application of the Canadian regional climate model to the Laurentian great lakes region: Implementation of a lake model

Abstract

This study reports on the implementation of an interactive mixed‐layer/thermodynamic‐ice lake model coupled with the Canadian Regional Climate Model (CRCM). For this application the CRCM, which uses a grid mesh of 45 km on a polar stereographic projection, 10 vertical levels, and a timestep of 15 min, is nested with the second generation Canadian General Circulation Model (GCM) simulated output. A numerical simulation of the climate of eastern North America, including the Laurentian Great Lakes, is then performed in order to evaluate the coupled model. The lakes are represented by a “mixed layer” model to simulate the evolution of the surface water temperature, and a thermodynamic ice model to simulate evolution of the ice cover. The mixed‐layer depth is allowed to vary spatially. Lake‐ice leads are parametrized as a function of ice thickness based on observations. Results from a 5‐year integration show that the coupled CRCM/lake model is capable of simulating the seasonal evolution of surface temperature and ice cover in the Great Lakes. When compared with lake climatology, the simulated mean surface water temperature agrees within 0.12°C on average. The seasonal evolution of the lake‐ice cover is realistic but the model tends to underestimate the monthly mean ice concentration on average. The simulated winter lake‐induced precipitation is also shown, and snow accumulation patterns on downwind shores of the lakes are found to be realistic when compared with observations.     

The relationship between anomalies of interannual net radiation and 37 GHz polarization difference over North Africa

We show evidence that interannual variations in the net radiation balance at the top-of-atmosphere over North Africa may be linked to variations at the land surface within the Sahelian zone. By utilizing concurrent monthly time series of earth radiation budget and passive microwave measurements obtained from the Nimbus 7 satellite over a five year period from 1979 to 1983, we have examined the spatial distribution of the amplitude of interannual variability of the net radiation at the top of the atmosphere. A strong association is found between the radiation budget anomalies derived from ERB-WFOV measurements and the anomalies of 37 GHz polarized brightness temperature difference derived from SMMR measurements. Previous research has shown that the interannual anomalies in the top-of-atmosphere net radiation balance in the Sahelian zone mostly arise from changes taking place at the surface, not from variations in cloudiness which brings variable rainfall throughout the African Sahel. Since microwave polarization difference is related in part to vegetation cover, we suggest that the main control of the modulation of North Africa's radiation balance may be year to year fluctuations of vegetation cover within the Sahelian zone, likely stemming from fluctuations in the rainfall. This relationship is relevant to the problem of radiation control and feedback on global climate through processes taking place within the biosphere of a semi-arid zone.     

青藏高原地区积雪及其变化的不确定性:3种积雪观测资料的对比分析

为了探究青藏高原积雪不同观测资料间的差异,本文通过定义积雪覆盖率(Snow Cover Percentage,SCP)对比了NOAA-CDR卫星可见光遥感积雪资料、卫星被动微波遥感积雪资料和我国146个台站观测的积雪资料在高原地区的气候态及年际变动特征。从年平均气候态看,微波与可见光资料的SCP分布较为接近,高值区均位于念青唐古拉山与喜马拉雅山南缘之间的山区。而台站资料SCP的高值区范围则相对较小,在高原东部的巴颜喀拉山及南部的念青唐古拉山。3种资料的积雪低值区均位于高原中南部沿雅鲁藏布江一带、阿尔金山北侧以及东边界的内陆省份。从季节平均场看,不同资料的积雪分布在冬季及秋季,无论是气候态还是年际变动均较为类似。在春季时,微波和台站资料间较为一致。而在夏季,资料间差异很大,不同资料间的两两相关接近于零,甚至为负数。本文同时选取了青藏高原地区4个典型台站(索县、清水河、康定、甘孜),将卫星资料插值于台站上,对比3种资料间的异同,以及与地表气温异常间的关系。结果表明,在这4个典型站上,台站SCP在过去36 a中为线性减少的趋势,而卫星SCP主要为线性增加的趋势,且台站年平均SCP与地表气温异常的协同性最好。     

Alpine snow cover in a changing climate: a regional climate model perspective

An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 % by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 % by mid century and to 30–80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.     

Detecting Arctic Climate Change Using KÖppen Climate Classification

Ecological impacts of the recent warming trend in the Arctic are already noted as changes in tree line and a decrease in tundra area with the replacement of ground cover by shrubs in northern Alaska and several locations in northern Eurasia. The potential impact of vegetation changes to feedbacks on the atmospheric climate system is substantial because of the large land area impacted and the multi-year persistence of the vegetation cover. Satellite NDVI estimates beginning in 1981 and the Köppen climate classification, which relates surface types to monthly mean air temperatures from 1901 onward, track these changes on an Arctic-wide basis. Temperature fields from the NCEP/NCAR reanalysis and CRU analysis serve as proxy for vegetation cover over the century. A downward trend in the coverage of tundra group for the first 40 yr of the twentieth century was followed by two increases during 1940s and early 1960s, and then a rapid decrease in the last 20 yr. The decrease of tundra group in the 1920–40 period was localized, mostly over Scandinavia; whereas the decrease since 1990 is primarily pan-Arctic, but largest in NW Canada, and eastern and coastal Siberia. The decrease in inferred tundra coverage from 1980 to 2000 was 1.4 × 10⁶ km², or about a 20% reduction in tundra area based on the CRU analyses. This rate of decrease is confirmed by the NDVI data. These tundra group changes in the last 20 yr are accompanied by increase in the area of both the boreal and temperate groups. During the tundra group decrease in the first half of the century boreal group area also decreased while temperate group area increased. The calculated minimum coverage of tundra group from both the Köppen classification and NDVI indicates that the impact of warming on the spatial coverage of the tundra group in the 1990s is the strongest in the century, and will have multi-decadal consequences for the Arctic.     

Update of Canadian Historical Snow Survey Data and Analysis of Snow Water Equivalent Trends, 1967–2016

ABSTRACT

In situ observations of snow water equivalent (SWE) from manual snow surveys and automated sensors are made at approximately 1000 sites across Canada in support of water resource planning for flood control and hydroelectricity production. These data represent an important source of information for research (e.g., validation of hydrological and climate models), for applied studies (e.g., ground snow loads), and for climate monitoring. This note describes the process to update a Canadian historical snow survey dataset to 2016 and the production of a 0.1° gridded version for research applications. Analysis of trends in SWE, snow depth (SD), and density over the 50-year period from 1967 to 2016 revealed large spatial variability in trend sign and strength, with a relatively small percentage of points showing statistically significant trends. Where SWE and SD trends were significant, they tended to be negative, which is consistent with previous investigations of snow cover changes in Canada. The results show evidence of a latitudinal dependence in SWE trends, with the largest negative trends occurring over lower latitudes, and a tendency for mainly positive trends in Arctic SWE, which is consistent with observations from Russia and climate model projections of the response of Arctic snow cover to climate warming. Arctic sites also showed evidence of an increasing trend in 1 April snowpack density of 6.6?kg m^?3 per decade but little corresponding change in SD. This has potentially important consequences for the soil thermal regime because it provides a cooling influence from an increase in the snowpack effective thermal conductivity. The snow survey dataset is available from the Government of Canada Open Data portal.     

Boreal forest and tundra ecosystems as components of the climate system

The effects of terrestrial ecosystems on the climate system have received most attention in the tropics, where extensive deforestation and burning has altered atmospheric chemistry and land surface climatology. In this paper we examine the biophysical and biogeochemical effects of boreal forest and tundra ecosystems on atmospheric processes. Boreal forests and tundra have an important role in the global budgets of atmospheric CO₂ and CH₄. However, these biogeochemical interactions are climatically important only at long temporal scales, when terrestrial vegetation undergoes large geographic redistribution in response to climate change. In contrast, by masking the high albedo of snow and through the partitioning of net radiation into sensible and latent heat, boreal forests have a significant impact on the seasonal and annual climatology of much of the Northern Hemisphere. Experiments with the LSX land surface model and the GENESIS climate model show that the boreal forest decreases land surface albedo in the winter, warms surface air temperatures at all times of the year, and increases latent heat flux and atmospheric moisture at all times of the year compared to simulations in which the boreal forest is replaced with bare ground or tundra. These effects are greatest in arctic and sub-arctic regions, but extend to the tropics. This paper shows that land-atmosphere interactions are especially important in arctic and sub-arctic regions, resulting in a coupled system in which the geographic distribution of vegetation affects climate and vice versa. This coupling is most important over long time periods, when changes in the abundance and distribution of boreal forest and tundra ecosystems in response to climatic change influence climate through their carbon storage, albedo, and hydrologic feedbacks.     

Modelling spatially distributed snowmelt and meltwater runoff in a small Arctic catchment with a hydrology land‐surface scheme (WATCLASS)

Abstract

The fully distributed hydrology land‐surface scheme WATCLASS is used to simulate spring snowmelt runoff in a small Arctic basin, Trail Valley Creek, dominated by open tundra and shrub tundra vegetation. The model calculates snowmelt rates from a full surface energy balance, and a three‐layer soil model is used to simulate the infiltration into and the exchange of heat and moisture within the ground. The generated meltwater is delivered to the stream channel network by overland flow, interflow, and baseflow and subsequently routed out of the catchment. Subgrid spatial variability is handled by the model through the use of grouped response units (GRUs). The GRUs in WATCLASS are chosen according to vegetation land cover.

Five spring snowmelt periods with a variety of initial end‐of‐winter snow cover and melt conditions were simulated and compared with observed runoff data. In a second step, the model's ability to simulate spatially variable snow covered area (SCA) within the basin was tested by comparing model predictions to remotely sensed SCA. WATCLASS was able to predict runoff volumes (on average within 15% over five years of modelling) as well as timing of snowmelt and meltwater runoff for open tundra fairly accurately. However, the model underestimated melt in the energetically more complex shrub tundra areas of the basin. Furthermore, the observed high spatial variability of the SCA at a 1‐km resolution was not captured well by the model.

Several recommendations are made to improve model performance in Arctic basins, including a more realistic implementation of the gradual deepening of the thawed layer during the spring, and the use of topographic information in the definition of land cover classes for the GRU approach.     

Rapid vegetation responses and feedbacks amplify climate model response to snow cover changes

We investigate the response of a climate system model to two different methods for estimating snow cover fraction. In the control case, snow cover fraction changes gradually with snow depth; in the alternative scenarios (one with prescribed vegetation and one with dynamic vegetation), snow cover fraction initially increases with snow depth almost twice as fast as the control method. In cases where the vegetation was fixed (prescribed), the choice of snow cover parameterization resulted in a limited model response. Increased albedo associated with the high snow caused some moderate localized cooling (3–5°C), mostly at very high latitudes (>70°N) and during the spring season. During the other seasons, however, the cooling was not very extensive. With dynamic vegetation the change is much more dramatic. The initial increases in snow cover fraction with the new parameterization lead to a large-scale southward retreat of boreal vegetation, widespread cooling, and persistent snow cover over much of the boreal region during the boreal summer. Large cold anomalies of up to 15°C cover much of northern Eurasia and North America and the cooling is geographically extensive in the northern hemisphere extratropics, especially during the spring and summer seasons. This study demonstrates the potential for dynamic vegetation within climate models to be quite sensitive to modest forcing. This highlights the importance of dynamic vegetation, both as an amplifier of feedbacks in the climate system and as an essential consideration when implementing adjustments to existing model parameters and algorithms.     

Interannual and decadal variations of snow cover over Qinghai-Xizang Plateau and their relationships to summer monsoon rainfall in China

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.     

1988～1998年北半球积雪时空变化特征分析

利用NOAA提供的北半球近10年(1988～1998)逐周雪盖观测资料，通过引入年或季节累积雪盖周数作为对雪量累积情况的定量衡量，对北半球雪盖变化时空特征进行了分析。结果表明:近10年来，北半球积雪年际变化的关键区位于青藏高原、蒙古高原、欧洲阿尔卑斯山脉及北美中西部，其中青藏高原是北半球积雪异常变化最强烈的区域。青藏高原和欧亚大陆其他地区积雪变化的关联表现为两种不同的时空变化型，第一种型表现为青藏高原地区和其他地区(如欧洲、俄罗斯远东地区)积雪的同位相趋势性增多；第二种型表现为青藏高原地区和中亚地区积雪变化同位相，而和蒙古高原－我国东北地区积雪变化反位相的年际振荡。     

Spatial and Temporal Variability of Snow Depth Derived from Passive Microwave Remote Sensing Data in Kazakhstan

Snow cover plays an important role in the hydrological cycle and water management in Kazakhstan.However, traditional observations do not meet current needs. In this study, a snow depth retrieval equation was developed based on passive microwave remote sensing data. The average snow depth in winter(ASDW),snow cover duration(SCD), monthly maximum snow depth(MMSD), and annual average snow depth(AASD) were derived for each year to monitor the spatial and temporal snow distribution. The SCD exhibited significant spatial variations from 30 to 250 days. The longest SCD was found in the mountainous area in eastern Kazakhstan, reaching values between 200 and 250 days in 2005. The AASD increased from the south to the north and maintained latitudinal zonality. The MMSD in most areas ranged from 20 to30 cm. The ASDW values ranged from 15 to 20 cm in the eastern region and were characterized by spatial regularity of latitudinal zonality. The ASDW in the mountainous area often exceeded 20 cm.     

Variability of seasonal-mean fields arising from intraseasonal variability: part 1, methodology

We propose a method for studying the influence of intraseasonal variability on the interannual variability of seasonal mean fields. The method, using monthly mean data, provides estimates of the interannual variance and covariance, in the seasonal mean field, associated with intraseasonal variability. These estimates can be used to derive patterns of interannual variability associated with meteorological phenomena that vary significantly within a season, such as atmospheric blocking, or intraseasonal oscillations. By removing this intraseasonal component from the total interannual variance/covariance, one can define a slow component of interannual variability that is closely related to very slowly varying (interannual/supra-annual) external forcings and internal dynamics. Together these patterns may help in our understanding of the source of climate predictive skill, and also the influence of intraseasonal variability on interannual variability. To show the efficacy of our methodology, we have tested it on synthetic data, using Monte Carlo simulations of the 500-hPa geopotential heights for boreal winter over the North Pacific/North American region. The synthetic data has been constructed in such a way that the intraseasonal and slow components of interannual variability are known a priori. It is demonstrated that our methodology can effectively separate the spatial patterns of both components of variability. The methodology is also applied to diagnose meteorological phenomena that play major roles in the variability and predictability of DJF New Zealand temperatures.     

Persistence of Snow Cover Anomalies over the Tibetan Plateau and the Implications for Forecasting Summer Precipitation over the Meiyu-Baiu Region

The present reported study investigated the persistence of snow anomalies over the Tibetan Plateau（TP） from the preceding seasons to summer and the relationship between the previous snow cover anomaly and summer precipitation over East Asia. The results showed that, relative to other snow indices, such as the station observational snow depth（SOSD） index and the snow water equivalent（SWE） index, the snow cover area proportion（SCAP） index calculated from the SWE and the percentage of visible snow of the Equal-Area Scalable Earth Grids（EASE-grids） dataset has a higher persistence in interannual anomalies, particularly from May to summer. As such, the May SCAP index is significantly related to summer precipitation over the Meiyu-Baiu region. The persistence of the SCAP index can partly explain the season-delayed effect of snow cover over the TP on summer rainfall over the Meiyu-Baiu region besides the contribution of the soil moisture bridge. The preceding SST anomaly in the tropical Indian Ocean and ENSO can persist through the summer and affect the summer precipitation over the Meiyu-Baiu region. However, the May SCAP index is mostly independent of the simultaneous SSTs in the tropical Indian Ocean and the preceding ENSO and may affect the summer precipitation over the Meiyu-Baiu region independent of the effects of the SST anomalies. Therefore, the May SCAP over the TP could be regarded as an important supplementary factor in the forecasting of summer precipitation over the Meiyu-Baiu region.     

Impacts of snow cover on vegetation phenology in the arctic from satellite data

The dynamics of snow cover is considered an essential factor in phenological changes in Arctic tundra and other northern biomes. The Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite data were selected to monitor the spatial and temporal heterogeneity of vegetation phenology and the timing of snow cover in western Arctic Russia (the Yamal Peninsula) during the period 2000-10. The magnitude of changes in vegetation phenology and the timing of snow cover were highly heterogeneous across latitudinal gradients and vegetation types in western Arctic Russia. There were identical latitudinal gradients for "start of season" (SOS) (r2 = 0.982, p<0.0001), "end of season" (EOS) (r2 = 0.938, p<0.0001), and "last day of snow cover" (LSC) (r2 = 0.984, p<0.0001), while slightly weaker relationships between latitudinal gradients and "first day of snow cover" (FSC) were observed (r2 = 0.48, p<0.0042). Delayed SOS and FSC, and advanced EOS and LSC were found in the south of the region, while there were completely different shifts in the north. SOS for the various land cover features responded to snow cover differently, while EOS among different vegetation types responded to snowfall almost the same. The timing of snow cover is likely a key driving factor behind the dynamics of vegetation phenology over the Arctic tundra. The present study suggests that snow cover urgently needs more attention to advance understanding of vegetation phenology in the future.