A new strategy, based on the adjustment of initialized simulations, to understand the origin of coupled climate models errors Benoît Vannière, Eric Guilyardi, Gurvan Madec, Thomas Tonniazzo, and Steve Woolnough 4th WGNE Workshop on CGCMs systematic errors Exeter - April 2013

CGCMs mean state biases in the tropical Pacific S 10S 5S 0 5N 10N 15N CMIP3 + CMIP5 SST biases Precipitation zonally averaged over the tropical band in CMIP3 CGCMs Cold tongue bias : - too strong zonal wind in the atmospheric component - amplification by coupled feedbacks (ex Bjerknes feedback) East Pacific warm bias : - coastal upwelling - stratus cloud cover The possible origins of SST biases are difficult to distinguish among all the sources because of bias compensation, feedback amplification and non-linearities. The first analysis shows that there is only small improvement of mean state systematic biases in the tropical Pacific between CMIP3 and CMIP5. Double ITCZ : - already present in AMIP simulation - influence of the EP warm bias - amplification by coupled feedbacks (ex, WES feedback) Bias Origins generally invoked Lin 2007 Clim. Dyn. mm/day 1/12

Using adjustment of initialized experiment Goal : to attribute the coupled biases to specific fields and processes Mean : the adjustment of initialized experiments close to observations Schematic of seasonal ensemble forecast errors Initialization of CGCMs in weather forecast mode have already been used to identify fast physics errors in transpose-AMIP protocols This study relates fast physics to long term and persistent errors Parameterization Fast physics errors Long term coupled errors transpose AMIP this study Atm. and Oce. component errors 2/12

Q1. What is the spatial pattern and the seasonality of SST biases in a control/historical experiment? Q2. What is the time scale of the bias adjustment (from monthly to decadal time scale), the chronology of errors appearance, the propagation of the bias? Q3. Is the source of the bias remote or local? Q4. Is the bias due to a direct effect of an atm/oce component bias or does it involve an amplification via coupled feedbacks? Q5. What is the atmospheric / oceanic field responsible for the bias? Protocol in five steps Strategy this systematic strategy that can be applied to any coupled model 3/12

Protocol Strategy Historical or control simulation Seasonal to decadal hindcasts Initialized close to observation and ensemble generation Regionally corrected simulation SST restoring / wind correction Ocean only experiments Associated experiments Ocean model Atm. model Ocean model obs Bulk formulation Low level atmospheric field prescribed Q1. spatial pattern / seasonality ? Q2. time scale / chronology / propagation? Q3. remote or local? Q4. direct effect / coupled feedbacks? Q5. atm./oce. field responsible for the bias? Atm. low level field Atm model 4/12

The strategy is applied to four tropical SST biases of IPSLCM5A- LR:  East Pacific warm bias  Cold tongue bias  Warm bias in convective region  Spurious spring upwelling Identifying the source of tropical SST biases in IPSLCM5A-LR Time Seasonal cycle at the equator Mean state 5/12

> Q2 : Bias time scale and propagation → EP warm bias a warm bias develops from the Peruvian coast and propagates westward at seasonal time scale it appears at all start dates and dominate during the upwelling season Init in Aug. ldtime 7 month Origin of the east Pacific warm bias Initialized from a SST nudged experiment 4 start dates 7-month integration 10 years Init in Aug. ldtime 7 month 6/12

SST nudging Wind response “turned off” > Q3 : Geographical origin → EP warm bias The amplification of the warm bias doesn’t involve dynamical coupling and Bjerknes feedback Advection is a key process in the westward propagation of the bias Origin of the east Pacific warm bias The warm bias is generated in the east Pacific and is propagated by oceanic advection. Initialized exp. with wind globally corrected Initialized exp. with SST regionally restored 7/12

> Q4 : Field responsible for the bias → EP warm bias Hindcasts SST errors after 3 months leadtime All forcings come from hindcasts Impact of hindcast 10m-wind Impact of hindcast SW heat flux Ocean only experiment forced by hindcasts fields give decomposition of SST biases contribution 10m-wind is likely to be the main contribution to the warm bias Origin of the east Pacific warm bias Ocean only 8/12

> Q5 : Degree of coupling → EP warm bias Hindcasts SST error after 3 months Hindcast forcing AMIP forcing 10m-wind impactSW heat flux impact All forcings come from hindcasts Origin of the east Pacific warm bias EP warm bias is a direct effect of the alongshore coastal winds biases Meridional coastal winds (m/s-1) 9/12

> Q2 : Bias time scale and propagation → Cold tongue bias > Q3 : Origin → Cold tongue bias The cold tongue bias takes 30 years to adjust When the cold bias in gyre region is corrected the cold tongue bias doesn’t form Initialised simulation restored toward observed SST in midlatitudes Hindcast Historical Origin of the cold tongue bias 10/12 20-yr leadtime

> Q4 : Field responsible for the bias → Cold tongue bias restoring toward hindcast midlatitudes cold SST bias during 30 yrs Origin of the cold tongue bias Experiment300m HC cooling trend at the equator after (J.m -2.mth -1 ) Hindcast Ocean-only with SST rest Ocean-only without SST rest The prescription of the midlatitude cold bias in the ocean-only experiment produces a cooling at the equator with a similar trend as in the hindcasts. One possible mechanism is the equatorward advection of the cold midlatitude cold bias via oceanic subtropical cells. Ocean-only 300m heat content 11/12 Hindcast drift STC pathway according to Izumo et al. 2002

 East Pacific warm bias Too weak alongshore component of winds at the Peruvian coast and propagation by advection  Cold tongue bias Equatorward propagation of the extratropical cold bias via the advection of STCs  Two warm bands on both side of the equator Too strong SW heat flux and too weak latent heat flux  Spring upwelling Coupled interaction with the East Pacific warm bias Conclusion We built a new strategy, attributing coupled model systematic biases to an error of its component. This strategy is based on the adjustment of coupled simulations initialized close to observation and simulations using different degrees of coupling. This method has shown success to attribute the origin of the SST biases in the tropical Pacific in IPSLCM5A-LR. Together with transpose AMIP, it forms a complete end-to-end strategy to attribute a coupled systematic bias to a model parameterization. Shared by CMIP models Model specific 12/12