Low-Level Cloud Response to the Gulf Stream Front in Winter Using CALIPSO*

2014 ◽  
Vol 27 (12) ◽  
pp. 4421-4432 ◽  
Author(s):  
Jing-Wu Liu ◽  
Shang-Ping Xie ◽  
Joel R. Norris ◽  
Su-Ping Zhang
Keyword(s):  
Boundary Layer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Gulf Stream ◽  
Satellite Observations ◽  
Sea Surface ◽  
Warm Water ◽  
Low Level ◽  
Temperature Front ◽  
The Cross

Abstract A sharp sea surface temperature front develops between the warm water of the Gulf Stream and cold continental shelf water in boreal winter. This front has a substantial impact on the marine boundary layer. The present study analyzes and synthesizes satellite observations and reanalysis data to examine how the sea surface temperature front influences the three-dimensional structure of low-level clouds. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite captures a sharp low-level cloud transition across the Gulf Stream front, a structure frequently observed under the northerly condition. Low-level cloud top (<4 km) increases by about 500 m from the cold to the warm flank of the front. The sea surface temperature front induces a secondary low-level circulation through sea level pressure adjustment with ascending motion over the warm water and descending motion over cold water. The secondary circulation further contributes to the cross-frontal transition of low-level clouds. Composite analysis shows that surface meridional advection over the front plays an important role in the development of the marine atmospheric boundary layer and low-level clouds. Under cold northerly advection over the Gulf Stream front, strong near-surface instability leads to a well-mixed boundary layer over the Gulf Stream, causing southward deepening of low-level clouds across the sea surface temperature front. Moreover, the front affects the freezing level by transferring heat to the atmosphere and therefore influences the cross-frontal variation of the cloud phase.

scholarly journals Coupling Ocean Currents and Waves with Wind Stress over the Gulf Stream

2019 ◽  
Vol 11 (12) ◽  
pp. 1476 ◽  
Author(s):  
Qi Shi ◽  
Mark A. Bourassa
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Latent Heat Flux ◽  
Wind Stress ◽  
Gulf Stream ◽  
Satellite Observations ◽  
Sea Surface ◽  
Current Stress ◽  
Induced Changes

This study provides the first detailed analysis of oceanic and atmospheric responses to the current-stress, wave-stress, and wave-current-stress interactions around the Gulf Stream using a high-resolution three-way coupled regional modeling system. In general, our results highlight the substantial impact of coupling currents and/or waves with wind stress on the air–sea fluxes over the Gulf Stream. The stress and the curl of the stress are crucial to mixed-layer energy budgets and sea surface temperature. In the wave-current-stress coupled experiment, wind stress increased by 15% over the Gulf Stream. Alternating positive and negative bands of changes of Ekman-related vertical velocity appeared in response to the changes of the wind stress curl along the Gulf Stream, with magnitudes exceeding 0.3 m/day (the 95th percentile). The response of wind stress and its curl to the wave-current-stress coupling was not a linear combination of responses to the wave-stress coupling and the current-stress coupling because the ocean and wave induced changes in the atmosphere showed substantial feedback on the ocean. Changes of a latent heat flux in excess of 20 W/m2 and a sensible heat flux in excess of 5 W/m2 were found over the Gulf Stream in all coupled experiments. Sensitivity tests show that sea surface temperature (SST) induced difference of air–sea humidity is a major contributor to latent heat flux (LHF) change. Validation is challenging because most satellite observations lack the spatial resolution to resolve the current-induced changes in wind stress curls and heat fluxes. Scatterometer observations can be used to examine the changes in wind stress across the Gulf Stream. The conversion of model data to equivalent neutral winds is highly dependent on the physics considered in the air–sea turbulent fluxes, as well as air–sea temperature differences. This sensitivity is shown to be large enough that satellite observations of winds can be used to test the flux parameterizations in coupled models.

scholarly journals Midlatitude Wind Stress–Sea Surface Temperature Coupling in the Vicinity of Oceanic Fronts

2007 ◽  
Vol 20 (15) ◽  
pp. 3785-3801 ◽  
Author(s):  
Michael A. Spall
Keyword(s):  
Boundary Layer ◽  
Pressure Gradient ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Turbulent Mixing ◽  
Sea Surface ◽  
Strong Positive Correlation ◽  
Surface Winds ◽  
Near Surface ◽  
The Cross

Abstract The influences of strong gradients in sea surface temperature on near-surface cross-front winds are explored in a series of idealized numerical modeling experiments. The atmospheric model is the Naval Research Laboratory Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model, which is fully coupled to the Regional Ocean Modeling System (ROMS) ocean model. A series of idealized, two-dimensional model calculations is carried out in which the wind blows from the warm-to-cold side or the cold-to-warm side of an initially prescribed ocean front. The evolution of the near-surface winds, boundary layer, and thermal structure is described, and the balances in the momentum equation are diagnosed. The changes in surface winds across the front are consistent with previous models and observations, showing a strong positive correlation with the sea surface temperature and boundary layer thickness. The coupling arises mainly as a result of changes in the flux Richardson number across the front, and the strength of the coupling coefficient grows quadratically with the strength of the cross-front geostrophic wind. The acceleration of the winds over warm water results primarily from the rapid change in turbulent mixing and the resulting unbalanced Coriolis force in the vicinity of the front. Much of the loss/gain of momentum perpendicular to the front in the upper and lower boundary layer results from acceleration/deceleration of the flow parallel to the front via the Coriolis term. This mechanism is different from the previously suggested processes of downward mixing of momentum and adjustment to the horizontal pressure gradient, and is active for flows off the equator with sufficiently strong winds. Although the main focus of this work is on the midlatitude, strong wind regime, calculations at low latitudes and with weak winds show that the pressure gradient and turbulent mixing terms dominate the cross-front momentum budget, consistent with previous work.

Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations

2014 ◽  
Vol 142 (11) ◽  
pp. 4284-4307 ◽  
Author(s):  
Natalie Perlin ◽  
Simon P. de Szoeke ◽  
Dudley B. Chelton ◽  
Roger M. Samelson ◽  
Eric D. Skyllingstad ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coupling Coefficient ◽  
Eddy Viscosity ◽  
Sea Surface ◽  
Coupling Coefficients ◽  
Wind Response ◽  
Speed Response

Abstract The wind speed response to mesoscale SST variability is investigated over the Agulhas Return Current region of the Southern Ocean using the Weather Research and Forecasting (WRF) Model and the U.S. Navy Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model. The SST-induced wind response is assessed from eight simulations with different subgrid-scale vertical mixing parameterizations, validated using Quick Scatterometer (QuikSCAT) winds and satellite-based sea surface temperature (SST) observations on 0.25° grids. The satellite data produce a coupling coefficient of sU = 0.42 m s−1 °C−1 for wind to mesoscale SST perturbations. The eight model configurations produce coupling coefficients varying from 0.31 to 0.56 m s−1 °C−1. Most closely matching QuikSCAT are a WRF simulation with the Grenier–Bretherton–McCaa (GBM) boundary layer mixing scheme (sU = 0.40 m s−1 °C−1), and a COAMPS simulation with a form of Mellor–Yamada parameterization (sU = 0.38 m s−1 °C−1). Model rankings based on coupling coefficients for wind stress, or for curl and divergence of vector winds and wind stress, are similar to that based on sU. In all simulations, the atmospheric potential temperature response to local SST variations decreases gradually with height throughout the boundary layer (0–1.5 km). In contrast, the wind speed response to local SST perturbations decreases rapidly with height to near zero at 150–300 m. The simulated wind speed coupling coefficient is found to correlate well with the height-averaged turbulent eddy viscosity coefficient. The details of the vertical structure of the eddy viscosity depend on both the absolute magnitude of local SST perturbations, and the orientation of the surface wind to the SST gradient.

scholarly journals Impact of a Narrow Coastal Bay of Bengal Sea Surface Temperature Front on an Indian Summer Monsoon Simulation

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Dhrubajyoti Samanta ◽  
Saji N. Hameed ◽  
Dachao Jin ◽  
Vishnu Thilakan ◽  
Malay Ganai ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Bay Of Bengal ◽  
Sea Surface ◽  
Temperature Front ◽  
Coastal Bay

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

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