Mean, Variability, and the Most Predictable Patterns

in CFS over the Tropical Atlantic Ocean

Zeng-Zhen Hu1

Bohua Huang1,2

(Contributors: K. Pegion and B. Jha)(Acknowledgments: S. Saha, S. Yang, K. Campana)

1Center for Ocean-Land-Atmosphere StudiesCalverton, Maryland, USA

2Department of Atmospheric, Oceanic, and Earth Sciences, College of ScienceGeorge Mason University, Fairfax, Virginia, USACOLA & NCEP CTB Joint Seminar: NCEP, Dec. 10, 2007

COLA

Why study Atlantic climate of CFS?(1)Atlantic Ocean is very important to US and European

(even global) climate. Hurricanes, Heat Waves, Meridional Overturning Circulation (MOC), North Atlantic Oscillation.

(2)CFS is the operational model in US to do the seasonal climate prediction

(3)We are the Atlantic research group at COLA and supported by NOAA Atlantic project.

(4)There are less published works of NCEP CFS about

Atlantic than about Pacific.

Main topics:Part 1: What is the ability of CFS in simulating the mean climate in the tropical Atlantic? What is the possible role of the cloud-radiation-SST feedback in the biases?

Part 2: What is the ability of CFS in simulating the leading variability modes and the associated physical processes in the tropical Atlantic?

Part 3: What is the predictive skill and most predictable patterns of CFS in the tropical Atlantic Ocean? What is their connection with ENSO ?

Data & CFSCFS Free Run• AGCM: NCEP T62&L64 • OGCM: MOM3, 1/3o(10oS-10oN), 1o(higher

latitudes); 74oS-64oN, L40• Free run: Jan 1985-Dec 2036, conducted by

Kathy Pegion at COLA

CFS Hindcasts:• All calender months: Jan. 1981-Dec. 2003

• 15 predictions of 9 month length from lead month 0 to 8

• Oceanic ICs: 11th, 21st of month 0, 1st of month 1 (Saha et al. 2006; Huang and Hu 2006).

• The atmospheric IC: the NCEP reanalysis II (Kanamitsu et al., 2002). Same day & those within two days before and after

• Data available from http://nomad6.ncep.noaa.gov/cfs/monthly

• AGCM: NCEP T62&L64

• OGCM: MOM3, 1/3o(10oS-10oN), 1o(higher latitudes); 74oS-64oN, L40

Observations

• NCEP/NCAR reanalysis I (Kalnay et al. 1996); ERA40 (Uppala et al. 2005)

• Cloud coverage data by ISCCP on 2.5ox2.5o, Jul1983-Dec2004 (Rossow and Dueñas 2004); Corresponding radiation calculated by Zhang et al. (2004)

• The SST dataset is the extended reconstruction (ER-v2) on 2ox2o (Reynolds et al. 2002)

• NCEP Global Ocean Data Assimilation (GODAS) (Behringer et al. 1998; Behringer and Xue 2004)

Part 1: Mean Biases & the Role of Cloud-Radiation-SST Feedback

Hu, Z.-Z., B. Huang, and K. Pegion, 2008: Leading patterns of tropical Atlantic variability in a coupled GCM. Climate Dynamics, 30 (7-8), 703-726, DOI 10.1007/s00382-007-0318-x.

Hu, Z.-Z., B. Huang, and K. Pegion, 2007: Low cloud errors over the southeastern tropical Atlantic in the NCEP CFS and their association with lower-tropospheric stability and air-sea interaction. JGR-Atmo. 113, D12114, doi:10.1029/2007JD009514.

Seasonal Mean and Variance of SST:

(1) General pattern and

zonal gradient well simulated

(2) Warm bias

(3) Too small variance in

southeastern Ocean

CFS Simulation of Tropical Atlantic SST Climatology

Mean Field Equatorial SST (Gradient)

OB

SC

FS

DJF MAM

Annual

JJA SON

CFS

OBS

CFS

OBS

CFS

CFS

OBS

CFS

OBS

OBS

For majority of current CGCMs, it is still a challenge in simulating the

zonal SST gradient along

equatorial Atlantic

(Davey et al. 2002)

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

No

Flu

x C

orre

ctio

nW

ith

Flu

x C

orre

ctio

n

Observed and CCSM Simulated JJA SST (Deser et al. 2006: J. Climate, 19, 2451-2481)

Observed and CCSM3 Simulated MAM SST (Deser et al. 2006: J. Climate, 19, 2451-2481)

Compared with other coupled models (CCSM, MPI, ECMWF, MO, and others), CFS did very good job in the tropical Atlantic, particularly in simulating the equatorial SST gradient.

However, most of these CGCMs show a similar error pattern in the tropical Atlantic: warm bias in the southeastern Ocean.

CFS Systematic Biases

MAM

JJA

SON

DJF

Low Clouds Short-Wave SST

There are coherent biases in the southeastern Atlantic Ocean:

(1) Too few low clouds

(2) Too much short-wave radiation reaching the sea surface

(3) Warm biases

(CFS-ISCCP//ERv2)

Do the differences of cloud definition between the CFS and ISCCP affect the results of the

comparison?

The answer is “YES or NO”.

ISCCP: Low clouds is accounted only in regions without being covered

by higher clouds (underestimated)CFS: Low clouds=BL+low clouds (overestimated)

SO: CFS-ISCCP: underestimate the amplitudes of negative differences; overestimate the positive ones

ISCCP(Wear, 1999; Rossow& Dueñas 2004)

CFS(Xu& Randall 1996)

High clouds < 440 hPa < 350 hPa

Middle clouds 680-440 hPa 650-350 hPa

Low clouds Surface-680 hPa (Shallower)

~900-650 hPa (Deeper)

Boundary layer clouds

no Surface-900 hPa (about 10% of atmosphere mass)

Difference of the Cloud Definition

JJA Low Clouds & Vertical T Gradient

T700-T850

Low Clouds

T850-T925

CFS: Region A OBS: Region B

St. Helena Radiosonde Station

A B

Low Clouds & Vertical T Gradient

CFS Region A OBS Region B

T850-T925

T700-T850

Low Clouds

Inversion layerNO inversion layer

Quasi-inversion layerNO quasi-inversion layer

CFS Has More High Clouds than in ISCCPThe Atmosphere is more Stable in Real World than in CFS

BLCloud Amount

Region B

CFS ISCCP CFS

Region A

ISCCP

T70

0-T

850

T85

0-T

925

<5%

<30% <30%

<10%

Also low clouds may be

associated with different climate

modes:

CFS: Subtropical South Atlantic Mode

(SSA)

OBS: Southern Tropical Atlantic Mode (STA) or

Zonal Mode/Atlantic Nino

UV & SST Regression onto Low Clouds in region B

CFS OBS

There are also some errors in the seasonal cycle:

(1) Along Equator: 1 month delay of seasonal transitionOverestimated variability

(2) In the tropical South Atlantic:Without westward propagationSoutherly wind errors

Monthly-Annual Mean of SST& Wind Stress along Equator

CFS: one month delayed transitionsimilar wind stress

Related to the Differenceof African Monsoon ?

Overestimated: STA mode

Monthly-Annual Mean of SST& Wind Stress along 15˚S

Westward propagation

STA Mode

No propagation

Warm Bias:African Monsoon

&tropical transition?

Summary of Part 1: Mean & the Role of Cloud-

Radiation-SST Feedback in the Biases • The seasonal mean climate is well simulated; SST zonal gradient along the

equator is qualitatively correct in the CFS. However, there are warm SST biases and reduced variability in the southeastern Ocean. The annual cycle of SST along the equator is delayed by one month. Westward propagation of SST along 15oS is not seen in the CFS

• CFS: Deficit low-cloud and excessive SW radiation can cause the warm SST bias in the southeastern Atlantic. In return, the warm bias does not favor inversion layer and also more low-clouds. CFS can not correctly simulate the vertical inversion layer of the temperature and the formation of low cloud. Low-cloud formation: Observed: (T850-T925); CFS: (T700-T850)

• There are much more high clouds in CFS than in ISCCP. The atmosphere is more unstable in CFS than in the real world

• The variability of wind stress and heat flux is too small in the southeastern Atlantic. There are double ITCZ in MAM and DJF, and too much precipitation in the western tropical Atlantic

Part 2: Leading Variability Patterns and the Associated Physical Processes in the

Tropical Atlantic Ocean

Hu, Z.-Z., B. Huang, and K. Pegion, 2008: Leading patterns of tropical Atlantic variability in a coupled GCM. Climate Dynamics, 30 (7-8), 703-726, DOI 10.1007/s00382-007-0318-x.

CFS Well Simulates Leading ModesC

FS

ER

-v2

1st16%

2nd15%

NTA

3rd10%

SSASTA

3rd10%

2nd11%

1st12%

Power Spectrum of Leading Modes

STA

NTA

SSA

3.0, 1.5 0.75 years

2.3, 1.5, 1.05 years

4.0, 0.8 0.5 years

2.4, 1.1, 0.5 years

5.5, 1.45, 1.0, 0.65 years

5.0, 1.2 years

ER-v2 CFS

Seasonality (Variance) of the Leading Modes

ER-v2 CFS

STA

NTA

SSA

Links with ENSO

STA

NTA

SSA

CFS/ER-v2

not similar

similar

wrong

ENSO Leading ENSO Lagging

Associated downward net

HF

STA: thermodynamic

damping & dynamic processes (similar to

ENSO)

NTA: thermodynamic

processes &WES

SSA: thermodynamic

processes & WES & northwest shift

NCEP/NCAR CFS

NTA

SSA

STA

STA: Lead-Lag Correlation of ATL3 (3oS-3oN, 0-20oW)with SST, HC & Wind Stress Along Equator (10 YR HF)

CFSOBS:

ATL3 Leading

ATL3 Leading

ATL3 Lagging

ATL3 Lagging

HC

SST&UV

HC

SST&UV

NTA Patterns in MAM

MAM NTA: Total (NAO/AO)

OBS

CFS

OBS

CFS

Asymmetric: Tropical Pacific

SSA pattern in DJF

NW shift

DJF SSA: Total: AAO

OBS

CFS

OBS

CFS

Asymmetric: Tropical Pacific (Mo et al.)

SHIFT

Summary of Part 2: Leading Variability Patterns and the Associated Physical Processes

• The leading variability modes, their associated physical processes are reasonable well reproduced.

• STA associated anomalies are too strong in the CFS• SSA and NTA are associated with similar physical processes in

their hemispheres: zonal component (AO/NAO; AAO), zonal asymmetric variability (tropical Pacific Ocean)

• The shift may be associated with teleconnection difference

• The frequency feature of the leading variability modes and their links with ENSO, as well as the seasonality are similar/different between CFS and observations/reanalyses

• Overall, the simulation of NTA is better than that of STA and SSA, which may be due to the fact that the ENSO influence is (not) correctly simulated in tropical N. (S.) Atlantic in CFS

Part 3: What are Predictive Skills, Most Predictable Patterns and the

Influence of ENSO ?

Hu, Z.-Z. and B. Huang, 2007: The predictive skill and the most predictable pattern in the tropical Atlantic: The effect of ENSO. Mon. Wea. Rev., 135, 1786-1806.

SST Predictive Skill: Anomalous Corr. CoefficientLead 1 Lead 3 Lead 5 Lead 7

CFSHindcast

Persistence

CFSMinus

Persistence

*TB is the best and Dipole is the worst, NB and SB are in between*prediction with IC from JJA&SON is more accurate than that from MAM

JJA&SON

MAM

What are the most predictable patterns of SST, H200, and

precipitation in March?

Why choose March as target month?� High predictive skill in CFS (previous figure)

� Large variability (Nobre and Shukla 1996)

� Strong connection between TAV & ENSO (Tourre & White 2005)

� However, the general patterns are similar for different targeted months

Maximized signal-to-noise EOF is used to identify the most Maximized signal-to-noise EOF is used to identify the most predictable pattern predictable pattern (MSN PC: orthogonal; EOF: not orthogonal)(MSN PC: orthogonal; EOF: not orthogonal)

MSN EOF1 of SST: A basin wide warming or cooling

MSN EOF1 SST & ENSO (the observation & predictions; Nov. IC & 4 month lead)

CFS SST ER-v2 SST

MSN EOF1 of H200: A basin wide coherent

variation symmetric about the equator

Nino3.4& H200

MSN EOF1 of precipitation: Contrary variation of e. tropical Pacific and the tropical land

Cor

rela

tion

wit

h M

SN

EO

F1

CF

SO

BS

SST H200

Maximum Signal-to-Noise EOF1 (CFS Hindcast SST)M

arch

For

ecas

t: at

4-m

onth

s L

ead

12-8OS temperature regression onto MSN EOF1 of precipitation

Theory of Chiang &

Sobel (2002):

The existence of atmosphere

convection is crucial for

temperature variations to

propagate from troposphere to

surface

OBS

CFS

Warm Bias

X

Summary of Part 3: Predictive Skills, Most Predictable

Patterns and the Influence of ENSO

(1) There are reasonable predictive skills over the tropical Atlantic. The skill is higher in the tropical

North Atlantic than in the tropical South Atlantic, with IC in JJA&SON than in MAM

(2) The skills and the most predictable pattern are largely attributed to the influence of ENSO in the

tropical Atlantic Ocean

Summary• MEAN: The seasonal mean climate is well simulated. There are warm SST biases

and suppressed variability in the southeastern ocean. Deficit low-cloud and excessive SW radiation are associated with the warm SST bias in the southeastern Atlantic. In return, the warm biases do not favor inversion layer. CFS can not correctly simulate the vertical inversion layer of the temperature and the formation of low cloud. Low-cloud formation: Observed: (T850-T925); CFS: (T700-T850). There are much more high clouds in CFS than in ISCCP. The atmosphere in the tropical Atlantic is more stable in the real world than in CFS.

• MODES: The leading variability modes, their associated physical processes, as well as the seasonality are well reproduced. The frequency feature and the association with ENSO are partly simulated. STA associated anomalies are too strong in the CFS; SSA and NTA are associated with similar physical processes in their hemispheres: zonal component (AO/NAO; AAO), zonal asymmetric variability (tropical Pacific Ocean). The shift of SSA may be due to the teleconnection difference. Overall, N. Atlantic is better simulated than S. Atlantic

• PREDICTION: The predictive skill depends on region and initial condition month. The skill is higher in N. Atlantic than in S. Atlantic, and with IC in JJA&SON than in MAM. The predictive skill and most predictable pattern are associated with the influence of ENSO. The unrealistic part of the most predictable pattern of SST in the SE Atlantic is partly caused by the biases.

(1) Hu, Z.-Z., B. Huang, and K. Pegion, 2008: Leading patterns of tropical Atlantic variability in a coupled GCM. Climate Dynamics, 30 (7-8), 703-726, DOI 10.1007/s00382-007-0318-x.

(2) Hu, Z.-Z., B. Huang, and K. Pegion, 2008: Low-cloud errors over the southeastern tropical Atlantic Ocean in the NCEP CFS and the association with lower tropospheric instability and air-sea interaction. JGR-Atmos, 113, D12114, doi: 10.1029/2007JD009514.

(3) Hu, Z.-Z., B. Huang, and K. Pegion, 2009: Biases and the most predictable patterns in the NCEP CFS over the tropical Atlantic Ocean. The Atlantic Ocean: New Oceanographic Research, Nova Science Publishers, Inc., New York, USA.

(4) Hu, Z.-Z. and B. Huang, 2007: The predictive skill and the most predictable pattern in the tropical Atlantic: The effect of ENSO. Mon. Wea. Rev. 135, 1786-1806.

Comments or Ask reprints/PDF files, send e-mail to Hu@cola.iges.org

Related Publications

Future Work:

In cooperation with CFS experts, do some sensitive experiments by specifying low clouds in the model to explore the impact of low clouds on the CFS biases in the tropical oceans

What is the Maximized Signal-to-Noise EOF (I)• The MSN EOF is a method to derive patterns that optimize the signal-to-

noise ratio from all ensemble members. • This approach was developed by Allen and Smith (1997) and has been

used in Venzke et al. (1999), Sutton et al. (2000), Chang et al. (2000), and Huang (2004).

• An ensemble mean is supposedly including a forced and a random part, which may be attributed respectively to the prescribed external boundary conditions and the unpredictable internal noise. In our case, the forced part is associated with the predictable signals which show certain consistency among different members of the ensemble predictions.

• The leading MSN EOF mode is the one with the maximum ratio of the variance of the ensemble mean to the deviations among the ensemble members.

• In this work, we only analyze the leading MSN EOF mode, which is defined as the most predictable pattern and is significant at the 95% level using a F-test.

• Details of this method were documented in Allen and Smith (1997), Venzke et al. (1999), and Huang (2004).

MSN EOF (II)• Variable: Xensemble =Xforced+

Xrandom

• Covariance: Censemble =Cforced+ Crandom (If Xforced and Xrandom are temporally uncorrelated)

• Crandom = Cnoise/15 (Cnoise is the average noise covariance of the 15 ensemble members

• To find the eigenvector of Cforced, the key procedures is to eliminate the spatial covariance of the noise. Which is equivalent to a transformation F such that

FT CrandomF= I• The transformation guarantees

that FT CforcedF and FT CensembleF have identical eigenvalues.

• F is estimated from the first K weighted EOF patterns of the deviations X’i =Xi-Xensemble (i denotes the ith member within the ensemble).

• The matrix of eigenvectors (E) of FT CensembleF contains a set of optimal noise filters, which can be restored into physical space by É=FE.

• The optimal filter (the 1st column vector é= É) maximizes the ratio of the variances of the ensemble mean and within-ensemble deviations (Venzke et al., 1999).

• The optimally filtered time series of Xensemble (i.e., its projection onto é) gives the 1st MSN principal component (PC).

• In practice, one can simply first project Xensemble onto F to form the pre-whitened data in the noise EOF space and then conduct an SVD to get both E and all MSN PCs simultaneously (Huang 2004).

• The 1st MSN EOF pattern, which represents the dominant spatial response, is derived by projecting Xensemble onto the 1st MSN PC.

CFS Error Growth with Season

DEMETER Error Patterns (Nov. Mean)

Compared with other world famous coupled models (CCSM, MPI, ECMWF, MO, and so on), CFS did very good job in the tropical Atlantic, particularly in simulating the W-E SST gradient.

However, the error patterns in the tropical Atlantic are similar among these models.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Hu, Z.-Z. and B. Huang, 2007: The predictive skill and the most predictable pattern in the tropical Atlantic: The effect of ENSO. Mon. Wea. Rev. 135, 1786-1806.

Definition of clouds in ISCCP & CFS

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are needed to see this picture.

ISCCP (Weare 1999;

Rossow&Dueñas 2004)

CFS (Xu&

Randall 1996)

~900hPABL

~350hPA

Middle

~650hPA

Low

High

Inversion layer in CFS, Reanalyses & Radiosonde

Seasonal Mean

and STDV of

Wind Stress

(1) General

pattern and meridional STDV

gradient well simulated

(2) Small STDV along Equator

(3) Too small STDV in

southeastern

Mean & STDV of

Downward Surface

HF

(a)Following

solar activity

(b) Suppressed

variability in

southeastern

Mean & STDV

of Precip:

(1) Large biases in the

west

(2) Double ITCZ in MAM

& DJF

HC & Wind Stress regression on to STA (all seasons) (Signals too strong in CFS)OBS CFS

Clouds in ISCCP & CFSISCCP (Weare 1999;

Rossow&Dueñas

2004)

CFS (Xu&

Randall 1996)

BL (>900hPa)

Middle (350-

650hPa)

Low (650-

900hPa)

High

Middle (400-680

hPa)

High

Low (680-

surface)

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.Unstable &

convective cloudsStable & stratus clouds

ISCCP CFS

Connection of the Modes with SLP

Difference in the connection of SSA and SH

MSN EOF1 of H200 is also associated with ENSO

MSN EOF1 of precip is also associated with ENSO

CFS overestimates the contrary variation

Nino3.4 & observed P

Mean Low-Clouds and differences

ISCCPCFS CFS-ISCCP

MA

MJJ

AS

ON

DJF

shift

Mean SW (down-up) and differences

CFS ISCCP CFS-ISCCP

JJA

MA

MS

ON

DJF

Mean SST and differences

ERv2CFS CFS-ERv2

DJF

SO

NJJ

AM

AM

JJA SON DJFMAM

Possible Influence of the Cloud Definition

BL

Clo

uds/

CFS

BL

C+

LC

/CFS

CF

S(B

LC

+L

C)-

ISC

CP

(LC

)

Spatial Coherence between Low Clouds and Atmospheric Stratification

CF

S S

imul

atio

nO

BS

Low Cloud T(850-925) T(700-850)

Temperature profile in lower troposphere

Inverse layer is stronger in

observed than in CFS

CF

SO

BS

Low Cloud Amount

∆T

∆T(850-925) ∆T(700-850)

Scatterplot: Low Cloud vs. T Stratification

BL Clouds in CFS & T StratificationC

FS

ISC

CP

Low Cloud Amount

BL Cloud AmountBL Cloud Amount

∆T(850-925) ∆T(700-850)

∆T

Mixed Relation of Stratification and Middle Clouds

CFS ISCCP

Comparable Middle Cloud Amount in CFS & ISCCP (Region A; Region B)

T70

0-T

850

T85

0-T

925

T92

5-T

100

Cloud-Radiation-SST feedback in observation and model

(a) Observation: Warm SST in the southeastern Atlantic in early boreal spring suppress the formation of low-cloud. Lacking low-cloud results in excessive SW radiation reaching sea surface, the warm SST is further intensified and extend northwestward in boreal summer.

(b) CFS: Deficit low-cloud and excessive SW radiation may be the main reason resulting in the warm SST bias in the southeastern Atlantic. The cloud-radiation-SST feedback is not well simulated.

(c) CFS model can not correctly simulate the vertical inversion layer of the temperature and the formation of low cloud: Low-cloud formation: Observed: (T850-T925); CFS: (T700-T850)