The CONCEPTS Global Ice-Ocean Prediction System Establishing an Environmental Prediction Capability in Canada
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1 The CONCEPTS Global Ice-Ocean Prediction System Establishing an Environmental Prediction Capability in Canada WWOSC 2014 Montreal, Quebec, Canada Dorina Surcel Colan 1, Gregory C. Smith 2, Francois Roy 2, Mateusz Reszka 1, Zhongjie He 2, Fraser Davidson 3, Hal Ritchie 4, Youyu Lu 5, Marie Drevillon 6, Benoit Tranchant 7 1 Canadian Meteorological Centre, Environment Canada, Dorval, Canada 2 Meteorological Research Division, Environment Canada, Dorval, Canada 3 Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John s, Canada 4 Meteorological Research Division, Environment Canada, Dartmouth, Canada 5 Bedford Institute of Oceanography, Fisheries and Oceans Canada, Bedford, Canada 6 Mercator-Océan, Toulouse, France 7 Collecte Localisation Satellite,Toulouse, France
2 Contents CONCEPTS and the need for a Canadian Ice-Ocean Forecasting Capability Global Ice-Ocean Prediction System (GIOPS) at CMC Model configuration and status Assimilation methodology SAM2 Ocean data sets availability and monitoring Comparison of innovation statistics with Mercator and GODAE Evaluation of analysis and trial fields Evaluation of sea surface temperature (SST) forecasts Evaluation of sea ice forecasts Ongoing and future developments
3 CONCEPTS Canadian Operational Network of Coupled Environmental Prediction Systems - establish a research capability - to develop and implement Operational Coupled Atmosphere-Ocean-Ice Assimilation and Prediction Systems for Canada.
4 CONCEPTS Canadian Operational Network of Coupled Environmental Prediction Systems - establish a research capability - to develop and implement Operational Coupled Atmosphere-Ocean-Ice Assimilation and Prediction Systems for Canada.
5 CONCEPTS Canadian Operational Network of Coupled Environmental Prediction Systems - establish a research capability - to develop and implement Operational Coupled Atmosphere-Ocean-Ice Assimilation and Prediction Systems for Canada.
6 CONCEPTS Canadian Operational Network of Coupled Environmental Prediction Systems - establish a research capability - to develop and implement Operational Coupled Atmosphere-Ocean-Ice Assimilation and Prediction Systems for Canada.
7 CONCEPTS Canadian Operational Network of Coupled Environmental Prediction Systems - establish a research capability - to develop and implement Operational Coupled Atmosphere-Ocean-Ice Assimilation and Prediction Systems for Canada. Canada requires ice-ocean forecast and information services for: - Weather prediction (days to seasons) - Ocean and sea ice prediction - Coast Guard Operations, e.g.: navigation - Fisheries and aquaculture management - Increased understanding of biological field observations - Attribution and mitigation of regional climate change impacts - Risk assessment for extreme events (sea level, waves, currents) - Emergency response: Search and Rescue, dispersion of pollutants
8 Ice-ocean modelling systems Global 1/4 resolution (GIOPS) Medium range forecasting Gulf of St. Lawrence 5km Short-term forecasting N. Atlantic and Arctic 1/12 (RIOPS) Short-to-medium range forecasting Global 1 resolution Monthly-to-seasonal forecasting Great Lakes 2km
9 Global Ice-Ocean Prediction System Produces daily analysis and 10 day forecasts at 00GMT - Numerical model NEMO-CICE - Tri-polar ORCA grid 1/4 resolution (ORCA025), < 15km in Arctic - 50 vertical levels (Smith et al., 2014) Based on Mercator operational system with following modifications: - Coupling with CICE sea ice model - Assimilate CMC-SST analysis - SAM2 ocean analysis is blended with CMC-3DVAR ice analysis - Atmospheric forcing from the bottom level of CMC GDPS forecasts. Status: running in operations at CMC
10 GIOPS Analysis System Système d Assimilation Mercator (SAM2) - Analysis method based on a reduced-order Kalman filter using a SEEK formulation - Background error covariances based on the statistics of a collection of 3D ocean state anomalies derived from a multi-year hindcast simulation (Lellouche et al., 2013) SAM2 assimilates: - Sea surface temperature (from satellite and in situ observations) - Subsurface temperature and salinity (from Argo, XBT, moorings,etc.) (CLS) - Sea level anomalies from satellite altimeters (AVISO) currently data from JASON2, CRYOSAT2 and SARAL-ALTIKA are assimilated
11 Ocean data availability and monitoring Delays Sea level anomalies of SLA data from satellite from altimeters AVISO 24 Temperature to 48 hours and salinity profiles Delays of in situ data from CLS (ARGO,XBT,moorings, ) After 7 days only 80% of data are available, after 14 days around 93% of data available 11
12 GIOPS Analysis System Analysis produced every Wednesday from two 7-day cycles one week period required to have sufficient data for optimality of analysis two cycles required because in situ and altimetry data have delays Addition of daily analysis cycle (SST and ice only) Initialize with GD analysis from previous week Analysis time GD GR 10 day forecast T=-14d T=-7d T=0 T=+7 T=+10 GD analysis Analysis time T=-14d GD T=-7d GR T=0 10 day forecast T=+7 T=+10 GD analysis
13 Evaluation of innovation statistics Obs-trial sea level anomaly misfits for 2011 Comparison of statistics for GIOPS and Mercator PSY3V3 Note: data assimilated not 100% identical Globally, GIOPS SLA RMS misfits are about 30% higher than Mercator s Due mostly to use of updated mean dynamic topography in PSY3 6cm 8cm
14 Evaluation of innovation statistics Obs-trial sea surface temperature misfits for 2011 Comparison of statistics for GIOPS and Mercator PSY3V3 Note: Different SST datasets assimilated Globally, GIOPS and Mercator SST misfits both about 0.5 C
15 Evaluation of innovation statistics Obs-trial temperature and salinity misfits for 2011 Comparison of statistics for GIOPS and Mercator PSY3V3 For most regions, GIOPS shows slightly higher biases and RMS misfits than Mercator for both temperature and salinity Mercator uses a 3DVAR bias correction algorithm for temperature and salinity.
16 GODAE Oceanview Intercomparison Intercomparison of various global systems: UK Metoffice (FOAM) Mercator (PSY3) CMC (GIOPS) NOAA/NCEP (RTOFS) Aus BofM (OceanMAPS) Near real-time comparison against shared data sets: Sea surface temperature Temperature and salinity profiles Sea level anomaly (Ryan et al., 2014)
17 Evaluation of analysis and trials Regional biases and RMS differences with AVHRR SST Forecasts beat persistence of SAM2 analysis for all regions Forecasts have lower RMS than persistence of CMC analyses for most regions Challenge to better constrain SST in polar regions Bias correction may be required to improve tropical forecasts Forecasts Persistence of GIOPS analyses Persistence of CMC analyses 17
18 Comparison with CMC SST Mean differences for to SST errors near ice edge problematic for RIPS (degradation of forecast skill) Modification included in GIOPSv1.1.1 (v1.8.2) SST adjusted depending of ice cover from 3D-Var analysis GIOPSv1.0 rejects SST data when ice concentration in trial > 0.0 GIOPSv1.1 uses 3DVar ice concentration analysis as proxy for freezing temperature; sets SST to freezing point when ice concentration>0.2; PSY3V3 full SST field is assimilated. V1.5.7 V1.8.2 Improved grid search algorithm used in collocalization of observations allowing the assimilation of an increased number of data specially in high latitudes.
19 Comparison with CMC SST RMS differences for to Previous experimental system: GIOPSv1.0.1 (SAMv1.5.7) Current system GIOPSv1.1.1 (SAMv1.8.2) Improved under-ice SST assimilation substantially reduces differences with CMCSST Improvements in Russian seas due partially to new grid search algorithm PSY3V3 V1.5.7 V1.8.2
20 Evaluation of sea ice forecast Sea-ice concentration as function of lead time GIOPS trial fields and forecast show an important reduction for RMSE in ice-concentration for both hemisphere (Smith et al., 2014)
21 Evaluation of sea ice forecast Sea-ice concentration at lead time 168h (Smith et al., 2014)
22 Ongoing and future developments Integrate recent Mercator improvements into GIOPS In situ temperature and salinity bias correction Updated mean dynamic topography Development of regional analysis system (RIOPS) Direct assimilation of SST observations Improvement to sea ice physics Landfast ice, deformation and leads, wave-ice coupling CONCEPTS is currently developing a suite of global and regional coupled environmental prediction systems
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