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Adeline Bichet, Lawrence Mudryk, Paul Kushner, Chris Derksen Observed and Modelled Northern Hemisphere Snow Trends: Uncertainty and Attribution CanSISE.

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Presentation on theme: "Adeline Bichet, Lawrence Mudryk, Paul Kushner, Chris Derksen Observed and Modelled Northern Hemisphere Snow Trends: Uncertainty and Attribution CanSISE."— Presentation transcript:

1 Adeline Bichet, Lawrence Mudryk, Paul Kushner, Chris Derksen Observed and Modelled Northern Hemisphere Snow Trends: Uncertainty and Attribution CanSISE East Meeting, July 2014

2 Motivation How can we disentangle the influence of natural ocean SST variability on regional SCE trends?

3 Observed trends show large variability with location, time period and season --- what portion of the observed trend is forced by anthropogenic emissions? We focus on three distinct sources of uncertainty in comparing observed and simulated estimates of snow trends: 1.observational uncertainty 2.model uncertainty 3.natural variability

4 1. Observation-Related Uncertainty NOAA Climate Data Record (Snow Cover only) Brown 2003 Snow Cover reconstruction (Snow Cover only) GlobSnow (passive microwave+climate stations) Global Land Data Assimilation System (GLDAS) MERRA ERA-Interim Land CROCUS Snow model Snow cover fraction calculated based on daily SWE values (> 4mm) similar to NOAA CDR reanalyses forced by observed meteorology with snow models of various sophistication. ]

5 Trend Maps of Snow Cover Fraction Single Observed Estimate vs Multiple Observed Estimates JFMAMJJASOND NOAA CDR 7-estimate mean %/dec (NOAA, Brown2003, MERRA, ERA,CROCUS, GLDAS,GlobSnow)

6 1. Observational Uncertainty

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10 CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled

11 1. Observational Uncertainty CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled

12 CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled 2. Model Uncertainty

13 CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled 2. Model Uncertainty

14 JFM Too much ocean and land warming in CAM4-coupled (Gent et al. 2011) Reduced ocean and land warming in CAM5 ensemble during all seasons CAM4-coupled - CAM5-coupled 2. Model Uncertainty

15 3. Natural Variability

16

17 Uncertainty in coupled model comes from internal variability, which seems to be at least partly caused by SST uncertainty. => Here, we attempt to separate uncertainty related to oceanic internal variability from the anthropogenically forced SST and sea ice signals. We perform AMIP-type experiments forced with anthropogenic SST and sea-ice (S_For): so that the atmospheric variability remains but the ocean forcing is only anthropogenic

18 Experiments CESM-CAM5, 1 degree, hist +RCP85 atmospheric forcing, CAM5-Coupled: 30 members, SST and sea-ice = interactive CAM5-Uncoupled: 6 members, SST and sea-ice = S_Obs CAM5-Forced: 10 members, SST and sea-ice = S_For

19 S_For: SST and sea ice data sets, representative of anthropogenic component of SST and sea ice. They cover the global scale at monthly resolution for We assume that: S_Obs(x,t)= S_For(x,t)+ S_Int(x,t) S_For(x,t)=g(t)h(x) For : 1. Derive g(t): Smoothed global-mean and annual-mean S_Obs(t) (or S_AOGCM(t)) 2. Compute h(x): Regress annual-mean S_Obs(x,t) onto g(t) at each grid point 3. S_F(x,t) = g(t)h(x) + Climatology of S_Obs (for seasonality) * Spatial pattern of h(x) is derived from S_Obs !new! (usually taken from AOGCM) * Once h(x) is obtain, S_F can be estimated for any time period covered by g(t) (e.g )-> In this case, derive g(t) from AOGCM Method: Estimate S_For

20 1. Derive g(t) Method: Estimate S_For 2. Derive h(x)3. Compute S_For = Regress S_Obs onto g(t)= g(t) * h(x) + S_Obs_Clim(x,m) - SST Obs - g(t)

21 SST component of S_For CAM5-Uncoupled (=Obs) Pacific Ocean: CAM5-Forced has no PDO, ENSO.. ● Southern Ocean (30S-60S): Much warmer in CAM5-Forced than in obs ● Northern Atlantic Ocean: Colder in CAM5-Forced than in obs

22 Sea Ice component of S_For JFM & AMJ: Much more melting in CAM5-Forced than in obs, in particular in the Beaufort, Chukchi, Bering, and Siberian Seas. ! Unrealistic ! CAM5-Uncoupled (=Obs) CAM5-Forced %/dec

23 Sea Ice component of S_For %/dec CAM5-Uncoupled (=Obs) CAM5-Forced (as it was done) CAM5-Forced (as it should have been done)

24 Obs (NOAA+Brown2003+MERRA+ERA+GLDAS+GS+CROCUS) Model Evaluation: Snow Cover, %/dec Obs (NOAA CDR only) CAM5-Uncoupled CAM5-Forced CAM5-Uncoupled: - N. America: Reproduce NOAA snow cover increase in northern N America in OND and JFM. Disagree with both obs in AMJ - Eurasia: Disagree with both obs in AMJ and OND JFMAMJJASND/OND

25 Model Evaluation: Snow Cover, %/dec Obs (NOAA CDR only) Obs (NOAA+Brown2003+MERRA+ERA+GLDAS+GS+CROCUS) JFMAMJJASND/OND CAM5-Uncoupled CAM5-Forced CAM5-Uncoupled: - N. America: Reproduce NOAA snow cover increase in northern N America in OND and JFM. Disagree with both obs in AMJ - Eurasia: Disagree with both obs in AMJ and OND CAM5-Forced: - N America: Mostly decrease in all seasons (except AMJ) - Eurasia: Decrease in northern Europe + JFM East-West dipole in Eurasia Attribution: - N. America: JFM and OND snow increase in northern N America is due to SST internal variability - Eurasia: Decrease in northern Europe is due to anth. forced SST and sea-ice

26 Attribution Processes: K/dec CAM5-Uncoupled CAM5-Forced Surface Air Temperature Trends, Sea Level Pressure Trends, hPa/dec CAM5-Uncoupled CAM5-Forced SAT: Cooling simulated in CAM5- Uncoupled in northwest N America is due to SST internal variability -> The cooling simulated in CAM5- Uncoupled could be induced by cool SSTs observed in this area (PDO<0-like pattern) SLP: High SLP simulated in CAM5-Uncoupled in N Pacific is due to SST internal variability -> The SLP simulated in CAM5- Uncoupled resembles the SLP associated with PDO<0

27 Attribution Processes: CAM5-Uncoupled CAM5-Forced Snow Water Equivalent Trends, Snowfall Trends, CAM5-Uncoupled CAM5-Forced cm/dec SWE: East-West dipole simulated by CAM5-Uncoupled in Eurasia in all seasons but JAS is due to anth SST and sea ice -> response to Sept sea-ice decrease (Ghatak et al., 2012)? Snowfall: JFM Snowfall increase in high northern latitudes (> 60N) is due to anth SST and sea ice -> SAT warming in this region leads to increase of moisture which trigger more precipitation. Because SAT are still below 0 in this region snowfall increases

28 Conclusions Observational Uncertainty Simulated October snow cover trends are consistent with observed snow cover products other than the NOAA climate data record, however observed fall snow cover reduction trends still have large spread Have we actually increased our confidence in observational estimates of trends given that reanalyses and snow charts potentially have different systematic biases? Model Uncertainty CAM4-coupled simulates overly strong snow cover reduction during the winter season. This is partly due to overly strong ocean+land warming trends and is reduced in cam5-coupled Snow cover reduction during the spring season is somewhat weak in CAM5 (and CAM4) models consistent with overly weak Arctic temperature sensitivity seen in other CMIP5 climate models Natural Variability Similar spread in trends seen in both coupled and uncoupled models despite reduced internal variability in SST trends

29 Snow cover In CAM5, the winter snow cover increase ( ) simulated in northern N America, along with the associated cold local temperatures and a high pressure over N Pacific are due to SST and sea-ice internal variability. We suggest that the PDO<0 pattern seen in observed SST trend trigged this snow cover decrease via changes in atmospheric circulation (in agreement with Mudryk et al., 2013). Snow water (SWE): In CAM5, the winter East-West dipole in snow depth simulated in Eurasia is due to anthropogenic SST and sea-ice. This is in agreement with Ghatak et al.(2012), who suggest that it is driven bu sea-ice melting Conclusions

30 Spring Trends OND JFM AMJ [ K / decade ] Northern Hemisphere Trends in TS land [ x10 6 km 2 / decade ] Northern Hemisphere Trends in Snow Cover Extent CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled OND JFM AMJ Surface temperture trends are well-simulated. CMIP5 climate models tend to have lower temperature sensitivities compared to observed estimates Simulations slightly underestimate observed spring SCA reductions +

31 1. Observational Uncertainty [ x10 6 km 2 / decade ] Eurasian Trends in Snow Cover Extent CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled OND JFM AMJ Positive trend in NOAA climate data record is inconsistent with other independent data sources (including surface obs, reanalysis and passive microwave retrievals) Increasing observational frequency and resolution over time, resulting in increased ability to detect small amount of snow could have led to such an internal trend + Spurious October trend over Eurasia x

32 1. Observational Uncertainty [ x10 6 km 2 / decade ] Eurasian Trends in Snow Cover Extent CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled OND JFM AMJ Positive trend in NOAA climate data record is inconsistent with other independent data sources (including surface obs, reanalysis and passive microwave retrievals) Increasing observational frequency and resolution over time, resulting in increased ability to detect small amount of snow could have led to such an internal trend + Spurious October trend over Eurasia x adjusting only October Eurasian trend in accordance with additional in situ observations brings it better in line with other trends estimates for all of OND

33 Trends in Total Snow Mass OND JFM AMJ [ kg / decade ] Northern Hemisphere Trends in SWE CAM4 Coupled CAM4 Uncoupled OBS CAM5 Coupled CAM5 Uncoupled spread in observational estimates of total snow mass trends is less than that of snow cover Models under-estimate SWE reductions compared to observational estimates (especially in winter) + +

34 Trend Maps of Snow Cover Fraction Single Observed Estimate vs Multiple Observed Estimates JFMAMJJASOND NOAA CDR 6-estimate mean %/dec (Brown2003, MERRA, ERA,CROCUS, GLDAS,GlobSnow)

35 Too Much Warming in CCSM4 Gent et al. 2011

36 SWE Trends JFM AMJOND CAM 4 Coupled CAM 4 Uncoupled Observations

37 Influence of North Pacific and North Atlantic Sea Level Pressure Trends Pacific and Atlantic SLP trends tend to affect western and eastern portions of NA snow cover respectively The influences of these trends on snow cover appear consistent despite the fact that they are weak with respect to SLP variability

38 Trends in Ensemble Mean JFMAMJJASOND %/dec coupled ensemble observations

39 Trends in Individual Realizations Snow Cover Extent Trends Surface Temperature Trends Sea Level Pressure Trends %/dec K/dec hPa/dec

40 Snow Precipitation Trends

41 coupled results: expect 40% of winter month trends to be significant Northern Hemisphere Eurasia North America coupled ensemble uncoupled ensemble uncoupled results: expect 10% of winter month trends to be significant

42 Simulated Climatology and Variability Both experiments reproduce the climatology quite well. Observed data based on NOAA snow chart climate data housed at the Rutgers University Global Snow Lab Variability is reasonable, but too low in October and June (snow- on and snow-off months) CCSM4 AMIP OBS Snow Cover Extent Variability [x 10 6 km 2 ] J F M A M J J A S O N D Northern Hemisphere North America Eurasia [x 10 6 km 2 ] Mean Snow Cover Extent J F M A M J J A S O N D

43 SCE Anomaly Brown and Derksen (2013) NOAA CDR October Snow Trend Bias NOAA CDR Trend AVG Reference Datasets Trend


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