Development	
  of	
  a	
  new	  type	  of	
  crop	  model	  for	
  impact	
  assessment	
  of	
  global	
  warming	  and	
  air	  pollu7on	
  (Ozone)	
20th	
  AIM	  WS@NIES	  24	
  Jan.	
  2015	
  
	
  

Yuji	
  Masutomi	  College	  of	
  Agriculture,	
  Ibaraki	  Univ.	

3. theResults
multipliers listed in Table 3 were applied to YXs,model.

Wedecrease
examined the ACP values in Asia without andproduction

4. D
fertilization effect (Figs. 6 and 7, respectively). Tabl
We
examined
the ACPthe
valuesAsiaPPD
withoutandthe Cvalues,including
SDCPinandvalues.
In withthe 2020
4.1.
fertilization
effect(Figs. of
6 and7, respectively).
6 lists thb
negative
impacts
climatechange (as Table
representedGC
includingthe SDCP and
PPD values.
the 2020s,mostthe inaver
3.1.
Spatial impact ofof climate
theCO2 fertilization
effect)wereInsmall—at!6
Examination
the change
averagechangesinyield values,
impacts of climate change (as represented by ACP with(=(YXs,model ! Y1990s, model) " 100/Y1990s, model) [%] without the CO2 negative
Table6)—but negative for all SRES scenarios. Ev
W
the CO2 fertilization effect) were small—at most !6.3% in A2 (Examinationoftheaveragechangesinyield
positive effect of CO fertilization was taken taintinto
fertilization effect (Fig. 4) revealed that climate change would(=(YXs,model ! Y1990s, model) " 100/Y1990s, model) [%] without the CO2
Table 6)—but negative for 2all SRES scenarios. Even when
proje
average changes
in production taken
were into
still account,negative
reduceyield over a large area of Asia. The regions that showed large positiveof CO
fertilization effect (Fig. 4) revealed that climate change would
Fig. 6. Average change effectin production(A2CP)fertilizationin Asia withoutwas
the CO2 fertilization
proce
Dingenen!et!al.!(2009)
scenarios.
the Pwerein the 2020s
decreases
in yield
werewestern
Japan,
China,
the southern
Fig.a
9. large
Average relative
yieldoflossAsia.
from2 metrics
foreasternthe 4 crops, yearthat
2000.
PD values
effect.
averagechangesMoreover,in production
still negative
for allwe
S
reduce yield
over
area
The
regions
showedlarge-ãĩ3. Spatial
Resultsimpact of climate change
3.1.
ý!

ĉĖĚ
ĔáÄý!

Impact	  of	
  global	
  warming	
  on	
  
rice	
  produc7vity	

decreases in yield were western Japan, eastern China, the southern

u$ĘģĦª-ãęC$

clima

scenarios. Moreover, the PPD values in the 2020s were highet for
al.

ÂĚü
P

Ģ1s

e yield loss from 2 metrics for the 4 crops, year 2000.

Masutomi!et!al.!(2009)
Fig. 7. Average change in production (A ) in Asia with the CO fertilization effect.
CP

2

Masutomi	
  et	
  al.	  (2009)	

Error bars indicate standard deviation of changes in production (i.e., SDCP).

SRES scenarios. This is because the negative impacts of climatechange for almost all climate scenarios were small but larger thanthe positive effects of CO2 fertilization in the 2020s.
In the 2050s, the ACP values without the CO2 fertilization effectwere greater than in the 2020s (Fig. 6, Table 6). On the other hand,the absolute values of ACP in the 2050s, taking into account the CO2
fertilization effect, were very small in all SRES scenarios (Fig. 7,
Table 6). This is because the increased average negative impacts ofclimate change were almost equal to the increased CO2 fertilization

Global	
  warming	
  will	  have	  signiﬁcant	
  impacts	
  on	
  rice	  produc7vity	
  over	  Asia.	

sivel
smal
prob
clima
clima
value
heav
denc
ACP v
dens
3, 5,
there
were
the p
of fu
way
impa
4.2.

W
clima
unce
for eacomp
taintNext	
  step	
  is…	• How	  to	
  adapt?	
  – Local	
  problem	  • We	
  must	  consider	
  local	  rice	  varie7es.	
  • Diﬀerent	
  rice	  varie7es	
  have	  diﬀerent	
  responses	
  to	
  high	  temperature.	
  Imperfect	
  grain	

ふさおとめ
越南222号
くまさんの力
つや姫
越路早生
コシヒカリ
元気つくし
*とちぎの星
ひとめぼれ
てんこもり
*朝の光
Kinuhikari	
キヌヒカリ
あきたこまちさがびより
日本晴*彩のみのりゆめまつり
アキニシキ*あさひの夢*あかね空
初星*彩のかがやき*さとじまん

Perfect	
  grain	*彩のきずなSai-­‐no-­‐kizuna	

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

Ra7o	
  of	
  perfect	
  grains	
  under	  high	  air	
  temperature	
  experiments	

図1 最近育成された品種の高温登熟性検定結果（平24）

白未熟粒
その他
整粒
*縞葉枯病抵
抗性品種

Arakawa	
  et	
  al.	  (2014)	

40%

6
4
2
0

20%

How	
  to	
  simulate	
  the	
  diﬀerence?	

0%～45 ～50 ～55 ～60 ～65 ～70 ～75 ～80 ～85 ～90• Leaf	  temperature	
  

整粒（％）
図2 「彩のきずな」県内産の玄米品質（整粒比）ヒストグラム

– 	
  is	
  one	  of	
  key	
  factors	
  for	  the	
  diﬀerent	
  response	
  Sai-­‐no-­‐kizuna	Kinuhikari	

Heading	
  stage	Arakawa	
  et	
  al.	  (2014)	

写真1 サーモグラフィーによる登熟期の群落表面温度

Same	
  condi7ons	
  including	
  weather,	
  but	
  diﬀerent	
  leaf	  temperature	
表1 出穂期の「彩のきずな」の光合成特性
品種名

光合成有効放射 葉表面温度
-2

蒸散速度
-2 -1

気孔コンダクタンス
-2 -1

光合成

-2

How	
  to	
  simulate	
  leaf	  temperature?	
• Basically,	
  temperature	
  of	
  any	
  objects	
  is	
  determined	
  by	
  input	
  and	
  output	
  of	
  energy.	
  energy	Ta!	
T?	

energy	Leaf	
  temperature	
  can	  be	
  simulated	
  by	
  energy	
  balance	
  of	
  the	
  crop.	
  Requirement	
  #1	
  
	
  
Energy	  balance	
  is	
  simulated	
  to	
  determine	
  leaf	  temperature	ĮĺőęXí
Impact	  of	
  Ozone	Įĺőè*

ŕræęT;ĮĺőVôöôúúùŖâDęĮĺő@ŕræęT;ĮĺőVô÷øôúúùŖ

36ppb	<<	
Įĺőè*%Ėĝü
(Provided	
  by	
  Dr.	
  Yonekura	
  (CESS))	
-ãĚ³õ÷ŔP

2ppb	

Ozone	
  reduces	
  crop	
  produc7vity	only slightly from 2000 levels with yields
þ0.1e1.8% for wheat, þ0.7e1.0% for soybean,

2000) for each crop individually and combined. The change inregionally aggregated and global CPL for each crop, as well as

Future	  impacts	  of	
  Ozone	LETTERS

NATURE | Vol 448 | 16 August 2007

Maize

SoybeanChinaaPresent JJA

90º N

India

90º N

45º N

Mexico45º NBrazil0ºCanadaAverage
M12
AOT40

90º W

3

4

5

c

60
[O3] (p.p.b.)

6

0

75

1

90
2

4

30

5

6

90º N
Total (A2)	
45º N

90º E

[O3] (p.p.b.)
7

75

8

High ozone sensitivity

90

Sitch	  et	
  al.	  (2007)	

0º

India
U.S.

45º S

0º
Average
M12
45 AOT4060

dChange in CPL (Million Metric Tons)

China

0º

90º W
15

3

Low ozone sensitivity

45º N

90º S
180º

90º E

Hungary
30
45

90º N
Wheat

0º

Romania15

Change in CPL (Million Metric Tons)

45º S

Italy

90º S
180º

(A2)	

2100 JJA

0ºFrance

45º S

2

b

U.S.

45º S

Brazil90º S
180º

90º Russia
W–30

Pakistan

–20

0º
–10

90º S
180º

90º E
–5

5

France

20

25

30

35

0

5

90º E

–5
5
10
Change in GPP due to O3 (%)

Figure 1 | Temporal changes of modelled ozone concentrations and grossprimary productivity. a, b, Modelled
Iran (24-h) mean surface [O3] in
Average diurnal
p.p.b. averaged over June, JulyM12
and August (JJA) for the present day (a) andEgyptscenario (b). c, d, Simulated
the year 2100 under the SRES AOT40
A2 emissions
15

0º

–30
Crop	
  produc7on	
  
l–20
oss	
–10

10

Turkeyin GPP due to O (%)
Change
3

10

90º W

10

percentage change in gross primary productivity (GPP) between 1901 andpre-industrial atmospheric [CO2] for ‘low’
2100 due to O3 effects at fixed
AverageM12
sensitivity.
(c) and ‘high’ (d) ozone plant
AOT40

15

20

25

30

35

Avnery	  et	
  al.	  (2011)	

interactions
between a changing climate, stomatal conductance,and ‘low’
Change in CPL (Million
MetricplantTons) O3 sensitivity models, respectively, both within the
Change in CPL(Million Metric Tons)
IPCC range22 of 24.3 Pg C yr21 to 20.9 Pg C yr21 with a mean ofozone uptake and plant productivity.
ction loss (CPL, millionWe
metric
tons)
for
the
ten
countries
withhighestabsolute
differenceinestimatedmean
CPL
and 2030 underthe A2(Supplementary
Table2). Figure 1 shows the impact
22.6
Pgbetween
C yr21 2000
use spatially explicit [O3] fields derived from the STOCHEM

gStatistical
Statistical
relationships
relationships

Additional
fraction
RCP4.5
RCP4.5under15 kcal
Combinede�ect: −0.64−0.64 ×× 10
1015
nourished
Combinede�ect:kcal

15 kcalProjectedPollutione�ectson+0.22
total
×
1015
Pollution e�ect:e�ect: +0.22
× 10
kcal
annualcrop productivitybb(106 kcal ha−1)

With pollution
and climatee�ectsCombined	
  eﬀect	
  of	
  CC	
  and	
  Ozone	Original
LETTERS15 kcalClimateClimate e�ect:e�ect: −0.80−0.80 ×× 10
1015 kcalagg

distribution
Fraction
LETTERSE�ects onon perper capita
capita foodfoodE�ectsPUBLISHED ONLINE: 27 JULY 2014 | DOI: 10.1038/NCLIMATE2317
underNATUREand
CLIMATECHANGE DOI: 10.1038/NCLIMATE2317
consumption
undernourishment
consumption
and undernourishment
nourishedcWith pollution
pollutionWithand climate
climateande�ectse�ectsThreat to future global
Additionalfood security from climate
Additional
MDER
DES distribution
fraction
fraction
Projectede�ectson
totalchangeand ozone underair pollution
underannual
annual crop
crop productivity
productivity

RCP4.5ozone andStatisticalclimaterelationships
projectionse�ects on total
Projected

M
M

Pollution e�ect: +0.22 × 10 kcal
RCP4.5nourished
6−1)
Original(10
nourished
Original
kcal haha−1)(106 kcal
1 †
2,3
1,4
b
Amos P. K. Tai * , Maria Val Martin and Colette L. HealdCombined
distributione�ect: −0.64
× 1015 kcal
distribution
Fraction
RCP4.5Fraction
Future2050
food production is highly vulnerable toboth climate for our methodology and summarizes our results. First, we use theunderunderemissions/changeand air pollution with implications for
global food Community E�ects
Earthon
SystemModel
per capita
food (CESM) to simulate present-day
land use
nourishednourishedconsumption
and undernourishment
security1–4 . Climate change
adaptation and ozone
regulation (2000) and
derive future(2050) projections of hourly temperature
15
15

dd

f
Pollution e�ect: −0.26
× 10 askcal
have been identified
important strategies to safeguard and ozone concentration consistent with the representative conClimate e�ect: −0.80 × 1015 kcal

eeStatistical
Statistical
relationships
relationships

MDER
gWith pollution
DES distribution
distribution
MDER
food production5,6 , but little is known about how climate
centration pathways(RCPs) represented
in the Intergovernmental
DES
climateand ozone pollution interact to a�ect agriculture, nor the PanelAdditionalon Climate Change Fifth andAssessment
Report14,15 (AR5). Oure�ectsfraction
Projected e�ects
total
relative e�ectiveness of these two strategies
forondi�erent
future ozone projections not only follow trends in anthropogenic
undercrop productivity
crops and CESM
regions. Here we present anannual
integrated
analysisof emissionsof precursor gases but also include the effects of climate
nourished
6−1
Original(10 kcal ha )the individual and combined e�ects of 2000–2050 climate and land use changes; these confounding
factors are known to sigdistribution
16,17
Fractionchange and ozone trends 15
on the production
of
four
major
crops
nificantly
impactfutureozone
projections
but are not consideredff
15 kcalPollutione�ect:
×10
kcal
under- crop impact studies. We consider two scenarios: RCP4.5,
d worldwide based on historical
Pollutione�ect:−0.26
× 10(wheat,rice,−0.26
maize andsoybean)in previous
nourishedobservations and model projections, specifically accounting representing an intermediate pathway with a global reduction infor ozone–temperature co-variation. The projections exclude surface ozoneMDER
due to pollution
control measures worldwide (except
DES distribution
RCP8.5the e�ectin South Asia)14 ; and RCP8.5, representing a more ‘pessimistic’,2050 of rising CO2 , which has complex and potentiallyo�setting
impacts on global food supply7–10 . We show that energy-intensive pathway with a worldwide increase in ozone except
emissions/land use
warming
reduces global crop production by >10% by 2050 in
the US and around Japan18 (Supplementary Fig. 1). The two scef
Pollution globale�ect: −0.26
× 1015 kcal
with a potential to substantially worsen
malnutrition
narios represent a range of policy options regarding ozone regulain all scenarios considered. Ozone
trends either exacerbate tion. Both scenarios project a global increase in surface temperaturee
RCP8.5or o�set a substantial fraction of climate impacts
depending (Supplementary Fig. 1), with similar effects on crop production as
RCP8.5on the scenario, suggesting the importance of air quality discussed below. Previous historical crop–temperature impact anal15
RCP8.515 15 planning. Furthermore, Combined
managementin agricultural
we find yses5,19 suggest a substantial
potential for crop-level adaptation to
15 kcalStatistical
15e�ect:
−1.07
×
10
ozone
and
15
kcal
Combinede�ect:
−1.07
×
10
kcal
Climatee�ect:−0.81
×10
kcal crops are primarily sensitive avoidRCP8.5
Climatee�ect:−0.81
×
10 somethat
dependingonregionlosses from warming, but they do not consider the concurrent
relationships
climatetoeither ozone (for example, wheat) or heat (for example, impacts of changing ozone levels
that may offset the benefits ofprojections
Combined e�ect: −1.07 × 1015 kcale�ect: −0.81
× 1015 kcalofmaize) alone, providing a measureClimateof relative
benefits
adaptation12 . We therefore exclude adaptation in our projections,climate adaptation versus ozone regulation for food security and focus on the potential of ozone regulation to combat the warmregions.−1 −0.5 0.0 0.5
impacts.20.0
Other environmental factors such as water scarcity−20 −10in di�erent−5 −2
1.0 2.0
5.0ing−20 −10 −5 −2−0.5 0.0
0.5 10.0
1.0 2.0 5.0 10.0 20.0
Global demand for food is expected
to increase by at−1least
50% andlanddegradation may influence future food production but are−1
from 2010 to 2050 mainly as a result
and a shift
outsidethe scope of this study.
106 kcal ha
6 of population−1 growth
6
10in
kcal
ha−1
towards a more ‘westernized’ dietdeveloping
regions11 . Assuming
From the CESM-simulated results we derive various metrics to
Figure 1 | Methodology
and results.
Using CESM,
we toderivefutureprojections
ozone exposurethe
indices
and agro-climaticwhich
are pollutionthat agricultural
production
is ablemeetthe (2050)
growingdemand for parameterize
influenceof climate variables,change andozone
used to estimate
subsequente�ects on total
crop productivity
based
on statistical crop–ozone
and crop–climate
relationships.
are andexpressed
througha combinationof annual
economic
growth andagricultural
on crop production:
growing
degree daysE�ects(GDDs)
killing degree

RCP8.5

Combined e�ect: −1.07 × 10 kcal

Climate e�ect: −0.81 × 10 kcal

−20 −10

−5

−2

−1

−0.5

0.0

0.5

1.0

2.0

5.0 10.0 20.0

Crop	
  physiological	
  interac7on	
O3

Both	
  CO2	
  and	  O3	  are	
  taken	
  up	
  into	  leaf	
  through	  stoma.	
  	• High	  CO2	  concentra7on	
  tends	
  to	
  close	  stoma	  • O3	
  uptake	
  will	  reduce	
  →	
  reduce	
  ozone	  damage	  • High	  O3	
  concentra7on	
  tends	
  to	
  close	  stoma	  • CO2	
  uptake	
  will	  reduce	
  →	
  reduce	
  photosynthesis	
  	
  
	
  	

What	
  will	  happen	
  under	  High	  CO2	
  and	  O3	  concentra7on?	

A	
  stomatal	
  response	
  model	  should	
  be	
  included.	
  Requirement	
  #2	
  
	
  
Stomatal	
  response	
  should	
  be	
  included	

So,	
  I	
  am	
  developing	
  MATCRO…	
• MATCRO	
  has	
  two	
  components	  – Land	  surface	  model	  	
  • Simulates	
  heat	  and	
  water	
  ﬂuxes	
  (energy	
  balance)	
  • Includes	
  a	
  stomatal	
  response	
  model	  • is	
  based	
  on	
  MATSIRO	
  (Takata	
  et	
  al.,	
  2003)	
  – MATSIRO	
  is	
  a	
  component	
  of	
  climate	
  models	  – Crop	  model	  • Phenology	
  and	  Par77oning	
  models	

MATSIRO	
  +	
  Crop	
  =	
  MATCRO	Structure	  of	
  MATCRO	
Temperature
Assimilate#Par<<oning#module

Sunshine#dura<on

DVI#module

Maintenance#Respira<on

GPP#

Leaf#

Stem#

Glucose#stock

Starch##stock#

Root#

Storage#

DVR

MATSIRO

LAI,#Height#
Root#

Leaf#biomass

Dead#leaf

Stem#biomass

Root#biomass

Growth#Respira<on

Storage#biomass

DVISimula7on	
  results	Model	  test	  site	  	  	  -­‐Mase-­‐	• Observa7on：climate	
  variables,	
  sensible	
  and	
  latent	
  heat	
  ﬂuxes,	
  CO2	
  and	
  CH4	
  ﬂuxes,	
  biomass	
  for	
  each	  organ,	
  LAI,	
  etc..	
  • Variety:	
  Koshihikari		  -­‐Total	  Biomass-­‐	-­‐Panicle	
  (Yields)	
  biomass-­‐	-­‐Latent	
  heat	  ﬂux-­‐	  	
  (evapora7on	
  and	  transpira7on)	

Heat	
  ﬂux	
  into	  ground	Summary	  	• A	
  new	  type	  of	
  crop	  model	  has	  been	
  developed	  – MATCRO	
  
• is	
  a	
  LSM	
  combined	
  with	  a	
  crop	  model	  • MATCRO	
  can	
  reproduce	
  well	  – Biomass	  – heat	  ﬂuxes	  • Leaf	  temperature	
  must	  be	
  reproduced	
  well	Challenges	
• Ozone	  response	
  and	
  interac7on	
  with	  CO2	  • Global	
  applica7on	
  – Global	  parameteriza7on	
  • Phenology	
  model	  and	  par77oning	
  model	  – Nitrogen	
  dynamics	Thank	  you	
  for	
  your	  aoen7on!	

Parameteriza7on	
  	
  -­‐Par77oning-­‐	Dead	
  leaf	Speciﬁc	
  leaf	  weight	

-­‐Sensible	
  heat	  ﬂux-­‐	

Parameteriza7on	Parameteriza7on	
  I -­‐Phenology-­‐	• The	
  method	  of	  Growing	
  degree	  days(GDDs)	
  is	
  used.	
  – GDDs	  are	  summa7on	
  of	
  air	
  temperatures	
  over	  growing	
  periods	  -­‐Leaf	
  biomass-­‐	Vol 458 | 23 April 2009 | doi:10.1038/nature07949

ETTERSThe	
  impact	
  of	
  changes	
  in	
  diﬀuse	
  radia7on	
  on	
  crops	

mpact of changes in diffuse radiation on the globalnd carbon •sink
Diﬀuse-­‐radia7on	
  fer7liza7on	
  eﬀect	  – Surface	  radia7on	
  has	
  two	
  components	
  of	
  Direct	
  and	
  Diﬀuse	  – Crops	
  can	
  use	
  diﬀuse	  radia7on	
  more	  eﬃciently	
  than	
  direct	  radia7on	  – Higher	
  frac7on	  of	
  diﬀuse	  radia7on	
  increase	
  crop	
  produc7vity.	
  photosynthesis tends to increase with irradiance. However,approach to scale photosynthesis from the leaf to the canopy. In thisM. Mercado1, Nicolas Bellouin2, Stephen Sitch2, Olivier Boucher2, Chris Huntingford1, Martin Wild3
ter M. Cox4t theoretical and observational studies have demonstrated
photosynthesis is also more efficient under diffuse lighttions1–5. Changes in cloud cover or atmospheric aerosol loadarising from either volcanic or anthropogenic emissions,both the total photosynthetically active radiation reachingurface and the fraction of this radiation that is diffuse, withtain overall effects on global plant productivity and the landn sink. Here we estimate the impact of variations in diffuse
on on the land carbon sink using a global model modified tont for the effects of variations in both direct and diffuse
tion on canopy photosynthesis. We estimate that variations
fuse fraction, associated largely with the ‘global dimming’d6–8, enhanced the land carbon sink by approximately oneer between 1960 and 1999. However, under a climate mitigascenario for the twenty-first century in which sulphateols decline before atmospheric CO2 is stabilized, thisse-radiation’ fertilization effect declines rapidly to near zeroe end of the twenty-first century.
e solar radiation reaching the Earth’s surface is the primary driver
nt photosynthesis. Leaf photosynthesis increases nonlinearly withnt photosynthetically active radiation (PAR), saturating at lightthat are often exceeded on bright days during the growing season1). Under clear-sky conditions, a fraction of the plant canopy isnated by direct solar radiation consisting of bright ‘sunflecks’,
he remaining portion of the canopy being in the shade. The sunlit
on of the canopy has leaves that are often light saturated andore have low light-use efficiency, whereas leaves in the shadestudy, we also separated each canopy layer into sunlit and shaded
regions11. Figure 1 shows a comparison of the simulated light
response of the gross primary productivity (GPP) with measurements inferred from the eddy correlation technique under direct
and diffuse irradiance conditions within a broadleaf temperate
forest12 and a needleleaf temperate forest13. The modified JULES• High	  concentra7on	
  of	
  aerosols	
  decrease	
  total	  radia7on	
  but	  increase	
  the	
  frac7on	
  of	
  diﬀuse	  radia7on.	
  
40

a

GPP (μmol m–2 s–1)

30

20

10

0
0
25

–1)

20

b

400

800

1,200

1,600

2,000

Diﬀuse	  	
Vol 458 | 23 April 2009 | doi:10.1038/nature07949

LETTERS
Impact of changes in diffuse radiation on the global
land carbon sink
Lina M. Mercado1, Nicolas Bellouin2, Stephen Sitch2, Olivier Boucher2, Chris Huntingford1, Martin Wild3& Peter M. Cox4

approach to scale photosynthesis from the leaf to the canopy. In thisstudy, we also separated each canopy layer into sunlit and shaded
regions11. Figure 1 shows a comparison of the simulated light
response of the gross primary productivity (GPP) with measurements inferred from the eddy correlation technique under direct
and diffuse irradiance conditions within a broadleaf temperate
forest12 and a needleleaf temperate forest13. The modified JULES
40

30

mol m–2 s–1)

Plant photosynthesis tends to increase with irradiance. However,recent theoretical and observational studies have demonstrated
that photosynthesis is also more efficient under diffuse light
conditions1–5. Changes in cloud cover or atmospheric aerosol loadings, arising from either volcanic or anthropogenic emissions,alter both the total photosynthetically active radiation reaching
the surface and the fraction of this radiation that is diffuse, withuncertain overall effects on global plant productivity and the landcarbon sink. Here we estimate the impact of variations in diffuse
fraction on the land carbon sink using a global model modified toaccount for the effects of variations in both direct and diffuse
radiation on canopy photosynthesis. We estimate that variations
in diffuse fraction, associated largely with the ‘global dimming’
period6–8, enhanced the land carbon sink by approximately onequarter between 1960 and 1999. However, under a climate mitigation scenario for the twenty-first century in which sulphate20a