Adeline Bichet, Lawrence Mudryk, Paul Kushner, Chris

Derksen

Observed and Modelled Northern Hemisphere Snow Trends: Uncertainty and Attribution

CanSISE East Meeting, July 2014

Motivation

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

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

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.]

Trend Maps of Snow Cover FractionSingle Observed Estimate vs Multiple Observed Estimates

JFM AMJ JAS OND

NO

AA

CD

R7

-est

imate

m

ean

-20 -5 -2.5 -1 -0.1 0.1 1 2.5 5 20 %/dec(NOAA, Brown2003,MERRA, ERA,CROCUS,GLDAS,GlobSnow)

1. Observational Uncertainty

1. Observational Uncertainty

1. Observational Uncertainty

1. Observational Uncertainty

1. Observational Uncertainty

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

1. Observational Uncertainty

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

2. Model Uncertainty

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

2. Model Uncertainty

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

3. Natural Variability

3. Natural Variability

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

Experiments

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

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)

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 point3. 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)

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)

- SSTObs

- g(t)

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

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

Sea Ice component of S_For

%/dec

CAM5-Uncoupled (=Obs)

CAM5-Forced (as it was done)

CAM5-Forced (as it should have been done)

Obs (NOAA+Brown2003+MERRA+ERA+GLDAS+GS+CROCUS)

Model Evaluation: Snow Cover, 1981-2010

%/dec

Obs (NOAA CDR only)

CAM5-Uncoupled

CAM5-Forced

CAM5-Uncoupled:- N. America: Reproduce NOAAsnow cover increase in northernN America in OND and JFM.Disagree with both obs in AMJ- Eurasia: Disagree with bothobs in AMJ and OND

JFM AMJ JAS ND/OND

Model Evaluation: Snow Cover, 1981-2010

%/dec

Obs (NOAA CDR only)

Obs (NOAA+Brown2003+MERRA+ERA+GLDAS+GS+CROCUS)

JFM AMJ JAS ND/OND

CAM5-Uncoupled

CAM5-Forced

CAM5-Uncoupled:- N. America: Reproduce NOAAsnow cover increase in northernN America in OND and JFM.Disagree with both obs in AMJ- Eurasia: Disagree with bothobs in AMJ and OND

CAM5-Forced:- N America: Mostly decrease inall seasons (except AMJ)- Eurasia: Decrease in northernEurope + JFM East-West dipole inEurasia

Attribution:- N. America: JFM and OND snowincrease in northern N America isdue to SST internal variability- Eurasia: Decrease in northernEurope is due to anth. forced SSTand sea-ice

Attribution Processes:

K/dec

CA

M5

-Un

cou

ple

dC

AM

5-F

orc

ed

Surface Air Temperature Trends, 1981-2010

Sea Level Pressure Trends, 1981-2010

hPa/dec

CA

M5

-Un

cou

ple

dC

AM

5-F

orc

ed

SAT: Cooling simulated in CAM5-Uncoupled in northwest N Americais due to SST internal variability-> The cooling simulated in CAM5-Uncoupled could be induced bycool SSTs observed in this area(PDO<0-like pattern)

SLP: High SLP simulated inCAM5-Uncoupled in N Pacific isdue to SST internal variability-> The SLP simulated in CAM5-Uncoupled resembles the SLPassociated with PDO<0

Attribution Processes:C

AM

5-U

nco

up

led

CA

M5

-Forc

ed

Snow Water Equivalent Trends, 1981-2010

Snowfall Trends, 1981-2010

CA

M5

-Un

cou

ple

dC

AM

5-F

orc

ed

cm/dec

cm/dec

SWE: East-West dipole simulatedby CAM5-Uncoupled in Eurasia inall seasons but JAS is due to anthSST and sea ice

-> response to Sept sea-icedecrease (Ghatak et al., 2012)?

Snowfall: JFM Snowfall increasein high northern latitudes (> 60N)is due to anth SST and sea ice

-> SAT warming in this regionleads to increase of moisturewhich trigger more precipitation.Because SAT are still below 0 inthis region snowfall increases

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

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

Conclusions

Spring Trends

OND JFM AMJ

1.0

0.5

0.0

-0.5

[ K

/ d

eca

de ]

Northern Hemisphere Trends in TSland

0.5

0.0

-0.5

[ x10

6 k

m2/

deca

de ]

Northern Hemisphere Trends in Snow Cover Extent

-1.0

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

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

+

1. Observational Uncertainty

0.5

0.0

-0.5

[ x10

6 k

m2/

deca

de ] Eurasian Trends in Snow Cover Extent

-1.0

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

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

1. Observational Uncertainty

0.5

0.0

-0.5

[ x10

6 k

m2/

deca

de ] Eurasian Trends in Snow Cover Extent

-1.0

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupled

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

Trends in Total Snow Mass

OND JFM AMJ

0.05

0.0

-0.05

-0.10

[ 10

15 k

g /

deca

de ]

Northern Hemisphere Trends in SWE

CAM4Coupled

CAM4Uncoupled

OBS

CAM5Coupled

CAM5Uncoupledspread 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)

+

+

Trend Maps of Snow Cover FractionSingle Observed Estimate vs Multiple Observed Estimates

JFM AMJ JAS OND

NO

AA

CD

R6

-est

imate

m

ean

-20 -5 -2.5 -1 -0.1 0.1 1 2.5 5 20 %/dec(Brown2003,MERRA, ERA,CROCUS,GLDAS,GlobSnow)

Too Much Warming in CCSM4

Gent et al. 2011

SWE TrendsJFM AMJ OND

CA

M 4

Cou

ple

dC

AM

4U

nco

up

led

Obse

rvati

ons

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

Trends in Ensemble Mean

JFM AMJ JAS OND

-20 -5 -2.5 -1 -0.1 0.1 1 2.5 5 20 %/dec

cou

ple

d

en

sem

ble

ob

serv

ati

on

s

Trends in Individual Realizations

Snow Cover Extent Trends

20

5

2.5

1

0.1

-0.1

-1

-2.5

-5

-20

-5 -2 -1 -0.5-0.10.1 0.5 12 5

Surface Temperature Trends

Sea Level Pressure Trends

%/dec

K/dec

5

3

1

0.2

-0.2

-1

-3

-5hPa/dec

Snow Precipitation Trends

coupled results: expect 40% of winter month trends to be significant

Northern HemisphereEurasia

North America

coupled ensembleuncoupled

ensemble

uncoupled results: expect 10% of winter month trends to be significant

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

km2]

J F M A M J J A S O N D

2.5

2.0

1.5

1.0

0.5

0

Northern Hemisphere

North America

Eurasia

50

40

30

20

10

0

[x

10

6

km2]

Mean Snow Cover Extent

J F M A M J J A S O N D

1982 1986 1990 1994 1998 2002 2006 2010

3

2

1

0

-1

-2

-3

SC

E

Anom

aly

Brown and Derksen (2013)

NOAA CDR October Snow Trend Bias

NOAA CDR Trend

AVG Reference Datasets Trend