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ENSEMBLES Final Symposium, 17-19 November 2009, Met Office Exeter

ENSEMBLES

Research Theme 1 and 2A partnersPresented by James Murphy, Met Office

The ensemble prediction system in global climate models


Two ensemble prediction systems

System for initialised seasonal-decadal time scales, incorporating three techniques for sampling modelling uncertainties

System for multidecadal climate change projections, based on perturbed parameter ensembles augmented by multi-model ensemble results.

Potential to develop a single “seamless”system across timescales in future


Seasonal-Decadal Prediction system

Three methods of sampling modelling uncertainties:

Multi-model (ECMWF, GloSea, DePreSys, Météo-France, IfM-Kiel, CERFACS, INGV)

Stochastic physics perturbations applied in a single model (ECMWF)

Sustained parameter perturbations applied to a single model (DePreSys).

These methods address complementary aspects of model uncertainty


Two streams of hindcast experimentsStream 1: Hindcast period 1991-2001

Seasonal hindcasts (7 months, May and November start date)Annual hindcasts (14 months, November start date)Two decadal cases (1965 and 1994)Nine member ensemblesDePreSys (Initial condition and perturbed parameter ensembles) 10-year runs in every instance.

Stream 2: Hindcast period 1960-2005Seasonal hindcasts (7 months, Feb, May, Aug, Nov start dates)Annual hindcasts (14 months, November start date)Ten decadal cases (1960, 65, 70,…, 2005)Nine member ensembles for seasonal/annual, minimum 3 members for decadalDePreSys (perturbed parameter ensembles) decadal runs from every year, 30 year runs started at 5 year intervals.

All three modelling systems completed stream 1.

Multi-model and perturbed parameter approaches in stream 2, plus a (growing) subset of hindcasts using the latest stochastic physics scheme in the ECMWF (IFS/HOPE) model.


Diagnostics and downscaling

Climate Explorer;Downscaling

portal

Hindcasts run/archived at ECMWF

common dataatmosphere

MARS

common dataoceanadditional data

ECFS

ECMWF firewall

Seasonal-decadal hindcast data archive

MARS client THREDDSserver

ENSEMBLES public data server (5 Tb)

common dataatmosphere

common dataocean


Initialisation of seasonal-decadal hindcasts

Initialisation of the ocean based on:Improved assimilation systems (building on ENACT)Improved EN3 database of observed temperature and salinity profilesSampling of uncertainties in initial conditions. Most groups did this via perturbations to wind stress and SST.Most contributors to the multi-model ensemble assimilated observations as absolute valuesIfM-Geomar initialised anomalies of SST only.Perturbed parameter ensemble (DePreSys) used anomaly initialisation for all runs, assimilating T and S reanalyses created off-line.

Most groups also initialised the atmosphere, based on ERA40 and ECMWF operational analyses

External forcing: Major GHGs included in all models; some include man-made aerosol forcing, plus solar and volcanic. No prior knowledge of major eruptions is assumed.


Ocean reanalyses in ENSEMBLES

1960 1970 1980 1990 2000 2010Years

-0.2

-0.1

0.0

0.1

0.2

Tem

pera

ture

(de

gree

C)

CERFACS (9 members)ECMWF (5 members)INGV (3 members)UKMO (3 member)

AVERAGED TEMPERATURE (0-300m) 1960-2005Global mean (80S-80N)

Data available from http://ensembles.ecmwf.int/download/ocean


Seasonal prediction skill

a) pp red. mm (9) full mm (45)

Anomaly correlation for surface air temperature spatially averaged over northern hemisphere extratropics

perturbed parameter ensemble

9-member multi-model ensemble



Seasonal prediction skillb) pp red. mm (9) full mm (45)

Anomaly correlation for precipitation averaged over the tropics

perturbed parameter ensemble




Probabilistic seasonal forecasts

Brier scores for temperature and precipitation events in upper or lower terciles for 21 Giorgi regions, for 2-4 and 5-7 months ahead


Control-GPCP

Stochastic physics-GPCP

Precipitation bias (DJF, 1-month lead, 1991-2001, CY29R2)

The stochastic physics scheme reduces the tropical bias

Stream 1 hindcasts: stochastic physics

control CASBS ERA40

Blocking frequency

Berner et al (2008)


Multi-model decadal hindcasts

Anomaly correlation for surface air temperature averaged over the northern hemisphere extratropics

1 year ahead 2-5 years ahead 6-10 years ahead


Regional skill for surface temperature projections 2-5 years ahead

Multi-model ensemble

Perturbed parameter ensemble


Effects of initialisation and sampling uncertain model parameters

Time series of pattern correlation with observations: 9 year mean surface temperature over land

Initialised (ensemble-mean) Initialised (single member)

Uninitialised (ensemble-mean)


Surface temperature anomalies for Dec 2005-Nov 2008, relative to 1961-2001


Summary of seasonal-decadal results

Multi-model ensemble provides seasonal-annual projections results competitive with, or slightly better than, other methods

But demonstration that systematically designed techniques for sampling uncertainties in a single model framework is an important new result.

Potential to develop and optimise stochastic and perturbed parameter methods, and combine with multi-model approaches to capitalise on their complementary strengths

First demonstration of potential benefits arising from ensemble approaches to decadal prediction, ahead of IPCC AR5.

Evidence of modestly enhanced skill in surface temperature projections at large regional scales, compared to uninitialised projections.

Further analysis of stream 2 experiments encouraged !


Different IPCC models give different projections, but we did not know whether the set of available projections fully captured the range of possible futures, or which changes were more likely than others

Probabilistic projections of 21st

century climate change

Change in summer precipitation (%), 2080-99 relative to 1980-99, SRES A2, IPCC AR4 models

Motivation: Develop a more systematic approach supporting estimates of the relative likelihood of different outcomes


A “perturbed parameter ensemble” approach to sample systematically a space of possible model

configurations

• Relatively large ensembles designed to sample modelling uncertainties systematically within a single model framework

• Executed by perturbing model parameters controlling key model processes, within expert-specified ranges

• Based on version 3 of the Met Office Hadley Centre model, HadCM3

• Key strength: Allows greater control over experimental design cf multi-model “ensembles of opportunity”

• Key limitation: does not sample “structural modelling uncertainties”, e.g. changes in resolution, or in the fundamental assumptions used in the model’s parameterisation schemes – need to include results from other models to account for these.


Inputs to probabilistic projections

ObservationConstraints

StructuralModel Errors

Carbon Cycle

Atmosphere

Sulphate Aerosol

Ocean

ProbabilisticClimate

Projections


Two stages

• Probabilistic projections of the equilibrium climate change in response to doubled CO2 at 300km resolution

• Further steps to obtain probabilistic projections of time-dependent climate change at 300km resolution


Simulations of equilibrium climate change

• Used the atmosphere-mixed layer (“slab”) ocean configuration of the model, HadSM3

• Obtained expert-specified prior distributions for multiple (31) uncertain model parameters controlling surface and atmospheric physical processes

• Ran an ensemble of 280 simulations (@300km horizontal resolution) of both present day climate and the equilibrium response to doubled CO2

• Allowed us to sample uncertainties in processes contributing the largest uncertainties to large-scale-regional climate changes at reasonable expense.


..gives a large sample of possible changes (e.g. summer UK rainfall)


Converting ensemble simulations into probabilistic projections of equilibrium climate change

• Bayesian framework designed for making future projections of real world systems using simulations from complex but imperfect models (Goldstein and Rougier, 2004; Rougier, 2007)

• Key ingredients included:

• An emulator, trained on the available ensemble runs and used to estimate model results for points in parameter space not sampled by a GCMsimulation

• Discrepancy, an estimate of the effects of structural model errors which cannot be resolved by varying model parameters

• A set of observations to use in estimating the relative likelihood that different model variants (i.e. different points in parameter space) give a true representation of the real climate system.

• Could then integrate over the model parameter space, weighting projections according to relative likelihood and accounting for effects of structural errors, to obtain probabilistic projections.


Global climate sensitivity

Mean impact of discrepancy


Simulations of time-dependent climate change using HadCM3 coupled atmosphere-ocean ensembles

• Smaller 17 member ensembles due to resource limitations

• Uses a subset of the multiple perturbation parameter sets used in the cheaper equilibrium simulations

• Can then build relationships between the equilibrium and transient responses…

• .. and hence produce large pseudo-ensembles of 21st century climate realisations by scaling large samples of possible equilibrium changes.

Observations

Historical + A1Bforcing


“Timescaling” approach to emulate large ensembles

of transient climate change projections

⇒

Equilibrium feedbacks (emulated)

Normalized equilibrium response pattern (emulated)for a doubling in CO2 conc.

Simple Climate Model projections for global surface temp. anomaly

Correction pattern representing differences between slab and dynamic ocean response

+

PDFs

×


Sampling uncertainties in other Earth system processes

• Further 17 member perturbed physics ensembles sampling uncertainties due to:

• Ocean transport processes, sulphur cycle processes and terrestrial ecosystem processes in HadCM3


Probabilistic projections in response to A1B emissions

Changes in temperature and precipitation for future 20 year periods, relative to 1961-90, at 300km scale.


Surface temperature changes for the 2080s

10th percentile 90th percentileMedian

Winter

Summer


Probabilistic projections of 21st

century climate changePreviously, national climate change scenarios have relied on projections from one or more models, with no formal information on how trustworthy the results are.

ENSEMBLES has moved from uncertainty to probability

Probabilistic projections for Europe which measurehow strongly different outcomes for climate change are supported by current evidence (models, observations, understanding)

Based on a comprehensive methodology for sampling key known uncertainties, and consistent with the UKCP09 probabilistic projections.

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