Albedo of firn and bare ice near the Trans-Antarctic Mountains to represent sea-glaciers on the tropical ocean of Snowball Earth

第6回極域科学シンポジウム[OM] 極域気水圏11月16日（月）　統計数理研究所 セミナー室2（D304

Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula : cryospheric, biotic and societal implications

DATA AVAILABILITY : ERA5 temperature datasets can be downloaded from https://cds.climate.copernicus.eu/. CMIP6 climate projections can be downloaded from https://esgf-node.llnl.gov/search/cmip6/. The dataset generated in this research with the precipitation extremes for each model at every grid point and for different durations are available via González-Herrero, Sergi. (2023). Zero-degree isotherm latitude (ZIL) position over Antarctica: Historical and Projections [Data set]. Zenodo. doi:10.5281/zenodo.10046608. The codes used in this study are available via Sergi González Herrero. (2023). sergigonzalezh/ZIL-Antarctica: Zero-degree isotherm latitude (ZIL) position over Antarctica [Code] (1.0). Zenodo. doi:10.5281/zenodo.10063849.DATA AVAILABILITY : All data and codes are available in open access repositories.Please read abstract in the article.The research group ANTALP (Antarctic, Arctic, Alpine Environments; 2021 SGR 00269) funded by the Agència de Gestió d'Ajuts Universitaris i de Recerca of the Government of Catalonia; the project NEOGREEN (PID2020-113798GB-C31) funded by the Spanish Ministerio de Economía y Competitividad; Agencia Estatal de Investigación; the ASICS-South Africa Biodiversa project and institutional funds from UKRI-NERC.https://www.elsevier.com/locate/scitotenvhj2024Plant Production and Soil ScienceSDG-15:Life on lan

Snow microstructure on sea ice: Importance for remote sensing applications

European Geosciences Union (EGU) General Assembly, 19-30 Apr 2021.-- 2 pagesSnow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic. The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples. This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges. First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instrumentsPeer reviewe

Observations, theory, and modeling of the differential accumulation of Antarctic megadunes

Antarctic megadunes are characterized by significant spatial differences in accumulation rate, with higher accumulation on the windward side and near-zero accumulation on the lee side. This leads to spatial differences in physical properties of snow and surface roughness, as well as to the upwind migration of the megadunes. While previous studies agree that megadunes are a result of complex interactions between wind, topography, and snow, it is not clear how they form or why they accumulate on the windward side. Here we use ICESat observations, dimensional analysis, and atmospheric flow modeling to investigate what conditions are responsible for the accumulation patterns and upwind migration of the megadunes. First, we use ICESat data to quantify the pattern of differential surface elevation change across the megadunes. We then use dimensional analysis based on supercritical-flow theory and atmospheric flow modeling to show that the megadunes topography and a stable atmosphere will always lead to upwind dune migration. We show that a combination of persistent katabatic winds, strong stability, and spatial variability in surface roughness is responsible for the accumulation on the upwind slope and hence the upwind migration of the megadunes. We further show that spatial differences in surface roughness are the primary control on accumulation magnitudes and hence dune migration velocity. The dune migration velocity in turn influences the degree of snow-metamorphism and the physical properties of snow that are relevant for paleoclimate records. Our findings pertain to the ongoing evolution of the megadunes, but their genesis remains an open question

The Uncertainty of Seasonal Snow Energy- and Mass Balance Simulations as a Function of Typical Input Uncertainty

The assessment of water resources and their change in time as well as many other applications from hydrology to meteorology require the successful modelling of the dynamics of the seasonal snow cover. Especially if extrapolation in time, e.g. for climate change scenario assessment, is aimed for, the use of energy balance models has been proven to be more trustworthy than simpler temperature index models. The disadvantage of the energy balance method is the requirement of a variety of input quantities, which are not always but become increasingly accessible. What is missing, however, is an in-depth assessment of the influence of typical errors in the individual input quantities on model performance. This contribution presents a sensitivity study, which compares the influence of typical errors and uncertainties in radiation, wind, temperature and humidity input, both measured and modeled, with errors caused by insufficient knowledge of solid precipitation rates. The analysis is carried out with the widely used SNOWPACK model and focuses on the build-up and melt of a mid-latitude seasonal snow cover. While mass input uncertainties still dominate the overall performance especially at high elevations (alpine zone), errors in wind and radiation can cause very significant effects as well. Wind is considered to be more critical in this context, because its spatial variation is both more pronounced and more difficult to estimate than the spatial variation in short- and longwave radiation. In absence of reliable radiation input, its value can successfully be calculated or parameterized if e.g. cloud information is available. Errors in air temperature input or a failure of a correct assessment of its spatial variability can be locally very important because of the feed-back mechanism via atmospheric stability. For the overall mass balance of a seasonal snow cover, humidity errors are less important. Notable exceptions are applications such as the correct assessment of surface hoar formation or prediction of melt rates during rain on snow events, for which good humidity input is required. A main conclusion is that input data through a combination of measurement stations and weather models are now widely available with sufficient quality to even allow a small-scale distributed application of energy balance models

Sensitivity of turbulent fluxes to wind speed over snow surfaces in different climatic settings

Local wind speed variations influence the energy and mass fluxes over snow through snow accumulation, sublimation of drifting and blowing snow, or variations in turbulent fluxes over static snow and ice surfaces. We use idealized model experiments to analyze the sensitivity of turbulent fluxes over static snow surfaces to variations in wind speed under different climatic conditions. We find that the sensitivity (change in the turbulent flux per change of unit wind speed) increases with increasing air temperature and relative humidity. The sensitivity of turbulent fluxes to wind speed is highest when the stability parameter ζ = 1, which occurs at wind speeds typical for glacierized catchments (3-5 m s -1), and exponentially decreases either side of that range. That peak in sensitivity is caused by atmospheric stability corrections in the model, and occurs independently of the flux-profile relationships we tested. Our results quantify the significant effect of local wind speed variations on turbulent fluxes over snow and ice and can be used to estimate potential model uncertainties in different climates, especially for the typical assumption in distributed hydrological models that the wind speed is spatially constant. © 2012 Elsevier Ltd. All rights reserved