Evaluation of the sea ice forecast at DMI

Transcription

1 DMI Evaluation of the sea ice forecast at DMI Till A. S. Rasmussen 1 Kristine S. Madsen 1, Mads H. Ribergaard 1, Leif T.Pedersen 1, Jacob L Høyer 1, Gorm Dybkjær 1, Mads Bruun Poulsen 2 and Sofie Abildgaard 3 1/ Danish Meteorological Institute 2/ University of Copenhagen 3/ Previous Universitity of Aarhus 7 th international workshop on sea ice modeling, assimilation and verification, 7 th

2 Motivation Shipping and tourism has increasing interest in Greenland and Arctic as the ice retreats and more areas are accessible DMI has operational obligations for Greenland (oil spill, ice charts) Relationship with remote sensing department at DMI The model system act as a test environment for the use of remote sensed data What is the quality of remote sensed data with respect to using it in models? Model feeds into Route guidance Oil spill Polar portal (polarportal.dk) Research projects Operations (experimental)

3 Outline Operational setup Baffin Bay Ice temperatures

4 Coupled ocean and sea ice setup Ocean model (HYCOM ) ~10 km horizontal resolution for the Arctic and the Atlantic to ~20 S 40 vertical levels (hybrid) Sea ice model (CICE 4.0) 5 ice thickness categories 4 vertical layers for each cat 1 Snow layer HYCOM and CICE are coupled using the ESMF 6.3 coupler Operational HYCOM+CICE domain. Modelled ice cover and sea surface temperature, July

5 Forcing ECMWF atmosphere (Deterministic forecast or ERA-Interim) Open boundaries: Tides and climatological temperature and salinity Body tides Rivers from monthly climatology presently 144h

6 Operational assimilation/nudging Nudging of OSISAF Rather use too little data than biased data Only when difference is higher than 10% (avoids unrealistic short term changes of OSISAF) Thickness added according to current ice distribution All inputs are being regridded to model grid We would like to add more sea ice sensors SST: GHRSST Level 4 DMI_OI global product (before 2011: OSTIA) Applied as correction to forcing (energy flux) No nudging of sst where there is sea ice. SSS: climatology (combined WOA and PHC)

7 Simulation history Spin-up ERA-Interim Hindcast ERA-Interim Operational 2014-present ECMWF deterministic forecast

8 Modelled sea ice thickness and SST 2013 Observed sea ice extent

9 Ice thickness comparison DMI Ground truth missing? Comparison of ice thickness from Cryosat 2 with error bars (red) Piomas (another model ) black line DMI points from ice thickness time series (previous slide) Green first year ice Blue multi year ice Figure extracted from Tiling et al DMI points added. Who validates who?

10 POLAR ICE Integration of data into one portal

11 DMI involvement Baffin Bay+Canadian Archipelago setup nested area Similar to the operational setup Resolution: ~3km DMI also provides Ice pressure (based on remote sensing) Arctic forecast Atmospheric forecast (HIRLAM)

12 Ice thickness evolution and DMI comparison with SMOS SMOS thin ice product Based on algorithms from Bremen and and HAMBURG Relatively coarse ~30km Compared to: Copernicus Global (NEMO+LIM). Copernicus Arctic HYCOM+Norwegian ice DMI Baffin Bay (3km) semi operational DMI operational

13 Sea ice thickness 1/ Myocean Global Upper left MyOcean Arctic Upper right (not half colorscale) DMI OPR Lower right DMI highres Lower left

14 Observed and modelled ice DMI thickness Green MyOcean global Yellow MyOcean Arctic Light Blue DMI OPR Magenta DMI Highres Red and Blue SMOS GLOBAL initiates with ~0.5m Freeze up starts early in Global More variation in DMI products (Potentially due to none spatial Assimilation/nudging Nr. 2 Bottom of Nares Strait Nr. 4 Mid Baffin

15 Ice drift Baffin Bay Comparison of ice displacements based on SAR drift and modeled ice velocities (data from Roberto Saldo DTU Space) Based on spring values Number of observations based on SAR drift Difference between model and observation only when observation is available

16 Ice drift comparison General directions are reasonable Displacement is over estimated by a factor of 2 Tuning of drag coefficients based on this Problems in Nares Strait landfast ice Same comparison to be made in full Arctic Expanded to include forecast skill

17 GLOBTEMP: Can ice surface temperatures be used to control the upper thermodynamic boundary? To demonstrate the effect of assimilating IST (ice surface temperatures) into a coupled ocean and sea ice model Evaluate IST product with observations. In this case buoys Motivation: Assimilation of sea ice is currently limited to ice concentration. Only partly constraints the state of the ice Surface boundaries are governed by atmospheric forcing with limited validation Comparison of IST from remote sensing, model and in situ observations Four experiments to be carried out (First two has been done) 1. No assimilation 2. Assimilation of ice concentration 3. Correction of atmospheric forcing temperatures 4. Apply surface temperatures directly 17

18 Challenges of using IST in model Observed sea ice extent SST (bottom color bar) IST right colorbar Challenges: IST measurements are daily Large daily variations Observations are not clearly better than model

19 Time series of remote sensing atmospheric temperature and model Same features present in all sensors, atmospheric forcing and model Metop data are colder. Model and atmosphere correlates well with IASI Suprising as this is the sensor with the coarsest resolution Is this used at ECMWF

20 Monthly means March Sep Mod RS Mod - RS

21 Buoys Mass balance buoys were deployed summer temperature sensors with 2cm intervals STD model ~3 (too warm Std rs ~3 (too cold) Profiles will be extracted from model as well Experiments will be more a sensitivity study than an atempt to do operational assimilation

22 Thickness categories Ice thickness are relatively constant for each category Obs is point meassurements following the same point whereas model is average fields Temperature profile selected based on ice categories close to buoy thickness

23 Temperature from cat 2-4 (Prelim) August 12 Feb 13 (y-axis) Layers (x-axis 0 snow) Coarse compared to obs More energy penetrates to ocean in low categories Full evaluation to be explored

24 Conclusion Sea ice volume is affected by the guess of ice thickness Assimilation can be valuable But needs to be handled with care Ice thickness compares well with SMOS ice thickness in Baffin Bay Model drift is too high Potential source for none assimilated underestimation of sea ice cover In order to use IST for assimilation level 2 of should be used and time window should be small

25 Future perspective Ocean assimilation SSH Move towards real assimilation instead of surface nudging Sea ice assimilation Multiple sensors Automated ice chart Update CICE to CICE 5 New modification to Hibler sea ice rheology Meltponds New hpc system Triple resources Increase resolution horizontally to 1/12 degree (present aim) Maybe we need to focus and increase resolution even more. This will reduce the domain size Coupling to atmospheric model at longer time scales Seasonal forecast Decadal forecast