uncertainties in climate modeling and climate change projections precis workshop tanzania...
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Uncertainties in climate modeling and climate change projectionsPRECIS WorkshopTanzania Meteorological Agency, 29th June – 3rd July 2015
Aims of this session
1. Examine fundamental concepts of climate uncertainties
2. Understand the cascade of uncertainties3. Provide detail on main sources of global
climate change uncertainties4. Provide detail on uncertainties in regional
climate change predictions
What is uncertainty?
Uncertainty = lack of certainty
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Uncertainties in climate prediction
Wilby and Dessai, 2010 Robust Adaptation to Climate Change
choices
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First, an analogy...
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Sources of climate projection uncertainty
1. Scenario uncertainty – Human and natural emissions of greenhouse gases– Translating emissions into concentrations of greenhouse gases and their effect on
system radiative forcings
2. Initial Condition (IC) uncertainty – Sparse/incomplete observations in time and space– Erroneous and uncertain observations
3. Model uncertainty– Model error and inadequacy– Parameter uncertainty
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What is “climate” uncertainty?
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Certainty
If we have:1. A perfect model AND2. Perfect initial observations
…then, and only then, can we make statements of certainty.
e.g. Probability of getting a 1 = 0Probability of getting a 2 = 0Probability of getting a 3 = 1Probability of getting a 4 = 0Probability of getting a 5 = 0Probability of getting a 6 = 0
UncertaintyIf we have:1. A perfect model AND2. Imperfect initial observations
…then the best we can do is make probabilistic statements.
e.g. Probability of getting a 1 = 1/6Probability of getting a 2 = 1/6Probability of getting a 3 = 1/6Probability of getting a 4 = 1/6Probability of getting a 5 = 1/6Probability of getting a 6 = 1/6
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Deeper uncertainty
If we have:1. An imperfect model AND2. Imperfect initial observations
…then we can say…nothing?
e.g. Probability of getting a 1 = ?Probability of getting a 2 = ?Probability of getting a 3 = ?Probability of getting a 4 = ?Probability of getting a 5 = ?Probability of getting a 6 = ?
The best we can do is make conditional probability statements. Probabilities are conditional on the model used in the analysis. The crucial question is:
How good is our model at representing “reality”?In weather prediction we can verify forecasts and update our models based on this information. In climate prediction, we don’t have this luxury. We have to rely on other sources of confidence, such as:
• model agreement• ability to represent driving (synoptic) processes • appropriate propagation and representation of teleconnections• ability to capture past climate behaviour
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Initial ConditionPredictability
Boundary ConditionPredictability
From Hewitson et al 2013
Predictability
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Uncertainty in predicting the weather
Imperfect initial observations
Perfect model scenario
We can make probabilistic statements.
“The probability of the temperature being at least 14 degrees is 0.7”
P(T=11) = 0.0P(T=12) = 0.1 P(T=13) = 0.2P(T=14) = 0.5P(T=15) = 0.2P(T=16) = 0.0P
roba
bilit
y
Temperature
In reality, we have imperfect models but we can verify our forecasts with observations of the system.
By identifying biases and sources of model error, we can update our models and improve predictions.
We have inherent irreducible uncertainty, so even armed with a perfect model, we have to consider probabilities. In the imperfect model, the probability distribution is conditional on the model assumptions.
“Climate is what you expect”
Prob
abili
ty
Temperature
μ
10th percentile
90th percentile
μ future
μ changeprob
abili
tyweather
forecast
climate
possible future climate
Natural internal variability
Model uncertainty+
90th percentile changeprob
abili
ty
“Future climate is what you expect to expect”
Climate uncertainty...is different
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Unpacking the sources of climate uncertainty
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1. Emission Scenario Uncertainty
Uncertainties in the key assumptions and relationship about future population, socio-economic development and technical changes.
We are currently working with 2 sets of scenarios: • SRES (used for CMIP3 / IPCC AR4) • RCPs (used for CMIP5 / IPCC AR5)
The IPCC does not assign probabilities to these scenarios.
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Previously: SRES Emissions Scenarios
1. Socio-economic scenarios
2. Emissions scenarios
3. Atmospheric CO2 concentrations
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Key Uncertainty: the carbon cycle
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CO2 concentration (ppm)
Fuss et al. 2014
Now: Representative Concentration Pathways (RCPs)
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ImpactsClimate
scenariosAtmospheric
concentrationsEmissions scenarios
Socio-economic scenarios
SRES: Sequential approach to developing climate scenarios
Climate modellers awaited results from socio-economic modellers
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Developing climate scenarios
Impacts
Emissions scenarios
Atmospheric concentrations (‘Representative Concentration Pathway’, RCPs)
Climate scenarios
Integrated assessment
modellers and climate modellers
work simultaneously
and collaboratively
Socio-economics
Policy Intervention (mitigation or adaptation)
Carbon cycle and atmospheric chemistry
RCPs: Parallel Approach to developing climate scenarios
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Developing climate scenarios
2. Initial Condition Uncertainty
Grigory Nikulin (Rossby Centre)© Crown copyright Met Office
3. Model Uncertainty
Only one planet Earth
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Uncertain processes and parameters in climate models
Large Scale Cloud
Ice fall speed
Critical relative humidity for formation
Cloud droplet to rain: conversion rate and threshold
Cloud fraction calculation
Convection
Entrainment rate
Intensity of mass flux
Shape of cloud (anvils) (*)
Cloud water seen by radiation (*)
Radiation
Ice particle size/shape
Cloud overlap assumptions
Water vapour continuum absorption (*)
Boundary layer
Turbulent mixing coefficients: stability-dependence, neutral mixing length
Roughness length over sea: Charnock constant, free convective value
Dynamics
Diffusion: order and e-folding time
Gravity wave drag: surface and trapped lee wave constants
Gravity wave drag start level
Land surface processes
Root depths
Forest roughness lengths
Surface-canopy coupling
CO2 dependence of stomatal conductance (*)
Sea ice
Albedo dependence on temperature
Ocean-ice heat transfer
Climateprediction.net
Parameter uncertainties and sub-grid scale processes can be explored using perturbed parameter experiments.
Uncertain processes and parameters in climate models
Hawkins and Sutton, 2009
Contributions to overall uncertainty
http://climate.ncas.ac.uk/research/uncertainty/plots.html© Crown copyright Met Office
Key Uncertainties in Regional Climate Modeling
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Regional Climate Uncertainties
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Many of the uncertainties associated with global change responses are relevant to regional scale responses, but not all.
The focus is on the manifestation of global scale changes at the sub-global scale – e.g. though the global temperature is rising, it isn’t rising at the same rate everywhere.
Model uncertainties and uncertainty introduced by natural internal variability tend to dominate at smaller scales.
Regional Climate Uncertainties
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Particular uncertainties are introduced in areas close to coasts, mountain ranges, areas of varied land cover and proximity to human influences.
RCMs better account for such issues, but sub-grid scale variability still exists.
Regional Projections (CORDEX)
Same RCM, different GCM
Same GCM, different RCM
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Regional Projections (CORDEX)
Same RCM, different GCM
Same GCM, different RCM
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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!
Summary
• 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
• Of those uncertainties that can be accounted for, not all are currently well described
• There is a lot more work for us all to do!
Thanks for listening.
Questions?