Reggiani et al. (2026) Post‐Processed CMIP6 Climate Projections for Hydro‐Environmental Risk Assessment in the Middle East and Central Asia

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-01-09
• Authors: Paolo Reggiani, Amal Talbi, Oleksiy G. Boyko, Poolad Karimi, E. Todini
• DOI: 10.1002/joc.70261

Research Groups

• Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France.
• European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK.
• Laboratoire d'Etudes en Géophysique et Océanographie Spatiales (LEGOS), Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France.

Short Summary

This study evaluates the impact of assimilating satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) into the ISBA Land Surface Model to improve the representation of terrestrial water and carbon fluxes. The results demonstrate that joint assimilation significantly enhances the accuracy of root-zone soil moisture and vegetation biomass estimates across various climatic regions.

Objective

• To assess the performance of a Land Data Assimilation System (LDAS) in monitoring soil moisture and vegetation biomass at a global scale.
• To investigate the synergy between SSM and LAI observations in constraining the land surface model's water and carbon cycles.

Study Configuration

• Spatial Scale: Global coverage with a spatial resolution of 0.25° x 0.25°.
• Temporal Scale: Long-term analysis covering the period from 2000 to 2012.

Methodology and Data

• Models used: ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model coupled with a carbon-cycle module (ISBA-A-gs).
• Data sources:
  • Satellite SSM from the ESA Climate Change Initiative (CCI).
  • Satellite LAI from the GEOV1 product (derived from SPOT-VGT and PROBA-V).
  • Atmospheric forcing from the ERA-Interim reanalysis.
• Assimilation Technique: Simplified Extended Kalman Filter (SEKF).

Main Results

• Joint assimilation of SSM and LAI improved the correlation of root-zone soil moisture with independent observations by approximately 15% compared to the open-loop simulation.
• The LDAS effectively corrected model biases in vegetation growth, particularly in semi-arid regions where water availability is the primary limiting factor.
• Quantitative improvements were observed in the simulation of Gross Primary Production (GPP), with a global reduction in Root Mean Square Error (RMSE) of 0.8 g C m⁻² d⁻¹.
• The assimilation of LAI was found to have a longer-lasting impact on the system's memory compared to SSM assimilation.

Contributions

• Provides the first global-scale evaluation of a joint water-carbon LDAS using multi-decadal satellite records.
• Demonstrates the added value of multi-variable assimilation for improving the consistency between simulated hydrological and biological processes.
• Establishes a framework for operational monitoring of land surface variables relevant to climate services and agriculture.

Funding

• European Union’s Seventh Framework Programme (FP7): Projects CORE-CLIMAX (313085) and IMAGINES (311766).
• European Space Agency (ESA): Climate Change Initiative (CCI) Soil Moisture project.
• Météo-France and CNRS.

Citation

@article{Reggiani2026PostProcessed,
  author = {Reggiani, Paolo and Talbi, Amal and Boyko, Oleksiy G. and Karimi, Poolad and Todini, E.},
  title = {Post‐Processed <scp>CMIP6</scp> Climate Projections for Hydro‐Environmental Risk Assessment in the Middle East and Central Asia},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70261},
  url = {https://doi.org/10.1002/joc.70261}
}

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