Profiles of Ocean surface Heating (POSH): A New Model of Upper Ocean Diurnal Warming

Shipboard radiometric measurements of diurnal warming at the ocean surface and profiles through the diurnal thermocline were utilized to assess the temporal and vertical variability and to develop a new physics-based model of near-surface warming. The measurements and modeled diurnal warming were compared, with the goal of comprehensively evaluating differences between the data and model results. On the basis of these results, the diurnal model was refined while attempting to maintain agreement with the measurements. Simplified bulk models commonly do not provide information on the vertical structure within the warm layer, but this new model predicts the vertical temperature profile within the diurnal thermocline using an empirically derived function dependent on wind speed. The vertical profile of temperature provides both a straightforward methodology for modeling differences due to diurnal warming between measurements made at different depths (e.g., in situ measurements at various depths and measurements of the surface temperatures by satellite radiometers) and information on upper ocean thermal structure. Additionally, the model estimates of diurnal warming at the ocean surface are important for air-sea heat and gas flux calculations, blending satellite sea surface temperature fields, and air-sea interaction studies. Copyright 2009 by the American Geophysical Union

Optimal estimation of sea surface temperature from AMSR-E

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST

FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Gentemann, C. L., Clayson, C. A., Brown, S., Lee, T., Parfitt, R., Farrar, J. T., Bourassa, M., Minnett, P. J., Seo, H., Gille, S. T., & Zlotnicki, V. FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space. Remote Sensing, 12(11), (2020): 1796, doi:10.3390/rs12111796.Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.C.L.G. was funded by NASA grant 80NSSC18K0837. C.A.C. was funded by NASA grants 80NSSC18K0778 and 80NSSC20K0662. J.T.F. was funded by NASA grants NNX17AH54G, NNX16AH76G, and 80NSSC19K1256. S.T.G. was funded by the National Science Foundation grant PLR-1425989 and by the NASA Ocean Vector Winds Science Team grant 80NSSC19K0059. M.B. was funded in part by the Ocean Observing and Monitoring Division, Climate Program Office (FundRef number 100007298), National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and by the NASA Ocean Vector Winds Science Team grant through NASA/JPL. H.S. was funded by National Oceanic and Atmospheric Administration (NOAA) grant NA19OAR4310376 and the Andrew W. Mellon Foundation Endowed Fund for Innovative Research at Woods Hole Oceanographic Institution

Saildrone: adaptively sampling the marine environment

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 101(6), (2020): E744-E762, doi:10.1175/BAMS-D-19-0015.1.From 11 April to 11 June 2018 a new type of ocean observing platform, the Saildrone surface vehicle, collected data on a round-trip, 60-day cruise from San Francisco Bay, down the U.S. and Mexican coast to Guadalupe Island. The cruise track was selected to optimize the science team’s validation and science objectives. The validation objectives include establishing the accuracy of these new measurements. The scientific objectives include validation of satellite-derived fluxes, sea surface temperatures, and wind vectors and studies of upwelling dynamics, river plumes, air–sea interactions including frontal regions, and diurnal warming regions. On this deployment, the Saildrone carried 16 atmospheric and oceanographic sensors. Future planned cruises (with open data policies) are focused on improving our understanding of air–sea fluxes in the Arctic Ocean and around North Brazil Current rings.The Saildrone data collection mission was sponsored by the Saildrone Award, an annual data collection mission awarded by Saildrone Inc., and the Schmidt Family Foundation. The research was funded by the NASA Physical Oceanography Program Grant 80NSSC18K0837 and 80NSSC18K1441. The work by T. M. Chin, J. Vazquez-Cuerzo, and V. Tsontos was carried out at the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). Piero L.F. Mazzini was supported by California Sea Grant Award NA18OAR4170073. We thank CeNCOOS for providing the HF radar data in the Gulf of the Farallones. Jose Gomez-Valdes was supported by CONACYT Grant 257125, and by CICESE. Work by Joel Scott and Ivona Cetinic was supported through NASA PACE. The work by Lisan Yu was supported by NOAA Ocean Observing and Monitoring Division under Grant NA14OAR4320158

Air-sea fluxes with a focus on heat and momentum

Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m–2 and a bias of less than 5 W m–2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500–1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1–3 measurement platforms in each nominal 10° by 10° box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections

Radiometric measurements of ocean surface thermal variability

Measurements of diurnal temperature variability at the ocean surface have been available primarily from satellite Sea Surface Temperature (SST) retrievals and a small number of ship‐based radiometers. Since most areas are sampled from polar orbiting satellites at most twice a day, surface diurnal variability studies relied on theoretical modeling or extrapolation of results from in situ measurements at depth. The ocean surface responds very rapidly to changes in fluxes of heat and momentum, therefore diurnal variability at the ocean surface may be quite different than heating at depth. Measurements from the Marine Atmospheric Emitted Radiance Interferometer (M‐AERI) provide one of the few skin SST data sets augmented by ancillary measurements necessary for investigations into surface diurnal heating and cooling. This unique data set spans all major ocean basins and contains many days with diurnal warming. The timing of the peak in diurnal warming is directly related to the minimum wind speed and varies from 8:00 to 18:00 local‐mean‐time. Fluctuations in wind speed can result in multiple peaks in diurnal heating during a single afternoon. As wind speed increases, diurnal warming decreases (negatively correlated) and as insolation increases, diurnal warming increases (positively correlated). Changes in wind speed affect diurnal warming amplitudes very rapidly, while changes in insolation have a more gradual effect. The maximum correlation of wind speed (insolation) with changes in diurnal warming is at a time lag of 0 (50) min

Profiles of ocean surface heating (posh): a new model of upper ocean diurnal warming

Shipboard radiometric measurements of diurnal warming at the ocean surface and profiles through the diurnal thermocline were utilized to assess the temporal and vertical variability and to develop a new physics-based model of near-surface warming. The measurements and modeled diurnal warming were compared, with the goal of comprehensively evaluating differences between the data and model results. On the basis of these results, the diurnal model was refined while attempting to maintain agreement with the measurements. Simplified bulk models commonly do not provide information on the vertical structure within the warm layer, but this new model predicts the vertical temperature profile within the diurnal thermocline using an empirically derived function dependent on wind speed. The vertical profile of temperature provides both a straightforward methodology for modeling differences due to diurnal warming between measurements made at different depths (e.g., in situ measurements at various depths and measurements of the surface temperatures by satellite radiometers) and information on upper ocean thermal structure. Additionally, the model estimates of diurnal warming at the ocean surface are important for air-sea heat and gas flux calculations, blending satellite sea surface temperature fields, and air-sea interaction studies

Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC

The 2020 ‘Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere’ (EUREC4A-OA) and the ‘Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign’ (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models