An Assessment of the Surface Turbulent Heat Fluxes from the NCEP–NCAR Reanalysis over the Western Boundary Currents

Abstract To analyze the probability density distributions of surface turbulent heat fluxes, the authors apply the two-parametric modified Fisher–Tippett (MFT) distribution to the sensible and latent turbulent heat fluxes recomputed from 6-hourly NCEP–NCAR reanalysis state variables for the period from 1948 to 2008. They derived the mean climatology and seasonal cycle of the location and scale parameters of the MFT distribution. Analysis of the parameters of probability distributions identified the areas where similar surface turbulent fluxes are determined by the very different shape of probability density functions. Estimated extreme turbulent heat fluxes amount to 1500–2000 W m−2 (for the 99th percentile) and can exceed 2000 W m−2 for higher percentiles in the subpolar latitudes and western boundary current regions. Analysis of linear trends and interannual variability in the mean and extreme fluxes shows that the strongest trends in extreme fluxes (more than 15 W m−2 decade−1) in the western boundary current regions are associated with the changes in the shape of distribution. In many regions changes in extreme fluxes may be different from those for the mean fluxes at interannual and decadal time scales. The correlation between interannual variability of the mean and extreme fluxes is relatively low in the tropics, the Southern Ocean, and the Kuroshio Extension region. Analysis of probability distributions in turbulent fluxes has also been used in assessing the impact of sampling errors in the Voluntary Observing Ship (VOS)-based surface flux climatologies, allowed for the estimation of the impact of sampling in extreme fluxes. Although sampling does not have a visible systematic effect on mean fluxes, sampling uncertainties result in the underestimation of extreme flux values exceeding 100 W m−2 in poorly sampled regions.

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What Drives Upper-Ocean Temperature Variability in Coupled Climate Models and Observations?

Journal of Climate ◽

10.1175/jcli-d-19-0295.1 ◽

2020 ◽

Vol 33 (2) ◽

pp. 577-596 ◽

Cited By ~ 4

Author(s):

R. Justin Small ◽

Frank O. Bryan ◽

Stuart P. Bishop ◽

Sarah Larson ◽

Robert A. Tomas

Keyword(s):

Heat Flux ◽

High Resolution ◽

Climate Model ◽

Heat Content ◽

Heat Fluxes ◽

Ocean Dynamics ◽

Upper Ocean ◽

Western Boundary ◽

Western Boundary Currents ◽

Boundary Currents

AbstractA key question in climate modeling is to what extent sea surface temperature and upper-ocean heat content are driven passively by air–sea heat fluxes, as opposed to forcing by ocean dynamics. This paper investigates the question using a climate model at different resolutions, and observations, for monthly variability. At the grid scale in a high-resolution climate model with resolved mesoscale ocean eddies, ocean dynamics (i.e., ocean heat flux convergence) dominates upper 50 m heat content variability over most of the globe. For deeper depths of integration to 400 m, the heat content variability at the grid scale is almost totally controlled by ocean heat flux convergence. However, a strong dependence on spatial scale is found—for the upper 50 m of ocean, after smoothing the data to around 7°, air–sea heat fluxes, augmented by Ekman heat transports, dominate. For deeper depths of integration to 400 m, the transition scale becomes larger and is above 10° in western boundary currents. Comparison of climate model results with observations show that the small-scale influence of ocean intrinsic variability is well captured by the high-resolution model but is missing from a comparable model with parameterized ocean-eddy effects. In the deep tropics, ocean dynamics dominates in all cases and all scales. In the subtropical gyres at large scales, air–sea heat fluxes play the biggest role. In the midlatitudes, at large scales >10°, atmosphere-driven air–sea heat fluxes and Ekman heat transport variability are the dominant processes except in the western boundary currents for the 400 m heat content.

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Radiating Instability of a Meridional Boundary Current

Journal of Physical Oceanography ◽

10.1175/2008jpo3853.1 ◽

2008 ◽

Vol 38 (10) ◽

pp. 2294-2307 ◽

Cited By ~ 26

Author(s):

Hristina G. Hristova ◽

Joseph Pedlosky ◽

Michael A. Spall

Keyword(s):

Western Boundary ◽

Far Field ◽

Boundary Current ◽

Important Conclusion ◽

Western Boundary Currents ◽

Horizontal Structure ◽

Boundary Currents ◽

Zonal Jets ◽

Eastern Boundary ◽

Eastern Boundary Current

Abstract A linear stability analysis of a meridional boundary current on the beta plane is presented. The boundary current is idealized as a constant-speed meridional jet adjacent to a semi-infinite motionless far field. The far-field region can be situated either on the eastern or the western side of the jet, representing a western or an eastern boundary current, respectively. It is found that when unstable, the meridional boundary current generates temporally growing propagating waves that transport energy away from the locally unstable region toward the neutral far field. This is the so-called radiating instability and is found in both barotropic and two-layer baroclinic configurations. A second but important conclusion concerns the differences in the stability properties of eastern and western boundary currents. An eastern boundary current supports a greater number of radiating modes over a wider range of meridional wavenumbers. It generates waves with amplitude envelopes that decay slowly with distance from the current. The radiating waves tend to have an asymmetrical horizontal structure—they are much longer in the zonal direction than in the meridional, a consequence of which is that unstable eastern boundary currents, unlike western boundary currents, have the potential to act as a source of zonal jets for the interior of the ocean.

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Pacific low-latitude western boundary currents and the Indonesian throughflow

Journal of Geophysical Research Atmospheres ◽

10.1029/96jc01204 ◽

1996 ◽

Vol 101 (C5) ◽

pp. 12209-12216 ◽

Cited By ~ 109

Author(s):

Roger Lukas ◽

Toshio Yamagata ◽

Julian P. McCreary

Keyword(s):

Western Boundary ◽

Indonesian Throughflow ◽

Low Latitude ◽

Western Boundary Currents ◽

Boundary Currents

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From the western boundary currents to the Pacific Equatorial Undercurrent: Modeled pathways and water mass evolutions

Journal of Geophysical Research Atmospheres ◽

10.1029/2011jc007477 ◽

2011 ◽

Vol 116 (C12) ◽

Cited By ~ 54

Author(s):

Mélanie Grenier ◽

Sophie Cravatte ◽

Bruno Blanke ◽

Christophe Menkes ◽

Ariane Koch-Larrouy ◽

...

Keyword(s):

Water Mass ◽

Western Boundary ◽

Western Boundary Currents ◽

Equatorial Undercurrent ◽

Boundary Currents ◽

The Pacific

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Estimation of turbulent heat fluxes and exchange coefficients for heat at Dumont d'Urville, East Antarctica

Antarctic Science ◽

10.1017/s0954102099000127 ◽

1999 ◽

Vol 11 (1) ◽

pp. 93-99 ◽

Cited By ~ 3

Author(s):

S. Argentini ◽

G. Mastrantonio ◽

A. Viola

Keyword(s):

Boundary Layer ◽

Heat Fluxes ◽

East Antarctica ◽

Turbulent Heat ◽

Convective Boundary ◽

Doppler Sodar ◽

Turbulent Heat Fluxes ◽

Unstable Boundary Layer ◽

Sodar Measurements ◽

The Antarctic

Simultaneous acoustic Doppler sodar and tethersonde measurements were used to study some of the characteristics of the unstable boundary layer at Dumont d'Urville, Adélie Land, East Antarctica during the summer 1993–94. A description of the convective boundary layer and its behaviour in connection with the wind regime is given along with the frequency distribution of free convection episodes. The surface heat flux has been evaluated using the vertical velocity variance derived from sodar measurements. The turbulent exchange coefficients, estimated by coupling sodar and tethered balloon measurements, are in strong agreement with those present in literature for the Antarctic regions.

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Searching for new physics: Using explainable AI to understand deep learned parameterizations of turbulent heat fluxes

10.5194/egusphere-egu21-3516 ◽

2021 ◽

Author(s):

Andrew Bennett ◽

Bart Nijssen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Coupled System ◽

Hydrologic Model ◽

Heat Fluxes ◽

Turbulent Heat ◽

Phase Lag ◽

Geophysical Research ◽

Turbulent Heat Fluxes

Machine learning (ML), and particularly deep learning (DL), for geophysical research has shown dramatic successes in recent years. However, these models are primarily geared towards better predictive capabilities, and are generally treated as black box models, limiting researchers&#8217; ability to interpret and understand how these predictions are made. As these models are incorporated into larger models and pushed to be used in more areas it will be important to build methods that allow us to reason about how these models operate. This will have implications for scientific discovery that will ensure that these models are robust and reliable for their respective applications. Recent work in explainable artificial intelligence (XAI) has been used to interpret and explain the behavior of machine learned models.Here, we apply new tools from the field of XAI to provide physical interpretations of a system that couples a deep-learning based parameterization for turbulent heat fluxes to a process based hydrologic model. To develop this coupling we have trained a neural network to predict turbulent heat fluxes using FluxNet data from a large number of hydroclimatically diverse sites. This neural network is coupled to the SUMMA hydrologic model, taking imodel derived states as additional inputs to improve predictions. We have shown that this coupled system provides highly accurate simulations of turbulent heat fluxes at 30 minute timesteps, accurately predicts the long-term observed water balance, and reproduces other signatures such as the phase lag with shortwave radiation. Because of these features, it seems this coupled system is learning physically accurate relationships between inputs and outputs.&#160;We probe the relative importance of which input features are used to make predictions during wet and dry conditions to better understand what the neural network has learned. Further, we conduct controlled experiments to understand how the neural networks are able to learn to regionalize between different hydroclimates. By understanding how these neural networks make their predictions as well as how they learn to make predictions we can gain scientific insights and use them to further improve our models of the Earth system.

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The Effect of Soil on the Summertime Surface Energy Budget of a Humid Subarctic Tundra in Northern Quebec, Canada

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0243.1 ◽

2021 ◽

Vol 22 (10) ◽

pp. 2547-2564

Author(s):

Georg Lackner ◽

Daniel F. Nadeau ◽

Florent Domine ◽

Annie-Claude Parent ◽

Gonzalo Leonardini ◽

...

Keyword(s):

Heat Flux ◽

Latent Heat ◽

Latent Heat Flux ◽

Land Surface ◽

Sensible Heat Flux ◽

Arctic Region ◽

Heat Fluxes ◽

Surface Energy Budget ◽

Turbulent Heat ◽

Turbulent Heat Fluxes

AbstractRising temperatures in the southern Arctic region are leading to shrub expansion and permafrost degradation. The objective of this study is to analyze the surface energy budget (SEB) of a subarctic shrub tundra site that is subject to these changes, on the east coast of Hudson Bay in eastern Canada. We focus on the turbulent heat fluxes, as they have been poorly quantified in this region. This study is based on data collected by a flux tower using the eddy covariance approach and focused on snow-free periods. Furthermore, we compare our results with those from six Fluxnet sites in the Arctic region and analyze the performance of two land surface models, SVS and ISBA, in simulating soil moisture and turbulent heat fluxes. We found that 23% of the net radiation was converted into latent heat flux at our site, 35% was used for sensible heat flux, and about 15% for ground heat flux. These results were surprising considering our site was by far the wettest site among those studied, and most of the net radiation at the other Arctic sites was consumed by the latent heat flux. We attribute this behavior to the high hydraulic conductivity of the soil (littoral and intertidal sediments), typical of what is found in the coastal regions of the eastern Canadian Arctic. Land surface models overestimated the surface water content of those soils but were able to accurately simulate the turbulent heat flux, particularly the sensible heat flux and, to a lesser extent, the latent heat flux.

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