Understanding the Tropical Biases in GCMs: Double-ITCZ, ENSO, MJO and Convectively Coupled Equatorial Waves

The tropical biases: One of the main bottlenecks for climate modeling

The major difficulties for understanding and alleviating these tropical biases 1.They all involve some forms of feedback, such as the ocean-atmosphere feedback and the wave-heating feedback, making it difficult to determine the real cause of the bias; 2.The biases need to be traced back to specific model characteristics, such as certain aspect of the physical parameterizations, in order to provide useful guidance on how to improve the model simulations.

Structure Analysis (Symptoms) Model Improvement (Treatments) How to attack the problem? Simulations and Predictions Feedback and Physical Relationship Analysis (Mechanisms) Difficult to try all combinations of schemes/parameters Possible missing physics in all existing schemes Difficult to understand the success of some schemes/ parameters

GCMs analyzed: 27 models including almost all the major GCMs used for predictions and projections 22 IPCC AR4 coupled GCMs (IPCC Fourth Assessment Report to be released in 2007; from PCMDI data archive) NCEP operational GFS and CFS (in collaboration with Wanqiu Wang of NCEP) ECMWF model (from DEMETER archive) NASA GMAO GEOS5 GCM currently under development (in collaboration with Siegfried Schubert, Max Suarez, Julio Bacmeister of NASA GMAO) GFDL next generation GCM currently under development (in collaboration with Leo Donner of GFDL) Seoul National University GCM (in collaboration with Myong-In Lee of NASA GMAO)

The double-ITCZ problem: Symptoms (1) Excessive (insufficient) precipitation over much of tropics (equatorial western Pacific); (2) Cold SST bias over much of tropics Shading: SST Contours: precipitation Double- ITCZ NCAR Obs GFDL From Lin (2006a)

The double-ITCZ problem: Mechanisms SST gradient - trade wind (Bjerknes) feedback (e.g. Bjerknes 1969, Neelin and Dijkstra 1995; Pierrehumbert 1995; Sun and Liu 1996; Jin 1996; Clement et al. 1996; Liu 1997; Cai 2003) SST - LHF feedback (e.g. Wallace 1992; Liu et al 1994; Zhang et al. 1995) SST - SWF feedback (e.g. Ramanathan and Collins 1991) From Lin (2006a) Neelin and Dijkstra (1995) showed that any excessive positive feedback (or insufficient negative feedback) tends to shift the whole system westward, leading to a double-ITCZ pattern. However, few previous studies have evaluated quantitatively the feedback parameters in GCMs. (1) Biases in AGCM’s climatology initiate the biases in the coupled runs; (2) Biases in ocean- atmosphere feedback parameters amplify or suppress the initial problems.

The double-ITCZ problem: Mechanisms (1) Excessive tropical precipitation in AGCMs leads to enhanced Walker circulation and surface flux cooling Annual mean along the equator (5N-5S) Excessive Precipitation Surface zonal wind stress Latent heat flux Surface downward shortwave flux Overly strong Excessive Insufficient

The double-ITCZ problem: Mechanisms (2) Overly positive ocean-atmosphere feedback parameters Linear regression for 5N-5S averaged monthly data Overly positive Bjerknes  x vs SST SST-LHF LHF vs SST SST-SWF SWF vs SST Overly positive Insufficiently negative Precip vs SST Qair vs SST Cld vs SST

The ENSO problem: Symptoms (1) Large scatter in ENSO variance (2) Too-short ENSO period in many models From Lin (2006b) Interannual variance of SST along the equator (5N-5S) Normalized spectrum of Nino3 SST CCSM3

Existing ENSO theories From Lin (2006c) (1) Slow coupled mode theory (Philander et al. 1984, Gill 1985, Hirst 1986, Neelin 1991, Jin and Neelin 1993, Wang and Weisberg 1996) (2) Delayer oscillator theory (Suarez and Schopf 1988, Battisti and Hirst 1989) (3) Advective-reflective oscillator theory (Picaut et al 1997) (4) Western Pacific oscillator theory (Weisberg and Wang 1997) (5) Recharge oscillator theory (Jin 1997a,b) (6) Stochastic forcing theory (McWilliams and Gent 1978, Lau 1985, Penland and Sardeshmukh 1995, Blanke et al. 1997, Kleeman and Moore 1997, Eckert and Latif 1997) Quasi-standing oscillation within Pacific basin triggered or forced by free oceanic waves

A new observation-based mechanism for ENSO: The coupled wave oscillator (Lin 2006c,d) ENSO amplitude and period are determined by circum-equatorial coupled equatorial waves, and their interactions with the off- equatorial Rossby waves

The ENSO Problem: Mechanism Incorrect representation of the coupled wave oscillator SSH xx xx CCSM3 ENSO Period=2.5 yrs MPI ENSO Period=4 yrs Too-fast phase speed Realistic phase speed

The MJO and CCEW problems: Symptoms Only half of the models have the waves, but usually with too weak variances and too fast phase speeds Obs GFDL NCAR

The MJO and CCEW problems: Symptoms The problem is especially severe for MJO, with very weak variance, no coherent eastward propagation, and no significant spectral peak Asian summer monsoon (Lin et al. 2006a,b,c, Lin 2007) All season North American monsoon West African monsoon Spectrum of precipitation at 0N85E CCSM3

The MJO and CCEW problems: Mechanisms Vertical heating profile Column-integrated diabatic heating has six major components (Mean state and higher- frequency modes affect the MJO through the nonlinear terms) In collaboration w/ Leo Donner Stratiform heating In collaboration w/ Myong-In Lee Moisture pre-conditioning In collaboration w/ Myong-In Lee Radiation feedback IPCC runs Air-sea coupling In collaboration w/ Wanqiu Wang Model resolution In collaboration w/ Ping Liu Shallow/midtop convection

The MJO and CCEW problems: Treatments Moisture trigger often significantly enhances the variances of CCEWs, and sometimes slows down the phase speeds Lin, Lee. Kim, Kang (2006d) No trigger Weak trigger Strong trigger No convection Effect on MJO is not monotonic

The MJO and CCEW problems: Treatments Moisture trigger significantly enhances the fraction of large-scale precipitation Lin, Lee, Kim, Kang (2006d) No trigger Weak trigger Strong trigger No convection

Structure Analysis (Symptoms) Model Improvement (Treatments) Recommendation: A model development strategy for alleviating the tropical biases Simulations and Predictions Feedback and Physical Relationship Analysis (Mechanisms) Understand the reasons of past successes/failures Save time and computer resources in testing parameters Know the directions of future improvements Difficult to try all combinations of schemes/parameters Possible missing physics in all existing schemes Difficult to understand the success of some schemes/ parameters