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Evaluation of the Snow Regime in Dynamic Vegetation Land Surface Models Using Field Measurements : Volume 7, Issue 3 (05/06/2013)

By Kantzas, E.

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Book Id: WPLBN0004022846
Format Type: PDF Article :
File Size: Pages 40
Reproduction Date: 2015

Title: Evaluation of the Snow Regime in Dynamic Vegetation Land Surface Models Using Field Measurements : Volume 7, Issue 3 (05/06/2013)  
Author: Kantzas, E.
Volume: Vol. 7, Issue 3
Language: English
Subject: Science, Cryosphere, Discussions
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Zakharova, E., Quegan, S., Kantzas, E., & Lomas, M. (2013). Evaluation of the Snow Regime in Dynamic Vegetation Land Surface Models Using Field Measurements : Volume 7, Issue 3 (05/06/2013). Retrieved from http://www.ebooklibrary.org/


Description
Description: Centre for Terrestrial Carbon Dynamics: National Centre for Earth Observation, University of Sheffield, Hicks Building, Hounsfield Rd, Sheffield S37RH, UK. An increasing number of studies have demonstrated the significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled, earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both the systems processes and its initial state. This study focuses on snow-related variables and extensively utilizes a historical data set (1966–1996) of field snow measurements acquired across the extend of the Former Soviet Union (FSU) to evaluate a range of simulated snow metrics produced by a variety of land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific issues in simulating snow dynamics such as magnitude and timings of SWE as well as evolution of snow density. We further employ the field snow measurements alongside novel and model-independent methodologies to extract for the first time (i) a fresh snow density value (57–117 kg m–3) for the region and (ii) mean monthly snowpack sublimation estimates across a grassland-dominated western (November–February) [9.2, 6.1, 9.15, 15.25] mm and forested eastern sub-sector (November–March) [1.53, 1.52, 3.05, 3.80, 12.20] mm; we subsequently use the retrieved values to assess relevant model outputs. The discussion session consists of two parts. The first describes a sensitivity study where field data of snow depth and snow density are forced directly into the surface heat exchange formulation of a land surface model to evaluate how inaccuracies in simulating snow metrics affect important modeled variables and carbon fluxes such as soil temperature, thaw depth and soil carbon decomposition. The second part showcases how the field data can be assimilated with ready-available optimization techniques to pinpoint model issues and improve their performance.

Summary
Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

Excerpt
GCOS: Implementation plan for the global observing system for climate in support of the unfccc (2010 update), WMO, 2010.; Bohren, C. F. and Beschta, R. L.: Snowpack albedo and snow density, Cold Reg. Sci. Technol., 1, 47–50, doi:10.1016/0165-232x(79)90018-1, 1979.; Boone, A., Mognard, N., Decharme, B., Douville, H., Grippa, M., and Kerrigan, K.: The impact of simulated soil temperatures on the estimation of snow depth over siberia from ssm/i compared to a multi-model climatology, Remote Sens. Environ., 101, 482–494, doi:10.1016/j.rse.2006.01.014, 2006.; Brun, E., Vionnet, V., Boone, A., Decharme, B., Peings, Y., Valette, R., Karbou, F., and Morin, S.: Simulation of northern eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses, J. Hydrometeorol., 14, 203–219, doi:10.1175/Jhm-D-12-012.1, 2013.; Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., and Kolli, R. K.: Regional climate projections, in: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, UK, 847–940, 2007.; Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.: The community climate system model version 3 (ccsm3), J. Climate, 19, 2122–2143, 2006.; Cox, P. and Stephenson, D.: Climate change – a changing climate for prediction, Science, 317, 207–208, doi:10.1126/science.1145956, 2007.; GCOS: Implementation plan for the global observing system for climate in support of the unfccc, gcos–92, WMO Technical Document no. 1219, WMO, Geneva, 2004.; Anderson, E. A.: A point energy and mass balance model for snow cover, technical report nws 19, in, NOAA, Office of Hydrology, National Weather Service, Silver Spring, MD, 1976.; Best, M.: Jules technical documentation, in: Met Office, Met Office, 2009.; Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R .L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, doi:10.5194/gmd-4-677-2011, 2011. % ### SELF-REFERENCE ###; Cramer, W., Bondeau, A., Woodward, F.&n

 

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