The Evolution of Lead-lag ENSO-Indian Monsoon Relationship in GCM Experiments Center for Ocean-Land-Atmosphere studies George Mason University Emilia K. Jin and James L. Kinter III 4th International CLIVAR Climate of the 20th Century Workshop 13-15th March 2007, Hadley Centre for Climate Change, Exeter, UK

International Climate of the Twentieth Century Project Background and Objectives  Characterize climate variability and predictability of the last ~130 years through analysis of both observational data and general circulation models, in particular the period since  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.  Objectives of this study Focusing on ENSO-monsoon relationship, To diagnose the problem in CGCM due to systematic error in ENSO characteristics To suggest “pacemaker” as an alternative solution to improve the predictability of coupled system To assess the advantages and shortcomings in “pacemaker” results “Pacemaker” Experiments

The Evolution of Lead-lag ENSO-Monsoon Relationship in GCM Experiments  Influence of CGCM’s Systematic Error On ENSO-Monsoon Predictability  Improvement through “Pacemaker”  Advantage vs. Shortcoming in “Pacemaker”  Influence of model deficiency in the long run on forecast skill  Systematic errors in ENSO characteristics and forced response  Simulation of Climatology  ENSO forced response  ENSO-monsoon relationship  Evolution of lead-lag ENSO-Indian monsoon relationship  Plausible sources of shortcomings

 Retrospective forecast Model and Experimental Design Lead month RunPeriod Initial Condition AtmOcean NCEP CFS (23 years) NCEP/DOE AMIP R2 GODAS (Behringer et al. 2005) Model AGCMOGCM NCEP CFS NCEP GFS (operational Global Forecast System) T62 L64 MOM o X1/3 to 1 o 40 Levels  The NCEP Climate Forecast System (CFS)  Free long run (Courtesy of K. Pegion)  52-year simulation  Analyzing last 50 years (50-yr climatology is subtracted)  52-year simulation  Analyzing last 50 years (50-yr climatology is subtracted)

Lead-lag Correlation (JJMS Extended IMR, NINO3.4)  Extended MR (Indian Monsoon Rainfall Index): Total rainfall over o E, 5-25 o N during JJAS  For retrospective forecasts, reconstructed data with respect to lead time (monthly forecast composite) is used.  Green solid line denotes 95% significance level 1 st month 2 nd month 3 rd month 4 th month 5 th month 6 th month 7 th month Observed

Lead-lag Correlation (JJMS Extended IMR, NINO3.4)  Purple line: CFS free long run during 52 years 1 st month 2 nd month 3 rd month 4 th month 5 th month 6 th month 7 th month Observed Long run

Temporal correlation of PC timeseries with observation Pattern correlation of eigenvector with free long run Forecast lead month Correlation Influence of Systematic Error on Forecast Skill in CFS SEOF 1 st mode of SST Anomalies (ENSO mode) Obs. Free long run 1 st month 9 th month 5 th month With respect to the increase of lead month, forecast ENSO mode is much similar to that of long run, while far from the observed feature.

(a) Observation (b) CFS CGCM (52 year long run) ENSO Characteristics in CFS CGCM NINO3.4 Index during

Calendar Month SST anomalies Calendar Month Longitude Observation CFS long run (a) Observation (b) CFS long run (c) NINO3 region ENSO Characteristics in CFS CGCM Standard Deviation of SST Anomalies over Tropics

Regression of DJF NINO3.4 Index to SST anomalies (a) Observation (b) CFS long run ENSO Characteristics in CFS CGCM  In CGCM, ENSO SST anomalies show westward penetration with narrow band comparing to the observed.

Overestimated ENSO forcing in CFS CGCM Correlation bet. SST and Latent heat flux GSSTF ver. 2 Surface latent heat flux during (a) Observation (b) CFS long run  In CGCM, the relationship between SST and latent head flux in the western Pacific shows the excessive ocean forcing atmosphere. It may be related with too coherent oceanic response, since the space and time scales of atmospheric internal dynamics (stochastic forcing) are too coherent (Kirtman and Wu, 2006) Positive: Ocean forces the Atmosphere Negative: Atmosphere forces the Ocean

Previous JJA Following JJA DJF ENSO-Monsoon in Observation Lead-Lag Regressed Map by NINO3.4 Index

ENSO-Monsoon in CFS long run Lead-Lag Regressed Map by NINO3.4 Index Previous JJAFollowing JJADJF

The Evolution of Lead-lag ENSO-Monsoon Relationship in GCM Experiments  Influence of CGCM’s Systematic Error On ENSO-Monsoon Predictability  Improvement through “Pacemaker”  Advantage vs. Shortcoming in “Pacemaker”  Influence of model deficiency in the long run on forecast skill  Systematic errors in ENSO characteristics and forced response  Simulation of Climatology  ENSO forced response  ENSO-monsoon relationship  Evolution of lead-lag ENSO-Indian monsoon relationship  Plausible sources of shortcomings

In this study, the deep tropical eastern Pacific where coupled ocean- atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. “Pacemaker” Experimental Design Pacemaker region Outside the pacemaker region To handle model drift To merge the pacemaker and non-pacemaker regions Other forcings (sea ice, greenhouse gases, etc)

In this study, the deep tropical eastern Pacific where coupled ocean- atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. “Pacemaker” Experimental Design Pacemaker region 165E-290E, 10S-10N Shaded region denotes that dynamic term prevails over thermodynamic term in 20-yr NCEP CFS simulation

In this study, the deep tropical eastern Pacific where coupled ocean- atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. “Pacemaker” Experimental Design To merge the pacemaker and non-pacemaker regions No blending Obs. with blending without beldning Global mean SST except pace-maker region

In this study, the deep tropical eastern Pacific where coupled ocean- atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. “Pacemaker” Experimental Design Outside the pacemaker region Slab ocean mixed-layer -γT clim AGCM (NCEP GFS) Heat fluxesBlended SST Prescribed mixed-layer depth: Seasonally varying 1/3 Smoothed Zonal mean Levitus climatology Except pacemaker region, zonal mean mixed-layer depth of each basin - Pacific, Atlantic, Indian Ocean - has not much differences

In this study, the deep tropical eastern Pacific where coupled ocean- atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. “Pacemaker” Experimental Design Outside the pacemaker region Slab ocean mixed-layer AGCM (NCEP GFS) Heat fluxesBlended SST To handle model drift Weak damping of 15W/m 2 /K with relaxation without relaxation Simulated minus observed global mean SST difference -γT clim

In this study, the deep tropical eastern Pacific where coupled ocean- atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST. “Pacemaker” Experimental Design Pacemaker region Outside the pacemaker region To handle model drift To merge the pacemaker and non-pacemaker regions 165E-290E, 10S-10N Weak damping of 15W/m 2 /K No blending Slab ocean mixed-layer Other forcings (sea ice, greenhouse gases, etc) Climatological sea ice Constant CO 2

Experiment ExtratropicsTropicsMixed-layer depthPeriodMember Pacemaker Slab ocean mixed- layer with weak damping of 15W/m 2 /K Observed interannual SST Zonal mean monthly Levitus climatology except Eastern Pacific (reduced as 1/3) 55 yr ( ) 4 Control Climatological SST Observed interannual SST none 55 yr ( ) 4 CGCM MOM352 yr1 Model and Experimental Design No air-sea interaction Local air-sea interaction Fully coupled system SST heat flux, wind stress, fresh water flux heat flux AGCM ( , 4runs) Mixed layer model + AGCM ( , 4runs) CGCM (52 yrs) -γT clim Atmosphere (GFS T62L64) Atmosphere (GFS T62L64) Atmosphere (GFS T62L64) Ocean (Full dynamics) Observed SST Slab ocean (No dynamics and advection) Observed SST Climatology SST

JJA Climatology of 55 years ( ) SSTRainfall Obs. Pace Control CGCM  Contour denotes difference from observation. Perfect SST Climatology Perfect SST Climatology in the Pacemaker

(a) Observation (b) CFS long run (c) PACE ENSO forcing in Experiments Correlation bet. SST and Latent heat flux

Correlation Map bet. DJF NINO3.4 and SST Previous JJAFollowing JJA DJF Obs. Pace Control CGCM  Green box denotes pacemaker region.

Regressed Map by DJF NINO3.4 Index Previous JJAFollowing JJA DJF Obs. Pace Control CGCM Rainfall and 850 hPa wind  Green box denotes pacemaker region.

Climatology of IMRStandard Dev. of IMR Indian Monsoon Rainfall Simulations Climatology and Variability  Extended MR (Indian Monsoon Rainfall Index): Total rainfall over o E, 5-25 o N during JJAS

Lead-lag Correlation (JJMS Extended IMR, NINO3.4) 26 years during Green line denotes 95% significant level Observation CFS long run PACE CONTROL

Lead-lag Correlation (JJMS Extended IMR, NINO3.4) 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. Observation CFS long run PACE CONTROL

The Evolution of Lead-lag ENSO-Monsoon Relationship in GCM Experiments  Influence of CGCM’s Systematic Error On ENSO-Monsoon Predictability  Improvement through “Pacemaker”  Advantage vs. Shortcoming in “Pacemaker”  Influence of model deficiency in the long run on forecast skill  Systematic errors in ENSO characteristics and forced response  Simulation of Climatology  ENSO forced response  ENSO-monsoon relationship  Evolution of lead-lag ENSO-Indian monsoon relationship  Plausible sources of shortcomings

Change of Lead-lag Correlation 20-year Moving Window during (HadSST and CMAP) Lag correlation with respect to 20-yr moving window during 55 years

Change of DJF Simultaneous Correlation 20-year Moving Window during Ensemble spread of Pace Ensemble spread of Control Observation PACE CONTROL Shading denotes ensemble spread among 4 members. Note that correlation for ensemble mean is not the average of correlations for four members.

Indian Monsoon Rainfall Simulations Year-to-year variability  3-year running mean of interannual IMR index Observation PACE CONTROL PeriodCor. Pacemaker Control

Contour denotes differences of regressed value: minus Change of Regressed Pattern of NINO34 Index vs Shading denotes regressed value during HadSSTPACE

Change of Regressed Pattern of NINO34 Index vs Contour denotes differences of regressed value: minus Shading denotes regressed value during HadSSTPACE

Plausible Sources for Recent Shortcoming Absence of influence of anthropogenic forcings such as CO2 increase etc.  Insufficient projection of climate change Inadequacies from “pacemaker” experimental design 1.Role of low-frequency ocean dynamics 2.Associated atmosphere-ocean coupled mode 3.Decadal change of monsoon forcing to alter the El Nino  To supplement this point of view, sensitivity experiments associated with decadal change are needed  For example, change of Q flux with respect to decades can be considered Imperfect model  Wrong atmosphere response The characteristics of recent decadal change is not found in “pacemaker”

Annual Mean Global Temperature Observation PACE  Even though interannual variability is well matched with observed, “pacemaker cannot mimic the global warming trend.

Summary and Conclusion  In CFS CGCM, lead-lag ENSO-monsoon relationship is weak and insignificant due to systematic errors of ENSO and its response.  In CGCM forecasts, systematic errors of couple models is major factor in limiting predictability after the influence of initial condition fades away with respect to lead month: mean error, phase shift, different amplitude, and wrong seasonal cycle, etc.  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 of the 20th century.  Surprisingly, “pacemaker” mimics the realistic ENSO-monsoon relationship compared to other experiments including control (POGA-type) and coupled (CGCM).  However, the recent change of ENSO-monsoon relationship is missed in “pacemaker” associated with absence of global warming signal.  To find out the cause of this discrepancy, supplementary “pacemaker” experiments can be performed based on this shortcoming.

Local SSTNINO 34 Obs. Pace Control CGCM Partial Correlation Correlation bet. DJF NINO3.4 and Previous JJA Rainfall

Calendar Month of IMR N34 LeadN34 LagN34 LeadN34 Lag Lead-Lag Relationship Monthly IMR and NINO3.4 Index

Latent Heat Flux - SST Correlation Conceptual Model Observational Estimates Based on NASA GFSST2 Data Control Coupled Model

Western Pacific Problem Hypothesis: Atmospheric Internal Dynamics (Stochastic Forcing) is Occurring on Space and Time Scales that are Too Coherent  Too Coherent Oceanic Response  Excessive Ocean Forcing Atmosphere  Test: Add White Noise to Latent Heat Flux

Contemporaneous Latent Heat Flux - SST Correlation Control Coupled Model Increased “Randomness” Coupled Model Add White Noise in Space and Time to Latent Heat Flux in the Western Pacific (Ad-Hoc) Observational Estimates Based on NASA GFSST2 Data

Based on What we Know About Atmosphere Forcing Ocean and Ocean Forcing Atmosphere, How Can we Fix the CGCM Problem in the Central Pacific?

(a) Local SST (b) NINO 3.4 (c) Ratio of COA of (a)/(b) (b) NINO 3.4 NINO3.4 local SST Partial Correlation (Edward, 1979)  Calculate the partial effect of local SST and NINO 3.4 SST on the precipitation anomalies by removing relationship between local and NINO3.4 SST  COA = COVARIANCE [A,B] / σA (Kang et al JMSJ)  To measure an actual magnitude of quantity of B related to the reference data A  Red denotes the effect of local SST is larger than that of remote forcing JJA Partial influence: Local SST vs. Remote forcing