scholarly journals Effects of pan-Arctic snow cover and air temperature changes on soil heat content

2016 ◽  
Author(s):  
Xiaogang Shi ◽  
Tara J. Troy ◽  
Dennis P. Lettenmaier
Keyword(s):  
North America ◽  
Surface Energy ◽  
Soil Temperature ◽  
Snow Cover ◽  
Air Temperature ◽  
Energy Flux ◽  
Land Surface ◽  
Heat Content ◽  
Dominant Factor ◽  
Surface Energy Flux

Abstract. Soil heat content (SHC) provides an estimate of the integrated effect of changes in the land surface energy balance. It considers the specific heat capacity, soil temperature, and phase changes of soil moisture as a function of depth. In contrast, soil temperature provides a much more limited view of land surface energy flux changes. This is particularly important at high latitudes, which have and are undergoing surface energy flux changes as a result of changes in seasonal variations of snow cover extent (SCE) and hence surface albedo changes, among other factors. Using the Variable Infiltration Capacity (VIC) land surface model forced with gridded climate observations, we simulate spatial and temporal variations of SCE and SHC over the pan-Arctic land region for the last half-century. On the basis of the SCE trends derived from NOAA satellite observations in 5° latitude bands from April through June for the period 1972–2006, we define a snow covered sensitivity zone (SCSZ), a snow covered non-sensitivity zone (SCNZ), and a non-snow covered zone (NSCZ) for North America and Eurasia. We then explore long-term trends in SHC, SCE, and surface air temperature (SAT) and their corresponding correlations in NSCZ, SCSZ and SCNZ for both North America and Eurasia. We find that snow cover downtrends have a significant impact on SHC changes in SCSZ for North America and Eurasia from April through June. SHC changes in the SCSZ over North America are dominated by downtrends in SCE rather than increasing SAT. Over Eurasia, increasing SAT more strongly affects SHC than in North America. Overall, increasing SAT during late spring and early summer is the dominant factor that has resulted in SHC changes over the pan-Arctic domain, whereas reduced SCE plays a secondary role that is only important in the SCSZ.

scholarly journals A dual-pass data assimilation scheme for estimating surface energy fluxes with FY3A-VIRR land surface temperature

2013 ◽  
Vol 10 (3) ◽  
pp. 3927-3972
Author(s):  
T. R. Xu ◽  
S. M. Liu ◽  
Z. W. Xu ◽  
S. Liang ◽  
L. Xu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Surface Energy Flux ◽  
Data Assimilation Scheme ◽  
Dual Pass ◽  
Assimilation Scheme

Abstract. A dual-pass data assimilation scheme is developed to improve predictions of surface energy fluxes. Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at the weekly temporal scale and pass 2 optimizes soil moisture at the daily temporal scale. Based on the ensemble Kalman filter (EnKF), land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) is assimilated into common land model (CoLM) for the first time. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun that include alpine meadow, grass, crop, and orchard land cover types. The results are compared with data set generated by a multi-scale surface energy flux observation system that includes an automatic weather station (AWS), an eddy covariance (EC) and a large aperture scintillometer (LAS). Results indicate that the CoLM can simulate the diurnal variations of surface energy flux, but usually overestimates sensible heat flux and underestimates latent heat flux and evaporation fraction (EF). With FY3A-VIRR LST data, the dual-pass data assimilation scheme can reduce model uncertainties and improve predictions of surface energy flux. Compared with EC measurements, the average model biases (BIAS) values change from 37.8 to 7.7 W m−2 and from −27.6 to 18.8 W m−2; the root mean square error (RMSE) values drop from 74.7 to 39.1 W m−2 and from 95.1 to 62.7 W m−2 for sensible and latent heat fluxes respectively. For evaporation fraction (EF), the average BIAS values change from −0.29 to 0.0 and the average RMSE values drop from 0.38 to 0.12. To compare the results with LAS-measured sensible heat flux, the source areas are calculated using a footprint model and overlaid with FY3A pixels. The four sites averaged BIAS values drop from 63.7 to −8.5 W m−2 and RMSE values drop from 118.2 to 69.8 W m−2. Ultimately, the error sources in surface energy flux predictions are investigated, and the results show that both soil moisture and vegetation parameters caused the big model biases in surface energy flux predictions. With Pass 1 and Pass 2, the dual-pass data assimilation scheme can cut down the surface energy flux prediction biases (BIAS) to nearly zero.

scholarly journals Deforestation reshapes land-surface energy-flux partitioning

2021 ◽  
Author(s):  
Kunxiaojia Yuan ◽  
Qing Zhu ◽  
Shiyu Zheng ◽  
Lei Zhao ◽  
Min Chen ◽  
...  
Keyword(s):  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Surface Energy Flux ◽  
Flux Partitioning

scholarly journals Regularities of soil temperature changes during the period with snow cover in modern climatic conditions of the Eurasian Subarctic

2021 ◽  
pp. 13-22
Author(s):  
Lev M. Kitaev
Keyword(s):  
Standard Deviation ◽  
Soil Temperature ◽  
Snow Cover ◽  
Air Temperature ◽  
Land Surface ◽  
Environmental Changes ◽  
Surface Air Temperature ◽  
Climatic Conditions ◽  
Winter Period

The influence of snow cover on the dynamics of soil temperature in the modern climatic conditions of the Eurasian Subarctic was investigated through a quantitative assessment of the features of the seasonal and long-term variation of parameters. Seasonal and long-term values of soil temperature for stable snow period decrease from west to east: a decrease of snow thickness and air temperature from west to east of Eurasia leads to a weakening of the heat-insulating properties of the snow cover with a significant decrease in regional air temperatures. With the emergence of a stable snow cover, the soil temperature seasonal and long-term standard deviation sharply decreases compared to the autumn and spring periods. With the appearance of snow cover, the soil temperature standard deviation drops sharply compared to the autumn and spring periods. An exception is the northeast of Siberia: here, a relatively small thickness of snow determines a noticeable dependence of the course of soil temperature on the dynamics of surface air temperature. There are no significant long-term trends in soil temperature due to its low variability during winter period. Analysis of the course of the studied characteristics anomalies showed an insignificant and non-systematic number of their coincidences. Currently, we have not found similar research results for large regions. The revealed patterns can be used in the analysis of the results of monitoring the state of the land surface, in the development of remote sensing algorithms, in the refinement of predictive scenarios of environmental changes.

scholarly journals Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 46 ◽  
Author(s):  
Prabhakar Shrestha ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Small Scale ◽  
Land Surface Models ◽  
Surface Energy Flux ◽  
Community Land Model ◽  
Flux Partitioning ◽  
Surface Models

An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.

Different energetics of global monsoon over land and ocean

2021 ◽  
Author(s):  
Arindam Chakraborty ◽  
Chetankumar Jalihal ◽  
Jayaraman Srinivasan
Keyword(s):  
Surface Energy ◽  
Energy Flux ◽  
Global Climate ◽  
Lower Troposphere ◽  
Summer Precipitation ◽  
Reanalysis Data ◽  
Vertical Transport ◽  
Net Energy ◽  
Global Monsoon ◽  
Surface Energy Flux

<p>Monsoons were traditionally considered to be land-based systems. Recent definitions of monsoons based on either the seasonal reversal of winds or the local summer precipitation accounting for more than 50% of the annual precipitation suggests that monsoon domains extend over oceanic regions as well. The concept of global monsoon combines all the monsoon domains into a single entity. Modern observations show that the variations in precipitation are nearly coherent across all the individual monsoon domains on decadal timescales. Using a transient simulation of the global climate over the last 22,000 years as well as reanalysis data of the modern climate, we have shown that tropical precipitation has different characteristics over land and ocean grids. This is due to the differences in the energetics of monsoon over land and ocean grids. With a lower thermal heat capacity, the net surface energy flux over land is negligible, whereas it is quite large over the ocean. In fact, the orbital scale variability of net energy flux into the atmosphere over the ocean is controlled by the surface energy flux. Another major difference between land and ocean grids of the global monsoon is in the vertical profile of the vertical pressure velocity. It is bottom-heavy over land and top-heavy over the ocean. This results in smaller vertical transport of moist static energy (which has a minimum in the lower troposphere) over land, and a larger vertical transport over the ocean. These differences between the land and ocean, suggest that the land and ocean grids should not be combined as is traditionally done. Global monsoon-land and global monsoon-ocean should be studied separately.</p>

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

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