Snow and glacier change modelling in the French Alps

Transcription

1 International Network for Alpine Research Catchment Hydrology Inaugural Workshop Barrier Lake Field Station, Kananaskis Country, Alberta, Canada October 2015 Snow and glacier change modelling in the French Alps M. Dumont, M. Lafaysse, S. Morin, V. Vionnet (CNRM-GAME) I. Zin (LTHE)

2 General framework for global changes in the Alps Warming is expected to accelerate throughout the 21st century Seasonal shifts in precipitation and relative humidity are expected Precipitation and temperature extremes are expected to intensify Snow cover is expected to drastically decrease below m Changes related to droughts and natural hazards are expected

3 Some addressed modelling issues Model complexity vs. accuracy Dealing with uncertainties Remote sensing and local data assimilation Examples on the proposed INARCH & surrounding sites

4 The SURFEX/ISBA-CROCUS soil-snow model 1D model Multi-layers max 50 couches Dynamical discretization of the layers Métamorphosis of snow grains Brun et al., 1989,1992; Vionnet et al., 2012

5 5 At local scale Col de Porte

6 Model complexity vs accuracy local scale Same land surface model with: Same multi-layer ground scheme 3-layers vs multi-layers snowpack scheme Small differences between the various models for bulk snow properties Strong impact of the input meteorological data Masson et al. 2013

7 Model complexity vs accuracy local scale Same multi-layer ground scheme 1701 combinations of multilayer parameterizations for albedo, fresh snow density, compaction, turbulent exchanges, snow cover fraction, thermal conductivity JULES Investigation Model, 4 years performance assessment Best models vary from year to year Essery et al Same kind of results on SWE and snow depth for experiments with different DDF schemes

8 At catchment scale Arve headwater SAFRAN meteorological analysis Air temperature Rel. humidity Windspeed Incoming solar radiation Incoming longwave radiation Rainfall Snowfall North South Conceptual 2-buckets module for groundwater storage

9 At catchment scale Arve headwater Elevation bands (300m) Slope classes (by 20 ) Exposition classes (by 45 ) 50 m 250 m Glacierized areas + soil and vegetation types km HRUs on the Arve headwater8 km

10 At catchment scale Arve headwater

11 At catchment scale Arve headwater Glacial retreat between 1986 (solid line) et 2012 (dashed line) low impact on discharge (consistent with observations)

12 Model complexity vs accuracy headwater scale Same surface model, with the same multi-layer ground scheme 3-layers snowpack scheme multi-layers snowpack scheme Significant differences during the snowmelt season (not expected, cf. Masson et al. 2013) Process (and parameter!) interaction effect at headwater scale

13 Dealing with uncertainties driving met data Arve headwater 12 years of ensemble precipitation predictions at catchment scale NCEP-GEFS 1 x1, 20 members + ctrl ECMWF-ENS 0.25 x0.25 members + ctrl CNR-OPALE-GFS 40 members Bellier et al., to be submitted Good performance of ECMWF-ENS even at small scale! Analog-based techniques unbias and make large scale ensemble predictions more reliable Diurnal cycle of performance

14 Dealing with uncertainties - Ensemble predictions PEARP-S2M Exemples: - 1st May 2015 in Mont-Blanc area Chamonix 02/05/2015 T = 5 yrs

15 Dealing with uncertainties - Ensemble predictions PEARP-S2M Exemples: - 1st May 2015 in Mont-Blanc area Chamonix 02/05/2015 T = 5 yrs

16 Dealing with uncertainties - Ensemble predictions PEARP-S2M Exemples: - 1st May 2015 in Mont-Blanc area Chamonix 02/05/2015 T = 5 yrs

17 Remote sensing and snow data assimilation MODIS Control simulation Charrois et al., to be submitted

18 Remote sensing and snow data assimilation Reflectances assimilation (Refl_DA) Reduced envelopes dispersion Reduced uncertainty on the ending melt date Need of regular observations Charrois et al., to be submitted

19 Remote sensing and snow data assimilation Reflectances + snow depth assimilation (Refl+SD_DA) Observations assimilation 0,07 0,04 18,5 9,5 Control simulation 10 days Charrois et al., to be submitted

20 Climate projections + uncertainties (Durance) Mean annual temperature (2000m) Snow cover duration at 1650m Winter precip (2000m) Snow cover duration at 2250m Dark blue : GCM uncertainty Green : downscaling uncertainty Cyan : residual Red : large scale internal variability Yellow : small scale internal variability Lafaysse et al., 2014