Evidence of climate change impact upon glaciers’ recession within the Italian Alps

We present evidence of climate change impact upon recent changes of glaciers within Lombardy region, in Northern Italy. We illustrate the recent area evolution of a set of 249 glaciers in the area using three surface area records for 1991, 1999 and 2003. The 1999 and 2003 surface area data are processed by combining glacier limits manually digitized upon registered color orthophotos and differential GPS (DGPS) glaciers’ surveys. Glaciers’ area was 117.4?km² in 1991, 104.7?km² in 1999, and to 92.4?km² 2003, with a 21% reduction. Glaciers smaller than 1?km² accounted for 53% of the total loss in area (13.1?km² during 1991–2003). The area change rate was higher lately, with ca. 11.7 % reduction during 1999–2003. We split Alps and fore Alps of Lombardy into six mountain groups, and we separately investigate relative area variations. We use climate series from local stations within each group to assess climate change during a 30-year window (1976–2005). We focus upon temperature and snow cover depth at thaw, known to impact glaciers’ changes. We compare local year-round temperature anomalies against global ones to evidence enhanced warming within this area, and we investigate the correlation of our target climate variables against NAO. Eventually, we highlight the link between the rate of change of our climate variables to the observed scaling of area loss against glaciers’ size, showing that in rapidly warming areas glaciers’ size affects less relative melting.     

Quantifying recent precipitation change and predicting lake expansion in the Inner Tibetan Plateau

Lake expansion since the middle of the 1990s is one of the most outstanding environmental change events in the Tibetan Plateau (TP). This expansion has mainly occurred in the Inner TP, a vast endorheic basin with an area of about 708,000 km² and containing about 780 lakes larger than 1 km². The total lake area of the Inner TP has increased from 24,930 km² in 1995 to 33,741 km² in 2015. The variability of the lake area in the coming decades is crucial for infrastructure planning and ecology policy for this remote region. In this study, a lake mass balance model was developed to describe the lake area response to climate change. First, the model was used to inversely estimate the change in precipitation from the change in lake volume. The result shows that precipitation has increased by about 21?±?7% since the middle of the 1990s, as seen in GPCC global data set. Then, the lake size in the coming two decades was predicted by the model driven with either current climate or a projected future climate, showing the lake area would expand continuously, but at a lower rate than before. Both predictions yield a total lake area of 36150?±?500 km² in 2025 and a rise of average lake level by about 6.6?±?0.3 m from 1995 to 2025. However, the two predictions become disparate in the second decade (2026–2035), as the future climate is more warming and wetting than the current climate. It is noted that the prediction of lake expansion is robust for the entire inner TP lake system but not always applicable to individual subregions or specific lakes due to their spatiotemporal heterogeneity.     

Potential spread of recently naturalised plants in New Zealand under climate change

Climate change and biological invasions are major causes of biodiversity loss and may also have synergistic effects, such as range shifts of invaders due to changing climate. Bioclimatic models provide an important tool to assess how the threat of invasive species may change with altered temperature and precipitation regimes. In this study, potential distributions of three recently naturalised plant species in New Zealand are modelled (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla), using four different general circulation models (CCCMA-CGCM3, CSIRO-Mk3.0, GFDL-CM2.0 and UKMO-HADCM3) with two emission scenarios (A2 and B1) each. Based on a maximum entropy approach, models were trained on global data using a small set of uncorrelated predictors. The models were projected to the country of interest, using climate models that had been statistically downscaled to New Zealand, in order to obtain high resolution predictions. This study provides evidence of the potential range expansion of these species, with potentially suitable habitat increasing by as much as 169 % (A. cunninghamiana; with up to 115,805 km² of suitable habitat), 133 % (P. guajava; 164,450 km²) and 208 % (S. actinophylla; 31,257 km²) by the end of the century compared to the currently suitable habitat. The results show that while predictions vary depending on the chosen climate scenario, there is remarkable consistency amongst most climate models within the same emission scenario, with overlaps in areas of predicted presence ranging between 81 % and 99.5 % (excluding CSIRO-Mk3.0). By having a better understanding of how climate change will affect distribution of invasive plants, appropriate management measures can be taken.     

Evaluating observed and projected future climate changes for the Arctic using the K?ppen-Trewartha climate classification

The ecosystems in the Arctic region are known to be very sensitive to climate changes. The accelerated warming for the past several decades has profoundly influenced the lives of the native populations and ecosystems in the Arctic. Given that the K?ppen-Trewartha (K-T) climate classification is based on reliable variations of land-surface types (especially vegetation), this study used the K-T scheme to evaluate climate changes and their impact on vegetation for the Arctic (north of 50°N) by analyzing observations as well as model simulations for the period 1900–2099. The models include 16 fully coupled global climate models from the Intergovernmental Panel on Climate Change Fourth Assessment. By the end of this century, the annual-mean surface temperature averaged over Arctic land regions is projected to increase by 3.1, 4.6 and 5.3°C under the Special Report on Emissions Scenario (SRES) B1, A1b, and A2 emission scenarios, respectively. Increasing temperature favors a northward expansion of temperate climate (i.e., Dc and Do in the K-T classification) and boreal oceanic climate (i.e., Eo) types into areas previously covered by boreal continental climate (i.e., Ec) and tundra; and tundra into areas occupied by permanent ice. The tundra region is projected to shrink by ?1.86?×?10⁶?km² (?33.0%) in B1, ?2.4?×?10⁶?km² (?42.6%) in A1b, and ?2.5?×?10⁶?km² (?44.2%) in A2 scenarios by the end of this century. The Ec climate type retreats at least 5° poleward of its present location, resulting in ?18.9, ?30.2, and ?37.1% declines in areal coverage under the B1, A1b and A2 scenarios, respectively. The temperate climate types (Dc and Do) advance and take over the area previously covered by Ec. The area covered by Dc climate expands by 4.61?×?10⁶?km² (84.6%) in B1, 6.88?×?10⁶?km² (126.4%) in A1b, and 8.16?×?10⁶?km² (149.6%) in A2 scenarios. The projected redistributions of K-T climate types also differ regionally. In northern Europe and Alaska, the warming may cause more rapid expansion of temperate climate types. Overall, the climate types in 25, 39.1, and 45% of the entire Arctic region are projected to change by the end of this century under the B1, A1b, and A2 scenarios, respectively. Because the K-T climate classification was constructed on the basis of vegetation types, and each K-T climate type is closely associated with certain prevalent vegetation species, the projected large shift in climate types suggests extensive broad-scale redistribution of prevalent ecoregions in the Arctic.     

Radar observations of snow formation in a warm pre‐frontal snowband

Abstract

Radar reflectivity measurements and sounding data were analyzed to investigate snowfall production in a long‐lasting snowband that formed in advance of a warm surface front moving across Alberta. The sounding data indicated that the band could have been forced by slantwise overturning during the release of moist symmetric instability combined with frontogenesis. The stability analysis presented here is novel in that it includes ice phase thermodynamics, neglected in previous studies of slantwise convection.

Radar reflectivity fields were analyzed to determine the total snow content and the mass outflow rate as factors of time. The peak value of total snow content was 17 kilotons per km of snowband, and the peak mass outflow rate was 10 tons s^‐1 km^‐1. The snowfall rate averaged across the cloud base was about 0.8 cm h^‐1, and the average snow content remained close to 0.2 g m^‐1. The characteristic time (defined as the ratio of total snow content over mass outflow rate) was about 30 minutes, which is approximately the time needed for the growth of snowflakes by aggregation in the observed temperature range. The precipitation efficiency of the snowband, defined as the ratio of snow mass outflow to water vapour inflow was estimated to be 14%. The precipitation production values observed in the Alberta snowband are compared with previous estimates reported for frontal rainbands and Alberta thunderstorms.     

Studying the spatiotemporal variation of snow-covered days over China based on combined use of MODIS snow-covered days and in situ observations

Based on the moderate resolution imaging spectro-adiometer (MODIS)-acquired snow-covered days data (MSCD), validation of MSCD is performed by using 529 in situ observations of snow-covered days (SCD) from 2001 to 2006 in China. For the different characteristics of snow cover in four major snow-covered regions including the Tibetan Plateau, Xinjiang, and north-eastern and inner Mongolia, the validation process is divided into five parts for all of China. Our results indicate that except in the south-eastern part of the Tibetan Plateau, the MSCD is usually lower than the in situ SCD measurement. It is found that the MSCD have good polynomial regression agreement with the in situ measurements in Xinjiang and north-eastern and inner Mongolia with an R ² values that reach 0.89, 0.78, and 0.87, respectively. Because the MSCD is smaller but with a good regression relationship with the in situ SCD, calibration of the MSCD images could significantly improve its precision in those regions. To be considered a stable snow-covered area, there must be greater than 60?days per year in which the pixels are covered by snow. Unstable snow-covered areas are ones in which fewer than 60?days but at least 1?day is covered by snow. The calibrated MSCD outcome indicates that the unstable snow-covered area can reach 555.2?×?10⁴?km², and the stable snow-covered area is approximately 273.1?×?10⁴?km². The area in the three major stable snow-covered regions of the Tibetan Plateau, Xinjiang, and north-eastern and inner Mongolia is approximately 100.4?×?10⁴, 54.4?×?10⁴, and 114.7?×?10⁴?km², respectively.     

Impacts of Aerosol Loading on Surface Precipitation from Deep Convective Systems over North Central Mongolia

The impacts of aerosol loading on surface precipitation from mid-latitude deep convective systems are examined using a bin microphysics model. For this, a precipitation case over north central Mongolia, which is a high-altitude inland region, on 21 August 2014 is simulated with aerosol number concentrations of 150, 300, 600, 1200, 2400, and 4800 cm^?3. The surface precipitation amount slightly decreases with increasing aerosol number concentration in the range of 150–600 cm^?3, while it notably increases in the range of 600–4800 cm^?3 (22% increase with eightfold aerosol loading). We attempt to explain why the surface precipitation amount increases with increasing aerosol number concentration in the range of 600–4800 cm^?3. A higher aerosol number concentration results in more drops of small sizes. More drops of small sizes grow through condensation while being transported upward and some of them freeze, thus increasing the mass content of ice crystals. The increased ice crystal mass content leads to an increase in the mass content of small-sized snow particles largely through deposition, and the increased mass content of small-sized snow particles leads to an increase in the mass content of large-sized snow particles largely through riming. In addition, more drops of small sizes increase the mass content of supercooled drops, which also leads to an increase in the mass content of large-sized snow particles through riming. The increased mass content of large-sized snow particles resulting from these pathways contributes to a larger surface precipitation amount through melting and collision-coalescence.     

气候变化对云南省小粒咖啡适生区的影响

为揭示气候变化对云南省小粒咖啡适生区的影响,基于最大熵（MaxEnt）模型,结合小粒咖啡物种分布数据、环境变量数据,构建云南省小粒咖啡适生区评估及预测模型,对当前气候条件下小粒咖啡在云南省的适生区进行评估,并对未来气候条件下,小粒咖啡在云南省的适生区进行预测,再对预测结果进行对比分析。结果显示:（1）构建的最大熵模型能够较精确地用于小粒咖啡在云南省适生区的评估和预测,当前气候条件下,评估模型的训练集与测试集的AUC （Area under ROC Curve）值均为0.941,达到评估结果为极好的标准。（2）影响云南省小粒咖啡种植的主导环境因子依次为11月平均最高气温、7月降雨量、海拔高度、2月平均最低气温、10月降雨量、坡度和最冷月最低气温,共占总贡献率的91.4%。（3）当前气候条件下,小粒咖啡的适生区主要分布在滇西、滇西南以及滇南的保山、德宏、普洱、临沧、西双版纳等地区,总适生区约为116300 km2,占云南省国土面积的29.51%,且总体上,高适生区外围分布中适生区,中适生区外围分布低适生区。RCP4.5、RCP8.5情景下,小粒咖啡总适生区的面积分别约为98300、69700 km2,分别占云南省国土面积的24.95%、17.69%,两种排放情景下小粒咖啡总适生区面积分别减少了18000、46600 km2,国土面积占比分别减少了4.56%、11.82%,且总适生区的质心均由东南向西北方向移动,与RCP4.5情景相比,RCP8.5情景的移动距离更远。（4）未来气候变化将会导致小粒咖啡在云南省的总适生区面积减小,总适生区的质心位置向海拔更高与纬度更高的方向移动,且高碳排放情景下这种变化幅度更大。      

基于TM和ETM~+数据的玛纳斯河流域积雪时空分布特征研究

为了进一步研究玛纳斯河流域积雪的时空分布特征以及影响因素,应用遥感技术,以Landsat TM(美国地球资源探测卫星系统上加载的专题绘图资料)以及ETM+(增强型专题绘图资料)为数据源,利用雪盖指数法对研究区进行了积雪信息提取。通过对提取积雪信息的研究,分析了研究区的积雪时空分布的特征,并详细分析了高程、坡度、坡向以及其他因素对于积雪分布的影响。结果表明研究区积雪空间分布随高度和地形变化非常明显,积雪主要分布高海拔地区,而山间河谷地带则相对较少,同时积雪受季节影响较大,主要集中在秋、冬、春3个季节;并得出高程、坡向对积雪分布影响比较大,而坡度对积雪分布影响则相对较小的规律特征。     

Lags in vegetation response to greenhouse warming

Fossil pollen in sediments documents vegetation responses to climatic changes in the past. Beech (Fagus grandifolia), with animal-dispersed seeds, moved across Lake Michigan or around its southern margin, becoming established in Wisconsin about 1000 years after populations were established in Michigan. Hemlock (Tsuga canadensis), with wind-dispersed seeds, colonized a 50,000 km² area in northern Michigan between 6000 and 5000 years ago. These tree species extended ranges northward at average rates of 20–25 km per century. To track climatic changes in the future, caused by the greenhouse effect, however, their range limit would need to move northward 100 km per °C warming, or about 300 km per century, an order of magnitude faster than range extension in the past. Yet range extension in the future would be less efficient than in the past, because advance disjunct colonies have been extirpated by human disturbance, and because the seed source is reduced due to reductions in tree populations following logging. Many species of trees may not be able to disperse rapidly enough to track climate, and woodland herbs, which have less efficient seed dispersal mechanisms, may be in danger of extinction.     

Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing

Snow is an important environmental factor in alpine ecosystems, which influences plant phenology, growth and species composition in various ways. With current climate warming, the snow-to-rain ratio is decreasing, and the timing of snowmelt advancing. In a 2-year field experiment above treeline in the Swiss Alps, we investigated how a substantial decrease in snow depth and an earlier snowmelt affect plant phenology, growth, and reproduction of the four most abundant dwarf-shrub species in an alpine tundra community. By advancing the timing when plants started their growing season and thus lost their winter frost hardiness, earlier snowmelt also changed the number of low-temperature events they experienced while frost sensitive. This seemed to outweigh the positive effects of a longer growing season and hence, aboveground growth was reduced after advanced snowmelt in three of the four species studied. Only Loiseleuria procumbens, a specialist of wind exposed sites with little snow, benefited from an advanced snowmelt. We conclude that changes in the snow cover can have a wide range of species-specific effects on alpine tundra plants. Thus, changes in winter climate and snow cover characteristics should be taken into account when predicting climate change effects on alpine ecosystems.     

Impact of climate changes over the extratropical land on permafrost dynamics under RCP scenarios in the 21st century as simulated by the IAP RAS climate model

Estimates of possible climate changes and cryolithozone dynamics in the 21st century over the Northern Hemisphere land are obtained using the IAP RAS global climate model under the RCP scenarios. Annual mean warming over the northern extratropical land during the 21st century amounts to 1.2–5.3°C depending on the scenario. The area of the snow cover in February amounting currently to 46 million km² decreases to 33–42 million km² in the late 21st century. According to model estimates, the near-surface permafrost in the late 21st century persists in northern regions of West Siberia, in Transbaikalia, and Tibet even under the most aggressive RCP 8.5 scenario; under more moderate scenarios (RCP 6.0, RCP 4.5, and RCP 2.6), it remains in East Siberia and in some high-latitude regions of North America. The total near-surface permafrost area in the Northern Hemisphere in the current century decreases by 5.3–12.8 million km² depending on the scenario. The soil subsidence due to permafrost thawing in Central Siberia, Cisbaikalia, and North America can reach 0.5–0.8 m by the late 21st century.     

Effects of climate warming on Olive and olive fly (Bactrocera oleae (Gmelin)) in California and Italy

Climate change is expected to alter the geographic distribution and abundance of many species. Here we examine the potential effects of climate warming on olive (Olea europaea) and olive fly (Bactrocera oleae) across the ecological zones of Arizona–California (AZ–CA) and Italy. A weather-driven physiologically-based demographic model was developed from the extensive literature and used to simulate the phenology, growth and population dynamics of both species. Observed weather for several years from 151 sites in AZ–CA and 84 sites in Italy were used in the study. Three climate-warming scenarios were developed by increasing observed average daily temperature 1°, 2° and 3°C. Predictions of bloom dates, yield, total fly pupae and percent infestation were mapped using GRASS GIS. Linear multiple-regression was used to estimate the effects of weather on yield and fly abundance. Olive has a much wider temperature range of favorability than olive fly. The model predicted the present distributions of both species and gave important insights on the potential effects of climate warming on them. In AZ–CA, climate warming is expected to contract the range of olive in southern desert areas, and expand it northward and along coastal areas. Olive fly is currently limited by high temperature in the southern part of its range and by cold weather in northern areas. Climate warming is expected to increase the range of olive fly northward and in coastal areas, but decrease it in southern areas. In Italy, the range of olive is expected to increase into currently unfavorable cold areas in higher elevations in the Apennine Mountains in central Italy, and in the Po Valley in the north. Climate warming is expected to increase the range of olive fly northward throughout most of Italy.     

Twenty-Seven Years of Manual Fresh Snowfall Density Measurements on Whistler Mountain,British Columbia

Snow density is important information for a wide range of activities including avalanche control, marketing, building-code development, weather forecasting, and water supply forecasting. Extended recent high-quality datasets from the mountainous regions of the Pacific Northwest coastal area are rare. This paper presents a study of an unusually long and continuous (January 1990 to April 2016) manually collected dataset of fresh snowfall measurements for Whistler Mountain, British Columbia, Canada. The dataset consists of snowboard core measurements that were collected by Whistler–Blackcomb ski patrol staff twice daily for avalanche control and resort-marketing purposes. These records were collated, transcribed, quality controlled, and made computer accessible in this study. A discussion of the characteristics of the data collection site and an assessment of data reliability are presented. Two examples of the many purposes to which this high-quality dataset might be put were studied. Climatic teleconnections to winter (December–February) mean snow density were examined, which revealed a positive relationship to the quadratic form of the Pacific Decadal Oscillation pattern (i.e., PDO²). In addition, an analysis of daily snow density relationships to air mass types was performed, which suggested that higher (lower) densities are associated with maritime inflow (arctic outflow) conditions. Both of these relationships appear to be mediated by the positive correlation between snow density and air temperature.

Based on the full dataset (N?=?1275 individual snow density measurements) for all months with measured snowfall, annual snowfall season (November to May) mean snow densities ranged from 77?kg?m^?3 to 109?kg?m^?3 with an overall mean of 91?kg?m^?3, giving an overall snow-depth to water-depth ratio of 11:1.     

庐山一次积冰天气过程冻雨滴谱及下落末速度物理特征个例研究

利用PARSIVEL激光雨滴谱仪和自动气象站观测资料及MICAPS数据,对2014年2月7~15日庐山地区积冰天气期间持续时间在5 h以上的2次冻雨过程[2月10日（个例1）和2月13日（个例2）]降水谱分布特征及下落末速度粒径分布进行研究。所观测到的两次个例均是以冻雨为主体的混合相态降水,下落末速度粒径分布偏离G-K曲线,与常规液态降水存在差异,低落速的冻雨滴随降水过程会逐渐向冰粒和干雪转化。结果表明:（1）个例1总降水粒子谱谱宽大于个例2,但峰值数密度比个例2小:个例1谱宽为10 mm,个例2谱宽为4.25 mm,两者峰值粒径均为0.5 mm;个例1降水粒子谱宽为干雪>冻雨>冰粒,个例2降水粒子谱宽为冻雨>干雪>冰粒。（2）Gamma分布更适合描述混合相态降水粒子谱以及冻雨滴谱,个例1中总降水粒子谱Gamma分布为:N（D）=20D-3.61exp（-0.08D）,冻雨Gamma分布:N（D）=76D-2.18exp（-1.11D）;个例2中总降水粒子谱Gamma分布为:N（D）=30D-4.68exp（-0.75D）,冻雨Gamma分布:N（D）=30D-4.67exp（-0.75D）。（3）混合相态降水因混有干雪或冰粒而使得下落末速度粒径谱分布表现出不同程度地向大粒径小落速方向或小粒径大落速方向延展的趋势,这为今后依据下落末速度粒径谱区分同时期降水类型提供了新的思路。     

Drivers of elevation-dependent warming over the Tibetan Plateau

The response of the warming magnitude over the Tibetan Plateau (TP; elevation ≥ 3000 m) to global climate change is not spatially uniform. Rather, it enhances with elevation, referred to as elevation-dependent warming (EDW). The degree of EDW over the TP is season-dependent, with the largest amplitude of 0.21°C km⁻¹ observed during boreal winter. Several factors have been proposed in previous studies as possible drivers of TP EDW, but the relative importance of these factors has been less studied. To quantitatively identify the major drivers of TP EDW in winter over recent decades (1979–2018), the authors applied the radiative kernels diagnostic method with several datasets. The results robustly suggest that, the surface albedo feedback associated with changes in snow cover plays the leading role in TP EDW. Observations show that the snow cover has reduced significantly over regions with high elevation during the winters of the past four decades, leading to reductions in outgoing shortwave radiation and thus EDW.摘要青藏高原 (海拔≥ 3000 m 地区) 对全球气候变化的变暖响应是空间不均匀的, 其增温幅度会随着海拔升高而增大, 被称为海拔依赖性增温. 青藏高原海拔依赖性增温具有季节依赖性, 在冬季最为显著, 达0.21°C km⁻¹. 在以往的研究中, 众多因素被认为是青藏高原海拔依赖性增温的可能驱动因素, 但关于这些因素相对重要性的研究较少. 基于多个数据集, 本文应用辐射核 (radiative kernel) 技术方法定量诊断了近几十年 (1979–2018年) 冬季不同物理过程对青藏高原海拔依赖性增温的贡献. 结果表明, 与积雪变化相关的地表反照率反馈在其中起主导作用. 观测数据分析显示, 在过去40年的冬季,高海拔地区的积雪覆盖率显著减少, 导致地表反射的短波辐射减少, 从而促进了海拔依赖性增温.     

Exchange rates,soybean supply response,and deforestation in South America

The advancement of South America's agro-pastoral frontier has been widely linked to losses in biodiversity and tropical forests, with particular impacts on the Brazilian cerrado, the Atlantic Forest, and the Amazon. Here we consider an important, yet largely overlooked, driver of South America's soybean expansion, namely the devaluation of local currencies against the US dollar in the late 1990s and early 2000s. Much interest has emerged in recent years over the environmental implications of soybean production in Brazil, with evidence of both direct incursions into moist tropical forest by soybean producers and of potential indirect effects, via the displacement of existing ranching operations. In this research we utilize historical trends in soybean prices, exchange rates, and cropland dedicated to soybean production in Bolivia, Paraguay, and Brazil to estimate the impact of currency devaluations on area of production. The results suggest that approximately 80,000 km², or 31% of the current extent of soybean production in these countries, emerged as a supply area response to the devaluation of local currencies in the late 1990s. The results also indicate that the more recent depreciation of the dollar and appreciation of the Brazilian real have counteracted a recent rise in global soybean prices, in the process sparing an estimated nearly 90,000 km² from new cropland, 40,000 km² of this in the Amazon alone. Amidst an increasingly neoliberal economic environment, where barriers to trade are jettisoned in favor of the free flow of commodities, relative currency values will occupy an important role in the future sourcing of both agricultural expansion and environmental degradation.     

The effect of snow on Antarctic sea ice simulations in a coupled atmosphere-sea ice model

 The effect of a snow cover on sea ice accretion and ablation is estimated based on the ‘zero-layer’ version sea ice model of Semtner, and is examined using a coupled atmosphere-sea ice model including feedbacks and ice dynamics effects. When snow is disregarded in the coupled model the averaged Antarctic sea ice becomes thicker. When only half of the snowfall predicted by the atmospheric model is allowed to land on the ice surface sea ice gets thicker in most of the Weddell and Ross Seas but thinner in East Antarctic in winter, with the average slightly thicker. When twice as much snowfall as predicted by the atmospheric model is assumed to land on the ice surface sea ice also gets much thicker due to the large increase of snow-ice formation. These results indicate the importance of the correct simulation of the snow cover over sea ice and snow-ice formation in the Antarctic. Our results also illustrate the complex feedback effects of the snow cover in global climate models. In this study we have also tested the use of a mean value of 0.16 Wm^-1 K^-1 instead of 0.31 for the thermal conductivity of snow in the coupled model, based on the most recent observations in the eastern Antarctic and Bellingshausen and Amundsen Seas, and have found that the sea ice distribution changes greatly, with the ice becoming much thinner by about 0.2 m in the Antarctic and about 0.4 m in the Arctic on average. This implies that the magnitude of the thermal conductivity of snow is of considerable importance for the simulation of the sea ice distribution. An appropriate value of the thermal conductivity of snow is as crucial as the depth of the snow layer and the snowfall rate in a sea ice model. The coupled climate models require accurate values of the effective thermal conductivity of snow from observations for validating the simulated sea ice distribution under the present climate conditions. Received: 20 November 1997/Accepted: 27 July 1998     

基于随机森林的湖北雪密度预测模型及其在雪压分析中的应用

雪密度、雪压等积雪参数资料的缺乏是南方地区雪灾精细化防御研究的难点之一,通过历史地面积雪气象观测资料来反演测站及周边的雪密度,是对现有积雪监测资料的有益补充。本文利用湖北省76站的逐日气象观测资料,分析并选取了积雪期的积雪日数、积雪深度、气温、日照等8个影响雪密度的自变量因子,构建了雪密度的随机森林回归(RF)模型,并通过RF模型反演数据,分析了湖北省雪密度和雪压分布情况。结果表明：①雪密度RF模型预测的均方根误差为0.04 g/cm3左右,可以用于湖北省雪密度资料反演。②湖北省平均雪密度在0.14～0.20 g/cm3之间,从中部以0.17 g/cm3为〖JP2〗界分为东西两个区,东部区雪密度较大。③湖北省近60年来最大雪压值在1.3～6.7 g/cm2之间,不同重现期最大雪压分布存在鄂西北和鄂东两个高值区,且鄂东区的中北部基本雪压值更大。〖JP〗     

A retrospective analysis of pan Arctic permafrost using the JULES land surface model

There is mounting evidence that permafrost degradation has occurred over the past century. However, the amount of permafrost lost is uncertain because permafrost is not readily observable over long time periods and large scales. This paper uses JULES, the land surface component of the Hadley Centre global climate model, driven by different realisations of twentieth century meteorology to estimate the pan-arctic changes in near-surface permafrost. Model simulations of permafrost are strongly dependent on the amount of snow both in the driving meteorology and the way it is treated once it reaches the ground. The multi-layer snow scheme recently adopted by JULES significantly improves its estimates of soil temperatures and permafrost extent. Therefore JULES, despite still having a small cold bias in soil temperatures, can now simulate a near-surface permafrost extent which is comparable to that observed. Changes in snow cover have been shown to contribute to changes in permafrost and JULES simulates a significant decrease in late twentieth century pan-Arctic spring snow cover extent. In addition, large-scale modelled changes in the active layer are comparable with those observed over northern Russia. Simulations over the period 1967–2000 show a significant loss of near-surface permafrost—between 0.55 and 0.81 million km² per decade with this spread caused by differences in the driving meteorology. These runs also show that, for the grid cells where the active layer has increased significantly, the mean increase is ~10 cm per decade. The permafrost degradation discussed here is mainly caused by an increase in the active layer thickness driven by changes in the large scale atmospheric forcing. However, other processes such as thermokarst development and river and coastal erosion may also occur enhancing permafrost loss.