Threat to future global food security from climate ... · Supplementary Information for Tai et al....

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Supplementary Information for Tai et al. (2014): Threat to future global food security

from climate change and ozone air pollution, Nature Climate Change

Methods

CESM simulations

We use the Community Earth System Model (CESM) v1.1.1 to simulate present-day

and future surface ozone concentration and climate variables in 2050. Our configuration uses

coupled atmosphere and land components but fixed data ocean and cryosphere consistent

with current and future climates, at a latitude-by-longitude resolution of 1.9°×2.5°. Our

simulations consider time varying, zonally averaged greenhouse gas distributions for CO2,

CH4, N2O and halocarbons. Anthropogenic emissions of greenhouse gases and ozone

precursors follow the RCP4.5 and RCP8.5 scenarios represented in IPCC AR5. These two

RCPs assume rapid decreases of emissions of most ozone precursors globally due to

aggressive air pollution abatement measures. Exceptions include ammonia, which increases

in both scenarios, and CH4, which increases by ~10% in RCP4.5 and ~125% in RCP8.5 by

2050. Meanwhile, there is significant regional variability of the trends in ozone precursor

emissions (Lamarque et al., 2011; van Vuuren et al., 2011). By 2050, surface ozone

concentration is projected to decrease in most regions for RCP4.5 (Supplementary Fig. 1).

Such decreases are dominated by changes in anthropogenic emissions of ozone precursors,

e.g., following the large decline in global NOx emissions. For example, anthropogenic

emission changes drive ~90% of the summertime surface ozone decreases over the US for

RCP4.5 (Val Martin et al., 2014). Under RCP8.5, increases in surface ozone concentration

(Supplementary Fig. 1) are primarily driven by elevated CH4 in most parts of the world

(Fiore et al., 2012). Details of these simulations are presented in Val Martin et al. (2014).

Ozone exposure metrics and crop-exposure response

In this study we consider four different ozone exposure metrics widely used to

quantify ozone exposure and impacts on vegetation/crop productivity in the literature. They

include three cumulative exposure metrics (AOT40, SUM06 and W126) and two mean

exposure metrics (M12 and M7). They are defined as:

AOT40 = [O3]i − 0.04( )i=1

n

∑ (1)

Threat to future global food security from climate change and ozone air pollution

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M12 = 1n

103[O3]ii=1

n

∑ (2)

SUM06 = [O3]ii=1

n

∑ (3)

W126 = wi[O3]ii=1

n

∑ (4)

where [O3]i is the hourly mean ozone concentration in ppm during the twelve hours of local

daylight (08:00-19:59); n is the number of hours in the 3-month growing season defined as

the three months prior to the start of the harvest period in every country according to crop

calendar data (Sacks et al., 2010); and wi is a weighting function assigning greater weight to

higher levels of hourly ozone with an inflection point at ~65 ppb (Lefohn & Runeckles,

1988), defined by:

wi =1

1+ 4403e−126[O3 ]i (5)

AOT40, SUM06 and W126 are in units of ppmh, and M12 in units of ppb. M7 is defined

similarly to M12 but uses a 7-hour (09:00-15:59) daytime window instead of 12-hour for

summation. The relative yield, i.e., RY = Y/Y0 = f(M) as defined in equation (2) in Methods in

the main text as a function of these ozone exposure metrics (M) is detailed in Supplementary

Table 1.

Climate metrics and crop-climate responses

In this study we consider two temperature-driven climate metrics to encapsulate the

impacts of climate variability on regional crop yield following the approach of Butler et al.

(2012), namely, growing degree day (GDD) and killing degree day (KDD) defined as

GDD = max min Tmean,Topt( )−Tbase , 0( )i=1

N

∑ (6)

KDD = max Tmax −Topt , 0( )i=1

N

∑ (7)

where Tmean is the daily mean temperature, Tmax is the daily maximum temperature, Topt is the

maximum optimal growth temperature, Tbase is the minimum temperature threshold, and N is

the number of days in the growing season for each crop. Topt and Tbase are specific for each

crop as listed in Supplementary Table 2. Growing season is defined as the period between the

mean planting date and harvesting date in every country according to crop calendar data

(Sacks et al., 2010).


Supplementary Fig. 2 shows the correlation coefficients between GDD and growing

season mean precipitation for 1961-2010. We find that KDD is strongly negatively correlated

with seasonal mean precipitation worldwide for all four crops considered, and thus at least in

part encapsulates the correlations with low precipitation and drought usually associated with

high temperature extremes. As the future projections of regional precipitation are among the

most uncertain climate variables predicted by climate models (IPCC, 2013), we resolve to use

KDD as a more robust metric to quantify the climate change effect.

To derive fine resolution maps (1.9°×2.5°) of annual crop yields for 1961-2010, we

apply a data fusion technique on national crop statistics from the Food and Agriculture

Organization FAOSTAT database (http://faostat3.fao.org/faostat-gateway/go/to/home/E)

using the approach of Monfreda et al. (2008). Briefly, for each crop and each 1.9°×2.5° grid

cell, the yield (Yt) for year t is estimated as

Yt = Y2000 ⋅Yt

∗

Y2000∗ ⋅εY (8)

where Y2000 is the fine-resolution crop yield data for year 2000 from Monfreda et al. (2008)

regridded to 1.9°×2.5°, Y*2000 and Y*

t are national crop yield data from FAOSTAT for the

country to which the grid cell belongs for year 2000 and t, respectively, and εY is an error

term that accounts for regridding and other errors and ensures that the summation of Yt over

all grid cells for a given country equals Y*t from FAOSTAT.

The observed sensitivities of crop yield to GDD and KDD (βGDD and βKDD) as found

by the multiple linear regression model equation (4) in Methods (main text) are shown in

Supplementary Figs. 3 amd 4.

The correlations between ozone exposure indices and climate metrics (GDD and

KDD) are estimated from NCEP/NCAR Reanalysis 1 meteorological data (Kalnay et al.,

1996) and hourly ozone observations from the AQS

(http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm) and CASTNET

(http://java.epa.gov/castnet/clearsession.do) networks in the US and EMEP

(http://www.nilu.no/projects/CCC/onlinedata/intro.html) network in Europe for 1993-2010,

using a simple linear regression model for each 1.9°×2.5° grid

M −Mm( ) = β0 + β1 D − Dm( ) (9)

where M is one of the four ozone exposure indices, D is one of the agro-climatic variables

(GDD or KDD), m denotes 5-year moving average for detrending the data, and β1 = dM/dD is

the observed sensitivity of M to GDD or KDD as used in equation (5) in Methods (main text)


to find the true, non-confounded sensitivity of crop yield to GDD and KDD. We find that

∂lnY/∂M*dM/dD as used in equation (5) in Methods (main text) varies spatially across the US

and Europe but approximately follows a lognormal distribution. We therefore apply its

geometric mean values for the rest of the world where long-term ozone data are not available

for evaluating dM/dD (see Supplementary Tables 3 and 4).

The uncertainties of crop projections as shown in Fig. 2 (main text) are quantified

using a Monte Carlo approach. We repeatedly and randomly sample the future model

projections and the statistical parameters for crop-climate and crop-ozone responses to derive

a probability distribution of crop projections for each region. The most important sources of

variability for crop projections arise from the variances of βGDD and βKDD in the regression

model, and the choice of ozone exposure index. Among the four ozone exposure indices, we

give less weight to the mean exposure indices (M7 and M12), because cumulative indices

(e.g., AOT40) that give higher weight to higher ozone concentrations are believed to be more

accurate predictors of crop yield losses than mean metrics (Lefohn & Runeckles, 1988).

FAO methodology to characterize undernourishment rate

The distribution of per capita food consumption for a population can be modeled as a

lognormal distribution

f (x) = 1xσ 2π

exp −ln x − µ( )22σ 2

⎡

⎣⎢

⎤

⎦⎥ (10)

where x is the per capita food consumption or dietary energy supply (DES) (kcal/person/day),

µ and σ are parameters of the lognormal distribution that can be related to the actual

arithmetic mean DES (xm) by

µ = ln xm − 0.5σ2 (11)

The rate of undernourishment (ru) is defined to be the fraction of population with a DES

below the minimum dietary energy requirement (xmin)

ru = f (x)dx0

xmin

∫ (12)

Assuming σ and xmin to remain constant, any change in mean DES (∆xm) caused by climate

change and/or ozone pollution can result in a shift in the distribution f(x), leading to a change

in ru as shown in Fig. 1g in the main text.


Table 1. Crop-exposure response equations used to calculate relative yield (RY) of wheat,

rice, maize and soybean from four different ozone exposure metrics (M).

Crop Exposure-yield relationship Reference

Wheat a RY = 1 − 0.0163AOT40 Mills et al., 2007 Van Dingenen et al., 2009

RY = 1 − 0.0228AOT40

(for winter variety in China only) b

Wang et al., 2012

RY = exp[−(SUM06/52.32)2.176] Wang and Mauzerall, 2004

RY = exp[−(W126/53.4)2.367] Lee and Hogsett, 1996

RY = exp[−(M7/186)3.2]/exp[−(25/186)3.2]

(for spring variety only)

Adams et al., 1989

RY = exp[−(M7/137)2.34]/exp[−(25/137)2.34]

(for winter variety only)

Lesser et al., 1990

Rice 1 − 0.00415AOT40 Mills et al., 2007 Van Dingenen et al., 2009

1 − 0.00949AOT40 (for China only) b Wang et al., 2012

RY = exp[−(M7/202)2.47]/exp[−(25/202)2.47] Adams et al., 1989

Maize RY = 1 − 0.00356AOT40 Mills et al., 2007 Van Dingenen et al., 2009



RY = exp[−(M12/124)2.83]/exp[−(20/124)2.83] Lesser et al., 1990

Soybean RY = 1 − 0.0113AOT40 Mills et al., 2007 Van Dingenen et al., 2009



RY = exp[−(M12/107)1.58]/exp[−(20/107)1.58] Lesser et al., 1990


(Table 1 continued)

a. Relationships are assumed for both spring and winter varieties unless otherwise noted.

b. Values for China are applied to a longitude range of 92.5°E-147.5°E and a latitude range

of 8.53°S-52.11°N.

Table 2. Equivalent food energy, maximum optimal temperature (Topt), and minimum

threshold temperature (Tbase) to define growing degree day and killing degree day for four

major crops considered in this study.

Crop Equivalent food energy

(106 kcal ton-1) a

Topt (°C) b Tbase (°C) b

Wheat 3.34 26 0

Rice 3.63 34 10

Maize 3.65 29 8

Soybean 4.46 30 10

a. From the U.S. Department of Agriculture National Nutrient Database for Standard

Reference: http://ndb.nal.usda.gov

b. Adopted from, e.g., Boschetti et al. (2009), Schlenker & Roberts (2009), Levis et al.

(2012), etc.


Table 3. Geometric mean values for ∂lnY/∂M*dM/dGDD as used in equation (5) in Methods

(main text) and described in Supplementary Methods for four major crops and four different

ozone exposure indices.

AOT40 SUM06 W126 M7 or M12

Wheat (spring) -2.159×10-4 -1.136×10-4 -4.716×10-5 -1.250×10-5

Wheat (winter) -9.148×10-5 -4.956×10-5 -2.001×10-5 -2.627×10-5

Rice -5.440×10-5 N/A N/A -2.505×10-5

Maize -4.295×10-5 -1.662×10-6 -2.818×10-6 -5.133×10-5

Soybean -1.464×10-4 -8.728×10-5 -6.159×10-5 -1.580×10-4

Table 4. Geometric mean values for ∂lnY/∂M*dM/dKDD as used in equation (5) in Methods

(main text) and described in Supplementary Methods for four major crops and four different

ozone exposure indices.

AOT40 SUM06 W126 M7 or M12

Wheat (spring) -7.248×10-4 -4.065×10-4 -1.859×10-4 -4.214×10-5

Wheat (winter) -9.655×10-4 -4.571×10-4 -1.911×10-4 -2.619×10-4

Rice -4.304×10-3 N/A N/A -1.844×10-3

Maize -4.132×10-4 -2.979×10-5 -4.245×10-5 -4.767×10-4

Soybean -2.045×10-3 -1.464×10-3 -1.047×10-3 -1.910×10-3


Table 5. Impacts of 2000-to-2050 climate change and ozone pollution trends on global total

crop production (in 1015 kcal a-1) using FAO 2050 projections as baseline for calculation,

complementary to Fig. 1 in the main text. In brackets are the percentage changes. Global total

crop production is projected to be 11.89×1015 kcal a-1 for 2050.

RCP4.5 RCP8.5

Pollution effect only +0.31 (+2.6%) -0.39 (-3.3%)

Climate effect only -1.47 (-12%) -1.52 (-13%)

Combined effect -1.27 (-11%) -1.92 (-16%)

Table 6. Regional definitions used to define major producing countries/regions in the pie

charts of Fig. 2 in the main text.

Major producing countries/regions Included countries

US All states of the United States

Europe All countries in Europe, not including Russia

China Mainland China only

S Asia All member countries of the South Asian Association for

Regional Cooperation (SAARC)

SE Asia All member countries of the Association of Southeast

Asian Nations (ASEAN)

S America All countries in South America (excluding Central

American countries, e.g., Panama)


Figure 1. Projected 2000-to-2050 changes in mean northern summer (JJA) ozone

concentration and surface temperature as a result of a combination of climate, land use and

anthropogenic emissions changes simulated by CESM, for two IPCC AR5 scenarios: RCP4.5

and RCP8.5. Ozone concentrations are maximum daily 8-hour average (MDA8) and

temperatures are daily mean. Stippling with dots indicates significant changes at 95%

confidence from Student’s t test.

Figure 2. Correlation coefficients between growing season killing degree day (KDD) and

mean precipitation over 1961-2010 for four major crops.


Figure 3. Observed sensitivities of crop yield to growing degree day (GDD) based on

multiple linear regression of data from 1961-2010 as represented in equation (4) in Methods

(main text). Values are in units of 10-3 (log unit) (degree day)-1. Only values with 90%

confidence level are shown.

Figure 4. Observed sensitivities of crop yield to killing degree day (KDD) based on multiple

linear regression of data from 1961-2010 as represented in equation (4) in Methods (main

text) and described in Supplementary Methods. Values are in units of 10-3 (log unit) (degree

day)-1. Only values with 90% confidence level are shown.


Figure 5. Relationships between the true sensitivities of crop yield to killing degree day

(KDD) and mean growing season temperature. Sensitivities are represented by βK̃DD (in log

unit per degree day) in equation (5) in Methods (main text). Values outside the 90%

interquartile range are excluded. Given also are the Pearson’s correlation coefficients (r).


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