GCOS SST&SI Working Group. Chairs Nick Rayner Tom Smith Executive committee Ken Casey Elisabeth Kent Alexey Kaplan Craig Donlon Ed Harrison Dick Reynolds. Sea ice subcommittee Søren Andersen, DMI/EUMETSAT OSISAF Florence Fetterer & Walt Meier, NSIDC Tony Worby & Steve Ackley, ASPeCt - PowerPoint PPT Presentation
GCOS SST&SI Working Group Chairs Nick Rayner Tom Smith Executive committee Ken Casey Elisabeth Kent Alexey Kaplan Craig Donlon Ed Harrison Dick Reynolds Sea ice subcommittee Søren Andersen, DMI/EUMETSAT OSISAF Florence Fetterer & Walt Meier, NSIDC Tony Worby & Steve Ackley, ASPeCt Per Gloersen, NASA GSFC Cathleen Geiger, CRREL/U. Delaware John Stark, UK Met Office Vasily Smolyanitsky, AARI/JCOMM ETSI Pablo Clemente-Colon, NIC Mark Drinkwater, ESA Stefan Kern & Dirk Notz, U. Hamburg Jinro Ukita, Chiba University
Transcript
GCOS SST&SI Working Group
ChairsNick RaynerTom Smith
Executive committeeKen Casey
Elisabeth KentAlexey KaplanCraig DonlonEd Harrison
Dick Reynolds
Sea ice subcommitteeSøren Andersen, DMI/EUMETSAT OSISAFFlorence Fetterer & Walt Meier, NSIDCTony Worby & Steve Ackley, ASPeCt
Per Gloersen, NASA GSFCCathleen Geiger, CRREL/U. Delaware
John Stark, UK Met OfficeVasily Smolyanitsky, AARI/JCOMM ETSI
Pablo Clemente-Colon, NICMark Drinkwater, ESA
Stefan Kern & Dirk Notz, U. HamburgJinro Ukita, Chiba University
Outline
• Background and summary of objectives– Organisation– Motivation and mission– Illustrative examples – Proposed list of activities
• Status– Events– Reports– Intercomparison
• Outlook• Recent relevant results
Organisation
Nick RaynerTom Smith
Sea ice subgroup
GCOS SST&SI WG
Motivation and mission
Motivation from a climate data user perspectiveUneven data coverage as well as inconsistencies between different passive microwave sea ice timeseries and ice charts have been identified by climate community constructing longterm SST&SI analyses:
• No uncertainty estimates in existing ice analyses.• An overwhelming number of data sets/algorithms with poorly known relative and
absolute skills.• For microwave data: Principal homogeneity issues with short overlap periods and
different equator crossing times.– The popular NSIDC gridded dataset has only 16 days of overlap between DMSP F8 and F11– SMMR and SSM/I overlap is 22 days
• For ice charts: Changes in analysis capabilities and practices are not always well documented
• Major challenge to reconcile ice chart, observation and satellite records.• The Antarctic has significantly less historical coverage than the Arctic
MissionTo provide analysis and recommendations on long term consistent sea ice fields with uncertainty estimates for use in SST & SI analyses. Initially focus on ice concentration but consider ice thickness as methods and data sets mature.
Rapid changes in MY sea ice
Nghiem et al., 2006
Arctic trends
Meier et al. – Ann. Glaciol. (46) 2007: Confirms the uniqueness of recent decline in ice coverage, while illustrating the sensitivity of trends to period and length of time series.
Analysis by Kaleschke, presented in Andersen et al. 2006 (b). Merged ESMR – SSM/I [Cavalieri et al.,2003] with NSIDC sea ice index. Recent developments have no correlation with AO, but Maslanik et al. (2007) suggest atm. circulation may still be significant
Trends in Antarctic
NSIDC sea ice indicator
May be due to increased snow ice formation with increased Precipitation. (Ice mass growth is a balance between insulation and snow ice), Powell et al., 2005.
NIC’s Sea Ice Climatology
Example from Partington et al showing change in chart detail , 1976 to 1993
Over time: Increase in quality, quantity, and effective resolution of charts
1976 1993
Courtesy Florence Fetterer, NSIDC
Differences in trends
Courtesy Nick Rayner, Hadley Centre
Arctic area trends from 7 Microwave algorithms
Ice chart and Microwave trends
1987-2004
1991-2004
Nearly a factor 2 betweenlowest and highest estimate
-42611
-42611-58111-33230
Some blind spots, the Antarctic
• Available time series– Arctic has systematic
observations back to late 1800’s
– Antarctic data is extremely scattered and spotty up to satellite era
• Systematic and comprehensive intercomparison of sea ice analyses– Help characterize important temporal and spatially distributed
differences – Provide starting points for further investigation and cooperation– Help evaluate effects of different assumptions– Provide a step towards meaningful accuracy and uncertainty estimates
• Error estimates– Develop and promote methods for standardized error estimates for
both ice charts and satellite analyses
• Data– Provide overview of the numerous data sets available– Promote easy and standardized access to field and ship observations– Examine data gaps and, if possible, recommend mitigation actions
Implementation
• Demonstration of intercomparison of a limited set of IC products– Products repository in common simple netCDF format– Develop statistics and comparison procedures (initial short list exists)– Presentation of analyses and download of data
• Ice chart based ice edge uncertainty project (NSF, Geiger)– Outcome of IICWG 2006– First step towards a more informed use of ice chart information in
climate science
• Not to forget, progress through cooperation– Structuring and collection of in situ observations and use of ice chart
data for ice thickness in ASPeCt– Structuring, collection and documentation through IICWG, ETSI– Scientific coordination through CliC/WCRP and others.
The full level of activity will depend on level of community commitment and funding. However, some initial activities are already committed to:
Status
Overview of eventsDocuments
Intercomparison
Recent events
• Group meetings: – Reformed in Exeter October 2005 with a wide initial representation
in sst and the decision to form a specific subgroup on sea ice– Inaugural sea ice meeting in Boulder, March 2006, established
initial plans– Participation in IICWG in Helsinki, September 2006, featuring a
dedicated morning session in plenary and several break outs. Resulting in an IICWG supported proposal to NSF for error analysis of ice chart based ice edge estimates.
• Interactions with other groups and bodies– Presentation at Antarctic Sea Ice thickness Workshop, Hobart (July
2006)– IGOS-Cryo meeting, Noordwijk (October 2006)– JCOMM-ETSI meeting, Geneva (March 2007), plans for a common
ice analyst workshop in 2008.• The SI group is leaving the formative phase, entering ops.
Reports
Intercomparison
• Intercomparison is not a new concept in sea ice retrieval. – Bad news:
• It can be implemented in many ways.• Small details and assumptions (e.g. spatial and temporal coverage,
products considered, filtering/averaging) may affect results.• Interpretation is often difficult (e.g. in terms of which product is
more correct)– Good news:
• Comparison of products efficiently reveals signatures of underlying algorithm and system sensitivities. Links well with efforts to understand the underlying processes (e.g. radiative/microphysical, retrieval/classification)
• Experience from existing intercomparison exercises may help identify best practices and limitations.
Systematic intercomparison
• In practice the idea is:– Get data in common simple,
self-contained format, example:
– Compute statistics and intercomparison products
– Make comparison products and data sets easily available
• Nearest neighbour regridding of GDSIDB product from 0.25 geographical to 25 km polar stereo projection – 2.5 % difference in extent trend (-13.9 to -14.2 x 103 km2/year)– 6.5 % difference in area trend (-8.9 to -9.5 x 103 km2/year)
RegriddedOriginal
Extent
Area
Development snapshot• Intercomparison implemented in Python using
matplotlib, numpy and Nio packages. • Possible evolution to web application via
CherryPy and TurboGears• Data sets: HadISST, NASA/Team, Bootstrap,
GDSIDB
Initial list of products• Linear trends of monthly mean values of sea
ice extent and area results in a measure of the spread in estimated retreat or increase in the sea ice cover. Taking one product as reference can be useful.
• Maps of linear trend in concentration or sea ice persistence provide the spatial structure of differences in estimated sea ice trends.
• Per pixel range of concentration based on several products or maps of anomaly with respect to wintertime average ice concentration provide spatial structure of single algorithm results.
• Maps of differences between algorithms on various time scales provide the spatial structure of inter-algorithm differences.
÷
Intended to augment SST intercomparison system at NODC
Outlook
• IPY– Snapshot activities will make large quantities of high resolution EO data
available and facilitate comprehensive validation activities– Field activities will help in developing and validating models that relate snow and
ice parameters to emissivity– Massive deployment of buoys will at least help relating observed signals to
meteorological conditions and ice drift patterns– The systematic data management will make the application of the data possible.
• New ice thickness measurement capabilities– Space based altimeters are becoming operational (IceSAT and follow-on,
Cryosat-II, Sentinel-3)– Development of AUV systems is picking up speed, in part thanks to IPY– The ASPeCt data set is close to 25000 observations spanning more than 2
decades in the Antarctic and still growing• General movement towards sustained operational satellite programmes,
e.g. ESA Sentinels, (JAXA GCOM?); also a movement towards free access to SAR data.
– SMMR+SSM/I is approaching 30 years of continuous operation - but there will most likely be a gap in AMSR class passive microwave observations and Seawinds scatterometer continuity is very unlikely.
Conclusions
• The group is coming out of its formative phase– Group members span ice charting, in-situ, satellite observations and to
some extent modelling disciplines– Activities and organisation have been outlined and agreed in a white-
paper document• Many of the defined activities have basic support from existing
activities of members. • An expanded set of activities, including standardisation of error
estimates, interpretation of differences and thorough investigations of ice chart records, may require external funding, some initiatives have started already.
• The group is seen to benefit from and add to the momentum of existing groups like ASPeCt and IICWG
• In the longer term, it is hoped that the group, in cooperation with the ice charting community, may help lessen the gap between ice chart and satellite observations to achieve longer data sets of sea ice concentration with improved confidence.
Examples of recent results by group members
ASPeCt: Tracks of 83 ASPeCt: Tracks of 83 “good” voyages“good” voyages
Worby et al., 2007
Annual mean icethicknessincludingridges andopen water
2.5 x 2.5º grid
Worby et al., 2007
AMSR-E v ASPeCt snow thickness data
Worby et al., 2007
OSISAF-NSIDC Passive Microwave Sea Ice Workshop, 26-28 Feb 2007
Arctic Sea Ice from Passive Microwave (PM) Sensors and Operational Ice Charts
NT-CHART BT-CHART TOTAL MULTIYEAR FIRST-YEAR THIN
4 O
CT
199
928
FE
B 2
000
15 255 35 45 55 65 908075 85 95 100
%+50-50 0
%
PM and chart total ice concentration difference, for two PM algorithms:
Total concentration from ice charts and partial concentrations by ice type – multiyear, first-year, and thin ice. Charts provide more information and are more accurate, but are less consistent than PM.NT = NASA Team algorithm
BT = Bootstrap algorithm From Meier, Fetterer, Fowler, Fall AGU 2006
NASA Team Comiso frequency Near 90GHz
Upper snow-layer density 100-410kg/m3
Above ice correlation length 0.14-0.32mm
MEMLSI simulations of ice concentration
NASA Team: sensitive to layer contrast.
Comiso frequency: moderately sensitive to scattering.
Near 90 GHz: moderately sensitive to deep scattering, sensitive to layer contrast.
Temporal analysis of ice index data: variability and linear trends in August for Eurasian (1900-2003) and Canadian Arctic (1968-2004)
Greenland, Barents, Kara Laptevs, Eastern-Siberian, Chukcha
Western CA (blue)
Eastern CA (green)
East Coast (red) (April)
Hudson Bay (light blue) From: V. Karklin, Z.Gudkovich, I.Frolov, V.Smolyanitsky, J.Falkingham “Interannual variability of Eurasian and Canadian Arctic sea ice in the 20th century and expectations for the 21st century”. JCOMM-II Scientific Conference “Operational Oceanography and Marine Meteorology for the 21st Century”. Septembr 15-17th, Halifax, Nova Scotia, Canada.
Wavelet analysis of sea ice extent variations for Eurasian Arctic Seas (based on 1900-2003 period) and Canadian Arctic Seas (based on 1968-2004 period) in August (red – more ice, blue – less ice)
Greenland Barents Kara
Laptev Eastern-Siberian Chukcha
Western CA Eastern CA Hudson Bay
Trends in Antarctic
NSIDC sea ice indicator
May be due to increased snow ice formation with increased Precipitation. (Ice mass growth is a balance between insulation and snow ice), Powell et al., 2005.
Differences
in 1996/1997 , NIC -Transitioned to digital imagery (OLS/AVHRR) and digital analysis in GIS formatStarted using SAR data in tactically significant areasNow, NIC uses Quicksat to compensate for deficiencies in SSM/I
MEMLSI emissivity model setup to explore effects of layering and coarse grains in the snow layers above sea ice.
LayeringLayering
Sca
tteri
ng
Sca
tteri
ng
1
2
Footprint convolution errors
Combined convolution and simple averaging
Reference
True low res.
Limaye et al. (2006): Particularly important in gradient areas,i.e. ice edge and coast. Additional errors come from mixing ofChannels with different footprint size
Deconvoluted
In summer: Melt ponds and others
Russ Hopcroft, from the University of Alaska Fairbanks, takes a sip from one of the many meltponds scattered around the ice. (Photo courtesy of Ian MacDonald.) – From NOAA Ocean Explorer site
In summer, melting snow and ice forms open freshwater ponds on top of the ice that, in the microwave regime, can be distinguished from the open ocean only by salinity. Additional complications occur e.g. due to refreezing of the ponds. Sea ice topography exerts an important control on the spatial extent and depth of the ponds. Spatial coverage can range from 20-50%
Applications/impacts
Impact in climate models
Singarayer et al. (2005) Fig.2+ Fig.3:Sea ice uncertainty is large but ocean - atm. gradient is small during summer.Other contributions: Rinke etal. (2006): Influence of sea ice on atmosphere. Parkinson et al.: Impact on climate models (2001)
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