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

Transcription

1 Recent Improvements in the U.S. Navy s Ice Modeling Efforts Using CryoSat-2 Ice Thickness for Model Initialization Richard Allard 1, David Hebert 1, Pamela Posey 1, Alan Wallcraft 1, Li Li 2, William Johnston 3, Michael Phelps 4, 1 NRL Oceanography Division, Stennis Space Center, MS 2 NRL Remote Sensing Division, Washington, D.C. 3 Computational Physics, Inc., Fairfax, VA 4 Jacobs Engineering, Stennis Space Center, MS November 2016 Sea Ice Thickness Workshop

2 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 November 2016 Sea Ice Thickness Workshop 2

3 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) ACNFS Ice Concentration (%) Sep/Oct 2016 Grid resolution is ~3.5 km at the North Pole November 2016 Sea Ice Thickness Workshop 3

4 Arctic Cap Nowcast/Forecast System NAVGEM atmospheric forcing SSMIS AMSR2 CICE Output: Ice concentration, ice thickness, ice drift, lead opening rate HYCOM Output: SSH, 3D temperature, salinity, and ocean currents November 2016 Sea Ice Thickness Workshop 4

5 Assimilated Ice Concentration (IC) 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 15 Aug 2012 SSMIS only 15 Aug 2012 AMSR2/IMS November 2016 Sea Ice Thickness Workshop 5

6 Satellite Sea Ice Thickness Observations CryoSat-2 1,2,3,4 (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. 1 Kurtz, 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, , doi: /tc Tilling, R., A. Ridout, A. Shepherd, and D. Wingham: Increased Arctic sea ice volume after anomalously low melting in 2013, Nature Geoscience, 8, , doi: /NGEO Ricker, 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), , doi: /tc November 2016 Sea Ice Thickness Workshop 6

7 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 15 MAR 2014 ACNFS NASA-CS2 ESA-CS November 2016 Sea Ice Thickness Workshop 7

8 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 November 2016 Sea Ice Thickness Workshop 8

9 ULS Comparisons A B A D B ULS ice draft data originally sampled at 0.5 Hz daily means converted to ice thickness D Apr-14 Oct-14 Apr-15 Oct November 2016 Sea Ice Thickness Workshop 9

10 Scatter Plot at ULS A November 2016 Sea Ice Thickness Workshop 10

11 CRREL Ice Mass Balance (IMB) Comparisons (1/2) 2013F Apr-14 Oct-14 Apr B END START Apr-14 Jun-14 Aug C November 2016 Sea Ice Thickness Workshop 11 Apr-14 Jun-14 Aug-14

12 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 November 2016 Sea Ice Thickness Workshop 12

13 Ice Thickness Summary Statistics WHOI Upward Looking Sonar Mean Bias RMSE R ULS-A 1.27 ACNFS NASA Mean Bias RMSE R ULS-B 1.61 ACNFS NASA Mean Bias RMSE R ULS-D 1.51 ACNFS NASA CRREL Ice Mass Balance Buoy Mean Bias RMSE R 2013-F 1.58 ACNFS NASA Mean Bias RMSE R 2014-C 1.61 ACNFS NASA Mean Bias RMSE R 2015-D 1.67 ACNFS NASA November 2016 Sea Ice Thickness Workshop 13

14 Ice Drift Comparisons Using Drift Data From IABP November 2016 Sea Ice Thickness Workshop 14

15 Laxon NASA IceBridge Laxon Line 2014 NASA IceBridge Mean Bias RMSE R ACNFS NASA ESA Canada Basin South Mean Bias RMSE R ACNFS NASA ESA November 2016 Sea Ice Thickness Workshop 15

16 Ice Volume Navy models consistently show larger volumes than PIOMAS; NASA is closer to PIOMAS through June November 2016 Sea Ice Thickness Workshop 16

17 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 November 2016 Sea Ice Thickness Workshop 17

18 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 days? What is best technique to reinitialize and bring model system to real time? November 2016 Sea Ice Thickness Workshop 18

19 Questions? November 2016 Sea Ice Thickness Workshop 19

20 Scatter Plots at ULS A Apr 14 Jun 14 Aug 14 Oct 14 Dec 14 Feb November 2016 Sea Ice Thickness Workshop 20

21 16-17 November 2016 Sea Ice Thickness Workshop 21