Using Arctic Ocean Color Data in ocean-sea ice-biogeochemistry seasonal forecasting systems

Transcription

1 Using Arctic Ocean Color Data in ocean-sea ice-biogeochemistry seasonal forecasting systems Matthieu Chevallier 1 The POLARIS project David Salas y Mélia 1, Roland Séférian 1, Marion Gehlen 2, Gilles Garric 3, Virginie Guémas 1,4, Marie-Noëlle Houssais 5, Martin Vancoppenolle 5 (1) CNRM-GAME, (Météo-France, CNRS), Toulouse, France (2) IPSL/LSCE, Gif-sur-Yvette, France (3) Mercator-Océan, Ramonville-Saint Agne, France (4) IC3, Barcelone, Espagne (5) IPSL/LOCEAN, Paris, France

2 POLARIS: scientific questions Main purpose: assessing the predictability of the Arctic ocean-sea ice system, including biogeochemistry, with 2 coupled atmosphere-ocean-sea ice models (CNRM-CM and EC-Earth) and 3 stand-alone ocean-sea ice models (MERCATOR, LOCEAN and CNRM). Scientific questions: Can we advance pan-arctic and regional sea ice seasonal-to-interannual predictions through model improvement? Are chlorophyll, net PP and plankton biomasses in the Arctic ocean potentially predictable? What are the sources of predictability of sea ice, and reciprocally, is sea ice a source of predictability for biogeochemistry and the atmosphere? Can we better understand recent events (ex: September 2012)? PIs: David Salas y Mélia (CNRM) and Marion Gehlen (LSCE)

3 Outlines Motivations Predicting climate at lead times 1 month-2 years? In the Arctic? With a fully coupled model? Using observations? Some existing results Interannual predictability of the marine net PP in the Tropical Pacific Seasonal predictability of the Arctic sea ice Future plans, possible collaborations Using ocean color data?

4 Seasonal-to-interannual climate predictability Long time scales: Seasonal: 1 month-1 year (operational) Interannual: 1 year-3 years (managment) Arctic sea ice: growing need of long-term predictions: Fishing, shipping, industry, research Summer and winter sea ice predictions Possible interest for long-term weather predictions (mid-lats) Arctic biogeochemistry Labrador sea, Barents sea Wide interest: WWRP-WCRP Polar Prediction Project Chantier Arctique (Ocean-Atmosphere) Green Mercator

5 Seasonal-to-interannual climate predictability IC BC Predictability from Initial Conditions (IC) Predictability from Boundary Conditions (BC) IC Predictability limits: Atmosphere: 10 days (barrier??) Ocean: up to 6 months BC Predictability limits: Atmosphere: 3-4 months Climate system: 3-10 years? Lorenz, 1963, JAS Branstator and Teng, 2010, JCLIM Predictability from IC in sea ice? Predictability from IC in Chl/NPP?

6 Seasonal-to-interannual predictability: sea ice Correlation between anomalies of X in January and X in February (lag 1), March (lag 2), June (lag 6). In the observations. Memory in the Arctic sea ice extent (2-5 months depending on the season) Role of the Arctic sea ice volume/thickness in sea ice predictability Also a role of the ocean (upper layer heat content ) Blanchard-Wrigglesworth et al., 2011, JCLIM Chevallier and Salas y Mélia, 2012, JCLIM

7 Seasonal-to-interannual predictability: sea ice Correlation between anomalies of X in January and X in February (lag 1), March (lag 2), June (lag 6). In the observations. It is possible to predict the Arctic sea ice extent a few months in advance, using a well-initialized forecast system. This predictability may drive a long-term predictability of marine Arctic productivity through bio-physical feedback.

8 Model predictions A fully coupled system (ocean+atmosphere+sea ice+ ) A global model ATM OCE+BGC Global Coupled A-O-I model t=0 t=5 months

9 Model predictions A fully coupled system (ocean+atmosphere+sea ice+ ) A global model Initialized every start date with observations and non-observable real quantities (Chl(z), sea ice thickness ) ATM Reality : OBS (T,S,SIC) ANA (SIT ) Chl(z)? OCE+BGC Global Coupled A-O-I model t=0 t=5 months

10 Model predictions A fully coupled system (ocean+atmosphere+sea ice+ ) A global model Initialized every start date with observations and non-observable real quantities (Chl(z), sea ice thickness ) Ensemble generation technics (to sample the uncertainty) ATM Reality : OBS (T,S,SIC) ANA (SIT ) Chl(z)? OCE+BGC Global Coupled A-O-I model t=0 t=5 months

11 Model predictions A fully coupled system (ocean+atmosphere+sea ice+ ) A global model Initialized every start date with observations and non-observable real quantities (Chl(z), sea ice thickness ) Ensemble generation technics (to sample the uncertainty) Reality : OBS (T,S,SIC) ANA (SIT ) Chl(z)? ATM OCE+BGC Global Coupled A-O-I model Forecast : Expected value +uncertainty Ex: SST, ice extent, thickness, NPP, Chl t=0 t=5 months

12 Model predictions A fully coupled system (ocean+atmosphere+sea ice+ ) A global model Initialized every start date with observations and non-observable real quantities (Chl(z), sea ice thickness ) Ensemble generation technics (to sample the uncertainty) Verification data (obs, analysis) for evaluation and calibration Reality : OBS (T,S,SIC) ANA (SIT ) Chl(z)? ATM OCE+BGC Global Coupled A-O-I model Forecast : Expected value +uncertainty Ex: SST, ice extent, thickness, NPP, Chl Verification : OBS t=0 t=5 months

13 Model predictions/hindcasts System evaluation: re-run the forecast system over as many past cases as possible for which we have the verification data. Hindcast mode over a long period (ex: ). Same initialization strategy over the entire period. Skill scores: correlation, RMS error. Reality : OBS (T,S,SIC) ANA (SIT ) Chl(z)? ATM OCE+BGC Global Coupled A-O-I model Forecast : Expected value +uncertainty Ex: SST, ice extent, thickness, NPP, Chl Verification : OBS t=0 t=5 months

14 Some scientific results Multi-year predictability of tropical marine productivity Séférian, R., et al., 2013, PNAS, in revision. Seasonal predictions of the Arctic sea ice Chevallier, M., et al., 2013, Journal of Climate. Germe, A., et al., 2014, Climate Dynamics, accepted.

15 Exemple 1: tropical marine biogeochemistry Séférian, R., Bopp, L., Gehlen, M., Swingedouw, D., Mignot, J., Guilyardi, E. and Servonnat, J., 2013: The multi-year predictability of tropical marine productivity. Proceedings of the National Academy of Sciences (PNAS), in revision.

16 Protocol 10-year long coupled forecasts Started Jan 1st Every year between 1987 and 2001 (predictions cover ) With IPSL-CM5A-LR coupled model Global model with a biogeochemical model (PISCES) 2 x 2 horizontal resolution Initialization Long coupled simulation with IPSL-CM5A-LR with restoring to the observed SST between 1949 and No constraint directly applied to the biogeochemical variabes (NPP, Chl) Séférian et al., 2013, PNAS, in revision

17 Correlation Result: net PP over Tropical Pacific Predictability of Net Primary Productivity over the Tropical Pacific (30 S- 30 N) years of prediction (lead time) NPP predictions: Comparison of model hindcasts with satellitederived chlorophyll for SeaWiFS captor ( ). Standard Error (RMSE) NPP natural variations can be predicted 3 years in advance in this model Séférian et al., 2013, PNAS, in revision

18 Correlation Result: net PP over Tropical Pacific Predictability of Net Primary Productivity over the Tropical Pacific (30 S- 30 N) NPP, Years 2-5 Fishing areas Standard Error (RMSE) NPP natural variations can be predicted 3 years in advance in this model Séférian et al., 2013, PNAS, in revision

19 Result: net PP over Tropical Pacific Mechanisms at play OBS NPP is initialized through SST nudging only. Séférian et al., 2013, PNAS, in revision

20 Exemple 2: Arctic ocean sea ice Chevallier, M., Salas y Mélia, D., Voldoire, A., Déqué, M. and Garric, G., 2013: Seasonal forecast of the pan-arctic sea ice extent using a GCM-based seasonal prediction system. Journal of Climate, 26. Germe, A., Chevallier, M., Salas y Mélia, D., Sanchez-Gomez, E. and Cassou, C., 2014: Interannual predictability of the Arctic sea ice in a global couped model: Regional disparity and temporal evolution. Climate Dynamics, accepted.

21 Protocol Coupled forecasts with 5-month lead time From May 1st until the end of September. From November 1st until the end of March. Every year between 1990 and With CNRM-CM5.1 coupled model (CNRM-CERFACS, Toulouse) Global model 1 x 1 horizontal resolution Initialization Atmosphere: ECMWF reanalysis (ERA-Interim) Ocean-sea ice:??? Chevallier et al., 2013, JCLIM

22 Initialization Full knowledge of the system at time t: from observations? Concentration/Extent: satellite since 1979 (NSIDC, CERSAT) Sea ice thickness: sparse in-situ data, reliable (?) satellite obs. since 2004 Upper/Deep ocean The solution: use the model to reconstruct the full dynamic/thermodynamic state of the system Ocean-sea ice component only (NEMO+Gelato) Forced by ERA reanalysis Over the period Sea ice thickness is consistent with ice drift Sea ice drift is driven by the winds Chevallier et al., 2013, JCLIM March average sea ice concentration (black: SSMI 15% ice-edge)

23 Initialization Full knowledge of the system at time t: from observations? Concentration/Extent: satellite since 1979 (NSIDC, CERSAT) Sea ice thickness: sparse in-situ data, reliable (?) satellite obs. since 2004 Upper/Deep ocean The solution: use the model to reconstruct the full dynamic/thermodynamic state of the system Ocean-sea ice component only (NEMO+Gelato) Forced by ERA reanalysis Over the period Sea ice thickness is consistent with ice drift Sea ice drift is driven by the winds The reconstructed thickness field should be close to its historical state. Chevallier et al., 2013, JCLIM March average sea ice thickness (m)

24 Initialization Full knowledge of the system at time t: from observations? Concentration/Extent: satellite since 1979 (NSIDC, CERSAT) Sea ice thickness: sparse in-situ data, reliable (?) satellite obs. since 2004 Upper/Deep ocean The solution: use the model to reconstruct the full dynamic/thermodynamic state of the system Ocean-sea ice component only (NEMO+Gelato) Forced by ERA reanalysis Over the period Sea ice thickness is consistent with ice drift Sea ice drift is driven by the winds The reconstructed thickness field is reasonably close to its historical state. Chevallier et al., 2013, JCLIM Model-ICESAT Winter (m)

25 September ice extent Result: September Arctic sea ice extent Forecasts: 1 May September with CNRM-CM5.1 (5 months) FOR MODEL FORECASTS (±1 STD) OBSERVATIONS (SSMI) Correlation obs vs model forecasts: CNRM-CM5.1: 0.6 CanSIPS (Environment Canada): < 0.2 (arbitrary thickness initial state) OBS Skillful predictions due to the initialization quality (thickness) Comparable skill for the winter sea ice extent (pan-arctic, Barents sea ) Still biases due to model physics (atmosphere), coupling flaws and insufficient resolution (horizontal and vertical) Chevallier et al., 2013, JCLIM

26 Conclusions Annual NPP in the Tropical Pacific can be predicted up to 3 years in advance. September and March Arctic sea ice extent can be predicted up to 5 months in advance. Skill of current forecasting systems Adapted initialization techniques Realistic simulation of physical processes Realistic simulation of bio-physical feedbacks Some caveats remain Biases in the individual model and the coupling (fluxes ) Horizontal and vertical resolution Missing processes (e.g. bio under/in sea ice: Martin s talk) Incomplete initialization (constraint in NPP, thickness ) Lack of long/homogenized verification data (ex: ocean color data)

27 Future plans: possible collaborations Our purposes in POLARIS: Improvements in the models Increase horizontal (1 ¼ ) and vertical resolution (10m 0.5m at z=0) New physical processes (e.g. snow over sea ice) Add bio-physical feedback in ocean/sea ice. New validation datasets (in-situ buoys, satellite-based products ). Model predictions of chlorophyll and PP in the Arctic Forecasts: CNRM-CM + PISCES = CNRM-ESM, ¼ horizontal resolution Initialization: NEMO-Gelato + PISCES, forced with ERA-Interim Verification data?

28 Future plans: possible collaborations Our purposes in POLARIS: Improvements in the models Increase horizontal (1 ¼ ) and vertical resolution (10m 0.5m at z=0) New physical processes (e.g. snow over sea ice) Add bio-physical feedback in ocean/sea ice. New validation datasets (in-situ buoys, satellite-based products ). Model predictions of chlorophyll and PP in the Arctic Forecasts: CNRM-CM + PISCES = CNRM-ESM, ¼ horizontal resolution Initialization: NEMO-Gelato + PISCES, forced with ERA-Interim Verification data? We need ocean color data and derived chlorophyll/pp profiles to validate the models, initialize the forecasts, and also as verification data! Long time coverage (at least , best ) Homogeneous datasets Reliable over the Arctic ocean (even near the sea ice edge) Best practices for the initialization (assimilation techniques)

29 Thank you for your attention Matthieu Chevallier Marion Gehlen David Salas y Mélia david.salas@meteo.fr Roland Séférian roland.seferian@meteo.fr