Downscaling biogeochemistry in the Benguela eastern boundary current

Machu, Éric; Goubanova, Katerina; Vu, Briac Le; Gutknecht, Elodie; Garçon, Véronique

doi:10.1016/j.ocemod.2015.01.003

Cited by 14 publications

(15 citation statements)

References 64 publications

(93 reference statements)

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“…Indeed, coastal SST bias in the CCS can be largely eliminated by an order of magnitude increase in the ocean resolution of GCMs (from ~100 to ~10 km), in combination with a moderate increase in the atmospheric resolution (from ~200 to ~50 km) (Delworth et al 2012). The fidelity of regional models in EBUS can be further improved by statistically downscaling the GCM winds to the regional model resolution prior to performing the dynamical downscaling (Machu et al 2015). Similarly, in the regional model a narrow coastal band is visible in which the net surface heat flux is negative while SST anomalies are positive during strong ENSO events (Fig.…”

Section: Implications For Regional Downscalingmentioning

confidence: 99%

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

et al. 2017

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underlie SST predictability. A simple persistence forecast provides considerable skill for lead times up to ~4 months, while skill above persistence is mostly confined to forecasts of late winter/spring and derives primarily from predictable evolution of ENSO-related variability. Specifically, anomalously weak (strong) equatorward winds are skillfully forecast during El Niño (La Niña) events, and drive negative (positive) upwelling anomalies and consequently warm (cold) temperature anomalies. This mechanism prevails during moderate to strong ENSO events, while years of ENSO-neutral conditions are not associated with significant forecast skill in the wind or significant skill above persistence in SST. We find also a strong latitudinal gradient in predictability within the CCS; SST forecast skill is highest off the Washington/Oregon coast and lowest off southern California, consistent with variable wind forcing being the dominant driver of SST predictability. These findings have direct implications for regional downscaling of seasonal forecasts and for short-term management of living marine resources.

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Section: Implications For Regional Downscalingmentioning

confidence: 99%

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

et al. 2017

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“…Among the main local atmospheric causes of the SETA SST biases is the insufficient low level humidity and stratocumulus cloud cover, resulting in an excessive shortwave radiation net flux into the ocean (Giese and Carton 1994;Ma et al 1996;Huang et al 2007;Hu et al 2008;Hourdin et al 2015). Another local cause is deficient coastal upwelling and subsequent increase of SSTs, which is generally attributed to the resolution of both the atmospheric (Cambon et al 2013;Machu et al 2015) and ocean components (Grodsky et al 2012) of the CGCMs being too coarse. On one hand, in coarse resolution atmospheric models, an alongshore wind stress that is generally too weak leads to an underestimated offshore Ekman transport of cold water (Milinski et al 2016;Goubanova et al 2018), whereas an unrealistic wind stress curl near the coast results in errors in vertical velocities and in a too strong alongshore warm southward meridional current (Small et al 2015;Koseki et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Role of wind stress in driving SST biases in the Tropical Atlantic

et al. 2019

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Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000-2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by 6 months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the southeastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the southeastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models.

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“…Indeed, even averaged within 1° off the coast the maximum vertical velocity in the Luderitz cell in CTRL-HR (1.68 m/day) is still significantly stronger than the 0.62 m/ day of the corresponding maximum vertical velocity in CTRL-LR. Figure 13e, j, o shows that in CTRL-LR the wind stress field in the Benguela system exhibits shortcomings typical for coarse resolution atmospheric models (Machu et al 2015;Patricola and Chang 2016): in general, too weak wind stress at the coast with the wind stress maximum located too far offshore and, in particular, a strongly underestimated local maximum of the wind stress over the Cape Frio region (17°S). These local atmospheric errors certainly contribute to the underestimation of the coastal upwelling in CTRL-LR.…”

Section: Impact Of the Model Resolution: Ctrl-hr Versus Ctrl-lrmentioning

confidence: 99%

“…Along with the oceanic model resolution, the representation of the upwelling in the model strongly depends on the realism and resolution of the wind stress forcing (Cambon et al 2013;Machu et al 2015;Small et al 2015;Milinski et al 2016). Indeed, even averaged within 1° off the coast the maximum vertical velocity in the Luderitz cell in CTRL-HR (1.68 m/day) is still significantly stronger than the 0.62 m/ day of the corresponding maximum vertical velocity in CTRL-LR.…”

Section: Impact Of the Model Resolution: Ctrl-hr Versus Ctrl-lrmentioning

confidence: 99%

“…Another local cause of the bias is associated with poor representation of the coastal upwelling and is generally attributed to a too-coarse resolution of both oceanic (Grodsky et al 2012) and atmospheric (Cambon et al 2013;Machu et al 2015;Small et al 2015;Milinski et al 2016) components of CGCMs. The intensity of the wind-driven coastal upwelling in the SETA is dynamically linked to the strength of the equatorward Benguela current.…”

Section: Introductionmentioning

confidence: 99%

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Respective roles of remote and local wind stress forcings in the development of warm SST errors in the South-Eastern Tropical Atlantic in a coupled high-resolution model

et al. 2018

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Processes involved in the development of the warm sea surface temperature (SST) bias in the Tropical SouthEastern Atlantic (SETA) in a high resolution (HR) version of the CNRM-CM model are evaluated based on full-field initialized seasonal hindcasts starting at 1 February of each year for 2000-2009. Whereas the initial SST growth is likely associated with local atmospheric forcing, its further development is due to remote oceanic processes. A mixed layer heat budget analysis in SETA indicates a spurious warm horizontal advection observed as far as south of 25°S that appears at the beginning of March. It is associated with an erroneous oceanic mean state at the equator resulting from the mean equatorial westerly wind bias. A sensitivity experiment with corrected wind stress over the equatorial region suggests that the remote forcing explains about 57% of the SETA SST bias in March-May. Comparison with a lower resolution (LR) version of the model reveals that in general similar processes are responsible for the SST bias in both models. A strong reduction of the bias in the HR model is observed only over the near-coastal Southern Benguela region due to a better representation of atmospheric and oceanic processes controlling the coastal upwelling. Overall, the results of the inter-comparison of the SETA SST bias evolution in different sensitivity experiments performed in this study can be interpreted in terms of the relative contributions of (erroneous) warm horizontal advection, associated with equatorial forcing, and cold horizontal advection, associated with local offshore Ekman transport.

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Downscaling biogeochemistry in the Benguela eastern boundary current

Cited by 14 publications

References 64 publications

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability

Role of wind stress in driving SST biases in the Tropical Atlantic

Respective roles of remote and local wind stress forcings in the development of warm SST errors in the South-Eastern Tropical Atlantic in a coupled high-resolution model

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