Assimilating sea ice concentration and SMOS sea ice thickness using a local SEIK filter August 18, 2014 Qinghua Yang, Svetlana N. Losa, Martin Losch, Xiangshan Tian-Kunze, Lars Nerger, Jiping Liu, Lars Kaleschke and Zhanhai Zhang

Content Introduction Model and Data assimilation (DA) Sea ice concentration DA in summer Sea ice thickness DA in cold season Summary & outlook

1. Introduction Arctic sea ice is in rapid decline in summer (IPCC, 2013) Arctic marine opportunities and risks: sea ice forecasts Factors affecting sea ice forecasts  Model biases  Atmospheric forcing  Sea ice data assimilation (DA)

Near real-time sea ice observations (Arctic ocean scale) in summer Sea ice concentration (NSIDC-SSMIS, OSISAF-SSM/I, UB- AMSR2) Sea ice drift (OSISAF-AVHRR; limited numbers) in cold season Sea ice concentration (NSIDC-SSMIS, OSISAF-SSM/I, UB- AMSR2) Sea ice drift (OSISAF-AMSR2/SSMI/ASCAT/AVHRR, IFREMER-SSMI/AMSR2) Sea ice thickness (UH-SMOS; the first operational thickness)

SMOS sea ice thickness data Derived from ESA-Soil Moisture and Ocean Salinity (SMOS) brightness temperatures The first daily near-real time sea ice thickness data; Only valid from October to April Maximum retrievable thickness: 0-1 m Uncertainty provided Ice thicknessThickness uncertainty

Sea ice data assimilation (DA) Sea ice concentration DA (Lisæter et al., 2003; Lindsay and Zhang, 2006; Stark et al., 2008; Wang et al., 2013)  large ice concentration improvement  small ice thickness improvement Question-1: Sea ice concentration DA with LSEIK? Sea ice thickness DA (Lisæter et al., 2007: assimilating synthetic ice thickness) Question-2: SMOS ice thickness DA with LSEIK?

2.1 Model configuration MITgcm ice-ocean model with an optimized Arctic regional configuration (Nguyen et al., 2011) ~ 18km horizontal resolution Forcing: Japan Meteorological Agency (JMA) analysis (‘hindcast’)

2.2 Data assimilation methodology Ensemble Kalman filter (local SEIK) in Parallel Data Assimilation Framework (PDAF, 24-hour forecast/analysis cycles Ensemble size 15 State vector (sea ice concentration + thickness) Assumed data errors ‘Forgetting factor’: inflate the ensemble error covariance Localization: 126 km radius (~ 7 grid points), weight on data errors

3. Sea ice concentration DA in summer Study period: June 1 to August 31, LSEIK-1: NSIDC SSMIS ice concentration (RMS=0.30, “relative weight” error) Ice thickness update: by the concentration observations and background error covariance. Independent data for comparison:  OSISAF sea ice concentration (  BGEP sea ice draft (  Ice mass-balance buoys (IMBs; (Yang et al., Ann. Glaciol., 2014)

RMSE evolution of sea ice concentration (Yang et al., Ann. Glaciol., 2014) NSIDCIndependent OSISAF

BGEP_2009A BGEP_2009D IMB_2010A IMB_2010B (Yang et al., Ann. Glaciol., 2014) Comparison with in-situ sea ice thickness IMB_2010A

4. Sea ice data assimilation in autumn-winter transition Study Period: November 1, 2011 to January 31, 2012 (A freeze-up period) LSEIK-1: SSMIS concentration (RMS=0.30); Same as summer LSEIK-2: SSMIS concentration (RMS=0.30) + SMOS thickness (0-1 m; provided uncertainty) In LSEIK-2, both ice concentration and thickness updates are influenced by the assimilated two data sets Independent data for comparison (Yang et al., JGR-Oceans, 2014, submitted)

RMSE evolution of sea ice concentration NSIDC Indepedent OSISAF

SMOS thickness (Yang et al., JGR-Oceans, 2014, submitted) RMSE evolution of sea ice thickness

LSEIK-1 LSEIK-2 Mean deviation RMS deviation Comparison with in-situ sea ice thickness Sea ice thickness evolution at BGEP_2011a (top left), BGEP_2011b (top right), BGEP_2011d (bottom left), IMB_2011K (bottom right) (Yang et al., JGR-Oceans, 2014, submitted) Impact on mean sea ice thickness forecasts

BGEP_2011aBGEP_2011b BGEP_2011d IMB_2011K BGEP_2011b (Yang et al., JGR-Oceans, 2014, submitted) Comparison with in-situ sea ice thickness

Summary In summer,  the ice concentration has been largely improved by the concentration assimilation, the ice thickness forecasts can also be improved. In the cold season,  the impact of assimilating only sea ice concentration is much smaller than in summer.  The SMOS ice thickness assimilation leads to much better thickness forecasts, and better concentration forecasts.  The SMOS ice thickness assimilation can also improve long- term (>5 days) sea ice forecasts.

Outlook Data assimilation of sea ice drift, SST and snow thickness observations Arctic sea ice data assimilation and ensemble forecasts using TIGGE ensemble forcing data (

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