Climateprediction.com The Physics of Risk: Understanding and Predicting Global Climate Change Emily...

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The Physics of Risk: Understanding and Predicting Global Climate The Physics of Risk: Understanding and Predicting Global Climate ChangeChange

Emily Shuckburgh, DAMTP Emily Shuckburgh, DAMTP with thanks to Myles Allen and David Stainforth, Department of Physics, Oxfordwith thanks to Myles Allen and David Stainforth, Department of Physics, Oxford

www.climateprediction.com


The World’s climate in The World’s climate in dangerdanger

““There is new and stronger evidence that most of the There is new and stronger evidence that most of the warming observed over the last 50 yearswarming observed over the last 50 years is is attributable to human activitiesattributable to human activities””

““Human influencesHuman influences will continue to change will continue to change atmospheric composition throughout the atmospheric composition throughout the 21st century21st century””

““The globally averaged surface temperature is The globally averaged surface temperature is projected to increase by projected to increase by 1.4 to 5.8° C by 21001.4 to 5.8° C by 2100””

The projected warming is very likely to be without The projected warming is very likely to be without precedent during at least the precedent during at least the last 10, 000 yearslast 10, 000 years””

Executive summary: Monday January 22, 2001


““A collective picture of a A collective picture of a warming world”warming world”

Over the 20th century:Over the 20th century: global-average global-average surfacesurface temperaturetemperature has has increasedincreased (0.6° C) (0.6° C) temperature in lowest 8km increased in past 4 decadestemperature in lowest 8km increased in past 4 decades global-average global-average sea levelsea level has has risenrisen (0.1- 0.2 m) (0.1- 0.2 m) snow & icesnow & ice extent extent decreaseddecreased - widespread retreat of glaciers - widespread retreat of glaciers precipitationprecipitation has has increasedincreased (up to 1% per decade) (up to 1% per decade)

In Northern hemisphere increase in In Northern hemisphere increase in temperaturetemperature in C20th in C20th likely to have been likely to have been largest of any centurylargest of any century, 1990s the , 1990s the warmest decadewarmest decade & 1998 the & 1998 the warmest yearwarmest year during during past past 1000 years1000 years..

Since 1750 there has been an increase of:Since 1750 there has been an increase of: COCO22 (31%), CH4 (151%) and N22O (17%) present COCO2 2 likely not exceeded in past 20, 000 yearslikely not exceeded in past 20, 000 years


The Arctic & AntarcticaThe Arctic & Antarctica


Indonesia & AustraliaIndonesia & Australia


Bangladesh & Bangladesh & MozambiqueMozambique


The science of climate The science of climate changechange

How can we How can we modelmodel weather and weather and climate?climate?

How can we predict How can we predict climateclimate when we when we can’t predict next week’s can’t predict next week’s weatherweather??

What are the main What are the main uncertaintiesuncertainties in in climate prediction?climate prediction?

Quantifying risk: the science of Quantifying risk: the science of probabilisticprobabilistic climate forecasting. climate forecasting.

Harnessing idle CPU on Harnessing idle CPU on YOURYOUR computer for global climate prediction.computer for global climate prediction.


Weather and ClimateWeather and Climate ““Weather”Weather” - tropospheric events associated with - tropospheric events associated with

atmospheric flows of few 100 m & few days or lessatmospheric flows of few 100 m & few days or less Weather phenomena - Weather phenomena - chaoticchaotic, but atmospheric data , but atmospheric data

averaged over a month - averaged over a month - more regularmore regular But exists interannual variabilityBut exists interannual variability ““Climate”Climate” - state of the atmosphere averaged over several - state of the atmosphere averaged over several

years +: the years +: the expectedexpected weather weather for a particular time of for a particular time of year.year.

It is determined by the It is determined by the boundary conditionsboundary conditions of the of the atmosphere-ocean system:atmosphere-ocean system: solar irradiance (power output of the sun)solar irradiance (power output of the sun) atmospheric composition (greenhouse gases...)atmospheric composition (greenhouse gases...) positions of continents, ice-sheets etc.positions of continents, ice-sheets etc.


FFss = 1375 W/m = 1375 W/m22, tube area , tube area aa22

~30% ~30% reflected awayreflected away into space: albedo into space: albedo = 0.3 = 0.3 ~70% ~70% emitted as thermalemitted as thermal infrared F infrared F00, from area 4, from area 4aa22

““effective temperature”, Teffective temperature”, Te e given by: given by: TTee44=F=F00=¼F=¼Fss(1-(1-) )

240 W/m 240 W/m22

gives gives TTee = 255K, lower than observed T = 255K, lower than observed Tss = ~285K = ~285K why? - greenhouse effectwhy? - greenhouse effect

A simple climate modelA simple climate model

Sun (energy input Fs) Earth (black body)


Solar and thermal Solar and thermal radiationradiation

Fg=F0(1+s)/(1+t) 1.6 F0 =Tg4

Tg 286 K

For doubled CO2, net radiation to space is

reduced from ~240W/m2 by ~4W/m2

Climate system adjusts to restore balance.

Atmosphere (Ta)Earth (Tg)Fg

F0Atmosphere:

More solar radiation transmitted s than thermal radiation t


Change in outgoing fluxes Change in outgoing fluxes on 1K tropospheric on 1K tropospheric warmingwarming

-2-1.5

-1-0.5

00.5

11.5

22.5

33.5

DirectWater v.AlbedoCloudsTotal

Direct emission: 4Direct emission: 4TTee33

more energy to spacemore energy to space Warm moist air Warm moist air

increases IR opacity: increases IR opacity: less energy emittedless energy emitted

Snow & ice melt: Snow & ice melt: less less energy reflectedenergy reflected

Cloud amount and Cloud amount and properties changeproperties change

Net feedback factor: Net feedback factor: = ~1.5W/m= ~1.5W/m22/K/K


History of numerical History of numerical modellingmodelling

Lewis Fry RichardsonLewis Fry Richardson (Kings, Nat. Sci., 1900) (Kings, Nat. Sci., 1900) Whilst working as an ambulanceman in WW1 he Whilst working as an ambulanceman in WW1 he

produced the produced the first numerical weather forecastfirst numerical weather forecast, , using a using a slide-ruleslide-rule. .

During intervals between transporting wounded During intervals between transporting wounded soldiers back from the front he made a soldiers back from the front he made a 6-hour 6-hour forecastforecast of pressure and wind, starting from of pressure and wind, starting from analysis of the conditions at 7am on 20 May 1910.analysis of the conditions at 7am on 20 May 1910.

It took at least It took at least 6 months6 months and was very and was very inaccurate.inaccurate.

Proposed a “forecast factory” with some 26, 000 Proposed a “forecast factory” with some 26, 000 accountantsaccountants

25 years later 25 years later Jule CharneyJule Charney formulated equations formulated equations to be solved on a to be solved on a computercomputer

First successful numerical prediction of weather in First successful numerical prediction of weather in April 1950 using ENIAC computerApril 1950 using ENIAC computer


What is a climate model?What is a climate model? Dynamical equations + parameterisationsDynamical equations + parameterisations


Dynamical equationsDynamical equations Newton’s 2nd law in horizontalNewton’s 2nd law in horizontal (forces: (forces:

pressure gradient and Coriolis)pressure gradient and Coriolis) Hydrostatic equationHydrostatic equation (gravity and (gravity and

pressure gradient)pressure gradient) Thermodynamic equationThermodynamic equation (temperature (temperature

can change by “advection” or can change by “advection” or evapouration/condensation)evapouration/condensation)

Continuity equationContinuity equation (conservation of (conservation of mass)mass)

Equation of StateEquation of State (perfect gas)(perfect gas) Water Vapour equationWater Vapour equation (amount of (amount of

water vapour)water vapour)


The equations for a sphereThe equations for a sphere


NWP parameterisationsNWP parameterisations RadiationRadiation Surface and Sub-Surface and Sub-

surface processessurface processes Large-scale cloud and Large-scale cloud and

precipitationprecipitation Convection and Convection and

convective convective precipitationprecipitation

Gravity wave dragGravity wave drag


Ocean-Atmosphere GCMsOcean-Atmosphere GCMsFor climate modelling, physical processes not important for weather modelling must be included, in particular a representation ofoceanic heat transfer


Evidence of “climate Evidence of “climate control”?control”?

Response to anthropogenic, solar and volcanic forcing


And how fast?And how fast? Surface oceans warm faster than Surface oceans warm faster than

depths.depths. Most initial warming occurs in top Most initial warming occurs in top

~100m.~100m. More sensitive climates (higher More sensitive climates (higher TT2xCO2xCO22) )

respond slower.respond slower. Ocean continues to adjust for centuries Ocean continues to adjust for centuries

after atmospheric composition after atmospheric composition stabilises. stabilises.


Climate model predictions: Climate model predictions: I I

Change In Near Surface Temperatures by the 2040s


Climate model Climate model predictions: IIpredictions: II

Predicted change at model resolution


How uncertain are these How uncertain are these model predictions?model predictions?

Models depend on “parameterisations” of Models depend on “parameterisations” of processes too small to resolve.processes too small to resolve.

Parameterisations represent the feedbacks Parameterisations represent the feedbacks between smaller and larger scales.between smaller and larger scales.

Many prescribed “parameters” (e.g. “ice fall Many prescribed “parameters” (e.g. “ice fall speed in clouds”) are poorly constrained.speed in clouds”) are poorly constrained.

What is the impact of different parameter What is the impact of different parameter choices on model predictions?choices on model predictions?

Harder question: impact of model structure.Harder question: impact of model structure.


Simulating global mean Simulating global mean TTss a simple climate a simple climate model:model:


Varying Varying TT2xCO2xCO22 in a simple in a simple climate model:climate model:


Varying ocean heat uptake Varying ocean heat uptake in a simple climate modelin a simple climate model


Varying both Varying both TT2xCO2xCO22 and and ocean heat uptakeocean heat uptake


Range of 50-year forecasts Range of 50-year forecasts consistent with recent consistent with recent changechange


But climate is more than But climate is more than TTss

Simple re-scaling of model predictions Simple re-scaling of model predictions only works for very large-scale variables.only works for very large-scale variables.

Better approach: vary parameters within Better approach: vary parameters within models and repeat the forecast.models and repeat the forecast.

But how much to vary parameters?But how much to vary parameters? The Monte Carlo solution: The Monte Carlo solution:

Vary parameters over very wide rangesVary parameters over very wide ranges Simulate 1950-2050 changes with many Simulate 1950-2050 changes with many

modelsmodels Down-weight predictions from runs that fail Down-weight predictions from runs that fail

to fit observed changes over 1950-2000to fit observed changes over 1950-2000


How many simulations?How many simulations? The problem of non-linearity: you can’t The problem of non-linearity: you can’t

just add up responses to different just add up responses to different perturbations.perturbations.

All combinations and permutations need All combinations and permutations need to be tried, at least in principle.to be tried, at least in principle.

5 settings each of 9 parameters gives 55 settings each of 9 parameters gives 599 permutations, or 2M simulations.permutations, or 2M simulations.

Current typical ensemble sizes with a Current typical ensemble sizes with a comprehensive climate model: 4.comprehensive climate model: 4.

An impossible task?An impossible task?


Climateprediction.comClimateprediction.com Most computing power is now on desks or in Most computing power is now on desks or in

bedrooms, not supercomputing centres.bedrooms, not supercomputing centres. 100-year simulation with HadCM3L would 100-year simulation with HadCM3L would

take 8 months on an up-to-date PC.take 8 months on an up-to-date PC. Over 2M people have participated in Over 2M people have participated in

SETI@home…SETI@home… So we plan to:So we plan to:

Distribute ~2M versions of HadCM3L set up for…Distribute ~2M versions of HadCM3L set up for… Pre-packaged (unique) simulation of 1950-2050 Pre-packaged (unique) simulation of 1950-2050 Estimate uncertainty from collated resultsEstimate uncertainty from collated results


Participants will be able to:Participants will be able to:

Run up to 110 years of the UM.Run up to 110 years of the UM. View model output.View model output. Compare their results on the web.Compare their results on the web. Run and view results from simplified Run and view results from simplified

models.models. Run impact models?Run impact models?


Viewing model diagnosticsViewing model diagnosticsSurface Temperatures


Impacts on WaterImpacts on Water

IS92a, GISS, 2050Large cost (21)

(6) (20) (11) (9)

No cost (90) (16) (3) (4)

(10)Large benefit (7)


The participants?The participants? To date we have:To date we have:

> 17,000 people> 17,000 people > 45,000 PCs> 45,000 PCs Aiming for ~ 2 million PCs.Aiming for ~ 2 million PCs. Who are they?Who are they?

IndividualsIndividualsSmall businessesSmall businessesSchoolsSchoolsLarge businesses ?Large businesses ?


How many is “enough”?How many is “enough”? There are many more than 9 There are many more than 9

underdetermined parameters in the model.underdetermined parameters in the model. We cannot screen all possible We cannot screen all possible

combinations.combinations. We can be more efficient by:We can be more efficient by:

Screening parameter combinations first with a Screening parameter combinations first with a simplified model (same atmosphere, slab simplified model (same atmosphere, slab ocean).ocean).

Using intelligent sampling techniques (e.g. Using intelligent sampling techniques (e.g. “genetic” algorithms).“genetic” algorithms).

We apply a range of future emissions We apply a range of future emissions scenarios to the most realistic models.scenarios to the most realistic models.


Current emissions Current emissions scenariosscenarios


Implications for future Implications for future forcingforcing


What of the real world?What of the real world? Should we expect climate change in the real Should we expect climate change in the real

world to lie inside the range of predictions?world to lie inside the range of predictions? It depends on the quantity of interest:It depends on the quantity of interest:

Has the ensemble converged, or does perturbing Has the ensemble converged, or does perturbing more parameters change the estimated range?more parameters change the estimated range?

Is the ensemble consistent with observations in Is the ensemble consistent with observations in this quantity?this quantity?

Do we expect this variable to be well-simulated?Do we expect this variable to be well-simulated? Basic problem: a probabilistic forecast Basic problem: a probabilistic forecast

cannot be tested with a single event -- the cannot be tested with a single event -- the world can always surprise us.world can always surprise us.


Project PlanProject Plan UGAMP / researchers release.UGAMP / researchers release. SoonSoon

Single parameter sensitivity tests with the slab model.Single parameter sensitivity tests with the slab model. Linux release.Linux release. Year EndYear End

Multiple parameter perturbations with the slab model.Multiple parameter perturbations with the slab model. Initial condition ensemble. Initial condition ensemble.

Main windows release.Main windows release. Next Next yearyear Casino-21: A physics ensemble experiment.Casino-21: A physics ensemble experiment.

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