scholarly journals Sea waves impact on turbulent heat fluxes in the Barents Sea according to numerical modeling

2020 ◽  
Author(s):  
Stanislav Myslenkov ◽  
Anna Shestakova ◽  
Dmitry Chechin
Keyword(s):  
Roughness Length ◽  
Barents Sea ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Sea Waves ◽  
Cold Air ◽  
Mean Values ◽  
The Barents Sea ◽  
Turbulent Heat Fluxes ◽  
Cold Air Outbreaks

Abstract. This paper investigates the impact of sea waves on turbulent heat fluxes in the Barents Sea. The COARE algorithm, meteorological data from reanalysis and wave data from the WW3 wave model results were used. The turbulent heat fluxes were calculated using the modified Charnock parameterization for the roughness length and several parameterizations, which explicitly account for the sea waves parameters. A catalog of storm wave events and a catalog of extreme cold-air outbreaks over the Barents Sea were created and used to calculate heat fluxes during extreme events. The important role of cold-air outbreaks in the energy exchange of the Barents Sea and the atmosphere is demonstrated. A high correlation was found between the number of cold-air outbreaks days and turbulent fluxes of sensible and latent heat, as well as with the net flux of long-wave radiation averaged over the ice-free surface of the Barents Sea during a cold season. The differences in the long-term mean values of heat fluxes calculated using different parameterizations for the roughness length are small and are on average 1–3 % of the flux magnitude. Parameterizations of Taylor and Yelland and Oost et al. on average lead to an increase of the magnitude of the fluxes, and the parameterization of Drennan et al. leads to a decrease of the magnitude of the fluxes over the entire sea compared to the Charnock parameterization. The magnitude of heat fluxes and their differences during the storm wave events exceed the mean values by a factor of 2. However, the effect of explicit accounting for the wave parameters is, on average, small and multidirectional, depending on the used parameterization for the roughness length. In the climatic aspect, it can be argued that the explicit accounting for sea waves in the calculations of heat fluxes can be neglected. However, during the simultaneously observed storm waves and cold-air outbreaks, the sensitivity of the calculated values of fluxes to the used parameterizations increase along with the turbulent heat transfer increase. In some extreme cases, during storms and cold-air outbreaks, the difference reaches 700 W m−2.

scholarly journals The impact of sea waves on turbulent heat fluxes in the Barents Sea according to numerical modeling

2021 ◽  
Vol 21 (7) ◽  
pp. 5575-5595
Author(s):  
Stanislav Myslenkov ◽  
Anna Shestakova ◽  
Dmitry Chechin
Keyword(s):  
Roughness Length ◽  
Barents Sea ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Sea Waves ◽  
Cold Air ◽  
The Barents Sea ◽  
Turbulent Heat Fluxes ◽  
Storm Wave ◽  
Cold Air Outbreaks

Abstract. This paper investigates the impact of sea waves on turbulent heat fluxes in the Barents Sea. The Coupled Ocean–Atmosphere Response Experiment (COARE) algorithm, meteorological data from reanalysis and wave data from the WAVEWATCH III wave model results were used. The turbulent heat fluxes were calculated using the modified Charnock parameterization for the roughness length and several parameterizations that explicitly account for the sea wave parameters. A catalog of storm wave events and a catalog of extreme cold-air outbreaks over the Barents Sea were created and used to calculate heat fluxes during extreme events. The important role of cold-air outbreaks in the energy exchange between the Barents Sea and the atmosphere is demonstrated. A high correlation was found between the number of cold-air outbreak days and turbulent fluxes of sensible and latent heat, as well as with the net flux of longwave radiation averaged over the ice-free surface of the Barents Sea during a cold season. The differences in the long-term mean values of heat fluxes calculated using different parameterizations for the roughness length are small and are on average 1 %–3 % of the flux magnitude. The parameterizations of Taylor and Yelland (2001) and Oost et al. (2002) lead to an increase in the magnitude of the fluxes on average, and the parameterization of Drennan et al. (2003) leads to a decrease in the magnitude of the fluxes over the entire sea compared with the Charnock parameterization. The magnitude of heat fluxes and their differences during the storm wave events exceed the mean values by a factor of 2. However, the effect of explicitly accounting for the wave parameters is, on average, small and multidirectional, depending on the parameterization used for the roughness length. With respect to the climatic aspect, it can be argued that explicitly accounting for sea waves in the calculations of heat fluxes can be neglected. However, during the simultaneously observed storm wave events and cold-air outbreaks, the sensitivity of the calculated values of fluxes to the parameterizations used increases along with the turbulent heat transfer increase. In some extreme cases, during storms and cold-air outbreaks, the difference exceeds 700 W m−2.

scholarly journals Constraining turbulent heat flux parameterization over a temperate maritime glacier in New Zealand

2013 ◽  
Vol 54 (63) ◽  
pp. 41-51 ◽  
Author(s):  
J.P. Conway ◽  
N.J. Cullen
Keyword(s):  
New Zealand ◽  
Wind Speed ◽  
Mass Balance ◽  
Roughness Length ◽  
Heat Fluxes ◽  
Large Temperature ◽  
Glacier Mass Balance ◽  
Turbulent Heat ◽  
Neutral Atmosphere ◽  
Turbulent Heat Fluxes

AbstractThe turbulent sensible and latent heat fluxes are important components of the surface energy balance over glaciers in the Southern Alps of New Zealand, contributing over half the energy available for ablation during large melt events. To calculate these terms confidently in glacier mass-balance models it is essential to use appropriate parameterizations for surface roughness and atmospheric stability. Eddy covariance measurements at Brewster Glacier were obtained over an ice surface to help facilitate an assessment of the calculation of the turbulent heat fluxes. The roughness length for momentum was found to be 3.6 x 10−3m, while the roughness lengths for temperature and humidity were two orders of magnitude smaller, in agreement with surface renewal theory. A Monte Carlo approach was used to assess the uncertainty in turbulent heat fluxes calculated using the bulk aerodynamic method. It was found that input-data and roughness-length uncertainty could not explain underestimates of observed sensible heat fluxes during periods with low wind speed and large temperature gradients. During these periods a katabatic wind speed maximum alters the formulation of the turbulent exchange coefficient to that typically observed in a neutral atmosphere and this has implications for glacier mass-balance sensitivity.

scholarly journals A Climatology of Cold Air Outbreaks and Their Impact on Air–Sea Heat Fluxes in the High-Latitude South Pacific

2014 ◽  
Vol 28 (1) ◽  
pp. 342-364 ◽  
Author(s):  
Lukas Papritz ◽  
Stephan Pfahl ◽  
Harald Sodemann ◽  
Heini Wernli
Keyword(s):  
Sea Ice ◽  
Dominant Role ◽  
Storm Track ◽  
South Pacific ◽  
Heat Fluxes ◽  
Specific Humidity ◽  
Extratropical Cyclones ◽  
Turbulent Heat ◽  
Cold Air ◽  
Cold Air Outbreaks

Abstract A climatology of cold air outbreaks (CAOs) in the high latitudes of the South Pacific and an analysis of the dynamical mechanisms leading to their formation are presented. Two major and distinct regions with frequent CAOs from autumn to spring are identified: one in the Ross Sea and another in the Amundsen and Bellingshausen Seas. Using an objective method to attribute CAOs to extratropical cyclones, it is shown that about 80% of the CAOs occur in association with the cyclonic flow induced by the passage of extratropical cyclones. Based on kinematic backward trajectories it is quantified that more than 40% of the air masses leading to CAOs originate from Antarctica and descend substantially, with the Ross Ice Shelf corridor as the major pathway. CAO trajectories descending from Antarctica differ from those originating over sea ice by a much lower specific humidity, stronger diabatic cooling, and much more intense adiabatic warming, while potential vorticity evolves similarly in both categories. In winter, CAOs are the major contributor to the net turbulent heat flux off the sea ice edge and CAO frequency strongly determines its interannual variation. Wintertime variations of the frequency of extratropical cyclones are strongly imprinted on the frequency of CAOs and the net turbulent heat and freshwater fluxes. In particular, much of the precipitation associated with the passage of extratropical cyclones is compensated by intense evaporation in cyclone-induced CAOs. This highlights the dominant role of the extratropical storm track in determining the variability of the buoyancy flux forcing of the Southern Ocean.

scholarly journals Predictors and prediction skill for marine cold air outbreaks over the Barents Sea

2021 ◽  
Author(s):  
Iuliia Polkova ◽  
Hilla Afargan‐Gerstman ◽  
Daniela I.V. Domeisen ◽  
Martin P. King ◽  
Paolo Ruggieri ◽  
...  
Keyword(s):  
Barents Sea ◽  
Prediction Skill ◽  
Cold Air ◽  
The Barents Sea ◽  
Cold Air Outbreaks

scholarly journals Stratospheric influence on marine cold air outbreaks in the Barents Sea

2020 ◽  
Author(s):  
Hilla Afargan-Gerstman ◽  
Iuliia Polkova ◽  
Lukas Papritz ◽  
Paolo Ruggieri ◽  
Martin P. King ◽  
...  
Keyword(s):  
Barents Sea ◽  
Polar Vortex ◽  
The Arctic ◽  
Stratospheric Polar Vortex ◽  
Cold Air ◽  
Norwegian Sea ◽  
The Barents Sea ◽  
Strong Surface ◽  
Dipole Pattern ◽  
Cold Air Outbreaks

Abstract. Marine cold air outbreaks (MCAOs) in the Arctic are associated with a range of severe weather phenomena, such as polar lows, strong surface winds and intense cooling of the ocean surface. While MCAO frequency has been linked to the strength of the stratospheric polar vortex, a connection to the occurrence of extreme stratospheric events, known as sudden stratospheric warmings (SSWs), has dominantly been investigated with respect to cold extremes over land. Here, the influence of SSW events on MCAOs in the Barents Sea is studied using observational and reanalysis datasets. Overall, more than a half of SSW events lead to more frequent MCAOs in the Barents Sea. SSW events with an enhanced MCAO response in the Barents Sea are associated with a ridge over Greenland and a trough over Scandinavia, leading to an anomalous dipole pattern of 500-hPa geopotential height and strong northerly flow over the Norwegian Sea. As SSW events tend to have a long-term influence on surface weather, these results can shed light on the predictability of MCAOs in the Arctic for winters with SSW events.

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

1999 ◽  
Vol 11 (1) ◽  
pp. 93-99 ◽  
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.

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

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

<p>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’ 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.</p><p>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. </p><p>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.</p>

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

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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