Coupled Climate Model SST Biases in the Eastern Tropical ...cpo.noaa.gov/sites/cpo/MAPP/Task...
Transcript of Coupled Climate Model SST Biases in the Eastern Tropical ...cpo.noaa.gov/sites/cpo/MAPP/Task...
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
10
14
Jan Dec
0.1
0.5
100oW 80oW 60oW 40oW 20oW 0o 20oE
40oS
30oS
20oS
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
20oS
10oS
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
-115
-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