© Crown copyright Met Office Uncertainties in Climate Scenarios Goal of this session: understanding the cascade of uncertainties provide detail on the

© Crown copyright Met Office

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


Uncertainties in the Development of Climate ScenariosPRECIS Workshop, MMD, KL, November 2012


Uncertainties

• Emissions

• Concentration

• GCMs

• Regional modelling

• Climate scenario construction

• Impacts

Stages required to provide climate scenarios


Uncertainties 1: 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)


ImpactsClimate

scenariosAtmospheric

concentrations

Emissions scenarios

Socio-economic scenarios

SRES: Sequential approach to developing climate scenarios

• 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.


RCPs: Parallel approach to generating 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


Representative Concentration Pathways (RCPs)


Uncertainties 2: Concentration Scenarios

Uncertainties in the understanding of the processes and physics in the carbon cycle and chemistry models

2 major sets of developments in recent years which affect how we address this uncertainty:

- Use of RCP scenarios

- Development of models with interactive carbon cycle and atmospheric chemistry (ESMs)

Older models with no interactive carbon cycle/chemistry use a single set of concentrations derived from 'offline' carbon cycle/chemistry models

Many models now include coupled carbon cycle and atmospheric chemistry models, Allows feedbacks to be represented


Carbon cycle model - HadCM3C

Coupled to standard HadCM3 atmosphere, ocean and interactive sulphur cycle.

Moses 2.1/ Triffid

land surface scheme:

Dynamic Vegetation

newHadOCC:

Ocean biology/carbon cycle model

Prescribe CO2 emissions

Photosynthesis

Respiration


Impact of perturbations on the atmospheric CO2

17 member ensemble of HadCM3C

Historical and A1B SRES future scenario

CO2 concentration (ppm)


Impact of perturbations on global mean temperature.

Relative impact of uncertainties in the terrestrial carbon cycle (green) and atmospheric feedbacks (blue)


Uncertainties 3: 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


Climate model formulation


Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere

Land surfaceLand surfaceLand surfaceLand surfaceLand surface

Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice

Sulphateaerosol

Sulphateaerosol

Sulphateaerosol

Non-sulphateaerosol

Non-sulphateaerosol

Carbon cycle Carbon cycle

Atmosphericchemistry

Ocean & sea-icemodel

Sulphurcycle model

Non-sulphateaerosols

Carboncycle model

Land carboncycle model

Ocean carboncycle model

Atmosphericchemistry

Atmospheric

chemistry

Off-linemodeldevelopment

Strengthening coloursdenote improvementsin models

1985 1992 1997

HADLEY CENTRE EARTH SYSTEM MODEL


Uncertainties in climate model

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

Rainfall change: IPCC CMIP3

Combination of pattern and some sign differences lead to lack of consensus


Temperature and precipitation changesAfrica, A1B, 2090s, CMIP3 ensemble

Figure 11.2


Uncertainties 4: 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


Source of uncertainties

Source of Uncertainty Represented in Climate Scenarios?

Ways to address it

Alternative emission scenarios Yes Scale GCM patterns by the ratio of the radiative forcing

Emissions to concentrations Beginning Use GCMs that include interactive chemistry

Modelling the climate response

Different responses by different GCMs for the same forcing.

Yes Use a range of GCMs

Signal (response)/noise (internal climate variability)

Not normally 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


Q: Which are the most ‘important’ sources of uncertainty?

A: That depends on the timescale that we are looking at…

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

IPCC AR4 SPM mean precipitation change summary figure

• Model consensus does not imply reliability- understanding mechanisms provides basis for a

prediction• Lack of consensus implies no information

- but assessed at grid-scale thus maybe misleading• Many tropical and sub-tropical regions appear uncertain

…”But, what about the white bits?”

• Cannot distinguish between ‘all models show small changes around zero’ and ‘large changes of different sign

• Consensus is measured at GCM grid-box scale, whilst signal might only be evident at larger spatial scale

• % change misleading in regions/seasons where rainfall is close to zero

0

x

xx

x

x

x

x

xx

xx

x

+

-

Key land areas where message has changed:

West Africa (DJF),South Asia (DJF), Australia (DJF) Southern South America (JJA), Northern Australia /SE Asia (JJA)

Remaining areas of disconsensus:

South America (DJF), North America (JJA), Northern/Central Africa (JJA)


To summarise

• There are many uncertainties which need to be taken into account when assessing climate change (and its impact) over a region

• The better we understand the uncertainties at each stage of the process, the better we are equipped to apply climate projections/scenarios appropriately.

• Confidence in climate model projections come from process understanding , not just model consensus


Questions

Documents

© Crown copyright Met Office Uncertainties in Climate Scenarios Goal of this session: understanding the cascade of uncertainties provide detail on the