Motivation – The Arctic

1. Influences on Late-Spring and Summertime Tropospheric Ozone in Western Siberia and the Russian Arctic T. Thorp1, S. R. Arnold1, R. J. Pope1,2, D. V. Spracklen1, L. Conibear1,3, C. Knote4, M. Arshinov5, B. Belan5, E. Asmi6, A. Skorokhod7. 1Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK. 2National Centre for Earth Observation, University of Leeds, Leeds, LS2 9JT, UK. 3Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training (CDT) in Bioenergy, University of Leeds, Leeds, LS2 9JT, UK. 4Meteorological Institute, Ludwig- Maximilians-University Munich, Theresienstr. 37, 80333 Munich, Germany. 5V.E Zuev Institute of Atmospheric Optics, Russian Academy of Sciences, Siberian Branch, Tomsk, Russia. 6Finnish Meteorological Institute, Climate and Global Change, Helsinki, Finland. 7A.M Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia.

2. Motivation – The Arctic • Arctic warming disproportionately relative to mid-latitudes • Predominantly controlled by well mixed greenhouse gases (CO2, CH4) • Warming from Short Lived Climate Pollutants (SLCPs) such as tropospheric ozone (O3) and aerosols known to contribute (Sand et al., 2016)

3. Motivation – The Arctic (Overland et al., 2016)

4. Motivation – Western Siberia • Array of potential precursor sources, both anthropogenic and natural • Acts as key ”gateway” for low-level export of European pollution moving poleward in winter and spring contributing to formation of Arctic Haze (Stohl, 2006). • Several large urban regions, with uncertain emissions and a severe lack of in-situ observations • Extensive vegetation likely acts as a substantial dry deposition sink for exported pollution (Hirdman et al., 2010; Engvall et al., 2012).

5. Spring / Summer Transport AMAP, 2015 • Spring: • Low level import of Eurasian pollution. Main pathway across Siberia into Russian Arctic. • Inefficient removal of pollution leading to Springtime peak (Arctic Haze) • Summer: • Removal of pollutants greater during summer (wet + dry deposition) • Wind direction change, and greater vertical mixing in Arctic vertical column

6. WRF-Chem v3.7.1 • 2 control simulations from April – September 2011 using EDGAR-HTAP v2.2 and ECLIPSE v5a anthropogenic emissions

7. Ozone Monitoring Instrument (OMI) AM JJ AS • Tropospheric column NO2 data from DOMINO product onboard NASA aura polar-orbiting satellite. • Increased data uncertainty above 65°N and data coverage between October – March poor, so restricted to Spring/Summer.

8. Tropospheric Column NO2 Mean Bias (WRF-Chem – OMI) AS JJ AM EH2 ECL • Low bias column NO2 seen in the troposphere. • High bias observed over major cities especially in April – May • Hatching shows significant difference between observations and model values.

9. Tropospheric Column NO2 Mean Bias (WRF-Chem – OMI) JJ AM AS EH2 Slope: 0.45 R: 0.70 FMB: -0.12 ECL Slope: 0.53 R: 0.76 FMB: -0.45 EH2 Slope: 0.14 R: 0.34 FMB: -0.24 ECL Slope: 0.43 R: 0.46 FMB: -0.53 EH2 Slope: 0.46 R: 0.56 FMB: 0.61 ECL Slope: 0.64 R: 0.60 FMB: 0.15 • ECLIPSE v5a emissions an improvement over EDGAR-HTAP v2.2 for region, but a continued low bias.

10. Tropospheric Column NO2 Mean Bias – Major City Focus (WRF-Chem – OMI) EH2 • ECLIPSE v5a emissions an improvement over EDGAR-HTAP v2.2 for cities within region (>100,000 population). • A continued low bias across all cities, especially in June/July and August/September • Norilsk is located 69.3 °N, so less confidence in satellite retrieval ECL

11. Surface O3 Comparison EH2 ECL Tomsk Slope: 0.51 R: 0.53 FMB: 0.17 ZOTTO Slope: 0.65 R: 0.64 FMB: 0.06 Tiksi Slope: 0.93 R: 0.44 FMB: 0.76 Tomsk Slope: 0.50 R: 0.61 FMB: 0.23 ZOTTO Slope: 0.65 R: 0.64 FMB: 0.02 Tiksi Slope: 0.94 R: 0.46 FMB: 0.78 • Similar patterns at remote sites (ZOTTO & Tiksi) as not impacted by anthropogenic emissions. Large positive bias in model at Tiksi in both emissions. • Most change seen at Tomsk, which is closest to large anthropogenic sources.

12. NO2 Source Contribution AS JJ AM Control Fires off Anthro off

13. O3 Source Contribution AS JJ AM Control Fires off Anthro off

14. O3 Dry Deposition AS JJ AM Control Fires off Anthro off

15. O3 Dry Deposition • Relative to control run, ozone deposition decreases by 16.5% in anthro_off simulation, and by 2.2% in fires_off simulation • Summer (JJA) greatest % change for both sensitivity simulations

16. O3 Dry Deposition to Surface Types • Forest land surface greatest ozone sink, for both total domain and above 60 °N. • Ozone deposition most sensitive to anthro_off simulation. Most significant difference seen in Forests in June (32% change). • Suggests that during this month, 1/3 of ozone deposition is from anthropogenic sourced ozone in domain. • Similar (30% change) for Forest cover above 60 69.3 °N

17. Conclusions • Through satellite comparison, large spatial evaluation of Siberian tropospheric column NO2 in spring and summer 2011. • WRF-Chem negative bias observed in tropospheric column NO2 when compared to OMI. • Major Siberian cities show varying model NO2 biases when compared with OMI, with an overestimation during spring. • ECLIPSE anthropogenic emissions provide improved NO2 results for domain compared to EDGAR-HTAP • Ozone is less sensitive to the different emission inventories. • Ozone most sensitive to anthropogenic emissions, particularly during summer. • Largest ozone sink is to Siberian forest vegetation – approximately 1/3 of this deposition from anthropogenic sources within the domain.

18. Extra Slides

20. Land Surface Type – Modified_IGBP_MODIS_NOAH scheme 14.2% 22.3% 0.1% 10.8% 0.2% 13.1% 10.3% 29.0%