Exploring the role of snow metamorphism on the isotopic composition of the surface snow at EastGRIP

Stable water isotopes from polar ice cores are invaluable high-resolution climate proxy records. Recent studies have aimed to improve knowledge of how the climate signal is stored in the water isotope record by addressing the influence of post-depositional processes on the surface snow isotopic composition. In this study, the relationship between changes in surface snow microstructure after precipitation/deposition events and water isotopes is explored using measurements of snow specific surface area (SSA). Continuous daily SSA measurements from the East Greenland Ice Core Project site (EastGRIP) situated in the accumulation zone of the Greenland Ice Sheet during the summer seasons of 2017, 2018 and 2019 are used to develop an empirical decay model to describe events of rapid decrease in SSA, driven predominantly by vapour diffusion in the pore space and atmospheric vapour exchange. The SSA decay model is described by the exponential equation SSA(t) = (SSA0 −26.8) e−0.54t + 26.8. The model performance is optimal for daily mean values of surface temperature in the range 0 °C to −25 °C and wind speed < 6 m s−1. The findings from the SSA analysis are used to explore the influence of surface snow metamorphism on altering the isotopic composition of surface snow. It is found that rapid SSA decay events correspond to decreases in d-excess over a 2-day period in 72 % of the samples. Detailed studies using Empirical Orthogonal Function (EOF) analysis revealed a coherence between the dominant mode of variance of SSA and d-excess during periods of low spatial variability of surface snow over the sampling transect, suggesting that processes driving change in SSA also influence d-excess. Our findings highlight the need for future studies to decouple the processes driving surface snow metamorphism in order to quantify the fractionation effect of individual processes on the snow isotopic composition.

Snow change detection from polarimetric SAR time-series at X-band (Svalbard, Norway)

&lt;p&gt;Due to recent climate change conditions, i.e. increasing temperatures and changing precipitation patterns, arctic snow cover dynamics exhibit strong changes in terms of extent and duration. Arctic amplification processes and impacts are well documented expected to strengthen in coming decades. In this context, innovative observation methods are helpful for a better comprehension of the spatial variability of snow properties relevant for climate research and hydrological applications.&lt;/p&gt;&lt;p&gt;Microwave remote sensing provides exceptional spatial and temporal performance in terms of all-weather application and target penetration. Time-series of Synthetic Active Radar images (SAR) are becoming more accessible at different frequencies and polarimetry has demonstrated a significant advantage for detecting changes in different media. Concerning arctic snow monitoring, SAR sensors can offer continuous time-series during the polar night and with cloud cover, providing a consequent advantage in regard of optical sensors.&lt;/p&gt;&lt;p&gt;The aim of this study is dedicated to the spatial/temporal variability of snow in the Ny-&amp;#197;lesund area on the Br&amp;#8709;gger peninsula, Svalbard (N 78&amp;#176;55&amp;#8217; / E 11&amp;#176; 55&amp;#8217;). The TerraSAR-X satellite (DLR, Germany) operated at X-band (3.1 cm, 9.6 GHz) with dual co-pol mode (HH/VV) at 5-m spatial resolution, and with high incidence angles (36&amp;#176; to 39&amp;#176;) poviding a better snow penetration and reducing topographic constraints. A dataset of 92 images (ascending and descending) is available since 2017, together with a high resolution DEM (NPI 5-m) and consistent in-situ measurements of meteorological data and snow profiles including glaciers sites.&lt;/p&gt;&lt;p&gt;Polarimetric processing is based on the Kennaugh matrix decomposition, copolar phase coherence (CCOH) and copolar phase difference (CPD). The Kennaugh matrix elements K&lt;sub&gt;0&lt;/sub&gt;, K&lt;sub&gt;3&lt;/sub&gt;, K&lt;sub&gt;4,&lt;/sub&gt; and K&lt;sub&gt;7&lt;/sub&gt; are, respectively, the total intensity, phase ratio, intensity ratio, and shift between HH and VV phase center. Their interpretation allows analysing the structure of the snowpack linked to the near real time of in-situ measurements (snow profiles).&lt;/p&gt;&lt;p&gt;The X-band signal is strongly influenced by the snow stratigraphy: internal ice layers reduce or block the penetration of the signal into the snow pack. The best R&lt;sup&gt;2&lt;/sup&gt; correlation performances between estimated and measured snow heights are ranging from 0.50 to 0.70 for dry snow conditions. Therefore, the use of the X-band for regular snow height estimations remains limited under these conditions.&lt;/p&gt;&lt;p&gt;Conversely, this study shows the benefit of TerraSAR-X thanks to the Kennaugh matrix elements analysis. A focus is set on the Copolar Phase Difference (CPD, Leinss 2016) between VV and HH polarization: &amp;#934; CPD = &amp;#934; &lt;sub&gt;VV&lt;/sub&gt; - &amp;#934; &lt;sub&gt;HH&lt;/sub&gt;. Our results indicate that the CPD values are related to the snow metamorphism: positive values correspond to dry snow (horizontal structures), negative values indicate recrystallization processes (vertical structures).&lt;/p&gt;&lt;p&gt;Backscattering evolution in time offer a good proxy for meteorological events detection, impacting on snow metamorphism. Fresh snowfalls or melting processes can then be retrieved at the regional scale and linked to air temperature or precipitation measurements at local scale. Polarimetric SAR time series is therefore of interest to complement satellite-based precipitation measurements in the Arctic.&lt;/p&gt;

Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

The presence of light-absorbing particles (LAPs) in snow leads to a decrease in short-wave albedo affecting the surface energy budget. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps and an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, a unique dataset collected at Col du Lautaret (2058 m a.s.l., above sea level, French Alps) for the two snow seasons 2016–2017 and 2017–2018 is presented. This dataset consists of spectral albedo measurements, vertical profiles of snow specific surface area (SSA), density and concentrations of different LAP species. Spectral albedos are processed to estimate SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of LAP concentrations and SSA measurements. Our dataset highlights, among others, large discrepancies between two measurement techniques of black carbon (BC) concentrations in snow (namely thermal-optical and laser-induced incandescence). Second, we present ensemble snowpack simulations of the multi-physics version of the detailed snowpack model Crocus, forced with in situ meteorological data, as well as dust and BC deposition fluxes from an atmospheric model. The temporal variations of near-surface LAP concentrations and SSA are most of the time correctly simulated. The simulated seasonal radiative forcing of LAPs is 33 % higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs is similar with 10 ± 5 and 11 ± 1 d for the first and the second snow seasons, respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance in accounting for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season (10 ± 5 d) also points out the sensitivity of model-based estimations of LAP impact on modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to the acceleration of the metamorphism by LAPs) is negligible for the 2 years considered here, which is contrary to what was found in previous studies for other sites.

In situ effective snow grain size mapping using a compact hyperspectral imager

Effective snow grain radius (re) is mapped at high resolution using near-infrared hyperspectral imaging (NIR-HSI). The NIR-HSI method can be used to quantify re spatial variability, change in re due to metamorphism, and visualize water percolation in the snowpack. Results are presented for three different laboratory-prepared snow samples (homogeneous, ice lens, fine grains over coarse grains), the sidewalls of which were imaged before and after melt induced by a solar lamp. The spectral reflectance in each ~3 mm pixel was inverted for re using the scaled band area of the ice absorption feature centered at 1030 nm, producing re maps consisting of 54 740 pixels. All snow samples exhibited grain coarsening post-melt as the result of wet snow metamorphism, which is quantified by the change in re distributions from pre- and post-melt images. The NIR-HSI method was compared to re retrievals from a field spectrometer and X-ray computed microtomography (micro-CT), resulting in the spectrometer having the same mean re and micro-CT having 23.9% higher mean re than the hyperspectral imager. As compact hyperspectral imagers become more widely available, this method may be a valuable tool for assessing re spatial variability and snow metamorphism in field and laboratory settings.

The genesis of a climate archive: snow pack studies at four polar sites

&lt;p&gt;Understanding the deposition history and signal formation in ice cores from polar ice sheets is fundamental for the interpretation of paleoclimate reconstruction based on climate proxies. Polar surface snow responds to environmental changes on a seasonal time scale by snow metamorphism, displayed in the snow microstructure and archived in the snowpack. However, the seasonality of snow metamorphism and its link to the deposited signal in isotopes and impurity load is poorly known.&lt;/p&gt;&lt;p&gt;Here, we apply core-scale microfocus X-ray computer tomography to continuously measure snow microstructure of four snow cores from Greenlandic (Renland ice cap-drill site (2m), EASTGRIP drill site (5m)) and Antarctic sites (EDML-drill site (3m), COFI7/Plateau station (4m)) covering a wide range of annual temperatures from -18&amp;#176;C down to -56&amp;#176;C. In our multi-parameter approach we compare the derived microstructural properties on the mm- to cm-scale to discretely measured trace components and stable water isotopes, commonly used as climate proxies. We will show how ice and pore intercepts, the geometrical anisotropy, specific surface area, crusts anomalies and small-scale density distributions are represented under different climate conditions. Their profiles will be discussed in the context of snow metamorphism and deposition history using trace components and isotopes as additional constraints on timing.&lt;/p&gt;

Using triple water isotopes signatures of surface snow to gauge metamorphism in Antarctica

&lt;p&gt;Water isotopic composition is a key proxy for past climate reconstructions using deep ice cores from Antarctica. As precipitation forms, the local temperature is imprinted in the snowfalls &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O. However, this climatic signal can be erased after snow deposition when snow is exposed to the atmosphere for a long time in regions with extremely low accumulation. Understanding this effect is crucial for the interpretation of ice core records from the extremely dry East Antarctic Plateau, where post-deposition processes such as blowing snow or metamorphism affect the physical and chemical properties of snow during the long periods of snow exposure to the atmosphere. Despite the importance of these processes for the reliable reconstruction of temperature from water isotopic composition in ice cores, the tools required to quantify their impacts are still missing. Here, we present a first year-long comparison between (a) time series of surface snow isotopic composition including d-excess and &lt;sup&gt;17&lt;/sup&gt;O-excess at Dome C and (b) satellite observations providing information on snow grain size, a marker of surface metamorphism. Long summer periods without precipitation tend to produce a surface snow metamorphism signature erasing the climatic signal in the surface snow &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O. Using a simple model, we demonstrate that d-excess and &lt;sup&gt;17&lt;/sup&gt;O-excess allow the identification of the latent fluxes induced by metamorphism, and their impact on surface snow isotopic composition. In turn, their measurements can help improve climate reconstructions based on &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O records ice by removing the influence of snow metamorphism.&lt;/p&gt;

Dust and Black Carbon size distributions in snow and some links to snow physics.

&lt;p&gt;&lt;span&gt;Snow contain many insoluble particles, some of which can absorb light (such as mineral dust and black carbon) and are responsible for a large climate forcing, both directly through their influence on snow albedo and indirectly by inducing snow metamorphism &amp;#8211; albedo feedbacks.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Light absorbing particles (LAPs) influence snow metamorphism and melting by changing the heat distribution in the snowpack. Conversely, some physical processes in snow influence the size distribution of LAPs in the snowpack: for example, melting partially redistributes the particles and dry metamorphism can induce vertical movement of particles. Yet&lt;!-- --&gt;, few studies investigate those couplings due to the scarcity of detailed physical and chemical characterization of snow.&lt;/span&gt;&lt;/p&gt;&lt;p&gt; &lt;span&gt;During two consecutive winters, such detailed characterization of snow was conducted at a high altitude site in the Alps (col du Lautaret, 2058m a.s.l.). The physical properties used here include detailed profiles of snow types enabling to investigate links between LAPs size distribution and snow evolution. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Size distributions analysis shows that for both black carbon (BC) and mineral &lt;/span&gt;&lt;span&gt;dust&lt;/span&gt;&lt;span&gt;, con&lt;/span&gt;&lt;span&gt;centrations are often underestimated due to a significant fraction of particles being too big to be detected by the instruments&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&lt;span&gt; The median value of this undetected fraction is at least 20% for dust and at least 5% for BC.&lt;/span&gt;&lt;span&gt; In more than 10% of the samples, &lt;/span&gt;&lt;span&gt;It&lt;/span&gt; &lt;span&gt;even &lt;/span&gt;&lt;span&gt;exceeds 60% for dust and &lt;/span&gt;&lt;span&gt;25% &lt;/span&gt;&lt;span&gt;for BC.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We then used stratigraphic data to explore the impact of partial melt and refreeze on LAP size distributions through an hypothetic coagulation mechanism induced by freeze-thaw cycles. No visible effect was found for dust, due to the higher variability of deposited particles size distributions. Conversely, freeze-thaw cycles seem to lead to a slight shift of BC size distributions toward the big particles.&lt;/span&gt;&lt;/p&gt;