scholarly journals The EUSTACE global land station daily air temperature dataset

2019 ◽  
Vol 6 (2) ◽  
pp. 189-204 ◽  
Author(s):  
Yuri Brugnara ◽  
Elizabeth Good ◽  
Antonello A. Squintu ◽  
Gerard Schrier ◽  
Stefan Brönnimann
Keyword(s):  
Air Temperature ◽  
Global Land ◽  
Land Station

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

Unnatural trend of global land long‐term surface air temperature change

2020 ◽  
Author(s):  
Yu Wang ◽  
Pengcheng Yan ◽  
Fei Ji ◽  
Shankai Tang ◽  
Liu Yang ◽  
...  
Keyword(s):  
Temperature Change ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Global Land

scholarly journals Trends and Interannual and Interdecadal Variations of Global Land Surface Air Temperature

1993 ◽  
Vol 71 (3) ◽  
pp. 367-375 ◽  
Author(s):  
Tsuyoshi Nitta ◽  
Jun Yoshimura
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Global Land

scholarly journals Global land surface air temperature dynamics since 1880

2017 ◽  
Vol 38 ◽  
pp. e466-e474 ◽  
Author(s):  
Jinfeng Wang ◽  
Chengdong Xu ◽  
Maogui Hu ◽  
Qingxiang Li ◽  
Zhongwei Yan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Dynamics ◽  
Global Land

scholarly journals The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill

2011 ◽  
Vol 12 (5) ◽  
pp. 805-822 ◽  
Author(s):  
R. D. Koster ◽  
S. P. P. Mahanama ◽  
T. J. Yamada ◽  
Gianpaolo Balsamo ◽  
A. A. Berg ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Rain Gauge ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Boreal Summer ◽  
Second Phase ◽  
Global Land ◽  
Soil Moisture Initialization ◽  
Coupling Experiment

Abstract The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forecast skill is much weaker than that for air temperature. The skill for predicting air temperature, and to some extent precipitation, increases with the magnitude of the initial soil moisture anomaly. GLACE-2 results are examined further to provide insight into the asymmetric impacts of wet and dry soil moisture initialization on skill.

scholarly journals The CCSM4 Land Simulation, 1850–2005: Assessment of Surface Climate and New Capabilities

2012 ◽  
Vol 25 (7) ◽  
pp. 2240-2260 ◽  
Author(s):  
David M. Lawrence ◽  
Keith W. Oleson ◽  
Mark G. Flanner ◽  
Christopher G. Fletcher ◽  
Peter J. Lawrence ◽  
...  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Climate Simulation ◽  
Biogeochemical Model ◽  
Earth System ◽  
Climate System Model ◽  
Community Land Model ◽  
Model Version ◽  
Surface Climate ◽  
Global Land

Abstract This paper reviews developments for the Community Land Model, version 4 (CLM4), examines the land surface climate simulation of the Community Climate System Model, version 4 (CCSM4) compared to CCSM3, and assesses new earth system features of CLM4 within CCSM4. CLM4 incorporates a broad set of improvements including additions of a carbon–nitrogen (CN) biogeochemical model, an urban canyon model, and transient land cover and land use change, as well as revised soil and snow submodels. Several aspects of the surface climate simulation are improved in CCSM4. Improvements in the simulation of soil water storage, evapotranspiration, surface albedo, and permafrost that are apparent in offline CLM4 simulations are generally retained in CCSM4. The global land air temperature bias is reduced and the annual cycle is improved in many locations, especially at high latitudes. The global land precipitation bias is larger in CCSM4 because of bigger wet biases in central and southern Africa and Australia. New earth system capabilities are assessed. The present-day air temperature within urban areas is warmer than surrounding rural areas by 1°–2°C, which is comparable to or greater than the change in climate occurring over the last 130 years. The snow albedo feedback is more realistic and the radiative forcing of snow aerosol deposition is calculated as +0.083 W m−2 for present day. The land carbon flux due to land use, wildfire, and net ecosystem production is a source of carbon to the atmosphere throughout most of the historical simulation. CCSM4 is increasingly suited for studies of the role of land processes in climate and climate change.

scholarly journals Impact of fire on global land surface air temperature and energy budget for the 20th century due to changes within ecosystems

2017 ◽  
Vol 12 (4) ◽  
pp. 044014 ◽  
Author(s):  
Fang Li ◽  
David M Lawrence ◽  
Ben Bond-Lamberty
Keyword(s):  
20Th Century ◽  
Air Temperature ◽  
Energy Budget ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Global Land

scholarly journals Evaluation of the Global Land Data Assimilation System (GLDAS) Air Temperature Data Products

2015 ◽  
Vol 16 (6) ◽  
pp. 2463-2480 ◽  
Author(s):  
Lei Ji ◽  
Gabriel B. Senay ◽  
James P. Verdin
Keyword(s):  
Data Assimilation ◽  
Air Temperature ◽  
Temperature Data ◽  
Mountainous Areas ◽  
Global Land ◽  
Land Data Assimilation ◽  
Weather Stations ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

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