Asha Vijayeta & Dietmar Dommenget

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Presentation transcript:

Asha Vijayeta & Dietmar Dommenget ENSO-dynamics in CMIP simulations in the framework of the recharge oscillator model. Asha Vijayeta & Dietmar Dommenget

Introduction ENSO statistics and dynamics in CMIP simulations exhibit wide spread uncertainties. Linear recharge oscillator concept is used to diagnose ENSO-dynamics. Questions: Do the models disagree with each other and have biases? Effect of model biases and model spread on the ENSO dynamics? Are the dynamical errors compensating and if so which ones? 

Linear Recharge Oscillator (ReOsc) Model T = NINO3 SST anomaly Burgers et al.(2005) Damping Effect of h on SST Stochastic forcing(random heating and wind stress) Stochastic forcing(random wind stress) Damping Effect of SST on h h = Mean equatorial Pacific thermocline depth anomaly ESTIMATION OF PARAMETERS

Data Used Dataset used: CMIP5 “Historical” and CMIP3 “20c3m” (1900-1999). Variables used: Thermocline depth anomaly in equatorial Pacific (20 0C isotherm, estimated from potential temperature) SST anomaly & net heat-flux anomaly in NINO3 region. Zonal windstress anomaly in NINO4 region. 1979–2014 HadISST1.1 Data for SST 1982-2002 BMRC 20degree isotherm depth 1979–2014 ERA Interim zonal surface wind stress 1984–2004 OAFlux surface fluxes. To calculate observed values

ReOsc model a good representation of ENSO in CMIP models? Standard deviation of SST Standard deviation of H Central period Mean correlation (4-8months lead) Observation(1982-2002) 1.0 6.8 3.0 0.54 Recharge oscillator toy model 0.99 8.9 3.5 0.65

Proof of Concept Proof 1: Standard deviation of SST anomaly Proof 2: Standard deviation of H anomaly

SST Equation parameters Model mean for both parameters within observed uncertainty range. Large model spread in a11 and a12.

Thermocline equation parameters Thermocline damping model mean outside of observed uncertainty range. Large model spread in a21 and a22. All CMIP5 models have stronger than observed thermocline damping.

Standard deviation of stochastic noises Model mean outside of observed uncertainty range. Large model spread in both noises. Majority of the CMIP models have underestimated noises.

Components of SST coupling parameters ReOsc toy model Separate SST damping parameter(a11) and effect of SST on thermocline(a21) into atmospheric and oceanic part Heat flux feedback Y15 model Bjerknes feedback

Atmospheric feedbacks: Bjerknes Feedback vs net Heatflux feedback Almost all CMIP models have underestimated windstress and net heatflux feedback

Ocean vs. atmospheric component of SST damping parameter(a11) Compensating atmospheric and oceanic parameter errors

Ocean vs. atmospheric component of thermocline effect on SST parameter(a21) Compensating atmospheric and oceanic parameter errors

Effect of the model spread on the ENSO dynamics. Effect of model biases on the ENSO dynamics. Effect of the model spread on the ENSO dynamics. Individual parameter set to model spread Individual parameter set to observation SST damping parameter model spread has maximum effect on stdv(SST) and stdv(H). Thermocline influence on SST (a12) parameter model spread has maximum effect on the central period and SST-H correlation. Thermocline damping parameter(a22) and the stochastic noises need to be corrected.

SUMMARY ENSO-dynamics and their diversity in CMIP ensemble well represented by ReOsc model. All CMIP models have biases, underestimated atmospheric feedbacks and stochastic noises, overestimated thermocline damping. No substantial improvement from CMIP3 to CMIP5. CMIP models get the right ENSO characteristics for wrong reasons. Compensating atmospheric and ocean parameter errors. Model spread of SST damping has maximum effect on stdv(SST) and stdv(h). Thermocline influence on SST (a12) model spread has maximum effect on the central period and correlation.

Thank you.

No. Model 1 ukmo_hadcm3 2 ncar_ccsm3_0 3 mri_cgcm2_3_2a 4 mpi_echam5 5 miroc3_2_hires 6 ipsl_cm4 7 iap_fgoals1_0_g 8 giss_model_e_r 9 giss_aom 10 gfdl_cm2_0 11 cnrm_cm3 12 cccma_cgcm3_1_t63 13 cccma_cgcm3_1

Frauen and Dommenget (2010) Yu et. al(2015) Y15 Model Frauen and Dommenget (2010) Yu et. al(2015) T(t)= NINO3 SST anomaly Ocean component of SST damping Effect of h on SST coupling parameter heatflux forcing heat capacity of mixed layer windstress forcing h Damping Ocean component of Effect of SST on h h(t) = Mean equatorial Pacific thermocline depth anomaly

ReOsc model fit Skill Proof 4: cross-correlation SST vs. thermocline depth anomaly(mean of 4-8 lead months) Proof 3: Central period of SST anomaly Power spectrum

SENSITIVITY TO PARAMETERS Individual parameter=observation Rest parameters=multi model mean

Influence of Model parameter spread on the statistical properties