1. 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 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

2. 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

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:
observational uncertainty
model uncertainty
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
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

5. 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

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

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

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

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

10. 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

11. 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

12. 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

13. 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

14. 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

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

16. 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

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
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

18. Experiments CAM5-Coupled: 30 members, SST and sea-ice = interactive
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
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

19. 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
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 :
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

20. 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)

21. 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

22. 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 !

23. 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)

24. 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

25. 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

26. Attribution Processes:
Surface Air Temperature Trends,
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,
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

27. Attribution Processes:
Snow Water Equivalent Trends,
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,
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

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
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

29. Conclusions 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
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

30. 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

31. 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

32. 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

33. 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

34. 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

35. Too Much Warming in CCSM4
Gent et al. 2011

36. 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

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
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

38. 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

39. 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

40. Snow Precipitation Trends
remove right hand side

41. 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

42. 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

43. 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