Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe
<p>Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations....
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-11-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/27/4087/2023/hess-27-4087-2023.pdf |
| _version_ | 1797628647342866432 |
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| author | S. Scherrer S. Scherrer G. De Lannoy Z. Heyvaert Z. Heyvaert M. Bechtold C. Albergel T. S. El-Madany W. Dorigo |
| author_facet | S. Scherrer S. Scherrer G. De Lannoy Z. Heyvaert Z. Heyvaert M. Bechtold C. Albergel T. S. El-Madany W. Dorigo |
| author_sort | S. Scherrer |
| collection | DOAJ |
| description | <p>Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture.</p>
<p>In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance.</p>
<p>The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.</p> |
| first_indexed | 2024-03-11T10:42:53Z |
| format | Article |
| id | doaj.art-6ddeabef306f45d09a8ad1633e545aba |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-03-11T10:42:53Z |
| publishDate | 2023-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-6ddeabef306f45d09a8ad1633e545aba2023-11-14T08:15:25ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382023-11-01274087411410.5194/hess-27-4087-2023Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over EuropeS. Scherrer0S. Scherrer1G. De Lannoy2Z. Heyvaert3Z. Heyvaert4M. Bechtold5C. Albergel6T. S. El-Madany7W. Dorigo8Department of Geodesy and Geoinformation, TU Wien, Vienna, AustriaDepartment of Earth and Environmental Sciences, KU Leuven, Heverlee, BelgiumDepartment of Earth and Environmental Sciences, KU Leuven, Heverlee, BelgiumDepartment of Geodesy and Geoinformation, TU Wien, Vienna, AustriaDepartment of Earth and Environmental Sciences, KU Leuven, Heverlee, BelgiumDepartment of Earth and Environmental Sciences, KU Leuven, Heverlee, BelgiumEuropean Space Agency Climate Office, ECSAT, Didcot, UKDepartment Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, GermanyDepartment of Geodesy and Geoinformation, TU Wien, Vienna, Austria<p>Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture.</p> <p>In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance.</p> <p>The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.</p>https://hess.copernicus.org/articles/27/4087/2023/hess-27-4087-2023.pdf |
| spellingShingle | S. Scherrer S. Scherrer G. De Lannoy Z. Heyvaert Z. Heyvaert M. Bechtold C. Albergel T. S. El-Madany W. Dorigo Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe Hydrology and Earth System Sciences |
| title | Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe |
| title_full | Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe |
| title_fullStr | Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe |
| title_full_unstemmed | Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe |
| title_short | Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe |
| title_sort | bias blind and bias aware assimilation of leaf area index into the noah mp land surface model over europe |
| url | https://hess.copernicus.org/articles/27/4087/2023/hess-27-4087-2023.pdf |
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