Towards determining ‘reliable’ 21st century precipitation and temperature change signal from IPCC CMIP3 climate simulations Abha Sood Brett Mullan, Stephen Stuart & Sam Dean Climate Variability Group, NIWA, Wellington

Task: To quantify 21st century regional Precipitation and Temperature climate change signal for end users in agriculture, industry, government, … Consistency and predictability & Accuracy and Reliability Can this be achieved? Model Credibility

Some problems!! Start from the beginning! Change in December-February precipitation (in %), between and , under an A2 emission scenario

The Experiment Control ‘Warm’ SST forcing, DJF, (20cL) ‘Warm’ - Control -1.8°C cutoff in SST -1.7°C cutoff in SST

Results: MSLP Control‘Warm’ MSLP Changes, DJF, 21cL-20cL ‘Warm’ - Control More blocking - but not as much as Ctl

Consistency and predictability... is realized in models based on physical principles  all components of climate system are represented  tuning models is restricted  more advanced model include crucial chemical and biological processes Advances are achieved by iteratively improving representation of climate relevant processes in all components of climate models guided by advances in understanding of -climate dynamics, -feedback mechanisms, -representation of climate states for initialization, -drivers of climate change

Accuracy and reliability... is realized in models by  evaluating the ability of reconstructing pertinent features of recent climate and wide range of past climates  more information allow probabilistic approach which leads to decrease in uncertainty and ultimately to ‘narrowing’ of the confidence levels - advances achieved by  sample CMIP3/5 model subset based on model performance over the region of interest  removing known biases in forcing fields (eg SST,SIC) and in projected climate data Caution: Future climate may still stray beyond IPCC projection some estimates may be too conservative

Approach:  Multi-Model Ensemble (MME): - more information reduces uncertainties - identification and quantification of modes of internal variability - determine extremes  Model Evaluation and Bias Correction (BC): - ‘realistic’ input for climate impact studies and risk evaluation - ‘realistic’ input for climate impact studies and risk evaluation - helps reveal key model errors - helps reveal key model errors  Model Improvement: - remove obvious forcing errors (SST, Sea Ice) - atmosphere ocean coupling at the marginal sea ice zone, - marine boundary layer clouds, - atmospheric chemistry, ♦ ♦ ♦

based on prior knowledge of the system alsoValidationandVerification based on prior knowledge of the system alsoValidationandVerification incompleteinformation based on limitedensembleincompleteinformation limitedensemble

Mullan, Dean (2008) CMIP3 - IPCC 20cL: cN: cM: cL: upper and lower bounds, ‘middle of road’ of SRES emission scenarios multidecadal ocean variability and initial ocean/climate state perturbations of SST forcing Selection of CMIP3 models bias correction of SST forcing SRESA2A1BB1 UKMO-HadCM3xxx MPI-ECHAM5xxo GFDL-CM2.1xoo CSIRO-MK3.5xoo

Regional Climate Model Statistical Downscaling GCM 1 GCM 2 GCM 3 GCM x Bias-Correction & Downscaling Downstream Models: River Snow Glacier Climate Change Studies Climate Change Studies Climate Change Studies Regional Modelling & Physical Impacts Emission Scenarios Present, Future, Paleo

Multi-Model Ensemble (NIWA-MME) ModelA2- control A2- warm A2- revised A1B- control A1B- revised B1- control B1- revised HadCM3GCMxabmlxackexackgxabmfoxabmho UKMO RCMxabmgxackfxackhxabmmoxabmno ECHAM5GCMxackaxackio MPIRCMxackbxackqo CM2.1GCMxackmoo GFDLRCMxackooo MK3.5GCMxacknoo CSIRORCMxackpoo

A2 SST-BC forcing: revised, 20cL DJF 20cL: cL:

Bias correction increases variability and corrects mean Validation: (VCSN) DJFJJA reanalysis driven

A2 SST-BC forcing: revised, 21cL - 20cL DJF 20cL: cL:

BC HadCM3 SST forcing (A2) : rev, DJF 20cL: cN: cM: cL: cN - 20cL 21cL - 20cL 21cM - 20cL Summer Precipitation Change (in %)

BC HadCM3 SST forcing (A2) : rev, DJF 20cL: cN: cM: cL: cN - 20cL 21cL - 20cL 21cM - 20cL Bias corrected Summer Precipitation Change (in %)

A2 SST-BC forcing: rev – ctl, 21cL- 20cL DJF 20cL: cL:

Transient climate change signal: DJF Precipitation 20cL: cN: cM: cL: revised - control

Transient climate change signal: JJA Precipitation 20cL: cN: cM: cL: revised - control

Transient climate change signal: JJA Precipitation 20cL: cN: cM: cL: revised - control

DJF MSLP & Precipitation – 20cL (Observed SST) Control observed SST Revised observed SST

DJF MSLP & Precipitation – 20cL (HadCM3 SST) HadCM3 SST Bias Corrected HadCM3 SST

DJF Southern Annular Mode Index + HadISST + HadCM3 bias corrected HadCM3

MSLP and Precipitation Changes, DJF, 21cL-20cL (MPI)

MSLP and Precipitation Changes, DJF, 21cL-20cL (MIROCM)

Conclusions  Regional climate change over NZ domain is dominated by changes in large scale circulation pattern  Large climate change signal  21st century trend may be nonlinear  bias correction reduces signal  Trends in temperature increase over time whereas precipitation may fluctuate  Climate change involves not only changes in mean but also in variability  Reasonably ‘reliable’ climate change signal by bias correcting but is superimposed by large internal variability – low precision Are there preferred climate states, climate attractors … associated with climate change?

Future Work  Climate model development and improvements  Run more ensemble members; physical and initial state  Projection of the behaviour of climate modes into future considering multi-decadal variability  Improvements in bias correction methodology and initialization concerns Are there preferred climate states, climate attractors … associated with climate change? &

Questions?

Summary Sensitivity in projections of rainfall change - caused by ‘trivially small’ differences in prescribed SSTs - result in changes in stationary wave patterns & rainfall - now have a number of GCM/RCM runs that reproduce this instability Similar sensitivity present for initial condition ensembles as for parameter/physics ensembles Challenges for interpretation of NZ climate changes - a problem with ‘climate scenario paradigm’ - ICs don’t matter!? - alternatively, maybe 30-year future climatology is too short a period - nevertheless, there is a climate change ‘signal’ What now? - explore with further ensemble runs & further analysis - use a different atmosphere GCM Slide 18 of 18