Earth's balanced climates in view of their energy budgets

2021 ◽  
Author(s):  
Thomas Anderl
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Energy Budget ◽  
Energy Budgets ◽  
Temperature Dependency ◽  
Energy Balancing ◽  
Atmospheric Water ◽  
Atmospheric Absorption ◽  
Rainfall Events ◽  
Characteristic Concentration

Abstract Earth’s well-known energy budget scheme is subjected to variations representing changes of insolation and atmospheric absorption. The Charney Report variability cases of doubled atmospheric CO2 concentration and insolation increase by 2 % are found reproducible. The planetary emissivity is revealed linear to surface temperature, conformant with measurements. Atmospheric water vapor with its characteristic concentration-temperature dependency appears as a major component in Earth’s energy balancing mechanisms. From this, shift towards fewer and stronger rainfall events is prescribed for rising temperatures.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

1994 ◽  
Vol 99 (C3) ◽  
pp. 5219 ◽  
Author(s):  
William J. Emery ◽  
Yunyue Yu ◽  
Gary A. Wick ◽  
Peter Schluessel ◽  
Richard W. Reynolds
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

Contribution of lakes in the ORCHIDEE land surface model

2020 ◽  
Author(s):  
Anthony Bernus ◽  
Catherine Ottle ◽  
Nina Raoult
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Energy Budget ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Heat Fluxes ◽  
Energy Budgets ◽  
Surface Model ◽  
Local Climate

<p>Lakes play a major role on local climate and boundary layer stratification. At global scale, they have been shown to have an impact on the energy budget, (see for example Le Moigne et al., 2016 or Bonan, 1995 ) . To represent the energy budget of lakes at a global scale, the FLake (Mironov et al, 2008) lake model has been coupled to the ORCHIDEE land surface model - the continental part of the IPSL earth system model. By including Flake in ORCHIDEE, we aim to improve the representation of land surface temperature and heat fluxes. Using the standard CMIP6 configuration of ORCHIDEE,  two 40-year simulations were generated (one coupled with FLake and one without) using the CRUJRA meteorological forcing data at a spatial resolution of 0.5°. We compare land surface temperatures and heat fluxes from the two ORCHIDEE simulations and assess the impacts of lakes on surface energy budgets. MODIS satellite land surface temperature products will be used to validate the simulations. We expect a better fit between the simulated land surface temperature and the MODIS data when the FLake configuration is used. The preliminary results of the comparison will be presented.</p>

scholarly journals Estimation of the Total Atmospheric Water Vapor Content and Land Surface Temperature Based on AATSR Thermal Data

Sensors ◽  
2008 ◽  
Vol 8 (3) ◽  
pp. 1832-1845 ◽  
Author(s):  
Tangtang Zhang ◽  
Jun Wen ◽  
Rogier Van der Velde ◽  
Xianhong Meng ◽  
Zhenchao Li ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Water Vapor Content ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Thermal Data

scholarly journals Seasonal Precipitation Predictability for the Northern Hemisphere Using Concurrent and Preseason Atmospheric Water Vapor Transport and Sea Surface Temperature

2021 ◽  
Vol 22 (1) ◽  
pp. 183-199
Author(s):  
Shida Gao ◽  
Pan Liu ◽  
Upmanu Lall
Keyword(s):  
Climate Change ◽  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Northern Hemisphere ◽  
Vapor Transport ◽  
Sea Surface ◽  
Water Vapor Transport ◽  
Seasonal Precipitation ◽  
Atmospheric Water

AbstractIntegrated atmospheric water vapor transport (IVT) is a determinant of global precipitation. In this paper, using the CERA-20C climate reanalysis dataset, we explore three questions in Northern Hemisphere precipitation for four seasons: 1) What is the covariability between the leading spatiotemporal modes of seasonal sea surface temperature (SST), the seasonal IVT, and the seasonal precipitation for the Northern Hemisphere? 2) How well can the leading spatial modes of seasonal precipitation be reconstructed from the leading modes of IVT and SST for the same season? 3) How well can the leading modes of precipitation for the next season be predicted from the leading modes of the current season’s SST and IVT? Wavelet analyses identify covariation in the leading modes of seasonal precipitation and those of IVT and SST in the 2–8-yr band, with the highest amplitude in the March–May (MAM) season, and provide a firm physical explanation for the potential predictability. We find that a subset of the 10 leading principal components of the seasonal IVT and SST fields has significant trends in connections with seasonal precipitation modes, and provides an accurate statistical concurrent reconstruction and one-season-ahead forecast of the leading seasonal precipitation modes, thus providing a pathway to improving the understanding and prediction of precipitation extremes in the context of climate change attribution, seasonal and longer prediction, and climate change scenarios. The same-season reconstruction model can explain 76% of the variance, and the next-season forecast model can explain 58% variance of hemispheric precipitation on average.

scholarly journals Land Surface Temperature Retrieval from Fengyun-3D Medium Resolution Spectral Imager II (FY-3D MERSI-II) Data with the Improved Two-Factor Split-Window Algorithm

2021 ◽  
Vol 13 (24) ◽  
pp. 5072
Author(s):  
Wenhui Du ◽  
Zhihao Qin ◽  
Jinlong Fan ◽  
Chunliang Zhao ◽  
Qiuyan Huang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cross Validation ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water ◽  
Medium Resolution ◽  
Split Window Algorithm ◽  
Spectral Imager

Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R2 of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy.

scholarly journals Profiling of atmospheric water vapor with MIR and LASE

2002 ◽  
Vol 40 (6) ◽  
pp. 1211-1219 ◽  
Author(s):  
J.R. Wang ◽  
P. Racette ◽  
M.E. Triesky ◽  
E.V. Browell ◽  
S. Ismail ◽  
...  
Keyword(s):  
Water Vapor ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water
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