Brown Shannon

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Shannon
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  • Article
    FluxSat: measuring the ocean-atmosphere turbulent exchange of heat and moisture from space
    (MDPI, 2020-06-03) Gentemann, Chelle L. ; Clayson, Carol A. ; Brown, Shannon ; Lee, Tong ; Parfitt, Rhys ; Farrar, J. Thomas ; Bourassa, Mark A. ; Minnett, Peter J. ; Seo, Hyodae ; Gille, Sarah T. ; Zlotnicki, Victor
    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.
  • Article
    Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites
    (Elsevier, 2021-02-14) Chu, Housen ; Luo, Xiangzhong ; Ouyang, Zutao ; Chan, W. Stephen ; Dengel, Sigrid ; Biraud, Sebastien ; Torn, Margaret S. ; Metzger, Stefan ; Kumar, Jitendra ; Arain, M. Altaf ; Arkebauer, Tim J. ; Baldocchi, Dennis D. ; Bernacchi, Carl ; Billesbach, Dave ; Black, T. Andrew ; Blanken, Peter D. ; Bohrer, Gil ; Bracho, Rosvel ; Brown, Shannon ; Brunsell, Nathaniel A. ; Chen, Jiquan ; Chen, Xingyuan ; Clark, Kenneth ; Desai, Ankur R. ; Duman, Tomer ; Durden, David J. ; Fares, Silvano ; Forbrich, Inke ; Gamon, John ; Gough, Christopher M. ; Griffis, Timothy ; Helbig, Manuel ; Hollinger, David ; Humphreys, Elyn ; Ikawa, Hiroki ; Iwata, Hiroki ; Ju, Yang ; Knowles, John F. ; Knox, Sara H. ; Kobayashi, Hideki ; Kolb, Thomas ; Law, Beverly ; Lee, Xuhui ; Litvak, Marcy ; Liu, Heping ; Munger, J. William ; Noormets, Asko ; Novick, Kim ; Oberbauer, Steven F. ; Oechel, Walter ; Oikawa, Patty ; Papuga, Shirley A. ; Pendall, Elise ; Prajapati, Prajaya ; Prueger, John ; Quinton, William L. ; Richardson, Andrew D. ; Russell, Eric S. ; Scott, Russell L. ; Starr, Gregory ; Staebler, Ralf ; Stoy, Paul C. ; Stuart-Haëntjens, Ellen ; Sonnentag, Oliver ; Sullivan, Ryan C. ; Suyker, Andy ; Ueyama, Masahito ; Vargas, Rodrigo ; Wood, Jeffrey D. ; Zona, Donatella
    Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.
  • Article
    Air-sea fluxes with a focus on heat and momentum
    (Frontiers Media, 2019-07-31) Cronin, Meghan F. ; Gentemann, Chelle L. ; Edson, James B. ; Ueki, Iwao ; Bourassa, Mark A. ; Brown, Shannon ; Clayson, Carol A. ; Fairall, Christopher W. ; Farrar, J. Thomas ; Gille, Sarah T. ; Gulev, Sergey ; Josey, Simon A. ; Kato, Seiji ; Katsumata, Masaki ; Kent, Elizabeth ; Krug, Marjolaine ; Minnett, Peter J. ; Parfitt, Rhys ; Pinker, Rachel T. ; Stackhouse, Paul W., Jr. ; Swart, Sebastiaan ; Tomita, Hiroyuki ; Vandemark, Douglas ; Weller, Robert A. ; Yoneyama, Kunio ; Yu, Lisan ; Zhang, Dongxiao
    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.