Atmosphere-Biosphere Interactions Prioritizing Climate Change - - PowerPoint PPT Presentation
Atmosphere-Biosphere Interactions Prioritizing Climate Change Adaptation and Ozone Pollution Control to Minimize Impacts on Public Health and Food Security in 2050 Amos P. K. Tai ( ) Earth System Science Programme, CUHK 5 th
5th Conference on East Asia and Western Pacific Meteorology and Climate (3 Nov 2013) Collaborators: Colette Heald (MIT) Maria Val Martin (CSU)
Climate Change
Warmth good Excess heat bad Drought bad Surface ozone very bad Air pollution meteorology Radiative forcing
Human health Various consequences
Community Earth System Model
Anthropogenic emissions, land use (2000, 2050 RCP4.5, 2050 RCP8.5) Climate and
(1.9° ×2.5° )
Warming Water vapor Isoprene NOx O3 O3
chemical loss high NOx low NOx photochemistry
Surface ozone is produced in situ in the atmosphere by photochemical reaction of ozone precursors (NOx, CO, hydrocarbon). It is detrimental to human health, vegetation and crop production.
driver, but climate change can partly offset (or enhance) the effects of changes in emissions.
2000-to-2050 changes in JJA surface (MDA8) O3 (ppbv)
climate only anthro emission only RCP4.5 RCP8.5
[ppb]
biogenic emis only
Community Earth System Model
Anthropogenic emissions, land use (2000, 2050 RCP4.5, 2050 RCP8.5) Statistical relationships to parameterize relative effects: climate and pollution Climate and
(1.92.5)
0 γclimateγpollution −1
Growing season heat and ozone exposure metrics for four major crops: Wheat Rice Maize Soybean Changes in crop production: Distribution of per capita food consumption
Additional fraction under- nourished Fraction under- nourished Original distribution With climate and pollution effects Minimum Dietary Energy Requirement
Methodology and data from Food and Agriculture Organization (FAO)
Pollution effect: +850 ×1012 kcal Climate effect: -750 ×1012 kcal
Combined effect: -87 ×1012 kcal
Pollution effect: -49 ×1012 kcal Climate effect: -770 ×1012 kcal
Combined effect: -900 ×1012 kcal
RCP4.5 RCP8.5
106 kcal ha-1
threaten food security in developing regions
damage of climate change; but this assumes flexible dietary habits and little barrier in international trade
Rate of undernourishment Based on FAO 2000 data Based on FAO 2050 projections Baseline 18% (current) 4.1% RCP4.5 19% 4.7% RCP8.5 26% 7.7%
Combined effects of 2000-to-2050 changes in climate and ozone pollution on rate of undernourishment in developing countries
2 4 6 8 1 100% climate 0% pollution 100% pollution 0% climate 50% each Contribution from climate vs. pollution effect
Projected 2000-to-2050 % change in production
US China S Asia Europe US China S Asia Europe RCP4.5 RCP8.5 Global Global
50 100
5 15 25
20
20 60
SE Asia S America SE Asia S America
Wheat Rice Maize Soybean
Major producers
Anthropogenic emissions are the dominant driver for future
enhanced by climate change depending on region Globally, ozone pollution and climate change together can potentially worsen malnutrition problems, but aggressive ozone control can offset impacts of climate change Climate adaptation will be important for climate-sensitive crops and regions: maize everywhere except in China, soybean in South America Ozone pollution control will be important for pollution-sensitive crops and regions: wheat everywhere, rice and maize in China Ozone pollution control have “triple” benefits of protecting public health, ensuring food security and mitigating climate change for China, where regions of high population, high ozone and croplands overlap.
Climate effect: we develop for each 1.9°×2.5° cell a constrained
multiple linear regression model, using 1961-2010 crop yield and meteorological data from FAO and NCEP/NCAR
GDD = growing degree days (GDD > 0) KDD = killing degree days (KDD < 0) *GDD and *KDD are the “true” effects after adjusting for confounding
factors (e.g., ozone also increases with T)
Ozone effect: we use relative yield (RY) as function of various ozone
exposure metrics found from literature
RY = 1 – a AOT40 RY = exp[-(M12/a)b]/exp[-(20/a)b] RY = exp[-(SUM06/a)b] RY = exp[-(W126/a)b]
2050
2000
∗
∗
We find relationships of 1961-2010 detrended annual crop
yield with growing degree days (GDD) and killing degree days (KDD) for each grid:
Constraints: GDD 0 and KDD 0
20 40 60 80 10 15 20 25 30 35 T.SRF.daily.m ean[i, j, 152:243, 1] - 273
Tmax Tmean Thigh Tbase Day since planting date
° C
0.0 0.5 1.0
2 4
Sensitivity of maize yield to growing season T
log unit /° C-day
0.0 0.5 1.0
Correlation of KDD with maize growing season mean precipitation
KDD not only captures the effect of extreme T but also associated drought conditions with low precipitation
Ozone pollution is strongly correlated with high temperature!
Therefore, part of GDD or KDD effect can be due to correlation with ozone pollution
Approach:
∗
Total effect Actual, non- confounded KDD effect M = ozone exposure metric (e.g. AOT40); obtained by differentiating historical O3- crop responses, e.g.: Evaluate from hourly ozone data (US AQS/CASTNET and Europe EMEP for 1993-2010)
∂lnY ∂AOT40 = − a 1− aAOT40
Regression to obtain dM/dKDD = KDD:
10 20 30 40
∗
For maize, ~7% of total KDD effect (KDD) is due to correlation
with ozone exposure. For wheat and soybean, it is ~20% and ~40%, respectively.
∗
∗
Population in developing countries
follows a lognormal distribution:
Food consumption per capita (kcal/person/day) MDER
2
With climate and pollution effect:
Fraction consumed by developing countries Fraction consumed as food
MDER = minimum dietary energy requirement