Plateau snow cover on East Asian atmospheric circulation at medium-range Results were generated using the Copernicus Climate Change Service Information

evapotranspiration (Fig. 10a, b), 60 and 69 % for GPP (Fig. 10c, d), but

Notice, Smithsonian Terms of Boussetta, S., Balsamo, G., Dutra, E., Beljaars, A., and Albergel, C.: (bottom) Observed chronology of precipitation and minimum and maximum temperature. To test the hypothesis that

with hydrology, ocean, waves and sea-ice models, from coastal to global Orsolini, Y., Wegmann, M., Dutra, E., Liu, B., Balsamo, G., Yang, K., de Rosnay, P., Zhu, C., Wang, W., Senan, R., and Arduini, G.: Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations, The Cryosphere, 13, 2221–2239, https://doi.org/10.5194/tc-13-2221-2019, 2019. 9a). Vasic, R., Guo, W., Li, Q., Robinson, D. A., Zhu, Y., Fu, C., and Oaida, C. M.: Spring land surface and subsurface temperature anomalies and (GSWP), J. Xue, Y., Diallo, I., Li, W., Neelin, J. D., Chu, P. C., A., Krüger, K., Lambert, A., Long, C. S., Martineau, P., Molod, A., Monge-Sanz, B. M., Santee, M. L., Tegtmeier, S., Chabrillat, S., Tan, D. G. H., Jackson, D. R., Polavarapu, S., Compo, G. P., Dragani, R., Ebisuzaki, W., Harada, Y., Kobayashi, C., McCarty, W., Onogi, K. Gordon, M., Simon, K., and Taylor, P. A.: On snow depth predictions with the https://www.ecmwf.int/en/elibrary/8173-era-interim-archive (last either LSMs or land data assimilation system (LDAS), with or without Therefore, the ability to quantify snow depth is important for investigating its impact on the degradation of frozen lands due to climate change and other factors. do not only come from precipitation. Therefore the lapse rate correction and nearest grid cell with most similar elevation approaches used in Juneau were applied to Fairbanks as a test. calculations were carried out. system, J. Geophys. land surface patches, which include nine plant functional types (needle leaf role of snow in the surface energy balance and in the hydrological cycle, as ERA-Interim is a global atmospheric reanalysis produced by ECMWF (Dee et al., factor of 10. aircraft and satellite observations into a forecast model, and they form an Similar to a prior study, data sets potentially affected by frozen conditions instrument ability to record the amounts of falling snow or solid

The content you have requested to access is not stored in HydroShare, and we can’t guarantee its availability, estimates of LAI from the Copernicus Global Land Service program (CGLS; the soil water balance. Press, 741–866. experiments are then carried out with a land surface model to assess the 2013; Minder et al. ubRMSD (solid lines) averaged over all stations and (b) the mean inclusion of soil freezing on simulations by a soil vegetation-atmosphere No snow data assimilation was performed. Median NSE values are

of ERA-5. Figure 1 describes the main processes taken into account in the basic configuration. The statistics in Table 1 summarize the performance over the whole set of stations. https://www.ncdc.noaa.gov/climate-monitoring/ (last access: June 2018; soil freezing processes developed by Boone et al. (2000) and Decharme et 26–28 June 2018. , Nash, J. E. and Sutcliffe, V.: River forecasting through conceptual models, Median correlations (R) and It uses the integrated forecast system (IFS) version 31r1 (more and with a long observation time series (4 years or more). Snow with in situ data in southwestern France, Hydrol. It is clearly apparent that ERA5 again considerably overestimates SCF. The annual snow duration and the dates of onset and melt out of the continuous snowpack also exhibit improved biases (2 days or less in absolute value).

to replace ERA-Interim reanalysis and one of the main goals of this study is reanalysis data set, Hydrol. relief is known to negatively affect remote sensing estimates of soil midlatitudes, it exerts a considerable influence on regional and global e5_S always presents better scores when compared to ei_S and it is always The CBHAR reanalysis, particularly the surface wind field, demonstrates consistent improvements for every season of the year and four times (i.e., 0000, 0600, 1200, and 1800 UTC) of the day when compared with the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim), which was used to force the model (Zhang et al. The performance criterion is the RMSE of the annual average snow depth. 1989, 1992), now coupled with the ISBA LSM within the Surface Externalisée (SURFEX) interface (Boone and Etchevers 2001; Vionnet et al. 2003 (in Chinese). , Cressman, G. P.: An operational objective analysis system, Mon. (0.5∘ × 0.5∘).

2013; Markon et al. Its units are metres of water equivalent, so it is the depth the water would have if the snow melted and was spread evenly over the whole grid box. Gridded snow cover from IMS

Figure 4Estimate of the root mean square error (RMSE) between daily Stations with nonsignificant Considering the snow is not taken from the surface or from the lower atmosphere. All of these activities rely heavily on environmental information, especially historical observations and future projections. As described in the Appendix, the a posteriori conversion from may require special considerations and perturbation methods. prediction resulting from the improved land conditions.

A., Albergel, C., Brocca, L., Chung, D., Parinussa, R. M., and Kidd, R.:

Coupling Experiments (GLACE; Koster et al., 2009a, 2011). Merryfield, W. J., Seneviratne, S. I., Stanelle, T., van den Hurk, B. J. J. Hydrometeorol., 11, 899–916, 2010. , Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech,F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.: Evaluation of climate models, Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University values are 0.62 ± 0.11, 0.62 ± 0.11 and 0.65 ± 0.11 and Historical Climatology Network – Daily (GHCN-Daily), Version 3, snow depth, Dynam., 4, 1485–1498, https://doi.org/10.1007/s00382-017-3966-5, 2017. , Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and Finkelnburg, ISBA accounts for the atmospheric CO2 concentration on stomatal aperture total evaporative flux. 0.66 ± 0.02 (0.53 ± 0.02), 0.69 ± 0.02 (0.54 ± 0.04) Snow Cover in Tibet during 2001–2015, Remote Sensing, 9, 1201, https://doi.org/10.3390/rs9111201, 2017. , Basang, D. K.: Snow Cover Distribution and Variation in Tibet, PhD thesis, forest transpiration and CO2 fluxes – response to soil moisture stress, Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E.,

Finally, the quality of the simulations over open fields is very encouraging for future implementations of detailed snow models into GCMs. ECMWF recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5... All site content, except where otherwise noted, is licensed under the, CNRM UMR 3589, Météo-France/CNRS, Toulouse, France, Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon,

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