Decadal co-variability of the summer surface air temperature and soil moisture in China under global warming

2007 ◽  
Vol 52 (11) ◽  
pp. 1559-1565 ◽  
Author(s):  
MingFeng Su ◽  
HuiJun Wang
Keyword(s):  
Global Warming ◽  
Soil Moisture ◽  
Air Temperature ◽  
Surface Air Temperature

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Numerical Simulations of Effects of Soil Moisture and Modification by Mountains over New Zealand in Summer

2011 ◽  
Vol 139 (2) ◽  
pp. 494-510 ◽  
Author(s):  
Yang Yang ◽  
Michael Uddstrom ◽  
Mike Revell ◽  
Phil Andrews ◽  
Hilary Oliver ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Moisture Content ◽  
Air Temperature ◽  
Land Surface ◽  
Soil Moisture Content ◽  
Surface Air Temperature ◽  
Shortwave Radiation ◽  
Lower Sensitivity ◽  
The South

Abstract Historically most soil moisture–land surface impact studies have focused on continents because of the important forecasting and climate implications involved. For a relatively small isolated mountainous landmass in the ocean such as New Zealand, these impacts have received less attention. This paper addresses some of these issues for New Zealand through numerical experiments with a regional configuration of the Met Office Unified Model atmospheric model. Two pairs of idealized simulations with only contrasting dry or wet initial soil moisture over a 6-day period in January 2004 were conducted, with one pair using realistic terrain and the other pair flat terrain. For the mean of the 6 days, the differences in the simulated surface air temperature between the dry and moist cases were 3–5 K on the leeside slopes and 1–2 K on the windward slopes and the central leeside coastal region of the South Island in the afternoon. This quite nonuniform response in surface air temperature to a uniformly distributed soil moisture content and soil type is mainly attributed to modification of the effects of soil moisture by mountains through two different processes: 1) spatial variation in cloud coverage across the mountains ranges leading to more shortwave radiation at ground surface on the leeside slope than the windward slope, and 2) the presence of a dynamically and thermally induced onshore flow on the leeside coast bringing in air with a lower sensitivity to soil moisture. The response of local winds to soil moisture content is through direct or indirect effects. The direct effect is due to the thermal contrast between land and sea/land shown for the leeside solenoidal circulations, and the indirect effect is through the weakening of the upstream blocking of the South Island for dryer soils shown by the weakening and onshore shift of the upstream deceleration and forced ascent of incoming airflow.

scholarly journals Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 335 ◽  
Author(s):  
Kishore Pangaluru ◽  
Isabella Velicogna ◽  
Geruo A ◽  
Yara Mohajerani ◽  
Enrico Ciracì ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Water Storage ◽  
Surface Air Temperature ◽  
Covariance Analysis ◽  
Positive Trend ◽  
Total Water ◽  
Maximum Covariance Analysis ◽  
Surface Atmosphere

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface–atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm3/cm3 per decade during the period ranging from 2002 to 2017.

scholarly journals Specifics of soil temperature under winter wheat canopy

2013 ◽  
Vol 43 (3) ◽  
pp. 209-223 ◽  
Author(s):  
Jana Krčmáŕová ◽  
Hana Stredová ◽  
Radovan Pokorný ◽  
Tomáš Stdŕeda
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Soil Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Soil Surface ◽  
Regression Equations ◽  
Experimental Plot ◽  
Near Surface ◽  
Soil Temperatures

Abstract The aim of this study was to evaluate the course of soil temperature under the winter wheat canopy and to determine relationships between soil temperature, air temperature and partly soil moisture. In addition, the aim was to describe the dependence by means of regression equations usable for phytopathological prediction models, crop development, and yield models. The measurement of soil temperatures was performed at the experimental field station ˇZabˇcice (Europe, the Czech Republic, South Moravia). The soil in the first experimental plot is Gleyic Fluvisol with 49-58% of the content particles measuring < 0.01 mm, in the second experimental plot, the soil is Haplic Chernozem with 31-32% of the content particles measuring < 0.01 mm. The course of soil temperature and its specifics were determined under winter wheat canopy during the main growth season in the course of three years. Automatic soil temperature sensors were positioned at three depths (0.05, 0.10 and 0.20 m under soil surface), air temperature sensor in 0.05 m above soil surface. Results of the correlation analysis showed that the best interrelationships between these two variables were achieved after a 3-hour delay for the soil temperature at 0.05 m, 5-hour delay for 0.10 m, and 8-hour delay for 0.20 m. After the time correction, the determination coefficient reached values from 0.75 to 0.89 for the depth of 0.05 m, 0.61 to 0.82 for the depth of 0.10 m, and 0.33 to 0.70 for the depth of 0.20 m. When using multiple regression with quadratic spacing (modeling hourly soil temperature based on the hourly near surface air temperature and hourly soil moisture in the 0.10-0.40 m profile), the difference between the measured and the model soil temperatures at 0.05 m was −2.16 to 2.37 ◦ C. The regression equation paired with alternative agrometeorological instruments enables relatively accurate modeling of soil temperatures (R2 = 0.93).

Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season

2017 ◽  
Vol 51 (4) ◽  
pp. 1295-1309 ◽  
Author(s):  
Jaison Thomas Ambadan ◽  
Aaron A. Berg ◽  
William J. Merryfield ◽  
Woo-Sung Lee
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Transition Season ◽  
Spring Transition

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.

Sign in / Sign up

Export Citation Format

Share Document