scholarly journals Estimation of turbulent heat flux over leads using satellite thermal images

2019 ◽  
Vol 13 (6) ◽  
pp. 1565-1582 ◽  
Author(s):  
Meng Qu ◽  
Xiaoping Pang ◽  
Xi Zhao ◽  
Jinlun Zhang ◽  
Qing Ji ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Surface Temperature ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Landsat 8 ◽  
Turbulent Heat ◽  
Thermal Images ◽  
Modis Data ◽  
Ice Leads

Abstract. Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as prime windows for heat exchange between the atmosphere and the ocean, especially in the winter. Lead geometry and distribution in the Arctic have been studied using optical and microwave remote sensing data, but turbulent heat flux over leads has only been measured on-site during a few special expeditions. In this study, we derive turbulent heat flux through leads at different scales using a combination of surface temperature and lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. First, ice surface temperature (IST) was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images using a split-window algorithm; then, lead pixels were segmented from colder ice. Heat flux over leads was estimated using two empirical models: bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that even though the lead area from MODIS is a little larger, the length of leads is underestimated by 72.9 % in MODIS data compared to TIRS data due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 56.70 % larger than that from MODIS data. When the fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 32.34 % higher than that from bulk formulae. In both cases, small leads accounted for more than a quarter of total heat flux over leads, mainly due to the large area, though the heat flux estimated using the fetch-limited model is 41.39 % larger. A greater contribution from small leads can be expected with larger air–ocean temperature differences and stronger winds.

scholarly journals Estimation of turbulent heat flux over leads using satellite thermal images

2019 ◽  
Author(s):  
Meng Qu ◽  
Xiaoping Pang ◽  
Xi Zhao ◽  
Jinlun Zhang ◽  
Qing Ji ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Heat Flux ◽  
Microwave Remote Sensing ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Landsat 8 ◽  
Turbulent Heat ◽  
Thermal Images ◽  
Modis Data ◽  
Ice Leads

Abstract. Sea ice leads are an important feature in pack ice in the Arctic. Even covered by thin ice, leads can still serve as the prime window for heat exchange between the atmosphere and the ocean, especially in winter seasons. Lead geometry and distribution in the Arctic have been studied in previous studies using optical or microwave remote sensing data. But turbulent heat flux over lead area has only been measured on site during a few special expeditions. In this study, we derive turbulent 10 heat flux through leads at different scale using a combination of lead distribution from remote sensing images and meteorological parameters from a reanalysis dataset. Firstly, ice surface temperature was calculated from Landsat-8 Thermal Infrared Sensor (TIRS) and MODIS thermal images using split-window algorithm at 30 m and 1 km scales, respectively, then lead pixels are segmented from colder ice. Heat flux over lead area is calculated using two empirical models, including bulk aerodynamic formulae and a fetch-limited model with lead width from Landsat-8. Results show that, even though lead area 15 from MODIS is generally a little higher, the length of leads is underestimated by 72.9 % in MODIS data compared to that from TIRS due to the inability to resolve small leads. Heat flux estimated from Landsat-8 TIRS data using bulk formulae is 42.33 % larger than that from MODIS data. When fetch-limited model was applied, turbulent heat flux calculated from TIRS data is 31.87 % higher than that from bulk formulae. In both cases, small leads account for more than a quarter of total heat flux over lead, mainly due to its large area, though the heat flux estimated using fetch-limited model is 42.26 % larger. More contribution 20 from small leads can be expected at larger air-ocean temperature difference and stronger winds.

scholarly journals Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

2021 ◽  
Author(s):  
Zhixiang Yin ◽  
Xiaodong Li ◽  
Yong Ge ◽  
Cheng Shang ◽  
Xinyan Li ◽  
...  
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Important Variable ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Turbulent Heat ◽  
Modis Imagery ◽  
Mixed Pixels ◽  
Thermal Infrared Imagery

Abstract. Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic. Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine spatial resolution SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.

scholarly journals Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

2021 ◽  
Vol 15 (6) ◽  
pp. 2835-2856
Author(s):  
Zhixiang Yin ◽  
Xiaodong Li ◽  
Yong Ge ◽  
Cheng Shang ◽  
Xinyan Li ◽  
...  
Keyword(s):  
Heat Flux ◽  
Spatial Resolution ◽  
Arctic Region ◽  
Open Water ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Turbulent Heat ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Thermal Infrared Imagery ◽  
Ice Surface Temperature

Abstract. The turbulent heat flux (THF) over leads is an important parameter for climate change monitoring in the Arctic region. THF over leads is often calculated from satellite-derived ice surface temperature (IST) products, in which mixed pixels containing both ice and open water along lead boundaries reduce the accuracy of calculated THF. To address this problem, this paper proposes a deep residual convolutional neural network (CNN)-based framework to estimate THF over leads at the subpixel scale (DeepSTHF) based on remotely sensed images. The proposed DeepSTHF provides an IST image and the corresponding lead map with a finer spatial resolution than the input IST image so that the subpixel-scale THF can be estimated from them. The proposed approach is verified using simulated and real Moderate Resolution Imaging Spectroradiometer images and compared with the conventional cubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine-spatial-resolution IST images and lead maps, thereby providing more accurate and reliable THF over leads.

scholarly journals Hydrography, transport and mixing of the West Spitsbergen Current: the Svalbard Branch in summer 2015

Ocean Science ◽  
2018 ◽  
Vol 14 (6) ◽  
pp. 1603-1618 ◽  
Author(s):  
Eivind Kolås ◽  
Ilker Fer
Keyword(s):  
Heat Flux ◽  
Heat Loss ◽  
Large Fraction ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Turbulent Heat ◽  
Fram Strait ◽  
The West ◽  
Transport And Mixing ◽  
West Spitsbergen

Abstract. Measurements of ocean currents, stratification and microstructure were made in August 2015, northwest of Svalbard, downstream of the Atlantic inflow in Fram Strait in the Arctic Ocean. Observations in three sections are used to characterize the evolution of the West Spitsbergen Current (WSC) along a 170 km downstream distance. Two alternative calculations imply 1.5 to 2 Sv (1 Sv = 106 m3 s−1) is routed to recirculation and Yermak branch in Fram Strait, whereas 0.6 to 1.3 Sv is carried by the Svalbard branch. The WSC cools at a rate of 0.20 ∘C per 100 km, with associated bulk heat loss per along-path meter of (1.1-1.4)×107 W m−1, corresponding to a surface heat loss of 380–550 W m−2. The measured turbulent heat flux is too small to account for this cooling rate. Estimates using a plausible range of parameters suggest that the contribution of diffusion by eddies could be limited to one half of the observed heat loss. In addition to shear-driven mixing beneath the WSC core, we observe energetic convective mixing of an unstable bottom boundary layer on the slope, driven by Ekman advection of buoyant water across the slope. The estimated lateral buoyancy flux is O(10−8) W kg−1, sufficient to maintain a large fraction of the observed dissipation rates, and corresponds to a heat flux of approximately 40 W m−2. We conclude that – at least in summer – convectively driven bottom mixing followed by the detachment of the mixed fluid and its transfer into the ocean interior can lead to substantial cooling and freshening of the WSC.

scholarly journals Sensitivity of Northern Hemisphere climate to ice–ocean interface heat flux parameterizations

2021 ◽  
Vol 14 (8) ◽  
pp. 4891-4908
Author(s):  
Xiaoxu Shi ◽  
Dirk Notz ◽  
Jiping Liu ◽  
Hu Yang ◽  
Gerrit Lohmann
Keyword(s):  
Heat Flux ◽  
Heat Exchange ◽  
Sea Ice ◽  
Temperature Difference ◽  
Climate Model ◽  
Turbulent Heat Flux ◽  
The Arctic ◽  
Ice Thickness ◽  
Turbulent Heat ◽  
Sea Ice Thickness

Abstract. We investigate the impact of three different parameterizations of ice–ocean heat exchange on modeled sea ice thickness, sea ice concentration, and water masses. These three parameterizations are (1) an ice bath assumption with the ocean temperature fixed at the freezing temperature; (2) a two-equation turbulent heat flux parameterization with ice–ocean heat exchange depending linearly on the temperature difference between the underlying ocean and the ice–ocean interface, whose temperature is kept at the freezing point of the seawater; and (3) a three-equation turbulent heat flux approach in which the ice–ocean heat flux depends on the temperature difference between the underlying ocean and the ice–ocean interface, whose temperature is calculated based on the local salinity set by the ice ablation rate. Based on model simulations with the stand-alone sea ice model CICE, the ice–ocean model MPIOM, and the climate model COSMOS, we find that compared to the most complex parameterization (3), the approaches (1) and (2) result in thinner Arctic sea ice, cooler water beneath high-concentration ice and warmer water towards the ice edge, and a lower salinity in the Arctic Ocean mixed layer. In particular, parameterization (1) results in the smallest sea ice thickness among the three parameterizations, as in this parameterization all potential heat in the underlying ocean is used for the melting of the sea ice above. For the same reason, the upper ocean layer of the central Arctic is cooler when using parameterization (1) compared to (2) and (3). Finally, in the fully coupled climate model COSMOS, parameterizations (1) and (2) result in a fairly similar oceanic or atmospheric circulation. In contrast, the most realistic parameterization (3) leads to an enhanced Atlantic meridional overturning circulation (AMOC), a more positive North Atlantic Oscillation (NAO) mode and a weakened Aleutian Low.

scholarly journals Sensitivity of Northern Hemisphere climate to ice-ocean interface heat flux parameterizations

2020 ◽  
Author(s):  
Xiaoxu Shi ◽  
Dirk Notz ◽  
Jiping Liu ◽  
Hu Yang ◽  
Gerrit Lohmann
Keyword(s):  
Heat Flux ◽  
Heat Exchange ◽  
Sea Ice ◽  
Mixed Layer ◽  
Climate Model ◽  
Turbulent Heat Flux ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Flux Parameterization

Abstract. We investigate the impact of three different parameterizations of ice-ocean heat exchange on modeled ice thickness, ice concentration, and water masses. These three parameterizations are (1) an ice-bath assumption with the ocean temperature fixed at the freezing temperature, (2) a turbulent heat-flux parameterization with ice-ocean heat exchange depending linearly on the temperature difference between the mixed layer and the ice-ocean interface, and (3) a similar turbulent heat-flux parameterization as (2) but with the temperature at the ice-ocean interface depending on ice-ablation rate. Based on model simulations with the standalone sea-ice model CICE, the ice-ocean model MPIOM and the climate model COSMOS, we find that (3) leads (in comparison to the other two parameterizations) to a thicker modeled sea ice, warmer water beneath high-concentration ice and cooler water towards the ice edge, and higher salinity in the Arctic Ocean mixed layer. Finally, in the fully coupled climate model COSMOS, the most realistic parameterization leads to an enhanced Atlantic meridional overturning circulation (AMOC), a more positive North Atlantic Oscillation (NAO) mode and a weakened Aleutian Low.

Augmentations to the Noah Model Physics for Application to the Yellow River Source Area. Part II: Turbulent Heat Fluxes and Soil Heat Transport

2015 ◽  
Vol 16 (6) ◽  
pp. 2677-2694 ◽  
Author(s):  
Donghai Zheng ◽  
Rogier van der Velde ◽  
Zhongbo Su ◽  
Xin Wang ◽  
Jun Wen ◽  
...  
Keyword(s):  
Heat Flux ◽  
Organic Matter ◽  
Surface Temperature ◽  
Heat Transport ◽  
Yellow River ◽  
Turbulent Heat Flux ◽  
Heat Fluxes ◽  
The Yellow River ◽  
Turbulent Heat ◽  
Turbulent Heat Fluxes

Abstract This is the second part of a study on the assessment of the Noah land surface model (LSM) in simulating surface water and energy budgets in the high-elevation source region of the Yellow River. Here, there is a focus on turbulent heat fluxes and heat transport through the soil column during the monsoon season, whereas the first part of this study deals with the soil water flow. Four augmentations are studied for mitigating the overestimation of turbulent heat flux and underestimation of soil temperature measurements: 1) the muting effect of vegetation on the thermal heat conductivity is removed from the transport of heat from the first to the second soil layer, 2) the exponential decay factor imposed on is calculated using the ratio of the leaf area index (LAI) over the green vegetation fraction (GVF), 3) Zilitinkevich’s empirical coefficient for turbulent heat transport is computed as a function of the momentum roughness length , and 4) the impact of organic matter is considered in the parameterization of the thermal heat properties. Although usage of organic matter for calculating improves the correspondence between the estimates and laboratory measurements of heat conductivities, it is shown to have a relatively small impact on the Noah LSM performance even for large organic matter contents. In contrast, the removal of the muting effect of vegetation on and the parameterization of greatly enhances the soil temperature profile simulations, whereas turbulent heat flux and surface temperature computations mostly benefit from the modified formulation. Further, the nighttime surface temperature overestimation is resolved from a coupled land–atmosphere perspective.

scholarly journals The bulk parameterizations of turbulent air-sea fluxes in NEMO4: the origin of Sea Surface Temperature differences in a global model study

2022 ◽  
Author(s):  
Giulia Bonino ◽  
Doroteaciro Iovino ◽  
Laurent Brodeau ◽  
Simona Masina
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Skin Temperature ◽  
Wind Stress ◽  
Ocean Model ◽  
Turbulent Heat Flux ◽  
Global Model ◽  
Sea Surface ◽  
Turbulent Heat

Abstract. Wind stress and turbulent heat fluxes are the major driving forces which modify the ocean dynamics and thermodynamics. In the NEMO ocean general circulation model, these turbulent air-sea fluxes (TASFs), which are components of the ocean model boundary conditions, can critically impact the simulated ocean characteristics. This paper investigates how the different bulk parametrizations to calculated turbulent air-sea fluxes in the NEMO4 (revision 12957) drives substantial differences in sea surface temperature (SST). Specifically, we study the contribution of different aspects and assumptions of the bulk parametrizations in driving the SST differences in NEMO global model configuration at ¼ degree of horizontal resolution. These include the use of the skin temperature instead of the bulk SST in the computation of turbulent heat flux components, the estimation of wind stress and the estimation of turbulent heat flux components which vary in each parametrization due to the different computation of the bulk transfer coefficients. The analysis of a set of short-term sensitivity experiments, where the only experimental change is related to one of the aspects of the bulk parametrizations, shows that parametrization-related SST differences are primarily sensitive to the wind stress differences across parametrizations and to the implementation of skin temperature in the computation of turbulent heat flux components. Moreover, in order to highlight the role of SST-turbulent heat flux negative feedback at play in ocean simulations, we compare the TASFs differences obtained using NEMO ocean model with the estimations from Brodeau et al. (2017), who compared the different bulk parametrizations using prescribed SST. Our estimations of turbulent heat flux differences between bulk parametrizations is weaker with respect to Brodeau et al. (2017) differences estimations.

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