Evaluation of Polar Regions in Ocean Reanalyses Status of the ORA-IP and EU-COST EOS projects in the polar oceans Keith Haines , University of Reading.

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Presentation transcript:

Evaluation of Polar Regions in Ocean Reanalyses Status of the ORA-IP and EU-COST EOS projects in the polar oceans Keith Haines , University of Reading Contributions from Petteri Uotila, Matthieu Chevallier, Hugues Goosse and many others GOW workshop 10-14 October 2016 – Toulouse - France

Outline The Ocean Reanalysis Intercomparison Project The ORA-IP Community What has been achieved The ORA-IP Data Repository The EOS COST Project for The Polar oceans: Ambitions for the project Arctic issues Antarctic issues Preliminary Results Arctic Sea ice (Results in Chevallier et al. 2016) Arctic Ocean Heat and Salt Content Arctic Hydrography (Stratification, T:S relations) Arctic Freshwater Content Antarctic Ocean Heat and Salt Content Antarctic Sea Ice Summary and Further Plans

CLIVAR GSOP/GODAE Ocean View Ocean Reanalysis Inter-comparison (ORA-IP) Magdalena Alonso Balmaseda (ECMWF) Takahiro Toyoda (MRI-JMA) Maria Valdivieso (UoReading) Andrea Storto (CMCC) Gregory Smith (Environment Canada) Matthew Palmer (UK MetOffice) Fabrice Hernandez (Mercator Ocean) Li Shi (BMRC) Keith Haines (UoReading) Matthieu Chevallier (CNRS) Tony Lee (JPL) Yosuke Fujii (MRI-JMA) Kirsten Wilmer-Becker (MetOffice) And All Reanalysis Providers AMOC NCAR MyOcean ¼ degree CMCC 6 Observation only products 13 Low resolution models 8 High resolution models (1/3 or ¼ degree) 4 Coupled DA products 6 Long reanalyses, starting 1950’s Summary Paper Balmaseda, M.A. et al., The Ocean Reanalysis Intercomparison project (ORA-IP) J.Op.Oceanogr. 2015 Special Issue Climate Dynamics: 9 papers published online in 2015 http://www.clivar.org/sites/default/files/documents/Exchanges64.pdf Only Arctic Sea Ice paper explicitly focussed on High Latitudes

Remon Sadikni The ORA-IP Data Server

Ocean Heat Content from ORA-IP Palmer et al 2016

http://www.eos-cost.eu

Polar ORA-IP Polar ORA-IP Petteri Uotila, Marika Marnela, Meri Korhonen (FMI Fi); Antoine Barthélemy, François Massonnet, Hugues Goosse (UC Louvain); Gilles Garric, Marie Drevillon (Mercator); Matthieu Chevallier (Meteo France); Lien Vidar (IMR) Jiping Xie (NERSC No); Dorotea Iovino, Verena Haid (CMCC IT); Frank Kauker (AWI De); Drew Peterson, (UKMO), Keith Haines (UoR UK), Steffen Tietsche (ECMWF); Li Shi (BoM Au), Neven Fuckar (BSC Sp) Make regional Arctic and Antarctic ORA comparisons More regional expertise (comparisons not done by reanalysis experts) Establish accessibility of ORA data repository Availability of ORA data on standard grid (Levitus 1°x1°) in standard format cf-netcdf Benefit from existing data OHC, OSC, Mixed layer depths, Sea Ice, Surface fluxes Updating for most recent ORA products New variables eg. Hydrography, Currents, Strait Transports…….. Work in progress: Meetings March 2016, Nov 2016 @ FMI Immediate Aims at 2 Papers : Arctic and Antarctic

Polar ORA-IP Some Polar Issues Atlantic water pathways; Fram strait and Arctic boundary currents Beaufort gyre freshwater storage variations Heat and Freshwater mean budgets/trends/transports for Arctic Sea Ice seasonal cycle and trends in Arctic Arctic halocline maintenance and variability Antarctic sea ice, mean and trends Mixed layer variability around ACC ACC transports OHC trends around Antarctic continent

ORA-IP : Arctic sea ice In Climate Dynamics Special Issue … First-time ever systematic intercomparison of Arctic sea ice fields from global ORA… Benefits from recent intercomparisons of observational datasets : Ivanova et al., 2014, 2015 (SIC); Zygmuntowska et al., 2014 (SIT); Sumata et al., 2014 (velocity)

Sea ice concentration/areas/extent Chevallier et al 2016 Sea ice extent (Mkm2) September 1990-2012 Figures avec les biais pour illustrer la difficulté liée à la calibration du système, et la solution intermédiaire consistant à prendre les sous-bassins.

Sea ice thickness Mean sea ice thickness (m) Difference wrt ICESAT Average March 2003-2007 Figures avec les biais pour illustrer la difficulté liée à la calibration du système, et la solution intermédiaire consistant à prendre les sous-bassins.

What about sea ice predictions? Statistical forecasts: ORA used to fit regression models using non-observed quantities (cf PIOMAS) Lagged correlations : Sea ice volume ➙ Sea ice extent (same period) Predictor : Sea ice Volume Figures avec les biais pour illustrer la difficulté liée à la calibration du système, et la solution intermédiaire consistant à prendre les sous-bassins. Predictand : Sea ice Extent Will result in very different statistical forecasts What is the truth? Possible collaboration with SIPN?

Observation only products Mean Arctic Ocean Heat Content Anomalies (1993-2010) Haines Mignac Observation only products (0 – 300m)

Observation only products Mean Arctic Ocean Salt Content Anomalies (1993-2010) Observation only products (0 – 300m)

Hydrography comparisons Uotila Stratification and T-S relations @ 3 sites Amerasian basin (80N 215E) Central Arctic (88N 10E) Nansen basin (84N 100E) Layer averages for 5 layers available 0-100, 100-300, 300-700, 700-1500, 1500-3000m depth Layer trend analyses also underway

Liquid freshwater content (m) in the Arctic Ocean averaged from 1993 to 2010 The reference salinity is 34.8 and the integration in the vertical is taken from surface to the depth where salinity is equal to the reference salinity. 3D salinity fields available from CGLORSv5, GLOSEA_GO1, GLOSEA_GO5, MOVEG2, MOVE-G2i, ORAP5, TOPAZ4 Iovino

Observation only products Mean Antarctic Ocean Heat Content Anomalies (1993-2010) Observation only products (0 – 700m)

Observation only products Mean Antarctic Ocean Salt Content Anomalies (1993-2010) Observation only products (0 – 700m)

Consistency with Antarctic sea ice thickness measurements from ASPeCt Massonnet Ship SIT data 23000+ Obs Evaluation conducted on 1993-2005 (longest common period between reanalyses and ASPeCt data). Label Institution Compatibility index Mean abs error (cm) GloSea5 UK Met Office 0.44 13 GECCO2 U. Hamburg 0.28 22 ECDA GFDL 0.48 GLORYSv1 MERCATOR 0.42 GLORYSv3 0.43 C-GLORS CMCC 0.49 ECCO NASA 12 Ship SIT data regridded to 1° bins Reanalysis data sampled monthly Consistency assessed against bins with 4+ SIT Obs None show >50% “compatibility” (ORA within ASPeCt SIT range with 4+ Obs)

Summary and Further Work The ORA-IP Data Repository is well established and accessible for new projects Polar ORA-IP should submit Arctic and Antarctic summary papers by early 2017 New studies likely to be needed Ocean Heat and Freshwater transports currently not widely available from ORA Polar regions may provide strong constraints on Earths Energy and water budget analyses Polar Climate change trends very large Align Polar Metrics with Operational community « State of the Oceans »

Sea ice volume Annually-averaged Arctic sea ice volume (103 km3) Figures avec les biais pour illustrer la difficulté liée à la calibration du système, et la solution intermédiaire consistant à prendre les sous-bassins.

Sea ice thickness Arctic sea ice volume (ICESat domain) Comparison with ICESat and CryoSAT Figures avec les biais pour illustrer la difficulté liée à la calibration du système, et la solution intermédiaire consistant à prendre les sous-bassins.

Sea ice velocity Arctic mean sea ice velocity: Annual mean Seasonal cycle Ice export through Fram Strait (annual mean) Figures avec les biais pour illustrer la difficulté liée à la calibration du système, et la solution intermédiaire consistant à prendre les sous-bassins.

Feb-Mar Kauker

Oct-Nov