Adeline Bichet, Lawrence Mudryk, Paul Kushner, Chris Derksen Observed and Modelled Northern Hemisphere Snow Trends: Uncertainty and Attribution This is an update on Mudryk et al. 2013 paper. For this talk we’ve updated the model version used in the large ensembles and included multiple observational estimates. Number of interesting differences Adeline Bichet, Lawrence Mudryk, Paul Kushner, Chris Derksen CanSISE East Meeting, July 2014

Motivation How can we disentangle the influence of natural ocean SST variability on regional SCE trends? SCE trends linked to ocean SST forcing. not capturing tropical ocean response. cite shin and sardeshmukh

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: observational uncertainty model uncertainty natural variability

] 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 Foremost we use the NOAA CDR which is only available for snow cover. All other products are actually daily SWE estimates for which we calculate the snow cover fraction based on SWE of 4mm or greater. (Era is actually era-i reconstruction for SCF) SCF: NOAA,GS,GLDAS,MERRA,ERA-I-recon/SWE: GS,MERRA,ERA-I-L,CROCUS ] reanalyses forced by observed meteorology with snow models of various sophistication. Snow cover fraction calculated based on daily SWE values (> 4mm) similar to NOAA CDR

Trend Maps of Snow Cover Fraction Single Observed Estimate vs Multiple Observed Estimates JFM AMJ JAS OND NOAA CDR Comparison of gross properties for simulated SCF trends and observed trends. It’s important to note that because the simulated trends are for the ensemble average they have many of the effects of natural variability removed. Even with several observational estimate being combing in the plots for observed trends, they still are only effectively measuring trends for a single climate realization. That being said the seasonal range of trends compares well. 7-estimate mean (NOAA, Brown2003, MERRA, ERA,CROCUS, GLDAS,GlobSnow) -20 -5 -2.5 -1 -0.1 0.1 1 2.5 5 20 %/dec

1. Observational Uncertainty Remind people that boxes/whisker don’t represent the same uncertainty in the models as in the observations

1. Observational Uncertainty Remind people that boxes/whisker don’t represent the same uncertainty in the models as in the observations

1. Observational Uncertainty Remind people that boxes/whisker don’t represent the same uncertainty in the models as in the observations

1. Observational Uncertainty Remind people that boxes/whisker don’t represent the same uncertainty in the models as in the observations

1. Observational Uncertainty CAM4 Coupled CAM4 Uncoupled Adjusting only october in accordance with in situ data available over Eurasia reduces the observational spread and brings it in better line with other trends for the fall. CAM5 Coupled CAM5 Uncoupled OBS

1. Observational Uncertainty CAM4 Coupled CAM4 Uncoupled Adjusting only october in accordance with in situ data available over Eurasia reduces the observational spread and brings it in better line with other trends for the fall. CAM5 Coupled CAM5 Uncoupled OBS

2. Model Uncertainty CAM4 Coupled CAM4 Uncoupled CAM5 Coupled CAM5 Examining the winter trends we see that three of the ensembles are in reasonable agreement with the observations CAM5 Coupled CAM5 Uncoupled OBS

2. Model Uncertainty CAM4 Coupled CAM4 Uncoupled CAM5 Coupled CAM5 Examining the winter trends we see that three of the ensembles are in reasonable agreement with the observations CAM5 Coupled CAM5 Uncoupled OBS

JFM 2. Model Uncertainty CAM4-coupled - CAM5-coupled Too much ocean and land warming in CAM4-coupled (Gent et al. 2011) Reduced ocean and land warming in CAM5 ensemble during all seasons this appears to be due to reduced ocean warming in the newer version of the model. Excessive ocean warming was an established trait of CCSM4. This is reduced in the new version, along with the warming over the NH land surface. JFM

3. Natural Variability Remind people that boxes/whisker don’t represent the same uncertainty in the models as in the observations

3. Natural Variability Let’s take a look at why we bother to run so many realizations for a given model/configuration. Emphasize that these are from the same model forced in the same way - initial condition ensemble - internal variability effect

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 might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS

Experiments CAM5-Coupled: 30 members, SST and sea-ice = interactive CESM-CAM5, 1 degree, hist +RCP85 atmospheric forcing, 1980-2010 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 might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS

Method: Estimate S_For 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 1980-2010. We assume that: S_Obs(x,t)= S_For(x,t)+ S_Int(x,t) S_For(x,t)=g(t)h(x) might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS For 1900-2008: 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. 1980-2040)-> In this case, derive g(t) from AOGCM

Method: Estimate S_For 1. Derive g(t) 2. Derive h(x) 3. Compute S_For = Regress S_Obs onto g(t) = g(t) * h(x) + S_Obs_Clim(x,m) might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS - SSTObs - g(t)

SST component of S_For Pacific Ocean: CAM5-Forced has no PDO, ENSO.. CAM5-Uncoupled (=Obs) might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS 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

Sea Ice component of S_For CAM5-Uncoupled (=Obs) %/dec might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS CAM5-Forced JFM & AMJ: Much more melting in CAM5-Forced than in obs, in particular in the Beaufort, Chukchi, Bering, and Siberian Seas. ! Unrealistic !

Sea Ice component of S_For CAM5-Uncoupled (=Obs) %/dec CAM5-Forced (as it was done) might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS CAM5-Forced (as it should have been done)

Model Evaluation: Snow Cover, 1981-2010 Obs (NOAA CDR only) 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 Obs (NOAA+Brown2003+MERRA+ERA+GLDAS+GS+CROCUS) %/dec might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS CAM5-Uncoupled CAM5-Forced JFM AMJ JAS ND/OND

Model Evaluation: Snow Cover, 1981-2010 Obs (NOAA CDR only) 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 Obs (NOAA+Brown2003+MERRA+ERA+GLDAS+GS+CROCUS) %/dec CAM5-Forced: - N America: Mostly decrease in all seasons (except AMJ) - Eurasia: Decrease in northern Europe + JFM East-West dipole in Eurasia might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS CAM5-Uncoupled 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 CAM5-Forced JFM AMJ JAS ND/OND

Attribution Processes: Surface Air Temperature Trends, 1981-2010 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) CAM5-Uncoupled K/dec CAM5-Forced might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS Sea Level Pressure Trends, 1981-2010 CAM5-Uncoupled 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 hPa/dec CAM5-Forced

Attribution Processes: Snow Water Equivalent Trends, 1981-2010 cm/dec CAM5-Uncoupled 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)? CAM5-Forced might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS Snowfall Trends, 1981-2010 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 CAM5-Uncoupled CAM5-Forced cm/dec

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 might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS

Conclusions Snow cover In CAM5, the winter snow cover increase (1980-2010) 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 might be interesting to look at arctic SWE for an explanation of why simulated trends are weaker than observed --- maybe observed estimates are poor because of high latitude/arctic dominance. also redo obs spread with GLDAS

Spring Trends Northern Hemisphere Trends in TSland 1.0 CAM4 Coupled Surface temperture trends are well-simulated. 0.5 [ K / decade ] CAM4 Uncoupled 0.0 Despite what appear to be accurately simulated trends in surface temperature, the models slightly underestimate the observed spring time SCA reductions. This is consistent with other CMIP5 models which tend to have lower temperature sensitivities compared to observed estimates (update to Derksen and Brown analysis from ??) -0.5 CAM5 Coupled OND JFM AMJ Northern Hemisphere Trends in Snow Cover Extent CAM5 Uncoupled 0.5 Simulations slightly underestimate observed spring SCA reductions 0.0 OBS [ x106 km2/ decade ] CMIP5 climate models tend to have lower temperature sensitivities compared to observed estimates -0.5 + -1.0 OND JFM AMJ

1. Observational Uncertainty 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 CAM4 Coupled CAM4 Uncoupled Adjusting only october in accordance with in situ data available over Eurasia reduces the observational spread and brings it in better line with other trends for the fall. CAM5 Coupled Eurasian Trends in Snow Cover Extent CAM5 Uncoupled 0.5 Spurious October trend over Eurasia 0.0 OBS [ x106 km2/ decade ] -0.5 + x -1.0 OND JFM AMJ

1. Observational Uncertainty 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 CAM4 Coupled CAM4 Uncoupled Adjusting only october in accordance with in situ data available over Eurasia reduces the observational spread and brings it in better line with other trends for the fall. CAM5 Coupled Eurasian Trends in Snow Cover Extent CAM5 Uncoupled 0.5 Spurious October trend over Eurasia 0.0 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 OBS [ x106 km2/ decade ] -0.5 + x -1.0 OND JFM AMJ

Trends in Total Snow Mass Northern Hemisphere Trends in SWE 0.05 CAM4 Coupled 0.0 [ 1015 kg / decade ] -0.05 CAM4 Uncoupled + -0.10 + observational estimates of SWE spread are less than that of SCE. CAM5 Coupled OND JFM AMJ 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) OBS

Trend Maps of Snow Cover Fraction Single Observed Estimate vs Multiple Observed Estimates JFM AMJ JAS OND NOAA CDR Comparison of gross properties for simulated SCF trends and observed trends. It’s important to note that because the simulated trends are for the ensemble average they have many of the effects of natural variability removed. Even with several observational estimate being combing in the plots for observed trends, they still are only effectively measuring trends for a single climate realization. That being said the seasonal range of trends compares well. 6-estimate mean (Brown2003, MERRA, ERA,CROCUS, GLDAS,GlobSnow) -20 -5 -2.5 -1 -0.1 0.1 1 2.5 5 20 %/dec

Too Much Warming in CCSM4 Gent et al. 2011

SWE Trends JFM AMJ OND CAM 4 Coupled Uncoupled CAM 4 Observations can see signal of overall reduction in SWE trends. East-west dipole over Eurasia is robust in simulations and there are hints of it in the observations. ghatak paper: Uncoupled CAM 4 Observations

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 remove right hand side The influences of these trends on snow cover appear consistent despite the fact that they are weak with respect to SLP variability

Trends in Ensemble Mean JFM AMJ JAS OND coupled ensemble observations -20 -5 -2.5 -1 -0.1 0.1 1 2.5 5 20 %/dec

Trends in Individual Realizations Snow Cover Extent Trends Surface Temperature Trends Sea Level Pressure Trends 20 5 2.5 1 0.1 -0.1 -1 -2.5 -5 -20 5 3 1 0.2 -0.2 -1 -3 -5 hPa/dec %/dec -5 -2 -1 -0.5 -0.1 0.1 0.5 1 2 5 K/dec

Snow Precipitation Trends remove right hand side

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

Simulated Climatology and Variability Northern Hemisphere North America Eurasia 50 40 30 20 10 [x 106 km2] Mean Snow Cover Extent J F M A M J J A S O N D Both experiments reproduce the climatology quite well. Variability is reasonable, but too low in October and June (snow-on and snow-off months) remove right hand side Snow Cover Extent Variability [x 106 km2] J F M A M J J A S O N D 2.5 2.0 1.5 1.0 0.5 CCSM4 AMIP OBS Observed data based on NOAA snow chart climate data housed at the Rutgers University Global Snow Lab

NOAA CDR October Snow Trend Bias Brown and Derksen (2013) 3 2 1 spring time trends too weak in both models despite relatively accurate temperature trends. weak albedo feedback? SCE Anomaly -1 -2 -3 1982 1986 1990 1994 1998 2002 2006 2010 NOAA CDR Trend AVG Reference Datasets Trend