D3.4 Product User Guide (PUG)
Contents
1. Convention for file names The Sea Ice Concentration dataset follows form 2 from RD 13 that is ESACCI SEAICE L4 SICONC lt INSTR gt lt AREA gt 25kmEASE2 lt YYYYMMDD gt fv lt VER gt nc where the values for each lt FIELD gt can be e lt INSTR gt SSMI or AMSR e lt AREA gt NH or SH e lt YYYYMMDD gt date string e lt VER gt product version lt XX YY gt File format Following RD 13 the Sea Ice Concentration datasets are netCDF files that follow the Climate and Forecast CF convention http cfconventions org The netCDF files are of type netCDF 4 classic model with internal compression deflate level 9 Most variables are stored as int16 short int with associated scale_factor in order to reduce requirements on disk space The status_flag dataset is encoded as byte ESA UNCLASSIFIED For Official Use 19 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 3 7 2 3 8 2 3 8 1 2 3 8 2 2 3 8 3 Access to data The SIC data can be accessed via a variety of sources e Via FTP on ftp osisaf met no sicci e Via the Arctic Data Portal on http arcticdata met no e Via the Integrated Climate Data Portal ICDC on http icdc zmaw de esa cci_sea ice ecvO html amp L 1 Dataset version history VO1 11 15 04 2014 Quality checked version of CRDP SSM I and AMSR E v01 11 has exactly the same scientific content as v01 10 but some dates were2. K Peubey C de Rosnay P Tavolato C Th paut J N and Vitart F 2011 The ERA Interim reanalysis configuration and performance of the data assimilation system Q J R Meteorol Soc 137 553 597 doi 10 1002 qj 828 Table 1 1 Reference Documents Acronyms and Abbreviations AMSR E Advanced Microwave Scanning Radiometer aboard EOS Announcement of Opportunity ASCII American Standard Code for Information Interchange ASIRAS Airborne Synthetic Aperture and Interferometric Radar Altimeter System ESA UNCLASSIFIED For Official Use 8 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 ed ma Notappicable OOO m moame OOOO Table 1 2 Acronyms ESA UNCLASSIFIED For Official Use 9 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 1 2 2 2 2 1 Sea Ice Concentration SIC Introduction This SIC part of the Product User Guide PUG provides the entry point to the European Space Agency Climate Change Initiative ESA CCI Sea Ice Concentration SIC dataset both from a scientific and a technical point of view Details of the scientific description of the processing chain and algorithms are however willingly kept out of this PUG and the interested readers are rather directed to the Algorithm Theoretical Basis Document RD 1 and Detailed Processing Model RD 2 Validation and evaluation results are not contained in this PUG either but in a
3. RD 9 The set of validation data available for the RRE did not yet allow making a quantitative statement with regard to the choice of the correct ice density If users have access to alternative sources of snow information and or ice density they are encouraged to calculate their own thicknesses from SICCI freeboard estimates ESA UNCLASSIFIED For Official Use 22 Ref SICCI PUG 13 07 3 2 1 4 3 2 2 3 3 3 3 1 D3 4 Product User Guide PUG Sampling error The pulse limited footprint of a traditional radar altimeter is several kilometres wide In consequence the radar altimeter may not sample the smallest floes and if the statistics of the sampled ice are different to the total ice cover then this will result in an error in the retrieved ice thickness Description of the processing chain and algorithm For detailed description of algorithm user should refer the ATBDv2 RD 1 The algorithm is based on distinguishing altimeter echoes from leads and ice floes retracking elevations for both surface types interpolating local sea level height from lead elevations and subtracting it from floe elevations This results into freeboard The thickness is then calculated from the freeboard with independent estimates of snow loading and ice density Technical description of the product Examples To ease and support the reading of the technical specifications we start this section by providing some visualization of maps extracte
4. dtu dk rf space dtu dk Henriette Skourup hsk space dtu dk Marko M kynen Eero Rinne Ari marko makynen fmi fi eero rinne fmi fi Seina Ari Seina fmi fi University of Stefan Kern stefan kern zmaw de Hamburg University of Georg Heygster heygster uni bremen de Bremen University of Peter Wadhams John Fletcher pwii cam ac uk jfaf2 damtp cam ac uk Vera Djepa vd256 cam ac uk MPM O M Dirk Notz dirk notz zmaw de Fanny Ardhuin Fanny Ardhuin ifremer fr ESA UNCLASSIFIED For Official Use 2 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 AWI Marcel Nicolaus Stefan marcel nicolaus awi de Hendricks stefan hendricks awi de ESA UNCLASSIFIED For Official Use 3 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 Table of Contents 1 Introduction ou cccccceee cece scence sees ee eeee esse seca eeeeeeseeaaeeeeeaeeaaeseeeeeeeaeeeeneneas 7 1 1 Document SUPUCCU PG sorier ani er ieo i en g Eo EEE E Eaa 7 1 2 DOCUIMENE Stat S eee e e sanscen nscieienedis sactg ised AE N EE 7 1 3 Reference Documents and DataSets ccecceee eee testes eee ee eee eee eeeeaee 7 1 4 Acronyms and Abbreviations cccceceeee eee eee eee eee eee ee eae eee eee eae eaeee 8 2 Sea Ice Concentration SIC s s sssssssssssus55555555u2u00002055uuunuunnnnnnnnnnnn 10 2 1 Introduction arne die an cree eet ncaa et ela tag cotey an ae EE es 10 2 2 Scientific Description
5. ice indeed does not act as a radiometric insulator for the PMR frequencies around 19 and 37 GHz that are the base for this SICCI dataset and many others Interpolation of missing values The SICCI SIC dataset aims at addressing needs from all users needing access to climate sea ice concentration data from interested general public to climate modellers Like for the OSISAF dataset it was decided to provide interpolated sea ice concentration values in places where original input satellite data was missing aiming at most complete daily maps Both temporal and spatial interpolation is used The locations were interpolation was used are clearly identified in the status_flag layer see later section These interpolated sea ice concentration values should generally not be used for scientific applications especially the values obtained from spatial interpolation ESA UNCLASSIFIED For Official Use 11 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 2 1 4 2 2 1 5 2 2 1 6 The AMSR E sea ice concentration time series The SICCI SIC dataset is composed of two CRDs one from the SSM I instrument and one from the AMSR E instrument These time series overlap partly Concerning the first version of the dataset v01 11 the project team warns potential users that the AMSR E SIC time series is less mature than the SSM I one and that the former should be used with extra care possibly after visual
6. red SIC datasets for CRDP of SICCI Phase 1 v01 10 Note that in v01 10 and subsequent CCI Phase 1 versions of the dataset SSM I FO8 was not used due to occurrence of bad scans in the CM 150 SSM I Tb FCDR Version 1 revision 1 This was later corrected in CM SAF FCDR Note also that the CM SAF SSM I Tb FCDR has last date on 31 December 2008 and although SSM I F13 continues operations after this date the current SIC SSM I dataset stops at that date 2 3 4 Product grid and geographic projection The SSM I and AMSR E SIC datasets are delivered on a set of two polar EASE2 grids with a grid spacing of 25 km The EASE2 projection is defined in RD 14 The two grids are defined in Table 2 3 below See also Figure 2 2 ESA UNCLASSIFIED For Official Use 18 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 3 5 2 3 6 geospatial_lat_min l X 5387 5 16 62393f U ca 5362 5 geospatial_lat_max lon_0 0 j NH25kmEASE2 datum wese4 72287 eG ellps WGS84 Y 5387 5 geospatial_lon_min lat_0 90 0 5362 5 E 180 f F 5387 5 geospatial_lon_max 180 f geospatial_lat_min 90 f proj laea geospatial_lat_max lon_0 0 16 62393 f SH25kmEASE2 datum WGS84 Same as above oe ellps WGS84 geospatial_lon_min lat_0 90 0 Miao geospatial_lon_max 180 f Table 2 3 Definition for the NH and SH grids used for the Sea Ice Concentration dataset
7. 13 07 Version 2 0 29 August 2014 lt End of Document gt ESA UNCLASSIFIED For Official Use 26
8. G document Reference Documents and Datasets Reference Details Algorithm Theoretical Basis Document ATBD v2 Issue 1 1 Feb 2014 Detailed Processing Model DPM v2 Issue 1 1 Feb 2014 Product Validation and Intercomparison Report PVIR v1 scheduled fall 2014 RD 4 Product Validation and Algorithm Selection Report PVASR vi Issue 1 0 June 2013 D 5 Comprehensive Error Characterisation Report CECR v1 Issue 1 1 August 2013 R Warren S G I G Rigor N Untersteiner V F Radionov N N Bryazgin Y I Aleksandrov and R Colony 1999 Snow depth on Arctic sea ice Journal of Climate 12 6 1814 1829 Kurtz N T and S L Farrell 2011 Large scale surveys of snow depth on Arctic sea ice from Operation IceBridge Geophys Res Lett 38 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 L Farrell and M Davidson 2013 CryoSat 2 estimates of Arctic sea ice thickness and volume Geophys Res Lett 40 1 6 EUMETSAT OSI SAF Global Reprocessed Sea Ice Concentration dataset v1 1 Product User Manual v1 3 October 2011 SAF OSI CDOP met no TEC MA 138 ESA UNCLASSIFIED For Official Use 7 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 1 4 RSE Vol 104 Iss 4 2006 http dx doi org 10 1016 j rse 2006 05 013 RD 13 Guidelines for Data Producer
9. Product Validation and Intercomparison Report RD 3 In short the SICCI SIC dataset is e Daily gridded SIC fields based on Passive Microwave Radiometer measurements e Global maps both Northern Hemisphere and Southern Hemisphere with 25 km grid spacing e Both a SSM I and a AMSR E dataset processed and delivered separately e Daily maps of total standard error uncertainty and quality control flags e Built upon the algorithms and processing software originally developed at the EUMETSAT OSI SAF for their SIC dataset RD 11 Contribution of the EUMETSAT OSI SAF to the production of this dataset is acknowledged EUMETSAT OSI SAF provided access and allowed re use of its SIC reprocessing software and data hosting facilities RD 11 The reprocessing chain was further updated with the new algorithms and knowledge from the ESA CCI Sea Ice project Scientific Description of the product This section gives a summary of the science features of the SIC dataset and describes first the known limitations and caveats the potential users should be aware of before analysing the dataset Note that this version of PUG is written before any extensive validation exercise of the dataset and that the results described below are based on an algorithm selection exercise described in a Product Validation and Algorithm Selection Algorithm RD 4 Known limitations and caveats All the aspects listed in this section apply in large extent to the othe
10. Ws Sea Ice Climate Change Initiative a esa Phase 1 European Space Agency D3 4 Product User Guide PUG This PUG is updated for datasets with versions SIC v01 11 SIT v0 9 Doc Ref SICCI PUG 13 07 Version 2 0 Date 29 August 2014 ak Consortium Members CGI A ime ay UCL as FMI freme P Max Planck Institut fur Meteorologie iti UNIVERSITY OF Lat Universitat Hamburg gt CAMBRIDGE Uy universit t Bremen DER FORSCHUNG DER LEHRE DER BILOUNG ESA UNCLASSIFIED For Official Use D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 Change Record Issue Date Reason for Change Author s 1 0 13 May 2014 First Issue Thomas Lavergne Eero Rinne 2 0 29 August 2014 Update dataset version history Clarify Thomas Lavergne link to EUMETSAT OSISAF Eero Rinne Authorship Role Names Name o signature O Written nc Thomas Lavergne SIC Eero Rinne a ee t Checked Checked by Gary Timms s Timms Distribution ESA Pascal Lecomte Pascal Lecomte esa int NERSC Stein Sandven Natalia Ivanova Stein Sandven nersc no natalia ivanova nersc no CGI Gary Timms Ed Pechorro gary timms cgi com previously ed pechorro cgi com Logica Met no Thomas Lavergne Lars Anders t lavergne met no larsab met no Breivik Steinar Eastwood s eastwood met no DMI Leif Toudal Pedersen Rasmus Itp dmi dk rtt dmi dk Tonboe Roberto Saldo Ren Forsberg rs space
11. d from the product files Note that there is a quality flag layer in addition N per grid cell Figure 3 1 Maps of sea ice thickness top left Freeboard top right and number of measurements per grid cell bottom All maps are for January 20 09 ESA UNCLASSIFIED For Official Use 23 Version 2 0 29 August 2014 rd Freebo D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 3 3 2 3 3 2 1 3 3 2 2 3 3 2 3 3 3 2 4 Content of product files The distributed product files are so called Level 3C files These contain monthly gridded maps of sea ice thickness and freeboard with some additional layers to assist the user in the interpretation of these maps The sea ice thickness and freeboard variables There are variables for sea ice thickness and freeboard sea_ice_thickness and sea_ice_freeboard respectively Note that the given values are mean values of successful altimeter measurements inside the grid cell They do not consider the fraction of open water if only one 3 m floe is measured in a 100 km x 100 km it will result into the sea_ice_thickness of 3 m Number of measurements per grid cell The number of measurements averaged to retrieve a freeboard value is vital to know when estimating effect of radar speckle in freeboard retrieval Thus this number is provided as a variable Low number of measurements will result in higher uncertainties and cells with only few
12. e used in this process The atmospheric correction scheme from Andersen et al 2006 RD 12 is implemented that includes an iteration to first correct the dynamic Tie Points and then use these corrected Tie Points along with the corrected brightness temperatures see RD 2 Per pixel uncertainty estimates The SICCI SIC dataset comes with uncertainty estimates for every grid cell with ice concentration value All uncertainties are intended as one standard deviation around the provided sea ice concentration value acting as the mean of the distribution Technical description of the product In this section the SIC product files are described in terms of content file name data format grid among others Examples To support the reading of the technical specifications we start this section by providing some visualization examples of maps extracted from the product files These are shown on the following page ESA UNCLASSIFIED For Official Use 14 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 3 2 Figure 2 2 Maps of Sea Ice Concentration left and total uncertainty right from the SICCI SSM I dataset valid for 1995 11 15 Content of product files The distributed product files are so called Level 4 files that are daily gridded maps of sea ice concentration and their uncertainties To achieve global coverage two product files are available per day one covering the Nort
13. ents should be used with caution ESA UNCLASSIFIED For Official Use 21 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 3 2 1 2 3 2 1 3 Radar penetration We assume that during cold winter months the dominating scattering surface for the radar is the snow ice interface However one of the outcomes of our Round Robin Exercise was that this is not always the case Thus the user is reminded that the freeboard given in the SICCI SIT product files is the radar freeboard which we assume to be the elevation of upper surface of ice measured from local sea level Nevertheless there is ongoing scientific discussion on how accurate this assumption is If the dominating scattering surface lies somewhere within the snowpack sea ice thickness retrieval using the radar freeboard with the incorrect assumption will result into too large thickness values Errors associated with the conversation of freeboard to thickness The freeboard is converted into thickness by assuming the ice to be in hydrostatic equilibrium This requires estimates of snow thickness as well as snow ice and water densities Uncertainty in all of these will contribute to the uncertainty of the thickness estimate Snow depth and density is estimated using the monthly snow depth climatology by Warren et al RD 6 which is based on measurements performed between 1954 and 1991 over multiyear ice The use of a climatology means that interannual a
14. hat the main variable ice_conc can contain interpolated ice concentration values that can be detected using the status flags see below and should generally be used with great care for any scientific purpose see section 2 2 1 3 3 2 2 The uncertainty variables There are three uncertainty variables in the product files the total uncertainty total_standard_error and its two components smearing_standard_error and algorithm_standard_error The total uncertainty is the sum of the smearing and algorithm uncertainties as variances The status flags The status_flag can take 10 values listed in Table 2 1 SIC values delivered with the nominal nominal uncertainty hiah t2m Warning that ERA Interim Tm is higher than 10 gn degrees Celsius this might be false ice f Sea water hemispheric coefficients was applied lake_ice oe over a lake the quality is unknown The region is flagged as having never seen sea max_ice_climo ice and SIC is accordingly set to O the total uncertainty as well The SIC value was corrected because in the 10 ne ee direct vicinity of land Vicinity of land usually leads to overestimation of the SIC values even after coastal correction is applied See 2 2 1 6 ESA UNCLASSIFIED For Official Use 16 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 The region is equatorward of 35 deg low_latitude latitude and SIC is set to O the total uncertaint
15. hern Hemisphere sea ice and the other the Southern Hemisphere Each file contains e atime information encoding date and time for the daily product average from 00 to 23 59 UTC e latitude and longitude for each grid point e a map of analysed daily averaged sea ice concentration e a map of processing aka status flags e three maps of uncertainties as standard deviations total algorithm and smear uncertainties ESA UNCLASSIFIED For Official Use 15 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 3 2 1 2 3 2 2 e a map of values retrieved outside the physical range 0 100 e a number of metadata information both pertaining to the given date and to the whole time series Note that all the variables encoding sea ice concentration both the ice concentration variables themselves and their uncertainties have unit that is they are ranging from 0 to 100 The sea ice concentration variables There are two Sea ice concentration variables in the product file one given the physical ice concentration bounded in 0 100 range ice_conc the second that only provides the grid cell values retrieved out of bounds by the algorithm ice_conc_off_range These non physical values are distributed to the special interested users that may benefit from accessing un biased distributions of the ice concentration distribution especially for use with Data Assimilation Note t
16. inspection or comparison to other data sources such as the SSM I time series during the overlap period Future versions of the SICCI dataset will bring the AMSR E time series to a similar maturity level as the SSM I one Grid resolution In RD 10 the Global Climate Observing System Requirement GCOS requires horizontal resolution of 10 15 km while a user survey conducted during the SICCI project confirmed need for SIC at resolutions below 10 km This requirement is incompatible with the requirement to take advantage of the 30 years long data record of PMW channels at 19 and 37 GHz SMMR SSM I SSMIS etc The present SICCI SIC products are delivered with a grid spacing of 25 km The same grid spacing is used both for the SSM I and the AMSR E SIC dataset Since the footprint of the SSM I channel at 19 35 GHz is roughly an ellipse of 45x70 km diameter and since no attempt was made in the SICCI dataset to use Resolution Enhancement techniques the true resolution of the SSM I dataset is expected to be larger than the 25 km grid spacing The footprint of the AMSR E 18 7 GHz channel being roughly an ellipse of 27x16 km diameter the a grid spacing of 25 km is much closer to the true resolution of the SIC dataset Coastal regions The radiometric signature of land is similar to sea ice at the wavelengths used for estimating the SIC Because of the large foot prints and the relatively high temperatures of land and ice compa
17. l Use 5 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 List of Tables Table 1 1 Reference DOCUMENES cecceee cece eee eee eee e nese eee sees sees seeaeeaeeeeeenenaanes 8 TFable 1 22 ACrONYMS ninenin na niaaa inana aa enact aceenennee ead eekeud LAA EEEa AENA 9 Table 2 1 Description of the status_flag valUeS sssssssrsrrrrsrssserrrrrrrrrrrrerrerrrrren 17 Table 2 2 Instrument and platforms entering the two SIC datasets for version v01 11 of the CRDP note that SSM I F08 is listed for reference but does not enter the GAataSet eich iis coi a tae T a E a N E a A EE cana at 17 Table 2 3 Definition for the NH and SH grids used for the Sea Ice Concentration ClALAS EE EA E A E O E T 19 Table 3 1 Description of status_flag valueS ssssssssssssrerrrrrrrrssrerrrrrrrnrnnrnrrerrrrrnna 25 ESA UNCLASSIFIED For Official Use 6 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 1 Introduction 1 1 Document Structure 1 2 1 3 This document describes in detail the datasets for the Sea Ice ECV project produced in Phase 1 of ESA s Climate Change Initiative The document includes the contributions for both the Sea Ice Thickness SIT and Sea Ice Concentration SIC aspects Chapter 2 describes the Sea Ice Concentration product user guide and chapter 3 describes the Sea Ice Thickness product user guide Document Status This is the 2nd issue of the PU
18. measurements sho uld be used with caution The uncertainty variables There are uncertainty fields in the product files but currently no uncertainty estimates are given This is because a decision was made that the uncertainty estimates for freeboard and in consequence sea ice thickness will stem from experimental validation of the product still to be carried out during 2014 When the uncertainties have been set a new product version will be published For now all uncertainty fields have been set to _Fillvalue The status flags The status_flag can take 5 values listed in Table 3 1 below meaning eomme O SIT and FB values given FB given but no SIT This is due to no valid snow 2 FB but estimate available point outside the central Arctic no SIT and thus Warren et al climatology is potentially not valid e Ocean but no FB measurements available Most unsucces likely open water sful No SIT data is provided because there is land in the grid cell either full or fractional cover The FillValue is used ESA UNCLASSIFIED For Official Use 24 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 3 3 3 3 3 4 3 3 5 3 3 6 3 3 7 No SIT data is provided because missing satellite 101 polehole input data due to the pole hole lat gt 82 5N The FillValue is used Table 3 1 Description of status_flag values Temporal coverage The dataset covers the Arctic win
19. nd cover aspects will be further investigated in later release of the SIC dataset Description of the processing chain and algorithms In this section we highlight the features of the SICCI processing chain and algorithm that make this SIC dataset different from others More details can be found in RD 1 and RD 2 Input data The SIC datasets produced in the SICCI project ingest the following input data e The EUMETSAT CMSAF SSM I Tb FCDR V001 RD 15 e The NSIDC Wentz AMSR E Tb FCDR V002 RD 16 e The ERA Interim daily atmosphere analysis fields RD 17 Contribution of these scientific teams and data providers to the SICCI SIC dataset is acknowledged Sea Ice Concentration algorithm The sea ice concentration algorithm used in the SICCI dataset is based on three PMR channels near 19 GHz Vertical polarization and 37 GHz both Vertical and Horizontal polarizations Like for the OSI SAF SIC dataset RD 11 the SICCI algorithm intercomparison exercise RD 4 concluded that these channels are best combined in a mixture of Comiso Bootstrap Frequency mode algorithm CF and Bristol algorithm BR CF is given more weights at low concentrations range while BR estimates dominate at high concentrations range The way this merging is done in SICCI was revised with respect to the OSI SAF Dynamic tuning of tie points One of the key features of the SICCI dataset is the dynamical adjustment of the algorithm Tie Points For SIC PMR algo
20. nd local spatial variability are not represented as is also shown in the PVASR RD 4 Furthermore the recent decline of multiyear ice has been shown to change the snow thicknesses in the Arctic The snow depth on first year ice is approximately 50 of that given by the Warren et al climatology RD 7 The geographical area from which snow depth measurements are used in the Warren et al climatology limits the region for which the freeboard to thickness conversion can be applied Snow depths from outside this geographical area such as the Hudson Bay are based on extrapolation and shall not be used for the conversion therefore Potential changes in the seasonal cycle of the snow density as provided by the Warren et al climatology in comparison to conditions today might exist but have not yet been investigated We recommend to keep using the seasonally varying snow density as provided by the Warren et al climatology The sea ice density is estimated to be constant over the whole Arctic regardless of the ice type There are direct measurements available that show density of ice to decrease with age However introducing ice type dependent ice density did not improve the fit between validation data and radar altimeter derived ice thicknesses during the round robin exercise This does not preclude however that usage of an ice type dependent ice density will not improve freeboard to thickness conversion as has been demonstrated e g by Laxon et al
21. of the Product cccece cece eee eee ee eee eeee eee eeeeaee 10 2 3 Technical description of the product cceeceeeee cece eee eee teen ee eeae teens 14 3 Sea Ice Thickness SIT sccseccceeseeeeeeeeeeeeeeeeeeeaeeaeeeeeeaeeseeeeeeaeeennees 21 3 1 TMEFOGUCTION iis ole lacca debit aa a A E E EAEE Ea 21 3 2 Scientific Description of the product ssssssssssssrerrrrrrssrerrrrrirrrrrrerrre 21 3 3 Technical description of the product s ssssssssssssrerrrrrrsrnerrrnrirrrrrrereere 23 ESA UNCLASSIFIED For Official Use 4 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 List of Figures Figure 2 1 Illustration of the mismatch between True SIC and most SIC retrieval based on Passive Microwave PMW algorithms as melted water grows at the SUITACEYOF SCAN COs e aa a esos a aa saehud ose de aaa vedesdee ceases abyddass cos 11 Figure 2 2 Maps of Sea Ice Concentration left and total uncertainty right from the SICCI SSM T dataset valid for 1995 11 15 oo cece ce ecee ee eeeteeeeeetteeseerees 15 Figure 2 3 Temporal coverage of the SSM I blue and AMSRE red SIC datasets for CRDP of SICCI Phase 1 VO1 10 ccc cccc cece eee ce eee eee sees eeeeeeeeaesaeeeseeaeeaneenenges 18 Figure 3 1 Maps of sea ice thickness top left Freeboard top right and number of measurements per grid cell bottom All maps are for January 20 O9 23 ESA UNCLASSIFIED For Officia
22. r existing Sea Ice Concentration datasets based on Passive Microwave Radiometer PMR measurements Users of these datasets including the SICCI one should be fully aware of these so that not to bias their conclusions ESA UNCLASSIFIED For Official Use 10 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 2 1 1 2 2 1 2 2 2 1 3 Summer melt ponding Virtually all Sea Ice Concentration algorithms based on the PMR channels around 19GHz 37GHz and 90GHz are very sensitive to melt pond water on top of the ice The depth of the emitting at these wavelengths indeed do not allow for distinguishing between ocean water in leads and melt water in ponds This is the main reason why PMR SIC datasets are underestimating sea ice concentration during summer Time melting atmosphere sea ice True SIC 0 True SIC 0 True SIC 1 0 True SIC Pmw SIC i Pmw SIC i Pmw SIC Pmw SIC True SIC SIC defining the ocean atm heat flux exchange T Lavergne OSI SAF Pmw SIC emitted SIC amount of water inside FoV Figure 2 1 Illustration of the mismatch between True SIC and most SIC retrieval based on Passive Microwave PMW algorithms as melted water grows at the surface of sea ice Thin sea ice Concentration of thin sea ice 5 30cm is underestimated by most of the classic PMR SIC algorithms due to the radiometric contribution of water below the ice A complete 100 cover of thin sea
23. red to water the land signature is spilling into the coastal zone open water and it will falsely look as intermediate concentration ice This is sometimes called land spill over and it is partly removed in the processing using a combination of physical and statistical methods However this coastal correction procedure is not perfect and a level of false sea ice remains along some coastlines This is less pronounced in the AMSR E than in the SSM I time series thanks to AMSR E s smaller foot prints In addition to the land spill over effect users of the SICCI datasets are made aware that SIC is only distributed in EASE2 25x25 km grid cells that have no fractional land cover conservative land mask This is unlike what is done in other SIC datasets like from OSISAF or NSIDC CDR that allow some amount of sub grid cell land cover to co exist with SIC values This choice was made in SICCI because the correction from land spill over effect is not performing well enough and the SIC values distributed in grid cells ESA UNCLASSIFIED For Official Use 12 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 2 2 2 2 2 1 2 2 2 2 2 2 2 3 with fractional land cover would have been mostly un reliable This might lead to systematic low bias of Sea Ice Extent or Sea Ice Area time series when computed from the SICCI datasets on hemispheric scales Both the land spill over correction and the fractional la
24. removed after manual and semi automatic inspection of the time series vO1 11 pertains of SSM I 01 01 1992 gt 31 12 2008 and AMSR E 19 06 2002 gt 03 10 2011 v01 10 11 03 2014 Release of CRDP only SSM I first vO1 10 pertains of SSM I 07 01 1991 gt 31 12 2008 Earlier versions Several earlier versions existed v00 10 v00 20 v01 00 for internal tests and validation ESA UNCLASSIFIED For Official Use 20 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 3 1 3 2 3 2 1 3 2 1 1 Sea Ice Thickness SIT Introduction This SIT part of the Product User Guide PUG provides an entry point to the European Space Agency Climate Change Initiative ESA CCI Sea Ice Thickness SIT dataset both from a scientific and a technical point of view Details of the scientific description of the processing chain and algorithms are however deliberately kept out of this PUG and the interested readers are rather directed to the Algorithm Theoretical Basis Document RD 1 and Detailed Processing Model RD 2 Validation and evaluation results are not contained in this PUG either but in a Product Validation and Intercomparison Report RD 3 In short the SICCI SIT dataset is e Monthly gridded SIT and sea ice freeboard FB fields with 100 km grid spacing for the Arctic for the freezing season October March based on radar altimeter measurements e Maps of uncertainties and quality con
25. rithms the tie points are typical radiometric signature of 0 ice Open Water TP and 100 ice Closed Ice TP In most other PMR datasets these tie points are either fixed or varying with seasons in a prescribed way For the SICCI SIC dataset the Tie Points to the algorithm are adapted to the sensor data and vary every day as a running average over a 7 days 7 days window a running average over a 15 days 15 days window was used in the OSISAF data record RD 11 In addition the Open Water Tie Point is selected in a buffer region at the outer limit of a monthly varying climatological mask to ensure that these Open Water signatures are representative for the typical weather conditions prevailing in the vicinity of the ice edge see RD 1 ESA UNCLASSIFIED For Official Use 13 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 2 2 2 4 2 2 2 5 2 3 2 3 1 Being derived separately for each sensor and platform dynamic Tie Points also ensure that residual inter satellite calibration offsets have minimal impact on the final ECV Atmospheric correction of Brightness Temperatures Following the approach of the OSI SAF dataset the SICCI processing chain includes correction of the brightness temperatures from atmospheric and surface effects using a Radiative Transfer Model RTM Fields of 10m wind speed total columnar water vapour and temperature at 2m from the ECMWF ERA Interim reanalysis ar
26. s Climate Change Initiative Phase 1 Issue 4 2 May 2013 RD 14 Brodzik M J Billingsley B Haran T Raup B Savoie M H EASE Grid 2 0 Incremental but Significant Improvements for Earth Gridded Data Sets ISPRS Int J Geo Inf 2012 1 32 45 RD 15 Fennig K Andersson A Schr der M 2013 Fundamental Climate Data Record of SSM I Brightness Temperatures Satellite Application Facility on Climate Monitoring RD 12 Andersen S Tonboe R Kern S Schyberg H Improved retrieval of sea ice total concentration from spaceborne passive microwave observations using numerical weather prediction model fields An intercomparison of nine algorithms DOI 10 5676 EUM_SAF_CM FCDR_SSMI V001 http dx doi org 10 5676 EUM_SAF_CM FCDR_SSMI V001 RD 16 Ashcroft P and F J Wentz 2013 AMSR E Aqua L2A Global Swath Spatially Resampled Brightness Temperatures Version 2 Boulder Colorado USA NASA DAAC at the National Snow and Ice Data Center http dx doi org 10 5067 AMSR E AE_L2A 002 RD 17 Dee D P Uppala S M Simmons A J Berrisford P Poli P Kobayashi S Andrae U Balmaseda M A Balsamo G Bauer P Bechtold P Beljaars A C M van de Berg L Bidlot J Bormann N Delsol C Dragani R Fuentes M Geer A J Haimberger L Healy S B Hersbach H H lm E V Isaksen L Kallberg P K hler M Matricardi M McNally A P Monge Sanz B M Morcrette J J Park B
27. ter months October November December January February and March of the Envisat operational period This results into a winter dataset from October 2002 to March 2012 Product grid and geographic projection Both the SIT dataset is delivered on a polar EASE2 grid with a grid spacing of 100 km The EASE2 projection is defined in RD 10 The grid is defined by a X Y boundaries Latitude Longitude cH ey and spacing m bounding box deg geospatial_lat_min proj laea X 5350000 17 22003 lon_0 0 5250000 geospatial_lat_max NH100k datum WGS8 5350000 90 0 MEASEZ 4 Y 5350000 geospatial_lon_min ellps WGS84 5250000 180 0 lat_0 90 0 5350000 geospatial_lon_max 180 0 Convention for file names The Sea Ice Thickness dataset file naming follows the form lt YYYYMM gt ESACCI L3C_SEAICE SIT lt INSTR gt fv lt VER gt nc where the values for each lt FIELD gt can be e lt INSTR gt RA2_ENVISAT lt YYYYMMDD gt date string e lt VER gt product version lt X Y gt File format Following RD 8 the Sea Ice Concentration datasets are netCDF files that follow the Climate and Forecast CF convention http cfconventions org Access to data The SIT data can be accessed via the Integrated Climate Data Portal ICDC at location http icdc zmaw de esa cci_sea ice ecv0 html amp L 1 ESA UNCLASSIFIED For Official Use 25 D3 4 Product User Guide PUG Ref SICCI PUG
28. trol flags Scientific Description of the product This section gives a summary of the science features of the SIT dataset and describes first the known limitations and caveats the potential users should be aware of before analysing the dataset Note that this version of PUG is written before any extensive validation exercise of the dataset and that the results described below stem from the Comprehensive Error Characterisation Report CECR RD 5 which in turn is based predominantly on past research and experience Known limitations and caveats Subsections below describe the main limitations and caveats of SIT estimation from radar altimetry These should be taken into account by all users of the product Users wanting more detailed information on limitations and uncertainties of or products should refer to the CECR and PVASR documents RD 5 and RD 4 Speckle All radar echoes exhibit a form of signal distortion known as speckle As the speckle de correlates between consecutive echoes summing over n echoes reduces the noise due to speckle Therefore for gridded ice thickness products the errors depend on the number of observations in a particular grid cell Speckle is the main reason why the number of observations per grid cell is included in the SIT product The effect of speckle in a single measurement is considerable when compared to expected freeboard Thus freeboard and thickness values in grid cells with only a few measurem
29. y as well The SIC value was obtained from spatial 13 interp_spatial F i interpolation caution The SIC value was obtained from temporal 14 interp_temporal interpolation caution No SIC data is provided because there is land 100 land in the grid cell either full or fractional cover The _FillValue is used See 2 2 1 6 No SIC data is provided because missing 101 missing satellite input data even after interpolation The _Fillvalue is used Table 2 1 Description of the status_flag values 2 3 3 Temporal coverage The Sea Ice Concentration dataset is made of two time series one merging all SSM I instruments aboard DMSP F10 Fil F13 F14 and F15 the second covering AMSR E instrument Table 2 2 and Figure 2 3 summarize the temporal coverage of the SIC datasets SIC Instrument First Date Last Date dataset and platform SSM I SSM I F08 9 July 1987 8 December 1991 SSM I F10 7 January 1991 14 November 1997 SSM I F11 1 January 1992 31 December 1999 Table 2 2 Instrument and platforms entering the two SIC datasets for version v01 11 of the CRDP note that SSM I FO8 is listed for reference but does not enter the dataset ESA UNCLASSIFIED For Official Use 17 D3 4 Product User Guide PUG Ref SICCI PUG 13 07 Version 2 0 29 August 2014 Satellite sensors for Sea Ice Concentration in CCI Phase 1 SSM I F15 1990 1995 2000 2005 2010 Figure 2 3 Temporal coverage of the SSM I blue and AMSRE
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