Ocean Reanalyses: Recent Advances and Unsolved Challenges

TLDR

Ocean reanalyses integrate ocean models, atmospheric forcing, and observations through data assimilation to produce four‑dimensional ocean fields that support climate monitoring, prediction, and model evaluation, yet they face challenges such as sparse observations, bias, and multi‑scale assimilation. This mini‑review documents recent advances and unsolved challenges in ocean reanalyses, aiming to guide future data‑assimilation improvements, inter‑comparison efforts, and backward‑time extensions for earth‑system applications. The review synthesizes community progress by evaluating data‑assimilation techniques, coordinated inter‑comparison studies, and data‑rescue strategies to enhance uncertainty estimates and fit‑for‑purpose assessments. Improved reliability metrics have enabled ocean reanalyses to become essential tools in climate services, delivering a more complete picture of ocean change to end users.

Abstract

Ocean reanalyses combine ocean models, atmospheric forcing fluxes, and observations using data assimilation to give a four-dimensional description of the ocean. Metrics assessing their reliability have improved over time, allowing reanalyses to become an important tool in climate services that provide a more complete picture of the changing ocean to end users. Besides climate monitoring and research, ocean reanalyses are used to initialize sub-seasonal to multi-annual predictions, to support observational network monitoring, and to evaluate climate model simulations. These applications demand robust uncertainty estimates and fit-for-purpose assessments, achievable through sustained advances in data assimilation and coordinated inter-comparison activities. Ocean reanalyses face specific challenges: i) dealing with intermittent or discontinued observing networks, ii) reproducing inter-annual variability and trends of integrated diagnostics for climate monitoring, iii) accounting for drift and bias due e.g. to air-sea flux or ocean mixing errors, iv) optimizing initialization and improving performances during periods and in regions with sparse data. Other challenges such as multi-scale data assimilation to reconcile mesoscale and large-scale variability and flow-dependent error characterization for rapidly evolving processes, are amplified in long-term reanalyses. The demand to extend reanalyses backward in time requires tackling all these challenges, especially in the emerging context of earth system reanalyses and coupled data assimilation. This mini-review aims at documenting recent advances from the ocean reanalysis community, discussing unsolved challenges that require sustained activities for maximizing the utility of ocean observations, supporting data rescue and advancing specific research and development requirements for reanalyses.