1. Major volcanic eruptions modelling with SOCOLv3-AER
T. Sukhodolov and the SOCOL group

2. Content
Background
Modelling issues
Some results

3. Direct and indirect effects on climate
Mechanisms
(Broenniman and Kraemer, 2016)
Direct and indirect effects on climate

4. Societal impact
“Volcanic eruption represents some of the most climatically important and societally disruptive short-term events in human history.”
(Volcanoes and Climate, PAGES, vol23, 2015 )
(Broenniman and Kraemer, 2016)

5. Assessing of climate impacts needs proper aerosol distribution
Modelling issues
(Broenniman and Kraemer, 2016)
(Kremser et al., 2016)
(Myhre et al. 2013)
Two ways to go:
Use prescribed aerosols for GCMs from observations or microphysical models
Directly include sulphur chemistry and aerosol microphysics to global models
Assessing of climate impacts needs proper aerosol distribution

6. Chemistry-Climate-Aerosol model SOCOLv3-AER (Sheng et al., 2015a)
Our models
Chemistry-Climate-Aerosol model SOCOLv3-AER (Sheng et al., 2015a)
Aerosol microphysics (AER)
40 bins (0.39nm – 3.2 μm)
Chemistry-Climate model SOCOLv3 (Stenke et al., 2013)

7. VolMIP CMIP, CCMI Modelling activities
Past: Ice core data and aerosol model
Future: Effects of future volcanoes are omitted!
(Zanchettin et al. 2016)

8. "Volcanic eruptions and their impact on future climate"
New project
"Volcanic eruptions and their impact on future climate"
Upgrade the model
Design future scenarios
Estimate future climate and economic effects

9. "Volcanic eruptions and their impact on future climate"
New project
"Volcanic eruptions and their impact on future climate"
SOCOLv3-AER  SOCOLv4-AER
Upgrade the model
Design future scenarios
Estimate future climate and economic effects
Better and faster representation of the solar irradiance
14 SW bands (instead of 6), faster (k-correlated)
Much better representation of the middle atmosphere
Improved gravity wave drag
Resolved QBO
Higher resolution and better scalability
T63L47 tuned and tested (compared to T42L39 before)
MPIESM
- Interactive vegetation dynamics (JSBACH)
- Coupled carbon cycle (JSBACH, HAMOCC)
- Coupled ocean

10. New project "Volcanic eruptions and their impact on future climate"
Historical volcanic eruptions from NGDC/NOAA
Upgrade the model
Design future scenarios
Estimate future climate and economic effects
(By Will Ball)
SO2 observations from AEROCOM

11. "Volcanic eruptions and their impact on future climate"
New project
"Volcanic eruptions and their impact on future climate"
Upgrade the model
Design future scenarios
Estimate future climate and economic effects
Spatial Production Allocation Model (SPAM, 42 crop groups) (Puma et al. 2016)

12. Some recent results
SOCOLv3-AER test of Pinatubo (focus on aerosol distributions)
SOCOLv3-AER test of Tambora (focus on aerosol transport and deposition)
Results with prescribed aerosols (focus on stratospheric warming)

13. Pinatubo modelling with SOCOLv3-AER
Background conditions (Sheng et al., 2015a)
Pinatubo conditions
Parameters
Pinatubo
Hudson
Eruption date
14-15 June 1991
12 September 1991
SO2 emission
14 Tg SO2 (REF) and 12 Tg SO2 (REF12)
2.3 Tg SO2 (Miles et al., 2017)
Latitude
97-112E, 1.8S-12N
45.5S, 72.58W
SO2 height injection
16-30 km (based on Sheng et al., 2015b)
16-20 km
Settings
5-member 5-year long

14. Pinatubo modelling with SOCOLv3-AER
Stratospheric aerosol burden
Evolution of model-calculated global (pole to pole, left) and tropical (20S-20N, right) stratospheric aerosol burden (Tg S) compared with the HIRS, SAGE4λ, and SAGE3λ observational data.

15. Pinatubo modelling with SOCOLv3-AER
Cumulative number distribution
Balloon-borne in situ OPC measurements above Lamarie, Wyoming (Deshler et al., 2003), and SOCOL-AER results for cumulative number distributions for two size channels with radii R > 0.15 and > 0.5 μm in August 1992, May 1992, and March 1993.

16. Pinatubo modelling with SOCOLv3-AER
Stratospheric aerosol optical depth
Time series of model-simulated zonal mean stratospheric aerosol optical depth at 525 nm (calculated by integrating the extinction above the tropopause). Lowermost right panel shows the sAOD derived from AVHRR (600 nm) measurements (Long and Stowe, 1994).

17. Pinatubo modelling with SOCOLv3-AER
Conclusions:
Model-derived aerosol distributions are already relatively good compared to available observations
Exact Pinatubo eruption strength needs further clarification
Model is able to reproduce the tropical lower stratospheric warming
Effects of nudged QBO, aerosol radiative coupling, different coagulation and sedimentation schemes are characterized
(Sheng et al., in prep)
Stratospheric temperature
Zonal mean temperature (upper panel) anomalies for tropics (20S—20N) at 30 hPa calculated by SOCOL-AER and derived from MERRA and ERA-Interim temperature reanalysis. Anomalies are calculated by removing the annual cycle.

18. Tambora modelling with SOCOLv3-AER
VolMIP - Model intercomparison project on climate response to volcanic forcing
Experimental Protocol for a well-defined volcanic forcing for Tambora eruption (VolMIP Tier 1 experiment)
Parameters
Values for Tambora
Eruption date
April 1, 1815
SO2 emission
60 Tg SO2
Eruption length
24 hours
Latitude
Centered at the equator
QBO phase at time of eruption*
Easterly phase (as for Pinatubo and El Chichón)
SO2 height injection**
Same as Pinatubo, 100% of the mass between 22 and 26 km, increasing linearly with height from zero at 22 to max at 24 km, and then decreasing linearly to zero at 26 km.
SST
Climatological from preindustrial control run
Other radiative forcing
Preindustrial CO2, other greenhouse gases, tropospheric aerosols (and O3 if specified)
Duration
5-years long to get the tail of the distribution
Ensemble size
5 members

19. Tambora modelling with SOCOLv3-AER
Zonal mean monthly (left) and total (right) volcanic sulphate deposition [kg SO4 km-2] for each model (ensemble mean). The red triangle marks the start of the eruption (1 April 1815). Volcanic sulphate deposition is calculated as the difference in total sulphate deposition (wet + dry) between the perturbed and control simulations. This anomaly is summed over the ~5 years of simulation to produce the total deposition maps.

20. Tambora modelling with SOCOLv3-AER
Simulated area-mean sulphate deposition [kg SO4 km-2 month-1] to the Antarctic ice sheet (top panel) and Greenland ice sheet (middle panel) for each model (colours). Solid lines mark the ensemble mean and shading is one standard deviation. In the bottom panel are deposition fluxes from two monthly resolved ice cores (DIV from Antarctica and D4 from Greenland). Note the reduced scale for the bottom panel. The grey triangles mark the start of the eruption.

21. Tambora modelling with SOCOLv3-AER
Conclusions:
Large divergence among models
Better deposition scheme is needed in SOCOL
Several bugs found in our code
(Marshall et al., in prep)
Background pre-industrial polar precipitation in each model control simulation (year average) (shading) and ice core accumulation (mm liquid water equivalent yr-1) in ice cores (filled circles). (Sigl et al. 2014). Antarctic ice core accumulation rates are an average of annual ice core accumulation from taken from Sigl et al. (2014). Greenland ice core accumulation rates are taken from Gao et al. (2006) (Table 1). WACCM, MAECHAM and SOCOL model data are averages of 60 months of control simulation; UKCA is an average of 48 months. Strong patches in SOCOL precipitation around coasts appear to be numerical artefacts due to SST files are will be repeated with new files.

22. To be continued… Thank you!

23. Volcanic signal in NH temperature reconstructions
Climate impact
(Guillet et al., 2017)
Volcanic signal in NH temperature reconstructions

24. Pinatubo modelling with SOCOLv3
Stratospheric aerosol data sets used in SOCOLv3 CCM simulations
CCMI
CMIP6
Period
Data used
SAGE II
SAGE I, SAGE II, SAM, CALIPSO, OSIRIS; OPC < 20 km
Data filling following Mt. Pinatubo eruption
Lidar measurements
CLAES observations
Ratio of H2SO4 mass in the CMIP6 and CCMI stratospheric aerosol data sets for 12 months around the Mt. Pinatubo eruption in June Black contours show the CCMI H2SO4 mass (109 molecules cm-3). The dashed black line shows the location of the tropopause.

25. Pinatubo modelling with SOCOLv3
Conclusion:
SOCOLv3 simulates stratospheric temperature and ozone changes following the Mt. Pinatubo eruption more accurately if CMIP5 aerosols are used.
(Revell et al., in prep)
Time series of temperature anomalies (calculated by removing the annual cycle from ) at 30 hPa, 15°N-15°S. The red and blue lines denote the ensemble mean of the SOCOLv3 CCMI and CMIP6 ensembles, respectively. The shaded areas denote the ensemble mean +/- 1σ.