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 http://www.icdc.zmaw.de/

Ice thickness Thickness 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 improvementQuestion-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, http://pdaf.awi.de).

• 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, 2010. • 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 (http://www.osi-saf.org/) BGEP sea ice draft (http://www.whoi.edu/beaufortgyre) Ice mass-balance buoys (IMBs; http://IMB.crrel.usace.army.mil)

(Yang et al., Ann. Glaciol., 2014)

RMSE evolution of sea ice concentration

(Yang et al., Ann. Glaciol., 2014)

NSIDC Independent 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)

(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

(Yang et al., JGR-Oceans, 2014, submitted)

Impact on mean sea ice thickness forecasts

BGEP_2011a BGEP_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 (http://tigge.ecmwf.int/)

Thanks for your attention!Qinghua.yang@awi.de