Dataset Open MESMAR-R: A Coupled Regional Reanalysis for the Mediterranean Region Covering the period 1993-2024 (daily min/mean/max for surface atmospheric variables)

Read full text
Authors: Andrea Storto, Vincenzo de Toma, Chunxue Yang
Cited by: 1

Abstract

MESMAR-R is a newly developed coupled atmosphere-ocean-hydrology reanalysis for the Mediterranean region. The system couples state-of-the-art numerical models with data assimilation in both the ocean and the atmosphere, producing a dynamically coherent reconstruction of the 1993–2024 period. MESMAR-R performs strongly in the ocean, particularly for sea level, sea-surface salinity, and upper-layer temperature, confirming its skill in the layers most relevant for air–sea interaction. The atmospheric component performs reasonably well given its simplified assimilation based on three-dimensional nudging, yielding basin-scale fields comparable to state-of-the-art reanalyses and maintaining internally consistent coupled air-sea interactions. Sensitivity experiments show that data assimilation in both the ocean and atmosphere is essential for optimal performance: coupling alone does not reach reanalysis skill, but its benefits are relevant for salinity and surface fluxes and amplify when complemented by data assimilation. The ocean and atmospheric trends reconstructed by MESMAR-R are physically coherent and consistent with observations, reproducing basin-wide salinification, thermo–halo compensation in steric sea level, mass-driven sea-level rise, and pronounced near-surface atmospheric warming and drying. MESMAR-R therefore provides a unique, balanced reconstruction of past Mediterranean climate, suitable for investigating coupled air–sea processes, compound events, marine extremes, and other phenomena where dynamically consistent atmosphere–ocean interactions are essential. The data include daily means, minima, and maxima for near-surface atmospheric variables.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
1 September 2026
Document type
Dataset