Download Climate scenarios

Uncertainties in the Development
of Climate Scenarios
PRECIS Workshop, MMD, KL, November 2012
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Uncertainties in Climate Scenarios
• Goal of this session:
• understanding the cascade of uncertainties
• provide detail on the uncertainties in emissions
scenarios
• provide detail on the uncertainties in regional climate
change predictions
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Uncertainties
• Emissions
• Concentration
• GCMs
• Regional modelling
• Climate scenario
construction
• Impacts
Stages required to provide climate scenarios
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Uncertainties: Emission Scenarios
• Uncertainties in the key assumptions and relationship
about future population, socio-economic development
and technical changes.
• The consequent uncertainties are unquantifiable as
IPCC does not assign probabilities to any of choices of
the key assumptions involved
• We are currently working with 2 sets of scenarios:
SRES (used for CMIP3/IPCC AR4) and RCPs (used for
CMIP5/AR5)
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SRES Emissions Scenarios
1. Socioeconomic
scenarios
3. Atmospheric
concentrations
2. Emissions
scenarios
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SRES: Sequential approach to
developing climate scenarios
Socioeconomic
scenarios
Emissions
scenarios
Atmospheric
concentration
s
Climate
scenarios
Impacts
• Climate modellers await results from socio-economic
modellers
• Emissions scenarios chosen early on are restrictive.. E.g.
no exploration of deliberate mitigation strategies, difficult
to explore uncertainties in carbon cycle feedbacks.
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Representative Concentration
Pathways (RCPs)
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RCPs: Parallel approach to
generating climate scenarios
Atmospheric concentrations
(‘Representative Concentration Pathway’, RCPs)
Emissions scenarios
Socio-economics
Policy Intervention
(mitigation or
adaptation)
Integrated
assessment
modellers and
climate modellers
work
simultaneously
and
collaboratively
Carbon cycle and
atmospheric chemistry
Impacts
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Climate
scenarios
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
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Carbon cycle model
Coupled to standard HadCM3 atmosphere, ocean and
interactive sulphur cycle.
Prescribe CO2
emissions
Photosynthesis
Respiration
(not atmospheric
concentration)
Moses 2.1/ Triffid
newHadOCC:
land surface scheme:
Ocean biology/carbon cycle
model
Dynamic Vegetation
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Impact of perturbations on
the atmospheric CO2
17 member ensemble of HadCM3C
Historical and A1B SRES future
scenario
CO2 concentration (ppm)
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Impact of perturbations on
global mean temperature.
Relative impact of uncertainties in the terrestrial carbon
cycle (green) and atmospheric feedbacks (blue)
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Uncertainties: Climate models
• Incorrect, incomplete or missing description of key
processes and feedbacks in the climate system e.g.
• Representation of clouds
• Complexity of sea-ice model
• Feedback from land-use change
• Internal (natural) variability of the climate system
• Decadal variability means that 30-year samples of a
climate state may differ substantially
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Climate model formulation
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HADLEY CENTRE EARTH SYSTEM MODEL
Atmosphere
1985
1992
1997
Atmosphere
Atmosphere
Atmosphere
Atmosphere
Atmosphere
Land surface
Land surface
Land surface
Land surface
Land surface
Ocean & sea-ice
Ocean & sea-ice
Ocean & sea-ice
Sulphate
aerosol
Sulphate
aerosol
Non-sulphate
aerosol
Sulphate
aerosol
Non-sulphate
aerosol
Carbon cycle
Carbon cycle
Ocean & sea-ice
Atmospheric
chemistry
Ocean & sea-ice
Off-line
model
model
development
Strengthening colours
denote improvements
in models
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Sulphur
cycle model
Land carbon
cycle model
Ocean carbon
cycle model
Atmospheric
chemistry
Non-sulphate
aerosols
Carbon
cycle model
Atmospheri
c
chemistry
Uncertainties in climate model
Boundary layer
Turbulent mixing coefficients: stabilitydependence, neutral mixing length
Large Scale Cloud
Ice fall speed
Roughness length over sea: Charnock constant,
free convective value
Critical relative humidity for formation
Dynamics
Cloud droplet to rain: conversion rate and
threshold
Diffusion: order and e-folding time
Cloud fraction calculation
Gravity wave drag: surface and trapped lee wave
constants
Convection
Entrainment rate
Gravity wave drag start level
Land surface processes
Intensity of mass flux
Root depths
Shape of cloud (anvils) (*)
Forest roughness lengths
Cloud water seen by radiation (*)
Surface-canopy coupling
Radiation
Ice particle size/shape
CO2 dependence of stomatal conductance (*)
Sea ice
Cloud overlap assumptions
Albedo dependence on temperature
Water vapour continuum absorption (*)
Ocean-ice heat transfer
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Change (%) in South Asian monsoon rainfall:
A1B, 2090s, CMIP3 ensemble
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Temperature and precipitation changes
Africa, A1B, 2090s, CMIP3 ensemble
Figure 11.2
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Perturbed physics approach
• The perturbed physics approach allows
uncertainties in various components of the
model to be systematically explored.
• This is done by:
• Identifying parameters in the model which are both
uncertain and important for the model response
• Using an ensemble of models to explore the
implications of these parameter uncertainties
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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 wellsimulated
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Source of uncertainties
Source of Uncertainty
Alternative emission scenarios
Emissions to concentrations
Represented in Climate Ways to address it
Scenarios?
Yes
Scale GCM patterns by the ratio of
the radiative forcing
Beginning
Use GCMs that include interactive
chemistry
Modelling the climate response
 Different responses by different
GCMs for the same forcing.
 Signal (response)/noise
(internal climate variability)
Yes
Not normally
Use a range of GCMs
Use ensemble simulations
Providing regional climate scenarios
 Baseline and future climates
Yes
Use observed or model baseline
and different methods for changes
 Adding high resolution detail
Yes
Use of a range of dynamical and
statistical techniques
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Main Sources of Uncertainty
Socio- Economic
Uncertainty
Natural annualdecadal
variability
(‘Internal
variability’)
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Uncertainty in
the model
representation
of physical
processes
Q: Which are the most important sources
of uncertainty?
Natural variability most
important on
timescales 0-20 years,
small by 100 years
Emissions
scenario
important on
timescales 40
years +
Model
uncertainty
important at all
timescales
A:
That depends on the timescale that we are looking at…
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To summarise
• There are many uncertainties which need to be taken
into account when assessing climate change (and its
impact) over a region
• Some account may currently be taken for most (BUT
NOT ALL) uncertainties
• Even those uncertainties that can be accounted for are
currently not well described
• There is a lot more work for us all to do!
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Questions
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