Physical Processes – Sea ice and Ocean Greg Smith Recherche en Prévision Numérique (RPN) Meteorological Research Division, Environment Canada WWRP, THORPEX, WCRP Polar Prediction Workshop, October 2010

Page 2 – January 21, 2016 Overview of Ice ocean processes

Page 3 – January 21, 2016 Contents Gulf of St. Lawrence (GSL): –Mini-Arctic Laboratory –What have we learned from coupled GSL system about ice-ocean processes important for polar prediction? Examples of key processes –Ocean: ▪Polynyas and their role in heat and moisture fluxes ▪Tides ▪Unresolved processes –Ice: ▪Effect of ice deformation in the marginal ice zone on surface roughness ▪Melt ponds (ice albedo)

Page 4 Development of the Gulf St-Lawrence forecast system A first operational fully coupled Atmosphere-Ocean-Ice Gulf of St. Lawrence N. Atlantic The Gulf of St. Lawrence (GSL) Between January and March the GSL is nearly entirely ice covered Ice conditions can change rapidly (storms, tides) Coastal weather forecasts are strongly affected by the ocean conditions. Coupled atmosphere-ice-ocean forecasting system To improve the atmospheric forecasts (icing, clouds, fog,…) To improve the ice-ocean forecasts (ice, currents, temperature, waves…) To improve the services: Major Seaway Users: Canadian ice service, coast-guard, maritime transportation Mini polar laboratory! –Semi-enclosed –Densely populated Circulation is controlled : by tides, exchanges with atmosphere, runoff from land, the seasonal ice cover, and the inflow through the bounding straits

Page 5 – January 21, 2016 The Gulf of St. Lawrence (GSL) Coupled System (forecast season, 2008) -5°C -15°C -25°C Better representation of sea ice improves the weather forecasts Dynamic ice cover allows ice- free water to open up, buffering atmospheric temperatures Demonstrates importance of air-ice-ocean coupling even for short-range weather forecasts

Page 6 Ice fraction 48h forecast 2 way coupled Atmosphere-Ocean-Ice An interesting Case Case: Particularly interesting given that the intense atmospheric circulation that dramatically changed the Ice conditions in only 48 hours was preceded by a cold and relatively quiet period.

Page 7 C d) A C % Anticosti Clouds Ice Water Ice Observation Forecast (coupled) Ice Valid: 14/03/97 20 Z after 44 hours Atmosphere-Ocean-Ice An interesting Case Ice Forecast

Page 8 Atmosphere-Ocean-Ice An interesting Case Difference Air temp. Coupled - Uncoupled Impact on surface air temperature

Page 9 Impacts on low level clouds (air-ocean exchanges) Coupled model forecast c) Water Ice Clouds AVHRR Nova-Scotia New-Brunswick P-E. I. Cape-Breton M. I. Clouds Ice Water Atmosphere-Ocean-Ice An interesting Case Cape-Breton P-E. I.

Page 10 – January 21, h forecasts – Winter Days (January 1 st to March 31 st ) WNH (1) Coupled Uncoupled YYY (2) WSF (3) WXS (4) WNE (5) Evaluation of 24hr surface temperature forecasts at stations around GSL Numerous “cold event” errors not present in coupled model Errors in surface fluxes can result in 5-10°C cold bias in uncoupled forecasts

Page 11 Surface temperature (TT) Forecast hour Dew point temperature (TD) Forecast hour Statistics for February 2008 Uncoupled Fully coupled

Page 12 Examples of ice-ocean processes

Page 13 – January 21, 2016 Polynyas Important sites for: –Atmosphere-ocean exchanges of heat and moisture –Ocean ventilation –Ecosystem effects Two types: –Sensible  driven by ocean heat flux –Latent  mechanically opened by winds or currents Comiso and Drinkwater (2007) Arctic Polynyas

Page 14 – January 21, 2016 Tides Importance of tides in ice-covered seas has been noted as early as Sverdrup (1926). However, complex interactions with sea ice not fully understood. Main effects: –Tidal fracturing opens and closes leads allowing greater ocean heat loss, more growth of ice, and more mobility for the ice cover –Polynyas: e.g. ▪“Great Siberian Polynya” ▪Gulf of St. Lawrence & Hudson Bay ▪Likely anywhere in Arctic where steep bathymetry is present Bareiss & Gorgen (2005) “Great Siberian Polynya” Interaction of tides with grounded icebergs

Page 15 – January 21, 2016 St. Lawrence Estuary Polynya Tides force relatively warm deep layer to surface Results in massive heat and moisture exchanges 10 W/m W/m 2 Mean winter SST Mean winter heat flux

Page 16 – January 21, 2016 Polynya in western Hudson Bay Hudson Bay generally exhibits low ice concentrations along western coast. Due mainly to westerly winds –Winds push ice offshore However, 3D regional modelling study shows that without tides, there is a large reduction in polynya: –Diapycnal mixing from tides provides important heat source –38% increase in ice volume Concentration Thermodynamic sea ice growth rate Ice thickness anomaly Heat flux anomaly Heat flux anomaly Impact of removing tides Saucier et al. (Clim. Dyn., 2004)

Page 17 – January 21, 2016 Arctic Tides Parameterization of tides: –Holloway & Proshutinsky (2007) ▪Enhanced ocean mixing ▪Fracturing and mobilization Net effect subtle: –thinning due to enhanced ocean heat flux competes with net ice growth during rapid openings and closings of tidal leads Tidal energy dissipation rate Temp at 320m no tides with tides diff

Page 18 – January 21, 2016 Neptune effect Interaction of eddies and topography drives a cyclonic rim current Parameterization required for accurate rim currents in OGCMs Holloway et al. (JGR, 2007) demonstrates importance of this effect using model simulations from AOMIP AOMIP: Arctic Ocean Model Intercomparison Study Implications for Atlantic layer heat supply to Arctic and tide/polynya heat fluxes Topostrophy: positive for cyclonic rim currents in N. Hemi. With Neptune Effect No Neptune Effect

Page 19 – January 21, 2016 Sea ice : Surface roughness Parameterization of atmospheric surface drag in the marginal ice zone Experiments with coupled Atm-ice-ocean model by Rinke et al. Impact of new roughness parameterization on surface air temperature and SLP Ice cover very sensitive to details of model parameterizations R. Gerdes (ECMWF Workshop on Ocean-Atmosphere Interaction, Nov. 2008)

Page 20 – January 21, 2016 Sea ice : Melt ponds Cover up to 50% of surface by end of melt season Many effects of melt ponds on sea ice heat and mass balance –Taylor & Feltham, 2004 Melt rate beneath ponds 2-3 times greater Albedo: – for ponds – for ice & snow Further work on parameterizations required… –Flocco et al. (JGR, 2010) Ice thickness Ice concentration Pond depth Pond area Flocco et al. (JGR, 2010)

Page 21 – January 21, 2016 Effect of ice thickness on predictability Processes affecting ice thickness especially important, as thickness is not well observed –Challenge for forecasting Ice forecasts very sensitive to thickness specification Canadian (CNOOFS) NW Atlantic 10day forecasts –Treatment of thickness when using 3DVAR-FGAT ice analyses has large impact on RMS forecast skill thickness concentration

Page 22 – January 21, 2016 Ice concentrationIce thickness (m) Radarsat coverage Bias Possible overestimation of heat flux? Need more resolution in the estuary? Too much wind drag? Pile up overestimation? Not enough ice categories? RMSE Water temperatures from climatology may not do the best job? Ice input from BI strait may improve results? What can we learn from ice forecast error?

Page 23 – January 21, 2016 A word of caution… Adequately representing processes in model parameterizations often not straightforward E.g Jourdain et al. (Clim Dyn., 2010): –Choice of coupling strategy has large impact on sensible heat fluxes –Impact of heat flux parameterization about twice the effect of coupling itself! Time series of the bottom potential density averaged over the Ross continental shelf. Uncoupled Coupled1 Coupled2

Page 24 – January 21, 2016 Summary remarks Polar regions exhibit a wide-range of coupled air-ice-ocean processes. As such, polar prediction requires the use of detailed models that can include as many of the processes as possible. However, due to scale of processes many will have to be parameterized Care is required in the formulation of parameterizations as models may be very sensitive to small details. Observations critical to supporting this activity: –Understand (and prioritize) processes –Development of parameterizations –Model evaluations –Forecast verification

Page 25 – January 21, 2016 Processes not well captured Ocean: –Deep convection, interior diapycnal mixing, boundary currents, shelf circulation, downslope flows that entrain new fluid during descent, thin cascading overflows, delicate upper ocean stratification by both heat and salt Ice/Snow: –Internal ice rheology and dynamics, melt pond formation and drainage, tidal straining, brine rejection, snow compaction and loss, land-fasting, polynya formation

Page 26 – January 21, 2016 Extras…

Page 27 Data: Hourly air & dew point temperature, surface pressure, cloud cover 6-hourly precipitation accumulation Objective Evaluation (Surface observations) 44 stations Operational implementation

Page 28 Impact on atmospheric variables Winter 2008 Forecast hour % Coupled System better (> 50%) Operational implementation Temperature Humidity Clouds Precipitation Surface pressure