Impact of Argo Observation on the Regional Ocean Reanalysis of China Coastal Waters and Adjacent Seas: A Twin-Experiment Study

Shao, Changwei; Li, Xuan; Cao, Yingzhi; Cui, Xiang; Gao, Siyu

doi:10.1155/2015/793825

Cited by 1 publication

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact of different observing networks on the quality of ocean reanalyses and forecast systems has been assessed in several studies. Observational networks like Argo and altimetry satellite data are crucial to correctly resolve the vertical structure of the water column and the mesoscale ocean circulation, respectively (e.g., Oke et al, 2015;Shao et al, 2015;Zhang et al, 2017). Their availability therefore has a direct impact in the quality of ocean reanalyses (Balmaseda et al, 2007a(Balmaseda et al, , 2013.…”

Section: Observational Requirementsmentioning

confidence: 99%

Ocean Reanalyses: Recent Advances and Unsolved Challenges

et al. 2019

View full text Add to dashboard Cite

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, and (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 flowdependent error characterization for rapidly evolving processes, are amplified in longterm 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.

show abstract