| References |
Belward, A., Estes, J., Kline, K., The IGBP-DIS Global 1-km Land-Cover Data Set DISCover: A project overview (1999) Photogramm. Eng. Remote Sens., 9, pp. 1013-1020; Benavidès Pinjosovsky, H.S., Thiria, S., Ottlé, C., Brajard, J., Badran, F., Maugis, P., Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6 (2017) Geosci. Model Dev., 10, pp. 85-104; Beringer, J., Lynch, A.H., Chapin, F.S., III, Mack, M., Bonan, G.B., The representation of Arctic soils in the land surface model: The importance of mosses (2001) J. Clim., 14 (15), pp. 3324-3335; Bicheron, P., Leroy, M., Brockmann, C., Krämer, U., Miras, B., Huc, M., Gross, D., (2006), pp. 538-542. , Globcover A 300 m global land cover product for 2005 using ENVISAT MERIS time series, in, Proceedings of the Second International Symposium on Recent Advances in Quantitative Remote Sensing, Serv. de Publ., Univ. de Valencia, Valencia, Spain; Bontemps, S., (2013), Consistent global land cover maps for climate modelling communities Current achievements of the ESA's land cover CCI, paper presented at the ESA Living Planet Symposium, Edinburgh, U. K., 9–13 Sept; Campolongo, F., Cariboni, J., Saltelli, A., An effective screening design for sensitivity analysis of large models (2007) Environ. Modell. Software, 22 (10), pp. 1509-1518; Campolongo, F., Saltelli, A., Cariboni, J., From screening to quantitative sensitivity analysis: A unified approach (2011) Comput. Phys. Commun., 182 (4), pp. 978-988; Carsel, R.F., Parrish, R.S., Developing joint probability distributions of soil water retention characteristics (1988) Water Resour. Res., 24 (5), pp. 755-769; Chadburn, S., Burke, E., Essery, R., Boike, J., Langer, M., Heikenfeld, M., Cox, P., Friedlingstein, P., An improved representation of physical permafrost dynamics in the JULES land-surface model (2015) Geosci. Model Dev., 8 (5), pp. 1493-1508; Chapin, F.S., Role of land-surface changes in Arctic summer warming (2005) Science, 310 (5748), pp. 657-660; Dankers, R., Burke, E.J., Price, J., Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme (2011) Cryosphere, 5 (3), pp. 773-790; De Rosnay, P., Polcher, J., Bruen, M., Laval, K., Impact of a physically based soil water flow and soil-plant interaction representation for modeling large-scale land surface processes (2002) J. Geophys. Res., 107 (D11); Dickinson, R.E., Henderson-Sellers, A., Kennedy, P.J., (1993), Biosphere-atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model,, Rep. N. T. N. NCAR/TN-387+STR, Boulder, Colo; Ducoudré, N., Laval, K., Perrier, A., SECHIBA, a new set of parameterizations of the hydrologic exchanges at the land-atmosphere interface within the LMD Atmospheric General Circulation Model (1993) J. Clim., 6 (2), pp. 248-273; Dufresne, J.L., Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5 (2013) Clim. Dyn., 40 (9-10), pp. 2123-2165; Frolov, I.E., Ashik, I.M., Kassens, H., Polyakov, I.V., Proshutinsky, A.Y., Sokolov, V.T., Timokhov, L.A., Anomalous variations in the thermohaline structure of the Arctic Ocean (2009) Dokl. Earth Sci., 429 (2), pp. 1567-1569; Gouttevin, I., Krinner, G., Ciais, P., Polcher, J., Legout, C., Multi-scale validation of a new soil freezing scheme for a land-surface model with physically-based hydrology (2012) Cryosphere, 6 (2), pp. 407-430; Groisman, P., Soja, A.J., Ongoing climatic change in Northern Eurasia: Justification for expedient research (2009) Environ. Res. Lett., 4 (4), p. 045002; Groisman, P.Y., Karl, T.R., Knight, R.W., Stenchikov, G.L., Changes of snow cover, temperature, and radiative heat balance over the Northern Hemisphere (1994) J. Clim., 7 (11), pp. 1633-1656; Gubler, S., Endrizzi, S., Gruber, S., Purves, R.S., Sensitivities and uncertainties of modeled ground temperatures in mountain environments (2013) Geosci. Model Dev., 6 (4), pp. 1319-1336; Guglielmo, F., Simulating hydrology with an isotopic land surface model in western Siberia: What do we learn from water isotopes? (2015) Hydrol. Earth Syst. Sci. Discuss., 12, pp. 9393-9436; Hinzmann, L.D., Evidence and implications of recent climate change in Northern Alaska and other Arctic regions (2005) Clim. Change, 72, pp. 251-298; Homma, T., Saltelli, A., Importance measures in global sensitivity analysis of nonlinear models (1996) Reliab. Eng. Syst. Safety, 52 (1), pp. 1-17; Hourdin, F., The LMDZ4 general circulation model: Climate performance and sensitivity to parametrized physics with emphasis on tropical convection (2006) Clim. Dyn., 27, pp. 787-813; Jansen, M.J.W., Analysis of variance designs for model output (1999) Comput. Phys. Commun., 117 (1-2), pp. 35-43; Koenigk, T., Brodeau, L., Graversen, R., Karlsson, J., Svensson, G., Tjernström, M., Willén, U., Wyser, K., Arctic climate change in 21st century CMIP5 simulations with EC-Earth (2013) Clim. Dyn., 40 (11-12), pp. 2719-2743; Koven, C.D., Ringeval, B., Friedlingstein, P., Ciais, P., Cadule, P., Khvorostyanov, D., Krinner, G., Tarnocai, C., Permafrost carbon-climate feedbacks accelerate global warming (2011) Proc. Natl. Acad. Sci. U. S. A., 108 (36), pp. 14,769-14,774; Koven, C.D., Riley, W.J., Stern, A., Analysis of permafrost thermal dynamics and response to climate change in the CMIP5 Earth System Models (2013) J. Clim., 26 (6), pp. 1877-1900; Krinner, G., Viovy, N., De Noblet-Ducoudré, N., A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system (2005) Global Biogeochem. Cycles, 19; Kuppel, S., Chevallier, F., Peylin, P., Quantifying the model structural error in carbon cycle data assimilation systems (2013) Geosci. Model Dev., 6, pp. 45-55; Loranty, M.M., Goetz, S.J., Shrub expansion and climate feedbacks in Arctic tundra (2012) Environ. Res. Lett., 7 (1), p. 011005; Lu, X., Wang, Y.-P., Ziehn, T., Dai, Y., An efficient method for global parameter sensitivity analysis and its applications to the Australian community land surface model (CABLE) (2013) Agric. For. Meteor., pp. 292-303; Morris, M.D., Factorial sampling plans for preliminary computational experiments (1991) Technometrics, 33 (2), pp. 161-174; Nicolsky, D.J., Romanovsky, V.E., Alexeev, V.A., Lawrence, D.M., Improved modeling of permafrost dynamics in a GCM land-surface scheme (2007) Geophys. Res. Lett., 34; Nossent, J., Elsen, P., Bauwens, W., Sobol' sensitivity analysis of a complex environmental model (2011) Environ. Modell. Software, 26 (12), pp. 1515-1525; Olson, J., Watts, J., Allison, L., (1983), p. 152. , Carbon in live vegetation of major world ecosystems,, Tech. Rep. W-7405-ENG-26, Oak Ridge Natl. Lab., Oak Ridge, Tenn; Ottlé, C., Lescure, J., Maignan, F., Poulter, B., Wang, T., Delbart, N., Use of various remote sensing land cover products for PFT mapping over Siberia (2013) Earth Syst. Sci. Data, 5, pp. 331-348; Päivänen, J., (1973), Hydraulic Conductivity and Water Retention in Peat Soils, Suomen Metsätieteellinen Seura, Helsinki, Finland; Paquin, J.P., Sushama, L., On the Arctic near-surface permafrost and climate sensitivities to soil and snow model formulations in climate models (2015) Clim. Dyn., 44 (1-2), pp. 203-228; Peng, S., Simulated high-latitude soil thermal dynamics during the past 4 decades (2016) Cryosphere, 10, pp. 179-192; Pujol, G., Iooss, B., Iooss, M.B., (2014), http://CRAN.R-project.org/package=sensitivity, Package ‘Sensitivity’, R Package Version 1.8–2; Rinke, A., Kuhry, P., Dethloff, K., Importance of a soil organic layer for Arctic climate: A sensitivity study with an Arctic RCM (2008) Geophys. Res. Lett., 35; Romanovsky, V.E., Thermal state of permafrost in Russia (2010) Permafrost Periglac. Process., 21, pp. 136-155; Saltelli, A., Making best use of model evaluations to compute sensitivity indices (2002) Comput. Phys. Commun., 145 (2), pp. 280-297; Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., (2008) Global Sensitivity Analysis: The Primer, , John Wiley, Hoboken, N. J; Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S., Variance based sensitivity analysis of model output, Design and estimator for the total sensitivity index (2010) Comput. Phys. Commun., 181 (2), pp. 259-270; Serreze, M.C., Barrett, A.P., Slater, A.G., Steele, M., Zhang, J.L., Trenberth, K.E., The large-scale energy budget of the Arctic (2007) J. Geophys. Res., 112; Sitch, S., Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model (2003) Global Change Biol., 9 (2), pp. 161-185; Slater, A.G., Lawrence, D.M., Diagnosing present and future permafrost from climate models (2013) J. Clim., 26 (15), pp. 5608-5623; Sobol, I.M., On sensitivity estimation for nonlinear mathematical models [in Russian] (1990) Mat. Model., 2 (1), pp. 112-118. , Math. Model. Comput. Exp, Engl. Transl; Sobol, I.M., Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates (2001) Math. Comput. Simul., 55, pp. 271-280; Valdayskikh, V., Nekrasova, O., Jouzel, J., Uchaev, A., Radchenko, T., Some characteristics of forest-tundra (West Siberia) soil groups distinguished on the basis of thermal properties (2013) Prace Geogr., 135, pp. 73-86; Vérant, S., Laval, K., Polcher, J., De Castro, M., Sensitivity of the continental hydrological cycle to the spatial resolution over the Iberian Peninsula (2004) J. Hydrometeorol., 5, pp. 267-285; Wang, F., Cheruy, F., Dufresne, J.L., The improvement of soil thermodynamics and its effects on land surface meteorology in the IPSL climate model (2016) Geosci. Model Dev., 9, pp. 363-381; Wang, T., Ottlé, C., Boone, A., Ciais, P., Brun, E., Morin, S., Krinner, G., Peng, S., Evaluation of an improved intermediate complexity snow scheme in the ORCHIDEE land surface model (2013) J. Geophys. Res. Atmos., 118, pp. 6064-6079 |