monitoring and modelling the urban component of the carbon cycle · 2016. 6. 22. · development...
Post on 22-Jan-2021
3 Views
Preview:
TRANSCRIPT
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
top related