Causes of recent changes in western North American snowpack

Monthly snow water equivalent (SWE) station observations and gridded temperature data are used to identify mechanisms by which warming affects the temporal and geographical structure of changes in western North American mountain snowpack. We first exploit interannual variability to demonstrate the sensitivity of snowpack to temperature during the various phases of the snow season. We show that mechanisms whereby temperature affects snowpack emerge in the mid to late portion of the snow season (March through May), but are nearly absent during the earliest phase (February), when temperatures are generally well below freezing. The mid to late snow season is precisely when significant loss of snowpack is seen at nearly all locations over the past few decades, both through decreases in snow accumulation and increases in snowmelt. At locations where April 1st SWE has been increasing over the past few decades, the increase is entirely due to a significant enhancement of accumulation during the earliest phase of the snow season, when the sensitivity analysis indicates that temperature is not expected to affect snowpack. Later in the snow season, these stations exhibit significant snowpack loss comparable to the other stations. Based on this analysis, it is difficult to escape the conclusion that recent snowpack changes in western North America are caused by regional-scale warming. Given predictions of future warming, a further reduction in late season snowpack and advancement in the onset of snowmelt should be expected in the coming decades throughout the region.     

The contribution of snow condition trends to future ground climate

Global climate models predict that terrestrial northern high-latitude snow conditions will change substantially over the twenty-first century. Results from a Community Climate System Model simulation of twentieth and twenty-first (SRES A1B scenario) century climate show increased winter snowfall (+10–40%), altered maximum snow depth (?5 ± 6 cm), and a shortened snow-season (?14 ± 7 days in spring, +20 ± 9 days in autumn). By conducting a series of prescribed snow experiments with the Community Land Model, we isolate how trends in snowfall, snow depth, and snow-season length affect soil temperature trends. Increasing snowfall, by countering the snowpack-shallowing influence of warmer winters and shorter snow seasons, is effectively a soil warming agent, accounting for 10–30% of total soil warming at 1 m depth and ~16% of the simulated twenty-first century decline in near-surface permafrost extent. A shortening snow season enhances soil warming due to increased solar absorption whereas a shallowing snowpack mitigates soil warming due to weaker winter insulation from cold atmospheric air. Snowpack deepening has comparatively less impact due to saturation of snow insulative capacity at deeper snow depths. Snow depth and snow-season length trends tend to be positively related, but their effects on soil temperature are opposing. Consequently, on the century timescale the net change in snow state can either amplify or mitigate soil warming. Snow state changes explain less than 25% of total soil temperature change by 2100. However, for the latter half of twentieth century, snow state variations account for as much as 50–100% of total soil temperature variations.     

Mid-Century Ensemble Regional Climate Change Scenarios for the Western United States

To study the impacts of climate change on water resources in the western U.S., global climate simulations were produced using the National Center for Atmospheric Research/Department of Energy (NCAR/DOE) Parallel Climate Model (PCM). The Penn State/NCAR Mesoscale Model (MM5) was used to downscale the PCM control (20 years) and three future(2040–2060) climate simulations to yield ensemble regional climate simulations at 40 km spatial resolution for the western U.S. This paper describes the regional simulations and focuses on the hydroclimate conditions in the Columbia River Basin (CRB) and Sacramento-San Joaquin River (SSJ) Basin. Results based on global and regional simulations show that by mid-century, the average regional warming of 1 to 2.5 °C strongly affects snowpack in the western U.S. Along coastal mountains, reduction in annual snowpack was about70% as indicated by the regional simulations. Besides changes in mean temperature, precipitation, and snowpack, cold season extreme daily precipitation increased by 5 to 15 mm/day (15–20%) along theCascades and the Sierra. The warming resulted in increased rainfall at the expense of reduced snowfall, and reduced snow accumulation (or earlier snowmelt) during the cold season. In the CRB, these changes were accompanied by more frequent rain-on-snow events. Overall, they induced higher likelihood of wintertime flooding and reduced runoff and soil moisture in the summer. Changes in surface water and energy budgets in the CRB and SSJ basin were affected mainly by changes in surface temperature, which were statistically significant at the 0.95 confidence level. Changes in precipitation, while spatially incoherent, were not statistically significant except for the drying trend during summer. Because snow and runoff are highly sensitive tospatial distributions of temperature and precipitation, this study shows that (1) downscaling provides more realistic estimates of hydrologic impacts in mountainous regions such as the western U.S., and (2) despite relatively small changes in temperature and precipitation, changes in snowpack and runoff can be much larger on monthly to seasonal time scales because the effects of temperature and precipitation are integrated over time and space through various surface hydrological and land-atmosphere feedback processes. Although the results reported in this study were derived from an ensemble of regional climate simulations driven by a global climate model that displays low climate sensitivity compared with most other models, climate change was found to significantly affect water resources in the western U.S. by the mid twenty-first century.     

Parametrization schemes of incident radiation in the North Water polynya

Abstract

An evaluation of the Canadian Land Surface Scheme (CLASS) 3.1 snow cover simulations at four sites included in the Snow Model Intercomparison Project (SnowMIP) revealed that CLASS was able to provide realistic representations of snow cover accumulation, melt and physical properties over a range of snow cover climates. The modified snow aging parametrization in CLASS 3.1 provided improved simulations of snowpack density which resulted in a marked reduction in the root‐mean‐square (rms) error for daily snow depth, and slight improvements in snow surface temperature. CLASS 3.1 still exhibited a tendency to overestimate snow cover duration which is attributed to the way shallow snow ablation is treated. CLASS provided generally realistic simulations of daily and seasonal variation in snow albedo although cold snow albedo was underpredicted by 0.10 to 0.15 at a site with a deep (> 2 m) cold snowpack. CLASS also exhibited a tendency to overpredict late spring snow albedo which was reduced by the addition of a snow layer subroutine that kept track of snow albedo by precipitation event. CLASS had a noticeable cold bias averaging 3°–4°C at two mountain sites included in the comparison. The bias was closely linked to atmospheric stability and could exceed 10°C under conditions of strong radiative cooling and low wind speeds. The CLASS energy deficit under these conditions was determined to be ～20–40 W m^?2 and was mostly accounted for by introducing a windless exchange coefficient into the calculation of sensible heat fluxes following the approach used in a number of other physical snowpack models. CLASS provided realistic simulations of daily snowmelt runoff with the exception of the Weissfluhjoch site which was characterized by a deep cold snowpack. A preliminary assessment of snow water equivalent (SWE) rms error for the 23 models participating in SnowMIP showed that CLASS was one of the better single layer snow models included in the comparison. CLASS performance was comparable to the multi‐layer CROCUS snowpack model in the evaluations carried out in this study.     

Stream temperature sensitivity to climate warming in California’s Sierra Nevada: impacts to coldwater habitat

Water temperature influences the distribution, abundance, and health of aquatic organisms in stream ecosystems, so understanding the impacts of climate warming on stream temperature will help guide management and restoration. This study assesses climate warming impacts on stream temperatures in California’s west-slope Sierra Nevada watersheds, and explores stream temperature modeling at the mesoscale. We used natural flow hydrology to isolate climate induced changes from those of water operations and land use changes. A 21 year time series of weekly streamflow estimates from WEAP21, a spatially explicit rainfall-runoff model were passed to RTEMP, an equilibrium temperature model, to estimate stream temperatures. Air temperature was uniformly increased by 2°C, 4°C, and 6°C as a sensitivity analysis to bracket the range of likely outcomes for stream temperatures. Other meteorological conditions, including precipitation, were unchanged from historical values. Raising air temperature affects precipitation partitioning into snowpack, runoff, and snowmelt in WEAP21, which change runoff volume and timing as well as stream temperatures. Overall, stream temperatures increased by an average of 1.6°C for each 2°C rise in air temperature, and increased most during spring and at middle elevations. Viable coldwater habitat shifted to higher elevations and will likely be reduced in California. Thermal heterogeneity existed within and between basins, with the high elevations of the southern Sierra Nevada and the Feather River watershed most resilient to climate warming. The regional equilibrium temperature modeling approach used here is well suited for climate change analysis because it incorporates mechanistic heat exchange, is not overly data or computationally intensive, and can highlight which watersheds are less vulnerable to climate warming. Understanding potential changes to stream temperatures from climate warming will affect how fish and wildlife are managed, and should be incorporated into modeling studies, restoration assessments, and licensing operations of hydropower facilities to best estimate future conditions and achieve desired outcomes.     

Modelling climate change effects on the spatial distribution of mountain permafrost at three sites in northwest Canada

Spatial models of present-day mountain permafrost probability were perturbed to examine potential climate change impacts. Mean annual air temperature (MAAT) changes were simulated by adjusting elevation in the models, and cloud cover changes were examined by altering the partitioning of direct beam and diffuse radiation within the calculation for potential incoming solar radiation (PISR). The effects of changes in MAAT on equilibrium permafrost distribution proved to be more important than those due to cloud cover. Under a ?2 K scenario (approximating Little Ice Age conditions), permafrost expanded into an additional 22?C43% of the study areas as zonal boundaries descended by 155?C290 m K^???1. Under warming scenarios, permafrost probabilities progressively declined and zonal boundaries rose in elevation. A MAAT change of +5 K, caused two of the areas to become essentially permafrost-free. The absolute values of these predictions were affected up to ±10% when lapse rates were altered by ±1.5 K km^???1 but patterns and trends were maintained. A higher proportion of diffuse radiation (greater cloud cover) produced increases in permafrost extent of only 2?C4% while decreases in the diffuse radiation fraction had an equal but opposite effect. Notwithstanding the small change in overall extent, permafrost probabilities on steep south-facing slopes were significantly impacted by the altered partitioning. Combined temperature and PISR partitioning scenarios produced essentially additive results, but the impact of changes in the latter declined as MAAT increased. The modelling illustrated that mountain permafrost in the discontinuous zone is sensitive spatially to long-term climate change and identified those areas where changes may already be underway following recent atmospheric warming.     

Snow pack in the Romanian Carpathians under changing climatic conditions

Snow pack characteristics and duration are considered to be key indicators of climate change in mountain regions, especially during the winter season (herein considered to last from the 1st of November to the 30th of April). Deviations recorded in the regime of the main explanatory variables of snow pack changes (i.e. temperature and precipitation) offer useful information on winter climate variability, in the conditions of the winter warming trend already seen in some areas of the Romanian Carpathians. The present work focuses on changes and trends in snow pack characteristics and its related parameters, registered at the 15 weather stations located in the alpine, sub-alpine and forest belts in all the three Romanian Carpathian branches (>1,000 m) over the 1961–2003 period. Changes in the snow pack regime were investigated in relation with the modifications of winter temperature and precipitation having been detected mostly at the end of the twentieth century. A winter standardized index was calculated to group winters over the 43-year period into severity classes and detect the respective changes. Links between the number of snow cover days and seasonal NAO index were also statistically analysed in this study. The general results show large regional and altitudinal variations and the complex character of the climate in the Romanian Carpathians, leading to the idea of an ongoing warming process associated with a lower incidence of snow cover, affecting to a large extent the forested mountain areas located below 1,600–1,700 m altitude. Also negative and weak correlations were found, particularly over the December–March interval, between the number of snow cover days and seasonal NAO index values.     

Simulation of snow processes beneath a boreal scots pine canopy

A physically-based multi-layer snow model Snow-Atmosphere-Soil-Transfer scheme(SAST)and a land surface model Biosphere-Atmosphere Transfer Scheme(BATS)were employed to investigate how boreal forests influence snow accumulation and ablation under the canopy.Mass balance and energetics of snow beneath a Scots pine canopy in Finland at different stages of the 2003-2004 and 2004 2005 snow seasons are analyzed.For the fairly dense Scots pine forest,drop-off of the canopy-intercepted snow contributes,in some cases,twice as much to the underlying snowpack as the direct throughfall of snow.During early winter snow melting,downward turbulent sensible and condensation heat fluxes play a dominant role together with downward net longwave radiation.In the final stage of snow ablation in middle spring,downward net all- wave radiation dominates the snow melting.Although the downward sensible heat flux is comparable to the net solar radiation during this period,evaporative cooling of the melting snow surface makes the turbulent heat flux weaker than net radiation.Sensitivities of snow processes to leaf area index(LAI)indicate that a denser canopy speeds up early winter snowmelt,but also suppresses melting later in the snow season. Higher LAI increases the interception of snowfall,therefore reduces snow accumulation under the canopy during the snow season;this effect and the enhancement of downward longwave radiation by denser foliage outweighs the increased attenuation of solar radiation,resulting in earlier snow ablation under a denser canopy.The difference in sensitivities to LAI in two snow seasons implies that the impact of canopy density on the underlying snowpack is modulated by interannual variations of climate regimes.     

Estimated impacts of climate warming on California’s high-elevation hydropower

California’s hydropower system is composed of high and low elevation power plants. There are more than 150 high-elevation power plants, at elevations above 1,000 feet (300 m). Most have modest reservoir storage capacities, but supply roughly 74% of California’s in-state hydropower. The expected shift of runoff peak from spring to winter due to climate warming, resulting in snowpack reduction and increased snowmelt, might have important effects on power generation and revenues in California. The large storage capacities at low-elevation power plants provide flexibility to operations of these units under climate warming. However, with climate warming, the adaptability of the high-elevation hydropower system is in question as this system was designed to take advantage of snowpack, a natural reservoir. With so many high-elevation hydropower plants in California, estimation of climate warming effects by conventional simulation or optimization methods would be tedious and expensive. An Energy-Based Hydropower Optimization Model (EBHOM) was developed to facilitate practical climate change and other low-resolution system-wide hydropower studies, based on the historical generation data of 137 high-elevation hydropower plants for which the data were complete for 14 years. Employing recent historical hourly energy prices, the model was used to explore energy generation in California for three climate warming scenarios (dry warming, wet warming, and warming-only) over 14 years, representing a range of hydrologic conditions. The system is sensitive to the quantity and timing of inflows. While dry warming and warming-only climate changes reduce average hydropower revenues, wet warming could increase revenue. Re-operation of available storage and generation capacities help compensate for snowpack losses to some extent. Storage capacity expansion and to a lesser extent generation capacity expansion both increase revenues, although such expansions might not be cost-effective.     

Analysis of climatic variability and snow cover in the Kaligandaki River Basin,Himalaya, Nepal

Various remote sensing products and observed data sets were used to determine spatial and temporal trends in climatic variables and their relationship with snow cover area in the higher Himalayas, Nepal. The remote sensing techniques can detect spatial as well as temporal patterns in temperature and snow cover across the inaccessible terrain. Non-parametric methods (i.e. the Mann–Kendall method and Sen's slope) were used to identify trends in climatic variables. Increasing trends in temperature, approximately by 0.03 to 0.08 °C year^?1 based on the station data in different season, and mixed trends in seasonal precipitation were found for the studied basin. The accuracy of MOD10A1 snow cover and fractional snow cover in the Kaligandaki Basin was assessed with respect to the Advanced Spaceborne Thermal Emission and Reflection Radiometer-based snow cover area. With increasing trends in winter and spring temperature and decreasing trends in precipitation, a significant negative trend in snow cover area during these seasons was also identified. Results indicate the possible impact of global warming on precipitation and snow cover area in the higher mountainous area. Similar investigations in other regions of Himalayas are warranted to further strengthen the understanding of impact of climate change on hydrology and water resources and extreme hydrologic events.     

Impact of snow initialization on sub-seasonal forecasts

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004–2009, with either realistic initialization of snow variables based on re-analyses, or else with “scrambled” snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This “warm Arctic—cold continent” difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses.     

Predicting thermal vulnerability of stream and river ecosystems to climate change

We use a predictive model of mean summer stream temperature to assess the vulnerability of USA streams to thermal alteration associated with climate change. The model uses air temperature and watershed features (e.g., watershed area and slope) from 569 US Geological Survey sites in the conterminous USA to predict stream temperatures. We assess the model for predicting climate-related variation in stream temperature by comparing observed and predicted historical stream temperature changes. Analysis of covariance confirms that observed and predicted changes in stream temperatures respond similarly to historical changes in air temperature. When applied to spatially-downscaled future air temperature projections (A2 emission scenario), the model predicts mean warming of 2.2 °C for the conterminous USA by 2100. Stream temperatures are most responsive to climate changes in the Cascade and Appalachian Mountains and least responsive in the southeastern USA. We then use random forests to conduct an empirical sensitivity analysis to identify those stream features most strongly associated with both observed historical and predicted future changes in summer stream temperatures. Larger changes in stream temperature are associated with warmer future air temperatures, greater air temperature changes, and larger watershed areas. Smaller changes in stream temperature are predicted for streams with high initial rates of heat loss associated with longwave radiation and evaporation, and greater base-flow index values. These models provide important insight into the potential extent of stream temperature warming at a near-continental scale and why some streams will likely be more vulnerable to climate change than others.     

Pre-Melt Energy Budget of an Arctic Snowpack on Landfast First-Year Sea Ice

The pre-melt energy budget of a snowpack on landfast first-year sea ice at a remote site in the Canadian Arctic Archipelago was analyzed. Over a 19-day period, the total heat conducted into the snowpack at the snow–sea-ice interface was the largest single energy transfer to the snowpack, while each of the turbulent heat fluxes removed comparable amounts of energy. The total energy transferred from the snowpack (∑Q?≈??7027?kJ?m^?2) should have reduced its temperature; however, the opposite occurred. The snowpack’s temperature at both the 7 and 13?cm depths increased over the pre-melt period. The total change in internal energy and latent heat of the snowpack (ΔU_snowpack)_, derived from 15-minute changes in the snowpack’s temperature over the pre-melt period, was approximately 672?kJ?m^?2. Closure of the energy budget was not achieved for either the daily or the total pre-melt period. The terms of the energy budget were determined independently; thus, the failure to close the energy budget was the result of the accumulation of errors associated with all the terms. However, for snow on first-year sea ice, the parameterization of the salinity and temperature dependence of the “specific heat” of the basal layer of the snowpack was likely the primary source of error. The snowpack plays a central role in the transfer of energy across the ocean–sea-ice–atmosphere interface, but an adequate method for modelling the evolution of snow on Arctic sea ice including the energy budget, which determines the warming rate and subsequent melt rate of the snow, has yet to be developed.     

Using a global climate model to evaluate the influences of water vapor, snow cover and atmospheric aerosol on warming in the Tibetan Plateau during the twenty-first century

We examine trends in climate variables and their interrelationships over the Tibetan Plateau using global climate model simulations to elucidate the mechanisms for the pattern of warming observed over the plateau during the latter half of the twentieth century and to investigate the warming trend during the twenty-first century under the SRES A1B scenario. Our analysis suggests a 4°C warming over the plateau between 1950 and 2100. The largest warming rates occur during winter and spring. For the 1961–2000 period, the simulated warming is similar to the observed trend over the plateau. Moreover, the largest warming occurs at the highest elevation sites between 1950 and 2100. We find that increases in (1) downward longwave radiation (DLR) influenced by increases in surface specific humidity (q), and (2) absorbed solar radiation (ASR) influenced by decreases in snow cover extent are, in part, the reason for a large warming trend over the plateau, particularly during winter and spring. Furthermore, elevation-based increases in DLR (influenced by q) and ASR (influenced by snow cover and atmospheric aerosols) appear to affect the elevation dependent warming trend simulated in the model.     

The potential impact of climate change on seasonal snow in New Zealand: part I—an analysis using 12 GCMs

Seasonal snow directly affects New Zealand??s economy through the energy, agriculture and tourism sectors. In New Zealand, little is known about the long-term variability of the snow cover and the expected impacts of climate change on snow cover. The lack of systematic historical snow observations in New Zealand means that information on interannual variability, trends and projections of future seasonal snow must be generated using simulation models. We use a temperature index snow model to calculate the accumulation and ablation of the current (1980?C1999) snowpack for more than 37,000 third-order river basins with 100?m contour intervals, resulting in over 200,000 individual model elements in New Zealand. Using this model, which captures the gross features of snow under the current climate, we assess the range of likely effects of climate change on seasonal snow in New Zealand using downscaled temperature and precipitation changes from the middle of the road (A1B) climate change projections from 12 general circulation models (GCMs). For each of the 12 GCMs, we consider two future time periods 2030?C2049 (mid-point reference 2040) and 2080?C2099 (mid-point reference 2090). These future time periods are compared to simulations of current, 1980?C1999 (mid-point reference 1990), seasonal snow. Our results show that on average at a national scale, at nearly all elevations, the 2040s and 2090s result in a decrease in snow as described by all of our summary statistics: snow duration, percentage of precipitation that is snow and peak snow accumulation in each year. This decrease in snow is more marked at elevations below 1,000?m but is evident at all but the very highest elevations. Relative to snow simulations for average peak snow accumulation for the present, we observe that by the 2040s, depending on the GCM used, there is a reduction of between 3 and 44?% at 1,000?m, and an increase of 8?% through to a reduction of 22?% at 2,000?m. By the 2090s, the average reduction is greater, with a decrease of between 32 and 79?% at 1,000?m and between 6 and 51?% at 2,000?m. More substantial reductions are observed below these elevations. When we consider the elevation where snow duration exceeds 3?months, we see a rise in this elevation from 1,550?m in the 1990s to between 1,550 and 1,750?m by the 2040s and 1,700 and 2,000?m by the 2090s, depending on the GCM used. The results of this work are consistent with our understanding of snow processes in general and with work from other similar mid-latitude locations.     

THE IMPACT OF CLIMATE CHANGE ON THE SNOW COVER PATTERN IN ESTONIA

The paper deals with problems of temporal and spatial variability of snow cover duration, of correlation between snow cover and winter mean air temperature patterns and of the impact of climate change on the snow cover pattern in Estonia. Snow cover fields are presented in form of IDRISI raster images. Snow cover duration measured at ca 100 stations and observation points have been interpolated into raster cells. On the base of time series of raster images, a map of mean territorial distribution of snow cover duration is calculated. Estonia is characterized by a great spatial variability of snow cover mostly caused by the influence of the Baltic Sea. General regularities of snow cover pattern are determined. A 104-year time series of spatial mean values of snow cover duration is composed and analyzed. A decreasing trend and periodical fluctuations have detected. Standardized principal component analysis is used for the time series of IDRISI raster images. It enables to study the influence of different factors on the formation of snow cover fields and territorial extent of coherent fluctuations. Correlation between snow cover duration and winter mean air temperature fields is analyzed. A spatial regression model is created for estimation of the influence of climate change on snow cover pattern in Estonia. Using incremental climate change scenarios (2 °C, 4 °C and 6 °C of warming in winter) mean decrease of snow cover duration in different regions in Estonia is calculated. According to results of model calculation, the highest decrease of snow cover duration will be take place on islands and in the coastal region of West Estonia. A permanent snow cover may not form at all. In the areas with maximum snow cover duration in North-East and South-East Estonia, that decrease should be much lower.     

Sensitivity of climate change projections to uncertainties in the estimates of observed changes in deep-ocean heat content

The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed twentieth century temperature changes. The impact on climate change projections of using the smallest and largest estimates of twentieth century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference (“business as usual”) scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by twentieth century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4’s multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However, the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the twentieth century changes in surface and upper air temperatures, combined with the expert prior for climate sensitivity.     

基于MODIS白天地温产品的青藏高原海拔依赖型变暖特征分析

基于2001～2018年中分辨率成像光谱仪（MODIS）探测的白天地面温度（简称MODIS 白天地温）资料,与青藏高原（简称高原）122个气象站点观测的最高气温资料,在年尺度上评估了MODIS 白天地温在高原的适用性,研究了高原五个干湿分区下MODIS 白天地温的海拔依赖型变暖特征,得到以下主要结论:（1）MODIS白天地温能够基本再现观测的最高气温的时空以及海拔依赖型变暖特征;（2）高原整体上,MODIS白天地温存在显著的海拔依赖型变暖特征,平均海拔每增加100 m,其趋势增加0.02°C (10a)?1,且受积雪—反照率反馈主导;（3）干湿分区下,海拔依赖型变暖特征在高原表现为偏湿润地区强于偏干旱地区;季风区强于西风区。海拔依赖型特征强弱:半湿润地区＞湿润半湿润地区＞半干旱地区＞湿润地区＞干旱地区。平均海拔每增加100 m,以上区域的地温趋势分别增加0.06,0.03,0.03,0.01,0.01°C (10a)?1。半湿润和湿润半湿润地区年均温在0°C左右,在气候变暖背景下积雪—反照率反馈作用最为强烈,是其海拔依赖型变暖的主导因素;干旱与半干旱地区年均温相对更低,气候变暖程度对积雪影响相对较小,积雪—反照率反馈作用被限制,但仍对上述地区的海拔依赖型变暖起主导作用;而湿润地区的积雪覆盖率的上升可能是由于降雪（固态降水）增加抵消了积雪融化损耗,云辐射、水汽等其他因素主导了其海拔依赖型变暖。     

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.     

近30年来我国雪量变化的初步探讨

本文根据2300多个地面气象台站资料,对近30年来我国雪量波动进行了监测与诊断研究。发现全国尺度的雪量变化趋势与全球平均气温成正相关,其年际波动与火山活动相位相反,多雪冬季与厄尼诺-南方涛动相同步。CO_2增温将加剧雪量分布的区域差异,导致北方平原、盆地积雪日数减少,青藏高原、高山地区和长江中下游降雪量增加。