scholarly journals Probability Distribution Characteristics for Surface Air–Sea Turbulent Heat Fluxes over the Global Ocean

2012 ◽  
Vol 25 (1) ◽  
pp. 184-206 ◽  
Author(s):  
Sergey K. Gulev ◽  
Konstantin Belyaev
Keyword(s):  
Probability Density ◽  
Interannual Variability ◽  
Probability Distributions ◽  
Heat Fluxes ◽  
Western Boundary ◽  
Turbulent Heat ◽  
Boundary Current ◽  
Turbulent Heat Fluxes ◽  
The Mean ◽  
The Impact

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.

scholarly journals Averaging-Related Biases in Monthly Latent Heat Fluxes

2012 ◽  
Vol 29 (7) ◽  
pp. 974-986 ◽  
Author(s):  
Paul J. Hughes ◽  
Mark A. Bourassa ◽  
Jeremy J. Rolph ◽  
Shawn R. Smith
Keyword(s):  
Latent Heat ◽  
Classical Method ◽  
Surface Flux ◽  
Heat Fluxes ◽  
Western Boundary ◽  
State University ◽  
Boundary Current ◽  
Surface Energy Fluxes ◽  
Temporal Averaging ◽  
The Impact

Abstract Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m−2 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m−2 in western boundary current regions during winter and can exceed 40 W m−2 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial.

An Explicit Algebraic Model for Turbulent Heat Transfer in Wall-Bounded Flow With Streamline Curvature

2006 ◽  
Vol 129 (4) ◽  
pp. 425-433 ◽  
Author(s):  
B. A. Younis ◽  
B. Weigand ◽  
S. Spring
Keyword(s):  
Heat Transfer ◽  
Boundary Layer ◽  
Heat Flux ◽  
Algebraic Model ◽  
Heat Fluxes ◽  
Turbulent Heat Transfer ◽  
Turbulent Heat ◽  
Streamline Curvature ◽  
Turbulent Heat Fluxes ◽  
The Mean

Fourier’s law, which forms the basis of most engineering prediction methods for the turbulent heat fluxes, is known to fail badly in capturing the effects of streamline curvature on the rate of heat transfer in turbulent shear flows. In this paper, an alternative model, which is both algebraic and explicit in the turbulent heat fluxes and which has been formulated from tensor-representation theory, is presented, and its applicability is extended by incorporating the effects of a wall on the turbulent heat transfer processes in its vicinity. The model’s equations for flows with curvature in the plane of the mean shear are derived and calculations are performed for a heated turbulent boundary layer, which develops over a flat plate before encountering a short region of high convex curvature. The results show that the new model accurately predicts the significant reduction in the wall heat transfer rates wrought by the stabilizing-curvature effects, in sharp contrast to the conventional model predictions, which are shown to seriously underestimate the same effects. Comparisons are also made with results from a complete heat-flux transport model, which involves the solution of differential transport equations for each component of the heat-flux tensor. Downstream of the bend, where the perturbed boundary layer recovers on a flat wall, the comparisons show that the algebraic model yields indistinguishable predictions from those obtained with the differential model in regions where the mean-strain field is in rapid evolution and the turbulence processes are far removed from local equilibrium.

scholarly journals Effects of Periodic Tidal Elevations on the Air-Sea Momentum and Turbulent Heat Fluxes in the East China Sea

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 90
Author(s):  
Yuting Han ◽  
Yuxin Liu ◽  
Xingwei Jiang ◽  
Mingsen Lin ◽  
Yangang Li ◽  
...  
Keyword(s):  
East China Sea ◽  
Wind Stress ◽  
Heat Fluxes ◽  
Surface Elevation ◽  
East China ◽  
Turbulent Heat ◽  
Sensible Heat ◽  
The East China Sea ◽  
Turbulent Heat Fluxes ◽  
The Mean

Using bulk formulas, two-year platform (fastened to the seabed) hourly observations from 2016 to 2017 in the East China Sea (121.6° E, 32.4° N) are used to investigate the role of the tide-induced surface elevation in changing the fixed observational height and modifying the momentum and air-sea turbulent heat fluxes. The semidiurnal tide-dominated elevation anomalies ranging from −3.6 to 3.9 m change the fixed platform observational height. This change causes hourly differences in the wind stress and latent and sensible heat fluxes between estimates with and without considering surface elevation, with values ranging from −1.5 × 10−3 Nm−2, −10.2 Wm−2, and −3.6 Wm−2 to 2.2 × 10−3 Nm−2, 8.4 Wm−2, and 4.6 Wm−2, respectively. More significant differences occur during spring tides. The differences show weak dependence on the temperature, indicating weak seasonal variations. The mean (maximum) difference percentage relative to the mean magnitude is approximately 3.5% (7%), 1.5% (3%), and 1.5% (3%) for the wind stress and latent and sensible heat fluxes, respectively. The boundary layer stability (BLS) can convert from near-neutral conditions to stable and unstable states in response to tide-induced changes in the observational height, with a probability of occurrence of 2%. Wind anomalies play dominant roles in determining the hourly anomalies of the latent heat flux, regardless of the state of the BLS. Extreme cases, including the cold air outbreak in 2016, tropical cyclones Meranti in 2016, and Ampil in 2018, are also examined. This study will facilitate future observation-reanalysis comparisons in the studied coastal region where ocean–atmosphere-land interactive processes are significant.

Global surface energy and water cycle variability 2001-2011 from satellite data

2020 ◽  
Author(s):  
Bo Dong ◽  
Keith Haines ◽  
Chris Thomas ◽  
Chunlei Liu ◽  
Richard Allan
Keyword(s):  
Surface Energy ◽  
Interannual Variability ◽  
Satellite Data ◽  
Water Budget ◽  
Water Cycle ◽  
Water Storage ◽  
Heat Fluxes ◽  
Turbulent Heat ◽  
Water Budgets ◽  
Turbulent Heat Fluxes

<p>We derive internally consistent, monthly to interannual, energy and water budgets, with uncertainties, for all the main continents and ocean basins over 2001-2011 based principally on satellite data. An inverse model is used following the Thomas et al (2019) climatology study and the NASA energy and water cycle study (NEWS), L’Ecuyer et al. (2015), Rodell et al. (2015).<br>Input data include CERES and Cloud_CCI AATSR (radiation), FluxCOM (land turbulent heat fluxes), JOFURO3 (ocean turbulent heat fluxes), GPCP2.3 (Precipitation), GRACE (total water storage), ERA5 (atmospheric water storage), GRUNv1 (land runoff), and we compare these with alternative products to assess component uncertainties. The different components are then brought together and adjusted within respective uncertainties to achieve balanced energy and water budgets.<br>Preliminary results focus on seasonal and interannual variability over land. Seasonal modifications to the water budget over Eurasia and N America include a delay in spring runoff (and reduced evapotranspiration over Eurasia) as GRACE data indicates retention of water mass over land. Evapotranspiration adjustments to FluxCOM are strongly seasonal and also result in bringing the land seasonal energy budget closer to the DEEPC Liu et al (2015) results demonstrating the value of coupling the energy and water cycles.<br>Strong correlated interannual variability in African precipitation, runoff and GRACE derived water storage is found, and we assess the relative consistency of different data products, particularly for precipitation, where multiple datasets are available and uncertainties are large. Consistent African precipitation variability is found in the TAMSAT data, which further supports the water cycle change scheme around year 2006 over Africa. Clear ENSO signals are seen, particularly over South America in 2010 and Australia in 2010-11, with correlated variability in rainfall, runoff and water storage distributions. <br>Optimisation is sensitive to the uncertainty of each energy and water budget component expressed in their spatial and temporal error covariances.  We introduce spatial error covariance for turbulent heat fluxes between major ocean basins as well as temporal error covariances for all components expressing the expectation of time mean bias adjustments. The results show improved net surface energy flux pattern with larger heat loss over North Atlantic and Arctic Ocean and more heat uptake for other basins and an intensified water cycle, with increased precipitation, evapotranspiration and runoff and stronger ocean-land water transports. </p>

Asteroid and Comet Impact Speeds upon Mars: Significance for Panspermia and the Supply of Organics

1997 ◽  
Vol 161 ◽  
pp. 197-201 ◽  
Author(s):  
Duncan Steel
Keyword(s):  
Probability Distributions ◽  
Regions Of Interest ◽  
Significant Fraction ◽  
Organic Chemicals ◽  
Impact Speed ◽  
The Mean ◽  
The Impact

AbstractWhilst lithopanspermia depends upon massive impacts occurring at a speed above some limit, the intact delivery of organic chemicals or other volatiles to a planet requires the impact speed to be below some other limit such that a significant fraction of that material escapes destruction. Thus the two opposite ends of the impact speed distributions are the regions of interest in the bioastronomical context, whereas much modelling work on impacts delivers, or makes use of, only the mean speed. Here the probability distributions of impact speeds upon Mars are calculated for (i) the orbital distribution of known asteroids; and (ii) the expected distribution of near-parabolic cometary orbits. It is found that cometary impacts are far more likely to eject rocks from Mars (over 99 percent of the cometary impacts are at speeds above 20 km/sec, but at most 5 percent of the asteroidal impacts); paradoxically, the objects impacting at speeds low enough to make organic/volatile survival possible (the asteroids) are those which are depleted in such species.

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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