Does Surface Temperature Respond to or Determine Downwelling Longwave Radiation?

2019 ◽  
Vol 46 (5) ◽  
pp. 2781-2789 ◽  
Author(s):  
L. R. Vargas Zeppetello ◽  
A. Donohoe ◽  
D. S. Battisti
Keyword(s):  
Surface Temperature ◽  
Longwave Radiation ◽  
Downwelling Longwave Radiation

Plot-scale retrieval of land surface temperature and emissivity estimation

2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Kaniska Mallick ◽  
Ivonne Trebs
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Longwave Radiation ◽  
Landscape Scale ◽  
The Earth ◽  
Downwelling Longwave Radiation

<p>The surface energy balance (SEB) is defined as the balance between incoming energy from the sun and outgoing energy from the Earth’s surface. All components of the SEB depend on land surface temperature (LST). Therefore, LST is an important state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. LST can be estimated radiometrically, based on the infrared radiance emanating from the surface. At the landscape scale, LST is derived from thermal radiation measured using  satellites.  At the plot scale, eddy covariance flux towers commonly record downwelling and upwelling longwave radiation, which can be inverted to retrieve LST  using the grey body equation :<br>             R<sub>lup</sub> = εσ T<sub>s</sub><sup>4</sup> + (1 − ε) R<sub> ldw         </sub>(1)<br>where R<sub>lup</sub> is the upwelling longwave radiation, R<sub>ldw</sub> is the downwelling longwave radiation, ε is the surface emissivity, <em>T<sub>s</sub>  </em>is the surface temperature and σ  is the Stefan-Boltzmann constant. The first term is the temperature-dependent part, while the second represents reflected longwave radiation. Since in the past downwelling longwave radiation was not measured routinely using flux towers, it is an established practice to only use upwelling longwave radiation for the retrieval of plot-scale LST, essentially neglecting the reflected part and shortening Eq. 1 to:<br>               R<sub>lup</sub> = εσ T<sub>s</sub><sup>4 </sup>                       (2)<br>Despite  widespread availability of downwelling longwave radiation measurements, it is still common to use the short equation (Eq. 2) for in-situ LST retrieval. This prompts the question if ignoring the downwelling longwave radiation introduces a bias in LST estimations from tower measurements. Another associated question is how to obtain the correct ε needed for in-situ LST retrievals using tower-based measurements.<br>The current work addresses these two important science questions using observed fluxes at eddy covariance towers for different land cover types. Additionally, uncertainty in retrieved LST and emissivity due to uncertainty in input fluxes was quantified using SOBOL-based uncertainty analysis (SALib). Using landscape-scale emissivity obtained from satellite data (MODIS), we found that the LST  obtained using the complete equation (Eq. 1) is 0.5 to 1.5 K lower than the short equation (Eq. 2). Also, plot-scale emissivity was estimated using observed sensible heat flux and surface-air temperature differences. Plot-scale emissivity obtained using the complete equation was generally between 0.8 to 0.98 while the short equation gave values between 0.9 to 0.98, for all land cover types. Despite additional input data for the complete equation, the uncertainty in plot-scale LST was not greater than if the short equation was used. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) were strongly correlated with our plot-scale estimates, but on average higher by 0.5 to 9 K, regardless of the equation used. However, for most sites, the correspondence between MODIS TERRA LST and retrieved plot-scale LST estimates increased significantly if plot-scale emissivity was used instead of the landscape-scale emissivity obtained from satellite data.</p>

scholarly journals On the Contribution of Longwave Radiation to Global Climate Model Biases in Arctic Lower Tropospheric Stability

2014 ◽  
Vol 27 (19) ◽  
pp. 7250-7269 ◽  
Author(s):  
Neil P. Barton ◽  
Stephen A. Klein ◽  
James S. Boyle
Keyword(s):  
Surface Temperature ◽  
Global Climate ◽  
Longwave Radiation ◽  
Reanalysis Data ◽  
The Arctic ◽  
Transfer Model ◽  
Polar Night ◽  
Clear Sky ◽  
Lower Tropospheric Stability ◽  
Downwelling Longwave Radiation

Abstract Previous research has found that global climate models (GCMs) usually simulate greater lower tropospheric stabilities compared to reanalysis data. To understand the origins of this bias, the authors examine hindcast simulations initialized with reanalysis data of six GCMs and find that four of the six models simulate within five days a positive bias in Arctic lower tropospheric stability during the Arctic polar night over sea ice regions. These biases in lower tropospheric stability are mainly due to cold biases in surface temperature, as very small potential temperature biases exist aloft. Similar to previous research, polar night surface temperature biases in the hindcast runs relate to all-sky downwelling longwave radiation in the models, which very much relates to the cloud liquid water. Also found herein are clear-sky longwave radiation biases and a fairly large clear-sky longwave radiation bias in the day one hindcast. This clear-sky longwave bias is analyzed by running the same radiation transfer model for each model’s temperature and moisture profile, and the model spread in clear-sky downwelling longwave radiation with the same radiative transfer model is found to be much less, suggesting that model differences other than temperature and moisture are aiding in the spread in downwelling longwave radiation. The six models were also analyzed in Atmospheric Model Intercomparison Project (AMIP) mode to determine if hindcast simulations are analogous to free-running simulations. Similar winter lower tropospheric stability biases occur in four of the six models with surface temperature biases relating to the winter lower tropospheric stability values.

scholarly journals The relevance of mid-Holocene Arctic warming to the future

2019 ◽  
Vol 15 (4) ◽  
pp. 1375-1394 ◽  
Author(s):  
Masakazu Yoshimori ◽  
Marina Suzuki
Keyword(s):  
Surface Temperature ◽  
General Circulation ◽  
Temperature Response ◽  
Longwave Radiation ◽  
General Circulation Models ◽  
Cross Model ◽  
Arctic Warming ◽  
Temperature Changes ◽  
The Future ◽  
Arctic Climate Change

Abstract. There remain substantial uncertainties in future projections of Arctic climate change. There is a potential to constrain these uncertainties using a combination of paleoclimate simulations and proxy data, but such a constraint must be accompanied by physical understanding on the connection between past and future simulations. Here, we examine the relevance of an Arctic warming mechanism in the mid-Holocene (MH) to the future with emphasis on process understanding. We conducted a surface energy balance analysis on 10 atmosphere and ocean general circulation models under the MH and future Representative Concentration Pathway (RCP) 4.5 scenario forcings. It is found that many of the dominant processes that amplify Arctic warming over the ocean from late autumn to early winter are common between the two periods, despite the difference in the source of the forcing (insolation vs. greenhouse gases). The positive albedo feedback in summer results in an increase in oceanic heat release in the colder season when the atmospheric stratification is strong, and an increased greenhouse effect from clouds helps amplify the warming during the season with small insolation. The seasonal progress was elucidated by the decomposition of the factors associated with sea surface temperature, ice concentration, and ice surface temperature changes. We also quantified the contribution of individual components to the inter-model variance in the surface temperature changes. The downward clear-sky longwave radiation is one of major contributors to the model spread throughout the year. Other controlling terms for the model spread vary with the season, but they are similar between the MH and the future in each season. This result suggests that the MH Arctic change may not be analogous to the future in some seasons when the temperature response differs, but it is still useful to constrain the model spread in the future Arctic projection. The cross-model correlation suggests that the feedbacks in preceding seasons should not be overlooked when determining constraints, particularly summer sea ice cover for the constraint of autumn–winter surface temperature response.

scholarly journals Surface Heat Budget estimation for Laurance Lake, US

2021 ◽  
Vol 877 (1) ◽  
pp. 012005
Author(s):  
Dahlia S. Abed-Zaid ◽  
Hussein A. M. Al-Zubaidi
Keyword(s):  
Surface Temperature ◽  
Water Surface ◽  
Heat Budget ◽  
Meteorological Data ◽  
Longwave Radiation ◽  
Atmospheric Conditions ◽  
Large Lakes ◽  
Water Surface Temperature ◽  
Maximum Water ◽  
The Many

Abstract Estimating heat budget factors are important to understand the many physical processes of large lakes and their reaction to the atmosphere. Some of these components are affected by water temperature, while the other depends on atmospheric conditions. This paper estimates the total heat flux for Lawrence lake via a code developed in MATLAB environment. The code can deal with different time resolutions if the lake water surface temperature data were at different time resolutions from the meteorological data. Results showed that solar energy peaks at 842 Watt/m2 at 540 Julian day, which is very normal for a sunny summer day, while the longwave radiation has 204 Watt/m2 as a min value. The back radiation did not make any reaction for the variation, but it revealed a small gradient. Furthermore, evaporation recorded - 67 Watt/m2 as a minimum value at 659 Julian day and 360 Watt/m2 as a maximum value at 578.43 Julian day close to the maximum water surface temperature event.

scholarly journals Optimization of Noah and Noah_MP WRF Land Surface Schemes in Snow-Melting Conditions over Complex Terrain

2017 ◽  
Vol 145 (12) ◽  
pp. 4727-4745 ◽  
Author(s):  
Elena Tomasi ◽  
Lorenzo Giovannini ◽  
Dino Zardi ◽  
Massimiliano de Franceschi
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Winter Season ◽  
Wrf Model ◽  
Longwave Radiation ◽  
Shortwave Radiation ◽  
Land Use Classification ◽  
Near Surface ◽  
Thermal Inversion ◽  
Alpine Valley

The paper presents the results of high-resolution simulations performed with the WRF Model, coupled with two different land surface schemes, Noah and Noah_MP, with the aim of accurately reproducing winter season meteorological conditions in a typical Alpine valley. Accordingly, model results are compared against data collected during an intensive field campaign performed in the Adige Valley, in the eastern Italian Alps. In particular, the ability of the model in reproducing the time evolution of 2-m temperature and of incoming and outgoing shortwave and longwave radiation is examined. The validation of model results highlights that, in this context, WRF reproduces rather poorly near-surface temperature over snow-covered terrain, with an evident underestimation, during both daytime and nighttime. Furthermore it fails to capture specific atmospheric processes, such as the temporal evolution of the ground-based thermal inversion. The main cause of these errors lies in the miscalculation of the mean gridcell albedo, resulting in an inaccurate estimate of the reflected solar radiation calculated by both Noah and Noah_MP. Therefore, modifications to the initialization, to the land-use classification, and to both land surface models are performed to improve model results, by intervening in the calculation of the albedo, of the snow cover, and of the surface temperature. Qualitative and quantitative analyses show that, after these changes, a significant improvement in the comparability between model results and observations is achieved. In particular, outgoing shortwave radiation is lowered, 2-m temperature maxima increased accordingly, and ground-based thermal inversions are better captured.

scholarly journals Intraseasonal Sea Surface Temperature Variability across the Indonesian Seas*

2015 ◽  
Vol 28 (22) ◽  
pp. 8710-8727 ◽  
Author(s):  
Asmi M. Napitu ◽  
Arnold L. Gordon ◽  
Kandaga Pujiana
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Intraseasonal Variability ◽  
Longwave Radiation ◽  
Active Phase ◽  
Ocean Model ◽  
Sea Surface ◽  
Indonesian Seas ◽  
Sst Variability ◽  
Slab Ocean

Abstract Sea surface temperature (SST) variability at intraseasonal time scales across the Indonesian Seas during January 1998–mid-2012 is examined. The intraseasonal variability is most energetic in the Banda and Timor Seas, with a standard deviation of 0.4°–0.5°C, representing 55%–60% of total nonseasonal SST variance. A slab ocean model demonstrates that intraseasonal air–sea heat flux variability, largely attributed to the Madden–Julian oscillation (MJO), accounts for 69%–78% intraseasonal SST variability in the Banda and Timor Seas. While the slab ocean model accurately reproduces the observed intraseasonal SST variations during the northern winter months, it underestimates the summer variability. The authors posit that this is a consequence of a more vigorous cooling effect induced by ocean processes during the summer. Two strong MJO cycles occurred in late 2007–early 2008, and their imprints were clearly evident in the SST of the Banda and Timor Seas. The passive phase of the MJO [enhanced outgoing longwave radiation (OLR) and weak zonal wind stress) projects on SST as a warming period, while the active phase (suppressed OLR and westerly wind bursts) projects on SST as a cooling phase. SST also displays significant intraseasonal variations in the Sulawesi Sea, but these differ in characteristics from those of the Banda and Timor Seas and are attributed to ocean eddies and atmospheric processes independent from the MJO.

Downwelling longwave radiation and atmospheric winter states in the western maritime Arctic

2014 ◽  
Vol 35 (9) ◽  
pp. 2339-2351 ◽  
Author(s):  
R. L. Raddatz ◽  
T. N. Papakyriakou ◽  
B. G. Else ◽  
M. G. Asplin ◽  
L. M. Candlish ◽  
...  
Keyword(s):  
Longwave Radiation ◽  
Downwelling Longwave Radiation
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