Assessment of CMIP5 climate change projections and uncertainties over North America and Eastern Canada. Marko Markovic Ramon de Elia Anne Frigon

Transcription

1 Assessment of CMIP5 climate change projections and uncertainties over North America and Eastern Canada Marko Markovic Ramon de Elia Anne Frigon CMOS, Montreal 30/05/2012

2 Outline - Introduction to CMIP5 and what is new with respect to CMIP3. - Regional climate projections, spatial mean timeseries. - Regional climate projections, spatial maps and intermodel spreads over N. America. - Evaluation of uncertainties in global climate projections over N. America and Quebec. - Importance of pattern scaling techniques: a possibility to connect simulation results across scenarios. - Future work and conclusions.

3 Motivation for CMIP5 model assessment - Climate Model Intercomparison Project 5 (CMIP5) is a multi-model (and multiensemble) database of most modern climate models. - To explore CMIP5 data base for their future use in climate, impact and adaptation studies at Ouranos. - To Assess CMIP3 and CMIP5 differences - different forcing scenarios - latest generation climate models in CMIP5 and more ensemble members - To answer questions such as: - How the new CMIP5 experiment will impact the way we provide information? - Should we try to connect the results two experiments in order to have more information?

4 CMIP5 scenarios development Representative Concentration Pathways (RCPs) According to IPCC, RCPs should represent an envelope of already available climate scenarios with one baseline scenario (8.5Wm -2 ), two stabilisation scenarios (i.e. 4.5 Wm -2 et 6 Wm -2 ) and a mitigation scenario (i.e. 2.6 Wm -2 ) Why different «pathways»? Each RCP contain info about: concentration and emission of green house gasses, aerosols, land use and other socio-economic parameters. Radiative Forcing W/m2 Adapted from CNRM-GAME, Meteo France Overshoot Stabilization Mitigation

5 Motivation for the CMIP5 experiment Introduction of mitigation and stabilization scenarios (radiative forcing). Impact of Carbon Cycle to climate change (Earth System Models [ESMs]) for better understanding complex climate feedbacks. In this work we focus on RCP experiments, which are ensemble simulations future climate projections with different radiative forcing (e.g. 8.5W/m2, 6W/m2). We compare multimodel-ensembles against CMIP3 SRES scenarios. 14 cmip3 models: bccr_bcm2_0 cccma_cgcm3_1 cnrm_cm3 csiro_mk3_0 csiro_mk3_5 gfdl_cm2_0 gfdl_cm2_1 inmcm3_0 ipsl_cm4 miroc3_2_medr miub_echo_g mri_cgcm2_3_2a ncar_ccsm3_0 ukmo_hadcm3 9 cmip5 models: CCSM4 CSIRO-Mk3-6-0 GISS-E2-R HadGEM2-ES IPSL-CM5A-LR MIROC-ESM MIROC-ESM-CHEM MIROC5 NorESM1-M

6 Near surface temperature spatial mean: N. America and Quebec - Shaded colors are ± 1 stdev. range individual model annual mean. - CMIP5 RCPs cover more possible outcomes at the end of 21 st century compared to CMIP3. - RCP8.5 is more pessimistic than SRESA2

7 Near surface temperature spatial maps and spread CMIP5/3 - Winter season multi-model mean with respect to recent past ( ). CMIP3 CMIP5 SRESA2 RCP85 RCP60 SRESA1B RCP45 SRESB1 RCP26

8 Precipitation (mm/day) spatial maps and spread CMIP5/3 - Winter season multi-model mean with respect to recent past ( ). CMIP3 CMIP5 SRESA2 RCP85 RCP60 SRESA1B RCP45 SRESB1 RCP26

9 Fractional Uncertainty (Hawkins and Sutton BAMS, 2009) - Uncertainty in climate projections with GCMs: - internal variability - model choice - scenario choice - Fractional uncertainty is an estimate of the relative importance of each source of variability Calculation starts from global annual mean temperature or precipitation mean for each model and each scenario - Raw prediction for each model and each scenario - Smooth fit (4 th order) anomaly relative to the mean - Residual from the smooth fit (internal variability) for each model and scenario

10 Fractional Uncertainty (Hawkins and Sutton BAMS, 2009) Simplified notation of Hawkins and Sutton is used here. I have the real formulas on the backup slides. Scenario uncertainty Model uncertainty Internal uncertainty (calculated from decadal means supposing internal variability does not change much during one decade) Total uncertainty Fractional uncertainty, G(t) represents total mean for all models and scenarios (e,g, signal)

11 Fractional Uncertainty (calculated as in Hawkins and Sutton BAMS, 2009) CMIP3 N. America

12 Fractional Uncertainty (calculated as in Hawkins and Sutton BAMS, 2009) CMIP3 CMIP5 N. America Quebec

13 Pattern Scaling example (Cabré et al 2010) -Technique relies on the assumption that anthropogenic CC signal is linearly related with the global temperature change CSIRO (CMIP5): CC divided by the global mean temperature for a given period (linear scaling) Pr.[mm/day/K] Tas [K/K] WP = DT(local,t) DT(global,t) Warming pattern is invariant to the global mean temperature.

14 Conclusions and future work - CMIP3 and CMIP5 experiment are different in terms of radiative forcing and climate models. - Results of climate simulation suggest more climate change outcomes (scenario effect) at the end of 21 st century for temperature and precipitation. - Climate change patterns for mean showed similarly orientated gradients with some differences. - We found that differences between CMIP5 and CMIP3 are largely influenced by different radiative forcing and not by different (more modern) climate models. Not shown in this presentation. - Uncertainty sources show similar distributions for two experiment with exception of scenario source (more scenarios in CMIP5). - Pattern scaling is a potential technique to connect CMIP3 and CMIP5. - Test the robustness of the warming pattern to the model choice.

15 Thank you We thank Damon Matthews of University Concordia and Vivek Arora of CCCma Environment Canada on their valuable comments.