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

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.

Assessment of radiative heating errors in Tropical Atmosphere Ocean array marine air temperature measurements

We assess the radiative heating error affecting marine air temperature (MAT) measurements in the Tropical Atmosphere Ocean array. The error in historical observations is found to be ubiquitous across the array, spatially variable and approximately stationary in time. The error induces spurious warming during daytime hours, but does not affect night-time temperatures. The range encompassing the real, unknown daily- and monthly-mean values is determined using daytime and night-time mean temperatures as upper and lower limits. The uncertainty in MAT is less than or equal to 0.5 °C and 0.2 °C for 95% of daily and monthly estimates, respectively. Uncertainties impact surface turbulent heat flux estimates, with potentially significant influences on the quantification of coupled ocean-atmosphere processes.

A New Anisotropic Four-Parameter Turbulence Model for Low Prandtl Number Fluids

Due to their interesting thermal properties, liquid metals are widely studied for heat transfer applications where large heat fluxes occur. In the framework of the Reynolds-Averaged Navier–Stokes (RANS) approach, the Simple Gradient Diffusion Hypothesis (SGDH) and the Reynolds Analogy are almost universally invoked for the closure of the turbulent heat flux. Even though these assumptions can represent a reasonable compromise in a wide range of applications, they are not reliable when considering low Prandtl number fluids and/or buoyant flows. More advanced closure models for the turbulent heat flux are required to improve the accuracy of the RANS models dealing with low Prandtl number fluids. In this work, we propose an anisotropic four-parameter turbulence model. The closure of the Reynolds stress tensor and turbulent heat flux is gained through nonlinear models. Particular attention is given to the modeling of dynamical and thermal time scales. Numerical simulations of low Prandtl number fluids have been performed over the plane channel and backward-facing step configurations.

Impact of Fully Coupled Hydrology-Atmosphere Processes on Atmosphere Conditions: Investigating the Performance of the WRF-Hydro Model in the Three River Source Region on the Tibetan Plateau, China

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region.

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

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.

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

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.

RANS predictions of trailing-edge slot flows using heat-flux closures developed with CFD-driven machine learning

Accurate prediction of the wall temperature downstream of the trailing-edge slot is crucial to designing turbine blades that can withstand the harsh aerothermal environment in a modern gas turbine. Because of their computational efficiency, industry relies on low-fidelity tools like RANS for momentum and thermal field calculations, despite their known underprediction of wall temperature. In this paper, a novel framework using a branch of machine learning, geneexpression programming (GEP) [Zhao et al. 2020, J. Comp. Physics, 411:109413] is used to develop closures for the turbulent heat-flux to improve upon this underprediction. In the original use of GEP (“frozen” approach), the turbulent heat-flux from a high-fidelity database was used to evaluate the fitness of the candidate closures during the symbolic regression, however, the resulting closure had no information of the temperature field during the optimisation process. In this work, the regression process of the GEP instead incorporates RANS calculations to evaluate the fitness of the candidate closures. This allows the inclusion of the temperature field from RANS to advance the iterative regression, leading to a more integrated heat-flux closure development, and consequently more accurate and robust models. The GEP-based CFD-driven framework is demonstrated on a trailing edge slot configuration with three blowing ratios. Full a posteriori predictions from the new closures are compared to high-fidelity reference data and both conventional RANS closures and closures obtained from the “frozen” approach.