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Perspectives from across Sciences Road: What atmospheric chemistry research can tell us about the Earth’s surface* (and vice versa)
Jenny A. Fisher Centre for Atmospheric Chemistry, UOW !& many collaborators from UOW and beyond!
*as defined by an atmospheric chemist…
GeoQuEST Seminar 26 September 2014
What goes on across the road in the Centre for Atmospheric Chemistry?
TRACE GAS MEASUREMENTS
FIRE EMISSIONS
AGRICULTURAL EMISSIONS
SOLAR RADIATION & AEROSOLS
MODELLING & ANALYSIS
h"p://smah.uow.edu.au/cac/index.html(as of 2012)
Why do we care about the stuff in the atmosphere?
HEALTHOutdoor air pollution deaths, 2008
CLIMATEwarmingcooling
World Health Organization, 2011 Intergovernmental Panel on Climate Change, AR5, 2013
How are the atmosphere and surface related — from an atmospheric chemist’s perspective?
Anthropogenic NaturalChemical
Industry TransportAgriculture
The Financial Times Limited, 2013
+
VolcanoesOcean Fires Vegeta>on Dust
How can we study this complicated system?
Surface Aircraft Satellite
? ?
Data and models work best together!
Model
What kind of “model” do I use?
GEOS-Chem: chemical transport model
Global, gridded 3D model with:
• Transport: “assimilated meteorology” — aka external winds, temperature, etc.
• Chemistry: solves simultaneous gas-phase and particulate chemistry for >100 species
• Sources: from external inventories or process-based parameterisations
• Sinks: wet and dry deposition, exchange with surface reservoirs
Represents the community’s best current understanding of sources & processes
Where have I been looking?
Cam McNaughton
NASA
ARCTIC Apr./Jul. 2008 field campaign
SOUTHEAST US Aug.-Sep. 2013 field campaign
AUSTRALASIA Current workVector Templates Halans
!
!
!
!
What explains elevated mercury in the Arctic atmosphere & ocean?
Part 1: when models & data disagree
Mercury in the Arctic is an ecological and health concern
AMAP 2011
Mercury is a neurtoxin that bioaccumulates in marine food webs
!
Some of the highest amounts have been found in Arctic wildlife
!
Traditional diets include these species
!
Atmosphere-ocean cycling is complex and uncertain.
The Arctic mercury cycle (simplified*)
Hg
Hg Hg
Ocean subsurface
Snow/iceOcean mixed layer
* no chemistry! no terrestrial exchange!
ArcLc atmosphereatmospheric transport
from mid-latitudes
Arctic atmospheric mercury has a unique seasonal cycle
Observations
spring minimum: Atmospheric Mercury Depletion Events
summer maximum
Can we explain this using our current understanding (i.e., the model)?
Model
We use the model to test hypotheses, pushing parameters to the limits of their uncertainties
J F M A M J J A S O N D1.0
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1.4
1.6
1.8
2.0
Hg
0, ng.m
-3
Month
standard simulaLon oceanic Hg reducLon snowpack Hg reducLon
“Traditional” explanations (atmospheric transport, snowpack processing, ocean chemistry) cannot explain the summer peak
We use the model to test hypotheses, pushing parameters to the limits of their uncertainties
J F M A M J J A S O N D1.0
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1.4
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Hg
0, ng.m
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standard simulaLon + river/erosion source
ArcLc Rivers
With a large source from Arctic rivers & coastal erosion, the model comes much closer to the observations — our working hypothesis!
Fisher et al., 2012
With this working hypothesis, we can use the model to probe drivers of change
NSIDC
May sea ice extent trend: 1979-‐2010
GISS
Air temperature change: 1979-‐2010
Asia
USSR
2 Gg
0Streets et al. (2011)
Hg emissions growth: 1979-‐2010
Over the last 30 years…
What are the implications for past — and future — variability in Arctic Hg?
With this working hypothesis, we can use the model to probe drivers of change
OceanAtmosphere
high temperature
GCMs predict future ⬆ cloudiness & ⬇ spring-‐summer ΔT → less Hg may be added to the ArcJc Ocean in future!
Fisher et al., 2013
!
!
!
!
What influences atmospheric composition in Australasia?
Part 2: when models & data agree
Australasia is influenced by distant sources
Satellite observations: October 2003
Edwards et al., 2006
Known influence from fires, especially African & South American
But what about other sources? human activity? vegetation?
How much do local vs distant sources matter?
We can track source influence with carbon monoxide (CO)
Mix of sources (fires, fossil fuel, vegetation)
Lifetime of weeks to months
Measured in Australasia:Darwin
Wollongong
Lauder
CO Colum
n, 101
8 molecules/cm
2
0
3
1
2
3
1
2
3
1
2
Month J F M A M J J A S O N D
Darwin
0.5
1.0
1.5
2.0
2.5
3.0
Wollongong
0.5
1.0
1.5
2.0
2.5
3.0
Lauder
F A J A O D0.0
0.5
1.0
1.5
2.0
2.5
3.0
J M M J S N
CO
Colu
mn (
10
18 m
ole
c c
m-2
)
Obs.: 5-yr mean, 2009Model
Model-observation comparison for 2009Observations (2005-2009, 2009)GEOS-Chem (2009)
The model provides a realistic picture, so we can use it to run “experiments”, and extrapolate in time/space
Test the influence of emission source types (or regions):
Impact on this region:
Fossil fuels
Summer (DJF) 2009
0 10 20 30 40 50
CO [ppbv]
0
2
4
6
8
10
12
Altitu
de
[km
]
The model provides a realistic picture, so we can use it to run “experiments”, and extrapolate in time/space
Test the influence of emission source types (or regions):
Impact on this region:
Fossil fuels
Summer (DJF) 2009
0 10 20 30 40 50
CO [ppbv]
0
2
4
6
8
10
12
Altitu
de
[km
] Wildfires
The model provides a realistic picture, so we can use it to run “experiments”, and extrapolate in time/space
Test the influence of emission source types (or regions):
Impact on this region:
Fossil fuels
Summer (DJF) 2009
0 10 20 30 40 50
CO [ppbv]
0
2
4
6
8
10
12
Altitu
de
[km
] Wildfires
Vegetation*
*mostly
The model provides a realistic picture, so we can use it to run “experiments”, and extrapolate in time/space
Test the influence of emission source types (or regions):
Impact on this region: Summer (DJF) 2009
0 10 20 30 40 50
CO [ppbv]
0
2
4
6
8
10
12
Altitu
de
[km
]
mainly from Australia
mainly from Africa & South America
The model provides a realistic picture, so we can use it to run “experiments”, and extrapolate in time/space
Test the influence of emission source types (or regions):
Impact on this region: Summer (DJF) 2009
0 10 20 30 40 50
CO [ppbv]
0
2
4
6
8
10
12
Altitu
de
[km
]
But these vegetation sources & their chemistry remain very poorly understood!
!
!
!
!
What are the atmospheric consequences of biogenic emissions?
Part 3: when we need more data
The Southeast US is a hotspot for “biogenics” (compounds emitted from vegetation)
2006 2007 2008
Aug
Sep
HCHO (biogenic proxy)
D. Jacob & L. Zhu (Harvard), J. Reid (NRL)
The Southeast US is a hotspot for “biogenics” — with air quality consequences!
2006 2007 2008
Aug
Sep
Aug
Sep
HCHO (biogenic proxy)
Aerosol
D. Jacob & L. Zhu (Harvard), J. Reid (NRL)
But we don’t understand the atmospheric chemistry in this region at this time of year
Month (2001)!
Ozo
ne (p
pb)!
observed!
Fiore et al., 2009
multiple models
We needed more data - with unprecedented chemical detail
NASA DC-‐8
Studies of Emissions & Atmospheric Composition, Clouds, & Climate by Regional Surveys (SEAC4RS)
August-September 2013 (Houston, TX)
We needed more data - with unprecedented chemical detail
DC-‐8 Exterior DC-‐8 Interior
DC-‐8 Wing & Probe ER-‐2 Flightsuit Learjet
Photos: Houston Daily News
Modellers do fieldwork, too!
0.00e+00 1.50e+12 3.00e+12 4.50e+12 6.00e+12atoms C/cm2/s
GEOS-Chem nested domain
0.00e+00 1.50e+12 3.00e+12 4.50e+12 6.00e+12atoms C/cm2/s
Southeast Region
Biogenic (vegetaJon) emissions: 1 Aug -‐22 Sep
After months of work, model results look promising. Now the real analysis can begin…
0.00 0.15 0.30 0.450.00NO [ppb]
0
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6SEAC4RS - August 2013 :Southeast: 4x5
DC8GEOS-Chem
0.0 0.2 0.4 0.6 0.80.0NO2 [ppb]
DC8GEOS-Chem
50 100 150 200 2500CO [ppbv]
DC8GEOS-Chem
0.0 0.2 0.4 0.6 0.8 1.00.0PAN [ppbv]
DC8GEOS-Chem
20 40 60 80 1000O3 [ppbv]
Bias: O3 7.ppbv
DC8GEOS-Chem
0.2 0.6 1.0 1.41.60.0ISOP [ppbv]
0
1
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DC8GEOS-Chem
0 1 2 3 4 50HCHO [ppbv]
DC8GEOS-Chem
0.2 0.6 1.0 1.41.60.0MVK+MACR [ppbv]
DC8GEOS-Chem
0 100 200 300 400 500ISOPOOH [pptv]
DC8GEOS-Chem
BB Filter CH3CN > 0.225
Plume Filter NO2 > 4.
0 1 3 50H2O2 [ppbv]
DC8GEOS-Chem
0.00 0.15 0.30 0.450.00NO [ppb]
0
1
2
3
4
5
6SEAC4RS - August 2013 :Southeast: 4x5
DC8GEOS-Chem
0.0 0.2 0.4 0.6 0.80.0NO2 [ppb]
DC8GEOS-Chem
50 100 150 200 2500CO [ppbv]
DC8GEOS-Chem
0.0 0.2 0.4 0.6 0.8 1.00.0PAN [ppbv]
DC8GEOS-Chem
20 40 60 80 1000O3 [ppbv]
Bias: O3 7.ppbv
DC8GEOS-Chem
0.2 0.6 1.0 1.41.60.0ISOP [ppbv]
0
1
2
3
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DC8GEOS-Chem
0 1 2 3 4 50HCHO [ppbv]
DC8GEOS-Chem
0.2 0.6 1.0 1.41.60.0MVK+MACR [ppbv]
DC8GEOS-Chem
0 100 200 300 400 500ISOPOOH [pptv]
DC8GEOS-Chem
BB Filter CH3CN > 0.225
Plume Filter NO2 > 4.
0 1 3 50H2O2 [ppbv]
DC8GEOS-Chem
K. Travis (Harvard)
Biogenic species Ozone
Most extensive sampling of Southeast US atmosphere + new high-resolution model will greatly enhance our understanding of biogenic sources, chemistry, & impacts - stay tuned!
Take-home messages
1. Atmosphere, ocean, land, and biosphere are linked — with consequences for health, climate, & ecosystems
Take-home messages
1. Atmosphere, ocean, land, and biosphere are linked — with consequences for health, climate, & ecosystems
2. Combining models and observations can provide added value to both
Take-home messages
1. Atmosphere, ocean, land, and biosphere are linked — with consequences for health, climate, & ecosystems
2. Combining models and observations can provide added value to both
J F M A M J J A S O N D1.0
1.2
1.4
1.6
1.8
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Hg
0,
ng
.m-3
Month
3. Model-observation disagreement provides an opportunity to test our current understanding — and improve it
Take-home messages
1. Atmosphere, ocean, land, and biosphere are linked — with consequences for health, climate, & ecosystems
2. Combining models and observations can provide added value to both
3. Model-observation disagreement provides an opportunity to test our current understanding — and improve it
4. Model-observation agreement facilitates “experiments” we couldn’t otherwise run
Summer (DJF) 2009
0 10 20 30 40 50
CO [ppbv]
0
2
4
6
8
10
12
Altitu
de
[km
]
Take-home messages
1. Atmosphere, ocean, land, and biosphere are linked — with consequences for health, climate, & ecosystems
2. Combining models and observations can provide added value to both
3. Model-observation disagreement provides an opportunity to test our current understanding — and improve it
4. Model-observation agreement facilitates “experiments” we couldn’t otherwise run
There are lots of unanswered questions at the atmosphere-surface interface —
let’s keep options open for Chemistry-SEES collaboration!
Funding & Resources: UOW, NASA, National Computational Infrastructure
Collaborators: Stephen Wilson, Clare Murphy, Rebecca Buchholz, Nicholas Jones (UOW) Guang Zeng, John Robinson (NIWA) Daniel Jacob, Elsie Sunderland, Anne Soerensen, Patrick Kim, Katherine Travis (Harvard) Louisa Emmons, Christine Wiedinmyer (NCAR) Lee Murray (Columbia/Lamont Doherty) Jingqiu Mao (Princeton/GFDL)
Photo: Don Leonard, Humboldt County CVB
Acknowledgements
