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Observation-based snow analyses and modeling applications Chris Derksen and Ross Brown Climate Research Division Environment Canada Lawrence Mudryk, Paul.

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Presentation on theme: "Observation-based snow analyses and modeling applications Chris Derksen and Ross Brown Climate Research Division Environment Canada Lawrence Mudryk, Paul."— Presentation transcript:

1 Observation-based snow analyses and modeling applications Chris Derksen and Ross Brown Climate Research Division Environment Canada Lawrence Mudryk, Paul Kushner, Bill Merryfield (Snow initialization in CanSIPS) Stephane Belair, Bernard Bilodeau, Marco Carrera, Natalie Gauthier (Snow assimilation in CaLDAS) Thanks to our data providers: Rutgers Global Snow Lab ● National Snow and Ice Data Center ● World Climate Research Programme Working Group on Coupled Modelling ● University of East Anglia – Climatic Research Unit ● NASA Global Modeling and Assimilation Office ● European Centre for Midrange Weather Forecasting

2 Outline 1.Brief overview of ‘observational’ snow datasets 2.Applications related to CanSISE: Observed variability and trends Evaluation of CMIP5 simulations Land surface initialization – CanSIPS CaLDAS 3.Observational gaps

3 Hemispheric Snow Datasets DescriptionPeriodResolutionData Source NOAA weekly snow/no-snow kmRutgers University, Robinson et al [1993] NOAA IMS daily 24 km snow/no-snow kmNational Snow and Ice Data Center (NSIDC), Ramsay [1998] NOAA IMS daily 4 km snow/no-snow kmNSIDC, Helfrich et al [2007] AVHRR Pathfinder daily snow/no-snow kmCanada Centre for Remote Sensing, Zhao and Fernandes, [2009] MODIS 0.05° snow cover fraction ~5 kmNSIDC, Hall et al [2006] GlobSnow snow cover fraction kmEuropean Space Agency, Metsamaki et al [2012] ERA-40 reconstructed snow cover duration (temperature-index snow model) ~275 km (5 km elev. adjustment) Environment Canada, Brown et al [2010] QuikSCAT derived snow-off date ~5 kmEnvironment Canada, Wang et al [2008] Daily snow depth analysis (in situ obs + snow model forced by GEM forecast temp/precip fields) ~35 kmCanadian Meteorological Centre, Brasnett [1999] Daily snow depth analysis (in situ obs + snow model forced by reanalysis temp/precip fields) ~35 kmEnvironment Canada, Brown et al [2003] MERRA reanalysis snow water equivalent (CATCHMENT LSM) x 0.67 degNASA, Rienecker et al [2011] GlobSnow snow water equivalent (satellite passive microwave + climate station obs) kmFinnish Meteorological Institute, Takala et al [2011]

4 June snow cover extent (2002) CMCIMS-24IMS-4MODISNCEPNOAAPMWQSCATAvg ± 1 SD May Avg SCE ± 0.80 June Avg SCE ± 1.06 Inter-Dataset Variability Brown et al. (2010) J. Geophys. Res.

5 Snow Cover Extent (km 2 x 10 6 x decade -1 ) NAEUR April May June Linear trends ( ) in SCE (km 2 x 10 6 x decade -1 ) derived from the NOAA snow chart CDR. (Mann-Kendall; serial autocorrelation removed) Observed Spring Snow Cover Extent Trends, (NOAA CDR) Bold = significant at 95%; Bold italics =significant at 99%. Derksen, C Brown, R (2012) Geophys. Res. Letters (updated)

6 Changes in Snow vs. Sea Ice Extent Over the 1979 – 2013 time period, NH June snow extent decreased at a rate of -19.9% per decade (relative to mean). September sea ice extent decreased at -13.7% per decade. Northern Hemisphere June snow cover extent; June and September Arctic sea ice extent, Derksen, C Brown, R (2012) Geophys. Res. Letters (updated)

7 How Reliable are the NOAA Snow Chart CDR Trends? Tendency for NOAA to consistently map less spring snow (~0.5 to 1 x 10 6 km 2 ) than the multi-dataset average since Accounting for this difference reduces the June NH SCE trend from km 2 x 10 6 to km 2 x 10 6 NH June SCE time series, NOAA snow chart CDR (red); average of NOAA, MERRA, ERAint (blue) Evidence of an artificial trend (~+1.0 x 10 6 km 2 per decade) in October snow cover. EUR Oct SCE: difference between NOAA snow chart CDR and 4 independent datasets, Brown, R Derksen, C (2013) Env. Res. Letters

8 Model AcronymInstitutionn latn lonResolution (°) CanESM2CCCma (Canadian Centre for Climate Modelling and Analysis, Canada) x 2.81 CCSM4NCAR (National Center for Atmospheric Research, USA) x 1.25 CNRM-CM5CNRM-CERFACS (Centre National de Recherches Meteorologiques / Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique, France) x 1.40 GISS-E2-RNASA-GISS (NASA-Goddard Institute for Space Studies, USA) x 2.50 INMCM4Institute for Numerical Mathematics, Russian Academy of Sciences, Russia x 2.00 MIROC5AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Japan), NIES (National Institute for Environmental Studies, Japan), and JAMSTEC (Japan Agency for Marine-Earth Science and Technology) x 1.40 MPI-ESM-LRMax Planck Institute for Meteorology, Germany x 1.88 MRI-CGCM3Meteorological Research Institute, Tsukuba, Japan x 1.13 CMIP5 Climate Model Simulations of Arctic Snow Cover (historical + rcp8.5)

9 Historical + projected (8 CMIP5 models; rcp85 scenario) and observed (NOAA snow chart CDR) snow cover extent for April, May and June. SCE normalized by the maximum area simulated by each model. Marked reductions in June SCE observed since 2005 fall below the zone of model consensus defined by +/-1 standard deviation from the multi-model ensemble mean. Simulated vs. Observed Arctic SCE Updated from Derksen, C Brown, R (2012) Geophys. Res. Letters NA EUR

10 Trends: Simulated (hist+rcp8.5) and observed (NOAA; CRU) Arctic SCE and surface temperature trends, Simulations underestimate observed spring SCE reductions Spring surface temperature trends slightly underestimated Slight warm bias in the simulations (not shown) but CRU may be biased cold in spring/summer due to station coastal bias. NA EUR SCE Tsurf

11 32-yr Running Trends – NA Arctic (as for sea ice in Massonnet et al, 2012) May June CMIP5 Models (hist + rcp8.5) CMIP5 Ensemble Mean and Observations

12 32-yr Running Trends – EUR Arctic (as for sea ice in Massonnet et al, 2012) CMIP5 Models (hist + rcp8.5) CMIP5 Ensemble Mean and Observations May June

13 Why do CMIP5 Models Underestimate Observed Spring SCE Reductions? North AmericaEurasia Model vs observed temperature sensitivity (dSCE/dTs), Models exhibit lower temperature sensitivity (change in SCE per deg C warming) than observations. Magnitude of observational dSCE/dTs depends on choice of observations -NOAA versus CMC -CRU versus ERAint

14 SCE Projections: Sensitivity to  and dSCE/dTs Consistent with a priori expectations, models with a higher dSCE/dTs generate a stronger projected  SCE Tendency for lower  and greater projected  SCE for EUR NAEUR

15 CMC+GlobSnow (mm) 10 model avg (mm) 10 model avg bias (mm) CMIP5 Simulated vs. Observed Arctic Snow Water Equivalent Models overestimate SWEmax over Arctic land areas/high elevation regions The multi-model ensemble agrees more closely with the observed data than any individual model Large ensemble experiment (Mudryk et al., Clim. Dyn. in press): natural climate variability generates widely different regional trends across realizations; these patterns related to temperature, precipitation and circulation trends Annual maximum monthly SWE (SWEmax)

16 TOE - the time at which the signal of climate change emerges from the noise of natural climate variability (Hawkins and Sutton, 2012) Previous work (e.g. Brown and Mote, 2009) showed that the time response of snow cover to warming and increased precipitation varied with snow cover variable, climate region and elevation TOE applied to observations (NOAA/CRU) and CMIP5 models Time-of-Emergence (TOE) Analysis for Snow Cover from CMIP5 Simulations TOE- CMIP5 Observations affected by local noise in dSCD/dT estimated from NOAA and CRU Some evidence that spring snow cover over northern Russia and Canada should exhibit an earlier TOE than indicated by the models (the zone where models project an increase in maximum snow accumulation)

17 Evaluation of Snow Initial Conditions in Canadian Seasonal to Interannual Prediction System (CanSIPS) How close are initial conditions to observations of SWE? Hindcasts Assimilation Runs Historical Runs freely running CanCM3\CanCM4 Observations GlobSnow (station + PMW) MERRA (reanalysis) CMC (station + snow model) 1 year duration serve as initial conditions for hindcasts assimilate observed T, u, v, q, SST, sea ice begin on 1st of month

18 Springtime Bias in SWE Initial Conditions

19 Generally too much NH SWE from February to May in CanCM3/CanCM4 assimilation run climatologies. Somewhat reduced in CanCM4 consistent with differences in temperature biases. Mean drift of hindcasts from assimilation runs? Springtime Bias in SWE Initial Conditions

20 The Canada Land Data Assimilation System (CaLDAS) LAND MODEL (SPS) OBS ASSIMILATION EnKF + EnOI xbxb y EnKF x a = x b + K { y – H(x b ) } K = BH T ( HBH T +R) -1 with CaLDAS IN OUT Ancillary land surface data Atmospheric forcing Observations Surface Temperature Soil moisture Snow depth or SWE Vegetation* Screen-level (T, Td) Surface stations snow depth L-band passive (SMOS, SMAP) Microwave SWE (AMSR-E) *Optical / IR (MODIS, VIIRS) Combined products (GlobSnow) T, q, U, V, Pr, SW, LW Orography, vegetation, soils, water fraction,... Analyses of… *) not done yet…

21 Field Observations of Snow Cover: ~4000 km transect across Alaska and Canadian tundra March-April 2007

22 High Arctic Measurements: Near Eureka, Ellesmere Island (~80N) April, 2011

23 Snow Radar Campaign: Near Inuvik, NT and Toolik, AK March/April 2013

24 Observations show rapid reductions in spring snow cover extent over the past decade (care must be taken when working with the NOAA snow cover extent record in the fall). The rate of June snow cover extent reductions (-19.9% per decade since 1979) is greater than the rate of summer ice loss (-13.7% per decade over the same time period). While Arctic surface temperatures in the spring are well simulated by CMIP5 models, they exhibit reduced snow cover extent sensitivity (dSCE/dTs ) to temperature compared to observations, hence the lower rate of snow cover loss. Interannual variability (  ) and dSCE/dTs are good predictors of  SCE (projections to 2050). CanSIPS: generally too much NH SWE from February to May in CanCM3/CanCM4 climatologies from assimilation runs (somewhat reduced in CanCM4 climatology). Strong spatially coherent drifts in forecasts relative to assimilation that peak for April/May forecasts. Conclusions

25 Future Work Address current observational gaps: - High resolution SWE for hydrological modeling and downscaling (i.e. ESA CoReH20 concept) - Snow depth on sea ice (new CSA GRIP project; participation in NASA IceBridge) Further examine the relative importance of interannual variability and snow cover temperature sensitivity in climate model projections of snow cover extent. Determine if the tendency for models to overestimate precipitation over high latitudes is contributing to a dampening of the climate change signal in Arctic snow cover extent. Compare CanSIPS surface temperature forecast errors with uncertainty in time lagged SWE initial conditions.

26 Questions?

27 Models vs Observations: Variability

28 Temperature Sensitivity vs. Variability Models have peak interannual variability (  ) and temperature sensitivity (dSCE/dTs) in May; observations in June Higher  = higher dSCE/dTs NAEUR Models strongly underestimate dSCE/dTs

29 Multi-Dataset SWE Time Series DatasetMethodDomainTime PeriodResolutionReference GlobSnowPassive microwave + climate stationPan-Arctic kmTakala et al., 2011 ERA-intSnow accumulation model driven by reanalysis temp and precip Pan-Arctic ~275 kmUppala et al., 2005 B2003Surface analysis + snow modelNorth America ~35 kmBrown et al, 2003 CMCSurface analysis + snow modelPan-Arctic ~35 kmBrasnett, 1999 MERRASWE field from reanalysisPan-Arctic ~35 kmRienecker et al., 2011 Seasonal SWEmax time series (+/- standard error) from multi-datasets SWE datasets are reasonably consistent (+/- 10 to 25 mm) with no significant trends in the multi-dataset average; SWEmax shows a step change over Eurasia in the late 1990s.

30 The intermodel standard deviation of local SAF contribution to climate change (Qu and Hall, 2013)

31 Snow Water Equivalent Trends from Large Ensemble Experiment (CCSM4)

32 SCDfal l SCDs pr avg std 8-model average and stdev in EYE for Fall and Spring snow cover duration Earliest EYE over coastal margins of continents with high precipitation regimes e.g. BC and Scandinavia EYE is earlier in the fall over NA and earlier in the spring over Eurasia Between-model variance in EYE is much lower over NA than Eurasia Coherent zone of late EYE over Tibetan Plateau and Siberia (region of largest between-model variance in EYE) NO EVIDENCE OF THE ARCTIC AMPLIFCATION OF SNOW COVER TRENDS SEEN IN HISTORICAL SATELLITE DATA!!!

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34 How quickly do hindcasts drift from initial conditions to model climatology? Hindcasts Assimilation Runs Historical Runs freely running CanCM3\CanCM4 Observations GlobSnow (station + PMW) MERRA (reanalysis) CMC (station + snow model) 1 year duration serve as initial conditions for hindcasts assimilate observed T, u, v, q, SST, sea ice begin on 1st of month Evaluation of Snow Initial Conditions in Canadian Seasonal to Interannual Prediction System (CanSIPS)

35 Mean Drift of Hindcasts from Assimilation Runs

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