Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics

Abstract. Evapotranspiration (ET) influences land-climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedbacks to large-scale hydrological processes and its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called aggregation bias that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale-dependence of this aggregation bias and show that it can lead to overestimation of daily ET fluxes by as much as 21 % averaged over the whole country. We show how our approach can be used to identify the dominant drivers of aggregation bias, and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale 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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Supplementary material to "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-supplement ◽

2020 ◽

Author(s):

Miguel Nogueira ◽

Clément Albergel ◽

Souhail Boussetta ◽

Frederico Johannsen ◽

Isabel F Trigo ◽

...

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Land Surface Models ◽

Surface Models ◽

Supplementary Material

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The influence of global sea surface temperature variability on the large-scale land surface temperature

Climate Dynamics ◽

10.1007/s00382-014-2332-0 ◽

2014 ◽

Vol 44 (7-8) ◽

pp. 2159-2176 ◽

Cited By ~ 7

Author(s):

Nicholas L. Tyrrell ◽

Dietmar Dommenget ◽

Claudia Frauen ◽

Scott Wales ◽

Mike Rezny

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Temperature Variability ◽

Sea Surface ◽

Large Scale Land ◽

Global Sea Surface Temperature

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Aggregating over land surface heterogeneity systematically biases evapotranspiration estimates in large-scale evaporation models

10.5194/egusphere-egu21-15460 ◽

2021 ◽

Author(s):

Elham Rouholahnejad Freund ◽

Massimiliano Zappa ◽

Kirchner James

Keyword(s):

Land Surface ◽

Large Scale ◽

Land Surface Model ◽

Spatiotemporal Variability ◽

Aggregation Bias ◽

Surface Model ◽

Land Surface Models ◽

Time Step ◽

Surface Models ◽

Large Scale Land

<p>Land surface models are highly uncertain in estimating evapotranspiration (ET) fluxes, and differ substantially in their projections of how ET will evolve in the future. Biases in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter simulated temperatures and model predictions of droughts and heatwaves. One potential source of bias is the "aggregation bias" that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate that this aggregation bias leads to substantial overestimates in ET fluxes in a typical large-scale land surface model. The proposed methodology can be used to correct for aggregation biases in ET estimates by quantifying the effects of finer-resolution spatiotemporal variability in ET drivers at each modeling time step, without explicitly representing sub-grid heterogeneities in large-scale land surface models.&#160;</p>

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Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5015-2020 ◽

2020 ◽

Vol 24 (10) ◽

pp. 5015-5025

Author(s):

Elham Rouholahnejad Freund ◽

Massimiliano Zappa ◽

James W. Kirchner

Keyword(s):

Land Surface ◽

Large Scale ◽

Hydrological Cycle ◽

Land Surface Model ◽

Aggregation Bias ◽

Surface Model ◽

Land Surface Models ◽

Evaporation Model ◽

Surface Models ◽

Large Scale Land

Abstract. Evapotranspiration (ET) influences land–climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedback to large-scale hydrological processes and its impact on atmospheric dynamics, ET is one of the drivers of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called “aggregation bias” that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale dependence of this aggregation bias and show that it can lead to an average overestimation of daily ET fluxes by as much as 10 % across the whole country (calculated as the median of the daily bias over the growing season). We show how our approach can be used to identify the dominant drivers of aggregation bias and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

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