Spring melt pond fraction (f p) has been shown to influence September sea ice extent and, with a growing need to improve melt pond physics in climate and forecast models, observations at large spatial scales are needed. We present a novel technique for estimating f p on sea ice at high spatial resolution from the Sentinel-1 satellite during the winter period leading up to spring melt. A strong correlation (r = À0.85) is found between winter radar backscatter and f p from first-year and multiyear sea ice data collected in the Canadian Arctic Archipelago (CAA) in 2015. Observations made in the CAA in 2016 are used to validate a f p retrieval algorithm, and a f p prediction for the CAA in 2017 is made. The method is effective using the horizontal transmit and receive polarization channel only and shows promise for providing seasonal, pan-Arctic, f p maps for improved understanding of melt pond distributions and forecast model skill. Plain Language Summary Recent and well-documented changes in Arctic sea ice have introduced the need for timely and accurate seasonal forecasts of ice conditions. Seasonal forecasts of ice conditions will reduce the risks to humans and help preserve the fragile Arctic ecosystem by preventing accidents and spills. Recent studies have shown a link between the amount of surface meltwater flooding that occurs on sea ice in the spring, termed melt pond fraction, and the extent of sea ice that remains at the end of summer. This link is due to the ability of surface meltwater to absorb more sunlight compared to bare ice and snow. This study provides a new way to estimate the amount of surface meltwater flooding expected to occur on the sea ice in spring, using satellite data collected during the winter period. The results presented here provide a key link between winter and late summer sea ice conditions that will enhance the ability of forecasters to make accurate seasonal predictions several months in advance of the active summer period.

Sea ice surface roughness is a geophysical property which can be defined and quantified on a variety scales, and consequently affects processes across various scales. The sea ice surface roughness influences various mass, gas, and energy fluxes across the ocean-sea ice-atmosphere interface. Utilizing synthetic aperture radar (SAR) data to understand and map sea ice roughness is an active area of research. This thesis provides new techniques for the estimation of sea ice surface roughness in the Canadian Arctic Archipelago using synthetic aperture radar (SAR). Estimating and isolating sea ice surface properties from SAR imagery is complicated as there are a number of sea ice and sensor properties that influence the backscattered energy. There is increased difficulty in the melting season due to the presence of melt ponds on the surface, which can often inhibit interactions from the sensor to the sea ice surface as shorter microwaves cannot penetrate through the melt water. An object-based image analysis is here used to quantitatively link the winter first-year sea ice surface roughness to C-band RADARSAT-2 and L-band ALOS-2 PALSAR-2 SAR backscatter measured at two periods: winter (pre-melt) and advanced melt. Since the sea ice in our study area, the Canadian Arctic Archipelago, is landfast, the same ice can be imaged using SAR after the surface roughness measurements are established. Strong correlations between winter measured surface roughness, and C-and L-band SAR backscatter acquired during both the winter and advanced melt periods are observed. Results for winter indicate: (1) C-band HH-polarization backscatter is correlated with roughness (r=0.86) at a shallow incidence angle; and (2) L-band HH-and VV-polarization backscatter is correlated with roughness (r=0.82) at a moderate incidence angle. Results for advanced melt indicate: (1) C-band HV/HH polarization ratio is correlated with roughness (r=-0.83) at shallow incidence angle;(2) C-band HHpolarization backscatter is correlated with roughness (r=0.84) at shallow incidence angle for deformed first-year ice only; and (3) L-band HH-polarization backscatter is correlated with roughness (r=0.79) at moderate incidence angle. Retrieval models for surface roughness are developed and applied to the imagery to demonstrate the utility of SAR for mapping roughness, also as a proxy for deformation state, with a best case RMSE of 5 mm in the winter, and 8 mm during the advanced melt. iv

Abstract:The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (f p ) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two~5.2 km long profiles over FYI-and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring f p using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring f p . A stronger negative correlation was observed between FYI thickness and f p (Spearman r s = −0.85) compared to FYI roughness and f p (r s = −0.52). The association between MYI thickness and f p was also negative (r s = −0.56), whereas there was no association between MYI roughness and f p . 47% of spring f p variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high f p ) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low f p ). This work provides an important contribution towards the parameterizations of f p in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring f p .

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ABSTRACT. Changing Arctic sea-ice extent and melt season duration, and increasing economic interest in the Arctic have prompted the need for enhanced marine ecosystem studies and improvements to dynamical and forecast models. Sea-ice melt pond fraction f p has been shown to be correlated with the September minimum ice extent due to its impact on ice albedo and heat uptake. Ice forecasts should benefit from knowledge of f p as melt ponds form several months in advance of ice retreat. This study goes further back by examining the potential to predict f p during winter using backscatter data from the commonly available Sentinel-1 synthetic aperture radar. An object-based image analysis links the winter and spring thermodynamic states of first-year and multiyear sea-ice types. Strong correlations between winter backscatter and spring f p , detected from high-resolution visible to near infrared imagery, are observed, and models for the retrieval of f p from Sentinel-1 data are provided (r 2 ≥ 0.72).The models utilize HH polarization channel backscatter that is routinely acquired over the Arctic from the two-satellite Sentinel-1 constellation mission, as well as other past, current and future SAR missions operating in the same C-band frequency. Predicted f p is generally representative of major ice types firstyear ice and multiyear ice during the stage in seasonal melt pond evolution where f p is closely related to spatial variations in ice topography.

The legend should have been reversed with FYI shown in blue and MYI in red. Both original and corrected versions of Figures 7, 9, 11, and 12 are given below.

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