A Plan to Develop Open Science’s Green Shoots into a Thriving Garden

Au cours des dernières décennies, le mouvement de la science ouverte, promet une approche plus inclusive, une façon efficace et fiable de mener la diffusion de la recherche scientifique qui s’est développée. Animée par la croyance que le partage ouvert du savoir soit sous toutes ses formes... qu\u27il s\u27agisse des documents, des données, des logiciels, des méthodes..

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

Using saildrones to validate arctic sea-surface salinity from the smap satellite and from ocean models

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the-2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations

Comparison of SST diurnal variation models over the Tropical Warm Pool region

Four sea surface temperature (SST) diurnal variation (DV) models have been compared against Multi-functional Transport Satellite - 1R (MTSAT-1R) SST measurements over the Tropical Warm Pool region (TWP, 90°E-170°E, 25°S-15°N) for four months from January to April 2010. The four models include one empirical model formulated by Chelle Gentemann (hereafter CG03), one physical model proposed by Zeng and Beljaars in 2005 (ZB05) and its updated version (ZB+T), and one air-sea coupled model (the Met Office Unified Model Global Coupled configuration 2, GC2) with ZB05 warm layer scheme added on top of the standard configuration. The sensitivity of the v3 MTSAT-1R data to the “true” changes in SST is first investigated using drifting buoys and is estimated to be 0.60 ± 0.05. This being significantly different from 1, the models are validated against MTSAT-1R data and the same data scaled by the inverse of the sensitivity (representing an estimate of the true variability). Results indicate that all models are able to capture the general DV patterns but with differing accuracies and features. Specifically, CG03 and ZB+T underestimate strong (> 2 K) DV events’ amplitudes especially if we assume that sensitivity-scaled MTSAT-1R variability is most realistic. ZB05 can effectively capture the DV cycles under most DV and wind conditions, as well as the DV spatial distribution. GC2 tends to overestimate small-moderate (< 2 K) DV events but can reasonably predict large DV events. 1-3 hr lags in warming start and peak times are found in GC2

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

Observations of Coupling between Surface Wind Stress and Sea Surface Temperature in the Eastern Tropical Pacific

Satellite measurements of surface wind stress from the QuikSCAT scatterometer and sea surface temperature (SST) from the Tropical Rainfall Measuring Mission Microwave Imager are analyzed for the three-month period 21 July–20 October 1999 to investigate ocean–atmosphere coupling in the eastern tropical Pacific. Oceanic tropical instability waves (TIWs) with periods of 20–40 days and wavelengths of 1000–2000 km perturb the SST fronts that bracket both sides of the equatorial cold tongue, which is centered near 1°S to the east of 130°W. These perturbations are characterized by cusp-shaped features that propagate systematically westward on both sides of the equator. The space–time structures of these SST perturbations are reproduced with remarkable detail in the surface wind stress field. The wind stress divergence is shown to be linearly related to the downwind component of the SST gradient with a response on the south side of the cold tongue that is about twice that on the north side. The wind stress curl is linearly related to the crosswind component of the SST gradient with a response that is approximately half that of the wind stress divergence response to the downwind SST gradient. The perturbed SST and wind stress fields propagate synchronously westward with the TIWs. This close coupling between SST and wind stress supports the Wallace et al. hypothesis that surface winds vary in response to SST modification of atmospheric boundary layer stability

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