the Most Predictable Patterns in CFS over the Tropical Atlantic Ocean 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 Studies Calverton, Maryland, USA 2Department of Atmospheric, Oceanic, and Earth Sciences, College of Science George Mason University, Fairfax, Virginia, USA COLA COLA & NCEP CTB Joint Seminar: NCEP, Dec. 10, 2007

Why study Atlantic climate of CFS? Atlantic Ocean is very important to US and European (even global) climate. Hurricanes, Heat Waves, Meridional Overturning Circulation (MOC), North Atlantic Oscillation. CFS is the operational model in US to do the seasonal climate prediction We are the Atlantic research group at COLA and supported by NOAA Atlantic project. 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 & CFS Observations CFS Hindcasts: CFS Free Run 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) CFS 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

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) CFS CFS OBS OBS OBS DJF MAM CFS OBS Annual CFS CFS CFS OBS JJA SON OBS

For majority of current CGCMs, it is still a challenge in simulating the zonal SST gradient along equatorial Atlantic (Davey et al. 2002) No Flux Correction With Flux Correction

Observed and CCSM Simulated JJA SST (Deser et al. 2006: J 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 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 (CFS-ISCCP//ERv2) MAM 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 JJA SON DJF Low Clouds Short-Wave SST

Do the differences of cloud definition between the CFS and ISCCP affect the results of the comparison? The answer is “YES or NO”.

Difference of the Cloud Definition 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) 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

JJA Low Clouds & Vertical T Gradient T700-T850 St. Helena Radiosonde Station Low Clouds B A T850-T925 CFS: Region A OBS: Region B

Low Clouds & Vertical T Gradient Quasi-inversion layer NO quasi-inversion layer T700-T850 Inversion layer NO inversion layer T850-T925 CFS Region A OBS Region B Low Clouds

Region A Region B <5% <10% T700-T850 BLCloud Amount <30% CFS Has More High Clouds than in ISCCP The Atmosphere is more Stable in Real World than in CFS Region A Region B <5% <10% T700-T850 BLCloud Amount <30% <30% T850-T925 CFS ISCCP CFS ISCCP

UV & SST Regression onto Low Clouds in region B 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 transition Overestimated variability (2) In the tropical South Atlantic: Without westward propagation Southerly wind errors

Monthly-Annual Mean of SST& Wind Stress along Equator CFS: one month delayed transition similar wind stress Related to the Difference of African Monsoon ? Overestimated: STA mode

Monthly-Annual Mean of SST& Wind Stress along 15˚S No propagation Westward propagation STA Mode 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 Modes STA NTA SSA ER-v2 1st 16% 2nd 15% 3rd 10% CFS 3rd 10% 2nd 11% 1st 12%

Power Spectrum of Leading Modes 2.3, 1.5, 1.05 years 3.0, 1.5 0.75 years STA 4.0, 0.8 0.5 years 2.4, 1.1, 0.5 years NTA 5.5, 1.45, 1.0, 0.65 years 5.0, 1.2 years SSA ER-v2 CFS

Seasonality (Variance) of the Leading Modes STA NTA SSA ER-v2 CFS

Links with ENSO CFS/ER-v2 not similar STA similar NTA wrong SSA 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 STA NTA SSA NCEP/NCAR CFS

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

NTA Patterns in MAM MAM NTA: Total (NAO/AO) OBS CFS Asymmetric: Tropical Pacific OBS CFS

SSA pattern in DJF OBS CFS OBS SHIFT CFS DJF SSA: Total: AAO Asymmetric: Tropical Pacific (Mo et al.) NW shift OBS SHIFT CFS

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. Coefficient Lead 1 Lead 3 Lead 5 Lead 7 CFS Hindcast Persistence CFS Minus Persistence

TB is the best and Dipole is the worst, NB and SB are in between *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 predictable pattern (MSN PC: orthogonal; EOF: not orthogonal)

MSN EOF1 of SST: A basin wide warming or cooling

MSN EOF1 SST & ENSO (the observation & predictions; Nov MSN EOF1 SST & ENSO (the observation & predictions; Nov. IC & 4 month lead) ER-v2 SST CFS 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 MSN EOF1 of precipitation: Contrary variation of e. tropical Pacific and the tropical land

Maximum Signal-to-Noise EOF1 (CFS Hindcast SST) March Forecast: at 4-months Lead OBS Correlation with MSN EOF1 CFS SST H200

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 X CFS Warm Bias 12-8OS temperature regression onto MSN EOF1 of precipitation

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.

Related Publications 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, 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. 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. 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

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 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. 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.

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.

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 ISCCP (Weare 1999; Rossow&Dueñas 2004) High CFS (Xu& Randall 1996) ~350hPA Middle ~650hPA Low ~900hPA BL

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 & CFS ISCCP CFS ISCCP (Weare 1999; Rossow&Dueñas 2004) CFS (Xu& Randall 1996) ISCCP CFS High High Middle (350-650hPa) Middle (400-680 hPa) Unstable & convective clouds Stable & stratus clouds Low (650-900hPa) Low (680-surface) BL (>900hPa)

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 MAM JJA shift SON DJF CFS ISCCP CFS-ISCCP

Mean SW (down-up) and differences MAM JJA SON DJF CFS ISCCP CFS-ISCCP

Mean SST and differences MAM JJA SON DJF CFS ERv2 CFS-ERv2

Possible Influence of the Cloud Definition MAM JJA SON DJF BL Clouds/CFS BLC+LC/CFS CFS(BLC+LC)-ISCCP (LC)

Spatial Coherence between Low Clouds and Atmospheric Stratification CFS Simulation OBS Low Cloud T(850-925) T(700-850)

Temperature profile in lower troposphere Inverse layer is stronger in observed than in CFS

Scatterplot: Low Cloud vs. T Stratification CFS ∆T OBS Low Cloud Amount

BL Clouds in CFS & T Stratification BL Cloud Amount ∆T ISCCP Low Cloud Amount

Mixed Relation of Stratification and Middle Clouds Comparable Middle Cloud Amount in CFS & ISCCP (Region A; Region B) T700-T850 T850-T925 T925-T100 CFS ISCCP

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)

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