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 Influence of radiative forcing factors on ground–air temperature coupling during the last millennium: implications for borehole climatology

2018 ◽  
Vol 14 (11) ◽  
pp. 1583-1606 ◽  
Author(s):  
Camilo Melo-Aguilar ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro-Montesinos ◽  
Norman Steinert
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Radiative Forcing ◽  
Spatial Scales ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Small Variation ◽  
Last Millennium ◽  
Reconstruction Methods

Abstract. Past climate variations may be uncovered via reconstruction methods that use proxy data as predictors. Among them, borehole reconstruction is a well-established technique to recover the long-term past surface air temperature (SAT) evolution. It is based on the assumption that SAT changes are strongly coupled to ground surface temperature (GST) changes and transferred to the subsurface by thermal conduction. We evaluate the SAT–GST coupling during the last millennium (LM) using simulations from the Community Earth System Model LM Ensemble (CESM-LME). The validity of such a premise is explored by analyzing the structure of the SAT–GST covariance during the LM and also by investigating the evolution of the long-term SAT–GST relationship. The multiple and single-forcing simulations in the CESM-LME are used to analyze the SAT–GST relationship within different regions and spatial scales and to derive the influence of the different forcing factors on producing feedback mechanisms that alter the energy balance at the surface. The results indicate that SAT–GST coupling is strong at global and above multi-decadal timescales in CESM-LME, although a relatively small variation in the long-term SAT–GST relationship is also represented. However, at a global scale such variation does not significantly impact the SAT–GST coupling, at local to regional scales this relationship experiences considerable long-term changes mostly after the end of the 19th century. Land use land cover changes are the main driver for locally and regionally decoupling SAT and GST, as they modify the land surface properties such as albedo, surface roughness and hydrology, which in turn modifies the energy fluxes at the surface. Snow cover feedbacks due to the influence of other external forcing are also important for corrupting the long-term SAT–GST coupling. Our findings suggest that such local and regional SAT–GST decoupling processes may represent a source of bias for SAT reconstructions from borehole measurement, since the thermal signature imprinted in the subsurface over the affected regions is not fully representative of the long-term SAT variations.

Impact of Land Initial States Uncertainty on Subseasonal Surface Air Temperature Prediction in CFSv2 Reforecasts

2020 ◽  
Vol 21 (9) ◽  
pp. 2101-2121 ◽  
Author(s):  
Chul-Su Shin ◽  
Paul A. Dirmeyer ◽  
Bohua Huang ◽  
Subhadeep Halder ◽  
Arun Kumar
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Air Temperature ◽  
Land Surface ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Prediction Skill ◽  
Temperature Prediction ◽  
Initial States ◽  
Air Temperature Prediction

AbstractThe NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

scholarly journals Development of Global Hourly 0.5° Land Surface Air Temperature Datasets

2013 ◽  
Vol 26 (19) ◽  
pp. 7676-7691 ◽  
Author(s):  
Aihui Wang ◽  
Xubin Zeng
Keyword(s):  
Air Temperature ◽  
Diurnal Cycle ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Linear Interpolation ◽  
Research Unit ◽  
Climate Studies ◽  
Modern Era

Abstract Land surface air temperature (SAT) is one of the most important variables in weather and climate studies, and its diurnal cycle is also needed for a variety of applications. Global long-term hourly SAT observational data, however, do not exist. While such hourly products could be obtained from global reanalyses, they are found to be unrealistic in representing the SAT diurnal cycle. Global hourly 0.5° SAT datasets are developed here based on four reanalysis products [Modern-Era Retrospective Analysis for Research and Applications (MERRA for 1979–2009), 40-yr ECMWF Re-Analysis (ERA-40 for 1958–2001), ECMWF Interim Re-Analysis (ERA-Interim for 1979–2009), and NCEP–NCAR reanalysis for 1948–2009)] and the Climate Research Unit Time Series version 3.10 (CRU TS3.10) for 1948–2009. The three-step adjustments include the spatial downscaling to 0.5° grid cells, the temporal interpolation from 6-hourly (in ERA-40 and NCEP–NCAR reanalysis) to hourly using the MERRA hourly SAT climatology for each day (and the linear interpolation from 3-hourly in ERA-Interim to hourly), and the bias correction in both monthly-mean maximum (Tmax) and minimum (Tmin) SAT using the CRU data. The final products have exactly the same monthly Tmax and Tmin as the CRU data, and perform well in comparison with in situ hourly measurements over six sites and with a regional daily SAT dataset over Europe. They agree with each other much better than the original reanalyses, and the spurious SAT jumps of reanalyses over some regions are also substantially eliminated. One of the uncertainties in the final products can be quantified by their differences in the true monthly mean (using 24-hourly values) and the monthly averaged diurnal cycle.

Simulation of Global Land Surface Conditions from 1948 to 2004. Part I: Forcing Data and Evaluations

2006 ◽  
Vol 7 (5) ◽  
pp. 953-975 ◽  
Author(s):  
Taotao Qian ◽  
Aiguo Dai ◽  
Kevin E. Trenberth ◽  
Keith W. Oleson
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Reanalysis Data ◽  
Freshwater Discharge ◽  
Land Surface Models ◽  
Surface Conditions ◽  
Surface Models

Abstract Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions. Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. Here a carefully constructed global forcing dataset for 1948–2004 with 3-hourly and T62 (∼1.875°) resolution is described, and historical simulations using the latest version of the Community Land Model version 3.0 (CLM3) are evaluated using available observations of streamflow, continental freshwater discharge, surface runoff, and soil moisture. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis, which is shown to have spurious trends and biases in surface temperature and precipitation. Surface downward solar radiation from the reanalysis was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite observations during recent decades. Surface specific humidity from the reanalysis was adjusted using the adjusted surface air temperature and reanalysis relative humidity. Surface wind speed and air pressure were interpolated directly from the 6-hourly reanalysis data. Sensitivity experiments show that the precipitation adjustment (to the reanalysis data) leads to the largest improvement, while the temperature and radiation adjustments have only small effects. When forced by this dataset, the CLM3 reproduces many aspects of the long-term mean, annual cycle, interannual and decadal variations, and trends of streamflow for many large rivers (e.g., the Orinoco, Changjiang, Mississippi, etc.), although substantial biases exist. The simulated long-term-mean freshwater discharge into the global and individual oceans is comparable to 921 river-based observational estimates. Observed soil moisture variations over Illinois and parts of Eurasia are generally simulated well, with the dominant influence coming from precipitation. The results suggest that the CLM3 simulations are useful for climate change analysis. It is also shown that unrealistically low intensity and high frequency of precipitation, as in most model-simulated precipitation or observed time-averaged fields, result in too much evaporation and too little runoff, which leads to lower than observed river flows. This problem can be reduced by adjusting the precipitation rates using observed-precipitation frequency maps.

Seasonal Variability in the Mechanisms Behind the 2020 Siberian Heatwaves

2022 ◽  
pp. 1-44
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Land Surface ◽  
Arctic Oscillation ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Stratospheric Polar Vortex

Abstract Record breaking heatwaves and wildfires immersed Siberia during the boreal spring of 2020 following an anomalously warm winter. Springtime heatwaves are becoming more common in the region, with statistically significant trends in the frequency, magnitude, and duration of heatwave events over the past four decades. Mechanisms by which the heatwaves occur and contributing factors differ by season. Winter heatwave frequency is correlated with the atmospheric circulation, particularly the Arctic Oscillation, while the frequency of heatwaves during the spring months is highly correlated with aspects of the land surface including snow cover, albedo, and latent heat flux. Idealized AMIP-style experiments are used to quantify the contribution of suppressed Arctic sea ice and snow cover over Siberia on the atmospheric circulation, surface energy budget, and surface air temperature in Siberia during the winter and spring of 2020. Sea ice concentration contributed to the strength of the stratospheric polar vortex and Arctic Oscillation during the winter months, thereby influencing the tropospheric circulation and surface air temperature over Siberia. Warm temperatures across the region resulted in an earlier than usual recession of the winter snowpack. The exposed land surface contributed to up to 20% of the temperature anomaly during the spring through the albedo feedback and changes in the ratio of the latent and sensible heat fluxes. This, in combination with favorable atmospheric circulation patterns, resulted in record breaking heatwaves in Siberia in the spring of 2020.

Understanding the Soil Temperature Variability at Different Depths: Effects of Surface Air Temperature, Snow Cover, and the Soil Memory

2021 ◽  
Author(s):  
Haoxin Zhang ◽  
Naiming Yuan ◽  
Zhuguo Ma ◽  
Yu Huang
Keyword(s):  
Soil Temperature ◽  
Snow Cover ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Thermal Conditions ◽  
Northeastern China ◽  
Memory Effects ◽  
Causality Analysis ◽  
Time Lagged ◽  
Warming Hiatus

<p>The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, by using linear analysis and nonlinear causality analysis—convergent cross mapping (CCM) and its time-lagged version (time-lagged CCM), significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land−atmosphere interactions.</p>

scholarly journals Assessments of the Northern Hemisphere snow cover response to 1.5 and 2.0 °C warming

2018 ◽  
Vol 9 (2) ◽  
pp. 865-877 ◽  
Author(s):  
Aihui Wang ◽  
Lianlian Xu ◽  
Xianghui Kong
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Boreal Summer ◽  
Surface Hydrology ◽  
Internal Climate Variability ◽  
Global Mean Temperature

Abstract. The 2015 Paris Agreement set a goal to pursue a global mean temperature below 1.5 °C and well below 2 °C above preindustrial levels. Although it is an important surface hydrology variable, the response of snow under different warming levels has not been well investigated. This study provides a comprehensive assessment of the snow cover fraction (SCF) and snow area extent (SAE), as well as the associated land surface air temperature (LSAT) over the Northern Hemisphere (NH) based on the Community Earth System Model Large Ensemble project (CESM-LE), the CESM 1.5 and 2 °C projects, and the CMIP5 historical RCP2.6 and RCP4.5 products. The results show that the spatiotemporal variations in those modeled products are grossly consistent with observations. The projected SAE magnitude change in RCP2.6 is comparable to that in 1.5 °C, but lower than that in 2 °C. The snow cover differences between 1.5 and 2 °C are prominent during the second half of the 21st century. The signal-to-noise ratios (SNRs) of both SAE and LSAT over the majority of land areas are greater than 1, and for the long-term period, the dependences of SAE on LSAT changes are comparable for different ensemble products. The contribution of an increase in LSAT to the reduction of snow cover differs across seasons, with the greatest occurring in boreal autumn (49–55 %) and the lowest occurring in boreal summer (10–16 %). The snow cover uncertainties induced by the ensemble variability are invariant over time across CESM members but show an increase in the warming signal between the CMIP5 models. This feature reveals that the physical parameterization of the model plays the predominant role in long-term snow simulations, while they are less affected by internal climate variability.

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 Influence of radiative forcing factors on ground-air temperature coupling during the last millennium: implications for borehole climatology

2018 ◽  
Author(s):  
Camilo Melo-Aguilar ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro-Montesinos ◽  
Norman Steinert
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Radiative Forcing ◽  
Spatial Scales ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Small Variation ◽  
Last Millennium ◽  
Reconstruction Methods

Abstract. Past climate variations may be known from reconstruction methods that use proxy data as predictors. Among them, borehole reconstructions is a well established technique to recover the long term past surface air temperature (SAT) evolution. It is based on the assumption that SAT changes are strongly coupled to ground surface temperature (GST) changes and transferred to the subsurface by thermal conduction. We evaluate the SAT-GST coupling during the last millennium (LM) using simulations from the Community Earth System Model LM Ensemble (CESM-LME). The validity of such premise is explored by analyzing the structure of the SAT-GST covariance during the LM and also by investigating the evolution of the long term SAT-GST relationship. The multiple and single-forcing simulations in the CESM-LME are used to analyze the SAT-GST relationship within different regions and spatial scales and derive the influence of the different forcing factors on producing feedbacks mechanisms that alter the energy balance at the surface. The results indicate that SAT-GST coupling is strong at global and above multi-decadal time scales in the CESM-LME however a relative small variation in the long term SAT-GST relationship is also represented. Although at global scale such variation does not impact significantly the SAT-GST coupling, at local to regional scales this relationship experiences considerable long term changes mostly after the end of the 19th century. Land use land cover (LULC) changes are the main driver for decoupling SAT and GST locally and regionally since they modify the land surface properties such as albedo, surface roughness and hydrology, and thus the energy fluxes at the surface. Snow cover feedbacks due to the influence of other external forcing are also important for corrupting the long term SAT-GST coupling. Our findings suggest that such local and regional SAT-GST decoupling processes may represent a source of bias for SAT reconstructions from borehole measurement since the thermal signature imprinted in the subsurface over the affected regions is not fully representative of the long term SAT variations.

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