Extending the QuikSCAT record of seasonal melt–freeze transitions over Arctic sea ice using ASCAT

Remote Sensing of Environment 141 (2014) 214–230

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Extending the QuikSCAT record of seasonal melt–freeze transitions over Arctic sea ice using ASCAT Jonas Mortin a,⁎, Stephen E.L. Howell b, Libo Wang b, Chris Derksen b, Gunilla Svensson a, Rune G. Graversen a, Thomas M. Schrøder c a b c

Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden Climate Research Division, Environment Canada, Toronto, Ontario, Canada Formerly at Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

a r t i c l e

i n f o

Article history: Received 6 September 2013 Received in revised form 7 November 2013 Accepted 8 November 2013 Available online 3 December 2013 Keywords: Active microwave measurements Satelliteborne scatterometry Arctic sea ice and snow Surface processes Melt–freeze retrieval Arctic climate

a b s t r a c t The seasonal melt–freeze transitions are important to continuously monitor over Arctic sea ice in order to better understand Arctic climate variability. The Ku-band scatterometer QuikSCAT (13.4 GHz), widely used to retrieve pan-Arctic seasonal transitions, discontinued its decadal long record in 2009. In this study, we show that the C-band scatterometer ASCAT (5.3 GHz), in orbit since 2006 and with an anticipated lifetime through 2021, can be used to extend the QuikSCAT record of seasonal melt–freeze transitions. This is done by (1) comparing backscatter measurements over multiyear and first-year ice, and by (2) retrieving seasonal transitions from resolution-enhanced ASCAT and QuikSCAT measurements and comparing the results with independent datasets. Despite operating in different frequencies, ASCAT and QuikSCAT respond similarly to surface transitions. However, QuikSCAT measurements respond slightly stronger to the early melt of first-year ice, making it less sensitive to sea-ice dynamics. To retrieve the transitions, we employed an improved edge-detector algorithm, which was iterated and constrained using sea-ice concentration data, efficiently alleviating unreasonable outliers. This gives melt–freeze transitions over all Arctic sea ice north of 60°N at a 4.45 km resolution during 1999–2009 and 2009–2012 for QuikSCAT and ASCAT, respectively. Using the sensor overlap period, we show that the retrieved transitions retrieved from the different instruments are largely consistent across all regions in the Arctic sea-ice domain, indicating a robust consistency. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Seasonal melt–freeze transitions over sea ice are key for understanding the Arctic climate system. Currently, the Arctic sea-ice cover ranges annually from about 15 to 3.5 million km2 (NSIDC, 2013)—more than 10 million km2 of sea ice are thus subject to complete melt and subsequent re-formation each year. Sea ice modulates the surface energy balance mainly through two characteristics. First, as sea ice has a significantly higher albedo than the underlying ocean, areas devoid of ice during the melt season can absorb and store much more insolation. Over a melt season, the absorbed insolation over seasonal ice has been estimated to be about 300 MJ m−2 (~30%) larger than over multiyear ice (MYI; Perovich & Polashenski, 2012). Second, sea ice dampens the ocean–atmosphere heat exchange by almost 1 and 2 orders of magnitude during the melt season and winter, respectively (Smith, Muench, & Pease, 1990). Due to this large influence on the surface energy budget, the duration and extent of the sea-ice cover have implications for the local and regional climate conditions (e.g. Maksimovich & Vihma, ⁎ Corresponding author at: Department of Meteorology, Stockholm University, Svante Arrhenius Väg 16C, 106 91 Stockholm, Sweden. Tel.: +46 8 162413. E-mail address: [email protected] (J. Mortin). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.11.004

2012; Persson, 2011), which have been suggested to scale to well beyond the Arctic domain (e.g. Hopsch, Cohen, & Dethloff, 2012; Parmentier et al., 2013; Screen, Simmonds, Deser, & Tomas, 2013). The seasonal duration of the sea-ice cover and the overlying snow is inherently coupled to the melt–freeze transitions. Even small changes in the timing of these transitions have a large impact on the region energy budget. In fact, the total heat input into the ocean during a melt season is more dependent on the timing of the seasonal transition than on the total incident energy (Perovich, Nghiem, Markus, & Schweiger, 2007). As an estimate, for every day earlier that melt begins, an additional 8.7 MJ m− 2 (~ 3 cm of ice melt) is absorbed by the ocean, and each day freeze-up occurs later, an additional 1.5 MJ m− 2 is absorbed (Perovich et al., 2007). This energy heats primarily the oceanic mixed layer and is released in the fall (Kurtz, Markus, Farrell, Worthen, & Boisvert, 2011), potentially delaying the freeze-up and fostering a thinner ice cover for the subsequent year (Dumas, Flato, & Brown, 2006; Laxon, Peacock, & Smith, 2003; Lindsay & Zhang, 2005). In turn, a delayed freeze-up can decrease the snowpack thickness in winter through decreased snow accumulation on the sea ice (Hezel, Zhang, Bitz, Kelly, & Massonnet, 2012). While this enhances the sea-ice growth rate, thus mitigating the influence of delayed freeze-up on the ice thickness (Notz, 2009), a thinner snow cover can ablate quicker during the melt

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season (Hezel et al., 2012). As mentioned above, this allows for more solar input to be stored in the ocean, which might delay the freeze-up. In recent decades, the temperatures in the Arctic have been rising two to four times faster than the global mean (Bekryaev, Polyakov, & Alexeev, 2010), and are expected to continue to do so (e.g. Abe, Shiogama, Nozawa, & Emori, 2011; Knutti & Sedláček, 2012). Since the melt season length and MYI coverage are very sensitive to changes in the surface air temperatures (Mortin, Graversen, & Svensson, 2013; Polyakov, Walsh, & Kwok, 2012), the seasonal transitions are effective indicators of Arctic warming. Because of these implications, it is important to continuously monitor the timing of the seasonal melt–freeze transitions over sea ice in space and time. Microwave instruments are well suited to observe the seasonal seaice transitions because, in contrast to optical instruments, they function independently of solar illumination and operate in wavelengths in which clouds are essentially transparent. Consequently, they provide measurements of the surface all year. Furthermore, they are sensitive to the dielectric properties of the surface, which are strongly related to the amount of liquid water at the surface. The state of liquid water changes over the year when the surface undergoes seasonal melt and freeze transitions, which induces distinct signals in microwave measurements. For this reason, passive microwave instruments have previously been utilized to retrieve the transitions over sea ice (e.g. Belchansky, Douglas, Mordvintsev, & Platonov, 2004; Drobot & Anderson, 2001; Markus, Stroeve, & Miller, 2009). However, due to their coarse spatial resolution (12–25 km), passive microwave instruments are unable to resolve variability and geographical features on small scales, such as coastlines and the narrow channels within the Canadian Arctic Archipelago (CAA). Synthetic aperture radar (SAR) instruments have also been utilized (e.g. Kwok, Cunningham, & Nghiem, 2003; Winebrenner, Holt, & Nelson, 1996, Winebrenner, Nelson, Colony, & West, 1994), but their fine spatial resolution (≤ 100 m) is achieved at the expense of spatial coverage and temporal resolution. Scatterometers (i.e. real aperture radar) offer a balance between resolution and coverage in the polar regions: resolution-enhanced data using the Scatterometer Image Reconstruction (SIR; Long, Hardin, & Whiting, 1993) provide essentially full coverage of the Arctic region each day at 4.45 km resolution. The Ku-band scatterometer QuikSCAT (QSCAT) was extensively utilized to retrieve the surface transitions over both land and sea-ice domains (e.g. Bartsch, 2010; Brown, Derksen, & Wang, 2007; Howell, Derksen, & Tivy, 2010; Rawlins et al., 2005; Wang et al., 2011), but the antenna ceased to rotate in late 2009, which discontinued its roughly 10-year long record. The C-band instrument Advanced Scatterometer (ASCAT) was launched in 2006 and 2012 on the MetOp-A and MetOp-B satellites, respectively, and is approved for another platform with a life expectancy beyond 2021 (Vogelzang & Stoffelen, 2012). Resolution-enhanced ASCAT SIR data are available from 2009 to the present. ASCAT has been widely utilized to retrieve the surface-wind speed, which it is primarily intended for, and to retrieve the surface-soil state (e.g. Naeimi et al., 2012; Wagner et al., 2013). However, currently the applications over sea ice are relatively few (e.g. Girard-Ardhuin & Ezraty, 2012). In this study, we show that ASCAT can be used to extend the 10-year transition record of QSCAT over sea ice. Since these instruments differ, most notably in frequency (5.3 and 13.4 GHz, respectively), we compare and discuss measurement time series from the instruments with an emphasis on the seasonal transitions over a full year over both MYI and first-year ice (FYI). Fortunately, both instruments acquired coincident measurements in 2009, making a direct comparison possible. To our knowledge, no such comparison exists between ASCAT and QSCAT. Furthermore, the seasonal melt–freeze transitions are retrieved from both QSCAT and ASCAT. In order to pinpoint the backscatter changes that most likely correspond to the transitions, we utilize an updated version of the edge-detection algorithm applied to QSCAT by Mortin, Schrøder, Hansen, Holt, and McDonald (2012). The algorithm updates include several significant improvements employed on both

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QSCAT and ASCAT data. The retrieval gives melt and freeze transitions during 1999–2009 and 2009–2012 from QSCAT and ASCAT, respectively, over all sea ice north of 60°N. These results and the algorithm improvements are evaluated using transitions from other independent data sources, such as the passive microwave radiometer melt–freeze algorithm by Markus et al. (2009) and temperatures from the ERA-Interim reanalysis. This paper is outlined as follows. Section 2 presents the data sources in greater detail, and Section 3 compares backscatter time series from QSCAT (Ku-band) and ASCAT (C-band) over sea ice for a full year. Section 4 describes the retrieval methodologies, and Section 5 evaluates the ASCAT transitions. In Section 6, we show that ASCAT successfully extends the QSCAT transition record over sea ice, and Section 7 provides conclusions of the study.

2. Data 2.1. ASCAT ASCAT is an active microwave instrument mounted on three polarorbiting European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)/European Space Agency (ESA) satellites: MetOp-A, launched in October 2006 and MetOp-B, launched in September 2012 (both presently active), as well as MetOp-C, planned to be launched in 2016 or 2017 with a life expectancy through 2021 (Vogelzang & Stoffelen, 2012). ASCAT should thus provide a long, consistent record. ASCAT is an upgraded successor to the widely utilized scatterometers onboard the European Remote Sensing (ERS)-1/2 platforms (Figa-Saldaña et al., 2002). It emits pulses in the C-band at 5.26 GHz—equivalent to a 5.7 cm wavelength—in vertical co-polarization (VV) and measures the returned backscatter signal (sigma naught; σ°). It has two sets of three fan-beam antennae measuring at incidence angles between 25° and 65°, each set covering a swath of 550 km separated by about 360 km, which yields a daily, global coverage of 80%. ASCAT measures the surface at all times due to two advantageous properties of microwave instruments: efficient penetration of atmosphere and clouds, and independence of solar illumination. The nominal resolution of the ASCAT standard backscatter product is 25 or 50 km (Figa-Saldaña et al., 2002); however, we utilize a resolution-enhanced product in this study. The SIR algorithm utilizes the side lobes and overlap of measurements due to the several overpasses made over the pole each day to enhance the spatial resolution to 4.45 km (Early & Long, 2001; Lindsley & Long, 2010; Long et al., 1993). This fine spatial resolution is achieved at the expense of the temporal resolution, which is reduced. Since the spatial coverage of ASCAT is insufficient to cover the full Arctic region on a daily basis, ASCAT SIR data are given as 2-day aggregates. Nevertheless, data are provided for all days, giving a data overlap equivalent to a temporal smoothing of σ°. While the diurnal cycle of backscatter provides useful information for other applications (e.g. Garreaud & Muñoz, 2005), the temporal resolution of SIR data is sufficient for studying the seasonal melt–freeze transitions (Mortin et al., 2012). Because the SIR data better resolve small-scale geographical features, such as coastlines, narrow channels, leads and polynyas, the shift of information from the temporal domain to the spatial is a good trade-off. The SIR algorithm is a true reconstruction algorithm that has been applied to several microwave instruments and is distributed by the Scatterometer Climate Record Pathfinder project. In this study, we utilize the all passes product in which σ° is normalized to a 40° inclination angle. We use data north of 60°N covering the entirety of 2009–2012, prior to which no SIR data are available. The product includes the standard deviation of the measurements from the multiple overpasses constituting the SIR data, as well as topography and bathymetry data, which are used as a sea mask—grid cells in which the altitude is less than 0 m are marked as sea. The ASCAT SIR data record is continuously updated.

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2.2. QSCAT QSCAT was originally intended as a quick recovery mission to replace the NASA Scatterometer (NSCAT), but acquired measurements for more than 10 years: from mid-July 1999 to mid-November 2009, when antenna rotation ceased. It operated in the Ku-band at 13.4 GHz— equivalent to a 2.2 cm wavelength. It used a conically scanning antenna with a pencil-beam configuration: the outer beam was vertically co-polarized (VV) at a 54° inclination angle, and the inner horizontally co-polarized (HH) at a 46° inclination angle. This gave swath widths of 1800 km and 1400 km, respectively, yielding a 90% global coverage daily. In this study, we utilize a similar SIR product as for ASCAT: daily backscatter values and standard deviation of all passes in both polarizations, available on the same 4.45 km grid as ASCAT data (Long & Hicks, 2010). It should be noted that QSCAT SIR data are 1-day aggregates due to the larger coverage than that of ASCAT, and that the inclination angle of each beam is preserved in the SIR data. Also, unlike ASCAT, a data void is present for QSCAT north of roughly 87.7°N, due to a combination of the orbital inclination angle and the incidence angle of measurements. 2.3. Ancillary data To obtain contextual information and data for constraining unreasonable transitions, as will be described in Section 4.2, we utilize seaice concentration (SIC) data retrieved from passive microwave measurements from the instruments Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMI/S). Using a combination of frequencies and polarizations acquired by these instruments onboard several platforms, the NASA Team algorithm provides SIC from late 1978 to the present (Cavalieri, Parkinson, Gloersen, & Zwally, 1996). In the time period we investigate, 1999– 2012, data were collected by the SSM/I instrument onboard the DMSP-F13 satellite from 1999 through April 2009, after which data were collected by the SSMI/S instrument onboard DMSP-F17. The difference in SIC between these sensors is very small (Cavalieri, Parkinson, DiGirolamo, & Ivanoff, 2012). The data have a daily temporal resolution and are provided on a 25 km polar stereographic grid by the National Snow and Ice Data Center (Cavalieri et al., 1996). To provide both contextual information and data from which we retrieve melt and freeze onset, as will be described in Section 4.3, we utilize temperature data from the ERA-Interim reanalysis provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim outputs a plethora of climate and weather related parameters at a spatial resolution of 0.75° in both latitude and longitude in 6-hour time steps from 1979 to the present (Dee et al., 2011). We use the 2-meter temperature (T2m) and the skin temperature (Tskin). ERAInterim data are widely utilized and have been evaluated extensively for the Arctic region (e.g. Jakobson et al., 2012; Zygmuntowska, Mauritsen, Quaas, & Kaleschke, 2012). We use melt and freeze timing estimates from Markus et al. (2009) to relate the results of this study to another, independent dataset of seasonal transitions. They retrieved early and permanent (late) melt and freeze over Arctic sea ice during 1979 to the present using radiometers Scanning Multichannel Microwave Radiometer (SMMR), SSM/I, Advanced Microwave Scanning Radiometer (AMSR-E), and SSMI/S. The measurements from these instruments are sensitive to the amount of liquid water at the surface, similar to ASCAT and QSCAT measurements. The retrieval approach is to incorporate several previously published indicators, each sensitive to different stages of the transitional processes, and to explore their agreement. The resulting transitions are available at a 25 km resolution through the NASA Cryosphere Science Research Portal. We also use freeze transitions over open water derived from the Interactive Multisensor Snow and Ice Mapping System (IMS) dataset. To produce IMS, analysts utilize a large number of satellite sensors, snow and ice mapping algorithms, and other ancillary data to distinguish land

from snow and open water from sea ice (Helfrich, McNamara, Ramsay, Baldwin, & Kasheta, 2007; Ramsay, 1998). IMS is binary and is provided as daily maps at a spatial resolution of 4 km by the U.S. National Ice Center and the National Snow and Ice Data Center. The freeze-up of open water areas can be retrieved since IMS data distinguishes sea ice from open water: the first day a grid cell covered with open water in the melt season becomes sea-ice covered is marked as freeze-up. Only freeze-up is utilized in this study from IMS, since the sole parameter associated with melt that can be retrieved is ice off. 3. Backscatter response of ASCAT and QSCAT 3.1. Background For comprehensive description of the snow–ice–ocean surface interactions of microwave measurements, refer to Ulaby, Moore, and Fung (1986) and Carsey (1992). Here, a brief description is provided. In general, microwave backscatter (σ°) signatures (acquired at a given frequency and incidence angle) are moderated by the dielectric properties and the roughness of the observed surface. In regions where the surface undergoes seasonal melt–freeze cycles, the dielectric properties have the largest influence on the annual time series (e.g. Gogineni et al., 1992; Stiles & Ulaby, 1980; Ulaby et al., 1986). However, due to seaice dynamics—e.g. advection and ridging—and the presence of open water during the melt season, surface roughness has a non-negligible influence on the annual time series. The dielectric properties of a medium can be quantified by its complex dielectric constant, describing its propensity to interact with an external electromagnetic field. For snow-covered sea ice, the largest component of the complex dielectric constant is the permittivity (e.g. Shokr, 1998; Ulaby et al., 1986), which describes the medium's ability to permit microwave energy to penetrate the material. A high permittivity inhibits penetration, making surface scattering more dominant. This ranges from diffuse (higher σ°) to specular (lower σ°) depending primarily on the surface roughness on scales of the incident wavelength. A low permittivity allows the microwave energy to penetrate the medium and to scatter within the material, back to the sensor (i.e. volume scattering). For components of the cryosphere, surface scattering generally yields lower σ° than volume scattering. The permittivity of natural media is closely related to its liquid water content: for example, liquid water has a permittivity more than 20 times larger than ice. Since sea ice and snow are predominately ice media with inclusions of water and brine, the relative volumetric ratio of these inclusions dominates the volume's bulk permittivity. The penetration depth of the microwave energy in a medium is an important concept to understand the temporal evolution of σ°. The penetration depth can be estimated with the permittivity of the medium and the incident wavelength; therefore, the penetration depth differs between ASCAT and QSCAT and varies with the liquid water content. When the liquid water content of an ice medium increases, leading to an increase of the permittivity, the penetration depth decreases drastically. For example, for snow volumes with liquid water contents of 0%, 1%, and 3%, the penetration depth at C-band (ASCAT) is about 20 m, 0.55 m, and 0.11 m, respectively (Howell, Yackel, De Abreu, Geldsetzer, & Breneman, 2005; Ulaby et al., 1986). At Ku-band (QSCAT), the equivalent depths are roughly 2 m, 0.2 m, and 0.04 m. Thus, dry snow is essentially transparent in the ASCAT and QSCAT wavelengths (5.7 and 2.2 cm, respectively), which is attributed to the typically much smaller snow grain size than the wavelengths (0.25–0.5 mm; Tucker, Perovich, Gow, Weeks, & Drinkwater, 1992), providing little scattering. Also in bare sea ice, QSCAT has a lower penetration depth than ASCAT: about 0.4 m and 1 m in MYI, respectively, and roughly half of that in FYI, attributed primarily to the different brine content between the ice types (Ulaby et al., 1986). The ability to use σ° to detect the seasonal melt–freeze transitions originates in the distinct changes of the permittivity between water in

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its solid and liquid phases. Changes in the liquid water content of the surface induce alterations in the permittivity and thus the penetration depth, which weakens and strengthens surface and volume scattering mechanisms. As a result, the surface liquid water content changes associated with the seasonal melt–freeze transitions induce the most notable σ° changes in an annual time series. To delineate the temporal evolution of σ° for Arctic sea ice from ASCAT and QSCAT and the differences thereof, time series of both instruments are plotted for MYI (Fig. 1) and FYI (Fig. 2) over a full year. SIC data are included as an indicator of sea-ice coverage and dynamics, and as contextual information in addition to T2m from ERAInterim. Further, to distinguish the thermodynamic processes from the

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dynamic, we show sites with landfast and mobile ice in each of these figures. The ice type and ice mobility of each site has been confirmed with high-resolution SAR imagery (~ 100 m) in conjunction with Canadian Ice Service analysis (not shown). Additionally, Fig. 3 illustrates time series of areas in the marginal seas in which the σ° evolution is more complex, such as areas with mixed ice types, very strong sea-ice dynamics, or thin sea ice. 3.2. Winter In winter, σ° is high and stable from MYI for both QSCAT and ASCAT due to strong volume scattering from air bubbles within the ice and the 100

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Month 2009 Fig. 1. Sites of MYI with time series of vertically polarized (VV) ASCAT and QSCAT, ERA-Interim T2m, and SIC: for landfast MYI (a, b) and mobile MYI (c, d). Over MYI, both ASCAT and QSCAT respond with a σ° decrease at melt and a σ° increase at freeze-up, elaborated upon in Sections 3.3 and 3.4. The σ° thresholds for distinguishing MYI from FYI are shown for the time period in which the ice-type evaluation is performed, discussed in Section 4.1. Dot and star denotes retrieved thaw and freeze transition, respectively. Both early and late melt and freeze by Markus et al. (2009) are included. IMS freeze-up is retrieved in open water, and is thus only available for b). All ASCAT and QSCAT transitions refer to a posteriori transitions, discussed in Section 4.1. Note that the scales differ between the panels. The ice type as estimated by the algorithm, described in Section 4.1, is consistent with the ice type confirmed using highresolution SAR imagery denoted in the panels.

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typically rough surface that gives strong surface scattering, both for landfast ice (Fig. 1a–b) and mobile ice (Fig. 1c–d). ASCAT σ° is consistently weaker than QSCAT σ° likely because the air bubbles in the ice are small relative to the operating wavelength, which give a stronger volume scattering in the shorter wavelength of QSCAT. In absence of large leads and polynyas, the impact of sea-ice dynamics on both the QSCAT and ASCAT σ° in winter is negligible (Fig. 1c–d); that is, SIC and σ° vary very little. The σ° level from FYI in winter is lower and less stable compared to σ° from MYI for both QSCAT and ASCAT (cf. Figs. 1 and 2). This is likely attributed to more brine in the FYI and in the snow basal layer, allowing less microwave energy to penetrate into the sea ice (Barber & Nghiem, 1999; Crocker, 1992). Additionally, compared with MYI, the volume scattering in the FYI is weaker due to fewer and smaller air bubbles, and the surface scattering is more specular due to a typically smoother surface. In comparison, ASCAT gives a slightly stronger response from

FYI than QSCAT (the opposite from MYI). This is most likely attributed to the larger penetration depth of ASCAT, which increases the volume scattering in the FYI. The impact of sea-ice dynamics is greater in winter close to the ice edge than in the central Arctic and can give a variability in ASCAT and QSCAT σ° that usually is unproblematic when retrieving the seasonal transitions. However, if the sea-ice dynamics is sufficiently strong, σ° changes of equivalent strength as the transitions are induced. For example, Fig. 2c shows SIC variability during winter that induces weak σ° edges, while Fig. 3c shows a nearby location in which strong SIC variability induces edges that are misinterpreted as melt while temperatures are still below freezing. 3.3. Melt and melt season Over MYI, melt causes a drastic drop in σ° that occurs simultaneously for QSCAT and ASCAT (Fig. 1). When the air temperature approaches

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Month 2009 Fig. 3. As Figs. 1 and 2, but for sites in areas in which the temporal evolution of σ° is more complex. The thresholds distinguishing MYI from FYI are excluded. Also, no confirmation with SAR imagery was performed for these sites. However, the algorithm estimates FYI coverage, as described in Section 4.1, at all these sites from both QSCAT and ASCAT data.

0 °C and the liquid water content within the snowpack increases, the permittivity is increased and the penetration depth is drastically reduced. While the increased liquid enhances volume scattering in the snowpack, it also inhibits the much stronger volume scattering in the MYI; σ° thus drops (e.g. Barber, Papakyriakou, Ledrew, & Shokr, 1995; Winebrenner et al., 1994). QSCAT gives a slightly stronger response than does ASCAT (Fig. 1), as the MYI volume scattering that is inhibited is stronger for QSCAT. Also, ASCAT (SIR) measurements are less responsive due to being 2-day aggregates, as discussed in Section 2.1. In regions where MYI is mobile, sea-ice dynamics may introduce sufficiently large σ° variability to indicate melt while air temperatures are still below freezing (Fig. 1c–d). However, this influence is normally small since the extensive and compacted ice cover before melt inhibits movement.

Over FYI, the melt signal indicator is the opposite: an increase in measured σ° (Fig. 2). The reason is twofold. First, at or near an air temperature of −5 °C, the brine content increases at the snow–ice interface (Assur, 1960), coating the snow grains in the basal layer. These grains have also been enlarged due to metamorphism during the fall and winter (Colbeck, 1982). When brine coated, they act as sufficiently large high-permittivity scattering centers to enhance the volume scattering at the snow–ice interface. Thus, σ° increases distinctly (Barber & Nghiem, 1999). Moreover, the increasing liquid water in the snowpack enhances volume scattering in the snow, which further increases σ° (e.g. Barber et al., 1995; Drinkwater, 1989). Second, later in the melt process, when melt ponds are forming that are progressively more wind roughened as they become larger in number and extent, σ° increases (e.g. Comiso & Kwok, 1996; Scharien & Yackel, 2005; Yackel &

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Barber, 2000). The first process (brine volume scattering) is typically the primary melt indicator over Arctic FYI for both ASCAT and QSCAT (e.g. Barber & Nghiem, 1999; Barber et al., 1995; Howell et al., 2005; Yackel, Barber, Papakyriakou, & Breneman, 2007). However, if the first process has a sufficiently weak impact on σ°, such as for rough FYI (Yackel et al., 2007), ASCAT can be unresponsive while QSCAT responds readily because of its shorter wavelength. As a result, QSCAT can detect an earlier melt than ASCAT. This is clearly seen in Fig. 2d, where ASCAT instead shows the largest σ° increase later in the melt process, most likely due to wind roughening of the progressively growing melt ponds. Another implication of the larger sensitivity to the primary melt indicator is that QSCAT is less sensitive to processes unrelated to melt, such as sea-ice dynamics (e.g. Fig. 3d). It should be noted, however, that the areas in which this difference is substantial typically are of limited extent, as will be shown in Section 5.2. Other surface conditions can weaken the melt signal indicator. For example, the snowmelt and sea-ice deterioration might occur simultaneously in parts of the marginal ice zone where the sea ice and snow cover are thin, lacking the increased volume scattering at melt (e.g. Fig. 3b). Also, because MYI and FYI give opposite melt responses, areas of mixed ice types can give a small σ° increase, or even a decrease, depending on the areal ratio of the ice types. During the melt season, σ° is dominated by wind roughening of water, to which QSCAT σ° is generally more responsive than ASCAT σ° (e.g. Fig. 2c–d), most likely because of its shorter wavelength and the ASCAT SIR data being 2-day aggregates. Early in the melt season when ponds are forming (i.e. advanced melt), σ° is controlled by melt pond fraction and wind speed (Comiso & Kwok, 1996; Yackel & Barber, 2000) and an increase in σ° is common, as discussed above. When the sea ice deteriorates, wind roughening of the open water surface gives strong σ° variability from both ASCAT and QSCAT (Figs. 1–3; Howell, Tivy, Yackel, & Scharien, 2006; Scharien & Yackel, 2005). The σ° variability is larger over open water than over sea ice during the melt season, most likely attributed to less fetch over melt ponds (cf. Figs. 1 and 2; Scharien & Yackel, 2005). 3.4. Freeze-up At freeze-up over ice that has remained throughout the melt season (i.e. MYI), σ° from both ASCAT and QSCAT increase distinctly when the melt ponds and other open water formations freeze (Fig. 1), re-enabling strong volume scattering within the MYI (Beaven & Gogineni, 1994; Carlström & Ulander, 1993). However, the freeze-up process can occur gradually when the surface undergoes multiple melt–refreeze cycles, as was observed during the Surface Heat Budget of the Arctic Ocean campaign (SHEBA; Persson, 2011). This yields a less distinct increase in σ° and/or multiple indicators for both ASCAT and QSCAT (Fig. 1c–d). Late in the melt season, sea-ice dynamics can have a significant impact also in areas with landfast MYI (Fig. 1a–b), but is less influential on σ° than in areas with mobile ice (Fig. 1c–d). However, once the surface has frozen completely, the influence of sea-ice dynamics on σ° becomes small for both landfast and mobile ice; thus, σ° from both ASCAT and QSCAT stabilize (Fig. 1). When ice forms in open water (i.e. FYI), σ° from both ASCAT and QSCAT increases and stabilizes after the variable melt season (Fig. 2). But the σ° response at FYI freeze-up is complex. From open water, σ° can increase or decrease at ice formation depending on the wind and fetch conditions (Onstott, 1992). As the ice thickens, volume scattering and thus σ° increase, thereby returning to the higher winter values (Winebrenner et al., 1996). Other factors affecting σ° at freeze-up are frost flowers, giving a strong response from diffuse surface scattering, and grease ice, giving a weak σ° due to dampened wind roughening (Onstott, 1992; Tucker et al., 1992). The resulting σ° response of these processes is an increase, although typically weak. In fact, in the marginal seas the σ° increase associated with freeze-up is commonly weaker than the increases associated with the σ° variability induced by wind

roughening during the melt season, for both ASCAT and QSCAT (e.g. Figs. 2c and 3a–d). Because of this, freeze-up in open water is very difficult to reliably estimate from automated algorithms on a large scale, particularly in the marginal seas. Nevertheless, the σ° increase related to freeze-up is coincident for ASCAT and QSCAT. 4. Transition retrieval 4.1. Backscatter time series analysis In order to obtain the seasonal melt–freeze transitions from both ASCAT and QSCAT, we use an edge-detection algorithm that locates the largest backscatter changes, or edges, which usually correspond to the seasonal melt–freeze transitions. The algorithm is applied to σ° time series twice. An initial (a priori) iteration retrieves first estimates of the transitions. These are subsequently used to construct a climatology that is utilized in a second (a posteriori) iteration to improve the transition dates in some areas. For consistency, the term climatology is here used also for ASCAT, despite the short record. The algorithm has previously been used with QSCAT data by Mortin et al. (2012). Here, we incorporate several improvements to the algorithm and apply it to both QSCAT and ASCAT data. Additionally, to obtain more reliable transitions, we employ SIC data to constrain unrealistic outliers, as will be described in Section 4.2. Edges can efficiently be detected by convoluting time series of σ° with the first derivative of a Gaussian function according to Z CNV ðt Þ ¼

∞ −∞

′

∘

f ðxÞσ ðt−xÞdx

ð1Þ

where f(x) is the Gaussian function (Canny, 1986). Values of CNV correspond to σ° edges of the same sign. To mitigate the influence of noise and processes introducing σ° edges unrelated to melt–freeze transitions, we employ Gaussians of different width in Eq. (1), corresponding to different time scales of variability in the signal. Widths of 1 through 30 days in steps of 1 are used. From the minimum value of σ° found in the melt season, the algorithm searches for the numerically largest values of CNV in both temporal directions to capture melt and freeze, respectively. We assume that no melt occurs prior to March 1 and that no freeze occurs after December 31; after the latter date, spatial growth of the sea-ice cover occurs primarily outside the study domain, i.e. south of 60°N (NSIDC, 2013). Because MYI and FYI give opposite σ° responses at melt, as discussed in Section 3.3, the algorithm must first evaluate whether or not a grid cell is ice covered and, if so, with what ice type. To evaluate whether a grid cell is ice covered or not, we exploit that the variability of σ° is large over open water and low over sea ice, as discussed in Section 3. The evaluation is performed for both the winter and the melt season. Specifically, we follow Haarpaintner, Tonboe, Long, and Van Woert (2004), who distinguished open water from sea ice by utilizing thresholds of QSCAT σ° in both polarizations, daily standard deviation of σ°, and a dual-polarization ratio. Here, the same thresholds as Haarpaintner et al. (2004) are applied for QSCAT (see their Table 1), while for ASCAT, the same thresholds are applied but on its single

Table 1 Approximate MYI area in winter 2009 retrieved using the σ° thresholds to distinguish ice types (i.e. −16.25 dB for ASCAT and −14.5 dB for QSCAT) and alterations thereof, given in million km2. For ASCAT (partial), detected MYI is excluded for the data void at the pole present in QSCAT measurements, as well as the Hudson and Baffin Bays and the eastern parts of the CAA.

thr thr thr thr thr

+ 0.5 dB + 0.25 dB − 0.25 dB − 0.5 dB

ASCAT

ASCAT (partial)

QSCAT

2.47 2.72 2.90 3.23 3.57

2.26 2.47 2.60 2.86 3.11

2.31 2.44 2.57

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polarization. These criteria are evaluated for 90% of the days during January through April and July through September for the winter and melt season, respectively. This gives a more realistic sea-ice cover with QSCAT than estimated by Mortin et al. (2012), who used 99% (cf. Fig. 5 and their Figs. 10–11). Using the only available polarization of ASCAT gives a less reliable ice cover than obtained with QSCAT, resulting in some falsely detected sea ice in the North Atlantic and in the northern Labrador Sea in winter. Therefore, we only include grid cells in which SIC exceeds 15% at least one day annually. With this exception, the thresholds yield good estimates of the sea-ice cover in both the winter and the melt season compared with SIC data, for both ASCAT and QSCAT (not shown). In grid cells that are evaluated as ice covered in winter, we distinguish MYI from FYI with σ°. The consistently stronger σ° from MYI, discussed in Section 3.2, makes a single σ° threshold feasible to separate the ice types. However, the different magnitudes of ASCAT and QSCAT σ° in winter (Figs. 1 and 2) necessitates different thresholds. For QSCAT, − 14.5 dB is used (Kwok, 2004) and for ASCAT, − 16.25 dB. The latter is obtained by visual calibration against the QSCAT MYI distribution and other datasets of MYI coverage (not shown; Maslanik, Stroeve, Fowler, & Emery, 2011; Swan & Long, 2012). These thresholds are indicated in Figs. 1 and 2 for the time period they are evaluated, January through April. The ASCAT threshold is lower than those previously derived for C-band SAR data (e.g. Kwok & Cunningham, 1994; Steffen & Heinrichs, 1994; Winebrenner et al., 1996) because of a mixture of ice types, which is more common in the coarser resolution of ASCAT (~5 km compared with ~100 m). The utilized thresholds give MYI distributions from QSCAT and ASCAT largely consistent with other, ancillary datasets (Maslanik et al., 2011; Swan & Long, 2012), with the exception of ASCAT overestimating MYI in the eastern parts of the CAA and in the Hudson and Baffin Bays (not shown). This is attributed to the smaller MYI–FYI σ° difference for ASCAT than for QSCAT, making the ice-type distinction less reliable with ASCAT (cf. Figs. 1 and 2; Onstott, 1992; Ulaby et al., 1986). The sensitivity of the thresholds used to distinguish ice types is indicated in Table 1. Geographically, the differences in MYI area are evenly distributed over areas where the algorithm detects MYI—for QSCAT, in the central Arctic and parts of the western CAA, and for ASCAT, also along the Eurasian coastline, in the eastern CAA as well as in the Hudson and Baffin Bays (not shown). Note that no ice-type distinction is performed during the melt season, since sea ice that remains throughout the whole melt season is by definition MYI. Once the sea-ice analysis is performed, the algorithm retrieves the largest value of CNV in Eq. (1) of the proper sign—depending on ice type and transition—for each of the 30 utilized Gaussian functions corresponding to σ° changes on different time scales. This gives 30 suggestions of transition dates each for melt and freeze-up. The algorithm subsequently settles on one transition date by using the median of the 30 suggestions. Refer to Mortin et al. (2012) for a detailed description of this methodology. These retrieved (a priori) transitions constitute the first estimates that are utilized in the second (a posteriori) iteration of the algorithm. While the use of multiple Gaussian functions mitigates the noise in the σ° time series, processes unrelated to the seasonal transitions (e.g. sea-ice dynamics) can cause σ° edges that are misinterpreted as transitions. To reduce these unrealistic outliers, the transition dates from the first (a priori) iteration are used to construct climatologies—for ASCAT and QSCAT separately—that are used in the second (a posteriori) iteration. The climatologies are constructed by fitting functions to the medians of transitions from all available years (1999–2009 and 2009– 2012 for QSCAT and ASCAT, respectively) in 1°-bins of latitude, yielding expected transition dates for each grid cell. Different functions can be used. Mortin et al. (2012) used two sinusoidal functions of latitude: one for melt and one for freeze, both for all sea ice. Here, we use three second-order polynomials of latitude on both ASCAT and QSCAT: two functions at freeze-up—for MYI and FYI—and one function at melt over

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all sea ice. The justification for using three different functions is as follows. Because the typically well-mixed upper oceanic layer takes longer to cool below the freezing point than do the shallow water formations (melt ponds) associated with existing ice, freeze-up over open water (FYI) occurs substantially later than over existing ice (MYI) at a given latitude. This is taken into account by the separate functions at freezeup. At melt, however, ice type should have a limited influence on the transition timing (Mortin et al., 2013). These characteristics are corroborated by the a priori transition medians used to construct the climatologies (not shown). Once constructed, the climatology functions gives expected dates for each grid cell and are utilized in the second iteration of the algorithm: instead of using the median of the 30 suggestions to settle on one transition date, the expected day from the climatology is used. See Mortin et al. (2012) for a detailed method description. This procedure enables the algorithm to settle on the identified σ° edges more likely to correspond to the seasonal transition than to other processes. Areas in which the σ° response is concise and edges are abundantly related to melt–freeze transitions on several time scales (1–30 days; e.g. Fig. 1a) remain unchanged from the iteration, while a significant decrease of unrealistic outliers is achieved in large areas, increasing the spatial coherence substantially. This is most notable for the melt transition. The impact of the iteration using these updated climatologies is equally large for QSCAT and ASCAT, and is similar to what was attained by Mortin et al. (2012; see their Fig. 6). 4.2. Constraining ASCAT transitions with sea-ice concentration The second iteration using the climatology improves primarily the melt transitions. This is because the σ° edge related to melt is typically strong enough to be captured by at least one Gaussian function convolution in Eq. (1), i.e., the edge constitutes the largest σ° change in at least one time scale (1–30 days). This makes the edge corresponding to the transition available for the algorithm amongst the 30 suggestions. Moreover, the processes inducing σ° edges unrelated to melt, such as sea-ice dynamics, vary spatially between years. At freeze-up, however, the edge related to ice formation in open water—where transitions are difficult to retrieve—is often less pronounced than the σ° variability during the melt season (e.g. Figs. 2c and 3a–c). Sea-ice dynamics can have a similar effect in some areas (Fig. 3d). Also, the problem is spatially consistent between years, which hinder the algorithm's ability to detect the actual freeze-up based on the numerical value of CNV in Eq. (1). As a response, we constrain the (a posteriori) transitions using SIC information. SIC data are relatively crude with respect to surface processes compared with ASCAT and QSCAT, particularly during the seasonal transitions (Agnew & Howell, 2003), and are difficult to implement as a sole indicator of melt and freeze-up over the central MYI pack. However, they are a suitable complement to σ° in areas where the transition signal indicators are weak and transitions are thus difficult to retrieve. Parallel to retrieving the seasonal transitions from ASCAT and QSCAT, we retrieve transitions from SIC. The last day the SIC drops below 80% prior to the annual SIC minimum is marked as melt, and the first day the SIC exceeds 15% after the annual SIC minimum is marked as freeze. We use these transition estimates based on SIC data whenever the transitions retrieved from ASCAT or QSCAT are most likely unrealistic. At melt, whenever the transition from ASCAT or QSCAT is later than that from SIC, we instead use the SIC date as our best estimate. At freeze-up, whenever the ASCAT or QSCAT transition is more than 14 days earlier than the freeze from SIC, we instead use the SIC date as our best estimate. The justification for this approach is as follows. At melt, it is expected that σ° will show a strong response when the liquid water content increases in the snow (over MYI) or when the brine increases at the snow–ice interface (over FYI), as discussed in Section 3.3. This will usually take place considerably earlier than the melt ponding and/or ice disintegration required for SIC to drop below 80% (for the last time before the melt season). Therefore, we constrain

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melt when the transition from ASCAT or QSCAT occurs later than the SIC estimate. At freeze-up, when ice forms in open water (i.e. FYI), the induced σ° increase is usually weak, as discussed in Section 3.4. Instead, if the σ° variability in the melt season from wind roughening and seaice dynamics is strong, ASCAT and QSCAT may retrieve an erroneously early freeze-up. We therefore constrain freeze-up dates from ASCAT and QSCAT that take place more than 14 days earlier than the freezeup from SIC. The 14-day criterion is arbitrary, but was selected because σ° responds to an earlier stage of the ice formation than that corresponding to a SIC of 15%. In the affected areas, the ASCAT and QSCAT dates are typically just a few days earlier than the SIC dates (not shown). Note that this methodology implies that no constraint is invoked at freeze-up where SIC never goes below 15% during the melt season. This is not necessary, however, since the freeze-up over summer sea ice (i.e. MYI) yields a strong, clear σ° response when melt ponds on top of the ice freezes. The areas affected by the SIC constraint are smaller for melt than for freeze-up, but are located in the same regions: Bering Sea, near-coastal areas in the Laptev and Kara Seas, and parts of Baffin Bay, Hudson Bay, and the CAA. Also, the areas affected are almost identical for ASCAT and QSCAT. Figs. 2 and 3 provide time series in these areas where the SIC constraint greatly improves the estimated transitions, as indicated by the contextual information of SIC and T2m. At the sites shown in Fig. 2, the σ° edges are weak at freeze-up, but the SIC constraint improves the retrieved dates, compared with those from IMS and passive microwave radiometer. Fig. 3 shows sites at which the melt transition gives a weak response in σ° and where the SIC constraint improves the retrieval at both melt and freeze. The site shown in Fig. 3d is exceptionally difficult, located in an area close to the ice edge that is subject to very strong sea-ice dynamics as the central ice mass is advected through it. This effect dominates the σ° time series, making the transitions very difficult to retrieve reliably.

2009

Melt

Freeze

2010

2011

4.3. Retrieval from ERA-Interim temperatures To relate the transitions from ASCAT and QSCAT to temperatures, transitions are retrieved from Tskin and T2m from ERA-Interim. The methodology is identical to that of Mortin et al. (2013), who provide a detailed description. In brief, the days when the 14-day running median of temperature passes a threshold of −1 °C before and after the annual maximum temperature are used as melt and freeze onset, respectively. This threshold is an intermediate of the melting point of the snow cover, the melting point of sea ice, the freezing point of the saline ocean water, and the freezing point of the freshwater in melt ponds. This retrieval methodology corresponds to the long-term conditions and mitigates influences from transient warming and cooling events passing the threshold but of insufficient magnitude or duration to induce melting or freezing at the surface. 5. Evaluation of ASCAT transitions

2012

60

90

120

150

Julian Day

180

210 180 210 240 270 300 330 360

Julian Day

5.1. Spatial variability Fig. 4 presents the SIC-constrained a posteriori melt–freeze transitions from ASCAT during 2009–2012. Melt begins in the marginal seas around Julian Day 120 (early May) and reaches the pole around Julian Day 170 (late June, early July), thus spanning almost 2 months. The large spatial variability of the melt dates reflects the high resolution of the scatterometer measurements and the variability of the melt transition. However, it also reflects the processes hampering reliable transition retrieval, discussed in Section 3.3. The primary issue is most likely sea-ice dynamics inducing σ° edges sufficiently strong to be misinterpreted as melt transitions. This is most notable in areas where the sea-ice dynamics is particularly strong, such as in the East Greenland current and Hudson Bay. Sea-ice dynamics also act to mix MYI and FYI during the fall and winter, partly through ice formation in leads and polynyas

Fig. 4. Seasonal melt (left column) and freeze (right column) transitions in Julian Day retrieved from ASCAT—SIC-constrained a posteriori transitions—for the years 2009–2012. The scale was chosen to clearly see outliers.

(e.g. Maslanik et al., 2011; Smith et al., 1990). Since MYI and FYI give opposite σ° responses at melt, the combined response is weak and of ambiguous sign. These characteristics are seen in the melt transitions in Fig. 4 as a spatial incoherence, primarily on the Eurasian side of the Arctic Ocean, but also in the Beaufort and Chukchi Seas, and seem to be more frequent for 2009 than the other years. Another situation discussed in Section 3.3 is the weak melt signal due to contemporaneous snowmelt and sea-ice breakup, common where the ice formed late. This is most notable close to the Eurasian coastline in Fig. 4. Lastly, although the melt transition is variable in the CAA (Howell, Tivy, Yackel, Else, &

J. Mortin et al. / Remote Sensing of Environment 141 (2014) 214–230

Duguay, 2008), unrepresentative ice-type estimation in the CAA, mentioned in Section 4.1, contributes to spatial incoherence when the algorithm searches for edges of the wrong sign. Freeze-up begins in the central Arctic around Julian Day 210 (early August), when the sea-ice cover is still shrinking due to the warm upper ocean. In the outskirts of the domain, the freeze-up occurs as late as day 360 (late December; as noted in Section 4.1, the algorithm retrieves transitions up to December 31). This gives a range of freeze-up dates of 5 months over the domain, substantially longer than at melt. Freeze-up is spatially more coherent than melt, because the primary problem of a weak σ° response at ice formation in open water is efficiently alleviated by the SIC constraint. Furthermore, MYI and FYI give a σ° response in the same direction at freeze-up—unrepresentative ice-type estimation is thus not a factor.

223

a) Melt

5.2. Comparison with QSCAT transitions In order to further investigate the differences between ASCAT and QSCAT, a point-to-point comparison of ASCAT and QSCAT transitions for year 2009 is mapped for melt (Fig. 5a) and freeze-up (Fig. 5b). At melt, 60% of the differences are smaller than 5 days, and 70% are smaller than 10 days, most notably in the central Arctic where MYI gives a distinct and coincident melt transition indicator for both ASCAT and QSCAT (Fig. 5a). In the marginal seas and the Eurasian sector, however, melttiming differences can be large—more than a month in some areas— and show a substantial spatial incoherence. This is primarily attributed to the larger sensitivity of QSCAT than ASCAT to the early melt signal indicator over FYI: increased volume scattering in the brine-laden snow basal layer, as discussed in Section 3.3. The implication of this on the retrieved transitions is twofold. First, where the impact of the brine process is weak on σ°, such as for rough FYI (Yackel et al., 2007), ASCAT reacts more strongly to the wind roughening of the progressively larger melt ponds that takes place later. This is seen in Fig. 5a as redcolored areas most notably in the Beaufort and Chukchi Seas, north of the East Laptev Sea, as well as in the Kara Sea. Fig. 2d provides time series from a site in these areas. Second, the more distinct edges associated with melt acquired by QSCAT than ASCAT makes the retrieval less susceptible to edges unrelated to melt, such as sea-ice dynamics. During the early spring, prior to a widespread melt, the dynamics of the main MYI cover is strong in the sector north of the Laptev, Kara, and Barents Seas. Because of the different winter levels of σ° from MYI and FYI (Section 3.2), the MYI dynamics induces substantial σ° edges to which QSCAT is less sensitive than ASCAT when detecting the primary edge. As a result, ASCAT indicates a substantially earlier melt, seen as blue areas in Fig. 5a. Fig. 3d provides time series from a site in these areas, where the QSCAT melt transition seems reasonable, unlike the ASCAT transition, when considering the contextual information from T2m and the radiometer transitions by Markus et al. (2009). The early melt from ASCAT can also be seen in Fig. 4 as blue and spatially incoherent areas in this sector, which seem to be more frequent for 2009 than for the other years. Around Baffin Bay, the spatial pattern of differences is incoherent, primarily owing to the sea-ice type estimate performed by the algorithm differing between the instruments: ASCAT detects MYI and QSCAT FYI, which have opposing σ° responses. At freeze-up, transitions from ASCAT and QSCAT are more consistent: 70% of the differences are smaller than 5 days, and 85% are smaller than 10 days (Fig. 5b). This corroborates the discussion in Section 3.4: the σ° response to freeze-up over MYI is distinct for both ASCAT and QSCAT, and while the σ° increase from ice formation in open water (i.e. FYI) is weaker than over MYI, it is sufficiently strong to detect in large parts of the domain. In both cases, the σ° edge acquired by ASCAT and QSCAT is coincident. Where the induced σ° edges are weak over FYI, the SIC constraint is applied to both ASCAT and QSCAT, which affects the same areas, as noted in Section 4.2. At the end of the melt season, when ice advects relatively freely, sea-ice dynamics is particularly strong, most likely causing the ASCAT–QSCAT differences at

b) Freeze

−40

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−20

−10

0

10

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40

Difference (ASCAT−QSCAT) [days] Fig. 5. Point-to-point difference between transition dates retrieved from ASCAT and QSCAT in 2009. The blue (red) color indicates an earlier (later) transition from ASCAT. Both the dates from QSCAT and ASCAT are SIC-constrained a posteriori dates, as described in Section 4.2. The data void at the pole is a result of the lack of QSCAT coverage.

freeze-up. It is notable that although the QSCAT record ends in midNovember, after which freeze-up typically occurs in the margins of the domain (e.g. Fig. 4), this point-to-point comparison can be done because of the applied SIC constraint. 5.3. Comparison with transitions from other datasets To relate the (constrained) transitions from ASCAT to other datasets, Fig. 6 shows differences of transition dates from ASCAT and from the datasets described in Sections 2 and 4: QSCAT (constrained), ERA-Interim T2m and Tskin, IMS, and passive microwave radiometer (i.e., Markus et al., 2009). At melt, the differences between ASCAT and QSCAT are small over both ice types (Fig. 6a–b), albeit slightly larger over FYI (b10 days) than over MYI (b5 days), consistent with Fig. 5. Comparing with transitions retrieved from passive microwave measurements, melt transitions from ASCAT (and QSCAT) generally occur

J. Mortin et al. / Remote Sensing of Environment 141 (2014) 214–230

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Fig. 6. Annual differences between SIC-constrained melt (left column) and freeze (right column) transitions from ASCAT and the corresponding transitions from independent reference datasets described in Sections 2 and 4. The ice type is retrieved by ASCAT: in winter at melt, and in summer at freeze-up (FYI is open water); all refers to all sea ice. Boxes are the first to third quartiles and the whiskers the 20-percentile and 80-percentile. Dots indicate the average. The number of data points (temporal average; bnN) constituting each dataset over each ice category is shown. Both the early and late transitions by Markus et al. (2009) are included, which have the same number of data points. Only the intersection of sea-ice category from each compared dataset is included. Except for comparison with QSCAT, ASCAT data are bi-linearly interpolated to the other grid. The statistics including ERA-Interim are area-normalized.

between the early and late melt. However, over FYI, the median of the differences of the early melt from passive microwave is close to 0 days (Fig. 6b), indicating that nearly half of the ASCAT transitions occur earlier. This is most likely attributed to the FYI melt from ASCAT being primarily related to the brine increase that occurs earlier than the increase of liquid water content in the snow pack (see Section 3.3), to which the radiometer transitions are sensitive (Markus et al., 2009). The melt dates retrieved from temperatures commonly occur earlier

than ASCAT, more so over MYI (Fig. 6a–b). This is expected, since air temperatures approaching the melting point precondition the increase of brine in the snow basal layer and the increase of liquid water content in the snow pack, which controls the ASCAT transitions. Moreover, the dates from Tskin are closer to ASCAT and QSCAT transitions than are dates from T2m, because Tskin is more intimately related to the surface processes: it responds instantaneously to changes in the surface energy budget because the skin layer in ERA-Interim has no heat capacity

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(ECMWF, 2013). In general, the ranges of the differences of all datasets are larger over FYI than over MYI (cf. Fig. 6b and a). This is most likely attributed to the large and heterogeneous area constituted by FYI, giving a wide range of transition dates, as well as the previously discussed difficulties of the ASCAT melt retrieval. At freeze-up, the ASCAT–QSCAT differences are very small (b 5 days; Fig. 6d–e) over both MYI and open water (i.e. FYI), consistent with Fig. 5. Differences with the IMS freeze-up dates are small over FYI (≲10 days; Fig. 6e), suggesting that the retrieval and SIC constraint methodologies are robust. Similar to melt, the ASCAT freeze-up generally occurs between the early and late freeze-up from passive microwave radiometer (Fig. 6d–f). Over MYI, however, ASCAT seems to respond slightly earlier than both the early and late radiometer retrievals to the freezing of the melt ponds and the related increase of volume scattering in the ice, perhaps due to the lower frequency of ASCAT (i.e. larger sensitivity to MYI volume scattering) or to a finer spatial resolution (Markus et al., 2009). The radiometer transitions over FYI are significantly earlier during 2011–2012 than during 2009–2010 (Fig. 6e). This is because spurious (too early) freeze-up dates are present in the radiometer transition dataset along all coastlines during these years (the reason for this is currently unclear; Jeffrey Miller, personal communication). The freeze transitions in open water (i.e. FYI) from T2m precede those from ASCAT by 2–3 weeks (Fig. 6e). This is expected, as it takes time before the well-mixed upper ocean is cooled below freezing and ice is formed after air temperatures go below freezing. The differences of ASCAT and Tskin are considerable despite that Tskin responds instantaneously to changes in the surface energy budget, because the skin layer in ERA-Interim has no heat capacity (ECMWF, 2013). These differences are related to the temperature threshold in the transition retrieval from ERA-Interim data (− 1 °C; Section 4.3) being higher than the freezing point of Arctic ocean water (~−1.86°; Serreze & Barry, 2009): an investigation in which the temperature threshold is changed to − 1.86 °C gives very small differences between transitions from ASCAT and Tskin (b5 days; not shown). The relation is the opposite over MYI for the same reasons (Fig. 6d), where the freezing point of the melt ponds, constituted primarily by fresh water, is close to 0 °C. 6. Time series of transitions In order to examine how consistently ASCAT extends the QSCAT record in different parts of the Arctic sea-ice domain, the time series of the seasonal transitions retrieved from both instruments are shown for melt (Fig. 7) and freeze (Fig. 8) for different regions during 1999– 2012. Both SIC-constrained and unconstrained transitions are indicated to illustrate the effects of the constraint and to examine if the consistency of the record extension depends on the constraint. At melt, the constrained transitions from QSCAT and ASCAT show good agreement in every region (Fig. 7). The largest inconsistency is found in the Chukchi and Beaufort Seas (Fig. 7h), where the QSCATtransition median occurs roughly 10 days earlier, consistent with the discussion in Section 5.2. The intra-annual spatial variability within a region, given by the whiskers in Fig. 7, are of similar magnitude for ASCAT and QSCAT, and changes in general little between years within each region. The SIC constraint at melt improves the transitions significantly in some regions, as compared with the transitions from passive microwave radiometer (i.e. Markus et al., 2009), most notably in the Bering Sea, Hudson and Baffin Bays, and the Kara and Barents Seas (Fig. 7a–b and d). The majority of the areas in which the melt transitions are constrained are located in these regions. Also the unconstrained transitions from ASCAT and QSCAT show good agreement. The exceptions are Chukchi and Beaufort Seas (Fig. 7h), as expected from the discussion in Section 5.2 (earlier melt from QSCAT), and Bering Sea (Fig. 7a), where the melt signal indicator typically is weak and the (unconstrained) retrieved transitions thus a result of the σ° variability during the melt season.

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At freeze-up, the constrained transitions show very good agreement in every region (Fig. 8), consistent with Fig. 5. The intra-annual spatial variability (whiskers) is large in some regions, most notably in the Kara and Barents Seas (Fig. 8d) and the Chukchi and Beaufort Seas (Fig. 8h). These regions are partly covered by sea ice during the melt season, and because open water freezes substantially later than does existing ice (Mortin et al., 2013), the variability becomes large. For this reason, the intra-annual spatial variability was greater prior to year 2005 in the Laptev and East Siberian Seas (Fig. 8g), after which the summer sea ice increasingly retreated from this region (NSIDC, 2013). Similarly, the central Arctic experienced strong sea-ice retreat in 2007 and 2012, giving a large variability (Fig. 8e). East Greenland also exhibits a large intra-annual variability (Fig. 8c) because the transitions are difficult to retrieve due to some of the largest ice motions found in the domain (Kwok, Schweiger, Rothrock, Pang, & Kottmeier, 1998), which cause a strong inter-annual variability also seen in the radiometer transitions. The SIC constraint improves the freeze transitions in every region except the Central Arctic, albeit to varying degree, as compared with transitions from passive microwave radiometer (Fig. 8). The largest improvements are achieved in Hudson and Baffin Bays (Fig. 8b), where sea-ice dynamics is strong, and the Bering Sea (Fig. 8a), where the σ° response to ice formation is weak. The latter region is the only region where the unconstrained transitions are inconsistent between ASCAT and QSCAT, owing to spurious retrieval dates with both ASCAT and QSCAT when the σ° variability in the melt season is substantially larger than the freeze signal indicator, as discussed in Section 3.4. 7. Summary and conclusions The duration of the Arctic sea ice cover—inherently linked to the seasonal melt–freeze transitions—moderates the energy budget through its high albedo and its dampening effect on the ocean–atmosphere heat flux (e.g. Bitz, Battisti, Moritz, & Beesley, 1996; Perovich et al., 2007; Smith et al., 1990). This has significant implications on the climatic conditions of the Arctic region and potentially beyond it (e.g. Parmentier et al., 2013; Persson, 2011; Screen et al., 2013). Therefore, in order to understand the Arctic climate system and its variability, it is important to continuously monitor the seasonal melt–freeze transitions over sea ice. In late 2009, QSCAT discontinued its 10-year record of Ku-band measurements extensively used to retrieve seasonal transitions over both the land and sea ice domains (e.g. Bartsch, 2010; Howell et al., 2010; Wang et al., 2011). Fortunately, the C-band scatterometer ASCAT acquired coincident measurements with QSCAT in 2009 and has an anticipated lifetime beyond 2021, since it is included on several platforms (Vogelzang & Stoffelen, 2012). In this study, we have shown that the record of seasonal melt–freeze transitions of QSCAT over sea ice can be extended using ASCAT, for both melt and freeze-up. We have also emphasized the differences between the two instruments that need to be considered for this application. This was done by (1) comparing the annual σ° time series from ASCAT and QSCAT over both MYI and FYI juxtaposed to contextual information, such as time series of SIC and T2m, and by (2) retrieving the transitions from both instruments during their full records—1999–2009 and 2009–2012, for QSCAT and ASCAT, respectively—and comparing the results. Moreover, the transitions were compared to other, independent datasets of transitions: retrieved from passive microwave radiometers (Markus et al., 2009), ERA-Interim T2m and Tskin, and IMS. In order to retrieve the seasonal melt–freeze transitions from resolution-enhanced ASCAT and QSCAT data, we employed an updated version of the edge detector Mortin et al. (2012) utilized on QSCAT data. The algorithm detects σ° changes typically corresponding to the transitions on several time scales, and it was iterated twice using an internal climatology. This mitigates the influence of temporal noise and the impact of processes unrelated to the surface transitions, such as sea-ice dynamics, alleviating erroneous transition outliers. Moreover, we applied

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Fig. 7. Time series of retrieved melt transitions from QSCAT and ASCAT during 2000–2012. They are shown for both SIC-constrained and unconstrained transitions, described in Section 4.2. For constrained transitions, medians (line), 20-percentiles and 80-percentiles (whiskers), averages (hollow circles), and the average number of measurements for each region, are shown. For unconstrained transitions, medians (dashed lines) are shown. Transition medians from passive microwave radiometer (i.e. Markus et al., 2009) are depicted as red filled triangles, indicating early and late melt. The map delineates the regions and the spatial data availability for 2009; the latter is very similar for ASCAT and QSCAT.

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Fig. 8. As Fig. 7, but for the freeze transition during 1999–2012.

SIC data as a constraint when the σ° response to melt or freeze was weak, causing the algorithm to misidentify the σ° variability during the melt season as transitions. The areas affected by the SIC constraint are smaller for melt than for freeze-up, but are located in the same

regions: Bering Sea, near-coastal areas in the Laptev and Kara Seas, and parts of Baffin Bay, Hudson Bay, and the CAA. Although ASCAT and QSCAT operate in different frequencies (5.3 and 13.4 GHz, respectively), they respond similarly to the variability of the

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dielectric constant associated with changes in the liquid water content of the surface, in turn strongly related to the seasonal melt–freeze transitions. That is, the σ° changes induced by the transitions generally occur near-simultaneously for ASCAT and QSCAT. However, ASCAT exhibits a weaker response than does QSCAT to the early melt over FYI. This makes QSCAT less sensitive to processes unrelated to the surface melt, such as sea-ice dynamics. While the difference is minor, the timing of the transitions from ASCAT and QSCAT can differ substantially over FYI in some areas, most notably in the Eurasian sector of the Arctic Ocean and in the Beaufort and Chukchi Seas. In addition, sea-ice types estimated from ASCAT measurements are less reliable than estimated from QSCAT due to the single polarization of ASCAT. Thus, in regions with mixed ice types or very strong sea-ice dynamics, QSCAT performs better than ASCAT. Except for these differences, QSCAT and ASCAT yield very similar seasonal transitions. When comparing the full record of seasonal transitions from ASCAT and QSCAT, jointly spanning 1999–2012, both the SIC-constrained and the unconstrained transitions are consistent in most regions of the Arctic domain. This consistency is thus independent of the SIC constraint. However, the constrained transitions are more reliable than the unconstrained, as compared with transitions from passive microwave radiometer measurements (Markus et al., 2009). When comparing the ASCAT transitions with those from other datasets, the agreement is generally good. Melt onset from T2m precedes the melt retrieved from ASCAT (and QSCAT) by about one week, since temperatures approaching the melting point precondition the snow and ice melt. At freeze-up over open water, temperatures go below the freezing point 2–3 weeks prior to ice formation, to which ASCAT (and QSCAT) responds. But since this time lag differs depending on the surface and atmospheric conditions, scatterometry is better suited to study the transitional surface processes over sea ice in detail than air temperatures. Because seasonal transitions from scatterometry directly correspond to physical changes in the snow and ice surface, the derived transitions are more closely linked to changes in surface albedo and energy budget. Similarly, SIC data are relatively crude with respect to the detailed surface processes compared with scatterometers, particularly during the seasonal transitions (Agnew & Howell, 2003), and is difficult to implement as an indicator of melt and freeze-up over the central MYI pack. However, SIC data are a suitable complement to σ° in areas where the transition signal indicators are weak and transitions thus difficult to retrieve. While the causes and implications of Arctic sea-ice retreat have been studied intensely, the terrestrial snow-cover reduction in spring has gained little attention despite being a stronger decline (Derksen & Brown, 2012). Therefore, it is important to continuously monitor also the seasonal snow-cover duration over the landmass. Mortin et al. (2012) retrieved the seasonal melt–freeze transitions over Arctic land during the full QSCAT record. However, ASCAT and QSCAT have markedly different sensitivities over land: ASCAT is primarily sensitive to the soil and QSCAT to the overlying snow (e.g. Naeimi et al., 2012; Wang et al., 2011). As a result, the transition record of QSCAT cannot be directly extended with ASCAT, unlike over sea ice. However, the Indian Oceansat-2 Scatterometer (OSCAT), operating at Ku-band since late 2009 can potentially be used to extend the QSCAT record, both over land and sea ice. Additionally, the ASCAT record can be extended back using ERS-1/2, both carrying a C-band scatterometer and jointly covering the time period 1992–2001. Acknowledgments The authors gratefully acknowledge the constructive remarks and valuable input by the three anonymous reviewers, as well as Ben Holt at Jet Propulsion Laboratory for fruitful discussion. Enhanced resolution QuikSCAT and ASCAT data were obtained from the NASA sponsored Scatterometer Climate Record Pathfinder at Brigham Young University (scp.byu.edu) through the courtesy of David G. Long. Furthermore,

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