Use of satellite-based sensing in land surface climatology

1994 ◽  
Vol 18 (1) ◽  
pp. 1-15 ◽  
Author(s):  
David Greenland
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Sensible Heat Flux ◽  
Longwave Radiation ◽  
Climate Modelling ◽  
Area Index ◽  
Surface Climate

Common types of satellite-derived measurements are reviewed with respect to how they are used to provide information on variables important to land surface climatology. The variables considered include solar radiation, surface albedo, surface temperature, outgoing longwave radiation, cloud cover, net radiation, soil moisture, latent and sensible heat flux, surface cover and leaf area index. A selection of land surface climate modelling schemes is identified and considered with a view to their practicality for use with satellite-derived data. Issues arising from the foregoing considerations include the absence from satellite data of some variables required by land surface climate models, the importance of extreme pixel values in model parameterization, the importance of matching spatial resolution in satellite data and climate model, and the need to have concurrent, independently observed, meteorological data in order to make full use of the satellite data.

scholarly journals Climate-model-informed deep learning of global soil moisture distribution

2021 ◽  
Vol 14 (7) ◽  
pp. 4429-4441
Author(s):  
Klaus Klingmüller ◽  
Jos Lelieveld
Keyword(s):  
Soil Moisture ◽  
Atmospheric Chemistry ◽  
Missing Values ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
European Space Agency ◽  
Moisture Distribution ◽  
Machine Learning Techniques ◽  
Climate Modelling

Abstract. We present a deep neural network (DNN) that produces accurate predictions of observed surface soil moisture, applying meteorological data from a climate model. The network was trained on daily satellite retrievals of soil moisture from the European Space Agency (ESA) Climate Change Initiative (CCI). The predictors precipitation, temperature and humidity were simulated with the ECHAM/MESSy atmospheric chemistry–climate model (EMAC). Our evaluation shows that predictions of the trained DNN are highly correlated with the observations, both spatially and temporally, and free of bias. This offers an alternative for parameterisation schemes in climate models, especially in simulations that use but may not focus on soil moisture, which we illustrate with the threshold wind speed for mineral dust emissions. Moreover, the DNN can provide proxies for missing values in satellite observations to produce realistic, comprehensive and high-resolution global datasets. As the approach presented here could be similarly used for other variables and observations, the study is a proof of concept for basic but expedient machine learning techniques in climate modelling, which may motivate additional applications.

scholarly journals weather@home 2: validation of an improved global–regional climate modelling system

2017 ◽  
Vol 10 (5) ◽  
pp. 1849-1872 ◽  
Author(s):  
Benoit P. Guillod ◽  
Richard G. Jones ◽  
Andy Bowery ◽  
Karsten Haustein ◽  
Neil R. Massey ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Modelling ◽  
Modelling System

Abstract. Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes in such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows one to generate a very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of the weather@home system (weather@home 2) with a higher-resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid-scale land-surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM temperature biases are overall reduced, in particular the warm bias over eastern Europe, but large biases remain. Precipitation is improved over the Alps in summer, with mixed changes in other regions and seasons. The model is shown to represent the main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

scholarly journals Updated cloud physics improve the modelled near surface climate of Antarctica of a regional atmospheric climate model

2013 ◽  
Vol 7 (4) ◽  
pp. 3231-3260
Author(s):  
J. M. van Wessem ◽  
C. H. Reijmer ◽  
J. T. M. Lenaerts ◽  
W. J. van de Berg ◽  
M. R. van den Broeke ◽  
...  
Keyword(s):  
Heat Flux ◽  
Climate Model ◽  
Sensible Heat Flux ◽  
Longwave Radiation ◽  
Radiative Flux ◽  
Sensible Heat ◽  
Near Surface ◽  
Surface Climate ◽  
Downward Longwave Radiation ◽  
Regional Atmospheric Climate Model

Abstract. The physics package of the polar version of the regional atmospheric climate model RACMO2 has been updated from RACMO2.1 to RACMO2.3. The update constitutes, amongst others, the inclusion of a parameterization for cloud ice super-saturation, an improved turbulent and radiative flux scheme and a changed cloud scheme. In this study the effects of these changes on the modelled near-surface climate of Antarctica are presented. Significant biases remain, but overall RACMO2.3 better represents the near-surface climate in terms of the modelled surface energy balance, based on a comparison with > 750 months of data from nine automatic weather stations located in East Antarctica. Especially the representation of the sensible heat flux and net longwave radiative flux has improved with a decrease in biases of up to 40 %. These improvements are mainly caused by the inclusion of ice super-saturation, which has led to more moisture being transported onto the continent, resulting in more and optically thicker clouds and more downward longwave radiation. As a result, modelled surface temperatures have increased and the bias, when compared to 10 m snow temperatures from 64 ice core observations, has decreased from −2.3 K to −1.3 K. The weaker surface temperature inversion consequently improves the representation of the sensible heat flux, whereas wind speed remains unchanged.

scholarly journals Climate model-informed deep learning of global soil moisture distribution

2021 ◽  
Author(s):  
Klaus Klingmüller ◽  
Jos Lelieveld
Keyword(s):  
Soil Moisture ◽  
Atmospheric Chemistry ◽  
Missing Values ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
European Space Agency ◽  
Moisture Distribution ◽  
Machine Learning Techniques ◽  
Climate Modelling

Abstract. We present a deep neural network (DNN) that produces accurate predictions of observed surface soil moisture, based on meteorological data from a climate model. The network was trained on daily satellite retrievals of soil moisture from the European Space Agency (ESA) Climate Change Initiative (CCI). The predictors precipitation, temperature and humidity were simulated with the ECHAM/MESSy atmospheric chemistry-climate model (EMAC). Our evaluation shows that predictions of the trained DNN are highly correlated with the observations, both, spatially and temporally, and free of bias. This offers an alternative for parametrisation schemes in climate models, especially in simulations that use, but may not focus on soil moisture, which we illustrate with the threshold wind speed for mineral dust emissions. Moreover, the DNN can provide proxies for missing values in satellite observations to produce realistic, comprehensive, high resolution global datasets. As the approach presented here could be similarly used for other variables and observations, the study is a proof of concept for basic but expedient machine learning techniques in climate modelling, which may motivate additional applications.

scholarly journals weather@home 2: validation of an improved global-regional climate modelling system

2016 ◽  
Author(s):  
Benoit P. Guillod ◽  
Andy Bowery ◽  
Karsten Haustein ◽  
Richard G. Jones ◽  
Neil R. Massey ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Modelling ◽  
Modelling System

Abstract. Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes of such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows to generate very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of weather@home system (weather@home 2) with a higher resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid scale land surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 km to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM biases are overall reduced, in particular the warm and dry bias over eastern Europe, but large biases remain. The model is shown to represent main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

scholarly journals Coupling of Integrated Biosphere Simulator to Regional Climate Model Version 3

2009 ◽  
Vol 22 (10) ◽  
pp. 2743-2757 ◽  
Author(s):  
Jonathan M. Winter ◽  
Jeremy S. Pal ◽  
Elfatih A. B. Eltahir
Keyword(s):  
Heat Flux ◽  
Regional Climate Model ◽  
Latent Heat Flux ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Sensible Heat Flux ◽  
Longwave Radiation ◽  
Sensible Heat ◽  
Integrated Biosphere Simulator

Abstract A description of the coupling of Integrated Biosphere Simulator (IBIS) to Regional Climate Model version 3 (RegCM3) is presented. IBIS introduces several key advantages to RegCM3, most notably vegetation dynamics, the coexistence of multiple plant functional types in the same grid cell, more sophisticated plant phenology, plant competition, explicit modeling of soil/plant biogeochemistry, and additional soil and snow layers. A single subroutine was created that allows RegCM3 to use IBIS for surface physics calculations. A revised initialization scheme was implemented for RegCM3–IBIS, including an IBIS-specific prescription of vegetation and soil properties. To illustrate the relative strengths and weaknesses of RegCM3–IBIS, one 4-yr numerical experiment was completed to assess ability of both RegCM3–IBIS (with static vegetation) and RegCM3 with its native land surface model, Biosphere–Atmosphere Transfer Scheme 1e (RegCM3–BATS1e), to simulate the energy and water budgets. Each model was evaluated using the NASA Surface Radiation Budget, FLUXNET micrometeorological tower observations, and Climate Research Unit Time Series 2.0. RegCM3–IBIS and RegCM3–BATS1e simulate excess shortwave radiation incident and absorbed at the surface, especially during the summer months. RegCM3–IBIS limits evapotranspiration, which allows for the correct estimation of latent heat flux, but increases surface temperature, sensible heat flux, and net longwave radiation. RegCM3–BATS1e better simulates temperature, net longwave radiation, and sensible heat flux, but systematically overestimates latent heat flux. This objective comparison of two different land surface models will help guide future adjustments to surface physics schemes within RegCM3.

scholarly journals A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models

2003 ◽  
Vol 16 (9) ◽  
pp. 1261-1282 ◽  
Author(s):  
Valéry Masson ◽  
Jean-Louis Champeaux ◽  
Fabrice Chauvin ◽  
Christelle Meriguet ◽  
Roselyne Lacaze
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Area Index ◽  
Surface Parameters ◽  
Global Database ◽  
Climate Maps ◽  
Land Cover Maps

Abstract Ecoclimap, a new complete surface parameter global dataset at a 1-km resolution, is presented. It is intended to be used to initialize the soil–vegetation–atmosphere transfer schemes (SVATs) in meteorological and climate models (at all horizontal scales). The database supports the “tile” approach, which is utilized by an increasing number of SVATs. Two hundred and fifteen ecosystems representing areas of homogeneous vegetation are derived by combining existing land cover maps and climate maps, in addition to using Advanced Very High Resolution Radiometer (AVHRR) satellite data. Then, all surface parameters are derived for each of these ecosystems using lookup tables with the annual cycle of the leaf area index (LAI) being constrained by the AVHRR information. The resulting LAI is validated against a large amount of in situ ground observations, and it is also compared to LAI derived from the International Satellite Land Surface Climatology Project (ISLSCP-2) database and the Polarization and Directionality of the Earth's Reflectance (POLDER) satellite. The comparison shows that this new LAI both reproduces values coherent at large scales with other datasets, and includes the high spatial variations owing to the input land cover data at a 1-km resolution. In terms of climate modeling studies, the use of this new database is shown to improve the surface climatology of the ARPEGE climate model.

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

2011 ◽  
Vol 115 (5) ◽  
pp. 1171-1187 ◽  
Author(s):  
Hua Yuan ◽  
Yongjiu Dai ◽  
Zhiqiang Xiao ◽  
Duoying Ji ◽  
Wei Shangguan
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Climate Modelling ◽  
Area Index

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 The DeepMIP contribution to PMIP4: experimental design for model simulations of the EECO, PETM, and pre-PETM (version 1.0)

2017 ◽  
Vol 10 (2) ◽  
pp. 889-901 ◽  
Author(s):  
Daniel J. Lunt ◽  
Matthew Huber ◽  
Eleni Anagnostou ◽  
Michiel L. J. Baatsen ◽  
Rodrigo Caballero ◽  
...  
Keyword(s):  
Experimental Design ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Geological Data ◽  
Time Model ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations

Abstract. Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( >  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

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