© Crown copyright Met Office Climate predictions – from the coal face Richard Wood, Met Office...

© Crown copyright Met Office

Climate predictions – from the coal faceRichard Wood, Met Office Hadley Centre(with thanks to many colleagues)


Contents

• What are we trying to do?

• Climate models

• Seasonal forecasts

• Climate change

• Decadal forecasts

• How well do we do?

• How to do better?


Contents


• Climate models


• Climate change





Who cares about climate predictions?

• Seasonal: wide range of potential users, e.g. energy/power supply, health, …

• Interannual-decadal: Longer term investments, infrastructure, international development, reinsurance?, …

• Century (climate change): a few major infrastructure projects (e.g. Thames barrier), international emissions control policy (UNFCCC – “dangerous anthropogenic interference”)


One person’s signal is the next person’s noise

Global mean temperature

Northern Europe temperature

As time and space scales reduce from century to a few decades, global to local, signal to noise ratio of climate change reduces.


One person’s signal is the next person’s noise (2)

“Signal to noise” = predictable variance/total variance

Timescale

Season Decade Century

© Crown copyright Met Office© Crown copyright Met Office

Initial value climate prediction

What are the key limiters to prediction?

• Inherent predictability limits, model variability errors, model bias, observations, initialisation/DA methods, … ?

NWP Seasonal Decadal

Type I . High signal/noise

Type I (mostly) .Low signal/noise

Type I/II. (How much I?)Low signal/noise

Initialise ‘best’ one-shot modelIC ensemble with lower resolutionMulti-model ensemble (informal)

Any scope for ‘best’ one-shot prediction?IC ensemble .Multi-model ensemble (EuroSIP)

No ‘best-shot’ .IC/physics ensemble

NWP initialisation NWP/operational ocean initialisation

Initialise and predict anomalies vs. model climatology

Can we really predict climate?

Bad news:At regional to local scales, climate models can’t agree on future changes in precipitation

‘Good’ news (for climate prediction):Even at the gridpoint scale, models agree that the climate change signal provides substantial potential predictability (if only they could agree on what it was!)

IPCC AR4 WGI Fig. 10.27

(Source: IPCC 4th Assessment Report)


Contents


• Climate models


• Climate change





Modelling the Climate System

GJJ1999

OCEAN

PrecipitationSea-ice

LAND

Ice- sheetssnow

Biomass

Clouds

Solarradiation

Terrestrialradiation

Greenhouse gases and aerosol

ATMOSPHEREMet.Office Hadley Centre


38 levels in atmosphere

1.25° lat

1.875° lon

1.25°1.25°*

The Met Office climate model “HadGEM1”

Predict climate variables (temperature, wind, cloud etc) in each box (covering whole world), every 30 minutes

30km

-5km * Decreasing to 1/3deg at the equator40 levels in ocean


At the model’s core:

Standard dynamics of rotating fluid

Thermodynamics

Navier-Stokes on rotating sphere. Various approximations possible (e.g. hydrostatic, Boussinesq,…). Non-Newtonian rheologies for ice.

Forces: gravity, pressure gradients, Coriolis, internal stresses …

Heat and water conservation, latent heat effects for changes of phase, radiative absorption/emission,

Plus:Closure problem for all the things that are happening within the gridbox (e.g. cloud physics, turbulent transfers, surface heterogeneity …)


Influence of multiple scales on ocean circulation(even before worrying about turbulence!)

Water mass transformations in the North Atlantic (McCartney et al., 1996)

Warm surface inflow

Cooling creates dense waterCold deep outflow

through narrow channels


Dependence of large scale solution on small scale details of model setup

Overturning streamfunction vs. potential density

(Discrete) model topography across Greenland-Scotland Ridge

(Roberts & Wood J. Phys. Oceanogr. 1997)


Vertical coordinate can influence the physics

depth-coordinate model Isopycnic (potential density)-coordinate model

(Marsh et al. J. Phys. Oceanogr. 1996)

Overturning streamfunction vs. potential temperatureNote mixing of overflow water with warmer ambient water in z-coordinate model


Vertical coordinates influence the physics

Z-level model has excessive mixing/entrainment due to “lego” bottom boundary

Isopycnal model has “layer” allowing overflow to have little or no mixing/entrainment


Contents


• Climate models


• Climate change





Ensemble seasonal prediction

(Anna Maidens, Craig McLachlan, Met Office)

Models have biases in their preferred climate state. Initialise model with observed state forecast drifts back towards its own climate

Solution 1: ‘Bias correction’: perform large set of hindcasts, subtract out mean drift (seasonal system). Also provides skill measure.

Solution 2: Initialise model using observed anomalies from mean state (decadal system)

© Crown copyright Met Office Guilyardi, Climate Dynamics 2006

Can we really model El Nino?

Lag correlations of “Trans-Nino index” with Nino3 SST

Blue on top: eastward propagation

Yellow on top: westward propagation

(Guilyardi, Climate Dyn., 2006)

Identical atmosphere, different ocean


Teleconnections: late winter European surface climate response to El Niño

Pattern 1 Pattern 2

Observed European surface pressure response to El Nino events (composites). Tends to follow one of two patterns

(Toniazzo and Scaife, 2006)

Modelled composite sea-level pressure response associated with years with and without sudden stratospheric warmings. Response seems to require interaction of stratosphere.

(Ineson and Scaife, Nature Geoscience 2009) El Niño years with SSWs El Niño years with no SSWs


Seasonal prediction using GCM with ensemble of initial conditionsTercile probability forecasts for March-May 09 (issued Feb 09)

Temperature Precipitation

Upper

Middle

Lower

See www.metoffice.gov.uk


Measuring skill/value of forecastsExample: ROC scores

Temperature Precipitation

See www.metoffice.gov.uk


Contents


• Climate models


• Climate change





Many sources of uncertainty in predicting future climate

Future trajectory of greenhouse gases (scenario uncertainty)

Internal variability (initial value)

Model uncertainty

Scenario uncertainty may reduce in future as a result of global emissions agreements (Copenhagen 2009)


Commitment to ecosystem loss

Broadleaf tree fraction

•The change you see today may not be the change you end up with

•Different timescales for growth and decay mean that recovery may take many centuries

(Courtesy Chris Jones, Met Office)


Irreversibility and thresholds

Ice sheets initiated at intervals of 10% area

Climate from HadCM3 forces an off-line ice sheet model

Time series of ice sheet evolution shows steps as driving climate is updated

(Jeff Ridley, Met Office)

Multiple equilibria of Greenland ice sheet

Idealised representation Decline of ice sheet will proceed at rate determined by forcing (scenarios in colours)

Recovery of ice sheet will depend on time spent in decline and if thresholds (horizontal lines) are passed.

(Jeff Ridley, Met Office)


How to deal with model uncertainty?

Future trajectory of greenhouse gases (scenario uncertainty)

Internal variability (initial value)

Model uncertainty

Scenario uncertainty may reduce in future as a result of global emissions agreements (Copenhagen 2009)


Bayesian prediction using perturbed physics ensemble (Murphy et al. Nature 2004) • Histogram based on 53-

members each with a perturbation to one parameter

• Emulated prior distribution based on uniform parameter distributions and linear modelling (accounting for some non-linearity)histogram of

“perturbed physics” ensemble

“emulated” prior predictive distribution

likelihood weighting via comparison with real world

posterior predictive distribution

• Likelihood, L0, of each model based on comparison with observations

• CPI: log L0(m) ~ -Σ(mi-oi)2 Model “error” term


Do observations of present day climate constrain future predictions?

(IPCC 4th Assessment Report)

Ability to simulate present day may allow some models to be eliminated

OBS.

But it doesn’t provide a strong constraint on future response


Process-based metrics may provide a way forward

Snow-albedo feedback

Seasonal cycle (observable) appears to be a good metric for future response



Shukla et al 2006

(Shukla et al. Geophys. Res. Lett. 2006)

Simulation of recently observed change may help

Error in simulating 20th Century temperature change


Predicting detail

But there may be robust relationships between regional climate change and local extremes – so maybe we don’t need to predict everything explicitly

Change in local extreme precip. vs. change in regional mean precip for two regions and 19 models (Good & Lowe 2006)

High resolution models give more realistic regional detail – so more confidence in local prediction?

N96

N216

N144

Obs.

(Malcolm Roberts/UJCC)


Probabilistic end-to-end prediction (UKCP 2009)

• Ocean, sulphur and carbon-cycle ensemble results incorporated via their influence on global mean temperature in the EBM component of the time scaling

• Use the ensemble results to fit relevant parameters in the EBM and then sample those parameters

EBMTime-scaling Down-

scalingAtmosphere PPE

Ocean PPE

Aerosol PPE

Carbon cycle PPE

(Mat Collins, Met Office)


Pinpointing and reducing Uncertainty


Temperature

Precipitation

• Sources of uncertainty in determining the width of PDF (local grid point values)

• Can then target research to reduce uncertainty e.g.

• Reduce time-scaling uncertainty by running more transient simulations

• Potentially reduce internal variability by initialising simulations with obs

• Reduce modelling uncertainty by developing better models, developing better constraints, taking newer and better observations

• Constrain carbon-cycle feedbacks

(Mat Collins, Met Office)


Natural variability – resampling: -34 to +17 Emissions – B1 to A1FI: -14 to - 9 GCM structure – 5 GCMs: -13 to +41 Natural variability – 3xGCM ICs: -25 to - 5 Downscaling – RCM v statistical: -22 to - 8 RCM structure – 8 RCMs: -5 to +8 Hydro’ model structure – 2 models: -45 to - 22 Hydro’ model parameters: +1 to + 7

Climate Impacts Uncertainty

% c

hang

e in

flo

od f

requ

encyChanges in 50-year flood

(%) from different drivers: River Beult in Kent

Q1: Are ranges additive?

Q2: Should model or observed climates be used as the baseline?

Q3: Are flow changes reliable enough to apply to observed flows?

Q4: Do reliable changes require full spectrum variability changes? (Richard Jones, Met Office)


Contents


• Climate models


• Climate change




First attempts at decadal climate prediction

10-year hindcasts, start dates from 1982 to 1996

Hindcasts include predictable elements of forcing (but not volcanic eruptions)

Note: value of initial conditions is probably understimated due to improved modern observational network

2007 obs 1980 obs 1960 obs

Decadal hindcasts of global mean surface temperature

Lead time of forecast (years)

With observed initial conditions

With random initial conditions

RM

S f

ore

cast

err

or

(°C

)

(Smith et al., Science 2007)


Contents


• Climate models


• Climate change





Empirical vs. dynamical prediction

Focus dynamical modelling on areas where there is a real chance of achieving better skill than from empirical model – note that empirical models can be re-tuned along with a slowly varying climate, but struggle with regime shifts.

Empirical (NAO)

cold warm

cold 19 10

warm 10 18

Dynamical cold warm

cold 70 65

warm 65 70

Test dynamical models against the best available statistical model (rather than persistence) – the ‘pragmatist’s null hypothesis’: if it’s cheaper and does as good a job, why not use it?

Winter CET seasonal forecast: only empirical method has skill

Verification

Fo

recast

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

year

tem

pera

ture

observedpredicted

last 15 years1961-90 mean

15-yr trend

3-year prediction of 13-year mean January Midlands temperature: dynamical beats simple empirical (courtesy Richard Graham)

(Richard Graham, Met Office)


Confidence in climate change projections

Stippling: Magnitude of multi-model mean > inter-model standard deviation

Note greater agreement among models for temperature than for precipitation

Agreement does not necessarily imply correctness, but adds to confidence if backed up by physical understanding



Contents


• Climate models


• Climate change





Do we use observations optimally to initialise models?

Observing points

• Estimating the Atlantic MOC at 26°N from a few sparse observations (a, RAPID array)

• In an ocean GCM, MOC reconstruction from a few profiles matches directly diagnosed MOC (b)

(Hirschi et al. GRL 2003)

• Key large scale information is contained here in sparse observations (geostrophic gradient across basin)

• Yet no standard DA scheme captures this information (covariance scale ~ 400 km)

• Standard DA schemes may produce lots of detailed information about T,S near this section, but miss the large scale dynamical signal (MOC)

• Can we make better use of observations to constrain large scale elements of the climate system?


Traceability of the first kindRunning ensembles for climate prediction implies compromising on model cost. How do we know that cheaper models have the right processes?

Equatorial Pacific Temperature

Eddy heat flux convergence

Mitigation scenario

Standard res. model

High res. model

Obs.Run a few high resolution versions of your ‘workhorse’ model. Are the critical processes the same in both models?

(Malcolm Roberts, Met Office/UJCC)


Traceability of the second kind

MAGICC best fit to HadCM3LC

Tuning scenario

Mitigation scenario

For some problems can only afford to run very cheap models (rapid response, low probability events)

Easy toys to play with!

To have any credibility need to demonstrate link to key processes in more complete models (and through them to observations)

Two simple climate models used to study mitigation scenario (emissions set to 0 in 2050).

Models can both be tuned to give similar answers for increasing CO2 scenarios. But get different answers for mitigation.

Differentiate by appealing to small number of runs of more complex model.

(Jason Lowe, Met Office)


Conclusions

• Short timescales: initial conditions dominate. Long timescale: forced changes dominate. Predictability ‘dip’ in the middle (decadal)?

• Irreversibility and commitment are issues for longer term predictions

• Probabilistic predictions have skill on all timescales where testable.

• Uncertainty comes from initial conditions, forcing, and models. Ensemble methods to quantify.

• ‘Traceable hierarchy’ of models to provide robust probabilistic predictions at practical computing cost.

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