Download - Uncertainties in climate modeling and climate change projections PRECIS Workshop Tanzania Meteorological Agency, 29 th June – 3 rd July 2015

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

© Crown copyright Met Office

Uncertainties in climate prediction

Wilby and Dessai, 2010 Robust Adaptation to Climate Change

choices


First, an analogy...


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


What is “climate” uncertainty?


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


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


Initial ConditionPredictability

Boundary ConditionPredictability

From Hewitson et al 2013

Predictability


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


Unpacking the sources of climate uncertainty


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.


Previously: SRES Emissions Scenarios

1. Socio-economic scenarios

2. Emissions scenarios

3. Atmospheric CO2 concentrations


Key Uncertainty: the carbon cycle


CO2 concentration (ppm)

Fuss et al. 2014

Now: Representative Concentration Pathways (RCPs)


ImpactsClimate

scenariosAtmospheric

concentrationsEmissions scenarios

Socio-economic scenarios

SRES: Sequential approach to developing climate scenarios

Climate modellers awaited results from socio-economic modellers


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


Developing climate scenarios

2. Initial Condition Uncertainty

Grigory Nikulin (Rossby Centre)© Crown copyright Met Office

3. Model Uncertainty

Only one planet Earth

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

http://climate.ncas.ac.uk/research/uncertainty/plots.html

Key Uncertainties in Regional Climate Modeling


Regional Climate Uncertainties


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


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


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?

Download - Uncertainties in climate modeling and climate change projections PRECIS Workshop Tanzania Meteorological Agency, 29 th June – 3 rd July 2015

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