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Abstract/Summary
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<jats:p>Abstract. Land surface temperature (LST) plays an essential role in water and energy exchanges between the Earth's surface and atmosphere. Recent advancements in high-quality satellite-derived LST data and land data assimilation systems present a unique opportunity to bridge the gap between global observational data and land surface models (LSMs) to better constrain the water and energy budgets in a changing climate. In this vein, this study focuses on the assimilation of the ESA CCI-LST product into the ORCHIDEE LSM (the continental part of the Institut Pierre-Simon Laplace Earth system model) with the aim of optimizing key parameters to improve the simulation of LST and surface energy fluxes. We use the land data assimilation system for the ORCHIDEE model (ORCHIDAS) to conduct a series of synthetic twin data assimilation experiments accounting for actual data availability and uncertainty from ESA CCI-LST to find an optimal strategy for assimilating LST. Here, we test different strategies of assimilation, notably investigating (i) two optimization methods (a random search technique and a gradient-based technique) and (ii) different ways to assimilate LST using the only raw data and/or different characteristics of the LST diurnal cycle (e.g. mean daily, daily amplitude, maximum and minimum temperatures, and morning and afternoon gradients). Upon identifying the optimal approach, we use ORCHIDAS to assimilate ESA CCI-LST data across 34 European sites provided by the Warm Winter database. Our results demonstrate the effectiveness of assimilating 3 h CCI-LST data in ORCHIDEE over a single year in 2018, thereby improving the accuracy of simulated LST and fluxes. This improvement, assessed against CCI-LST and in situ observations, reaches up to a 60 % reduction in the root-mean-square deviation, with a median decrease of 20 % over the entire validation period (2009–2020). Furthermore, we evaluate the effectiveness of optimized parameters for application at larger scales using the median of optimized parameters per vegetation type across sites. Notably, the performance for both LST and fluxes exhibits consistent stability over the years, comparable to using site-specific parameters, and indicates a significant improvement in the modelled fluxes. Future work will be focused on refining the utilization of the observation uncertainties provided by the ESA CCI-LST product (e.g. decomposed uncertainties and spatio-temporal variability) in the assimilation process.
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Hinweis
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DeepGreen-ID: 142fc763bb7d49749af61dd4a2285568 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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