1. - Climate Change and Development Group -
A Comparison of Climate Change in Central Europe and Developing Countries
DRAFT!!
Dr. Matthias Lüdeke
- Climate Change and Development Group -
MDG Workshop, June 2012
Potsdam Institute for Climate Impact Research

2. Major concepts to understand anthropogenic Climate Change:
Global Climate Models
Certainty of projections
Assessment of sensitivity
and adaptation options
(future)
global GHG
emissions
(future)
Climate
Change
(future)
impacts of
Climate Change
CO2, NH4, N2O, etc.
Who contributes what?
Fair distribution of
emission reduction?
Free riding problem
Influence of a changed
climate on important
functions of, e.g., urban
systems
Change in statistics
of temperature,
precipitation etc.
Important differences between Europe and the developing world:
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

3. per cap CO2 (eq) emissions and population
Per capita and total emissions of greenhouse gases in the year For each of the indicated
continental regions, the height of the bar indicates annual per capita emissions, expressed as tonnes of carbon equivalent
(i.e., the amount of carbon that would have the same warming effect if embodied in CO2), and the
width indicates the population expressed in millions. Thus, the areas of the bars for each region represent total
annual emissions in millions of tonnes of carbon-equivalent (millions of people multiplied by tonnes per person).
The subdivisions of the bars indicate the contributions of different sources (or sinks, if negative), color-coded as
Follows (from black to light blue): coal , oil , natural gas, CH4, other gases, land use change, net biospheric release of carbon , other sources of
CO2. The multi-colored scale in the lower-left corner of the figure shows, as an example, equal annual per capita emissions
of 1 tonne of carbon-equivalent per person in each of the seven source categories, for a population of 500 million.
[Graph prepared by P. Kolp of the International Institute for Applied Systems Analysis (IIASA) based on Grübler et al., 2006]
UN-SEG 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

4. (future) global GHG emissions
Developing world:
Necessary economic growth has to be decoupled from
CO2 emissions to keep the per cap emissions close to the
desired 2t/cap/year
Europe:
CO2 emissions have to be drastically reduced to reach
per cap emissions close to the desired 2t/cap/year
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

5. (future) Climate Change
Major concepts to understand anthropogenic Climate Change:
Global Climate Models
Certainty of projections
(future)
global GHG
emissions
(future)
Climate
Change
CO2, NH4, N2O, etc.
Who contributes what?
Fair distribution of
emission reduction?
Free riding problem
Change in statistics
of temperature,
precipitation etc.
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

6. Change in annual air temperature until 2100 under Climate Change (A1B)
Multi-model mean of annual mean surface warming (surface air temperature change, °C) for the scenario A1B,
time period 2080 to Stippling is omitted for clarity
Meehl et al., 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

7. Change in annual air temperature until 2100, T(B1)/T(A1B)
Ratios of ensemble mean and annual mean temperature changes from to Ratio between the B1 and A1B scenarios.
Christensen et al., 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

8. Change in winter precipitation until 2100 under Climate Change (A1B)
Relative changes in precipitation (in percent) for the period 2090–2099, relative to 1980–1999. Values are multi-model
averages based on the SRES A1B scenario for December to February. White areas are where less than 66% of the models agree
in the sign of the change and stippled areas are where more than 90% of the models agree in the sign of the change
Meehl et al., 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

9. Change in summer precipitation until 2100 under Climate Change (A1B)
Relative changes in precipitation (in percent) for the period 2090–2099, relative to 1980–1999. Values are multi-model
averages based on the SRES A1B scenario for June to August. White areas are where less than 66% of the models agree
in the sign of the change and stippled areas are where more than 90% of the models agree in the sign of the change
Meehl et al., 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

10. Dr. Matthias Lüdeke, Climate Change and Development Group
Changes in spatial patterns of simulated precipitation intensity (defined as the annual total precipitation divided by the number of wet days) between two 20-year means (2080–2099 minus 1980–1999) for the A1B scenario.
Stippling denotes areas where at least fi ve of the nine models concur in determining that the change is statistically significant.
Each model’s time series was centred on its 1980 to 1999 average and normalised (rescaled) by its standard deviation computed (after de-trending) over the period 1960 to The models were then aggregated into an ensemble average, both at the global and at the grid-box level. Thus, changes are given in units of standard deviations.
Meehl et al., 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities

11. Main Methodological Elements:
Climate Change projections and their uncertainty for Hyderabad/India
Main Methodological Elements:
two global CO2-emission scenarios, describing a high and low emission future
A2: business as usual and B2: global emission reduction from about 2035 on
model runs of 17 global circulation models using the above emission scenarios (done for IPCC, AR4)
downscaling of global model runs for the Hyderabad area
degree of certainty is assessed by consensus amongst the model runs
weighting: ability of a model to reproduce the present climate is considered in final projection results
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 11 11

12. Climate Change projections and their uncertainty for Hyderabad/India
Lüdeke et al., 2011
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 12 12

13. Climate Change projections and their uncertainty for Hyderabad/India
Lüdeke et al., 2011
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 13 13

14. (future) Climate Change
Developing world:
Temperature change more responsive to emission reduction compared to Europe
Europe:
North-South divide of direction of precipitation change – transition zone uncertain
Both:
Temperature change comparable
Depending on climate variable and location projections may show high certainty
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 14 14

15. Major concepts to understand anthropogenic Climate Change:
Assessment of sensitivity
and adaptation options
Global Climate Models
Certainty of projections
(future)
global GHG
emissions
(future)
Climate
Change
(future)
impacts of
Climate Change
CO2, NH4, N2O, etc.
Who contributes what?
Fair distribution of
emission reduction?
Free riding problem
Influence of a changed
climate on important
functions of, e.g., urban
systems
Change in statistics
of temperature,
precipitation etc.
Major differences between Europe and the developing world:
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 15 15

16. Dr. Matthias Lüdeke, Climate Change and Development Group
Ecological vulnerability to future climate change (2061–2070) based on expected changes in
vegetation. Simulations by the Lund-Potsdam-Jena (LPJ) Dynamic Global Vegetation Model (Sitch et al., 2003),
on a continuous global grid, of a change-in-vegetation metric (ΔV: Sykes et al., 1999) that indicates the amount of
dissimilarity between vegetation conditions prior to and after climate change. The ΔV metric is based on the
relative importance of different plant life forms (e.g., tree, grass) in each vegetation class, and a series of life-form
attributes (e.g., evergreen or deciduous, tropical or nontropical) with a weight for each attribute. Due to the
generally slow adaptation of diversity in complex ecosystems, any change is considered a “loss” (except for the
encroachment of vegetation on previously barren land). Vulnerability was considered “high” when the model
simulated substantial losses in the cover of any vegetation type between the present day and the period 2061 to
2070, indicated by ΔV > 0.3, while “low” vulnerability was assigned to ΔV < 0.1
UN-SEG 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 16 16

17. Dr. Matthias Lüdeke, Climate Change and Development Group
Agro-economic vulnerability to future climate change (2061–2070) based on loss of agricultural
productivity. Solar radiation, atmospheric CO2 concentration, temperature, soil moisture, nutrient availability, and
farming practices are represented using nonlinear (process-based or empirical) functions, implemented through
the agricultural crops component in the LPJ model (Bondeau et al., 2007). Adaptation of farming practices is
considered by allowing shifts in planting dates, varieties, and irrigation (Rosenzweig and Iglesias, 2003). If a
significant yield loss in at least one important crop was identified in a country where the GDP share of agriculture
is greater than 5%, then vulnerability was ranked as “high.” In the case of low dependency on agriculture and a
decrease in only one significant crop yield (or no decrease at all), vulnerability was ranked as “low.” The remaining
two combinations were ranked as “medium.”
UN-SEG 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 17 17

18. Dr. Matthias Lüdeke, Climate Change and Development Group
Social vulnerability to future climate change (2061–2070) based on population exposed to hydrometeorological
disasters. Records in the Emergency Disasters Database (EM-DAT; OFDA/CRED, 2006) show a
general and substantial increase in the number of disasters and the number of people affected from 1950 onwards.
Part of this trend is considered to be due to the increase in population density in vulnerable areas like cities or
coastal zones and to other non-climatic factors, for geological as well as for hydro-meteorological disasters.
Comparing the increase in geological and climate-related hydro-meteorological disasters indicates the remaining
effect of climate change on the latter. The portion of people killed and affected (injured, homeless, or needing
substantial assistance) in hydro-meteorological disasters has been linked to global warming (for droughts by global
extrapolation, whereas for floods and windstorms with some regional modifications). Coastal floods and river
floods were discriminated using the Dynamic Interactive Vulnerability Assessment (DIVA; DINAS-COAST
Consortium, 2006) for coastal floods. Winter storms and tropical storms were discriminated using data from
Munich Re (Berz and Siebert, 2004).
UN-SEG 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 18 18

19. Sterzel 2009, based on UN-SEG 2007
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 19 19

20. Rapid coastal urbanisation: typical vulnerability patterns
Port-au-Prince
Mumbai
Manila
Rio de Janeiro
Lagos
Relative changes in precipitation (in percent) for the period 2090–2099, relative to 1980–1999. Values are multi-model
averages based on the SRES A1B scenario for June to August. White areas are where less than 66% of the models agree
in the sign of the change and stippled areas are where more than 90% of the models agree in the sign of the change
Kok et al., 2010
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 20 20

21. Rapid coastal urbanisation: typical vulnerability patterns
3 example clusters:
Presently low flood exposure but
high sensitivity -> unprecedented
damage types under future CC
Increasing damage from all
types of extreme events under
poor adaptation ability
Declining natural protection
against prevailing floods and
future SLR, moderate coping
abilities
Kok et al., 2010
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 21 21

22. Hyderabad/India under Climate Change
Extreme Daily Precipitation in Hyderabad
once-in-2-years percentile
Lüdeke et al, 2010
Locations of
Critical Flow
Accumulation
50% B1 A2 B1 A2
2000
2050
2100
all IPCC-AR4 runs
Increase of people
severely affected B1 A2
Kit et al.
2011
50% impact reduction under the Low Emission scenario
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 22 22

23. Present and future urban slums under Climate Change
Lacunarity based slum identification
from actual QuickBird (0.6x0.6m)
satellite imagery
Kit et al., 2011
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 23 23

24. (future) impacts of Climate Change Developing world:
Major sensitivities, in particular due to specific urban development patterns (growing informal settlements)
Europe:
Much higher adaptive capacity
For some less sensitive and exposed cities mitigation will be the major task – enforced by severe climate change impacts elsewhere at the latest
Both:
Detailed local/regional impact studies are the condition for effective adaptation planning – identification of, e.g. city clusters with similar sensitivities and exposures will simplify this demanding task
Dr. Matthias Lüdeke, Climate Change and Development Group
Research Domain II – Climate Impacts and Vulnerabilities 24 24