LEGOS Altimetric Sea Ice Thickness Data Product v1.0

Transcription

1 LEGOS Altimetric Sea Ice Thickness Data Product v1.0 (a) Ice thickness c2 02/2015 (m) Authors: Kevin Guerreiro & Sara Fleury September 2017 (b) Incertitude c2 02/2

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3 Contents 1 Versions history 4 2 Product description 5 3 Parameters description 6 4 Sea ice thickness processing Freeboard height methodology Sea ice thickness estimation Correction of the LRM freeboard bias Correction of the slow wave propagation into snow Freeboard-to-thickness conversion Sea Ice Thickness uncertainties Product references 15 6 List of acronyms and abbreviations 16 7 Bibliography 17 8 Acknowledgments 18 2

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5 1 Versions history Version name Date Main changes from previous version v1.0 01/09/2017 none (first version) Table 1: Description of Sea Ice Thickness Data Product changes from one version to another. 4

6 Table 2: My caption Availability period Maximum latitude Spatial resolution Temporal resolution DOI ENV 81.5 SIT Nov-Mar N 12.5 km monthly CS SIT Nov-Mar N 12.5 km monthly CS2 88 SIT Nov-Mar N 12.5 km monthly 2 Product description In the LEGOS Altimetric Sea Ice Thickness Data Product v1.0, Sea Ice Thickness (SIT) is estimated from the Envisat ( ) and CryoSat-2 (2010-present) ESA satellite missions. The processing algorithms used to derive these products are detailed in section 4 and are based on the publication by [Guerreiro et al., 2016]. For both Envisat and CryoSat-2, a sea ice thickness dataset is provided. Giving the respective orbit of each satellite mission, the Envisat SIT dataset (ENV 81.5 SIT) provides ice thickness up to 81.5 N and the CryoSat-2 SIT dataset (CS2 88 SIT) provides ice thickness up to 88 N. In order to allow the continuity between the two satellite missions, an additional CryoSat-2 SIT dataset (CS SIT) provides with ice thickness estimates up to 81.5 N 1. For each dataset, sea ice thickness is provided on a monthly basis and onto a 12.5 km 12.5 km regular grid for all winter months (November-March). In addition to sea ice thickness, intermediate parameters such as snow depth, snow density and sea ice density are also provided on the same regular grid and on the same monthly basis. The methodology used to estimate these parameters is detailed in section 4. Finally, an uncertainty field is provided for each sea ice thickness map. The methodology used to estimate sea ice thickness uncertainties is provided in section 4.3. The LEGOS Altimetric Sea Ice Thickness Data Product is provided in NetCDF v.4 files (Network Common Data Form, Each NetCDF file contains monthly gridded arrays for each parameter (SIT, snow depth, etc) as well as the corresponding lon/lat coordinates. 1 CAUTION: Because of the gridding processing (see section 4), the SIT up to 81.5 N cannot be directly derived from the CS2 88 SIT product. 5

7 3 Parameters description NetCDF name Full name Units Short description latitude latitude degrees North Latitude coordinates corresponding to the EASE-Grid Sea Ice Age, Version 3 product longitude longitude degrees East Longitude coordinates corresponding to the EASE-Grid Sea Ice Age, Version 3 product freeboard freeboard height meters Freeboard height corrected from slow wave propagation in snow see section 4.1 snow depth snow depth meters see section snow density snow density kg/m see section sea ice density sea ice density kg/m see section sea ice thickness sea ice thickness meters see section 4 sea ice thickness unc sea ice thickness meters see section 4.3 uncertainty myi fraction multi year ice fraction no units Myi fraction estimated from the Sea Ice Age, Version 3 product Table 3: Short description of all parameters available in each monthly NetCDF files of the LEGOS Altimetric Sea Ice Thickness Data Product v1.0 6

8 4 Sea ice thickness processing The LEGOS Altimetric Sea Ice Thickness processing mainly consists of two steps: Estimating the freeboard height from altimetric measurements Converting the freeboard height to sea ice thickness In section 4.1, we describe how the freeboard height is estimated from Envisat and CryoSat-2 level 1b SGDR data. The freeboard height retrieval is identical for CryoSat-2 and Envisat and the bias related to the LRM mode of Envisat is corrected with the so-called pulse-peakiness correction [Guerreiro et al., 2016]. In section 4.2, the freeboard-to-thickness conversion methodology is described. In particular, the methodologies used to derive the snow depth, ice density, sea water density and snow density fields are detailed. In section 4.3, the uncertainty related to the ice thickness estimates is analysed and the methodology to retrieve monthly uncertainty fields corresponding to each ice thickness field is described. 4.1 Freeboard height methodology The first step of the freeboard height methodology consists to gather all required datasets. The Envisat v2.1 and CryoSat-2 Baseline C level 1b SGDR data both contain the satellite altitude, waveform echoes, atmospheric corrections and barometer tide corrections at a 20 Hz sampling frequency ( 300 m) and can be obtained on the ESA website portal ( These datasets are combined with additional parameters (DTU 15 Mean Sea Surface product [Andersen and Knudsen, 2015] and the FES 14 ocean tide correction [Carrere et al., 2015]) by the CTOH and are finally converted to NetCDF files. The CTOH SGDR files can be found on the CTOH ftp server (ftp://ftp.legos.obs-mip.fr). The next step of the freeboard retrieval consists in estimating the altimetric range from Envisat and CryoSat-2 waveform echoes. This step is completed with the Threshold First Maximum Retracker Algorithm (TFMRA, [Helm et al., 2014]) used with a 50% threshold. Once the altimetric range is obtained, the surface height (H) is estimated according to the following equation: H = Alt (range (tropo dry + tropo wet + iono + MSS DT U15 + tide ocean + tide bar )) (1) Where Alt is the satellite altitude, range is the altimetric range, tropo dry is the dry tropospheric correction, tropo wet is the wet tropospheric correction, iono is the ionospheric correction, MSS DT U15 is the DTU15 Mean Sea Surface correction, tide ocean is the oceanic tide corrections and tide bar is the barometer tide correction. 7

9 Once the surface height is estimated for the entire Arctic domain, it is necessary to distinguish ocean surfaces (leads) from ice surfaces (floes). This surface classification is performed by analysing the waveform pulse-peakiness (PP) parameter given by the following expression: P P = max(w F ) i=nw F i=0 (W F i ) (2) Where WF represents the echo and N W F is the number of range bins. According to [Guerreiro et al., 2016], surfaces with a PP larger than 0.3 are considered as leads while surfaces with a PP smaller than 0.1 are considered as ice floes. Observations with a PP found between 0.1 and 0.3 are considered as mixed echoes and are discarded. The following step consists of smoothing the surface height of ice floes and leads along each satellite track. For this purpose, a 25-km median filter is applied to the 20 Hz surface height of ice floes and leads. Once the surface height of ice floes and leads are smoothed (respectively H floe25km and H lead25km), the freeboard height is estimated according to the following equation: fb = H floe25km H lead25km (3) The last step of the freeboard height processing consists in gridding the along-track freebord onto a regular grid. The sea ice mask we use to grid the freeboard height is the EASE-Grid Sea Ice Age, Version 3 product ( This product is provided onto a 12.5 km 12.5 km regular grid (12.5 km Northern Hemisphere EASE-Grid). To grid the freeboard height, a median filter with a 100 km radius is applied for each grid cell where the mask indicates the presence of sea ice. This operation is repeated for every winter months (Nov-Mar) and for each satellite mission in order to obtain monthly gridded freeboard height fields for the entire period of interest. 8

10 4.2 Sea ice thickness estimation Correction of the LRM freeboard bias The study by [Guerreiro et al., 2016] has demonstrated that the freeboard retrieved with LRM altimeters, such as Envisat, must be corrected from a bias driven by the large radar footprint combined with the ice roughness variability. To correct this bias, a function based on the pulse-peakiness parameter is applied as follows: fb = fb + y(p P ) (4) Where fb is the corrected freeboard, fb is the uncorrected radar freeboard and y(p P ) is the PP-correction function. For Envisat the PP-correction function is given by 2 : y(p P ) = A.P P 3 + B.P P 2 + C.P P + D (5) Note that the Envisat freeboard height provided in the LEGOS Altimetric Sea Ice Thickness Data Product v1.0 is already corrected as prescribed bellow. Thus, the user does not need to apply any further correction. Also, note that this correction is not applied to SAR altimeters such as Cryosat-2 as this bias only concerns LRM altimeters Correction of the slow wave propagation into snow Before starting with the freeboard-to-thickness conversion, it is necessary to correct the radar freeboard from the slow wave propagation into the snow pack. As suggested by [Beaven, 1995], the main interface of the Ku-band signal over sea ice is located at the snow/ice interface. This result implies that the altimetric signal goes through the snow pack twice (goings and comings). While going through snow, the radar signal is slowed by the successive scatterings driven by snow grains. To take into account the slower wave propagation in the snow pack, a correction factor (α) is added to the freeboard height. This coefficient is estimated from snow depth (h s ) and snow density (ρ s ) according to the study by [Kwok and Cunningham, 2015] 3 : α = h s (1 ( ρ s ) 1.5 ) (6) 2 Note that the function is slightly different from the one provided in the study by [Guerreiro et al., 2016] due to improvements brought since the study was published 3 The methodology to estimate h s and ρ s will be described in section

11 4.2.3 Freeboard-to-thickness conversion The next step of the processing chain consists to convert the freeboard height into sea ice thickness using an equation for the hydrostatic equilibrium that exists between the snow covered sea ice and the ocean [Laxon et al., 2003]: SIT = ρ wfb + ρ s h s ρ w ρ i (7) In this equation, ρ w, ρ s and ρ i represent respectively the sea water, snow and ice densities and h s represents snow depth. In order to derive these parameters, diverse methodologies are described in the literature. We explain bellow how each of these parameters is obtained in the LEGOS Altimetric Sea Ice Thickness Data Product v1.0 processing chain. Snow depth: While promising studies have demonstrated the potential of passive and active microwave sensors to monitor snow depth at the top of sea ice, these methodologies must still be improved in order to provide accurate snow depth measurements at pan-arctic scale [Maaß et al., 2013, Guerreiro et al., 2016]. In the mean time, the current methodologies used to derive Snow depth (h s ) fields are generally based on the so-called Warren climatology (W99). The W99 climatology is based on snow depth measurements obtained from drifting stations mainly located over Multi Year Ice in the Central Arctic Ocean [Warren et al., 1999]. These individual measurements are then converted into monthly pan-arctic fields using a 2D quadratic fit function. While the study by [Kurtz and Farrell, 2011] showed that the W99 climatology could be used to obtain snow depth at the top of MYI, it suggested that the climatology should be corrected by a factor 0.5 over FYI to account for the thinner snow layer. Following this result and based on the approach by [Kwok and Cunningham, 2015], snow depth fields are derived as follows: h s (X, t, f MY ) = h W s 99 (X, t)f MY hws (X, t)(1 f MY ) (8) 2 Where h W s 99 (X, t) represents the time and space varying W99 climatology and f MY represents the Multi Year Ice fraction. The methodology employed to derive the Multi Year Ice fraction fields is described further down. Sea water, sea ice and snow densities: The sea water density is set to a constant value of kg.m 3 for the entire Arctic region and for the whole winter period [Wadhams et al., 1992]. The monthly snow density fields are prescribed by the 10

12 W99 climatology. As sea ice density is relatively different for MYI (882 kg.m 3 ) and FYI (917 kg.m 3 ) [Alexandrov et al., 2010], it is necessary to distinguish both ice types to derive sea ice density fields. Based on the study by [Kwok and Cunningham, 2015], sea ice density is estimated as follows: ρ i = ρ MY i f MY + ρ F i Y (1 f MY ) (9) Where ρ MY i = 882kg.m 3 and rho F Y i = 882kg.m 3 and f MY represents the Multi Year Ice fraction. Multi Year Ice fraction: As discussed above, it is necessary to use a Multi Year Ice fraction dataset to derive the snow depth and ice density fields in order to convert the freeboard height to ice thickness. In the literature, different approaches are used to derive MY/FY classification from satellite measurements. These methodologies are generally based on radiometric imager or/and on scatterometers data. As the LEGOS Altimetric Sea Ice Thickness Data Product v1.0 aims to provide continuity in the sea ice thickness estimates throughout the entire period of data availability, the choice of the MY/FY classification was mainly motivated by the continuity of the dataset since Based on this main criterion, the EASE-Grid Sea Ice Age, Version 3 product was elected to parametrize snow depth and ice density fields ( In this product, the sea ice age maps are derived using data from satellite passive microwave instruments, drifting buoys and a weather model. This approach allows to provide ice age maps at a 12.5 km resolution. In addition to provide a relatively long times series of MY/FY classification ( present), this dataset does not make any assumption between sea ice roughness and sea ice age as done, for example, in the OSI-SAF product. To convert the EASE-Grid Sea Ice Age fields to Multi Year Ice fraction fields, we apply a spatial filter with a radius of 100 km (similarly to the freeboard data) and estimate the ratio of MYI observation within the filtering area to build MYI concentration maps. These fields can then be directly applied in equations (8) and (9) to estimate snow depth and ice density. Once all intermediate parameters have been computed, equation (7) can be applied to derive monthly Sea Ice Thickness fields for each satellite mission. 4 And since 1991 for a potential extension of the time series to the ERS-1/2 missions. 11

13 4.3 Sea Ice Thickness uncertainties To derive Sea Ice Thickness uncertainties, it is necessary to take into account the uncertainties related to the freeboard measurement as well as the uncertainties related to the freeboard-to-thickness conversion. The SIT uncertainties must therefore be estimated from the combination of equation (3), (6) and (7), which can be re-written as follows: SIT = ρ w((h floe H lead ) + h s (1 ( ρ s ) 1.5 )) + ρ s h s ρ w ρ i (10) To estimate the uncertainties associated to the above equation, we assume that errors are independent from one another, which allows us to apply the following Gaussian propagation law: ɛ 2 SIT = n i=1 SIT x i 2 ɛ 2 xi (11) Where i is the index of the different variables x (ice density, snow depth, etc); ɛ xi is the uncertainty associated to x i and SIT is the function provided in equation (10). The combination of (10) and (11) gives access to the following uncertainties expression: ɛ 2 H g = [ ((H floe H lead ) + h s (1 ( ρ s ) 1.5 ))ρ i + ρ w h s (ρ w ρ i ) 2 ] 2 ɛ 2 ρ w +[ ρ w((h floe H lead ) + h s (1 ( ρ s ))) + ρ s h s (ρ w ρ i ) 2 ] 2 ɛ 2 ρ i +[ ρ w(1 ( ρ s ) 1.5 ) + ρ s ] 2 ɛ 2 h (ρ w ρ i ) s +[ ρ w( 1.5)(0.51)( ρ s ) h s ] 2 ɛ 2 ρ (ρ w ρ i ) s ρ w +[ (ρ w ρ i ) ]2 ɛ 2 H floe ρ w +[ (ρ w ρ i ) ]2 ɛ 2 H lead (12) The uncertainty values of snow depth, snow density, ice density and sea water density (respectively ɛ hs, ɛ ρs, ɛ ρi and ɛ ρw ) are given in Table 3. Regarding the uncertainties associated to the freeboard height measurement (ɛ Hlead and ɛ Hlead ), they have to be estimated for each satellite mission. This estimation must take into account uncertainties related to the radar measurement (speckle noise, atmospheric effects, etc) as well as uncertainties related to the retracking step. As operated over ocean surfaces, the uncertainty associated to individual surface height measurements 12

14 Parameters Typical value Uncertainty Reference Snow depth 0-40 m m [Warren et al., 1999] Snow density kg.m kg.m 3 [Warren et al., 1999] Ice density (FY) 917 kg.m kg.m 3 [Alexandrov et al., 2010] Ice density (MY) 882 kg.m kg.m 3 [Alexandrov et al., 2010] Sea water density kg.m kg.m 3 [Wadhams et al., 1992] Table 4: Table summarizing the typical values of snow depth, snow density, ice density and sea water density as well as the associated uncertainties. can be estimated from the local (i.e. within along-track sections of 25 km) standard deviation of surface height estimated in leads. Regarding the ice floes, the surface heigh standard deviation is strongly impacted by the freeboard variability and can therefore not be used to estimate uncertainties. As there is no any other way to estimate the surface height uncertainty over ice floes, we make the assumption that the individual uncertainty of surface height over ice floes is identical to the individual uncertainty over leads. The first step consists therefore to estimate the average value of the Sea Level Anomaly standard deviation (σ SLA ) in each along-track section of 25 km for each mission and for the entire Arctic Ocean. Average σ SLA values are presented in table 4 for both Envisat and CryoSat-2. To take into account the impact of the data averaging that reduces the uncertainty within the 25 km averaged sections, the uncertainties associated to the surface height of leads and floes is considered as Gaussian and is estimated as follows: σ SLA ɛ Hfloe = Nobsfloe (13) σ SLA ɛ Hlead = Nobslead Where Nobs floe and Nobs lead represent the number of floes and leads within each section of 25 km. This approach allows to account for the impact of the lead and ice floe densities on the freeboard height estimate. The association of equations (12) and (13) allows to estimate monthly Sea Ice Thickness uncertainties maps. An example of CryoSat-2 uncertainty maps is provided bellow. CryoSat-2 Envisat σ SLA m m Table 5: Table showing the average local Sea Level Anomaly standard deviations (σ SLA ) for CryoSat-2 et Envisat. 13

15 (m) (kg/m³) (m) (kg/m³) (m) (m) Figure 1: Examples of sea ice parameters shown for CryoSat-2 in November

16 5 Product references ESA Envisat SGDR: https : //earth.esa.int/web/guest/ /ra 2 sensor data record 1471 ESA CryoSat-2 SGDR: https : //earth.esa.int/web/guest/missions/cryosat/ipf baseline CTOH Envisat SGDR: CTOH CryoSat-2 SGDR: NSDIC Sea ice age classification: http : //dx.doi.org/ /p F SV F ZA9Y 85G 15

17 6 List of acronyms and abbreviations CS-2: CryoSat-2 CTOH: Centre de Topographie des Ocans et de l Hydrosphre. DTU: Danmarks Tekniske Universitet ENV: Envisat ESA: European Space Agency FB: Freeboard FYI: First Year Ice LRM: Low Resolution Mode MYI: Multi Year Ice PP: Pulse Peakiness SAR: Synthetic Aperture Radar SGDR: Sensor Geophysical Data Record SIT: Sea Ice Thickness SLA: Sea Level Anomaly TFMRA: Threshold First Maximum Retracking Algorithm W99: Warren climatology 16

18 7 Bibliography References [Alexandrov et al., 2010] Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. (2010). relation between sea ice thickness and freeboard in the arctic. The Cryosphere, 4(3): The [Andersen and Knudsen, 2015] Andersen, O. B., G. P. L. S. and Knudsen, P. (2015). The dtu15 mean sea surface and mean dynamic topography- focusing on arctic issues and development,. In oral presentation, in the 2015 OSTST Meeting, Reston, USA. [Beaven, 1995] Beaven, S. G. (1995). Sea ice radar backscatter modeling, measurements, and the fusion of active and passive microwave data. Technical report, DTIC Document. [Carrere et al., 2015] Carrere, L., Lyard, F., Cancet, M., and Guillot, A. (2015). Fes 2014, a new tidal model on the global ocean with enhanced accuracy in shallow seas and in the arctic region. In EGU General Assembly Conference Abstracts, volume 17, page [Guerreiro et al., 2016] Guerreiro, K., Fleury, S., Zakharova, E., Rémy, F., and Kouraev, A. (2016). Potential for estimation of snow depth on arctic sea ice from cryosat-2 and saral/altika missions. Remote Sensing of Environment, 186: [Helm et al., 2014] Helm, V., Humbert, A., and Miller, H. (2014). Elevation and elevation change of greenland and antarctica derived from cryosat-2. The Cryosphere, 8(4): [Kurtz and Farrell, 2011] Kurtz, N. T. and Farrell, S. L. (2011). Large-scale surveys of snow depth on arctic sea ice from operation icebridge. Geophysical Research Letters, 38(20). [Kwok and Cunningham, 2015] Kwok, R. and Cunningham, G. (2015). Variability of arctic sea ice thickness and volume from cryosat-2. Phil. Trans. R. Soc. A, 373(2045): [Laxon et al., 2003] Laxon, S., Peacock, N., and Smith, D. (2003). High interannual variability of sea ice thickness in the arctic region. Nature, 425(6961): [Maaß et al., 2013] Maaß, N., Kaleschke, L., Tian-Kunze, X., and Drusch, M. (2013). Snow thickness retrieval over thick arctic sea ice using smos satellite data. The Cryosphere, 7(6): [Wadhams et al., 1992] Wadhams, P., Tucker III, W., Krabill, W. B., Swift, R. N., Comiso, J. C., and Davis, N. (1992). Relationship between sea ice freeboard and draft in the arctic basin, and implications for ice thickness monitoring. J. Geophys. Res, 97(20): [Warren et al., 1999] Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R. (1999). Snow depth on arctic sea ice. Journal of Climate, 12(6):

19 8 Acknowledgments The development and distribution of the LEGOS Altimetric Sea Ice Thickness Data Product v1.0 was supported by the TOSCA SickAys project. We thank the CTOH for providing CryoSat-2 and Envisat SGDRs in a convenient format with all required parameters. We also thank the NSIDC for providing the EASE-Grid Sea Ice Age, Version 3 product, which became essential for the building of or ice thickness product. 18