Gulf Stream Region Research Articles

Regionalizing the sea-level budget with machine learning techniques

Abstract. Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ-MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ-MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.

Simulation Framework and Case Studies for the Design of Sea Surface Salinity Remote Sensing Missions

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band microwave radiometers have now been used to measure sea surface salinity (SSS) from space for over a decade with the SMOS, Aquarius, and SMAP missions, and it is expected that the launch of the CIMR mission in the later half of this decade will ensure measurement continuity in the near future. Beyond these missions, it is useful to consider how future missions can be designed to meet different scientific objectives and performance requirements as well as to fit within different cost spaces. In this article, we present a software simulator for remote sensing measurements of ocean state capable of generating L1- and L2- equivalent data products for an arbitrary spacecraft mission including multifrequency fixed-pointing or scanning microwave radiometers.This simulator is then applied to case studies of SSS measurement over selected areas of interest, including the Gulf Stream, Southern Ocean, and Pacific tropical instability wave regions. These simulations illustrate how different design choices concerning receiver bandwidth and revisit time can improve the detection of SSS features in these regions from the mesoscale to the seasonal scale.

Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> terms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

Properties of the Lateral Mesoscale Eddy-Induced Transport in a High-Resolution Ocean Model: Beyond the Flux–Gradient Relation

Abstract Lateral mesoscale eddy-induced tracer transport is traditionally represented in coarse-resolution models by the flux–gradient relation. In its most complete form, the relation assumes the eddy tracer flux as a product of the large-scale tracer concentration gradient and an eddy transport coefficient tensor. However, several recent studies reported that the tensor has significant spatiotemporal complexity and is not uniquely defined, that is, it is sensitive to the tracer distributions and to the presence of nondivergent (“rotational”) components of the eddy flux. These issues could lead to significant biases in the representation of the eddy-induced transport. Using a high-resolution tracer model of the Gulf Stream region, we examine the diffusive and advective properties of lateral eddy-induced transport of dynamically passive tracers, reevaluate the utility of the flux–gradient relation, and propose an alternative approach based on modeling the local eddy forcing by a combination of diffusion and generalized eddy-induced advection. Mesoscale eddies are defined by a scale-based spatial filtering, which leads to the importance of new eddy-induced terms, including eddy-mean covariances in the eddy fluxes. The results show that the biases in representing these terms are noticeably reduced by the new approach. A series of targeted simulations in the high-resolution model further demonstrates that the approach outperforms the flux–gradient model in reproducing the stirring and dispersing effect of eddies. Our study indicates potential to upgrade the traditional flux–gradient relation for representing the eddy-induced tracer transport.

Electromagnetic Ducting in the Near‐Shore Marine Environment: Results From the CASPER‐East Field Experiment

AbstractWe used the observations from the east coast of the USA and model simulations to elicit marine atmospheric surface layer (MASL) processes that controlled the spatial variability of electromagnetic ducting conditions in the near‐shore (60 km) marine environment during CASPER‐East field campaign. The estimated evaporation duct height (EDH) showed an average decrease of ∼2.5 m over every 10 km distance from the shore. The surface layer stability conditions chiefly determined the duct heights. While the mean duct height remained ∼10 m under unstable conditions, deeper (&gt;20 m) evaporation ducts were frequently evident (∼40% of the time) during stable MASL conditions, which were responsible for the observed near‐shore EDH variability. Episodes of warm offshore flow and advection of hot and humid air from the Gulfstream region created ideal environmental conditions for stable MASL development and thus deeper ducts over the coastal ocean. A detailed case study of warm and humid Gulfstream air advection over the cooler coastal ocean is presented to elucidate the temporal evolution of the surface layer stability and associated changes in the spatial distribution of the evaporation duct height.

Reconstructing ocean subsurface salinity at high resolution using a machine learning approach

Abstract. A gridded ocean subsurface salinity dataset with global coverage is useful for research on climate change and its variability. Here, we explore the feed-forward neural network (FFNN) approach to reconstruct a high-resolution (0.25∘ × 0.25∘) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25∘ × 0.25∘) satellite remote-sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse-resolution (1∘ × 1∘) gridded salinity product. We show that the FFNN can effectively transfer small-scale spatial variations in ADT, SST, and SSW fields into the 0.25∘ × 0.25∘ salinity field. The root-mean-square error (RMSE) can be reduced by ∼11 % on a global-average basis compared with the 1∘ × 1∘ salinity gridded field. The reduction in RMSE is much larger in the upper ocean than the deep ocean because of stronger mesoscale variations in the upper layers. In addition, the new 0.25∘ × 0.25∘ reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1∘ × 1∘ resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25∘ × 0.25∘ data are consistent with the 1∘ × 1∘ gridded salinity field, suggesting the persistence of the large-scale signals in the high-resolution reconstruction. The successful application of machine learning in this study provides an alternative approach for ocean and climate data reconstruction that can complement the existing data assimilation and objective analysis methods. The reconstructed IAP0.25∘ dataset is freely available at https://doi.org/10.57760/sciencedb.o00122.00001 (Tian et al., 2022).

How sensitive are Lagrangian coherent structures to uncertainties in data?

Lagrangian coherent structures (LCSs) are time-varying entities which capture the most influential transport features of a flow. These can for example identify groups of particles which have greatest stretching, or which maintain a coherent jet or vortical structure. While many different LCS methods have been developed, the impact of the inevitable measurement uncertainty in realistic Eulerian velocity data has not been studied in detail. This article systematically addresses whether LCS methods are self-consistent in their conclusions under such uncertainty for nine different methods: the finite time Lyapunov exponent, hyperbolic variational LCSs, Lagrangian averaged vorticity deviation, Lagrangian descriptors, stochastic sensitivity, the transfer operator, the dynamic Laplacian operator, fuzzy c-means clustering and coherent structure colouring. The investigations are performed for two different realistic data sets: a computational fluid dynamics simulation of a Kelvin–Helmholtz instability, and oceanographic data of the Gulf Stream region. Using statistics gleaned from stochastic simulations, it is shown that the methods which detect full-dimensional coherent flow regions are significantly more robust than methods which detect lower-dimensional flow barriers. Additional insights into which aspects of each method are self-consistent, and which are not, are provided.

A comparative analysis of the mesoscale thermohaline features across subarctic frontal zones in the Northern Hemisphere

Mesoscale eddies and their thermohaline features across the two subarctic frontal (SAF) zones, the Oyashio Extension (OE) and the Gulf Stream (GS), in the Northern Hemisphere are investigated and compared using both satellite-based observations and Argo float data. Our results show that the thermohaline properties (temperature, salinity and associated density) induced by mesoscale eddies have significant differences between the south and north sides of each SAF zone. The relationships between the sea surface height anomaly (SSHA) and the sea surface temperature anomaly (SSTA), called the H-T amplitude relationship, are examined and used to depict the mesoscale sea surface characteristics. In both the OE and GS regions, SSTAs on the north sides of the SAFs are more sensitive to the SSHA than that on their southern sides, and this sensitivity is stronger in the GS than in the OE. It is found that in the OE, the difference in the H-T amplitude relationships between the north and south sides is mainly due to the thermal expansion coefficient, which is contingent on the ambient sea surface temperature. In the GS, the difference between the north and south sides in the H-T amplitude relationships is mainly due to the change of the vertical profiles of mesoscale eddies, which are related to the ambient meridional density front.

Four-dimensional temperature, salinity and mixed-layer depth in the Gulf Stream, reconstructed from remote-sensing and in situ observations with neural networks

Abstract. Despite the ever-growing number of ocean data, the interior of the ocean remains undersampled in regions of high variability such as the Gulf Stream. In this context, neural networks have been shown to be effective for interpolating properties and understanding ocean processes. We introduce OSnet (Ocean Stratification network), a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The proposed scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T−S) profiles down to 1000 m and the mixed-layer depth (MLD) from surface data covering 1993 to 2019. OSnet is trained to fit sea surface temperature and sea level anomalies onto all historical in situ profiles in the Gulf Stream region. To achieve vertical coherence of the profiles, the MLD prediction is used to adjust a posteriori the vertical gradients of predicted T−S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4∘ daily grid, producing four-dimensional fields of temperature and salinity, with their associated confidence interval issued from the bootstrap. OSnet profiles have root mean square error comparable with the observation-based Armor3D weekly product and the physics-based ocean reanalysis Glorys12. The lowest confidence in the prediction is located north of the Gulf Stream, between the shelf and the current, where the thermohaline variability is large. The OSnet reconstructed field is coherent even in the pre-Argo years, demonstrating the good generalization properties of the network. It reproduces the warming trend of surface temperature, the seasonal cycle of surface salinity and mesoscale structures of temperature, salinity and MLD. While OSnet delivers an accurate interpolation of the ocean stratification, it is also a tool to study how the ocean stratification relates to surface data. We can compute the relative importance of each input for each T−S prediction and analyse how the network learns which surface feature influences most which property and at which depth. Our results demonstrate the potential of machine learning methods to improve predictions of ocean interior properties from observations of the ocean surface.

Barents-Kara sea-ice decline attributed to surface warming in the Gulf Stream

Decline in winter sea-ice concentration (SIC) in the Barents-Kara Sea significantly impacts climate through increased heat release to the atmosphere. However, the past Barents-Kara SIC decrease rate is underestimated in the majority of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. Here we show that climate model simulations can reproduce the Barents-Kara SIC trend for 1970–2017 when sea surface temperature (SST) variability in the Gulf Stream region is constrained by observations. The constrained warming of the Gulf Stream strengthens ocean heat transport to the Barents-Kara Sea that enhances the SIC decline. The linear trends between the SIC and SST are highly correlated in the CMIP6 ensemble, suggesting that the externally forced component of the Gulf Stream SST increase explains up to 56% of the forced Barents-Kara SIC trend. Therefore, future warming of the Gulf Stream can be an essential pacemaker of the SIC decline.

Surface Wind Responses to Mesoscale Sea Surface Temperature over Western Boundary Current Regions Assessed by Spectral Transfer Functions

Abstract Satellite observations have revealed that mesoscale sea surface temperature (SST) perturbations can exert distinct influence on sea surface wind by modifying the overlying atmospheric boundary layer. Recently, spectral transfer functions have been shown to be useful to elucidate the wind response features. Spectral transfer functions can represent spatially lagged responses, their horizontal scale dependence, and background wind speed dependence. By adopting the transfer function analysis, the present study explores seasonality and regional differences in the wind response over the major western boundary current regions. Transfer functions estimated from satellite observations are found to be largely consistent among seasons and regions, suggesting that the underlying dominant dynamics are ubiquitous. Nevertheless, the wind response exhibits statistically significant seasonal and regional differences depending on background wind speed. When background wind is stronger (weaker) than 8.5 m s−1, the wind response is stronger (weaker) in winter than in summer. The Agulhas Retroflection region exhibits stronger wind response typically by 30% than the Gulf Stream and Kuroshio Extension regions. Although observed wind distributions are reasonably reconstructed from the transfer functions and observed SST, surface wind convergence zones along the Gulf Stream and Kuroshio Extension are underrepresented. The state-of-the-art atmospheric reanalysis and regional model represent well the structure of the transfer functions in the wavenumber space. The amplitude is, however, underestimated by typically 30%. The transfer function analysis can be adapted to many other atmospheric responses besides sea surface wind, and thus provide new insights into the climatic role of the mesoscale air–sea coupling.

Local Air‐Sea Interactions at Ocean Mesoscale and Submesoscale in a Western Boundary Current

AbstractWe present results from a new, global, high‐resolution (∼3‐km for ocean and ∼6‐km for atmosphere) realistic earth system simulation. This simulation allows us to examine aspects of small‐scale air‐sea interaction beyond what previous studies have reported. Our study focuses on recurring intermittent wind events in the Gulf Stream region. These events induce local air‐sea heat fluxes above Sea Surface Temperature (SST) anomalies with horizontal scales smaller than 500‐km. In particular, strong latent heat bursts above warm SST anomalies are observed during these wind events. We show that such wind events are associated with a secondary circulation that acts to fuel the latent heat bursts by transferring dry air and momentum down to the surface. The intensity of this secondary circulation is related to the strength of small‐scale SST fronts that border SST anomalies. The study of such phenomena requires high‐resolution in both the atmospheric and oceanic components of the model.

Meridional Eddy Heat Transport Variability in the Surface Mixed Layer of the Atlantic Ocean

AbstractWe analyze the interannual variability and trends of the eddy heat transport (EHT) in the surface mixed layer of the Atlantic Ocean from covariances of sea surface temperature (SST) and geostrophic velocities from satellite observations between 1993 and 2018. The EHT is largest along the path of the Gulf Stream in the Northern Hemisphere and the vicinity of the Agulhas Retroflection and Argentine basin in the Southern Hemisphere. On average, meridional EHT in the mixed layer leads to a divergence of heat away from the subtropics in both hemispheres toward the equator and higher latitudes. Depending on latitude, the divergence of the EHT accounts for around 1%–5% of the atmospheric net surface heat flux, but reaches as much as 20% for certain regions. The EHT can be linked to eddy kinetic energy (EKE) and meridional SST gradients using mixing length hypothesis, but the variability of EKE and SST gradients are not enough to capture local EHT variability. For different latitudes, the EHT shows different behavior over time, with a strong increase in northward EHT in the Gulf Stream region, and a decadal oscillation of poleward EHT in the tropical and subtropical regions. This oscillation on larger scales is highly correlated with spatially averaged EKE and the large‐scale SST gradients. A comparison with climate indices indicates a relation to Atlantic climate variability, especially the meridional modes of the Tropical Atlantic Variability and the North Atlantic Oscillation (NAO).

On the relationship between CYGNSS surface heat fluxes and the lifecycle of low-latitude ocean extratropical cyclones

AbstractSurface latent and sensible heat fluxes are important for extratropical cyclone evolution and intensification. Because extratropical cyclone genesis often occurs at low-latitude, CYGNSS surface latent and sensible heat flux retrievals are composited to provide a mean picture of their spatial distribution in low-latitude oceanic extratropical cyclones. CYGNSS heat fluxes are not affected by heavy precipitation and offer observations of storms with frequent revisit times. Consistent with prior results obtained for cyclones in the Gulf Stream region, the fluxes are strongest in the wake of the cold fronts, and weakest to negative in the warm sector in advance of the cold fronts. As cyclone strength increases, or mean precipitable water decreases, the maximum in surface heat fluxes increases while the minimum decreases. This impacts the changes in fluxes during cyclone intensification: the post-cold frontal surface heat flux maximum increases due to the increase in near surface winds. During cyclone dissipation, the fluxes in this sector decrease, due to the decrease in winds and in temperature and humidity contrast. The warm sector minimum decreases throughout the entire cyclone lifetime and is mostly driven by sea-air temperature and humidity contrast changes. However, during cyclone dissipation, the surface heat fluxes increase along the cold front in a narrow band to the east, independently from changes in the cyclone characteristics. This suggests that, during cyclone dissipation, energy transfers from the ocean to the atmosphere are linked to frontal in addition to synoptic-scale processes.

Smoother versus sharper Gulf Stream and Kuroshio sea surface temperature fronts: effects on cyclones and climatology

Abstract. The Gulf Stream and Kuroshio regions feature strong sea surface temperature (SST) gradients that influence cyclone development and the storm track. Previous studies showed that smoothing the SSTs in either the North Atlantic or North Pacific yields a reduction in cyclone activity, surface heat fluxes, and precipitation, as well as a southward shift of the storm track and the upper-level jet. To what extent these changes are attributable to changes in individual cyclone behaviour, however, remains unclear. Comparing simulations with realistic and smoothed SSTs in the atmospheric general circulation model AFES, we find that the intensification of individual cyclones in the Gulf Stream or Kuroshio region is only marginally affected by reducing the SST gradient. In contrast, we observe considerable changes in the climatological mean state as well as a reduced cyclone activity in the North Atlantic and North Pacific storm tracks that are shifted equatorward in both basins. The upper-level jet in the Atlantic also shifts equatorward, while the jet in the Pacific strengthens in its climatological position and extends further east. Surface heat fluxes, specific humidity, and precipitation also respond strongly to the smoothing of the SST, with a considerable decrease in their mean values on the warm side of the SST front. This decrease is more pronounced in the Gulf Stream than in the Kuroshio region, due to the larger decrease in SST along the Gulf Stream SST front. Considering the differences of the different variables occurring within/outside of a 750 km radius of any cyclone over their entire lifetime, we find that cyclones play only a secondary role in explaining the differences in the mean state between the smoothed and realistic SST experiments.

Mechanisms shaping wind convergence under extreme synoptic situations over the Gulf Stream region

AbstractAir-sea interaction processes over the Gulf Stream have received particular attention over the last decade. It has been shown that sea surface temperature (SST) gradients over the Gulf Stream can alter the near surface wind divergence through changes in the marine atmospheric boundary layer (MABL). Two mechanisms have been proposed to explain the response: the Vertical Mixing Mechanism (VMM) and the Pressure Adjustment Mechanism (PAM). However, their respective contribution is still under debate. It has been argued that the synoptic perturbations over the Gulf Stream can provide more insight on the MABL response to SST fronts. We analyze the VMM and PAM under different atmospheric conditions obtained from a classification method based on the deciles of the statistical distribution of winter turbulent heat fluxes over the Gulf Stream. Lowest deciles are associated with weak air-sea interactions and anticyclonic atmospheric circulation over the Gulf Stream, whereas highest deciles are related to strong air-sea interactions and a cyclonic circulation. Our analysis includes the low and high-resolution versions of the ARPEGEv6 atmospheric model forced by observed SST, and the recently released ERA5 global reanalysis. We find that the occurrence of anticyclonic and cyclonic perturbations associated with different anomalous wind regimes can locally modulate the activation of the VMM and the PAM. In particular, the PAM is predominant in anticyclonic conditions, whereas both mechanisms are equally present in most of the cyclonic conditions. Our results highlight the role of the atmospheric circulation and associated anomalous winds in the location, strength and occurrence of both mechanisms.

Global oscillatory modes in high-end climate modeling and reanalyses

Interannual oscillatory modes, atmospheric and oceanic, are present in several large regions of the globe. We examine here low-frequency variability (LFV) over the entire globe in the Community Earth System Model (CESM) and in the NCEP-NCAR and ECMWF ERA5 reanalyses. Multichannel singular spectrum analysis (MSSA) is applied to these three datasets. In the fully coupled CESM1.1 model, with its resolution of 0.1 times 0.1 degrees in the ocean and 0.25 times 0.25 degrees in the atmosphere, the fields analyzed are surface temperatures, sea level pressures and the 200-hPa geopotential. The simulation is 100-year long and the last 66 yr are used in the analysis. The two statistically significant periodicities in this IPCC-class model are 11 and 3.4 year. In the NCEP-NCAR reanalysis, the fields of sea level pressure and of 200-hPa geopotential are analyzed at the available resolution of 2.5 times 2.5 degrees over the 68-years interval 1949–2016. Oscillations with periods of 12 and 3.6 years are found to be statistically significant in this dataset. In the ECMWF ERA5 reanalysis, the 200-hPa geopotential field was analyzed at its resolution of 0.25 times 0.25 degrees over the 71-years interval 1950–2020. Oscillations with periods of 10 and 3.6 years are found to be statistically significant in this third dataset. The spatio-temporal patterns of the oscillations in the three datasets are quite similar. The spatial pattern of these global oscillations over the North Pacific and North Atlantic resemble the Pacific Decadal Oscillation and the LFV found in the Gulf Stream region and Labrador Sea, respectively. We speculate that such regional oscillations are synchronized over the globe, thus yielding the global oscillatory modes found herein, and discuss the potential role of the 11-year solar-irradiance cycle in this synchronization. The robustness of the two global modes, with their 10–12 and 3.4–3.6 years periodicities, also suggests potential contributions to predictability at 1–3 years horizons.

Variability and upward trend in the kinetic energy of western boundary currents over the last century: impacts from barystatic and dynamic sea level change

Global sea level reconstruction (RecSL) for 1900–2015 was used to estimate the variations in oceanic kinetic energy (OKE) and compare OKE with changes in wind patterns and wind kinetic energy (WKE); the comparison was done for each latitude and for 5 western boundary currents (WBCs). Two contributors to variability in sea level were analyzed: gravitational, rotational and deformational effects (GRD) related to changes in water masses (barystatic sea level change), and changes in the sterodynamic sea level (SDSL), associated with changes in wind, steric sea level and ocean circulation. GRD changes were responsible for latitudinal multidecadal variations with time scale of ~ 60 to 80 years, while SDSL changes were responsible for interannual and decadal variability, and together with the Greenland ice melt, to sea level acceleration since the 1960s. Regional changes near WBCs show a coherent upward trend in OKE (+ 24% ± 3 increase per century), while trends in WKE over the same regions changed widely from -11% (decrease) over the Gulf Stream region to + 28% (increase) over the Brazil Current region. Low frequency oscillations of wind and oceanic kinetic energy are correlated in some WBCs (e.g., R = 0.5 in the Kuroshio region) but not in others (e.g., R = -0.05 in the Gulf Stream region). The study suggests that several forcing mechanisms contribute to the increased OKE, they include an increased wind-stress curl over subtropical gyres, local changes in wind patterns that impact some WBCs, and large uneven warming near WBCs that increased sea level gradients and thus intensified OKE.

Air-Sea interaction over the Gulf Stream in an ensemble of HighResMIP present climate simulations

A dominant paradigm for mid-latitude air-sea interaction identifies the synoptic-scale atmospheric “noise” as the main driver for the observed ocean surface variability. While this conceptual model successfully holds over most of the mid-latitude ocean surface, its soundness over frontal zones (including western boundary currents; WBC) characterized by intense mesoscale activity, has been questioned in a number of studies suggesting a driving role for the small scale ocean dynamics (mesoscale oceanic eddies) in the modulation of air-sea interaction. In this context, climate models provide a powerful experimental device to inspect the emerging scale-dependent nature of mid-latitude air-sea interaction. This study assesses the impact of model resolution on the representation of air-sea interaction over the Gulf Stream region, in a multi-model ensemble of present-climate simulations performed using a common experimental design. Lead-lag correlation and covariance patterns between sea surface temperature (SST) and turbulent heat flux (THF) are diagnosed to identify the leading regimes of air-sea interaction in a region encompassing both the Gulf Stream system and the North Atlantic subtropical basin. Based on these statistical metrics it is found that coupled models based on “laminar” (eddy-parameterised) and eddy-permitting oceans are able to discriminate between an ocean-driven regime, dominating the region controlled by the Gulf Stream dynamics, and an atmosphere-driven regime, typical of the open ocean regions. However, the increase of model resolution leads to a better representation of SST and THF cross-covariance patterns and functional forms, and the major improvements can be largely ascribed to a refinement of the oceanic model component.

The Importance of Including Sea Surface Current when Estimating Air–Sea Turbulent Heat Fluxes and Wind Stress in the Gulf Stream Region

AbstractUsing buoy observations from 2004 to 2010 and newly released atmospheric reanalysis and satellite altimetry-derived geostrophic currents from 1993 to 2017, the quantitative contribution of daily mean surface currents to air–sea turbulent heat flux and wind stress uncertainties in the Gulf Stream (GS) region is investigated based on bulk formulas. At four buoy stations, the daily mean latent (sensible) heat flux difference between the estimates with and without surface currents range from −18 (−4) to 20 (4) W m−2, while the daily mean wind stress difference ranges from −0.04 to 0.02 N m−2. The positive values indicate higher estimates with opposite directions between surface currents and absolute winds. The transition between positive and negative differences is significantly associated with synoptic-scale weather variations. The uncertainties based on buoy observations are approximately 7% and 3% for wind stress and turbulent heat fluxes, respectively. The new reanalysis and satellite geostrophic currents confirm the uncertainties identified by buoy observations with acceptable discrepancies and provide a spatial view of the uncertainty fields. The mean geostrophic currents are aligned with the surface wind along the GS; therefore, the turbulent heat fluxes and wind stress will be “underestimated” with surface currents included. However, on both sides of the GS, the surface flow can be upwind due to possible mechanisms of eddy–mean flow interactions and recirculations, resulting in higher turbulent heat flux estimations. The wind stress and turbulent heat flux uncertainties experience significant seasonal variations and show long-term trends.