Impact of the snow cover scheme on snow distribution and energy budget modeling over the Tibetan Plateau

This paper presents the impact of two snow cover schemes (NY07 and SL12) in the Community Land Model version 4.5 (CLM4.5) on the snow distribution and surface energy budget over the Tibetan Plateau. The simulated snow cover fraction (SCF), snow depth, and snow cover days were evaluated against in situ snow depth observations and a satellite-based snow cover product and snow depth dataset. The results show that the SL12 scheme, which considers snow accumulation and snowmelt processes separately, has a higher overall accuracy (81.8%) than the NY07 (75.8%). The newer scheme performs better in the prediction of overall accuracy compared with the NY07; however, SL12 yields a 15.1% underestimation rate while NY07 overestimated the SCF with a 15.2% overestimation rate. Both two schemes capture the distribution of the maximum snow depth well but show large positive biases in the average value through all periods (3.37, 3.15, and 1.48 cm for NY07; 3.91, 3.52, and 1.17 cm for SL12) and overestimate snow cover days compared with the satellite-based product and in situ observations. Higher altitudes show larger root-mean-square errors (RMSEs) in the simulations of snow depth and snow cover days during the snow-free period. Moreover, the surface energy flux estimations from the SL12 scheme are generally superior to the simulation from NY07 when evaluated against ground-based observations, in particular for net radiation and sensible heat flux. This study has great implications for further improvement of the subgrid-scale snow variations over the Tibetan Plateau.     

Long-term snow and weather observations at Weissfluhjoch and its relation to other high-altitude observatories in the Alps

Snow and weather observations at Weissfluhjoch were initiated in 1936, when a research team set a snow stake and started digging snow pits on a plateau located at 2,540?m asl above Davos, Switzerland. This was the beginning of what is now the longest series of daily snow depth, new snow height and bi-monthly snow water equivalent measurements from a high-altitude research station. Our investigations reveal that the snow depth at Weissfluhjoch with regard to the evolution and inter-annual variability represents a good proxy for the entire Swiss Alps. In order to set the snow and weather observations from Weissfluhjoch in a broader context, this paper also shows some comparisons with measurements from five other high-altitude observatories in the European Alps. The results show a surprisingly uniform warming of 0.8°C during the last three decades at the six investigated mountain stations. The long-term snow measurements reveal no change in mid-winter, but decreasing trends (especially since the 1980s) for the solid precipitation ratio, snow fall, snow water equivalent and snow depth during the melt season due to a strong temperature increase of 2.5°C in the spring and summer months of the last three decades.     

Improving the treatment of the vertical snow burial fraction over short vegetation in the NCAR CLM3

One deficiency of the NCAR Community Land Model (CLM3) is the disappearance of the simulated snow even in the middle of winter over a boreal grassland site due to unrealistically modeled high downward turbulent fluxes. This is caused by the inappropriate treatment of the vertical snow burial fraction for short vegetation. A new snow burial fraction formulation for short vegetation is then proposed and validated using in situ observations. This modification in the CLM3 largely removes the unrealistic surface turbulent fluxes, leading to a more reasonable snowmelt process, and improves the snow water equivalent (SWE) simulation. Moreover, global offline simulations show that the proposed formulation decreases sensible and latent heat fluxes as well as the ground temperature during the snowmelt season over short vegetation dominant regions. Correspondingly, the SWE is enhanced, leading to the increase in snowmelt-induced runoff during the same period. Furthermore, sensitivity tests indicate that these improvements are insensitive to the exact functional form or parameter values in the proposed formulation.     

Climate change impact on snow and soil temperature in boreal Scots pine stands

Scenarios indicate that the air temperature will increase in high latitude regions in coming decades, causing the snow covered period to shorten, the growing season to lengthen and soil temperatures to change during the winter, spring and early summer. To evaluate how a warmer climate is likely to alter the snow cover and soil temperature in Scots pine stands of varying ages in northern Sweden, climate scenarios from the Swedish regional climate modelling programme SWECLIM were used to drive a Soil-Vegetation-Atmosphere Transfer (SVAT)-model (COUP). Using the two CO₂ emission scenarios A and B in the Hadley centres global climate model, HadleyA and HadleyB, SWECLIM predicts that the annual mean air temperature and precipitation will increase at most 4.8°C and 315 mm, respectively, within a century in the study region. The results of this analysis indicate that a warmer climate will shorten the period of persistent snow pack by 73–93 days, increase the average soil temperature by 0.9–1.5°C at 10 cm depth, advance soil warming by 15–19 days in spring and cause more soil freeze–thaw cycles by 31–38%. The results also predict that the large current variations in snow cover due to variations in tree interception and topography will be enhanced in the coming century, resulting in increased spatial variability in soil temperatures.     

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.     

Effect of snow cover on pan-Arctic permafrost thermal regimes

This study quantitatively evaluated how insulation by snow depth (SND) affected the soil thermal regime and permafrost degradation in the pan-Arctic area, and more generally defined the characteristics of soil temperature (T_SOIL) and SND from 1901 to 2009. This was achieved through experiments performed with the land surface model CHANGE to assess sensitivity to winter precipitation as well as air temperature. Simulated T_SOIL, active layer thickness (ALT), SND, and snow density were generally comparable with in situ or satellite observations at large scales and over long periods. Northernmost regions had snow that remained relatively stable and in a thicker state during the past four decades, generating greater increases in T_SOIL. Changes in snow cover have led to changes in the thermal state of the underlying soil, which is strongly dependent on both the magnitude and the timing of changes in snowfall. Simulations of the period 2001–2009 revealed significant differences in the extent of near-surface permafrost, reflecting differences in the model’s treatment of meteorology and the soil bottom boundary. Permafrost loss was greater when SND increased in autumn rather than in winter, due to insulation of the soil resulting from early cooling. Simulations revealed that T_SOIL tended to increase over most of the pan-Arctic from 1901 to 2009, and that this increase was significant in northern regions, especially in northeastern Siberia where SND is responsible for 50 % or more of the changes in T_SOIL at a depth of 3.6 m. In the same region, ALT also increased at a rate of approximately 2.3 cm per decade. The most sensitive response of ALT to changes in SND appeared in the southern boundary regions of permafrost, in contrast to permafrost temperatures within the 60°N–80°N region, which were more sensitive to changes in snow cover. Finally, our model suggests that snow cover contributes to the warming of permafrost in northern regions and could play a more important role under conditions of future Arctic warming.     

Evaluation of Snow Depth and Snow Cover Fraction Simulated by Two Versions of the Flexible Global Ocean–Atmosphere–Land System Model简

Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational data.The results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,respectively.Although the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the NH.Analysis showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two models.In addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period 1971-94.However,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.     

ＣＬＭ３０对中国区域陆面过程的模拟试验 及评估Ⅱ：土壤湿度

利用NCAR公用陆面模式（Community Land Model 3.0,CLM3.0）及普林斯顿大学1948—2001年1°×1°、3 h一次的全球大气近地面强迫资料,对中国地区1948-2001年的土壤温度进行了off-line模拟试验,通过对模拟结果和全国台站观测土壤温度资料的对比,评估了CLM3.0对中国区域不同层次土壤温度的模拟能力。结果表明：模式能模拟出中国地区多年平均土壤温度的空间分布型,除部分地区模拟比观测偏高外,模式模拟的土壤温度普遍偏低;模式能较好地反映出中国地区土壤温度的年际变化,对4月的模拟稍好于7月。对于划分的8个子区域,东部区域模拟好于其它各区,除高原一带外,表层的模拟均好于深层;模式基本能抓住土壤温度的变化趋势,而且模拟出了7月长江流域以南地区土壤温度显著降低这一趋势,但模拟的趋势比观测有所偏弱。     

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.     

Potential predictability of Eurasian snow cover

Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982–1998) and observations. Using remotely sensed observations of snow cover, we find significant point-wise correlation over the North Atlantic and North Pacific between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El Niño-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO.     

黄河源积雪期土壤温湿及冻融特征分析与模拟

利用2017～2018年黄河源地区野外观测站数据,对黄河源区两个积雪期内土壤温湿及冻融特征进行了分析,并与CLM4.5模式模拟的积雪期土壤温、湿度及辐射分量进行了对比,结果表明:CLM4.5能很好地模拟出整个积雪期土壤温度的变化趋势;对不同土壤层在不同冻结阶段土壤含水量的模拟有所差异:在完全冻结阶段,对5cm 土壤层含...     

青藏高原多雪年与少雪年土壤水热特征模拟分析

利用耦合了陆面过程模式(CLM4.5)的区域气候模式(RegCM4)分别对青藏高原的一个多雪年和少雪年进行了数值模拟.通过对比模拟雪深与遥感雪深、土壤温湿度的模拟值与观测值、多雪年与少雪年的土壤温湿度模拟值,结果表明,RegCM4-CLM4.5可以有效模拟出高原的多雪年与少雪年特征,模拟雪深大值中心比遥感雪深高10～2...     

The extent and intensity of the urban heat island in Iași city,Romania

The study underlines the characteristics of the urban heat island of Ia?i (Ia?i’s UHI) on the basis of 3 years of air temperature measurements obtained by fixed-point observations. We focus on the identification of UHI development and intensity as it is expressed by the temperature differences between the city centre and the rural surroundings. Annual, seasonal and daily characteristics of Ia?i’s UHI are investigated at the level of the classical weather observation. In brief, an intensity of 0.8 °C of UHI and a spatial extension which corresponds to the densely built area of the city were delineated. The Ia?i UHI is stronger during summer calm nights—when the inner city is warmer with 2.5–3 °C than the surroundings—and is weaker during windy spring days. The specific features of Ia?i’s UHI bear a profound connection to the specificity of the urban structure, the high atmospheric stability in the region and the local topography. Also, the effects of Ia?i’s UHI upon some environmental aspects are presented as study cases. For instance, under the direct influence of UHI, we have observed that in the city centre, the apricot tree blossoms earlier (with up to 4 days) and the depth of the snow cover is significantly lower (with up to 10 cm for a rural snow depth of 30 cm) than in the surrounding areas.     

Customization of regional climate model (RegCM4) over Indian region

The regional climate model (RegCM4) is customized for 10-year climate simulation over Indian region through sensitivity studies on cumulus convection and land surface parameterization schemes. The model is configured over 30° E–120° E and 15° S–45° N at 30-km horizontal resolution with 23 vertical levels. Six 10-year (1991–2000) simulations are conducted with the combinations of two land surface schemes (BATS, CLM3.5) and three cumulus convection schemes (Kuo, Grell, MIT). The simulated annual and seasonal climatology of surface temperature and precipitation are compared with CRU observations. The interannual variability of these two parameters is also analyzed. The results indicate that the model simulated climatology is sensitive to the convection as well as land surface parameterization. The analysis of surface temperature (precipitation) climatology indicates that the model with CLM produces warmer (dryer) climatology, particularly over India. The warmer (dryer) climatology is due to the higher sensible heat flux (lower evapotranspiration) in CLM. The model with MIT convection scheme simulated wetter and warmer climatology (higher precipitation and temperature) with smaller Bowen ratio over southern India compared to that with the Grell and Kuo schemes. This indicates that a land surface scheme produces warmer but drier climatology with sensible heating contributing to warming where as a convection scheme warmer but wetter climatology with latent heat contributing to warming. The climatology of surface temperature over India is better simulated by the model with BATS land surface model in combination with MIT convection scheme while the precipitation climatology is better simulated with BATS land surface model in combination with Grell convection scheme. Overall, the modeling system with the combination of Grell convection and BATS land surface scheme provides better climate simulation over the Indian region.     

Incorporating organic soil into a global climate model

Organic matter significantly alters a soil’s thermal and hydraulic properties but is not typically included in land-surface schemes used in global climate models. This omission has consequences for ground thermal and moisture regimes, particularly in the high-latitudes where soil carbon content is generally high. Global soil carbon data is used to build a geographically distributed, profiled soil carbon density dataset for the Community Land Model (CLM). CLM parameterizations for soil thermal and hydraulic properties are modified to accommodate both mineral and organic soil matter. Offline simulations including organic soil are characterized by cooler annual mean soil temperatures (up to ∼2.5°C cooler for regions of high soil carbon content). Cooling is strong in summer due to modulation of early and mid-summer soil heat flux. Winter temperatures are slightly warmer as organic soils do not cool as efficiently during fall and winter. High porosity and hydraulic conductivity of organic soil leads to a wetter soil column but with comparatively low surface layer saturation levels and correspondingly low soil evaporation. When CLM is coupled to the Community Atmosphere Model, the reduced latent heat flux drives deeper boundary layers, associated reductions in low cloud fraction, and warmer summer air temperatures in the Arctic. Lastly, the insulative properties of organic soil reduce interannual soil temperature variability, but only marginally. This result suggests that, although the mean soil temperature cooling will delay the simulated date at which frozen soil begins to thaw, organic matter may provide only limited insulation from surface warming.     

Treatment of frozen soil and snow cover in the land surface model SEWAB

Summary  The land surface model SEWAB (Surface Energy and Water Balance) is designed to be coupled to both, atmospheric and hydrological models. Its application in mid and high latitudes requires the inclusion of freezing and thawing processes within the soil and the accumulation and ablation of a snow cover. These winter processes are parameterised with a minimum number of empirical formulations in order to assure reasonable computation times for an application in climate and sensitivity studies yet accounting for all important processes. Meteorological forcing data and measurements of snow depth, soil temperature and liquid soil water content at two locations in the mid-west of North America are used to test the model. Generally the simulated snow depth matches the measurements, remaining differences in snow depth can be explained by uncertainties in snow density, blowing snow and errors in precipitation measurements. The simulated soil temperature and liquid soil water content compare well with the measurements, showing the isolating effect of the snow cover. Received August 25, 2000 Revised January 19, 2001     

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.     

The coupling of the Common Land Model (CLM0) to a regional climate model (RegCM)

Summary We replace the existing land surface parameterization scheme, the Biosphere-Atmosphere Transfer Scheme (BATS), in a regional climate model (RegCM) with the newly developed Common Land Model (CLM0). The main improvements of CLM0 include a detailed 10-layer soil model, the distinction between soil ice and water phases, a linked photosynthesis-stomatal conductance model, a multilayer snow model, and an improved runoff parameterization. We compare the performance of CLM0 and BATS as coupled to the RegCM in a one year simulation over East Asia. We find that the RegCM/CLM0 improves the winter cold bias present in the RegCM/BATS simulation. With respect to the surface energy balance, lower CLM0 albedos allow the absorption of more solar radiation at the surface. CLM0 tends to simulate higher sensible heat and lower latent heat fluxes than its BATS counterpart. The surface water balance also changes considerably between the two land surface schemes. Compared to BATS, CLM0 precipitation is reduced overall and surface runoff is increased, thereby allowing less water to enter the soil column. Evapotranspiration is lower in CLM0 due to lower ground evaporation, which leads to a wetter surface soil in CLM0 in spite of less precipitation input. However, transpiration is greater in CLM0 than BATS, which has an overall effect of less surface storage during the summertime. Comparison with station observations indicates that CLM0 tends to improve the simulation of root zone soil water content compared to BATS. Another pronounced difference between the two schemes is that CLM0 produces lower snow amounts than BATS because of different snow models and warmer CLM0 temperatures. In this case, BATS snow cover amounts are more in line with observations. Overall, except for the snow amounts, CLM0 appears to improve the RegCM simulation of the surface energy and water budgets compared to BATS.     

Warmer climate: less or more snow?

Changes in snow amount, as measured by the water equivalent of the snow pack (SWE), are studied using simulations of 21st century climate by 20 global climate models. Although the simulated warming makes snow season to shorten from its both ends in all of Eurasia and North America, SWE at the height of the winter generally increases in the coldest areas. Elsewhere, snow decreases throughout the winter. The average borderline between increasing and decreasing midwinter SWE coincides broadly with the ?20°C isotherm in late 20th century November–March mean temperature, although with some variability between different areas. On the colder side of this isotherm, an increase in total precipitation generally dominates over reduced fraction of solid precipitation and more efficient melting, and SWE therefore increases. On the warmer side, where the phase of winter precipitation and snowmelt are more sensitive to the simulated warming, the reverse happens. The strong temperature dependence of the simulated SWE changes suggests that projections of SWE change could be potentially improved by taking into account biases in simulated present-day winter temperatures. A probabilistic cross verification exercise supports this suggestion.     

Improved Parameterization of Snow Albedo in WRF + Noah: Methodology Based on a Severe Snow Event on the Tibetan Plateau

Snowfall and the subsequent evolution of the snowpack have a large effect on the surface energy balance and water cycle of the Tibetan Plateau (TP). The effects of snow cover can be represented by the WRF coupled with a land surface scheme. The widely used Noah scheme is computationally efficient, but its poor representation of albedo needs considerable improvement. In this study, an improved albedo scheme is developed using a satellite-retrieved albedo that takes snow depth and age into account. Numerical experiments were then conducted to simulate a severe snow event in March 2017. The performance of the coupled WRF/Noah model, which implemented the improved albedo scheme, is compared against the model’s performance using the default Noah albedo scheme and against the coupled WRF/CLM that applied CLM albedo scheme. When the improved albedo scheme is implemented, the albedo overestimation in the southeastern TP is reduced, reducing the RMSE of the air temperature by 0.7°C. The improved albedo scheme also attains the highest correlation between the satellite-derived and the model-estimated albedo, which provides for a realistic representation of both the snow water equivalent (SWE) spatial distribution in the heavy snowbelt (SWE > 6 mm) and the maximum SWE in the eastern TP. The underestimated albedo in the coupled WRF/CLM leads to underestimating the regional maximum SWE and a consequent failure to estimate SWE in the heavy snowbelt accurately. Our study demonstrates the feasibility of improving the Noah albedo scheme and provides a theoretical reference for researchers aiming to improve albedo schemes further.