1. WFM 6311: Climate Change Risk Management
Lecture-8:Uncertainties in the Development of Climate Scenarios
Akm Saiful Islam
Institute of Water and Flood Management (IWFM)
Bangladesh University of Engineering and Technology (BUET)
February, 2013
Disclaimer: Materials used for this presentation are based on PRECIS training by Met office, UK and used for academic purposes

2. Warning: Future projections contain uncertainty
We are trying to project how the future climate will change.
Challenges:
Future climate will be influenced by socio-economic changes outside the science.
We don’t have observations of the future to verify projections/our scientific understanding.
We can aim to quantify the impact of these factors.

3. Types of uncertainty
Socio-economic changes
Earth system processes

4. Types of uncertainty
Socio-economic changes

5. Why socio-economics will matter
Future population size, economic growth, use of technology, energy consumption and land use and agriculture will all influence the future emissions of greenhouse gases and aerosols.
As scientists we are not in a position to make statements of likelihood of how the socio-economics will change.
Instead we take a small number of “representative” scenarios of how the socio-economics are likely to change (SRES).

6. A qualitative description of the SRES scenarios
The current thinking on emissions scenarios is provided by the IPCC Special Report on Emissions Scenarios (SRES)
These explore uncertainties in the key assumptions and relationship about future population, socio-economic development and technical changes.
Emission scenarios are plausible representation of future development of emissions of substances that are potentially radiatively active (e.g., greenhouses and aerosols), based on a coherent and internally consistent set of assumptions about driving forces (such a demographic and socio-economic development, technological change) and their key relationships. The SRES scenarios set comprises four scenario families: A1, A2, B1, B2. The scenarios within each family follow the same storyline. The A1 family includes three groups reflecting a consistent variation of the storyline (A1T, A1FI and A1B). Hence the SRES scenarios consist of six distinct scenario groups, all of which are plausible and together capture the range of uncertainties associated with the driving forcing and emissions.
The SRES scenarios do not include additional climate initiatives, which means that no scenarios are included that explicitly assume implementation of the United Nations Framework Convention on Climate Change or the emissions targets of the Kyoto Protocol. In particular, none involves a stabilisation of concentrations of greenhouse gases.
A1.
The A1 storyline and scenario family describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family develops into three groups that describe alternative directions of technological change in the energy system. The three A1 groups are distinguished by their technological emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance across all sources (A1B) (where balanced is defined as not relying too heavily on one particular energy source, on the assumption that similar improvement rates apply to all energy supply and end use technologies).
A2.
The A2 storyline and scenario family describes a very heterogeneous world. The underlying theme is self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in continuously increasing population. Economic development is primarily regionally oriented and per capita economic growth and technological change more fragmented and slower than other storylines.
B: Sustainability push, service and information economy rather than material driven
Technology: choice of transportation: car/public/new design
Energy: fossil fuel or renewable
Agriculture: choice of crops, water usage
Land use: deforestation, city sprawl, agriculture
E.g. population growth: assumed to increase in regional world, assumed to reach maximum mid century in A1.
Note: none of these scenarios include any assumptions of policy intervention to directly limit emissions.

7. Scenarios constructed from storylines
Storylines are differentiated according to assumptions about how the global economy evolves:
Growth or environmental benefits are maximised
Regional economies converge or remain differentiated
Growth and convergence lead to the highest energy requirement
Environmental awareness and convergence lead to the lowest energy requirement
Regional differentiation results in energy requirements intermediate between the extrema above
Good picture in UKCP09 that could put in here instead.

8. SRES emission scenarios
Anthropogenic emissions of CO2, CH4, N2O and sulphur dioxide for the six illustrative SRES scenarios, A1B, A1FI, A1T, A2, B1 and B2. For comparison the IS92a is also shown.
It is evident that these scenarios encompass a wide range of emissions. Particularly noteworthy are the much lower future sulphur dioxide emissions for the six SRES scenarios, compared o the IS92 scenarios, due to structural changes in the energy system as well as concerns about local and regional air pollution.
A1FI: highest emissions of CO2, CH4 and N2O.
In GTC per yr.
© Crown copyright Met Office

9. Storyline emissions summaries
A1: maximum energy requirements – emissions differentiated dependent on fuel sources:
A1FI: Fossil Intensive
A1T: Technology development of non-fossil sources
A1B: Balance across sources
B1: minimum energy requirements and emissions
A2: high energy requirements – emissions less than A1FI
B2: lower energy requirements – emissions greater than B1
A1 family split into 3 by choice of energy production route.
Each scenario has numerous paths that make up the family i.e. an ensemble
These 6 scenarios cover the range of scenarios developed by the IPCC, i.e. limit the need to run all scenarios.
No specific climate initiatives to reduce GHGs have been assumed in these scenarios, however through choice of energy production this is in part covered.

10. Stages required to provide climate scenarios
A1 family A2 B2 B1
Different SRES storylines will produce different future emissions
This will cascade to the global and regional climate change and climate impacts
E.g. to investigate water resources in S. Africa in 2080 it is necessary to take a number of steps which are highlighted in this diagram.
To predict future climate change, we first need projections of emissions of greenhouse gases and other constituents. These emission scenarios have been developed in the IPCC Special Report on Emission Scenarios (SRES) and reflect a number of different ways in which the world might develop. The concentrations of these gases is calculated using carbon cycle and chemistry models taking as input the above emission scenarios. The concentration scenarios are used as input into the Global coupled climate models to compute global climate projections. The current resolution of the atmospheric part of a typical GCM is about 250 km in the horizontal, and of the ocean is 125 to 250 km. This resolution is not high enough to represent the fine-scale detail that characterises the climate in many regions of the world. Hence the application of some regionalization technique is advisable in order to conduct impact assessments.
Finally the impacts models require climate scenarios as inputs. The climate scenario will be constructed by combining the climate change prediction (from the RCM) with a description of the current climate as represented by the observational data (the observed “baseline” climate).
You will have a following talk about impact study methodologies. .

11. Climate system uncertainties: From emissions to concentrations
It is important to consider the processes which control the relationship between emissions and concentrations
E.g. to investigate water resources in S. Africa in 2080 it is necessary to take a number of steps which are highlighted in this diagram.
To predict future climate change, we first need projections of emissions of greenhouse gases and other constituents. These emission scenarios have been developed in the IPCC Special Report on Emission Scenarios (SRES) and reflect a number of different ways in which the world might develop. The concentrations of these gases is calculated using carbon cycle and chemistry models taking as input the above emission scenarios. The concentration scenarios are used as input into the Global coupled climate models to compute global climate projections. The current resolution of the atmospheric part of a typical GCM is about 250 km in the horizontal, and of the ocean is 125 to 250 km. This resolution is not high enough to represent the fine-scale detail that characterises the climate in many regions of the world. Hence the application of some regionalization technique is advisable in order to conduct impact assessments.
Finally the impacts models require climate scenarios as inputs. The climate scenario will be constructed by combining the climate change prediction (from the RCM) with a description of the current climate as represented by the observational data (the observed “baseline” climate).
You will have a following talk about impact study methodologies. .

12. Climate system uncertainties: From emissions to concentrations
Atmospheric concentrations of CO2, CH4 and N2O resulting from the six SRES scenarios and from the IS92a scenario computed with current methodology.
Step 2: Calculation of GHG concentrations
Necessary step as not all emitted gases stay in the atmosphere due to sources and sinks in the gaseous cycles. Most notable the role of the carbon cycle.
Traditional approach IPCC SRES also provide concentrations of GHGs that have been calculated by medium complexity chemistry and carbon cycle models. Two models Bern and ISAMS. They did calculate the uncertainty in this process but the range was not used by the atmospheric modelling community due to computer constraints.
The IPCC SRES scenarios are shown above. By 2100, carbon cycles models project atmospheric CO2 concentrations of 540 to 970 ppm for the illustrative SRES scenarios.
HC has initiated the impact of carbon and other chemistry schemes on the calculation of GHG concentrations, by inputting emissions directly into the model, which then calculates the resultant emissions throughout the projection. .

13. Uncertainties: Concentration Scenarios
Uncertainties in the understanding of the processes and physics in the carbon cycle and chemistry models
Models currently use a single set of concentrations derived from carbon cycle/chemistry models
Experiments to date indicate the uncertainties may be large
Coupling a carbon-cycle model into one AOGCM shows a large positive feedback
Coupling an atmospheric chemistry model into one AOGCM shows a small negative feedback
The imperfect understanding of some of the processes and physics in the carbon cycle and chemistry models generate uncertainties in the conversion of emissions to concentration. To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be included. For example, the effect of this biospheric feedback on the world climate may be very important. There is another feedback that arises from the effect that climate change has on the chemical reactions between species in the atmosphere, particularly as the amount of water vapour in the atmosphere increases. This affects the levels of methane and ozone considerably. These two feedbacks have been introduced in the Hadley Centre model to reduce the uncertainty in the conversion of emissions to concentrations.

14. Climate system uncertainties: From emissions to concentrations
Looking at CO2 as an example .. .

15. Climate system uncertainties: Climate response to concentrations
There is a very significant emphasis on improving our knowledge of climate system processes, yet there remains uncertainty in how the climate responds to changes in atmos. concentrations
E.g. to investigate water resources in S. Africa in 2080 it is necessary to take a number of steps which are highlighted in this diagram.
To predict future climate change, we first need projections of emissions of greenhouse gases and other constituents. These emission scenarios have been developed in the IPCC Special Report on Emission Scenarios (SRES) and reflect a number of different ways in which the world might develop. The concentrations of these gases is calculated using carbon cycle and chemistry models taking as input the above emission scenarios. The concentration scenarios are used as input into the Global coupled climate models to compute global climate projections. The current resolution of the atmospheric part of a typical GCM is about 250 km in the horizontal, and of the ocean is 125 to 250 km. This resolution is not high enough to represent the fine-scale detail that characterises the climate in many regions of the world. Hence the application of some regionalization technique is advisable in order to conduct impact assessments.
Finally the impacts models require climate scenarios as inputs. The climate scenario will be constructed by combining the climate change prediction (from the RCM) with a description of the current climate as represented by the observational data (the observed “baseline” climate).
You will have a following talk about impact study methodologies. .

16. Climate system uncertainties: Climate response to concentrations
Sources of uncertainties within physical climate models
Contribution of uncertainty
Implementation of numerics
small
Representation of dynamics
Representation of sub-gridscale physical processes
Significant (particularly on long/century timescales)
Natural climate variability
Significant (on short (seasonal to annual) timescales)
Impact of atmospheric composition on radiative balance
Less significant

17. Why the uncertainty?
For one thing, GCMs only resolve climate down to certain scales, meaning that smaller scale processes have to be parameterized.

18. Land surface processes
Climate processes which are parameterised
Boundary layer
Turbulent mixing coefficients: stability-dependence, neutral mixing length
Roughness length over sea: Charnock constant, free convective value
Large Scale Cloud
Ice fall speed
Critical relative humidity for formation
Cloud droplet to rain: conversion rate and threshold
Cloud fraction calculation
Dynamics
Diffusion: order and e-folding time
Gravity wave drag: surface and trapped lee wave constants
Gravity wave drag start level
Convection
Entrainment rate
Intensity of mass flux
Shape of cloud (anvils) (*)
Cloud water seen by radiation (*)
Land surface processes
Root depths
Forest roughness lengths
Surface-canopy coupling
CO2 dependence of stomatal conductance (*)
Parameters that are uncertain within the climate model.
Radiation
Ice particle size/shape
Cloud overlap assumptions
Water vapour continuum absorption (*)
Sea ice
Albedo dependence on temperature
Ocean-ice heat transfer
© Crown copyright Met Office

19. Process complexity in climate modelling – progress map
1985
1992
1997
Atmosphere
Atmosphere
Atmosphere
Atmosphere
Atmosphere
Atmosphere
Land surface
Land surface
Land surface
Land surface
Land surface
Ocean & sea-ice
Ocean & sea-ice
Ocean & sea-ice
Ocean & sea-ice
Sulphate
aerosol
Sulphate
aerosol
Sulphate
aerosol
Non-sulphate
aerosol
Non-sulphate
aerosol
Carbon cycle
Carbon cycle
Atmospheric
chemistry
Through time are models have become more complex. There are uncertainties in each area, some of which are described in the next slide.
Off-line
model
development
Strengthening colours
denote improvements
in models
Sulphur
cycle model
Non-sulphate
aerosols
Ocean & sea-ice
model
Land carbon
cycle model
Carbon
cycle model
Ocean carbon
cycle model
Atmospheric
chemistry
Atmospheric
chemistry

20. Climate system uncertainties: Climate response to concentrations
We also need to consider how we treat possible uncertainties in relationships between global or regional climate change and impacts
E.g. to investigate water resources in S. Africa in 2080 it is necessary to take a number of steps which are highlighted in this diagram.
To predict future climate change, we first need projections of emissions of greenhouse gases and other constituents. These emission scenarios have been developed in the IPCC Special Report on Emission Scenarios (SRES) and reflect a number of different ways in which the world might develop. The concentrations of these gases is calculated using carbon cycle and chemistry models taking as input the above emission scenarios. The concentration scenarios are used as input into the Global coupled climate models to compute global climate projections. The current resolution of the atmospheric part of a typical GCM is about 250 km in the horizontal, and of the ocean is 125 to 250 km. This resolution is not high enough to represent the fine-scale detail that characterises the climate in many regions of the world. Hence the application of some regionalization technique is advisable in order to conduct impact assessments.
Finally the impacts models require climate scenarios as inputs. The climate scenario will be constructed by combining the climate change prediction (from the RCM) with a description of the current climate as represented by the observational data (the observed “baseline” climate).
You will have a following talk about impact study methodologies. .
© Crown copyright Met Office

21. Uncertainties: Climate change scenarios and impacts
Climate change scenarios for impacts studies can be derived by:
Combining climate model and observed data
Using climate model data directly
Choices are often required when considering:
How to provide information at fine scales
How to apply changes in the mean climate or climate variability
As with climate modelling, the physical processes involved in studying climate impacts are often not well understood or well-simulated
How to interpret output from model for impacts studies?
Choice of data to use in impacts model: raw model output for future time period or combination of model and observations. Choice is dependent on what is being done.
Comparison of model and obs for baseline period will highlight any biases in the model data.
Obs: may be erroneous, short in length, non homogeneous.
Model: lack of detail (25km resolution), but homogeneous and long in length
Future projections:
take model if no biases found or
Apply percentage change found in model between past and future to observations. E.g. 20% increase in rainfall found in SE UK in model, apply 20% increase to current day observations of rainfall to get future rainfall quantities.
Final uncertainty: in impacts model itself – all models have uncertainties.
Previous notes:
First, a representation of the present day (baseline) climate is required which could either be taken from observed data or from climate models. The former may contain errors or they may be insufficient to characterise the climate to the required level of detail. Model-based estimates of current climate will certainly contain some errors, often greater than the estimated errors in the observed data, and will also suffer from a lack detail but may provide a more complete range of variables and a longer dataset. Second, if the baseline climate is represented by observations then there are various ways of obtaining the future climate scenario, from just adding mean changes from the model to the baseline to taking changes in parameters of daily distributions in the model and combining these with the observed daily distributions. Each of these options will clearly lead to different results and a range should be investigated to give some estimate of the resulting uncertainty. Finally, the techniques used to provide the climate impacts make assumptions and simplifications of the physical processes involved and so lead to another layer of uncertainty.

22. Rainfall change: IPCC AR4
Combination of pattern and some sign differences lead to lack of consensus
Is this relevant if monsoon processes are captured in the models?

23. More 21st century predictions
Source: Intergovernmental Panel on Climate Change (IPCC), WG1-AR4, Ch 10.

24. Thank you