Papers by Carlo Carmagnola
Http Www Theses Fr, Nov 22, 2013
Snow is a porous medium whose microstructure is constantly subjected to morphological transformat... more Snow is a porous medium whose microstructure is constantly subjected to morphological transformations. These transformations, which take the name of "metamorphism", are likely to affect the thermal, mechanical and electromagnetic properties of snow at the macroscopic level. Specifically, the exchange of energy and matter within the snowpack and between the snow and the atmosphere above are strongly impacted by the evolution over time of the snow microstructure. Therefore, an adequate representation of metamorphism in snowpack models is crucial.
Hydrological Processes, 2016
The Cryosphere Discussions, 2012
The broadband albedo of surface snow is determined both by the near-surface profile of the physic... more The broadband albedo of surface snow is determined both by the near-surface profile of the physical and chemical properties of the snowpack and by the spectral and angular characteristics of the incident solar radiation. Simultaneous measurements of the physical and chemical properties of snow were carried out at Summit Camp, Greenland (72 • 36 N, 38 • 25 W, 3210 m a.s.l.) in May and June 2011, along with spectral albedo measurements. One of the main objectives of the field campaign was to test our ability to predict snow spectral albedo by comparing the measured albedo to the albedo calculated with a radiative transfer model, using measured snow physical and chemical properties. To achieve this goal, we made daily measurements of the snow spectral albedo in the range 350-2200 nm and recorded snow stratigraphic information down to roughly 80 cm. The snow specific surface area (SSA) was measured using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement, . Samples were also collected for chemical analyses including black carbon (BC) and dust, to evaluate the impact of light absorbing particulate matter in snow. This is one of the most comprehensive albedo-related data sets combining chemical analysis, snow physical properties and spectral albedo measurements obtained in a polar environment. The surface albedo was calculated from density, SSA, BC and dust profiles using the DISORT model (DIScrete Ordinate Radiative Transfer, ) and compared to the measured values. Results indicate that the energy absorbed by the snowpack through the whole spectrum considered can be inferred within 1.10 %. This accuracy is only slightly better than that which can be obtained considering pure snow, meaning that the impact of impurities on the snow albedo is small at Summit. In the near infrared, minor deviations in albedo up to 0.014 can be due to the accuracy of radiation and SSA measurements and to the surface roughness, whereas deviations up to 0.05 can be explained by the spatial heterogeneity of the snowpack at small scales, the assumption of spherical snow grains made for DISORT simulations and the vertical resolution of measurements of surface layer physical properties. At 1430 and around 1800 nm the discrepancies are larger and independent of the snow properties; we propose that they are due to errors in the ice refractive index at these wavelengths. This work contributes to the development of physically based albedo schemes in detailed snowpack models, and to the improvement of retrieval algorithms for estimating snow properties from remote sensing data.
ABSTRACT We have measured vertical profiles of specific surface area (SSA), thermal conductivity ... more ABSTRACT We have measured vertical profiles of specific surface area (SSA), thermal conductivity (TC) and density in snow from 12 different climatic regions featuring seasonal snowpacks of maritime, Alpine, taiga and tundra types, on Arctic sea ice, and from ice caps in Greenland and Antarctica. We attempt to relate snow physical properties to climatic variables including precipitation, temperature and its yearly variation, wind speed and its short scale temporal variations. As expected, temperature is a key variable that determines snow properties, mostly by determining the metamorphic regime (temperature gradient or equi-temperature) in conjunction with precipitation. However, wind speed and wind speed distribution also seem to have an at least as important role. For example high wind speeds determine the formation of windpacks of high SSA and high TC instead of depth hoar with lower values of these variables. The distribution of wind speed also strongly affects properties, as for example frequent moderate winds result in frequent snow remobilization, producing snow with higher SSA and lower TC than regions with the same average wind speeds, but with less frequent and more intense wind episodes. These strong effects of climate on snow properties imply that climate change will greatly modify snow properties, which in turn will affect climate, as for example changes in snow SSA modify albedo and changes in TC affect permafrost and the release of greenhouse gases from thawing permafrost. Some of these climate-snow feedbacks will be discussed.
The Cryosphere, 2013
The permeability (K) of snow to air flow affects the transfer of energy, water vapor and chemical... more The permeability (K) of snow to air flow affects the transfer of energy, water vapor and chemical species between the snow and the atmosphere. Yet today little is known about the temporal evolution of snow permeability as a function of metamorphic regime. Furthermore, our ability to simulate snow permeability over the seasonal evolution of a snowpack has not been tested. Here we have measured the evolution of snow permeability in a subarctic snowpack subject to high temperature-gradient (TG) metamorphism. We have also measured the evolution of the same snowpack deposited over tables so that it evolved in the equi-temperature (ET) regime. Permeability varies in the range 31 × 10 −10 (ET regime) to 650 × 10 −10 m 2 (TG regime). Permeability increases over time in TG conditions and decreases under ET conditions. Using measurements of density ρ and of specific surface area (SSA), from which the equivalent sphere radius r is determined, we show that the equation linking SSA, density ρ and permeability, K = 3.0 r 2 e (−0.013ρ) (with K in m 2 , r in m and ρ in kg m −3 ) obtained in a previous study adequately predicts permeability values. The detailed snowpack model Crocus is used to simulate the physical properties of the TG and ET snowpacks. For the most part, all variables are well reproduced. Simulated permeabilities are up to a factor of two greater than measurements for depth hoar layers, which we attribute to snow microstructure and its aerodynamic properties. Finally, the large difference in permeabilities between ET and TG metamorphic regimes will impact atmosphere-snow energy and mass exchanges. These effects deserve consideration in predicting the effect of climate change on snow properties and snow-atmosphere interactions.
Remote Sensing of Environment, 2014
The albedo of the Greenland ice sheet plays a key role in the energy balance and climate of the a... more The albedo of the Greenland ice sheet plays a key role in the energy balance and climate of the arctic. Change in snow albedo values associated with changing climate conditions can be monitored remotely from satellite platforms viewing the entire Greenland ice sheet, yet comparisons to high quality surface measurements are necessary to assess the accuracy of satellite measurements as new snow albedo algorithms are developed with higher spatial and temporal resolution. During May, June, and July 2011, we obtained daily measurements of spectral albedo at Summit, Greenland with an Analytical Spectral Devices (ASD) spectroradiometer, scanning at 350-2200 nm. We compare our spectral albedo field measurements to the Moderate Resolution Imaging Spectrometer (MODIS), using both the Version 005 Direct Broadcast daily albedo product and the recently developed Version 006 MCD43A daily albedo product. The spectral field measurements allow calculation of weighted integrals to compare to seven MODIS narrow bandwidths ranging the UV through Infrared, as well as a broadband integration to compare to the MODIS shortwave albedo. We additionally compare our field measurements to albedo measured at the Baseline Surface Radiation Network (BSRN) station at Summit. Using the MODIS Version 005 Direct Broadcast product, high-quality retrievals only, comparison to field measurements results in root mean square error (RMSE) of 0.033 for the MODIS shortwave product, and RMSE for the MODIS narrow bandwidths ranging 0.022-0.077. The new MODIS Version 006 product shows considerable improvement, with shortwave RMSE of 0.026, and narrow bandwidths ranging 0.020-0.048. These error values for the Version 006 albedo product show an improvement in reported error values from previous MODIS field validations in Greenland, which have been limited to broadband data from the Greenland Climate Network Automatic Weather Stations.
Snow albedo plays a critical role in the energy balance of the Greenland Ice Sheet. In particular... more Snow albedo plays a critical role in the energy balance of the Greenland Ice Sheet. In particular, the snow albedo influences the extent to which absorbing aerosols over Greenland (i.e. dust and black carbon) force climate. With this in mind the spectral snow albedo, physical snow properties, and snow chemistry were measured during May, June, and July 2011 at Summit, Greenland to investigate the variability in snow spectral albedo and its impact on aerosol direct radiative forcing. Optical and chemical properties of aerosol and aerosol optical depth were also measured as part of this study. Strellis et. al. will present a preliminary assessment of aerosol radiative forcing at Summit in summer 2011, in a separate presentation at this meeting. Spectral albedo was measured from 350-2500 nm with an ASD FieldSpec Pro spectroradiometer daily at four permanent sites and a moving fifth site where snow was sampled for characterization, as well as in more intensive diurnal and spatial surveys...
ABSTRACT The albedo of snow is determined both by the near-surface profile of the physical and ch... more ABSTRACT The albedo of snow is determined both by the near-surface profile of the physical and chemical properties of the snowpack and by the spectral and angular characteristics of the incident solar radiation. Simultaneous measurements of the physical and chemical properties of snow at Summit, Greenland in May and June 2011 were carried out along with spectral albedo measurements. The main objective was to test our ability to predict snow albedo by comparing the measured snow spectral albedo with the albedo calculated with a radiative transfer model. Daily snow stratigraphies down to about 80 cm were recorded. The snow density and specific surface area (SSA) were measured at the highest possible vertical resolution. For SSA, we used DUFISSS (DUal Frequency Integrating Sphere for Snow Ssa), to measure the reflectance of snow in the near infrared (at 1310 nm and 1550 nm). In addition to regular SSA vertical profiles with a resolution of 1 to 4 cm, the SSA of the surface layer was measured during events such as snowfall, blowing snow, rime or surface hoar formation. During several periods of intensive sampling, we also measured simultaneously SSA and albedo at several spots in order to study the horizontal variability of these properties. The spectral albedo of the snow was measured in the range 350-2500 nm at the same spot (prior to the other measurements), using an ASD spectroradiometer. Samples were also collected for chemical analyses including trace elements and elemental carbon, to evaluate the impact of absorbers in snow. From these data sets, the surface albedo was calculated using the DISORT model (DISCrete Ordinate Radiative Transfer) and compared to the measured values. The overall agreement is very good but some differences are observed for several wavelengths. These discrepancies and their possible sources, such as shadows of the observer, ice optical indices, and uncertainties in snow SSA and radiometric measurements are discussed.
Advances in Water Resources, 2013
ABSTRACT The specific surface area of snow (SSA) is a useful variable to describe the physical an... more ABSTRACT The specific surface area of snow (SSA) is a useful variable to describe the physical and chemical properties of snow, including a quantitative link to snow metamorphism and the optical properties of snow. Here we present a series of 16 weekly profiles of snow physical properties including SSA measured using the DUFISSS instrument spanning the period from January to April 2010 at the Col de Porte field site in the French Alps near Grenoble. Measured SSA values for dry snow ranged between ca. 5 and 80 m2 kg−1, and generally decreased over time in a given snow layer. Wet snow conditions encountered towards the end of the snow season show SSA values between 3 and 10 m2 kg−1. This unique dataset is compared with simulations carried out using the Crocus snowpack model, using two parameterizations of snow SSA: one simply derived from the internal computation of the optical radius in Crocus, and the other one determined from density and snow type. Both parameterizations perform rather satisfactorily qualitatively and quantitatively, compared to the performance in terms of snow density profile. Ample room for improvement exists, in particular through the implementation of SSA as a fully fledged prognostic variable in Crocus, which is currently in progress.
The Cryosphere, 2013
The broadband albedo of surface snow is determined both by the near-surface profile of the physic... more The broadband albedo of surface snow is determined both by the near-surface profile of the physical and chemical properties of the snowpack and by the spectral and angular characteristics of the incident solar radiation. Simultaneous measurements of the physical and chemical properties of snow were carried out at Summit Camp, Greenland (72 • 36 N, 38 • 25 W, 3210 m a.s.l.) in May and June 2011, along with spectral albedo measurements. One of the main objectives of the field campaign was to test our ability to predict snow spectral albedo by comparing the measured albedo to the albedo calculated with a radiative transfer model, using measured snow physical and chemical properties. To achieve this goal, we made daily measurements of the snow spectral albedo in the range 350-2200 nm and recorded snow stratigraphic information down to roughly 80 cm. The snow specific surface area (SSA) was measured using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement, . Samples were also collected for chemical analyses including black carbon (BC) and dust, to evaluate the impact of light absorbing particulate matter in snow. This is one of the most comprehensive albedo-related data sets combining chemical analysis, snow physical properties and spectral albedo measurements obtained in a polar environment. The surface albedo was calculated from density, SSA, BC and dust profiles using the DISORT model (DIScrete Ordinate Radiative Transfer, ) and compared to the measured values. Results indicate that the energy absorbed by the snowpack through the whole spectrum considered can be inferred within 1.10 %. This accuracy is only slightly better than that which can be obtained considering pure snow, meaning that the impact of impurities on the snow albedo is small at Summit. In the near infrared, minor deviations in albedo up to 0.014 can be due to the accuracy of radiation and SSA measurements and to the surface roughness, whereas deviations up to 0.05 can be explained by the spatial heterogeneity of the snowpack at small scales, the assumption of spherical snow grains made for DISORT simulations and the vertical resolution of measurements of surface layer physical properties. At 1430 and around 1800 nm the discrepancies are larger and independent of the snow properties; we propose that they are due to errors in the ice refractive index at these wavelengths. This work contributes to the development of physically based albedo schemes in detailed snowpack models, and to the improvement of retrieval algorithms for estimating snow properties from remote sensing data.
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Papers by Carlo Carmagnola