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A. Langlois, J. Kohn, A. Royer, P. Cliche, L. Brucker, G. Picard, M. Fily, C. Derksen, and J. M. Willemet

Abstract

Snow cover plays a key role in the climate system by influencing the transfer of energy and mass between the soil and the atmosphere. In particular, snow water equivalent (SWE) is of primary importance for climatological and hydrological processes and is a good indicator of climate variability and change. Efforts to quantify SWE over land from spaceborne passive microwave measurements have been conducted since the 1980s, but a more suitable method has yet to be developed for hemispheric-scale studies. Tools such as snow thermodynamic models allow for a better understanding of the snow cover and can potentially significantly improve existing snow products at the regional scale.

In this study, the use of three snow models [SNOWPACK, CROCUS, and Snow Thermal Model (SNTHERM)] driven by local and reanalysis meteorological data for the simulation of SWE is investigated temporally through three winter seasons and spatially over intensively sampled sites across northern Québec. Results show that the SWE simulations are in agreement with ground measurements through three complete winter seasons (2004/05, 2005/06, and 2007/08) in southern Québec, with higher error for 2007/08. The correlation coefficients between measured and predicted SWE values ranged between 0.72 and 0.99 for the three models and three seasons evaluated in southern Québec. In subarctic regions, predicted SWE driven with the North American Regional Reanalysis (NARR) data fall within the range of measured regional variability. NARR data allow snow models to be used regionally, and this paper represents a first step for the regionalization of thermodynamic multilayered snow models driven by reanalysis data for improved global SWE evolution retrievals.

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J. Boutin, Y. Chao, W. E. Asher, T. Delcroix, R. Drucker, K. Drushka, N. Kolodziejczyk, T. Lee, N. Reul, G. Reverdin, J. Schanze, A. Soloviev, L. Yu, J. Anderson, L. Brucker, E. Dinnat, A. Santos-Garcia, W. L. Jones, C. Maes, T. Meissner, W. Tang, N. Vinogradova, and B. Ward

Abstract

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km2. In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

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