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1. Sea Spray and Its Feedback Effects: Assessing Bulk Algorithms of Air–Sea Heat Fluxes via Direct Numerical Simulations

   https://doi.org/10.1175/JPO-D-18-0193.1  

   Peng, Tianze ; Richter, David ( June 2019 , Journal of Physical Oceanography)

   Sea spray exchanging momentum, heat, and moisture is one of the major uncertainties in modeling air–sea surface heat fluxes under high wind speeds. As a result of several untested assumptions in existing models and low fidelity in the measurements, questions regarding the appropriate method for modeling the effects of spray on air–sea fluxes still exist. In this study, we implement idealized direct numerical simulations (DNS) via an Eulerian–Lagrangian model to simulate spray droplets in turbulent flows. Then, we verify the bulk spray models of Fairall et al. and Andreas et al. with the detailed physics from DNS. We find that the quality of the underlying assumptions of bulk models is sensitive to the time scales governing spray microphysics and lifetime. While both models assume that spray experiences a uniform and steady ambient condition, our results show that this assumption only works well for droplets with long thermodynamic time scales and relatively short lifetime. When the thermodynamic time scales are short, the models fail to predict the correct temperature and radius change of spray (e.g., condensation), thus spray-mediated heat fluxes, which in turn overestimates the total heat fluxes. Moreover, using our two-way coupled simulations, we find a negative feedback induced by the spray evaporation that may be missing in the bulk models, which could lead to further overestimates of the total heat flux when the spray-mediated flux is treated as an add-on to the corresponding interfacial flux. We further illustrate that the feedback effects are consistent under different flow Reynolds numbers, which suggests that the findings are relevant at practical scales.

    

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2. Machine Learning for Improving Surface-Layer-Flux Estimates

   https://doi.org/10.1007/s10546-022-00727-4  

   McCandless, Tyler ; Gagne, David John ; Kosović, Branko ; Haupt, Sue Ellen ; Yang, Bai ; Becker, Charlie ; Schreck, John ( September 2022 , Boundary-Layer Meteorology)

   Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946),$$z/L$$$z/L$. However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

    

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3. Data-Driven Characterization of Coherent Turbulence Structures in Atmospheric Boundary Layers

   Rezaie, Milad ; Momen, Mostafa ( January 2023 , https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/416487)

   Turbulence is a major source of momentum, heat, moisture, and aerosol transport in the atmosphere. Hence, it is crucial to understand and accurately characterize turbulence mechanisms in atmospheric flows. Many complex factors in the atmosphere influence the turbulence structures including stratification and background shear. However, our understanding of the interacting effects of these factors on coherent turbulence structure evolutions is still limited. In this talk, we aim to bridge this knowledge gap by using mode decomposition techniques and a wide range of large-eddy simulation (LES) data. By developing a data-driven technique, we will characterize unique features of atmospheric boundary layer (ABL) turbulence under different forcing scenarios. We will present 3D LES wind speed snapshots of different ABL flows that will be used as dynamic mode decomposition (DMD) input data. Then, the obtained modes and eigenvalues will be employed to gain insights into coherent turbulence structures in ABLs. We will explain the physical meaning of dominant modes and how each mode relates to the physical cause of turbulence structures. The dominant modes, which are selected based on the mode amplitude, contain the most important spatial and temporal characteristics of the flow. We will evaluate the accuracy of the performance of this method by reconstructing the flow field with only a small number of modes, and then calculate the mean average error between the real flow and the reconstructed flow fields. We will present different data frequencies, wind speeds, and surface heat fluxes. This allows us to elucidate the modes and determine the conditions in which the mode decomposition provides more accurate results for the ABL flows. Our findings can be used to identify the major causes of turbulence in real atmospheric flows and could provide a deeper insight into the dynamics of turbulence in ABLs. Our results will also be useful for developing reduced-order models that can rapidly predict the turbulent ABL flow fields. 

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4. Surface impacts and associated mechanisms of a moisture intrusion into the Arctic observed in mid-April 2020 during MOSAiC

   https://doi.org/10.3389/feart.2023.1147848  

   Kirbus, Benjamin ; Tiedeck, Sofie ; Camplani, Andrea ; Chylik, Jan ; Crewell, Susanne ; Dahlke, Sandro ; Ebell, Kerstin ; Gorodetskaya, Irina ; Griesche, Hannes ; Handorf, Dörthe ; et al ( April 2023 , Frontiers in Earth Science)

   Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformationsviacloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d^-1. For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm^-3(pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.

    

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5. A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

   https://doi.org/10.1175/JAMC-D-20-0232.1  

   Grachev, A. ( January 2022 , Journal of Applied Meteorology and Climatology)

   Abstract Measurements made in the Columbia River Basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modelled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin-Obukhov similarity and (ii) a modified Priestley-Taylor (P-T) algorithm with physically based ecophysiological constraints which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP 2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various timescales based on the modelled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP 2, a hybrid approach can be used for similar flux-tower sites and field campaigns. 

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