Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans

Eastern Tropical Oceans Synthesis US CLIVAR Working Group

WG Objectives include identifying sources for the tropical SST biases, and the remote impact of the biases

http://www.usclivar.org/working-groups/etos

Toniazzo&Woolnough, 2013

top: mean SST error in historical integrations of 25 coupled CMIP5 GCMs.bottom: mean precipitation errors relative to CMAP + 10-m wind errors relative to ERA-I

Eastern tropical SSTs typically too warm in CGCMS,too much southern hemisphere ocean rain,

not enough Amazonian/too much Congo rain

Z. Xu/Ping Chang

CMIP5 models provide only incremental improvement to known Atlantic equatorial SST biases

...and the zonal equatorial Atlantic SST gradient remains reversed in CMIP5

Toniazzo&Woolnough, 2013

top: mean SST error in historical integrations of 25 coupled CMIP5 GCMs.bottom: mean precipitation errors relative to CMAP + 10-m wind errors relative to ERA-I

what may be less appreciated is that the maximum SST bias in the Atlantic occurs in the southeast around 20S, not on the equator

model error diagnosis approaches:

• comparison to observations(have to identify the bias before you can correct it)

•examine initial error tendencies

•flux override experiments

structure of the southeast Atlantic and Pacific flux biases* differin the Pacific, dominated by the shortwave bias (too little cloud)

in the Atlantic, dominated by the turbulent fluxes=> oceanic contribution to the cooling must be more significant in the Atlantic

Pacific Atlantic

* compared to OAFLUX

this is in line with differences in the ocean climate

SST+surface currents SEA SST bias enhanced when Angola-Benguela frontis displaced too far south in CMIP5 models

Xu et al., 2014

examining initial error growth tendencies:

assesses fast error growth due to parameterizationdeficiencies before longer-time-scale & remote feedbacks

develop that make attribution more difficult

CFSv2-2011: solar-induced initial errorCanCM4+HadCM3: incorrect winds/precip => boreal-spring amplification

with all three models showing a sustaining oceanic component to the southeast Atlantic biases

most of bias development happened in 2-6 months

Toniazzo&Woolnough, 2013

initial error causes differed between the 3 models

NMME phase 2 models lend themselves to such analysis

• daily data on air-sea fluxes, precip, clouds, ocean+atmosphere state• forecasts made near beginning of each month; seasonal variation in

error growth provides insight into the mechanism (e.g., in Pacific awesterly wind bias leading to a basin-wide SST bias is most active in

boreal winter

Kirtman et al., 2013

7 models

example: NMME/CCSM4 ensemble mean SST error, initialized 0Z Dec 27 of years 1982-2009

averaged over first 5 days

averaged over days 361-365

warm bias already apparent after 5 days

another diagnostic technique: flux-override experiments

•impose observed fluxes/winds in a specific region•remove & allow full coupling to re-establish•evaluate for error growth

flux overrides have been done to assess remote impacts of the SEP/SEA biases

CAM4 (T31; CCSM4 Q !ux) CAM5 (T31; CESM Q !ux)CAM3 (T42)

SEA SURFACE TEMPERATURE BIAS

PRECIPITATION BIAS

K ; mm/day

adopted from Xu et al., 2014; Ed Schneider

experiment: internal heat flux reduced to zero in the southeast Pacific (5-30S, 70-135W) & redistributed elsewhere, in 3 models

in this case demonstrates a southward ITCZ shift in the Pacific (but not the Atlantic)

similar but weaker changes in CAM4/CAM5 reflects difference reference datasets

CAM4 (T31; CCSM4 Q !ux) CAM5 (T31; CESM Q !ux)CAM3 (T42)

SEA SURFACE TEMPERATURE BIAS

PRECIPITATION BIAS

K ; mm/day

similar experiment in the Atlantic demonstrates a southward shift in both Atlantic and Pacific basins

bias correction depends on how the bias is determined

initialization system dataset also ideal for model evaluation

model-independent datasets also useful

18

22

26Jan Dec

0.5

0.9

6

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Jan Dec

0.1

0.5

100oW 80oW 60oW 40oW 20oW 0o 20oE

40oS

30oS

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10oS

EQU

10oN

X

100oW 80oW 60oW 40oW 20oW 0o 20oE

40oS

30oS

20oS

10oS

EQU

10oN

cloud optical depth

17 17

20 20

24

longitude

latit

ude

Stratus

10S10W

100oW 80oW 60oW 40oW 20oW 0o 20oE

40oS

30oS

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EQU

10oN

o

September-mean TMI SST, MODIS cloud fraction, QuikScat coastal wind maxima climatology

surface flux products assessed at two ‘full-flux’ buoy sites

color contours - SST; filled grey contours - MODIS cloud fraction 0.6-0.9; filled color contours - coastal surface wind jets 7-9 m/s

85W, 20S

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

-200

-100

0

100

200

300flu

xes (

W m

-2)

10W, 10S

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

-200

-100

0

100

200

300

28.5

220

-50

-141

36.5

191

-42.5-112

buoySW

turbulent

LW

net

buoy

buoyCERES

buoyOAFLUX

thick black lines - buoy red+blue solid - CERES red+blue dashed - ISCCP green-OAFLUX

OAFLUX overestimates the amount of heat entering the ocean by almost 20 W m-2, because

of errors in ISCCP SW+LW surface products=> model bias is actually even worse

CERES SW worse, but LW better, than ISCCPJanuary 1, 2001-December 31, 2009

201

195

191

-43-39

-30

-112

-109

shortwave

2001 2002 2003 2004 2005 2006 2007 2008 2009

170

180

190

200

210

fluxe

s (W

m-2)

longwave

2001 2002 2003 2004 2005 2006 2007 2008 2009

-15-20

-25

-30

-35

-40

-45-50

fluxe

s (W

m-2)

turbulent

2001 2002 2003 2004 2005 2006 2007 2008 2009

-100

-105

-110

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-120

-125

-130

fluxe

s (W

m-2)

net

2001 2002 2003 2004 2005 2006 2007 2008 2009

10

20

30

40

50

60

70

fluxe

s (W

m-2) 56

52

36

STRATUS (85W, 20S)

buoy

buoyCERES

do OAFLUX/CERES fluxes capture observed interannual variability?

CERES

ISCCP

ISCCP

OAFLUX

buoy

so-so...SW yes, other components, no

low cloud errors assessed independently against MISR

low cloud errors occur independently of underlying

SST, pointing to parameterizations at fault

Southeast Atlantic

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

20

40

60

80

100

tota

l clo

ud a

mou

nt (%

)

Southeast Pacific

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

20

40

60

80

100

tota

l clo

ud a

mou

nt (%

)

Southeast Atlantic

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

20

40

60

80

100

120

cond

ense

d wa

ter p

ath

(g m

-2)

Southeast Pacific

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

20

40

60

80

100

120

cond

ense

d wa

ter p

ath

(g m

-2)

cesm-cam5

cesm-cam5

cesm-cam5

bnu-esm

access1-0

bcc-csm1-1-m

access1-3

cesm-cam5ccsm4

access1-0hadgem2-a

access1-0hadgem2-a

modis-lowisccp-totalsurface obs - total

amsre amsre

widely-varying seasonal cycles in CMIP5 low clouds vs observed

should correspond with differences in initial error growth causesKatinka Bellomo

does increasing resolution reduces the SST biases?

Small et al., 2015Kirtman et al., 2012

not necessarily: in CESM1, improvement in SEA (stronger upwelling) may be for wrong reason, how POP2 ocean model incorporates atmosphere winds

in CCSM4, a too diffuse thermocline is even more diffuse at higher resolution

the end