1. Overview of JPSS Ice Products
<insert name here>
Cooperative Institute for Meteorological Satellite Studies,
Madison, Wisconsin USA
CoastWatch/OceanWatch/PolarWatch Annual Meeting, 15 August 2018

2. The Cryosphere and JPSS✓
River and lake ice
Ice sheets, ice caps, ice shelves ✘ ✓
Sea ice
Permafrost and seasonally-frozen ground ✓
Snow ✘
The point of the labels is that the Cryosphere Teams actually only do floating ice and snow. There are other cryosphere elements that are not covered. ✘
Glaciers

3. JPSS Snow and Ice Products
NPP/JPSS VIIRS
Snow cover (binary)
Snow fraction
Ice thickness and age
Ice concentration
Ice surface temperature
Ice motion (experimental)
Sea ice leads (under devel.)
Polar winds
AMSR-2 on GCOM-W1
Snow cover
Snow depth
Snow water equivalent (SWE)
Ice characterization
Ice age class (first-, multi-year)
Ice concentration
Ice motion (experimental)

4. VIIRS Ice Surface Temperature
IST provides the radiating, or “skin”, temperature of the sea and fresh water ice surface under clear-sky conditions. It includes the aggregate temperature of objects comprising the ice surface, including snow and melt water on the ice. The VIIRS IST EDR provides surface temperatures retrieved at VIIRS moderate resolution for ice-covered oceans both day and night.

5. VIIRS Ice Surface Temperature
Advantages: High spatial resolution (750 m) and high accuracy, as determined through validation studies with surface temperature measurements from aircraft campaigns (NASA IceBridge). It has been shown to have a near-zero bias and a root-mean-square error (RMSE) of less than 1 K.
Limitations: It is a clear-sky only product, and errors in the cloud mask produce errors in IST.

6. VIIRS Ice Concentration
(animation)
Ice concentration provides the fraction of an area covered by ice. It is calculated for every VIIRS moderate resolution band pixel (750 m) over unfrozen ocean and inland water bodies.

7. VIIRS Ice Concentration
Weekly Composite, 27 Oct 2016
VIIRS Ice Concentration
Advantages: High spatial resolution (750 m) and detail relative to passive microwave. Comparisons to Landsat-8 indicate that differing concentrations have an absolute magnitude of less than 10%.
Limitations: Clear-sky only. Errors in the cloud mask produce errors in the ice concentration. The performance of cloud mask is generally better during the “day” (sunlit conditions) than at night. This difference can lead to cloud mask discontinuity in regions that include the terminator.

8. AMSR2 Ice Concentration
AMSR2 vs VIIRS
Right: Enterprise VIIRS Sea Ice Concentration (SIC) along the Alaska Coast on April 27, Left: Passive microwave-derived sea ice concentration from NSIDC for the same area and day.

9. AMSR2 Sea Ice Concentration
Examples of AMSR2 sea ice concentration over the Arctic (left) and Antarctic (right) on 1 May 2018.

10. Multiyear Ice Concentration
Initial comparison with independent ice age fields (Lagrangian tracking of ice parcels) indicates good agreement in terms of spatial distribution of multi-year ice cover.

11. VIIRS+AMSR2 Blended Ice Concentration
Passive infrared/visible ice concentration:
Con: clear-sky only
Pro: high spatial resolution
Passive microwave ice concentration:
Con: low spatial resolution
Pro: all-weather
Compared to AMSR, the blended snow depth showed improved ability to detect real time events, adjust based on surface obs, adjust for high elevations, fill observations between overpass observational gaps, and reduce errors in high desert areas.
Blended sea ice concentration at 1 km resolution on June 24, 2015 using AMSR-2 and the Suomi NPP VIIRS ice concentration products
Blended ice concentration:
high spatial resolution under all-weather conditions

12. Recent Arctic Climate Trends
Satellite-Derived Ice Thickness Products
APP-x
CryoSat-2
SMOS
IceBridge
ICESat
PIOMAS
Wang, X., J. R. Key, and Y. Liu (2010), A thermodynamic model for estimating sea and lake ice thickness with optical satellite data, J. Geophys. Res., 115, C12035, doi: /2009JC
Kwok, R., and G. F. Cunningham (2008): ICESat over Arctic sea ice: Estimation of snow depth and ice thickness, J. Geophys. Res., 113, C08010, doi: /2008JC
Laxon S. W., K. A. Giles, A. L. Ridout, D. J. Wingham, R. Willatt, R. Cullen, R. Kwok, A. Schweiger, J. Zhang, C. Haas, S. Hendricks, R. Krishfield, N. Kurtz, S. Farrell and M. Davidson (2013), CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, doi: /grl
N. T. Kurtz, S. L. Farrell, M. Studinger, N. Galin, J. P. Harbeck, R. Lindsay, V. D. Onana, B. Panzer, and J. G. Sonntag (2013), Sea ice thickness, freeboard, and snow depth products from Operation IceBridge airborne data, The Cryosphere, 7,  , 2013.
X. Tian-Kunze, L. Kaleschke, N. Maaß, M. Mäkynen, N. Serra, M. Drusch, and T. Krumpen (2014), SMOS-derived thin sea ice thickness: algorithm baseline, product specifications and initial verification, The Cryosphere, 8,  , 2014.
Zhang, J.L. and D.A. Rothrock (2003), Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates, Mon. Weather Rev., 131, , 2003.
SMOS instrument is Microwave Imaging Radiometer using Aperture Synthesis (MIRAS). L-band, 1.4 GHz, passive. Emission is a function of salinity and temperature; attenuation for very high salinity and high temperature is only a few cm; 1.5 m for low salinity and cold.
IceBridge snow radar from U. Kansas. 2-7 GHz (broadband); mainly C band with a little of S band on the low frequency end.
CryoSat-2 instrument is SIRAL (SAR/Interferometric Radar Altimeter), Ku band, GHz; 250 m footprint
These are the monthly mean results for March 2012, except for ICESat sea ice thickness, which is a 34-day average from 2 February to 31 March 2008.

13. Recent Arctic Climate Trends
VIIRS/AVHRR Sea Ice Thickness
Advantages: Solid physical foundation with all components of surface energy budget. Capable of retrieving daytime and nighttime sea, lake, and river ice thickness under both clear and cloudy sky conditions. Computationally efficient, easy to implement and maintain, flexible, and fast. Built-in parameterizations can be used if various satellite products are not available.
Limitations: The accuracy of input parameters can significantly impact the accuracy of the ice thickness estimates. Thickness is sensitive to rapid changes in surface temperature. Averages over time are usually better than instantaneous estimates. Daytime retrievals are less reliable than nighttime retrievals. The uncertainty is large for ice more than a few meters thick.

14. Recent Arctic Climate Trends
VIIRS Sea Ice Thickness
Alaska
Left: VIIRS ice thickness on the Ob River, western Siberia, on 16 January The actual river ice thickness on 7 February was cm, as determined by surface-based radar and drilled holes in the area indicated by the red circle.
Above: Collecting validation data on Green Bay.

15. Recent Arctic Climate Trends
VIIRS/AVHRR Sea Ice Thickness
Advantages: Solid physical foundation with all components of surface energy budget. Capable of retrieving daytime and nighttime sea, lake, and river ice thickness under both clear and cloudy sky conditions. Computationally efficient, easy to implement and maintain, flexible, and fast. Built-in parameterizations can be used if various satellite products are not available.
Limitations: The accuracy of input parameters can significantly impact the accuracy of the ice thickness estimates. Thickness is sensitive to rapid changes in surface temperature. Averages over time are usually better than instantaneous estimates. Daytime retrievals are less reliable than nighttime retrievals. The uncertainty is large for ice more than a few meters thick.

16. Ice Motion
The ice motion products provide the speed and direction of ice features over the past 24 hours. Ice motion is currently generated from AMSR2, VIIRS infrared window (M15), blended AMSR2+VIIRS(IR), and the VIIRS day-night band (DNB).

17. Ice Motion
Advantage: It clearly illustrates the medium- to large-scale motion of ice over a 24-hour period.
Limitations: AMSR2 is ambiguous for melting ice, so AMSR2 ice motion is of the highest quality during the winter, spring, and autumn. Cloud cover restricts the area coverage of VIIRS ice motion. Spatial averaging must be employed to achieve a useable result. Data latency and processing time constrain real-time generation.

18. Application: Detection of Warm Event
<Put some of the description in the Notes section here.>
The Enterprise VIIRS Ice Surface Temperature (IST) product clearly illustrates how warm the Arctic is now compared to a year ago. The figure below shows the surface temperature in mid-February 2017 and Weekly loops are available at for 2017 and for The temperature contrast is striking, with the western Arctic being much warmer for this particular week this year, and the Hudson Bay area being colder. The anomalously high temperatures over the Arctic is not confined to this week. The month of January was very warm compared to climatology, as shown using the same IST algorithm applied to over three decades of AVHRR data. This is discussed on the WMO Global Cryosphere Watch (GCW) website at
VIIRS ice surface temperature over the Arctic in mid-February of 2017 (left) and 2018 (right).

19. Application: Weddell Sea Polynya
<Put some of the description from the Notes section here>
VIIRS Ice Surface Temperature (IST) identifies early stages of Weddell Sea polynya formation. During the month of September 2017, a large polynya, called the Weddell Polynya, formed off the coast of Antarctica in the King Haakon VII Sea (Figure 20). A polynya is an irregularly shaped area of persistent open water in sea ice that is sustained by winds or ocean heat. On August 31, VIIRS showed an isolated region of relatively warm surface temperatures near 270 K surrounded by cooler temperatures in the range of K. By September 3, a small opening in the sea ice is observed with temperatures of K, expanding over the region around the opening. Later in the month, by September 25, the polynya becomes evident. The polynya is larger than The Netherlands and almost the size of the U.S. state of Maine. It is the first time a polynya of this magnitude has been observed in the Weddell Sea in 40 years.

20. AVHRR Polar Pathfinder-Extended (APP-x) A Climate Data Record
APP-x contains 19 variables:
Surface skin temperature, snow, ice, land
Surface broadband albedo, all-sky
Sea ice thickness
Surface type
Cloud mask
Cloud particle thermodynamic phase
Cloud optical depth
Cloud particle effective radius
Cloud top temperature
Cloud top pressure
Cloud type
Up/down shortwave radiation at the surface
Up/down longwave radiation at the surface
Up/down shortwave radiation at the TOA
Upwelling longwave radiation at the TOA
Shortwave cloud radiative forcing at the surface
Longwave cloud radiative forcing at the surface
APP-x characteristics:
1982 – present, updated daily
Arctic and Antarctic
25 km resolution , EASE grid
Twice daily centered on 04:00/02:00 (Arctic/Antarctic) and 14:00 local solar time
Available from NCEI

21. Application: Arctic sea ice thickness and volume trends
APP-x, 60-90N,
Jan
Apr
The first and second pairs of (S,P) in each panel are the trend and statistical significance level for ice thickness (red line) and ice volume (blue line).
JAN=January, APR=April, JUL=July, OCT=October, ANN=Annual, S=Slope (trend in per year), P=Statistical significance level
JUL
Oct
Year

22. Application: APP-x “Trackers”
Regional trackers are also being developed