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The work required to meet these project objectives has led to a detailed examination of snow depth models and the relationship between surface and under-ice topography for the APPLS survey region, incorporating unique in situ observations from the ARISE, SIPEX and SIPEX-II research voyages (Chapter 2). It has also provided an improved understanding of how system design and flight planning a↵ect final results (Chapter 3), which leads to an ability to determine an a priori uncertainty for every LiDAR point captured by the APPLS system. In turn, this allows a rigorous a priori estimation of uncertainty for every sea-ice thickness or snow depth estimate which is derived from the APPLS system. These advances are used collectively in Chapter 4, where APPLS LiDAR observations are used to estimate snow depth and sea ice thickness for specific region of sea ice o↵ East Antarctica. The hydrostatic model for estimating ice thickness from LiDAR points (Wadhams et al., 1992) is applied and validated using coincident in situ and AUV observations. The model is extended to make a qualitative comparison of regional-scale ice thickness estimates from LiDAR and ship based ASPeCt observations. Finally, the potential benefits of 3D photogrammetry using SfM (Snavely et al., 2006) as a tool for observing and improving understanding of sea ice is shown.
Using a validated model for estimating sea-ice thickness using airborne LiDAR allowed a broader view of ice conditions in the vicinity of in situ observations on the SIPEX II voyage. This demonstrated that in situ observations, while accurate, are planned and executed in a fashion which introduces a sampling bias to the record. This has substantial implications for current understanding of sea-ice thickness based on the use of the in situ record as a validation tool, which are discussed below.
5.2
Implications for estimation of sea ice thickness from
altimetry
A key message from this study is that the record of Antarctic sea-ice thickness from in situ and ASPeCt observations capture only one tail of the complete distribution. This conclusion is arrived at after validation of the methods used in this study to estimate sea-ice thickness from altimetry, and then applying them to a wider (albeit small) region of sea ice. Figure 4.26 shows that most sea ice for the SIPEX-II region was around 3 m thick, a value consistent with Williams et al. (2014b). Observations from drill holes and the ASPeCt program show a peak at around 1 m, but Figure 4.13 shows clearly that for at least the site studied here, drill holes only capture the least-deformed (and therefore among the thinnest) ice at the sampling site.
Remote sensing e↵orts have traditionally used in situ observations as standards for calibration (e.g. Kurtz et al., 2012), aiming for agreement at regional scales (e.g. AMSR- E pixels, or ICESat analysis scales). This study shows that doing so may result in a substantial under-estimation of ice thickness. While the community necessarily looks to scales of observation consistent with satellite data collection capabilities (e.g. Ozsoy-Cicek et al., 2013; Weissling et al., 2011), the detailed analysis of small scale datasets using appropriate combinations of in situ and remotely sensed observations is a critical tool for guiding larger-scale e↵orts at estimating sea-ice thickness.
5.2. IMPLICATIONS FOR ESTIMATION OF SEA ICE THICKNESS FROM
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5.2.1 Geophysical implications for modelling the ice/ocean interface from
altimetry
The data produced from observational studies are essential as inputs to models of sea ice in the Earth system. How ice thickness and under-ice topography is parameterised will a↵ect ice/ocean/atmospheric interactions in model systems (e.g. Martin et al., 2016; McPhee, 2002; Skyllingstad et al., 2003). With increased resolution of observations, the shape and distribution of modelled ice features need to be considered. Skyllingstad et al. (2003) investigated the role of keels in thermal heat flux to the underside of the ice. A key implication from their study was that smaller keels may result in a greater heat flux to the underside of sea-ice during the spring melt season. While large keels contribute to the movement of relatively warm water to the underside of sea ice, the turbulent heat flux is localised in the lee of the keel. For shorter keels, the motion of ice and ocean currents are less coupled and turbulent heat fluxes to the underside of the ice are spread over a wider area.
Figure 4.14 shows that spurious features (e.g. snow dune shaped keels) are prevalent in high resolution draft estimates derived from altimetry, and ice keels modelled below surface ridges are generally deeper and narrower than keels observed by AUV-based sonar. These factors potentially a↵ect derived sea-ice thickness and roughness. Martin et al. (2016) suggest that the orientation and spatial frequency of ridge keels is also important in modelling the interactions between ocean and ice, with keel orientation a↵ecting parameterised skin drag at the ice-ocean interface. How well these models translate to reality is uncertain, as the authors note that in situ ice ridges may not conform to idealised geometry used for modelling since the spatial distribution of keels beneath sea ice is poorly understood.
5.2.2 Operational implications for modelling the ice/ocean interface from
altimetry
The timely collection of high resolution sea-ice topography observations will eventually find applications in ship guidance and other operations in the pack ice zone. In a hypothetical scenario where LiDAR equipped drones are used for ice reconnaissance and observations are processed in real time (e.g. Skaloud et al., 2010b), the choice of model parameters used to generate ice thickness and draft from altimetry will impact operational decisions in the field. Under-estimating snow depth on level ice and underestimating the level ice thickness will impede the passage of ships, and are both outcomes of choosing inappropriate parameters for estimating snow depth and ice thickness from altimetry.
Large-scale ice thickness estimates are in use as an operational tool in the Arctic, based on SAR imagery and interpretation by experienced analysts (e.g. http://www.polarview.org/services/sea-ice-services/, accessed 2 May 2016). Automated ice thickness estimates derived from satellite-based instruments (e.g. CryoSAT-2, the