Review of „Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations”, by G. Duveiller et al., submitted to Earth System Science Data

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land-climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as to inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset is available at: https://doi.org/10.5281/zenodo.1182145.

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Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations

Earth System Science Data ◽

10.5194/essd-10-1265-2018 ◽

2018 ◽

Vol 10 (3) ◽

pp. 1265-1279 ◽

Cited By ~ 18

Author(s):

Gregory Duveiller ◽

Giovanni Forzieri ◽

Eddy Robertson ◽

Wei Li ◽

Goran Georgievski ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Cover Change ◽

Vegetation Cover ◽

Land Surface ◽

Satellite Observations ◽

Evaluation Framework ◽

Land Surface Models ◽

Mitigation And Adaptation ◽

Surface Models

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land–climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset consisting of both harmonized model simulation and remote sensing estimations is available at https://doi.org/10.5281/zenodo.1182145.

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Recent progresses in incorporating human land-water management into global land surface models toward their integration into Earth system models

Wiley Interdisciplinary Reviews Water ◽

10.1002/wat2.1150 ◽

2016 ◽

Vol 3 (4) ◽

pp. 548-574 ◽

Cited By ~ 48

Author(s):

Yadu N. Pokhrel ◽

Naota Hanasaki ◽

Yoshihide Wada ◽

Hyungjun Kim

Keyword(s):

Water Management ◽

Land Surface ◽

Earth System ◽

Land Surface Models ◽

System Models ◽

Surface Models ◽

Global Land ◽

Earth System Models ◽

Land Water

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Adding some discussion on (emerged) issues when using the data in land surface models of earth system models

10.5194/gmd-2019-360-sc1 ◽

2020 ◽

Keyword(s):

Land Surface ◽

Earth System ◽

Land Surface Models ◽

System Models ◽

Surface Models ◽

Earth System Models

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Inferring past land-use induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

Biogeosciences Discussions ◽

10.5194/bgd-9-12505-2012 ◽

2012 ◽

Vol 9 (9) ◽

pp. 12505-12542

Author(s):

J. P. Boisier ◽

N. de Noblet-Ducoudré ◽

P. Ciais

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Surface Albedo ◽

Vegetation Type ◽

Satellite Observations ◽

Land Surface Models ◽

Historical Changes ◽

Surface Models ◽

Past Land Use

Abstract. Cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land-use induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the magnitude of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We have derived monthly albedo climatologies for croplands and four other land-cover types from MODIS satellite observations. We have then estimated the changes in surface albedo since preindustrial times by combining these climatologies with the land-cover maps of 1870 and 1992 used by modelers in the context of the LUCID intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter and 2% in summer between 1870 and 1992 over areas that have experienced intense deforestation in the northern temperate regions. The MODIS-based reconstructions of historical changes in surface albedo were then compared to those simulated by the various models participating to LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the reconstructions, that is larger increases in winter than in summer driven by the presence of snow. However, individual models show significant differences with the satellite-based reconstructions, despite the fact that land-cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how land-surface models parameterize albedo. Another reason, of secondary importance, results from differences in the simulated snowpack. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point to major deficiencies within the models; we therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate global land-surface models.

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Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia

10.5194/gmd-2020-49 ◽

2020 ◽

Cited By ~ 1

Author(s):

Miguel Nogueira ◽

Clément Albergel ◽

Souhail Boussetta ◽

Frederico Johannsen ◽

Isabel F Trigo ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Vegetation Cover ◽

Land Surface ◽

Added Value ◽

Vegetation Coverage ◽

Daily Maximum ◽

Cold Bias ◽

Land Surface Models ◽

Surface Models

Abstract. Earth observations were used to evaluate the representation of Land Surface Temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – the European Centre for Medium Range Weather Forecasting (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (CHTESSEL) and the Météo-France Interaction between Soil Biosphere and Atmosphere model (ISBA) within the SURface EXternalisée modelling platform (SURFEX-ISBA) for the 2004–2015 period. The results show that the daily maximum LST simulated by CHTESSEL over Iberia is affected by a large cold bias during summer months when compared against the Satellite Application Facility on Land Surface Analysis (LSA-SAF), reaching magnitudes larger than 10 °C over wide portions of central and southwestern Iberia. This error is shown to be tightly linked to a misrepresentation of the vegetation cover. In contrast, SURFEX simulations did not display such a cold bias. We show that this was due to the better representation of vegetation cover in SURFEX, which uses an updated land cover dataset (ECOCLIMAP-II) and an interactive vegetation evolution, representing seasonality. The representation of vegetation over Iberia in CHTESSEL was improved by combining information from the European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset with the Copernicus Global Land Service (CGLS) Leaf Area Index (LAI) and fraction of vegetation coverage (FCOVER). The proposed improvement in vegetation also includes a clumping approach that introduces seasonality to the vegetation cover. The results show significant added value, removing the daily maximum LST summer cold bias completely, without reducing the accuracy of the simulated LST, regardless of season or time of the day. The striking performance differences between SURFEX and CHTESSEL were fundamental to guide the developments in CHTESSEL highlighting the importance of using different models. This work has important implications: first, it takes advantage of LST, a key variable in surface-atmosphere energy and water exchanges, which is closely related to satellite top-of-atmosphere observations, to improve model’s representation of land surface processes. Second, CHTESSEL is the land surface model employed by ECMWF in the production of their weather forecasts and reanalysis, hence systematic errors in land surface variables and fluxes are then propagated into those products. Indeed, we show that the summer daily maximum LST cold bias over Iberia in CHTESSEL is present in the widely used ECMWF fifth generation reanalysis (ERA5). Finally, our results provide hints into the interaction between vegetation land-atmosphere exchanges, highlighting the relevance of the vegetation cover and respective seasonality in representing land surface temperature in both CHTESSEL and SURFEX. As a whole, this work demonstrates the added value in using multiple earth observation products for constraining and improving weather and climate simulations.

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Nitrogen cycling in CMIP6 land surface models: progress and limitations

Biogeosciences ◽

10.5194/bg-17-5129-2020 ◽

2020 ◽

Vol 17 (20) ◽

pp. 5129-5148 ◽

Cited By ~ 1

Author(s):

Taraka Davies-Barnard ◽

Johannes Meyerholt ◽

Sönke Zaehle ◽

Pierre Friedlingstein ◽

Victor Brovkin ◽

...

Keyword(s):

Carbon Dioxide ◽

Nitrogen Cycle ◽

Land Surface ◽

Atmospheric Carbon Dioxide ◽

Earth System ◽

Land Surface Models ◽

System Models ◽

Surface Models ◽

Atmospheric Carbon ◽

Earth System Models

Abstract. The nitrogen cycle and its effect on carbon uptake in the terrestrial biosphere is a recent progression in earth system models. As with any new component of a model, it is important to understand the behaviour, strengths, and limitations of the various process representations. Here we assess and compare five land surface models with nitrogen cycles that are used as the terrestrial components of some of the earth system models in CMIP6. The land surface models were run offline with a common spin-up and forcing protocol. We use a historical control simulation and two perturbations to assess the model nitrogen-related performances: a simulation with atmospheric carbon dioxide increased by 200 ppm and one with nitrogen deposition increased by 50 kgN ha−1 yr−1. There is generally greater variability in productivity response between models to increased nitrogen than to carbon dioxide. Across the five models the response to carbon dioxide globally was 5 % to 20 % and the response to nitrogen was 2 % to 24 %. The models are not evenly distributed within the ensemble range, with two of the models having low productivity response to nitrogen and another one with low response to elevated atmospheric carbon dioxide, compared to the other models. In all five models individual grid cells tend to exhibit bimodality, with either a strong response to increased nitrogen or atmospheric carbon dioxide but rarely to both to an equal extent. However, this local effect does not scale to either the regional or global level. The global and tropical responses are generally more accurately modelled than boreal, tundra, or other high-latitude areas compared to observations. These results are due to divergent choices in the representation of key nitrogen cycle processes. They show the need for more observational studies to enhance understanding of nitrogen cycle processes, especially nitrogen-use efficiency and biological nitrogen fixation.

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