Recent Improvements in the U.S. Navy’s Ice Modeling ... · Recent Improvements in the U.S....

Recent Improvements in the U.S. Navy’s Ice Modeling Efforts Using CryoSat-2 Ice Thickness

for Model Initialization Richard Allard1, David Hebert1, Pamela Posey1,

Alan Wallcraft1, Li Li2, William Johnston3, Michael Phelps4, 1NRL Oceanography Division, Stennis Space Center, MS

2NRL Remote Sensing Division, Washington, D.C. 3Computational Physics, Inc., Fairfax, VA

4Jacobs Engineering, Stennis Space Center, MS

16-17 November 2016 Sea Ice Thickness Workshop

Outline

• ACNFS • Ice concentration assimilation • Model initialization and hindcast studies • Model ice thickness comparisons

– WHOI ULS, CRREL Ice Mass Balance Buoys, NASA IceBridge

• Ice Volume comparison • Summary and Future Plans

16-17 November 2016 Sea Ice Thickness Workshop 2

Arctic Cap Nowcast/Forecast System


• ACNFS consists of 3 components: ‒ Ice Model: Community Ice CodE (CICE) ‒ Ocean Model: HYbrid Coordinate Ocean

Model (HYCOM) ‒ Data assimilation: Navy Coupled Ocean

Data Assimilation (NCODA) • Operational since September 2013 • Runs daily at Naval Oceanographic

Office (NAVOCEANO) • ACNFS produces nowcast/7-day

forecasts for the Northern Hemisphere polar regions

• Forced with NAVy Global Environmental Model (NAVGEM) atmospheric products

• Products pushed daily to NOAA and the U.S. National Ice Center (NIC)

Grid resolution is ~3.5 km at the North Pole

ACNFS Ice Concentration (%) Sep/Oct 2016

http://www7320.nrlssc.navy.mil/hycomARC/

Arctic Cap Nowcast/Forecast System


SSMIS AMSR2

NAVGEM

atmospheric forcing

• CICE Output: Ice concentration, ice thickness, ice drift, lead opening rate • HYCOM Output: SSH, 3D temperature, salinity, and ocean currents

Assimilated Ice Concentration (IC)


15 Aug 2012 SSMIS only

15 Aug 2012 AMSR2/IMS

•Since the late 1990’s, DMSP SSM/I and SSMIS IC (25 km) have been assimilated into the Navy’s ice forecast systems

•Passive microwave sensors are known to underestimate sea ice extent in summer melt season

•Collaborated with National Snow and Ice Data Center (NSIDC) to develop a technique to assimilate AMSR2 (10 km) and NIC Interactive Multisensor Snow and Ice Mapping System (IMS) ice mask (4 km)

•NAVOCEANO implemented AMSR2 and IMS products into ACNFS and GOFS 3.1 in February 2015

•VIIRS IC testing underway at NRL

Satellite Sea Ice Thickness Observations

• CryoSat-21,2,3,4 (CS2) – Synthetic Aperture Interferometric Radar Altimeter – Repeat cycle: 369 days; 30 day “subcycle” – Algorithms are based on the measurements of freeboard. 3

different retracking approaches are available: • UCL: Threshold retracker at 50% (5 km) • NASA: Waveform fitting (25 km) • AWI: Threshold retracker at 40%

– Uncertainties depend on surface roughness and snow depth, which are not well quantified.


2Tilling, R., A. Ridout, A. Shepherd, and D. Wingham: Increased Arctic sea ice volume after anomalously low melting in 2013, Nature Geoscience, 8, 643-648, doi: 10.1038/NGEO2489. 3http://www.cpom.ucl.ac.uk/csopr/seaice.html 4Ricker, R., S. Hendricks, V. Helm, H. Skourup, and M. Davidson (2014), Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness On radar-waveform interpretation, The cryosphere, 8 (4), 1607-1622, doi:10.5194/tc-8-1607-2014.

1Kurtz, N. T., N. Galin, and M. Studinger (2014), An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting, The Cryosphere, 8, 1217-1237, doi:10.5194/tc-8-1217-2014.

Generation of CS2 Ice Thickness Field • Start with ACNFS monthly mean thickness for MM/YY • Compute difference in monthly means

– 28-day CS2 minus ACNFS monthly mean • Add difference to ACNFS thickness on 15 March (year) to get

desired ice thickness • Distribute thickness across CICE model categories the same as

ACNFS


NASA-CS2 ESA-CS2 ACNFS

15 M

AR 2

014

Experiments • 2 hindcast studies performed for the period of

March 15, 2014 – September 30, 2015 with initialization from thickness fields from CS2-NASA and CS2-ESA and compared with ACNFS.

• Identical forcing used for all experiments (NAVGEM atmospheric forcing, NCODA data assimilation etc.)

• Evaluations performed of: – modeled ice thickness versus WHOI ULS and CRREL

Ice Mass Balance Buoys – modeled ice drift versus IABP – ice volume vs PIOMAS


ULS Comparisons


http://www.whoi.edu/page.do?pid=137076

•A

•B

•D

ULS ice draft data • originally sampled at 0.5 Hz • daily means • converted to ice thickness

A

B

D

Apr-14 Oct-14 Apr-15 Oct-15

Scatter Plot at ULS “A”


CRREL Ice Mass Balance (IMB) Comparisons (1/2)

16-17 November 2016 Sea Ice Thickness Workshop 11 http://imb.erdc.dren.mil/buoysum.htm

2013F

2014B

2014C

END

START

Apr-14 Aug-14 Jun-14

Apr-14 Aug-14 Jun-14

Apr-14 Apr-15 Oct-14

CRREL Ice Mass Balance (IMB) Comparisons (2/2)


2015D

Little difference between ACNFS and CS-2 runs; Modeled ice thickness shows less melt after July ‘15

Ice Thickness Summary Statistics


Mean Bias RMSE R

ULS-D 1.51 ACNFS 2.89 1.38 1.56 0.73 NASA 2.00 0.49 0.98 0.63

Mean Bias RMSE R

ULS-B 1.61 ACNFS 3.17 1.58 1.65 0.60 NASA 1.99 0.38 0.69 0.53

Mean Bias RMSE R

ULS-A 1.27 ACNFS 2.64 1.37 1.59 0.82 NASA 1.74 0.47 0.95 0.67

WHOI Upward Looking Sonar CRREL Ice Mass Balance Buoy

Mean Bias RMSE R

2015-D 1.67 ACNFS 2.04 0.37 0.43 0.91 NASA 2.01 0.35 0.42 0.93

Mean Bias RMSE R

2014-C 1.61 ACNFS 3.19 1.50 1.59 0.90 NASA 1.48 -0.21 0.33 0.94

Mean Bias RMSE R

2013-F 1.58 ACNFS 3.34 1.76 1.77 0.84 NASA 1.89 0.31 0.48 0.85

Ice Drift Comparisons Using Drift Data From IABP


NASA IceBridge


2014 NASA IceBridge

Mean Bias RMSE R

3-28-14 2.52

ACNFS 3.62 1.09 1.17 0.52

NASA 3.28 0.75 1.12 0.41

ESA 3.56 1.04 1.33 0.43

Mean Bias RMSE R

3-14-14 2.07

ACNFS 2.92 0.86 1.04 -0.15

NASA 2.31 0.25 0.60 0.74

ESA 2.33 0.26 0.53 0.75

”Laxon Line”

“Canada Basin South”

Laxon

Ice Volume


Navy models consistently show larger volumes than PIOMAS; NASA is “closer” to PIOMAS through June 2015.

Summary • NASA CS2 initialization method shows significant improvement

at ULS locations (fixed) with lower bias versus observations; significant improvement from operational ACNFS; mean bias reduced by 0.99 m; ESA CS2 (not shown) mean bias reduced by 0.71 m.

• Bias at 4 CRREL IMBs is reduced from an average of 1.35 m (ACNFS) to 0.21 m; central Arctic results very similar for all 3 model experiments.

• Some improvement shown with NASA IceBridge, but bias and RMSE errors still large (compared to previous years)

• Little impact seen in ice drift analysis; some lower errors found during summer months for Beaufort/Chuk./Ber. Seas and Greenland/Norwegian Seas


Future Plans • Repeat experiments with blended CS2/SMOS data using

a weighted uncertainty technique • Repeat studies with melt ponds, different snow cover

options • Investigate reinitialization techniques for seasonal

applications (Fall, Winter, Spring) • Implement and test techniques to assimilate swath and

in situ ice thickness data on a more frequent basis (e.g., every 3 days…)

• Prepare for testing with ICESat-2 ‒ How to use data with latency of 30-90 days? ‒ What is best technique to reinitialize and bring model system

to real time?


Questions ?


Scatter Plots at ULS “A”


Apr ‘14 Jun ‘14 Aug ‘14

Oct ‘14 Dec ‘14 Feb‘15

Recent Improvements in the U.S. Navy’s Ice Modeling ... · Recent Improvements in the U.S....

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