Sea Ice Data Assimilation in the Arctic via DART/CICE5 in the CESM1

Transcription

1 Sea Ice Data Assimilation in the Arctic via DART/CICE5 in the CESM1 Yongfei Zhang and Cecilia Bitz 1 Jeffrey Anderson, Nancy Collins, Jonathan Hendricks, Tim Hoar, and Kevin Raeder 2 1 University of Washington, Seattle, WA 2 National Center for Atmospheric Sciences, Boulder, CO Nov. 16, 2016 Sea Ice Thickness Workshop, Boulder, CO 1

2 Outline Introduction of DART/CICE5 Perfect model experiments Openloop (free-run) DA with default settings DA holding hicen DA with inflation and localization distance Updating SST as well Joint assimilation of sea ice concentration and sea ice thickness observations Conclusions 2

3 DART/CICE5 Flowchart of ensemble data assimilation forecast analysis 3

4 Strong Coupling Weakly Coupled Assimilation Observations Observations Data Data Assimilation Assimilation Computer Forecasts Atmos DART CAM Sea Land Ice DART CLM CESM CESM DART Sea Land Ice DART CICE Ocean DART POP 4

5 Perfect model experiments Slab ocean/data atmosphere/cice5 Atmospheric forcing is from the 80-ensemble CAM4/DART reanalysis 30 out of 80 ensembles are used to drive CICE5 ensembles Two CICE model parameters are perturbed Truth and synthetic observations One of the ensembles is chosen as the truth A 15% perturbation is added to the true sea ice concentration to make our synthetic observations Openloop and data assimilation(da) experiments DA experiments are compared with the openloop (free-run) 5

6 1. DA with default settings DA_dflt The state vector includes aicen, vicen, and vsnon Basic post-processing, the analysis aice is confined within 0 and 1 No state inflation Localization distance is set to 0.05 radians (about 300 km) as a reasonable first-guess Jan 2001 to Sep

7 DA_dflt Openloop 7

8 2. DA holding hicen DA_dflt State vector includes aicen, vicen, and vsnon vicen is updated through EnKF, which depends on its covariance matrix with aicen hicen = vicen/aicen will be re-binned after each DA cycle DA_hicen State vector only includes aicen hicen remains the same as the forecast hicen - = vicen + /aicen - vicen + = hicen - x aicen + the aggregate thickness (hice) will still be changed as it s the areal weighted average of hicen 8

9 DA_hicen DA_dflt Openloop Jan 2001 to Sep

10 3. DA with inflation and reduced localization distance Inflation Localization distance Gabsri-Cohn half-with Reduce to 0.01 radians 10

11 DA_inf_loc DA_hicen DA_dflt Openloop Jan 2001 to Sep

12 DA_dflt Openloop aice5 aice Differences of absolute biases in SIC between the DA cases and the openloop DA_hicen Openloop DA_inf_loc Openloop 9-month mean Good Bad 12

13 4. Updating SST as well Strongly Weakly Coupled Coupled Ice-Ocean Assimilation Assimilation Observations Data Assimilation Atmos DA Atmos Land Sea Ice DA DA Land Sea Ice Coupled Earth System Model & DA & Ocean DA Ocean Ocean 13

14 DA_wSST DA_woSST Openloop 14

15 5. Different post-processing methods Simple squeeze (DA_SS) Basic squeezing method Prior weight (DA_PW) sums the increments of all the categories and redistribute them among the categories based on their areal weight in the forecast Tendency weight (DA_TW) sums the increments of all the categories and re-distribute them among the categories based on their tendency weight 15

16 DA_TW DA_PW DA_SS Openloop 2001 to

17 6. Assimilation of sea ice thickness hice = vice/aice A perturbation of 0.1m is added to the true hice to make sea ice thickness observations DA_aice Only sea ice concentration observations are assimilated DA_aice_hice Observations of sea ice concentration and thickness are assimilated jointly into CICE5 17

18 DA_aice_hice DA_aice Openloop 2001 to

19 DA_aice_hice DA_aice Openloop Not as good in sea ice volume 19

20 Conclusions Assimilation of sea ice concentration observations will improve the aggregate sea ice concentration (SIC), not necessary improve that of individual categories Small localization distance is needed to reduce sea ice area bias of each category, given the 30 ensemble size Updating sea surface temperature via data assimilation slightly improves SIC Need longer experiment time to decide which post-processing method works best Assimilating sea ice thickness will not only improve the aggregate SIC, but also significantly improve SIC of every category Need to look into the sea ice volume bias 20

21 Thanks! 21

22 aice(n) n=1 n=2 n=3 n=4 n=5 h 22

23 Observations Data Assimilation Reanalysis, Free-Run Forecasts, & Calibration Historical Obs DA Reanalysis Reanalysis restarts Coupled Earth System Retrospective Forecasts Model Forecast Calibration Current Obs DA Real Time Forecasts 23

24 DA slightly influences SST 24

25 DA_SS Openloop DA_PW Openloop Differences of absolute biases in SIC between the DA cases and the openloop DA_TW Openloop Good Bad 25

26 Correlation of selected points and their surroundings aice vice 26

27 DA_aice_hice DA_aice Openloop 27

28 28

29 DA_aice_hice DA_aice Openloop Arctic sea ice thickness 29

30 Differences of absolute biases in SIC between the DA cases and the openloop aice5 aice DA_aice Openloop DA_aice_hice Openloop Good Bad 30