Sea Ice Forecast Verification in the Canadian Global Ice Ocean Prediction System
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1 Sea Ice Forecast Verification in the Canadian Global Ice Ocean Prediction System G Smith 1, F Roy 2, M Reszka 2, D Surcel Colan, Z He 1, J-M Belanger 1, S Skachko 3, Y Liu 3, F Dupont 2, J-F Lemieux 1, C Beaudoin 1, B Tranchant 4, M Drevillon 5, G Garric 5, C-E Testut 5, P Pellerin 1, H Ritchie 1, Y Lu 3, F Davidson 3 and the rest of the CONCEPTS Team 1 Meteorological Research Division, RPN-E 2 Meteorological Service of Canada 3 Department of Fisheries and Oceans 4 Collecte Localisation Spatiale (CLS), Toulouse, France 5 Mercator-Ocean, Toulouse, France World Weather Open Science Conference, Montreal, August 18, 2014
2 Background CMC currently produces ice forecasts from 3 systems: Coupled Gulf of St. Lawrence Forecasting System (RDPS-CGSL) Regional Ice Prediction System (RIPS) Global Ice Ocean Prediction System Updates to all three accepted for CMC operations in May Overview Update to Coupled GSL system MoGSL -> NEMO Impact on atmospheric and sea ice forecast skill RIPS Increased resolution and improved downscaling GIOPS Update to SST assimilation and impact on ice forecast skill
3 CONCEPTS Global Ice-Ocean Prediction System Produces daily ice-ocean analyses and 10day forecasts NEMO-CICE (~1/4 ), < 15km in Arctic Mercator Ocean Assimilation System (SAM2): Sea surface temperature Temperature and salinity profiles Sea level anomaly from satellite altimeters Global 3DVar Ice analysis: SSM/I, SSM/IS, CIS charts, Radarsat image analyses Experimental implementation (March 2014) Purpose: Boundary conditions for regional systems Initialize seasonal forecasts Emergency response Global coupled forecasting Sea ice forecasting See presentation by D. Surcel-Colan, Thurs. 1:20pm Page 3 Smith et al., QJRMS, 2014, accepted
4 Ocean data assimilation Système d Assimilation Mercator (SAM) v2 SAM2 assimilates : Sea surface temperature (from both satellite and in situ observations) Subsurface temperature and salinity (from Argo, CTD, XBT, moorings, marine mammals) Sea level anomalies from satellite altimeters (AVISO) Altimeters: Jason 2 Cryosat2 Altika Lellouche et al., Ocean Sci., Smith et al., QJRMS, 2014, accepted
5 Blending with 3DVAR ice analyses Smith et al., QJRMS, 2014, accepted Require multicategory blending for CICE Method 1: Rescale distribution (RED) For each category i: Ca(i) = Cf(i)*Aa/Af Ca = analysis partial conc Cf = trial partial conc Aa = total conc from 3DVAR analysis Af = total conc from trial Method 2: Rescale Fcst Tendency (RFT) Ca t (i) = (Cf(i)-Ca t-1 (i))*aa/af Use of RFT results in a smaller impact on total ice volume Due to a reduction in the impact of assimilation on thick ice categories Some errors remain
6 Update to GIOPS (v1.1.0) Motivation for update SST errors near ice edge problematic for RIPS (degradation of forecast skill). Especially relevant when cycling CICE in RIPS (under development) Modification included in GIOPSv1.1.0 Under-ice sea surface temperature assimilation 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=freezing when ice conc>0.2. Full SST field assimilated. Improved ice blending algorithm (RFT) Page 6 September 9, 2014
7 Comparison with CMC SST analysis Mean differences for to Previous experimental system: GIOPSv1.0.1 (SAMv1.5.7) Updated system GIOPSv1.1.0 (SAMv1.8.2) Improved under-ice SST assimilation substantially reduces differences with CMC SST analysis V1.5.7 V1.8.0
8 Evaluation of sea ice forecasts using 3DVAR 10km global analyses Only evaluate points where the 3DVAR analysis changes by more than 10% (RIPS DGLA Verification) Pro: Only includes points where we have confidence in 3DVAR ice analysis Focus on ice edge in particular Con: Excludes areas of incorrect model changes E.g: coastal polynyas, false alarms along the ice edge Examine RMS error and biases: as a function of lead time spatially temporally
9 Verification of ice forecast skill Compared to 3DVar analyses (where ΔGL>0.1) Reduction in RMS in both hemispheres Improvements most notable in Southern Hemisphere
10 Verification of ice forecast skill Compared to 3DVar analyses (where ΔGL>0.1) Reduction in RMS in both hemispheres Improvements most notable in Southern Hemisphere Lower RMS error over Arctic in fall/winter
11 Verification of ice forecast skill Compared to 3DVar analyses (where ΔGL>0.1) Reduction in RMS in both hemispheres Improvements most notable in Southern Hemisphere Lower RMS error over Antarctic in fall/winter
12 Evaluation against 3DVAR ice analyses Weekly forecasts for 2011 (50 total), lead time of 168h GIOPS GIOPS 3DVAR 3DVAR Pers Pers Forecasts show lower RMS errors than persistence over most regions Largest forecast errors East of Greenland, Baffin Bay, Hudson Bay and Barents/Kara Sea
13 Verification of ice forecast skill Compared to 3DVar analyses (where ΔGL>0.1) V1.8.0 V1.5.7 V1.8.0 forecasts show lower RMS errors than v1.5.7 over most regions Largest improvements: East of Greenland, Baffin Bay, Hudson Bay and Barents/Kara Sea
14 Evaluation of cycling using IMS analyses Comparison with IMS Analyses: Interactive Multisensor Snow and Ice Mapping System (NOAA-NIC) Daily Northern Hemisphere ice analyses on 4km grid (ice/water) Assimilates : AVHRR, GOES, SSM/I Evaluation Methodology: Interpolate model forecasts to IMS grid Calculate contingency table values using 0.4 ice concentration cutoff Bin results on 1 lat-lon grid Forecast Ice Forecast No ice IMS Ice Hit ice Miss IMS No ice False Alarm Hit water
15 Verification of ice forecast skill Compared to IMS analyses Improved PCT in winter, summer and fall Due mostly to reduced false alarms by warming SST near ice edge
16 Evaluation against 3DVAR ice analyses Global domain, lead time of 168h Overall, forecasts show lower RMS errors than persistence Despite strong seasonality and differing regional errors, overall forecast skill remarkably constant (about 0.28) Strong sensitivity to vertical mixing and quality of ocean analysis With wave breaking parameterization Without additional mixing RMS Bias
17 Small-scale ocean variability Without additional mixing GIOPS CMC Global Ice-Ocean Prediction System (GIOPS) 7day RMS forecast error evaluated against analyses for 2011 (50 weekly forecasts) Restricted to points where analysis changed by more than 10% Ice forecast skill exhibits strong sensitivity to ocean mixing With/without parameterization for surface wave breaking Comparison with Argo shows better results with additional mixing Highlights need for more polar observations! Persistence With additional mixing Page 17 Smith et al., ECMWF Proc., 2013
18 Summary and Outlook Three sea ice forecast suites are now running in operations at CMC Coupled Gulf of St. Lawrence (5km; 48hr) Regional Ice Prediction System (Arctic-N. Atl, 3-8km; 48hr) Global Ice Ocean Prediction System (12-25km; 10days) All three systems have been verified with available observations and show significant forecast skill with respect to persistence Forecast skill sensitive to details of SST assimilation and vertical mixing parameterization Verification methods and observation error hinder our ability to improve these systems
19 Thank you!
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