Monitoring and modelling the urban component of the carbon cycle

Michael RaupachMichael Raupach ESSP Global Carbon Project

CSIRO Marine and Atmospheric ResearchCentre for Australian Weather and Climate Research

and Peter Rayner

Thanks: GCP colleagues, CSIRO colleagues

“Realizing Low Carbon Cities: Bridging Science and Policy”, 16-18 February 2009, Nagoya, Japan

Introduction and OutlineIntroduction and OutlineIntroduction

� Urbanisation is one of the great present trends in the earth system:g p yinteracts with population, human aspiration, connectedness, ecological limits

� Urbanisation involves changes to modes of production and consumption

Need to understand and influence urban patterns� Need to understand and influence - urban patternsurban processes urban management

Outline

� Background: global and regional trends in emissions, population, GDP, energy

� The fine spatial picture as seenm from space

Feedbacks in the carbon-climate-human systemFeedbacks in the carbon climate human system

4

6

8

10Emissions

Atmospheric GHG GHG sinks GHG emissions0

2

4

concentrationsR1 R2 R3 C1 C2 C3

340

360

380

400

[CO2]

Global climate

Human societiesand economies

C1 C2 C3

Land, ocean systems

R1 R2 R3

280

300

320

340

Global climate system

C1 C2 C3R1 R2 R3

Regional 0.4

0.6

0.8Temperature

Regional climates

R1 R2 R3-0.6

-0.4

-0.2

0

0.2

Global trends in urbanisation 1950-2015

UN Development Program data (August 2006)http://esa.un.org/unup/index.asp?panel=3

Global trends in urbanisation 1950 2015World

7000

8000

Urban > 10M

More Developed

1000

1500

6000

7000 Urban > 10MUrban 5M to 10MUrban 1M to 5MUrban 0.5M to 1MUrban < 0.5MRural

0

500

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Less Developed6000

4000

5000

on (m

illio

n)

Rural

3000

4000

5000

3000

4000

Popu

latio

1000

2000

3000

1000

200001950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015Least Developed

500

1000

01950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

01950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

Drivers of global emissions

Raupach et al. (2007) PNASUpdated to 2005 with IEA data

1.4

1.5 FEtotalPopulationg=Gp/P

Drivers of global emissions� Kaya Identity

G FE

1.2

1.3

1.4 g pe=E/Gpf=F/Eh=F/Gp

GP

PEGFF E

= × × ×

0 9

1

1.1

Fossil-fuel CO2emission

0.7

0.8

0.9

Population

Per-capita GDP

World 0.5

0.6

1980 1985 1990 1995 2000 2005 2010

Energy intensity of GDP

Carbon intensity of energy Carbon intensity of GDP

= F/G = (E/G) x (F/E)

Development trajectories: energy

Raupach (2008) unpublished

Per capita energy12

Development trajectories: energy� Plot per capita primary energy against income, from 1980 to 2005

) Per capita energy, E/P [kW/person]

10

12

USAEUkW

/per

son)

6

8

E/P

EUJapanD1FSUChinaI di

ener

gy (

k

?

4

6E IndiaD2D3WorldAustraliaFta

prim

ary

0

2FranceTaiw anKyotoA1

Per c

apit

Taiwan in 1980 = China in 2005

Taiwan in 2005 = China in 2030 ?

0 10 20 30 40Income (g=Gp/P)

Development trajectories: CO2 emissions

Raupach (2008) unpublished

P it i i6

Development trajectories: CO2 emissions� Plot per capita FF emissions against income, from 1980 to 2006

Per capita emissions, F/P [tC/y/person]

5

6

USA/y/p

erso

n)

3

4

/P

USAEUJapanD1FSUChinass

ions

(tC

/

2

3F/ IndiaD2D3WorldAustraliaFita

FF

emis

0

1FranceTaiw anKyotoA1

Per c

ap

0 10 20 30 40Income (g=Gp/P)

Urbanisation:global spatial data

global spatial data

� Population density(GRUMP)

min max

� Nightlights(DSMP-OLS)

Raupach, Rayner, Paget (2009)(Submitted to Energy Policy)

Nightlights (L) versus population density (D)

Raupach, Rayner, Paget (2009)(Submitted to Energy Policy)

Nightlights (L) versus population density (D)

Probability distributions, ranks, power lawsProbability distributions, ranks, power laws� Exceedance probability distribution of random variable is the probability of

exceeding a given value (x)

� Relationship to rank: if set of observed x is ranked in descending order,• EPD(x) = (rank of x) / (number of observations)

� Power-law hypothesis:

Zipf plot: Populations of largest urban regions and cities (2005)

100

Urban RegionsCities• EPD(x) ~ x−p

• A "scale-free" distribution 10atio

n (M

)

Citiesslope 1slope 2

• "Zipf's law" of city sizes10

Popu

la

11 10 100

Rank

Exceedance probability distribution (EPD)for nightlights and population density

Raupach, Rayner, Paget (2009)(Submitted to Energy Policy)

for nightlights and population density

Log(L) Log(L)Log(EPD) Log(EPD)

Nightlights Nightlights

USA Chi

g( ) g( )

Slope −1.8 Slope −1.8

USAEuropeJapanD1FSUWorld

ChinaIndiaD2D3World

World

Log(EPD) Log(EPD)Log(D) Log(D)

Population Density Population Density

Slope −1.8 Slope −1.8

Physical-economic indicators and nightlights

Raupach, Rayner, Paget (2009)(Submitted to Energy Policy)

Physical economic indicators and nightlights

Energy (kW/km2) Emissions (tC/y/km2)

r2 = 0 97 r2 = 0 96r = 0.97 r = 0.96

NLcorr NLcorr

GDP-PPP (k$/y/km2)

USAEuropeJapanD1FSU

ChinaIndiaD2D3World

r2 = 0.94FSU World

NLcorr

CO2 emissions map from data assimilation

Rayner, Raupach, Paget, Ciais (In prep)

CO2 emissions map from data assimilation� FFDAS = Fossil Fuel Data Assimilation System

• Assimilates nightlights, population density, national physical-economic data• Accounts for nightlights saturation and other data errors• Yields map of areal density of emissions (tC/y/km2)• In future, can also assimilate other data sources including CO2 from space

min max

SummarySummary� Nightlights data are a powerful resource, AFTER accounting for saturation

� Power-law Exceedance Probability Distribution (EPD) is observed for nightlights y ( ) g gand population density

� Power-law EPD allows estimate of nighlight lost to instrumental saturation:World 3.4%USA 16%Japan 54%

� Saturation-corrected nightlights, with population density, can provide spatial g g ydensities of energy consumption, emissions, GDP

� Fossil-fuel Data Assimilation System (FFDAS)• Yields emissions maps from nightlights, population, economyp g g , p p , y• Later can assimilate CO2 from space• Output is a high-resolution emissions map which is consistent with

– local and national information– aggregate emissions received by the global atmosphere

Hilary Talbot

Abstract and extra slidesAbstract and extra slides� Urbanisation represents not only the tendency of a population to live in cities and towns, but

also the adoption of associated modes of production and consumption. The latter factor is the reason why the global trend towards urbanisation has major implications for the carbon cycle, y g j p yclimate and the earth system. To explore these implications, three big questions need to be addressed: What are the space-time patterns of urbanisation and its effects? What are the governing processes? What are the opportunities for creative management? This talk will briefly address the research agenda implied in each of these questions.

� Patterns: The discernment of patterns requires better information, which needs to be both global and sufficiently finely resolved to “see” cities and towns. I will indicate the information available from combined use of nightlights (from satellite observations), population-density data and regional data on physical and financial economies.

� Processes: Urbanisation is one visible reflection of four great trends which are together determining the evolution of the earth system in the present anthropocene era: population and its dynamics in both developed and developing regions, human aspiration and its consequences, globalisation and interconnectedness of economies and cultures, and global

l i l li it t l i l di li t d th b l I decological limits on natural resource use, including climate and the carbon cycle. Improved understanding of the syndrome of processes linked with urbanisation can elucidate much about how these great trends operate and interact.

� Management: To reduce consumption, and its impacts on climate and ecosystems, new f furban forms are needed. The management challenge is both to imagine these new forms and

to devise the pathways for evolving our cities towards them.

Components of radiative forcing

CO2

other gases

nonnon-gas

IPCC (2007) WG1

Global temperature predictions for 1900 to 2100(IPCC Fourth Assessment Feb 2007)(IPCC Fourth Assessment, Feb 2007)

Future CO2 emissions (2000-2100)

High A2 1700 GtCMedium A1B 1500 GtCLow B1 920 GtCNone ~250 GtC

Actual CO2 emissions

Climate system inertia2 deg Climate system inertiag

IPCC (2007) Fourth Assessment, WG1 SPM, Fig 7

A phase transition in human ecology

Global per capita GDP10000

GWP per capita(Y2000 $US / person / y)in human ecology

� Since 1800, global wealth 1000

, gand per-person resource use have doubled every 45 years

doubling time = 45 y

� Growth in consumption:• ti l b f 1900

Global population and GDP100000

1000 500 1000 1500 2000

t e 5 y

AD 0 500 1000 1500 2000

• essential before 1900• disaster after 2050

10000

PopulationGDPpppPopulation (million)GWP (billion Y2000 $US / y)

1000

Angus Maddison (http://www.ggdc.net/maddison/)

1000 500 1000 1500 2000AD 0 500 1000 1500 2000

25

30

GtC

/y)

CDIACIEAallA1B(Av)A1FI(Av)A1T(A )

Global CO2 emissionsfrom fossil fuels to 2007

15

20

el E

mis

sion

(G

A1T(Av)A2(Av)B1(Av)B2(Av)� Emissions from fossil fuels

and industry (CDIAC data)

from fossil fuels to 2007

5

10

Foss

il Fu

e

Year Emissions (GtC/y)2004 7.692005 7.99

9 5

10CDIACIEAall

01850 1900 1950 2000 2050 2100

2006 8.232007 8.47

8

8.5

9

9.5

sion

(GtC

/y)

IEAallA1B(Av)A1FI(Av)A1T(Av)A2(Av)B1(Av)

� Growth rates (CDIAC data)

Decade Growth rate1980-89 1.90 % y−1

6.5

7

7.5

sil F

uel E

mis

s ( )B2(Av)

1980 89 1.90 % y1990-99 0.93 % y−1

2000-07 3.47 % y−1

5

5.5

6

1990 1995 2000 2005 2010

Foss

Graphs: Raupach et al. (2007) PNAS, with updated data: CDIAC to 2007, IEA to 2005

1.2

1.4

1.6

1.8

2

1.2

1.4

1.6

1.8

2 FEtotalPopulationg=Gp/Pe=E/Gpf=F/Eh=F/Gp

1.2

1.4

1.6

1.8

2

USA 0.2

0.4

0.6

0.8

1

EU 0.2

0.4

0.6

0.8

1

Japan 0.2

0.4

0.6

0.8

1

01980 1985 1990 1995 2000 2005 2010

01980 1985 1990 1995 2000 2005 2010

01980 1985 1990 1995 2000 2005 2010

1 4

1.6

1.8

2

1 4

1.6

1.8

2

2.5

3

0 4

0.6

0.8

1

1.2

1.4

0 4

0.6

0.8

1

1.2

1.4

1

1.5

2

D1 0

0.2

0.4

1980 1985 1990 1995 2000 2005 2010

FSU 0

0.2

0.4

1980 1985 1990 1995 2000 2005 2010

3 3 3

China 0

0.5

1980 1985 1990 1995 2000 2005 2010

1.5

2

2.5

1.5

2

2.5

1.5

2

2.5

India 0

0.5

1

1980 1985 1990 1995 2000 2005 2010

D2 0

0.5

1

1980 1985 1990 1995 2000 2005 2010

D30

0.5

1

1980 1985 1990 1995 2000 2005 2010

Regional Shift in Emissions Share

Emissions from developing countries are growing fastns 62%

Emissions from developing countries are growing fastal

Em

issi

on

62%57%

49.7%

50 3%

53%

obal

Ann

ua

KyotoKyotoProtocol

Current

47%38%

43%50.3%

age

of G

lo

FCCC

KyotoProtocoladopted

enters intoforce

Perc

ent

KyotoReference Year

J. Gregg and G. Marland, 2008, personal communication

Populations of largest urban regions and cities

http://en.wikipedia.org/wiki/List_of_metropolitan_areas_by_populationAugust 2006

Populations of largest urban regions and cities

� 100 largest urban regions Populations of largest urban regions and cities (2005)

20g g

1: Tokyo (35.2M)2: Mexico City (19.4M)100: Casablanca (3.1M)Total: 693M (10 7% of global)

urban regions and cities (2005)

14

16

18

Urban RegionsCities

Tokyo region(35M)

Total: 693M (10.7% of global)

� 60 largest cities 10

12

14

ulat

ion

(M) Cities

g1: Mumbai (12.78M)9: Mexico City (8.5M)60: Pune (3.06M)Total: 351M (5 4% of global) 4

6

8P

opu

Total: 351M (5.4% of global)

� We are talking about urban 0

2

4

0 20 40 60 80 100settlements of all sizes! 0 20 40 60 80 100Rank

Urban and rural incomes: Chinaincomes: China

� Per capita income of urban and rural phouseholds in China, 1978 - 1997

� Heilig, G.K. (1999) Can China feed itself? A system for evaluation of policy options. IIASA. (http://www.iiasa.ac.at/Research/LUC/ChinaFood/index_h.htm).htm)

� Caption: This chart partly explains the attraction of cities and towns for China's rural population. Whereas average household income has risen significantly in rural areas, incomes in urban areas have increased even more. The gap between urban and rural income has remainedgap between urban and rural income has remained almost unchanged.

� Source: China Statistical Yearbook, Beijing, 1998 (p.325)

� Note: Constant prices.

Power-law exponents and missing nightlights fractionPower law exponents and missing nightlights fraction� Power-law exponents: −1.2 to − 2.8 (smaller in brighter regions)

� Fraction of nightlights unseen because of saturation: 3.4% (World)54% (Japan)

Nightlights (L) Population density Region Exponent p Mean L

(counts) Exponent p Mean D

(person km−2) Missing nightlights fraction because of

isaturation, mL USA 1.57 5.63 1.64 32.80 0.162

Europe 1.73 8.57 1.73 125.11 0.137 Japan 1.21 10.13 1.18 327.82 0.543p

D1 2.10 1.34 1.21 8.76 0.020 FSU 2.39 2.19 1.72 16.80 0.009

China 2.27 2.33 2.10 139.02 0.013 India 2 62 3 63 1 95 342 21 0 005India 2.62 3.63 1.95 342.21 0.005D2 2.22 1.95 1.54 41.05 0.014 D3 2.76 0.40 1.54 39.21 0.001

World 1.97 2.22 1.77 51.13 0.034