Global Modelling of UTLS Ozone David Stevenson + many others Institute of Atmospheric and Environmental Science The University of Edinburgh Royal Met. Soc. 18 th October 2006, London Zoo

Very few observations of long-term trends in tropospheric ozone…

Surface ozone at Arosa, Switzerland Staehelin et al., 2001

NH mid-lats, mid-troposphere Logan et al., 1999; O 3 sonde data Even shorter time period of observations from the free atmosphere… Large interannual variability Regionally different trends; regionally different AQ measures

Models of tropospheric ozone  Limited observational evidence suggests that O 3T has increased substantially since pre-industrial times  No ice-core record of O 3 (too reactive)  Recent (last 30 years) trends show regional differences and are obscured by large interannual variations  We are dependent on models to produce a global picture of O 3T change (past and future)  Best we can do is produce models that closely match the limited set of observations of O 3 and its precursors, and hope they can reliably simulate the past/future  But it is difficult to know the true ‘ozone sensitivity’ – i.e.  O 3 /  emissions or  O 3 /  climate  However, we can assess the consistency (or otherwise) between models – i.e. intercomparisons

Trop. O 3 radiative forcing  O 3 (Stevenson et al., 1998) Zonal mean O 3 change Tropospheric O 3 radiative forcing Simulate pre-industrial and present-day O 3T, use the change to calculate a radiative forcing A large part of this is due to changes in UT O ° N: Cold, high tropopause, hot surface, clear skies

About ¼ of CO 2 forcing Warming from increases in GHGs  +3 W m -2

A commonly held view? “Nobody believes a modelling paper except the author; everybody believes an observational paper – except the author” One solution…

ACCENT model intercomparison for IPCC-AR4 26 different models perform same experiments –16 Europe: 4 UK (Edinburgh, Cambridge x2, Met. Office) 4 Germany (Hamburg x2, Mainz x2) 2 France (Paris x2) 2 Italy (Ispra, L’Aquila) 1 Switzerland (Lausanne) 1 Norway (Oslo) 1 Netherlands (KNMI) 1 Belgium (Brussels) –7 US –3 Japan Large ensemble reduces uncertainties, and allows them to be quantified

Intercomparison simulations Year 2000 – using EDGAR3.2 emissions –Fix biomass burning & natural emissions 3 Emissions scenarios for 2030 –‘Likely’: IIASA CLE (‘Current Legislation’) –‘High’: IPCC SRES A2 –‘Low’: IIASA MFR (‘Maximum technically Feasible Reductions’) Also assess climate feedbacks –expected surface warming of ~0.7K by 2030

Comparison of ensemble mean model with O 3 sonde measurements J F M A M J J A S O N D Observed ±1SD Model ±1SD 90-30°S 30°S-Eq30°N-Eq90-30°N UT 250 hPa MT 500 hPa LT 750 hPa Individual models in grey

E. Asian NOx emissions too low; Biomass burning emissions too high GOME NO 2 Tropospheric Column 2000 Mean of 3 retrieval methodsStd. Dev. of 3 retrieval methods Mean of 17 models Std. Dev. of 17 models

Models’ CO underestimates observations in Northern Hemisphere - Asian CO emissions too low

Where is modelled O 3T most uncertain? Zonal mean year 2000 O 3T

Year 2000 Ensemble mean of 26 models Annual Zonal Mean Annual Tropospheric Column

Year 2000 Inter-model standard deviation (%) Annual Zonal Mean Annual Tropospheric Column Models show large variations in the crucial tropical UT region

Annual Zonal Mean ΔO 3 / ppbv Annual Tropo- spheric Column ΔO 3 / DU ‘Likely’ IIASA CLE SRES B2 economy + Current AQ Legislation ‘Optimistic’ IIASA MFR SRES B2 economy + Maximum Feasible Reductions ‘Pessimistic’ IPCC SRES A2 High economic growth + Little AQ legislation Change in tropospheric O under 3 scenarios

Main candidates for inter-model differences in tropical UT O 3 Convection – Vertical mixing of both O 3 and its precursors – Lightning NOx production – In-cloud chemistry, washout – Distribution of water vapour Different treatments of emissions – Injection height of biomass burning – Biogenic VOCs and degradation chemistry – Lightning NOx (magnitude/profile) Stratosphere-troposphere exchange All of above also sensitive to climate change…

Effect of switching on convection in 2 models STOCHEM-HadAM3 (Doherty et al., 2005) MATCH-MPIC (Lawrence et al., 2003) Convection increases ozone everywhere Convection increases ozone in tropical MT Decreases elsewhere We don’t know what convection does to UT O 3 !

Convective mass fluxes differ markedly STOCHEM-HadAM3; Too strong/high? ERA-40 The truth? MATCH-MPIC Too weak/low? Or are differences in the chemical schemes the cause of the differences?

Impact of Climate Change on Ozone by 2030 (ensemble of 10 models) Mean Mean - 1SD Mean + 1SD Negative water vapour feedback Positive stratospheric influx feedback Positive and negative feedbacks – no clear consensus

Climate impact of aircraft NOx emissions ΔNO x NB negative scale expanded ΔO3ΔO3 ΔOH ΔCH 4 NB negative scale expanded Decay with e-folding timescale of 11.1 years Short-term warming from ozone Long-term cooling from methane Plus minor ozone long-term cooling UT crucial for correct quantification of aircraft NOx impacts…

Summary Models are essential to simulate past/future ozone (lack of observations) Comparison of models and observations suggest similar levels of uncertainty in both Uncertainties in modelled O 3 are large in the UT – translates directly into climate forcing Convection is poorly understood and a major source of uncertainty – not even clear if convection increases or decreases UT O 3 Likely effects of climate change (water vapour increases, STE changes) on O 3 even less well constrained Conclusion: plenty to do…