The Intersection of Climate, Air Quality, and Vegetation
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Transcript of The Intersection of Climate, Air Quality, and Vegetation
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The Intersection of Climate, Air Quality, and Vegetation
Harvard Climate SeminarApril 10, 2014
Colette L. HealdXuan Wang, David A. Ridley, Amos P.K. Tai, Maria Val Martin
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Climate Forcing
Climate Feedbacks
Altering Ecosystem Health (Nutrients, Toxics)
Emissions, Removal
AIR QUALITY
VEGETATION CLIMATE
Lots of interesting
stuff I won’t talk about
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Air Pollution is a Significant Public Health Concern(more premature deaths per year from PM than car accident fatalities in the US)
Particulate Matter (PM) is estimated to be the leading environmental cause of premature mortality. Overall , PM is the 3rd and 9th most deadly risk factor.
Ozone is a risk factor for aging populations (i.e. Europe)
3.5M deaths/yr
3.1M deaths/yr
[Lim et al., 2012]
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Ozone and PM Warm and Cool the ClimatePM is the Leading Source of Uncertainty in Global Climate
Forcing
[IPCC, 2013]
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Ozone and PM also Alter Ecosystem Health
ACID RAINCROP DAMAGE FERTILIZATION
Mahowald et al., (2011) suggests biogeochemical feedbacks from aerosols
constitute a large climate cooling
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Climate Forcing
Climate Feedbacks
Altering Ecosystem Health (Nutrients, Toxics)
Emissions, Removal
AIR QUALITY
VEGETATION CLIMATE
BC
Crops
DustOzone
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IPCC AR5 Estimates that Black Carbon is the 2nd Largest Warming Agent in the Atmosphere.
(but that’s not what models say)
How can these be
reconciled?
Absorption ↑Lifetime ↓
Aging
Emission
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Observations Suggest That Models Overestimate BC
[Koch et al., 2009]
AeroCom models overestimate BC over Americas by factor ~8, overestimate remote HIPPO BC by factor ~5.
[Schwarz et al., 2010]
AeroCom means in black, HIPPO obs in colourObs in black, AeroCom models in colour
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New Model Aging Processes for BC
Hydro-phobic
Hydro-philic
Old Assumptions
1.15 days
Hydro-phobic
Hydro-phobic
New Assumptions
Anthropogenic
Biomass burning
Hydro-philic
Hydro-philic
Sulfate, etc.
Organic components
4 hours(also increase fraction emitted as
hydrophillic to 70%)
(Moteki et al., 2007; Moffet et al., 2009; Friedman et al.,2009; Liu et al., 2010; Akage et al.,2012; Lack et al.,2012; Shamjad et al., 2012; Schwarz et al.,2008, Moteki et al., 2007; Moffet and Prather, 2009),
k = 1/τ = a [SO2] [OH] + b
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Impact of New Model Aging Processes on Simulation of BC
Good simulation near source (with or without new aging).Modified aging scheme results in shorter lifetime and better simulation of low concentrations in remote locations. Vastly
better than AeroCom. Generally within a factor of 2.
HIPPO Continental (Near-Source)
Good
Better
Still Bad
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Considering Absorption Enhancement and Brown CarbonSmaller size, wider size range
Absorption CoefficientMie calculation
Absorption enhancement from
coating (AE=1.1)
Absorption Coefficient
Absorption enhancement from
coating (AE=1.5)
Larger size, narrower size range
Mie calculation
Brown Carbon
Aromatic SOA
50% of POA Biomass/biofuel
Absorption Coefficient
Get RI from field measurements
Mie calculation
Anth BC
BB BC
(Akage et al., 2012; Schwarz et al., 2006; 2007; 2008; Lack et al., 2012; Dubovik et al., 2002; Shamjad et al., 2012; Moffet et al., 2009; Knox et al., 2009; Kondo et al., 2011; Lack et al., 2012; Moffet and Prather, 2009; Bond et al., 2006; Cappa et al., 2012)
Also “Most Absorbing” Simulation : Set AE=2 and standard aging mechanism (longer lifetime)
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Measurements Still Suggest Absorption is Underestimated
Better able to capture the spectral AAOD with our “best” simulation (including BrC), but still biased low (especially in biomass burning regions).
Can “scale up” our model to match observations (Bond et al., 2013) – emissions or optics?
*AAOD product here using lev2 SSA with lev1.5 AOD
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Our Work Suggests Smaller BC DRF Required to Match All Observational Constraints
Brown Carbon contributes 35% of the warming from carbonaceous aerosols.BC DRF is less than methane and tropospheric ozone.
Suggests that controlling BC is less effective for climate mitigation.[Wang, et al., in prep]
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Dust From North Africa
Air Quality (local & Americas)
Ocean Fertilization (C cycle)
Nutrient Supply(terrestrial productivity)
Relationship is breaking down?
Observations from Barbados suggest that increasing trend in dust from 1960s to 1980s may have reversed. Impact of greening of the Sahel on productivity of the Amazon?
Tropical Cyclone Genesis
[Mahowald et al., 2009]
Dust @ BarbadosNegative Sahel Precip Anomaly
Previous year’s Precip. Index
Sum
mer
dus
t con
c.
[Prospero and Lamb, 2003]
North African Dust Driven by Sahelian Precipitation
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Why is Dust From North Africa Decreasing?
African dust has been decreasing year-round at both source and down-wind by ~10%/decade from 1982-2008, with
substantial interannual variability. Model captures this! Use model to assess that trends & variability largely from surface
winds NOT vegetation changes.
Fixed meteorologyFixed vegetationFixed surface winds
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Vegetation Changes Are NOT Responsible for the Recent Trend in Dust From Africa
Vegetation(AVHRR NDVI)
Surface Winds(MERRA)
(2002-2006) – (1982-1986)
Regions of substantial
dust decrease
(black contours)
[Ridley et al., ACPD, 2014]
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Possible Mechanism: Aerosols Changing Aerosols (via Climate)?
Suggests that this may be a short-term trend. Let’s wait and see!
↓ anthropogenic aerosols ↑ SST
Northward shift of ITCZ
↓surface winds↓ dust emission
[Booth et al., 2012; Ackerley et al., 2011; Evan et al., 2009; Folland et al., 1986; Broccoli et al, 2006; Doherty et al., 2012]
↑precipitation↑greening Sahel
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Considering the Impact of Air Pollution and Climate on Global Food Security (in an era of rising food demand)
tolerant
sensitive
AIR POLLUTION(Snap peas damage due to ozone exposure)
CLIMATE(Illinois heat wave summer 2012)
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Estimating the Climate and Air Quality Impacts on Global Crop Productivity From Historical Record
Rel
ativ
e Y
ield
[Mills et al. 2007]
Wheat
Ozone exposure (ppm-hour)
Tmax
Tmean
Thigh
Tbase
Day since 1 June
°C
based on Butler and Huybers
[2013]
climate ozone(2050) (2000)P P
Estimate yield-O3 relationship from literature estimate of O3 exposure
indices
Estimate yield-climate relationship from a MLR of 1961-2010 FAO crop yields and NCEP/NCAR
reanalysis
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How Will Drivers Change in the Future?
2000-2050 changes in maximum daily 8-h average (MDA8) O3 (ppbv)
2000-2050 changes in surface air temperature (K)
Community Earth System Model (CESM 1.1) Simulation
Warming climate, similar in both scenarios. Ozone projections reflect regional pollution control, but RCP8.5 includes large increases in methane (increases O3 background)
stippling=significant
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Impacts of Climate Change and Ozone Pollution on Total Crop Production
Future crop productivity very sensitive to ozone pollution & climate change. Uncertainty associated largely with future air quality.
Pollution effect: +0.22 ×1015 kcal
Climate effect: -0.80 ×1015 kcalCombined effect: -0.64 ×1015 kcal
Pollution effect: -0.26 ×1015 kcal
Climate effect: -0.81 ×1015 kcal Combined effect: -1.07 ×1015 kcal
RCP4.5
RCP8.5
106 kcal ha-1-50 -20 -10 -5 -2 -1 0 1 2 5 10 20 50
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The rate of undernourishment
in developing countries in 2050
nearly doubles due to climate and
ozone.
Impact of Ozone Pollution and Climate Change on Crop Production Around the World
[Tai, val Martin and Heald, submitted]
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But Wait, There are Known Large Summertime Biases in the Simulation of Surface Ozone in Eastern US and Europe
[Lamarque et al., 2012]
[Fiore et al., 2009]
[Lapina et al., 2014]
EMEP
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The Importance of Getting Dry Deposition of Ozone Right
Correcting bugs in dry deposition parameterization in land model significantly reduces some the surface O3 bias…. So we thought about whether there might be other issues…
[val Martin et al., GRL, in press]
CLM (and MOZART) dry deposition schemes fail to account for leaf area density in calculation of leaf cuticular resistance and stomatal resistance
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Reducing stomatal conductance drastically improves simulation of dry deposition velocity and surface concentrations of ozone. Realistic? Implications for carbon and hydrological cycle?
How Vegetation Phenology Is Controlling Predictions of Surface O3
Observations (ppb)
Harvard Forest
RMNPOriginal SchemeCorrected SchemeOptimized SchemeObservations
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CONCLUSIONS
AQ → climate: Warming from BC overestimated in AR5. Not as effective a mitigation strategy for climate change.
AQ → climate → AQ: Recent decrease in North African dust due to stilling of winds (not vegetation), that we speculate is
due to decreasing anthropogenic aerosols from the U.S.
AQ + climate → vegetation: Together AQ & climate substantially threaten global food security
Vegetation → AQ: Vegetation seasonality & density controls surface O3 in Eastern U.S. and Europe (and
Amazon)