California reanalysis downscaling at 10 km using an ocean‐atmosphere coupled regional model system

Li, Haiqin; Kanamitsu, Masao; Hong, Song‐You

doi:10.1029/2011jd017372

Cited by 32 publications

(37 citation statements)

References 74 publications

(64 reference statements)

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“…Near the coast, the interaction of the flow with the coastal headlands and capes is clear in both ROM runs and in the EnsFull, with increased wind speeds. However, the uncoupled run has warmer SSTs, compared to the coupled run, which is a common feature in regional coupled models (H. Li et al, 2012Li et al, , 2014. The seasonality of the maximum 10-m wind speed near the coast is well related to the locations where BCLLJ occurs (Lima et al, 2019).…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 93%

How Will a Warming Climate Affect the Benguela Coastal Low‐Level Wind Jet?

et al. 2019

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The strong coastal upwelling associated to the Benguela eastern boundary upwelling system makes the ocean along coast of this current one of the most productive ecosystems in the world. The Benguela Coastal Low‐Level Jet (BCLLJ) is one of the most important mesoscale feature that shape the climate of this region. The main synoptic forcing of the BCLLJ is the Angola thermal low over land and the St. Helen anticyclone over the ocean, resulting in southwesterly winds along the coast. This study investigates how the BCLLJ might change due to climate warming, with the help of uncoupled and coupled simulations from a 25‐km horizontal resolution regional climate model (ROM). In general, the coupled simulation displays the best performance in representing the present time near‐surface wind speed, with a decrease on the known warm bias of sea surface temperature in the Benguela eastern boundary upwelling system region. The analysis of the projected changes of the BCLLJ climate toward the end of the 21st century (2070–2099), following the RCP8.5 emissions scenario, shows an increase in the frequency of the BCLLJ occurrence along the southern area with higher changes in the coupled simulation (between 6% and 8%). These changes are related to a southerly shift of the St. Helen High, which intensifies the flow offshore the west coast of South Africa and causes a sharpening of the land‐sea thermal contrasts. However, during spring, associated with the decrease in near‐surface wind speed due to higher sea surface temperatures, the future frequency and intensity of the BCLLJ are lower.

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Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 93%

How Will a Warming Climate Affect the Benguela Coastal Low‐Level Wind Jet?

et al. 2019

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“…Since then, the method has been successfully applied to several oceanographic numerical problems such as the estimation of boundary conditions (Marchesiello et al, 2001;Chen et al, 2013), downscaling (Li et al, 2012), and other DA problems (Verron, 1992;Haines et al, 1993;Blayo et al, 1994;Lewis et al, 1998;Killworth et al, 2001;Thompson et al, 2006). Concerning applications to DA problems, the weights given to the model and the observations are generally not based on any optimality condition, but are rather scalars or Gaussian-like functions constructed based on physical assumptions or empirical considerations.…”

Section: G a Ruggiero Et Al: Numerical Experiments With The Dbfnmentioning

confidence: 99%

Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model

Ruggiero

Ourmiéres

Blum

et al. 2015

Nonlin. Processes Geophys.

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Abstract. The diffusive back-and-forth nudging (DBFN)is an easy-to-implement iterative data assimilation method based on the well-known nudging method. It consists of a sequence of forward and backward model integrations, within a given time window, both of them using a feedback term to the observations. Therefore, in the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a bounded time domain. In this method, the backward integration is carried out thanks to what is called backward model, which is basically the forward model with reversed time step sign. To maintain numeral stability, the diffusion terms also have their sign reversed, giving a diffusive character to the algorithm. In this article the DBFN performance to control a primitive equation ocean model is investigated. In this kind of model non-resolved scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales. Thus, in this article, the DBFN approximations and their consequences for the data assimilation system set-up are analysed. Our main result is that the DBFN may provide results which are comparable to those produced by a 4Dvar implementation with a much simpler implementation and a shorter CPU time for convergence. The conducted sensitivity tests show that the 4Dvar profits of long assimilation windows to propagate surface information downwards, and that for the DBFN, it is worth using short assimilation windows to reduce the impact of diffusioninduced errors. Moreover, the DBFN is less sensitive to the first guess than the 4Dvar.

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“…This last application was first suggested by Anthes (1983) and has become an active area of research over the last few years (e.g. Li et al 2012;Stefanova et al 2011;Weisse et al 2009;Kanamaru and Kanamitsu 2007).…”

Section: Introductionmentioning

confidence: 99%