Predictability of ENSO-Monsoon Relationship in NCEP CFS Emilia K. Jin Center for Ocean-Land-Atmosphere studies (COLA) George Mason University (GMU) Hi! I’m Emilia Jin in COLA and George Mason University. Today, I’d like to talk about Predictability of ENSO-Monsoon Relationship in NCEP CFS. They are my colleagues in COLA. Thanks to J. Kinter, B. Kirtman, J. Shukla, and B. Wang* COLA/GMU, *IPRC/Univ. of Hawaii NOAA 32th Climate Diagnostics and Prediction Workshop (CDPW), 22-26 Oct COAPS/FSU, Tallahassee, FL

Contents ENSO-monsoon relationship in NCEP/CFS forecasts The role of perfect ocean forcing in coupled systems: CGCM vs. “Pacemaker” The role of air-sea interaction on ENSO-monsoon relationship Shortcoming in “Pacemaker”: Decadal change of ENSO-Indian monsoon relationship In this study we will show you the several characteristics of simulation of ENSO-monsoon relationship by comparing different experiments.

JJA Forecast Skill of Rainfall with respect to Lead Month Temporal correlation with respect to lead month 1st month 3rd month 5th month 9th month Area mean (60-140E, 30S-30N) For retrospective forecasts, reconstructed data with respect to lead time (monthly forecast composite) is used. Correlation This is forecast skill of monsoon rainfall with respect to lead month. In CFS forecasts, the monsoon predictability decrease with respect to lead month. Forecast lead month Retrospective forecast (Courtesy of NCEP) Lead month Run Period NCEP CFS 9 15 1981-2003 (23 years)

Relationship between NINO3.4 and Monsoon Indices Lead-lag correlation with respect to lead month WNPSM index Extended IMR index N34 lead N34 lag N34 lead N34 lag COR(OBS,CFS) Observation 1st month forecast 8th month forecast WNPSMI EIMR If we focus on the predictability of ENSO-monsoon relationship, monsoon index can be good indication. This is the lead-lag correlation of western North pacific summer monsoon index and extended Indian monsoon index. Black is for observation, red is for 1st month forecast, and blue for 8th month forecast. At first forecast month, the relationship is quite similar to observed. However, it show moderate skill in 8th month for both cases. 1st month 8th month 0.48 0.20 0.48 0.12 Western North Pacific Summer Monsoon Index (Wang and Fan, 1999) WNPSMI : U850(5ºN–15ºN, 100ºE–130ºE) minus U850(20ºN–30ºN, 110ºE–140ºE) Extended Indian Monsoon Rainfall Index (Wu and Kirtman 2004) EIMR: Rainfall (5ºN–25ºN, 60ºE–100ºE) Green line denotes 95% significant level.

Relationship between NINO3.4 and Monsoon Indices WNPSM index Extended IMR index Observation 1st month forecast 8th month forecast CFS long run If we compare this result with index from CFS long run simulation, purple line her, some feature shown in the 8th forecast looks similar to it. It means model’s systematic error has a role to degrading the forecast skill with respect to lead month. NCEP/CFS 52-year long run (Courtesy of Kathy Pegion)

Regressed field of 1st SEOF of 850 hPa zonal wind Observation 1 Shading: 500 hPa vertical pressure velocity Contour: 850 hPa winds 1 Shading: Rainfall (CMAP and PREC/L) Contour: SST COR (PC, NINO3.4) = 0.85 To summarize the model’s monsoon property associated with ENSO, we used the seasonal EOF analysis. SEOF is developed to describe the seasonally evolving anomalies throughout a full calendar year From summer to next spring, a covariance matrix was constructed using four consecutive seasonal mean anomalies for each year. In this case, we use 850 hap zonal wind as primary variable to obtain the PC timeseries and Principal Component time series shown here. The simultaneous correlation coefficient between PC1 and the ENSO index- the NINO 3.4 SST anomaly reaches 0.85, indicating that the seasonally evolving patterns of the leading mode concur with ENSO’s turnabout from a warming to a cooling phase. From the summer of Year 0, referred to as JJA(0), to the spring of the following year, called MAM(1), a covariance matrix was constructed using four consecutive seasonal mean anomalies for each year. SEOF (Wang and An 2005) of 850 hPa zonal wind over 40E-160E, 40S-40N High-pass filter of eight years The seasonally evolving patterns of the leading mode concur with ENSO’s turnabout from a warming to a cooling phase (Wang et al. 2007).

Regressed field of 1st SEOF of 850 hPa zonal wind Observation 1 Shading: 500 hPa vertical pressure velocity Contour: 850 hPa winds 1 Shading: Rainfall (CMAP and PREC/L) Contour: SST COR (1st PC timeseries of SEOF, N34) 1) Reversal of the monsoon anomalies from JJA(0) to JJA(1) 2) Two prominent off-equator anticyclonic anomalies: SIO and WNP The lead-lag relationship between PC time series and NINO3.4 index is shown like this. If we look at the circulation, there are two dominant off-equator anticyclonic anomalies over the Southern Indian Ocean and Western North Pacific for the preceding and following summer of ENSO. In patterns, significant rainfall anomalies in various regions of the A-AM are associated with the evolution of these two anoamlous anticyclones. Correlation N34 lead N34 lag

Impact of the Model Systematic Errors on Forecasts Pattern Cor. of EOF Eigenvector Patternl correlation of eigenvector with observation Pattern correlation of eigenvector with free long run Correlation With respect to the increase of lead month, forecast monsoon mode associated with ENSO is much similar to that of long run, while far from the observed feature. Forecast lead month COR (1st PC timeseries of SEOF, N34) In CFS forecasts, this mode is also well simulated in first month forecast while show moderate skill for 8th lead month. This feature also suggest that the influence of model deficiency on forecast drop the skill in the case of monsoon associated with ENSO. Pattern Correlation with respect to lead month show this feature more clearly. With respect to lead month, forecast monsoon mode is much similar to that of free long run, while far from the observed feature. Correlation Observation 1st month forecast 8th month forecast CFS long run N34 lead N34 lag

In CFS coupled GCM, what is responsible to drop the predictability of ENSO – monsoon relationship? Ocean forcing? Atmospheric response? Air-sea interaction? ….. In CFS coupled GCM, what is responsible to drop the predictability of ENSO – monsoon relationship? It must be the imperfect ocean forcing, and imperfect atmospheric response, and so on.

“Pacemaker” Experiments The challenge is to design numerical experiments that reproduce the important aspects of this air-sea coupling while maintaining the flexibility to attempt to simulate the observed climate of the 20th century. “Pacemaker”: tropical Pacific SST is prescribed from observations, but coupled air-sea feedbacks are maintained in the other ocean basins (e.g. Lau and Nath, 2003). Anecdotal evidence indicates that pacemaker experiments reproduce the timing of the forced response to El Niño and the Southern Oscillation (ENSO), but also much of the co-variability that is missing when global SST is prescribed. In this study, we use NCEP/GFS T62 L64 AGCM mainly. To investigate this problem, we performed the ideal experiment called “pacemaker” and compared with coupled model. In our experiment, tropical Pacific SST is prescribed from observation as perfect SST, but coupled air-sea feedbacks are maintained in the other ocean basins by using slab ocean.

“Pacemaker” Experimental Design In this study, the deep tropical eastern Pacific where coupled ocean-atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. Pacemaker region 165E-290E, 10S-10N To merge the pacemaker and non-pacemaker regions No blending Outside the pacemaker region Slab ocean mixed-layer This is simple description of To handle model drift Weak damping of 15W/m2/K to observed climatology Mixed-layer depth Zonal mean monthly Levitus climatology

Model and Experimental Design Pacemaker Atmosphere (GFS T62L64) CGCM Atmosphere (GFS T62L64) Local air-sea interaction Fully coupled system Ocean (Full dynamics) SST SST heat flux, wind stress, fresh water flux Observed SST heat flux -γTclim Slab ocean (No dynamics and advection) Mixed layer model + AGCM (1950-2004, 4runs) CGCM (52 yrs) This is simple summary of experiments. We performed 4 member ensemble simulation during 55 years and compared with CFS long run Experiment Extratropics Tropics Mixed-layer depth Period Member Pacemaker Slab ocean mixed-layer with weak damping of 15W/m2/K Observed interannual SST Zonal mean monthly Levitus climatology except Eastern Pacific (reduced as 1/3) 55 yr (1950-2004) 4 CGCM (CFS) MOM3 52 yr 1

Lead-lag correlation with Nino3.4 Index WNPSMI EIMR 1st PC timeseries of SEOF ISMI This is lead-lag correlation between monsoon indices and Nino3.4 index. Yellow band denotes the ensemble spread of 4 pacemaker simulations. In general, pacemaker shows some accordance with observed relationship suggesting the atmospheric response to the perfect eastern Pacific SST is reasonable in this model. While, CFS long run shows different results. For western North Pacific summer monsoon, it shows the delay of positive correlation associated with its long ENSO life cycle. While, in the case of indian monsoon indices, it shows the insignificant relationship with ENSO. So the imperfect ocean forcing in CFS must be responsible for this discrepancies. Observation PACE CFS N34 lead N34 lag N34 lead N34 lag ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE) Ensemble spread of 4 members of Pacemaker exp.

ENSO Characteristics in CFS CGCM NINO3.4 Index during 1950-2005 (a) Observation (b) CFS CGCM (52 year long run) For the delay of relationship, it must be associated with long life cycle of ENSO and associated summer peak in this model.

Lead-lag correlation with Nino3.4 Index WNPSMI EIMR 1st PC timeseries of SEOF ISMI This is lead-lag correlation between monsoon indices and Nino3.4 index. Yellow band denotes the ensemble spread of 4 pacemaker simulations. In general, pacemaker shows some accordance with observed relationship suggesting the atmospheric response to the perfect eastern Pacific SST is reasonable in this model. While, CFS long run shows different results. For western North Pacific summer monsoon, it shows the delay of positive correlation associated with its long ENSO life cycle. While, in the case of indian monsoon indices, it shows the insignificant relationship with ENSO. So the imperfect ocean forcing in CFS must be responsible for this discrepancies. Observation PACE CFS N34 lead N34 lag N34 lead N34 lag ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE) Ensemble spread of 4 members of Pacemaker exp.

ENSO Characteristics in CFS CGCM Regression of DJF NINO3.4 Index to SST anomalies (a) Observation (b) CFS long run And the spatial pattern of ENSO SST anomalies showing the westward penetration with narrow band comparing to the observed and so sst structure ove the Indian Ocean is somewhat different from oberved. It must be associated with the Indian monsoon. In CGCM, ENSO SST anomalies show westward penetration with narrow band comparing to the observed.

JJA Regression map of 1st SEOF of 850 hPa zonal wind Total field Difference from Obs. 850 hPa zonal wind and rainfall 850 hPa zonal wind and SST Obs. Pace Pace-Obs. This is summertime regression map of 1st SEOF for observation and simulations. Shading is for rainfall and contour is the zonal wind anomalies. In left panel, the westerlies associated with SST forcing is well related with rainfall pattern. In CFS, cooling over Indian Ocean due to westward shift of SST pattern is related with easterly anomalies over here and so dry region occurs southeastward comparing to observation. In pacemaker, it has warm bias over maritime continent but it has relatively small influence on Indian continent. CFS CFS-Obs. Contour: zonal wind Shading: rainfall Contour: zonal wind Shading: SST

Model and Experimental Design Control Atmosphere (GFS T62L64) Pacemaker Atmosphere (GFS T62L64) No air-sea interaction Local air-sea interaction SST Observed SST Observed SST -γTclim heat flux Climatology SST Slab ocean (No dynamics and advection) AGCM (1950-2004, 4runs) Mixed layer model + AGCM (1950-2004, 4runs) To investigate the role of air-sea interaction, we also performed the control run having pacemaker with climatological SST outside of tropics. We performed 4 member ensemble simulation during 55 years. Experiment Extratropics Tropics Mixed-layer depth Period Member Pacemaker Slab ocean mixed-layer with weak damping of 15W/m2/K Observed interannual SST Zonal mean monthly Levitus climatology except Eastern Pacific (reduced as 1/3) 55 yr (1950-2004) 4 Control Climatological SST none

Leag-lag correlation with Nino3.4 Index WNPSMI EIMR 1st PC timeseries of SEOF ISMI This is lead-lag correlation between monsoon indices and Nino3.4 index. Blue line is for control run and blue band is for ensemble spread. Because of perfect SST over the tropics, the SEOF PC has identical relationship with observed. It has also reasonable relationship of wnpsmi, but the indian monsoon relatinship is very weak, Observation PACE Control N34 lead N34 lag N34 lead N34 lag ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE) Ensemble spread of 4 members of Pacemaker exp.

JJA Regression map of 1st SEOF of 850 hPa zonal wind Total field Difference from Obs. 850 hPa zonal wind and rainfall 850 hPa zonal wind and SST Obs Pace Pace-Obs. Because this control run doesn’t have SST anomalies outside the Pacific, regressed pattern of difference shows the opposite sign of observed SST anomalies. Without air-sea interaction and indian ocean dipole pattern over here, the wind pattern altered toward westward. As a result, the rainfall over Indian continent show reversed sign. In pacemaker, warm bias over the maritime continent also weaken the Indian dipole pattern over the Indian Ocean, but the intensity is relatively small than control run. Ctl-Obs. Ctl Contour: zonal wind Shading: rainfall Contour: zonal wind Shading: SST

1st S-EOF modes: Observation 1956-76 1977-2004 Even though pacemaker show better relationship comparing to other experiment, there is a clear shortcoming in terms of decadal change and it can give a key to understand the ENSO-monsoon relationship. This is observed decadal change of monsoon variability for two period. Most dominant change of two period is strength of WNPM and weakening of SIOM associated with change of ENSO.

Lead-lag Correlation between NINO3.4 and Monsoon indices 56-76 77-04 Decadal change of ENSO-Monsoon relationship based on SEOF analysis (Wang et al. 2007) Remote El Niño/La Niña forcing is the major factor that affects A-AM variability. The mismatch between NINO3.4 SST and the evolution of the two major A-AM circulation anomalies suggests that El Niño cannot solely force these anomalies. 2. The monsoon-warm pool ocean interaction is also regards as a cause (a positive feedback between moist atmospheric Rossby waves and the underlying SST dipole anomalies) The enhanced ENSO variability in the recent period has increased the strength of the monsoon-warm pool interaction and the Indian Ocean dipole SST anomalies, which has strengthened the summer westerly monsoon across South Asia, thus weakening the negative linkage between the Indian summer monsoon rainfall and the eastern Pacific SST anomaly.  However, in pacemaker, the strengthen of the Indian Ocean dipole SST anomalies is not shown due to fixed mixed-layer depth and SST climatology. Pacemaker mimic the enhancement of wnpm but fail to reproduce the weakening of Indian monsoon. Considering the cause of decadal change based on SEOF analysis, even though pacemaker has a change of remote forcing, the absence of mechanism to generate the change of warm-ocean interaction with fixed mixed-layer depth and climatology through damping While, the reproduction of change of WNPMI suggest that the decadal change of WNPMI is strongly related with change of remote forcing. It also supported by previous results showing reasonable relationship in control run without air-sea interaction.

Change of Lead-lag Correlation (Extended IMR, NINO3.4) 20-year Moving Window during 1950-2004 OBS (IMR) (HadSST and CMAP) Lag correlation with respect to 20-yr moving window during 55 years This is the evolution of lead-lag relationship between indian monsoon rainfall and nino3.4 index with 20-year moving window. Accordance with previous plot, observation show weakening of relationship for recent years but pacemaker show strong relationship all the time.

Summary In CFS CGCM, the predictability of lead-lag ENSO-monsoon relationship drops with respect to lead month due to systematic errors of ENSO and its response. To improve the predictability, “pacemaker” experiment is designed and conducted to reproduce the important aspects of air-sea coupling while maintaining the flexibility to attempt to simulate the observed climate. Surprisingly, “pacemaker” mimics the realistic ENSO-monsoon relationship compared to other experiments including control and coupled (CGCM). However, the recent change of ENSO-Indian monsoon relationship is missed in “pacemaker”, possibly associated with the Indian Ocean dynamics, while the decadal change of western North Pacific summer monsoon is well related with that of eastern tropical Pacific SST anomalies. To find out the cause of this discrepancy, supplementary “pacemaker” experiments can be performed based on this shortcoming. systematic errors of couple models is major factor of limiting predictability (mean error, phase error, amplitude error, seasonal cycle) investigate the model capability in long simulation is the one key to understand the behavior of forecast error. more quantitative examination to distinguish the influence of initial condition and model capability will be followed.

Thank You ! Emilia K. Jin kjin@cola.iges.org

Change of DJF Simultaneous Correlation 20-year Moving Window during 1950-2004 Focusing on the simultaneous correlation, it is more clear. Black is for observed imr, red for pacemaker, and blue for control run. Till 70’s, the relationship in pacemaker is quite identical with observation. However, it cannot reproduce the weakening of relationship after climate shift near 1976 Observation PACE CONTROL Ensemble spread of Pace Ensemble spread of Control Shading denotes ensemble spread among 4 members. Note that correlation for ensemble mean is not the average of correlations for four members.