scholarly journals Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

2016 ◽  
Vol 10 (1) ◽  
pp. 287-306 ◽  
Author(s):  
W. Wang ◽  
A. Rinke ◽  
J. C. Moore ◽  
X. Cui ◽  
D. Ji ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Soil Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Ground Temperature ◽  
Diagnostic Methods ◽  
The Tibetan Plateau ◽  
Temperature Profiles ◽  
Land Surface Models ◽  
Surface Models

Abstract. We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.

scholarly journals Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

2015 ◽  
Vol 9 (2) ◽  
pp. 1769-1810 ◽  
Author(s):  
W. Wang ◽  
A. Rinke ◽  
J. C. Moore ◽  
X. Cui ◽  
D. Ji ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Soil Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Ground Temperature ◽  
Diagnostic Methods ◽  
The Tibetan Plateau ◽  
Temperature Profiles ◽  
Land Surface Models ◽  
Surface Models

Abstract. We perform a land surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies between 6 modern stand-alone land surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by 5 different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99–135 x 104 km2) between the two diagnostic methods based on air temperature which are also consistent with the best current observation-based estimate of actual permafrost area (101 x 104 km2). However the uncertainty (1–128 x 104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air temperature based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification and snow cover. Models are particularly poor at simulating permafrost distribution using definition that soil temperature remains at or below 0°C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in permafrost distribution can be made for the Tibetan Plateau.

scholarly journals Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

2016 ◽  
Vol 121 (20) ◽  
pp. 12,180-12,197 ◽  
Author(s):  
Peng Bai ◽  
Xiaomang Liu ◽  
Tiantian Yang ◽  
Kang Liang ◽  
Changming Liu
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
The Tibetan Plateau ◽  
Land Surface Models ◽  
Surface Models ◽  
Simulation Results ◽  
Streamflow Simulation

Recent Trends in Land Surface Temperature on the Tibetan Plateau

2006 ◽  
Vol 19 (12) ◽  
pp. 2995-3003 ◽  
Author(s):  
Yuichiro Oku ◽  
Hirohiko Ishikawa ◽  
Shigenori Haginoya ◽  
Yaoming Ma
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Precipitation Amount ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Diurnal Range

Abstract The diurnal, seasonal, and interannual variations in land surface temperature (LST) on the Tibetan Plateau from 1996 to 2002 are analyzed using the hourly LST dataset obtained by Japanese Geostationary Meteorological Satellite 5 (GMS-5) observations. Comparing LST retrieved from GMS-5 with independent precipitation amount data demonstrates the consistent and complementary relationship between them. The results indicate an increase in the LST over this period. The daily minimum has risen faster than the daily maximum, resulting in a narrowing of the diurnal range of LST. This is in agreement with the observed trends in both global and plateau near-surface air temperature. Since the near-surface air temperature is mainly controlled by LST, this result ensures a warming trend in near-surface air temperature.

Evaluation of WRF coupled with Noah using an improved albedo parameterization scheme during a severe snow event over the Tibetan Plateau

2020 ◽  
Author(s):  
Lian Liu ◽  
Massimo Menenti ◽  
Yaoming Ma ◽  
Weiqiang Ma
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Large Impact ◽  
Parameterization Scheme ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
Heavy Snow

<p>Snow falls frequently over the Tibetan Plateau, and is a vital component of the widespread cryosphere which has vital feedback to climate change. Snowfall and the subsequent evolution of the snowpack have a large effect on surface energy balance and water cycle. Albedo, the main determinant of net radiation flux, is a major driver of land surface processes. However, the current widely used Noah land surface model does not describe snow albedo correctly, although it keeps snow-related variables i.e. snow cover and age into account. In our study, the impact of an improved albedo parameterization scheme in WRF coupled with Noah was investigated. In the improved albedo scheme, albedo was parameterized as functions of snow depth and age which was developed using remote sensing retrievals of albedo. Numerical experiments were conducted to model a severe snow event in March 2017. The performance of WRF coupled with Noah applying the improved albedo scheme was compared with that of applying the default albedo scheme and with that of WRF coupled with CLM applying CLM’s complex albedo scheme. First, the improved albedo scheme largely reduces the WRF coupled with Noah albedo overestimation in the southeastern Tibetan Plateau, remarkably reducing the large cold bias estimates by 0.7 ℃ air temperature RMSE. Second, the improved albedo scheme gives the highest correlationship between the satellite-derived and the model estimated albedo, contributing to achieve the SWE spatial pattern, heavy snow belt and maximum SWE estimates in eastern Tibetan Plateau. Remarkable underestimation of albedo in WRF coupled with CLM contributes to regional maximum SWE underestimation and failure in heavy snow belt estimates.</p><p>In addition, WRF default land cover and green vegetation fraction were out of date but played a large impact on estimates of air temperature, albedo and SWE. Updated land parameters led to improve the model performance in simulating the severe snow event, by reducing albedo RMSE by 1%-4%. The choice of the algorithm to retrieve green vegetation fraction had a large impact on the accuracy of green vegetation fraction retrievals. It remains open to debate the optimal algorithm to estimate land surface properties in the complex topographic Tibetan Plateau.</p>

scholarly journals Modeling Evapotranspiration over China’s Landmass from 1979 to 2012 Using Multiple Land Surface Models: Evaluations and Analyses

2017 ◽  
Vol 18 (4) ◽  
pp. 1185-1203 ◽  
Author(s):  
Shaobo Sun ◽  
Baozhang Chen ◽  
Quanqin Shao ◽  
Jing Chen ◽  
Jiyuan Liu ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Scale ◽  
Temporal Variations ◽  
The Tibetan Plateau ◽  
Land Surface Models ◽  
Infiltration Capacity ◽  
Community Land Model ◽  
Variable Infiltration Capacity Model ◽  
Capacity Model ◽  
Surface Models

Abstract Land surface models (LSMs) are useful tools to estimate land evapotranspiration at a grid scale and for long-term applications. Here, the Community Land Model, version 4.0 (CLM4.0); Dynamic Land Model (DLM); and Variable Infiltration Capacity model (VIC) were driven with observation-based forcing datasets, and a multiple-LSM ensemble-averaged evapotranspiration (ET) product (LSMs-ET) was developed and its spatial–temporal variations were analyzed for the China landmass over the period 1979–2012. Evaluations against measurements from nine flux towers at site scale and surface water budget–based ET at regional scale showed that the LSMs-ET had good performance in most areas of China’s landmass. The intercomparisons between the ET estimates and the independent ET products from remote sensing and upscaling methods suggested that there were fairly consistent patterns between each dataset. The LSMs-ET produced a mean annual ET of 351.24 ± 10.7 mm yr−1 over 1979–2012, and its spatial–temporal variation analyses showed that (i) there was an overall significant ET increasing trend, with a value of 0.72 mm yr−1 (p < 0.01), and (ii) 36.01% of Chinese land had significant increasing trends, ranging from 1 to 9 mm yr−1, while only 6.41% of the area showed significant decreasing trends, ranging from −6.28 to −0.08 mm yr−1. Analyses of ET variations in each climate region clearly showed that the Tibetan Plateau areas were the main contributors to the overall increasing ET trends of China.

scholarly journals Improved parameterization of snow albedo in WRF + Noah. Part II: Applicability to snow estimates for the Tibetan Plateau

2021 ◽  
Author(s):  
Lian Liu ◽  
Yaoming Ma ◽  
Massimo Menenti ◽  
Rongmingzhu Su ◽  
Nan Yao ◽  
...  
Keyword(s):  
Heat Flux ◽  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Sensible Heat Flux ◽  
Parameterization Scheme ◽  
The Tibetan Plateau ◽  
Sensible Heat ◽  
Snow Albedo

Abstract. Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land-atmosphere interactions estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the WRF and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates, by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged RMSE relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27 % (5 %), 32 % (69 %), 13 % (17 %) and 21 % (108 %) respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

scholarly journals Impact of frozen soil processes on soil thermal characteristics at seasonal to decadal scales over the Tibetan Plateau and North China

2020 ◽  
Author(s):  
Qian Li ◽  
Yongkang Xue ◽  
Ye Liu
Keyword(s):  
Tibetan Plateau ◽  
Soil Temperature ◽  
Temperature Profile ◽  
Land Surface ◽  
North China ◽  
Thermal Characteristics ◽  
Frozen Soil ◽  
The Tibetan Plateau ◽  
Soil Processes ◽  
Freeze Thaw

Abstract. Frozen soil processes are of great importance in controlling surface water and energy balances during the cold season and in cold regions. Over recent decades, considerable frozen soil degradation and surface soil warming have been reported over the Tibetan Plateau and North China, but most land surface models have difficulty in capturing the freeze-thaw cycle and few validations focus on the effects of frozen soil processes on soil thermal characteristics in these regions. This paper addresses these issues by introducing a physically more realistic and computationally more stable and efficient frozen soil module (FSM) into a land surface model—the third-generation Simplified Simple Biosphere model (SSiB3-FSM). To overcome the difficulties in achieving stable numerical solutions for frozen soil, a new semi-implicit scheme and a physics-based freezing-thawing scheme were applied to solve the governing equations. The performance of this model, as well as the effects of frozen soil process on the soil temperature profile and soil thermal characteristics, were investigated over the Tibetan Plateau and North China using observation and models. Results show that the SSiB3 model with the FSM produces more realistic soil temperature profile and its seasonal variation than that without FSM during the freezing and thawing periods. The freezing process in soil delays the winter cooling, while the thawing process delays the summer warming. The time lag and amplitude damping of temperature become more pronounced with increasing depth. These processes are well simulated in SSiB3-FSM. The freeze-thaw processes could increase the simulated phase lag days and land memory at different soil depths, as well as the soil memory change with the soil thickness. Furthermore, compared with observations, SSiB3-FSM produces a realistic change of maximum frozen soil depth at decadal scales. This study shows the soil thermal characteristics at seasonal to decadal scales over frozen ground can be greatly improved in SSiB3-FSM and SSiB3-FSM can be used as an effective model for TP and NC simulation during cold reasons.

scholarly journals Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

2016 ◽  
Author(s):  
Hongbo Zhang ◽  
Fan Zhang ◽  
Guoqing Zhang ◽  
Xiaobo He ◽  
Lide Tian
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Estimation Accuracy ◽  
Daily Maximum ◽  
The Tibetan Plateau ◽  
Cloud Detection ◽  
Air Temperatures ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have played a significant role in estimating the air temperature (Tair) due to the sparseness of ground measurements, especially for remote mountainous areas. Generally, two types of air temperatures are studied including daily maximum (Tmax) and minimum (Tmin) air temperatures. MODIS daytime and nighttime LST are often used as proxies for estimating Tmax and Tmin, respectively. The Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %). The presence of clouds can affect the relationship between Tair and LST and can further affect the estimation accuracies. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from over the TP. Comparisons made between in-situ cloudiness observations and MODIS claimed clear-sky records show that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Our validation of the MODIS LST values under different cloudiness constraining conditions shows that the accuracy of MODIS nighttime LST is severely affected by undetected clouds. Large errors introduced by undetected clouds are found to significantly affect the Tmin estimations based on nighttime LST through cloud effect tests. However, clouds are mainly found to affect Tmax estimation by affecting the essential relationship between Tmax and daytime LST. The obviously larger errors of Tmax estimation than those of Tmin could be attributed to larger MODIS daytime LST errors resulting from higher degrees of daytime LST heterogeneity within MODIS pixel than those of nighttime LST. Constraining all four MODIS observations per day to non-cloudy observations can efficiently screen samples to build a strong fit of Tmin estimation using MODIS nighttime LST. The present study reveals the effects of clouds on Tair estimation through MODIS LST and will thus help improve the estimation accuracy levels while alleviating the problems associated with severe data sparseness over the TP.

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