Data Assimilation for Arctic Ice and Ocean Models

Richard Allard1, David Hebert1, E. Joseph Metzger1, Michael Phelps3, Pamela Posey1, Ole Martin

Smedstad2, Alan Wallcraft1

1NRL Oceanography Division2Vencore Services and Solutions, Inc. 

3Jacobs Engineering

13-15 July 2015 YOPP Summit 1

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.

13-15 July 2015 YOPP Summit 2

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

13-15 July 2015 YOPP Summit 3

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 ‘15

13-15 July 2015 YOPP Summit 4

Similar to systems such as Canadian Global Ice Ocean Prediction System (GIOPS), etc.

13-15 July 2015 YOPP Summit 5

U.S. Navy’s Data Assimilation System

Ocean DataQC

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

13-15 July 2015 YOPP Summit

Data Types for Assimilation/VerificationOcean

• 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

13-15 July 2015 YOPP Summit 7

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

13-15 July 2015 YOPP Summit 8

SWIFTBuoy

Example of Temperature Data Assimilated into ACNFS

13-15 July 2015 YOPP Summit 9

Ice Tethered Profilers

13-15 July 2015 YOPP Summit 10

Ice Modeling Assimilation from SatellitesHigh 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 fromSSMIS and AMSR2

NCODA Analysis

IMS

Modified NCODA Analysis

ACNFS and GOFS 3.1

ACNFS Ice Concentration15 Aug 2012SSMIS only

ACNFS Ice Concentration15 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)

13-15 July 2015 YOPP Summit 12

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, 2015

13-15 July 2015 YOPP Summit 13

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.

13-15 July 2015 YOPP Summit 14

CryoSat-2 Merged 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.

13-15 July 2015 YOPP Summit 15

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.?

13-15 July 2015 YOPP Summit 16

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?

13-15 July 2015 YOPP Summit 17

Questions ?

13-15 July 2015 YOPP Summit 18