Abstract. Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed.

<p>The effects of climate change on water resources are partly determined by the size and the spatial distribution of ice reservoirs around the world. While mountain glaciers represent only 1% of today’s global ice volume, they have contributed around 25% to sea-level rise during the last decades, and are expected to contribute within the same proportion during the rest of the 21st century [<em>SROCC, 2019</em>]. Mountain glaciers also represent sources of drinking water for millions of people. The glacierized drainage basins cover around 26% of the global land surface and are populated by more than two billions of people [<em>Huss et Hock, 2018</em>]. However, mountain glacier ice thickness estimates are largely uncertain due to the use of simplified retrieval approaches [<em>Rabatel et al., 2018</em>]. Furthermore, nearly 50% of the global glacier evolution uncertainty comes from the glacier model itself and the initial glacier representation in space [<em>Marzeion et al., 2019</em>]. The objective of this study is to understand the effects of initial glacier conditions, and specifically the ice thickness, on simulations of glacier evolution and their contribution to river runoff.</p><p><br>For that purpose, we used the Open Global Glacier Model [OGGM, <em>Maussion et al., 2019</em>], that simulates the surface mass balance and ice dynamics to estimate the evolution of any glacier in the world. We used three new different ice thickness dataset and proposed a framework to assimilate them within OGGM. We then focused on sensitivity analysis of future glacier evolution using different climate scenarios, initial conditions of ice thickness and internal model parameters such as the creep parameter, the spatial resolution, the spin-up initialization. These experiments were performed for different types of glaciers in terms of location, size and geometry. The results helped us to assess the importance of model initialization with respect to other model parameters, in the glacier evolution during the 21st century and specifically the changes in surface and volume. We also explored the differences induced in terms of glacier contribution to river runoff and peak water timing, which is of great importance for freshwater resources management.</p>

<p>Accurate mapping of subglacial bedrock topography is of prime importance to correctly simulate the past and future evolution of glaciers and ice sheets. As ocean warming is a major driver of recent changes in Greenland and Antarctica, mapping the bathymetry of the ocean seafloor in fjords and underneath ice shelves is crucial to accurately model warm water pathways up to the ice margins and grounding lines. A good knowledge of this bedrock topography also allows to better understand the past extent of the ice sheets and identify vulnerable regions that are sitting on retrograde bed slopes, hence that might be prone to the marine ice sheet instability. For mountain glaciers, accurately mapping the bedrock topography is mandatory to estimate ice thicknesses, which are used to simulate the contribution of glaciers to sea level rise, but also to quantify the amount of freshwater resources stored in glaciers. Because of their large number, remote locations, and difficult access conditions, only scarce in-situ data exists for bedrock topography. Hence, while being a fundamental variable for glacier modeling, it remains poorly constrained at the time. Here, we present how the use of multiple sensors remote sensing techniques has helped us to unravel the hidden relief beneath glaciers and ice sheets. In Greenland and Antarctica, we use airborne gravimetry measurements along with multibeam and radar echoe sounder to map the bathymetry in fjords and below ice shelves. We show that the use of these new bathymetric products help us to understand the retreat history of glaciers, revealing pathways for warm water, and contributes to better modeling ocean circulation up to the grounding lines of glaciers. For mountain glaciers, we mapped the ice velocity worldwide at an enhanced sampling resolution of 50 m, using massive cross correlation techniques on image pairs from both optical (ESA’s Sentinel-2; USGS/NASA’s Landsat-7/8) and radar imagery (ESA’s Sentinel-1a/b). Finally, we combine this mapping with airborne and ground penetrating radar to recover the ice thickness of all glaciers on Earth. These estimations reveal a different picture of the bedrock topography beneath glaciers, with a modified ice thickness distribution. Using these new estimations as initial state in the Open Global Glacier Model, we show the important impact on the evolution of freshwater resources, and specifically on the timing of the peak water.</p>