DLR contributions to WP 3.3.1 Katrin Obermaier, Volker Grewe, Rudolf Deckert

1. DLR contributions to WP 3.3.1Katrin Obermaier, Volker Grewe, Rudolf Deckert

2. WP 3.3.1 Impact of future climate change Investigate existing simluations with regard to impact of climate variability on traffic induced ozone changes. Impact of chemistry-climate feed-backs on traffic induced ozone changes. Each participating group performs 4 simulations (two may be covered in 3.3.1) A: Climate2000 Emissions2050 all emissions BR: Climate2000 Emissions2050 -5% for road traffic (recommended by WP 6) C: Climate2050 Emissions2050 all emissions DR: Climate2050 Emissions2050 -5% for road traffic DLR: investigates transient simulations 1960 to 2020 with and without climate change and the impact on traffic induced ozone changes Additionally: Calculation of the effect of lifetime changes on the transient behaviour of methane changes

3. WP 3.3.1 Impact of future climate change Future climate change simulations yet to be done Investigation of existing runs: a) Understanding the impact of changing atm. composition on chemistry and radiative forcing (Obermayer et al.) b) Understanding the impact of climate variability on regional ozone amounts (Deckert et al.) Appendix: calculation of methane changes, as suggested in Budapest (Volker Grewe)

4. WP 3.3.1 Existing DLR simulations Simulations with climate change ensemble of three simlulations 1960-1999 ensemble of four simulations 2000-2019 Simulation without climate change single simulation 1980-2019

5. WP 3.3.1 Existing DLR simulations In the simulations: tagging approach for O3 by Grewe (2004) tagged-O3 production rates calculated relatively to NOy concentrations four anthropogenic NOx emission sectors + NOx natural sources 2004-2019 growth rate of traffic NOx emissions varies between regions Obermaier et al.

6. WP 3.3.1 Changing atmospheric composition Simulations: evolution of emissions and tagged O3 matches tagging method works Lightning and air traffic: large tagged-O3 column despite weak NOx emissions Reason: emissions in upper troposphere result in high O3 production efficiency = high level of UV radiation = favourable background NOx concentrations Obermaier et al.

7. WP 3.3.1 Changing atmospheric composition Nonlinear dependence of O3 production efficiency on background NOx concentrations (Grooß et al. 1998) efficiency increases up to a NOx concentration of about 0.3 ppb efficiency decreases for higher NOx concentrations net O3 destruction for excessively low or high NOx concentrations Grooß et al. 1998

8. WP 3.3.1 Changing atmospheric composition Nonlinear dependence explains low production efficiency of industrial emissions 1960-1990 increase in production efficiency of air traffic emissions Obermaier et al. Grooß et al. 1998

9. WP 3.3.1 Changing atmospheric composition Further effect of nonlinear dependence: O3 production efficiencies from ground sources decrease during 1960-2019 due to increasing NOx background, in accordance with Lamarque et al. 2005 Exception for road traffic after 2005: emissions only increase in nonindustrial regions with low NOx background Obermaier et al.

10. WP 3.3.1 Changing atmospheric composition Investigation of climate change effects on tagged O3 not so easy Main reason: evolution of SSTs, CO2and CH4 different for simulations with and without climate change CH4 impacts on O3 chemistry via HO2 Work still in progress

11. WP 3.3.1 Changing atmospheric composition What is the radiative impact of each tagged-O3 species? - Consider radiative forcing Radiative forcing (RF) relates to surface temperature response (ΔTsurf) ΔTsurf = λ RF, λ: climate sensitivity parameter RF calculated for each tagged-O3 species defined by RF(all O3 s) minus RF(all O3 species except species of interest) radiation calculations similar to Stuber et al. (2001)

12. WP 3.3.1 Changing atmospheric composition Method works: similar evolution of RF and tagged O3 However: RF of an O3 molecule depends, among others, on surface temperature (hence on latitude) air temperature and pressure (hence on altitude) Obermaier et al.

13. WP 3.3.1 Changing atmospheric composition RF efficiency (RFE): the dependencies mentioned become apparent RFE increases towards the equator highest RFE for lightning (high altitude + low latitude) lowest RFE for road traffic and industry (low altitude + high latitude) RF efficiency decreases for many sectors since 1975: saturation effect due to increasing background O3 concentrations O3 long-wave absorption band saturates Obermaier et al.

14. WP 3.3.1 Impact of climate variability Analyse transient simulation with climate change: 1960-1999, 2000-2019 consider three industrialised regions: East Coast USA, Central Europe, East China average of nine grid points for each region tagged-O3 mass from surface to 500hPa

15. WP 3.3.1 Impact of climate variability Example of tagged-O3 time series: anthropogenic NOx emissions how investigate interannual variability? … multiple regression analysis accounts for annual cycles and trends relates interannual variability to other simulated quantities (e.g. wind velocity) model boundary conditions (e.g. SSTs) Deckert et al.

16. WP 3.3.1 Impact of climate variability Regression model adopted O3(t) = a * seascycle(t) +b1* trend(t) + b2* wind(t )+ b3* radiation(t) + b4* enso(t) +b5* qbo(t) + resid(t) predictors trend: linear, NOx emissions, ozone production efficiency wind: 700hPa horizontal-wind components and geopotential radiation: absorbed surface solar radiation enso: calculated from equatorial sea surface temperatures qbo: 50hPa equatorial zonal wind Seasonally dependent relationships bi=bi1+ bi2* cos(bi3+ t*π /6) + bi4* cos(bi5 + t*π /12) + ... Try to meet requirements for statistical inference resid(t): autoregressive model AR(n) seascycle(t) calculated prior to regression seasonal weighting same region as O3 (t) simulation boundary conditions

17. WP 3.3.1 Impact of climate variability Example of regression result: East China, industrial O3 95% confidence band for the regression line regression works Deckert et al.

18. WP 3.3.1 Impact of climate variability Example of regression result: East China, industrial O3 95% confidence band for the regression line regression works Without annual cycle more obvious: seasonally dependent trend predictors capture many prominent peaks, but not all Deckert et al.

19. WP 3.3.1 Impact of climate variability Example of regression result without annual cycle: East China and East Coast USA, industrial O3 regression explains a noticeable fraction of interannual variability for both cases variability greater for East China Deckert et al.

20. WP 3.3.1 Impact of climate variability Problem: Requirements for statistical inference not satisfied residuals autocorrelated, not normally distributed despite AR(n) stochastic model Therefore: confidence bands/intervals underestimated + statistical tests for predictor significance invalid Hence: adopt those predictors that reduce the residual sum of squares (SSQ) more strongly than same predictor with time lag other clearly unsuitable predictors Consider SSQ reduction for each predictor adopted = SSQ difference for regression with and without a given predictor measures interannual variability captured by this predictor problem: only rough measure and not strictly additive (due to correlated predictors?)

21. WP 3.3.1 Impact of climate variability East China: large SSQ reduction for industry, soils, lightning due to large interannual variability large interannual variability for soils Central Europe not well captured Deckert et al.

22. WP 3.3.1 Impact of climate variability Central Europe: dominance of wind due to transport? (prevailing westerlies and nearby Atlantik) Noticeable ship effect for the same reason? Deckert et al.

23. WP 3.3.1 Impact of climate variability Stratosphere: qbo and enso are known to affect the Brewer-Dobson circulation dominance of radiation due to dynamics and/or chemistry? Deckert et al.

24. WP 3.3.1 Impact of climate variability More thoughts needed to provide physical/chemical explanations Deckert et al.

25. Calculation of methane changes (Grewe&Stenke, 2008 ACP) Instantaneous change ‘Old’ method: CCMs/CTMs → Δτ(2000) → ΔCH4 (2000) → RF ‘New method’ CCMs/CTMs → Δτ(2000) → Δτ(t)=E× Δτ(2000) → ΔCH4 (t) → RF Change according to lifetime Unperturbed case Perturbed case Methane change: Input: Lifetime changes, emission evolution, background methane

26. Calculated of methane changes and RF changes: Inputs Percentage lifetime changes for 2000 : road traffic -1.69% air traffic -1.07% ship traffic -4.27%

27. Methane changes: Results RF changes are smoother Max. impact occurs later Methane RF lasts longer Timescale of changes: decade

28. Summary • ECHAM5/MESSY, resolution T42/L41, is being developed • Existing simulations Katrin’s findings show that • NOx emissions produce a realistic O3 response: dependence on altitude and background NOx • the associated RF and RFE is realistic: dependence on altitude, latitude, and background O3 • climate dependency of tagged O3: more work necessary My investigations of regional ozone show that • multiple regression explains a noticeable fraction of interannual variability • predictors: wind, radiation, qbo, enso • physical/chemical explanations: more work needed • statistical inference not allowed • Volker’s consideration of methane lifetime changes • smoother evolution of methane concentration and RF changes • later maximum impact of “ • longer lasting impact of “