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.     

中国冬季多种积雪参数的时空特征及差异性

利用1979~2006年冬季中国站点最大雪深和站点雪日、卫星遥感雪深、积雪覆盖率和雪水当量5种积雪资料,从多角度深入细致地分析了我国冬季积雪的时空变化特征。结果表明:5种积雪资料的经验正交分解第一模态都表现为中国南、北方反位相的特征,即当新疆和东北三省-内蒙古地区积雪偏多(少)时,青藏高原和南方地区积雪偏少(多)。新疆和东北三省-内蒙古地区的雪深、积雪覆盖率和雪日随时间有逐渐增多的趋势,而其中边缘山区的雪水当量表现出减少的趋势,青藏高原地区的积雪表现出与其完全相反的特征。南方地区站点最大雪深和雪日表现出随时间减少的趋势,卫星遥感难以监测到该区积雪。相比较而言,卫星遥感资料比较适合高原和山区缺少气象站的地区及北半球更大区域积雪的研究,而站点资料更适用于中国中东部和平原地区积雪的区域研究。雪深、雪日、积雪覆盖率和雪水当量这些多样性积雪参数存在一定的差异性,因此5种积雪资料结合使用才能得到更准确的结论。     

应用风云气象卫星及融合降水资料改进地表参数模拟

气象卫星资料不仅对天气、气候研究非常重要, 对于地表参数模拟和预报也具有重要意义。本文首次将全国自动站观测、卫星降水估计和地面观测融合降水资料(CMORPH)以及风云二号D星(FY-2D)积雪覆盖率数据应用到了高分辨率陆面资料同化系统(u-HRLDAS)。融合降水资料用于驱动u-HRLDAS, 同时用于计算雪水当量;积雪覆盖率资料作为u-HRLDAS强迫变量。区域模拟结果表明, 积雪覆盖率对于地表反照率、地表温度以及地气交换通量模拟有极其重要的影响。密云站土壤湿度模拟结果表明, 融合降水资料准确度优于全球陆面资料同化系统(GLDAS)再分析资料。小汤山站单点验证结果表明, 应用融合降水资料及卫星积雪覆盖率资料可以改进地表温度及地气交换通量的模拟。     

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.     

青藏高原冬春雪深分布与中国夏季降水的关系

利用SSMR和SSM／I卫星遥感雪深反演资料,通过与高原测站雪深观测资料的对比分析,揭示了高原雪深的时空分布特征,在此基础上对积雪异常年中国夏季降水异常和大气环流进行了对比分析。结果表明,卫星遥感雪深资料可较真实反映出高原积雪的状况,并可反映出高原西部积雪的变化;高原冬、春季积雪EOF分解第1模态具有相同的空间分布,反映了高原冬、春季积雪分布具有相当的一致性,而春季积雪的第2模态则反映高原积雪的东西差异;冬、春季雪深EOF第1模态的时间序列与中国夏季降水的相关分析表明,大致以长江为界,我国东部地区呈现出南涝北旱的分布模态,春季高原东（西）部多（少）雪与东（西）部少（多）雪年的夏季,我国东部降水表现出长江以南（北）地区为大范围的降水偏多（少）。     

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.     

An empirical formula to compute snow cover fraction in GCMs

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

Influence of the Eurasian snow cover on the Indian summer monsoon variability in observed climatologies and CMIP3 simulations

The present study is aimed at revisiting the possible influence of the winter/spring Eurasian snow cover on the subsequent Indian summer precipitation using several statistical tools including a maximum covariance analysis. The snow–monsoon relationship is explored using both satellite observations of snow cover and in situ measurements of snow depth, but also a subset of global coupled ocean–atmosphere simulations from the phase 3 of the Coupled Model Intercomparison Project (CMIP3) database. In keeping with former studies, the observations suggest a link between an east–west snow dipole over Eurasia and the Indian summer monsoon precipitation. However, our results indicate that this relationship is neither statistically significant nor stationary over the last 40 years. Moreover, the strongest signal appears over eastern Eurasia and is not consistent with the Blanford hypothesis whereby more snow should lead to a weaker monsoon. The twentieth century CMIP3 simulations provide longer timeseries to look for robust snow–monsoon relationships. The maximum covariance analysis indicates that some models do show an apparent influence of the Eurasian snow cover on the Indian summer monsoon precipitation, but the patterns are not the same as in the observations. Moreover, the apparent snow–monsoon relationship generally denotes a too strong El Niño-Southern Oscillation teleconnection with both winter snow cover and summer monsoon rainfall rather than a direct influence of the Eurasian snow cover on the Indian monsoon.     

北疆积雪深度和积雪日数的变化趋势

 选取新疆北疆20个站1961-2006年积雪及稳定积雪日数、最大积雪深度资料,同时选择冬季降水量和气温稳定通过0℃以下的日数作为积雪的影响因子,分析了46 a来北疆积雪的变化趋势。结果表明：46 a来最大积雪深度呈显著增加趋势,平均年增长0.8%,其变化与冬季降水量增加有关,呈正相关;积雪日数和稳定积雪日数也呈稍增加趋势,增加主要发生在1960-1980年代,1990年代以来有所减少,其变化与气温稳定通过0℃以下的日数呈显著正相关。     

北半球积雪监测诊断业务系统

利用卫星遥感和常规观测的积雪资料，确定了适合业务使用的北半球及中国积雪监测诊断方法，并初步建立了北半球和中国积雪监测业务。其相关业务产品主要有：北半球月积雪日数、中国月积雪日数、积雪深度的分布，北半球、欧亚、中国等不同区域积雪面积距平指数。     

积雪季节变化特征的数值模拟及其敏感性试验

文中利用综合陆面模式 (ComprehensiveLandSurfaceModel,CLSM )对法国ColdePorte 1 993/ 1 994 ,1 994 / 1 995年及BOREASSSA OJP 1 994 / 1 995年积雪个例进行了模拟试验 ,通过模拟结果与观测资料的对比 ,检验了CLSM对积雪变化特征的模拟能力 ,并通过敏感性试验探讨了降雪密度、积雪持水量等积雪参数化方案及植被对积雪模拟可能产生的影响。结果表明 :(1 )CLSM能够准确地模拟出积雪的变化过程 ,对积雪的演变特征作出了合理的描述 ;(2 )降雪密度、积雪持水量参数化方案对积雪模拟结果均具有一定的影响 :降雪密度参数化主要对积雪深度的模拟产生影响 ;而积雪持水量参数化方案对积雪的演变过程 ,尤其是积雪的消融 ,具有重要的作用 ;(3)有、无植被存在的情况下 ,积雪 土壤系统的变化过程存在显著的差别 ,植被通过改变积雪 /土壤表面的能量平衡 ,对积雪及土壤的变化过程产生重要影响 :植被的存在有利于积雪的维持 ,使得积雪融化进程推迟 ,冻结土壤的增温明显偏慢     

改进的CLDAS降水驱动对中国区域积雪模拟的影响评估

积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS-Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS-Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS-Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。     

Application of global snow model for the estimation of snow depth in the UK

Microwave imagery can be used successfully for mapping of snow and estimation of snow pack characteristics under almost all weather conditions. This research is a contribution to the field of space borne remote sensing of snow by means of passive microwave data imagery. The satellite data are acquired from the Special Sensor Microwave Imager (SSM/I). The SSM/I is a four frequency seven channels dual polarization (except 22 GHz which is only vertically polarized) scanning radiometer with channels located at 19, 22, 37, and 85 GHz frequencies. A radiative transfer theory based model is used to estimate the snow cover characteristics of different snow pack types in the UK. A revised form of the Chang et al. (Nord Hydrol 16:57–66, 1987) model is used for this purpose. The revised Chang model was calibrated for global snow monitoring and takes into account forest fractional coverage effects. Snow cover characteristics have significant effects on up-welling naturally emitted microwave radiation through the processes of forward scattering. The up-welling signal is more complex for snow covers that consist of free liquid water content. The aim of this study is to test the global snow depth model for the UK snow cover. The Chang model predicted snow depth bias results for January, February, and March 1995 are ?1.26, ?0.35, and ?0.63 cm, respectively. Similarly, the Chang model Mean Absolute Error (MAE) for January, February, and March 1995 have values 2.88, 2.38, and 1.91 cm, respectively. These results show that the Chang model underestimates the snow depth prediction for all the case studies. The results of this study led us to the conclusion that the global snow models (Chang model) when applied for the retrieval of local snow depth estimation (UK snow cover) underestimate snow depth.     

The Impact of “Bad” Argo Profiles on Ocean Data Assimilation

Recent studies have found cold biases in a fraction of Argo profiles (hereinafter referred to as bad Array for Real-time Geostrophic Oceanography (Argo) profiles) due to the pressure drifts during 2003 and 2006. These bad Argo profiles have had an important impact on in situ observation-based global ocean heat content estimates. This study investigated the impact of bad Argo profiles on ocean data assimilation results that were based on observations from diverse ocean observation systems, such as in situ profiles (e.g., Argo, expendable bathythermograph (XBT), and Tropical Atmosphere Ocean (TAO), remote-sensing sea surface temperature products and satellite altimetry between 2004 and 2006. Results from this work show that the upper ocean heat content analysis is vulnerable to bad Argo profiles and demonstrate a cooling trend in the studied period despite the multiple independent data types that were assimilated. When the bad Argo profiles were excluded from the assimilation, the decreased heat content disappeared and a warming occurred. Combination of satellite altimetry and mass variation data from gravity satellite demonstrated an increase, which agrees well with the increased heat content. Additionally, when an additional Argo profile quality control procedure was utilized that simply removed the profiles that presented static unstable water columns, the results were very similar to those obtained when the bad Argo profiles were excluded from the assimilation. This indicates that an ocean data assimilation that uses multiple data sources with improved quality control could be less vulnerable to a major observation system failure, such as a bad Argo event.     

气象卫星遥感资料在积雪监测中的应用

介绍了在地区级气象台站采用气象系统 92 1 0工程下发的FY 1D卫星遥感数据 ,在短红外波段的基础上建立多光谱提取积雪信息的计算方法及监测模型 ,并以2 0 0 2年 1 1月至 2 0 0 3年 5月新疆积雪遥感监测为例 ,介绍了卫星遥感监测积雪深度和积雪覆盖面积的方法 ,分析了积雪覆盖特征和变化规律。     

卫星反演积雪信息的研究进展

综合分析了积雪信息反演的主要遥感信息源和提取方法。在光学遥感方面，应用较广的主要是改进型甚高分辨率扫描辐射仪（AVHRR）资料和中分辨率成像光谱仪（MODIS）资料；提取积雪信息大多是根据积雪在可见光波段的高反射率和近红外波段的低反射率，并通过建立回归模型反演积雪面积和深度。由于传感器的改进，MODIS卫星资料在空间分辨率、积雪反演算法等方面明显优于AVHRR资料。光学仪器受云层和大气的影响很大，由于云和积雪在可见光和近红外波段上都具有高反射率。并且由于云层的遮挡。云下的地表信息不能被光学遥感仪器所接收到。微波遥感方面，被动微波遥感仪如微波辐射计成像仪（SSM／I）、高级微波扫描辐射计（AMSR—E）等可以全天候穿过云层进行监测，具有光学仪器所没有的优势，并通过提取地表的亮温差，建立雪深反演模型得到积雪深度。被动微波传感器存在分辨率低。无法监测浅雪区信息等问题。另外影响地表微波亮温的因素很多，这些都在一定程度上影响了反演结果的精确度。主动微波遥感仪如合成孔径雷达、微波散射计等利用积雪与其它地物的后向散射系数的不同来识别积雪，但也同样存在分辨率低等问题。最后探讨了卫星反演积雪信息中仍然存在的问题和进一步发展的方向。     

雷达径向风观测在华北区域数值预报系统中的实时三维变分同化应用试验

以实现业务应用为目标开展了区域多部雷达径向风观测资料的三维变分直接同化应用研究。重点对背景场误差协方差的方差和尺度因子进行调整,形成能够与其他常规观测资料协同同化的雷达径向风同化方案,并建立了京津冀6部多普勒雷达观测资料的实时预处理系统。基于上述工作开展2011年汛期京津冀多普勒雷达径向风观测资料在华北区域快速更新循环同化和预报系统中的实时同化和对比试验,并对应用效果进行了初步评估。实时同化试验期间京津冀地区6部雷达经过质量控制后的径向风数据质量和同化情况的分析结果表明,同化系统有效地吸收了雷达径向风的观测信息并形成合理的分析增量,其中,S波段雷达观测的径向风数据数量、质量和稳定度均明显优于C波段雷达;整体来看,雷达径向风同化对地面和高空要素预报性能的影响基本为中性,且主要影响时段集中在最初的6 h。但降水预报评分结果表明,雷达径向风同化从降水强度、落区和范围等方面均明显提升了系统对对流尺度降水的短时预报性能。同时也应该看到,受制于目前3 h一次的同化更新频率,雷达资料同化的效果往往到对流临近时次才能体现。     

Snow Cover Variation in the Past 45 Years (1959-2003) in the Tianshan Mountains, China

 Meteorological data at 17 weather stations in the Tianshan Mountains from 1959 to 2003 were analyzed to explore the variations in temperature and snow cover. The abrupt change test for snow depth was performed using Mann-Kendall statistic. The spatial distribution of maximum snow depth was calculated by employing GIDS interpolation and DEM data. The results show that mean temperature in winter had a rising trend at a rate of 0.44 ℃/10 a. The minimum temperature in winter increased more evidently at a rate of 0.79 ℃/10 a. The maximum snow depth has obviously deepened at a rate of 1.15 cm/10 a in the past 45 years, and it was about 16% higher than the average during 1991-2003. The Mann-Kendall statistic test of snow depth indicates that the abrupt change occurred in 1976. The maximum increment for snow cover depth occurred in Zhaoshu (Kunes) (39.3%) and Nilka (39.7%) in the west Tianshan Mountains. In contrast, the snow cover depth reduced by 17% in Barkol in the east Tianshan Mountains. There was a primary change periodicity of about 2.8 years in snow cover. In addition, snow cover days with a depth more than 10 cm increased distinctly, however, there was no obvious advance or delay in snow beginning and ending dates.     

1959-2003年中国天山积雪的变化

Mcteorological data at 17 weather stations in the Tianshan Mountains from 1959 to 2003 were analyzed to explore the variations in temperature and snow cover.The abrupt change test for snow depth was performed using Mann-Kendall statistic.The spatial distribution of maximum snow depth was calculated by employing GIDS interpolation and DEM data.The results show that mean temperature in winter had a rising trend at a rate of 0.44℃/10a.The minimum temperature in winter increased more evidently at a rate of 0.79℃/10a.The maximum snow depth has obviously deepened at a rate of 1.15 cm/10 a in the past 45 years,and it was about 16% higher than the average during 1991-2003.The Mann-Kendall statistic test of snow depth indicates that the abrupt change occurred in 1976.The maximum increment for snow cover depth occurred in Zhaoshu(Kunes)(39.3%)and Nilka(39.7%)in the west Tiansban Mountains.In contrast,the snow cover depth reduced by 17% in Barkol in the east Tianshan Mountains.There was a primary change periodicity of about 2.8 years in snow cover.In addition,snow cover days with a depth more than 10 cm increased distinctly,however,there was no obvious advance or delay in snow beginning and ending dates.     

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.