Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season

Type Article
Date 2019-08
Language English
Author(s) Smith Gregory C.1, Allard Richard2, Babin Marcel3, Bertino Laurent4, Chevallier Matthieu5, 6, Corlett Gary7, Crout Julia8, Davidson Fraser9, Delille BrunoORCID10, Gille Sarah T.11, Hebert David2, Hyder Patrick12, Intrieri Janet13, Lagunas Jose3, Larnicol Gilles14, Kaminski Thomas15, Kater Belinda16, Kauker Frank17, 18, Marec Claudie3, 19, Mazloff Matthew11, Metzger E. Joseph2, Mordy Calvin20, O'Carroll Anne7, Olsen Steffen M.21, Phelps Michael8, Posey Pamela8, Prandi Pierre14, Rehm Eric3, Reid Phillip22, Rigor Ignatius23, Sandven Stein4, Shupe Matthew13, 24, Swart Sebastiaan25, 26, Smedstad Ole Martin8, Solomon Amy27, Storto Andrea28, Thibaut Pierre14, Toole John29, Wood Kevin20, Xie Jiping4, Yang Qinghua30
Affiliation(s) 1 : Environm & Climate Change Canada, Meteorol Res Div, Environm Numer Predict Res Sect, Dorval, PQ, Canada.
2 : US Naval Res Lab, Stennis Space Ctr, Bay St Louis, MS USA.
3 : Univ Laval, CNRS, UMI 3376, Takuvik, Quebec City, PQ, Canada.
4 : Nansen Environm & Remote Sensing Ctr, Bergen, Norway.
5 : Meteo France, Div Marine & Oceanog, Toulouse, France.
6 : Univ Toulouse, CNRS, Meteo France, CNRM, Toulouse, France.
7 : European Org Exploitat Meteorol Satellites, Darmstadt, Germany.
8 : Perspecta Inc, Stennis Space Ctr, Bay St Louis, MS USA.
9 : Fisheries & Oceans Canada, Northwest Atlantic Fisheries Ctr, St John, NF, Canada.
10 : Univ Liege, Chem Oceanog Unit, Liege, Belgium.
11 : Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USA.
12 : Met Off, Exeter, Devon, England.
13 : NOAA, Phys Sci Div, Earth Syst Res Lab, Boulder, CO USA.
14 : Collette Localisat Satellites, Toulouse, France.
15 : Invers Lab, Hamburg, Germany.
16 : Arcadis Nederland BV, Zwolle, Netherlands.
17 : Ocean Atmosphere Syst, Hamburg, Germany.
18 : Alfred Wegener Inst Polar & Marine Res, Bremerhaven, Germany.
19 : CNRS, Lab Oceanog Phys & Spatiale, UMR 6523, IFREMER,IRD,UBO, Piouzane, France.
20 : Univ Washington, Joint Inst Study Atmosphere & Oceans, Seattle, WA 98195 USA.
21 : Danish Meteorol Inst, Copenhagen, Denmark.
22 : Bur Meteorol, Hobart, Tas, Australia.
23 : Univ Washington, Polar Sci Ctr, Seattle, WA 98195 USA.
24 : Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA.
25 : Univ Gothenburg, Dept Marine Sci, Gothenburg, Sweden.
26 : Univ Cape Town, Dept Oceanog, Rondebosch, South Africa.
27 : NOAA, Earth Syst Res Lab, Boulder, CO USA.
28 : Ctr Maritime Res & Expt, La Spezia, Italy.
29 : Woods Hole Oceanog Inst, Woods Hole, MA 02543 USA.
30 : Sun Yat Sen Univ, Guangdong Prov Key Lab Climate Change & Nat Disas, Sch Atmospher Sci, Zhuhai, Peoples R China.
31 : Polar Predict Project PPP Steering Grp, WWRP, Beijing, Peoples R China.
Source Frontiers In Marine Science (2296-7745) (Frontiers Media Sa), 2019-08 , Vol. 6 , N. 429 , P. 28p.
DOI 10.3389/fmars.2019.00429
WOS© Times Cited 18
Keyword(s) polar observations, operational oceanography, ocean data assimilation, ocean modeling, forecasting, sea ice, air-sea-ice fluxes, YOPP
Abstract

There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. In the former, this is driven by a declining ice cover accompanied by an increase in maritime traffic and exploitation of marine resources. Oceanographic predictions in the Antarctic are also important, both to support Antarctic operations and also to help elucidate processes governing sea ice and ice shelf stability. However, a significant gap exists in the ocean observing system in polar regions, compared to most areas of the global ocean, hindering the reliability of ocean and sea ice forecasts. This gap can also be seen from the spread in ocean and sea ice reanalyses for polar regions which provide an estimate of their uncertainty. The reduced reliability of polar predictions may affect the quality of various applications including search and rescue, coupling with numerical weather and seasonal predictions, historical reconstructions (reanalysis), aquaculture and environmental management including environmental emergency response. Here, we outline the status of existing near-real time ocean observational efforts in polar regions, discuss gaps, and explore perspectives for the future. Specific recommendations include a renewed call for open access to data, especially real-time data, as a critical capability for improved sea ice and weather forecasting and other environmental prediction needs. Dedicated efforts are also needed to make use of additional observations made as part of the Year of Polar Prediction (YOPP; 2017-2019) to inform optimal observing system design. To provide a polar extension to the Argo network, it is recommended that a network of ice-borne sea ice and upper-ocean observing buoys be deployed and supported operationally in ice-covered areas together with autonomous profiling floats and gliders (potentially with ice detection capability) in seasonally ice covered seas. Finally, additional efforts to better measure and parameterize surface exchanges in polar regions are much needed to improve coupled environmental prediction.

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Smith Gregory C., Allard Richard, Babin Marcel, Bertino Laurent, Chevallier Matthieu, Corlett Gary, Crout Julia, Davidson Fraser, Delille Bruno, Gille Sarah T., Hebert David, Hyder Patrick, Intrieri Janet, Lagunas Jose, Larnicol Gilles, Kaminski Thomas, Kater Belinda, Kauker Frank, Marec Claudie, Mazloff Matthew, Metzger E. Joseph, Mordy Calvin, O'Carroll Anne, Olsen Steffen M., Phelps Michael, Posey Pamela, Prandi Pierre, Rehm Eric, Reid Phillip, Rigor Ignatius, Sandven Stein, Shupe Matthew, Swart Sebastiaan, Smedstad Ole Martin, Solomon Amy, Storto Andrea, Thibaut Pierre, Toole John, Wood Kevin, Xie Jiping, Yang Qinghua (2019). Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season. Frontiers In Marine Science, 6(429), 28p. Publisher's official version : https://doi.org/10.3389/fmars.2019.00429 , Open Access version : https://archimer.ifremer.fr/doc/00512/62379/