Multi-decadal scenario simulation over Korea using a one-way double-nested regional climate model system. Part 2: future climate projection (2021–2050)

An analysis of simulated future surface climate change over the southern half of Korean Peninsula using a RegCM3-based high-resolution one-way double-nested system is presented. Changes in mean climate as well as the frequency and intensity of extreme climate events are discussed for the 30-year-period of 2021–2050 with respect to the reference period of 1971–2000 based on the IPCC SRES B2 emission scenario. Warming in the range of 1–4°C is found throughout the analysis region and in all seasons. The warming is maximum in the higher latitudes of the South Korean Peninsula and in the cold season. A large reduction in snow depth is projected in response to the increase of winter minimum temperature induced by the greenhouse warming. The change in precipitation shows a distinct seasonal variation and a substantial regional variability. In particular, we find a large increase of wintertime precipitation over Korea, especially in the upslope side of major mountain systems. Summer precipitation increases over the northern part of South Korea and decreases over the southern regions, indicating regional diversity. The precipitation change also shows marked intraseasonal variations throughout the monsoon season. The temperature change shows a positive trend throughout 2021–2050 while the precipitation change is characterized by pronounced interdecadal variations. The PDF of the daily temperature is shifted towards higher values and is somewhat narrower in the scenario run than the reference one. The number of frost days decreases markedly and the number of hot days increases. The regional distribution of heavy precipitation (over 80 mm/day) changes considerably, indicating changes in flood vulnerable regions. The climate change signal shows pronounced fine scale signal over Korea, indicating the need of high-resolution climate simulations     

Western Himalayan snow cover and Indian monsoon rainfall: A re-examination with INSAT and NCEP/NCAR data

Summary ?This study presents the monthly climatology and variability of the INSAT (Indian National Satellite) derived snow cover estimates over the western Himalayan region. The winter/spring snow estimates over the region are related to the subsequent summer monsoon rainfall over India. The NCEP/NCAR data are used to understand the physical mechanism of the snow-monsoon links. 15 years (1986–2000) of recent data are utilized to investigate these features in the present global warming environment. Results reveal that the spring snow cover area has been declining and snow has been melting faster from winter to spring after 1993. Connections between snow cover estimates and Indian monsoon rainfall (IMR) show that spring snow cover area is negatively related with maximum during May, while snow melt during the February–May period is positively related with subsequent IMR, implying that smaller snow cover area during May and faster snow melt from winter to spring is conducive for good monsoon activity over India. NCEP/NCAR data further shows that the heat low over northwest India and the monsoon circulation over the Indian subcontinent, in particular the cross-equatorial flow, during May are intensified (weakened) when the snow cover area during May is smaller (extensive) and snow melts faster (slower) during the February–May period. The well-documented negative relationship between winter snow and summer rainfall seems to have altered recently and changed to a positive relationship. The changes observed in snow cover extent and snow depth due to global warming may be a possible cause for the weakening winter snow–IMR relationship. Received January 15, 2002; revised May 5, 2002; accepted June 23, 2002     

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.     

Quantifying uncertainties in projections of extremes��a perturbed land surface parameter experiment

Uncertainties in the climate response to a doubling of atmospheric CO₂ concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO₂. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO₂ is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.     

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 thermoinsulation effect of snow cover within a climate model

We use a state of the art climate model (CAM3–CLM3) to investigate the sensitivity of surface climate and land surface processes to treatments of snow thermal conductivity. In the first set of experiments, the thermal conductivity of snow at each grid cell is set to that of the underlying soil (SC-SOIL), effectively eliminating any insulation effect. This scenario is compared against a control run (CTRL), where snow thermal conductivity is determined as a prognostic function of snow density. In the second set of experiments, high (SC-HI) and low (SC-LO) thermal conductivity values for snow are prescribed, based on upper and lower observed limits. These two scenarios are used to envelop model sensitivity to the range of realistic observed thermal conductivities. In both sets of experiments, the high conductivity/low insulation cases show increased heat exchange, with anomalous heat fluxes from the soil to the atmosphere during the winter and from the atmosphere to the soil during the summer. The increase in surface heat exchange leads to soil cooling of up to 20 K in the winter, anomalies that persist (though damped) into the summer season. The heat exchange also drives an asymmetric seasonal response in near-surface air temperatures, with boreal winter anomalies of +6 K and boreal summer anomalies of −2 K. On an annual basis there is a net loss of heat from the soil and increases in ground ice, leading to reductions in infiltration, evapotranspiration, and photosynthesis. Our results show land surface processes and the surface climate within CAM3–CLM3 are sensitive to the treatment of snow thermal conductivity.     

CHARACTERISTICS OF VEGETATION COVER, ROUGHNESS AND ALBEDO DISTRIBUTION OVER CHINA*

To build land surface dataset for climate model,with application of remote sensing technique as well as the Geographic Information System(GIS),the data of surface type,roughness and albedo over China in 1997 were retrieved,resolutions being 10 km×10 km.Based on these data,an analysis is conducted on the geographic distributions and seasonal variations of surface vegetation cover and roughness as well as albedo over China.Results show that surface vegetation cover is mainly located to the south of Yangtze River,in Southwest and Northeast China andsparse vegetation cover is in the Northwest.The variation of land surface cover affects the variations of land surface roughness and albedo.High albedo occurred in the north of Xinjiang Autonomous Region,the north of Northeast China and the Qinghai-Xizang Plateau in winter,in correspondence with the location of snow cover.For most part of China,surface roughness decreases and albedo increases in winter,while the roughness increases and the albedo decreases in summer,which could mainly result from land surface cover(snow cover and vegetation cover)and soil moisture changes.This shows that the geographic distribution and seasonal variation of the albedo are almost opposite to those of the roughness,in agreement with theoretical results.Temporally,the amplitude of surface roughness change is quite small in comparison with the roughness itself.     

Biogeophysical impacts of land use on present-day climate: near-surface temperature change and radiative forcing

Changes in land cover affect climate through the surface energy and moisture budgets, but these biogeophysical impacts of land use have not yet been included in General Circulation Model (GCM) simulations of 20th century climate change. Here, the importance of these effects was assessed by comparing climate simulations performed with current and potential natural vegetation. The northern mid-latitude agricultural regions were simulated to be approximately 1–2 K cooler in winter and spring in comparison with their previously forested state, due to deforestation increasing the surface albedo by approximately 0.1 during periods of snow cover. Some other regions such as the Sahel and India experienced a small warming due to land use. Although the annual mean global temperature is only 0.02 K lower in the simulation with present-day land use, the more local temperature changes in some regions are of a similar magnitude to those observed since 1860. The global mean radiative forcing by anthropogenic surface albedo change relative to the natural state is simulated to be −0.2 Wm², which is comparable with the estimated forcings relative to pre-industrial times by changes in stratospheric and tropospheric ozone, N₂O, halocarbons, and the direct effect of anthropogenic aerosols. Since over half of global deforestation has occurred since 1860, simulations of climate since that date should include the biogeophysical effects of land use.     

The effects of land-surface characteristics on the East Asian summer monsoon

 The Community Climate Model version 2 (CCM2) of the National Center for Atmospheric Research (NCAR) was used to investigate the effects of the land-surface characteristics on the East Asian summer monsoon. Four numerical experiments were performed in this study. They include the control run, the biosphere–atmosphere transfer scheme (BATS) run, the heavy snow run, and the light snow run. The results show that CCM2 can reasonably simulate many characteristics of the East Asian summer monsoon, such as the 850-hPa southwesterlies, 200-hPa easterlies, high precipitation rate, two monsoon subsystems, the low-level subtropical high, and the upper level South Asian anticyclone. Nevertheless, the model still exhibits some systematic errors, including oversimulation of the temperature over the Eurasian continent, which in turn intensifies the monsoon circulations. In the BATS run, the model can significantly relieve the temperature bias over the continent in spring and early summer. However, the effect of BATS decreases in the summer due to excessive incoming solar radiation. The Eurasian continent is still occupied by an oversimulated thermal low in summer. In the heavy snow case, the high albedo of snow and larger soil moisture suppress the warming rate of the surface and atmosphere in the early summer and hence the cooler troposphere results in a weaker monsoon circulation. Moreover, anomalous cyclonic flows are found in the leeside of Tibetan Plateau (i.e. the southwest vortex in China) in the heavy snow case. This may shed a light on the precipitation anomalies (floods) over Yangtze River Valley (Central China) and eastern Asia due to intensified baroclinic disturbances. Received: 8 September 1999 / Accepted: 5 June 2000     

Dependence of global radiation on cloudiness and surface albedo in Tartu, Estonia

Summary ?The dependence of global and diffuse radiation on surface albedo due to multiple reflection of radiation between the surface and the atmosphere (base of clouds) is found on the basis of data obtained at the Tartu–T?ravere Actinometric Station over the period 1955–2000. It is found that the monthly totals of global radiation increase by up to 1.38–1.88 times, particularly in the winter half-year between November and March, when snow cover albedo may be high. A semi-empirical formula is derived for calculating with sufficient accuracy the monthly totals of global radiation, considering the amount of cloudiness and the surface albedo. In the time series of the monthly total by global radiation a downward trend occurs in winter months. A decrease in global radiation by up to 20% in the past 46 years can be explained primarily by a relatively high negative trend in the snow cover duration and surface albedo (up to − 0.24). As a result, days are growing darker, a new phenomenon associated with climate change, which undoubtedly affects human mood to some extent. Received November 8, 2001; revised January 24, 2002; accepted February 2, 2002     

Influence of snow cover changes on surface radiation and heat balance based on the WRF model

The snow cover extent in mid-high latitude areas of the Northern Hemisphere has significantly declined corresponding to the global warming, especially since the 1970s. Snow-climate feedbacks play a critical role in regulating the global radiation balance and influencing surface heat flux exchange. However, the degree to which snow cover changes affect the radiation budget and energy balance on a regional scale and the difference between snow-climate and land use/cover change (LUCC)-climate feedbacks have been rarely studied. In this paper, we selected Heilongjiang Basin, where the snow cover has changed obviously, as our study area and used the WRF model to simulate the influences of snow cover changes on the surface radiation budget and heat balance. In the scenario simulation, the localized surface parameter data improved the accuracy by 10 % compared with the control group. The spatial and temporal analysis of the surface variables showed that the net surface radiation, sensible heat flux, Bowen ratio, temperature and percentage of snow cover were negatively correlated and that the ground heat flux and latent heat flux were positively correlated with the percentage of snow cover. The spatial analysis also showed that a significant relationship existed between the surface variables and land cover types, which was not obviously as that for snow cover changes. Finally, six typical study areas were selected to quantitatively analyse the influence of land cover types beneath the snow cover on heat absorption and transfer, which showed that when the land was snow covered, the conversion of forest to farmland can dramatically influence the net radiation and other surface variables, whereas the snow-free land showed significantly reduced influence. Furthermore, compared with typical land cover changes, e.g., the conversion of forest into farmland, the influence of snow cover changes on net radiation and sensible heat flux were 60 % higher than that of land cover changes, indicating the importance of snow cover changes in the surface-atmospheric feedback system.     

Permafrost Thaw Accelerates in Boreal Peatlands During Late-20th Century Climate Warming

Permafrost covers 25% of the land surface in the northern hemisphere, where mean annual ground temperature is less than 0°C. A 1.4–5.8 °C warming by 2100 will likely change the sign of mean annual air and ground temperatures over much of the zones of sporadic and discontinuous permafrost in the northern hemisphere, causing widespread permafrost thaw. In this study, I examined rates of discontinuous permafrost thaw in the boreal peatlands of northern Manitoba, Canada, using a combination of tree-ring analyses to document thaw rates from 1941–1991 and direct measurements of permanent benchmarks established in 1995 and resurveyed in 2002. I used instrumented records of mean annual and seasonal air temperatures, mean winter snow depth, and duration of continuous snow pack from climate stations across northern Manitoba to analyze temporal and spatial trends in these variables and their potential impacts on thaw. Permafrost thaw in central Canadian peatlands has accelerated significantly since 1950, concurrent with a significant, late-20th-century average climate warming of +1.32 °C in this region. There were strong seasonal differences in warming in northern Manitoba, with highest rates of warming during winter (+1.39 °C to +1.66 °C) and spring (+0.56 °C to +0.78 °C) at southern climate stations where permafrost thaw was most rapid. Projecting current warming trends to year 2100, I show that trends for north-central Canada are in good agreement with general circulation models, which suggest a 4–8 °C warming at high latitudes. This magnitude of warming will begin to eliminate most of the present range of sporadic and discontinuous permafrost in central Canada by 2100.     

Snow cover validation and sensitivity to CO2 in the UVic ESCM

Abstract

The University of Victoria's (UVic) Earth System Climate model is used to conduct equilibrium atmospheric CO₂ sensitivity experiments over the range 200–1600 ppm in order to explore changes in northern hemisphere snow cover and feedbacks on terrestrial surface air temperature (SAT). Simulations of warmer climates predict a retreat of snow cover over northern continents, in a northeasterly direction. The decline in northern hemisphere global snow mass is estimated to reach 33% at 600 ppm and 54% at 1200 ppm. In the most northerly regions, annual mean snow depth increases for simulations with CO₂ levels higher than present day. The shift in the latitude of maximum snowfall is estimated to be inversely proportional to the CO₂ concentration. The northern hemisphere net shortwave radiation changes are found to be greater over land than over the ocean, suggesting a stronger albedo feedback from changes in terrestrial snow cover than from changes in sea ice. Results also reveal high sensitivity of the snow mass balance under low CO₂ conditions. The amplification feedback (defined as the zonal SAT anomaly caused by doubling CO₂ divided by the equatorial anomaly) is greatest for scenarios with less than 300 ppm, reaching 1.9 at the pole for 250 ppm. The stronger feedback is attributed to the significant albedo changes over land areas. The simulation with 200 ppm triggers continuous accumulation of snow ('glaciation') in regions which, according to paleo‐reconstructions, were covered by ice during the last glacial cycle (the Canadian Arctic, Scandinavia, and the Taymir Peninsula).     

Radiative effect of black carbon aerosol on seasonal variation in snow depth in the Northern-Hemisphere

In this research, we studied the effects of black carbon (BC) aerosol radiative forcing on seasonal variation in the Northern Hemisphere (NH) using numerical simulations with the NASA finite-volume General Circulation Model (fvGCM) forced with monthly varying three-dimensional aerosol distributions from the Goddard Ozone Chemistry Aerosol Radiation and Transport Model (GOCART). The results show that atmospheric warming due to black carbon aerosols subsequently warm the atmosphere and land surfaces, especially those over Eurasia. As a result, the snow depth in Eurasia was greatly reduced in late winter and spring, and the reduction in snow cover decreased the surface albedo. Our surface energy balance analysis shows that the surface warming due to aerosol absorption causes early snow melting and further increases surface-atmosphere warming through snow/ice albedo feedback. Therefore, BC aerosol forcing may be an important factor affecting the snow/ice albedo in the NH.     

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

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

Statistical and dynamical assessment of vegetation feedbacks on climate over the boreal forest

Vegetation feedbacks over Asiatic Russia are assessed through a combined statistical and dynamical approach in a fully coupled atmosphere–ocean–land model, FOAM-LPJ. The dynamical assessment is comprised of initial value ensemble experiments in which the forest cover fraction is initially reduced over Asiatic Russia, replaced by grass cover, and then the climatic response is determined. The statistical feedback approach, adopted from previous studies of ocean–atmosphere interactions, is applied to compute the feedback of forest cover on subsequent temperature and precipitation in the control simulation. Both methodologies indicate a year-round positive feedback on temperature and precipitation, strongest in spring and moderately substantial in summer. Reduced boreal forest cover enhances the surface albedo, leading to an extended snow season, lower air temperatures, increased atmospheric stability, and enhanced low cloud cover. Changes in the hydrological cycle include diminished transpiration and moisture recycling, supporting a reduction in precipitation. The close agreement in sign and magnitude between the statistical and dynamical feedback assessments testifies to the reliability of the statistical approach. An additional statistical analysis of monthly vegetation feedbacks over Asiatic Russia reveals a robust positive feedback on air temperature of similar quantitative strength in two coupled models, FOAM-LPJ and CAM3–CLM3, and the observational record. CCR Contribution # 931.     

Underestimation of the Warming Trend over the Tibetan Plateau during 1998–2013 by Global Land Data Assimilation Systems and Atmospheric Reanalyses

Accurate surface air temperature (T2m) data are key to investigating eco-hydrological responses to global warming. Because of sparse in-situ observations, T2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system (LDAS) are widely used in research over alpine regions such as the Tibetan Plateau (TP). It has been found that the warming rate of T2m over the TP accelerates during the global warming slowdown period of 1998–2013, which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature. By evaluating two global LDASs, five global atmospheric reanalysis datasets, and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis, we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86% during 1998–2013. This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP, respectively. Although GLDAS2.0, ERA5, and MERRA2 reduce biases of T2m simulation from their previous versions by 12%-94%, they do not show significant improvements in capturing the warming trend. The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement, as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP. Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period, and the WRF dynamical downscaling dataset provides more accurate T2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.     

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.     

2000~2016年青藏高原地表反照率时空分布及动态变化

应用MODIS地表反照率产品MCD43C3,结合青藏高原自然带数据、积雪覆盖率和植被指数数据,采用一元线性回归方法分析了2000~2016年青藏高原地表反照率的分布及变化特征,结果表明:1）高原地表反照率空间分布差异大,整体上东南部低、西北部高,受地形和地表覆盖影响较大。2）高原地表反照率四季的空间分布变化明显,高海拔山脉和高寒灌丛草甸是高原地表反照率年内和年际变化的敏感地区。3）高原地表反照率年变化介于0.19~0.26,一定程度上表现为“双峰单谷”型,与地表覆盖类型的季节变化密切相关。4）高原地表反照率年际变化整体呈缓慢波动减小的趋势,平均变率约为－0.4×10-3 a-1,减小的区域约占高原总面积的66%,川西 —藏东针叶林带的西南部地区减小得最快,减小速率超过1.0×10-2 a-1。5）高原地表反照率减小与冰川消融和积雪减少密切相关,高原植被覆盖改善也是一个重要因素。