Data Assimilation for Arctic Ice and Ocean Models Richard Allard 1, David Hebert 1, E. Joseph Metzger 1, Michael Phelps 3, Pamela Posey 1, Ole Martin Smedstad 2, Alan Wallcraft 1 1 NRL Oceanography Division 2 Vencore Services and Solutions, Inc. 3 Jacobs Engineering July 2015YOPP Summit1

YOPP Objectives (1-7) 1.Improve the polar observing system to provide better coverage of high- quality observations in a cost effective manner. 2.Gather additional observations through field programmes aimed at improving understanding of key polar processes. 3.Develop improved representation of key polar processes in uncoupled and coupled models used for prediction, etc. 4.Develop improved data assimilation systems that account for challenges in the polar regions such as sparseness of observational data, steep orography, cryosphere uncertainties, model error and the importance of coupled processes. 5.Explore the predictability of the atmosphere-cryosphere-ocean, with a focus on sea ice, on time scales from days to a season. 6.Improve understanding of linkages between polar regions and lower latitudes and asses skill of models representing these. 7.Improve verification of polar weather and environmental predictions to obtain quantitative knowledge on model performance, etc July 2015YOPP Summit2

Modeling Systems Using Data Assimilation Regional, Pan-Arctic, Global Models –Individual ice, ocean, wave, atmosphere modeling systems –Coupled ice-ocean (forced with standalone atmosphere) –Coupled ice-ocean-land-atmosphere –Ensemble-based modeling systems July 2015YOPP Summit3

GOFS 3.1 consists of 3 state-of-the-art components: Ice Model: Community Ice CodE (CICE) Ocean Model: HYbrid Coordinate Ocean Model (HYCOM) Data assimilation: Navy Coupled Ocean Data Assimilation (NCODA) Transitioned to NAVOCEANO Sept 2014 Currently undergoing OPTEST Outputs nowcast/7-day forecasts of ice concentration, ice thickness, ice drift, sst, sss and ocean currents Products pushed daily to the NIC and NOAA Once operational, GOFS 3.1 will replace Arctic Cap Nowcast/Forecast System (ACNFS) Added capability of forecasting ice conditions in the southern hemisphere Global Ocean Forecast System (GOFS) 3.1 Black line is the independent ice edge location (NIC). Grid resolution ~3.5 km at the North Pole GOFS 3.1 ice concentration (%) Jun/Jul ‘ July 2015YOPP Summit4 Similar to systems such as Canadian Global Ice Ocean Prediction System (GIOPS), etc.

13-15 July 2015YOPP Summit5 U.S. Navy’s Data Assimilation System Ocean Data QC 3DVAR

Developing Cross-correlations Using Model and Observed Data Investigate relationships between ice thickness, ice concentration, ice drift, and ice age to – develop cross-correlation functions of these observation types to incorporate into NCODA ‒ Exploit ACNFS assimilative runs since 2007 (to present) If ice age is assimilated (for example), the 3DVAR analysis will produce a correction for ice thickness. 6 Rollenhagen et al. (2009) examined assimilation of sea ice drift by determining covariance between ice thickness/concentration and ice drift. ICE DRIFT ICE THICKNESS ICE AGE ICE EDGE ICE THICKNESS ICE CONCENTRATION July 2015YOPP Summit

Data Types for Assimilation/Verification Ocean Satellite SST Profile (e.g., ITP, UpTempO) Ship Buoy (SST) Argo Floats XBT, CTD Tagged Marine Mammals Glider –SLOCUM, Spray –WaveGlider Wave observations –SWIFT buoy Snow Airborne (radar) In situ Ice Concentration (e.g., SSMI, AMSR2) Ice Edge (e.g., IMS) Ice Age (NSIDC) Ice Thickness ‒ CryoSat-2 ‒ SMOS ‒ NASA IceBridge (Mar/Apr) ‒ CRREL/SAMS IMB (point) ‒ ULS (draft) ‒ Airborne (YOPP?) ‒ ICESat-2 (2018?) Ice Surface Temperature (VIIRS) Ice Drift July 2015YOPP Summit7 Data should be available from Global Telecommunications System (GTS). What additional data will be offered through YOPP partners?

Arctic Observations Available for Assimilation and Validation July 2015YOPP Summit8 SWIFT Buoy

Example of Temperature Data Assimilated into ACNFS July 2015YOPP Summit9

Ice Tethered Profilers July 2015YOPP Summit10

Ice Modeling Assimilation from Satellites High resolution ice concentration data Since the late 1990’s, DMSP SSMI and then SSMIS ice concentration (25km) has been assimilated in the Navy’s ice forecast systems. Passive microwave sensors have a known problem with underestimating sea ice especially during the summer. Worked with NSIDC to develop technique to assimilate: AMSR2 (10km) and NIC’s Interactive Multisensor Snow and Ice Mapping System (IMS) ice mask (4km). Worked with NAVO to implement new real-time data feeds NCODA Ice concentrations from SSMIS and AMSR2 NCODA Analysis IMSIMS Modified NCODA Analysis ACNFS and GOFS 3.1 ACNFS Ice Concentration 15 Aug 2012 SSMIS only ACNFS Ice Concentration 15 Aug 2012 AMSR2 and IMS

Ice Edge Error Improvements The blended product (black) during the summer period (Aug/Sep) shows the greatest reduction in daily mean error. Adv. Microwave Scanning Radiometer-2 (AMSR-2) Multisensor Analyzed Sea Ice Extent (MASIE) July 2015YOPP Summit12

Ice Thickness Data for Model Initialization Algorithm improvements have resulted in more realistic ice thickness estimates from CryoSat-2. However, some areas with ice (Bering Strait) are not represented. NRL is developing a composite ice thickness data set with CryoSat-2 and Surface Moisture and Ocean Salinity (SMOS) ice thickness; –this data set will tested in the CICE model for ice thickness initialization. CryoSat-2 28-day composite April 22 – May 19, July 2015YOPP Summit13

Ice Thickness Data for Model Initialization Algorithm improvements have resulted in more realistic ice thickness estimates from CryoSat-2. However, some areas with ice (Bering Strait) are not well represented. NRL is developing a composite ice thickness data set with CryoSat-2 and Surface Moisture and Ocean Salinity (SMOS) ice thickness; –this data set will tested in the CICE model for ice thickness initialization July 2015YOPP Summit14 CryoSat-2Merged CryoSat-2/SMOS March 2015

Observing System Simulation Experiments (OSSEs) Used to investigate the potential impacts of prospective observing systems. Can be used to investigate present observational and data assimilation systems to test the impact of new observations on them. Data denial experiments can be performed to assess impact of observations. OSSEs could be performed to identify observational requirements for YOPP (e.g., density of buoys, upper-air profiles) for a given target spatial domain July 2015YOPP Summit15

YOPP Issues Availability of observational data in near real- time to support modeling efforts. –Is the data in a WMO standard format? –Can the data be distributed via GTS? –Will it be available on the YOPP data archive? –If not, can the data be accessed through ftp? What is the latency in posting of data? Will in situ data be shared with YOPP partners? YOPP web portal to provide links to data, model forecasts, etc.? July 2015YOPP Summit16

Way Forward Identify anticipated “new” data sources that can be assimilated into modeling systems Coordination with MOSAIc and other field programs Perform model intercomparison studies (coupled, uncoupled) –What is impact of additional data (type, density etc.) on predictive skill? –What new parameterizations can be incorporated into these models based on new targeted observational data? July 2015YOPP Summit17

Questions ? July 2015YOPP Summit18