7 oktober 2009 Challenge the future Delft University of Technology ‘Simulation and Modeling...

7 oktober 2009

Challenge the future

DelftUniversity ofTechnology

‘Simulation and Modeling Hierarchies of our Climate System’

A. Pier SiebesmaKNMI & TU DelftThe Netherlands

2Simulation and modeling hierarchies

Uncertainties in Future Climate model Predictions

2.5-4.3°CIPCC 2007

Past FuturePresent190

0

3Verstoorde wolken in een opwarmend klimaat

Earth’s Global Energy Balance

342 W/m2

235 W/m2

107 W/m2

Temperature

Incoming solar radiation

Reflected solar radiation

Outgoing long wave radiation


Increase of Greenhouse Gases…….

342 W/m2

107 W/m2

….increase of temperature



…..decrease in outgoing long wave radiation


……restored new Equilibrium

342 W/m2

235 W/m2 107 W/m2

Higher equilibrium temperature



Outgoing long wave radiation


If climate would be static…………

0)1( lwsw FFR

swF

Top of the atmosphere

lwF swF

sw

sw

F

F :Planetary Albedo

R: net radiation at the TOA

External Perturbation

spss

TQTT

RQRo

Zero feedback gain


If climate would be static…………

0)1( lwsw FFR

swF

Top of the atmosphere

lwF swF

sw

sw

F

F :Planetary Albedo

R: net radiation at the TOA

External Perturbation

spss

TQTT

RQRo

Zero feedback gain

12

3

4.3280

4235

44

KWmx

T

FT

T

R

s

lws

sP

4effT

24 WmQ

KQT PPs 2.1/,

Planck Parameter

Forcing for 2XCO2

Direct Warming


Dynamical Climate Model Feedbacks

),........1(..),.........,,2,,( xnxRSnowCloudsOHTsforcingGHGsolarRR

ix

sispss

i

is

s

TTQTT

x

x

RT

T

RQRo

perturbation

zero feedback gain

feedback processesPs

xx

Ps

x P

xs T

gTT ,, 1

1

1

1


Dufresne & Bony, Journal of Climate 2008

Radiative effects only

Water vapor feedback

Surface albedo feedback

Cloud feedback

Cloud effects “remain the largest source of uncertainty”in model based estimates of climate sensitivity IPCC 2007

2XCO2 Scenario for 12 Climate Models


Primarily due to marine low clouds

“Marine boundary layer clouds are at the heart of tropical cloud feedback uncertainties in climate models”

(duFresne&Bony 2005 GRL)

Stratocumulus

Shallow cumulus


1.How did I get here?

~1m - 1m

~107 m ~105 m

~103 m

The planetary scaleCloud cluster scale

Cloud scaleCloud microphysical scale

10 m 100 m 1 km 10 km 100 km 1000 km 10000 km

turbulence Cumulus

clouds

Cumulonimbus

clouds

Mesoscale

Convective systems

Extratropical

Cyclones

Planetary

waves

Large Eddy Simulation (LES) Model

Cloud System Resolving Model (CSRM)

Numerical Weather Prediction (NWP) Model

Global Climate Model

No single model can encompass all relevant processes

DNS

mm

Cloud microphysics

Resolved

Scales

turbulence

~ 100 km

convection

clouds

radiation

Small scales Large scales

Schematic View of how scales are connected in traditional GCM’s

Depiction of the interaction between resolved and parameterized unresolved cloud-related processes (convection, turbulence, clouds and radiation) in present-day climate models. (from Siebesma et al, Perturbed Clouds in our climate system MIT)

Which are the problems, errors and uncertainties that we have to face with this approach?

1. Inherent lack of understanding of certain physical processes

< 100 m [100,600m] >800m

Source : Andrew Heymsfield

Uncertainties in ice and mixed phase microphysics:

•Supersaturation

•Liquid vs ice

•Habits

•Size distribution

•Sedimentation

•Interaction with radiation

No fundamental equations available describing these properties and processes.

crlcrl qqHqqKA With:

ql : cloud liquid water

ql : critical threshold

H : Heaviside function

A : Autoconversion rate

: Kessler Autoconversion Rate (Kessler 1969)

Example 1: Autoconversion of cloud water to precipitation in warm clouds

Autoconversion rate is a convex function:

_______

ll qAqA

Larson et al. JAS 2001

ql=1 g/kg ql=0

GCM grid box

Subgrid variability of liquid water needs to be known in order to estimate the autoconversion……….

2. Non-linear character of many cloud related processes

Example 2: Cloud fraction and Cloud liquid water

ststc qqHqqHa ______________

qsat

qt

T

qsat(T)

qt .(T,qt)

Cloud fraction:

ststststl qqHqqqqHqqq _________________________

Cloud liquid water:

Subgrid variability of temperature and humidity needs to be known in order to estimate the grid box cloud fraction ac and liquid water content ql

Plane parallel cloud

Scu

Clo

ud a

lbedo

Liquid water path (LWP)

x

x

(LWP) < (LWP)

Neglecting Cloud inhomogeneity causes a positive bias in the cloud albedo.

Example 3: Cloud Albedo Bias

•These biased errors slowly go away when increasing model resolution:

LWPLWP x 0

•Typically allowed if x < 100m

•So for all models operating at a coarser resolution additional information about the underlying Probability Density Function (pdf) is required of temperature, humidity (and vertical velocity).

),,( wqTP t

dTdqTqqHTqPqqHa tsttstc )(),(______________

For Example:

So if only….. we would know the pdf .

Resolved

Scales

turbulence

~ 250 km

convection

clouds

radiation

Small scales Large scales

3. Interactions between the various subgrid processes

•Subgrid processes strongly interact with each other while in (most) GCM’s they only interact indirectly through the mean state leading to inconsistencies and biases.

4. Statistical versus Stochastic Convection 4. Statistical versus Stochastic Convection

~500km

•Traditionally (convection) parameterizations are deterministic:

•Instantaneous grid-scale flow and mean state is taken as input and convective response is deterministic

•One to one correspondency between sub-grid state and resolved state assumed.

•Conceptually assumes that spatial average is a good proxy for the ensemble mean.

,.......),,,(t

q

t

TTqfac

e.g. subgrid cloud fraction :

4. Statistical versus Stochastic Convection4. Statistical versus Stochastic Convection

resolution

100m 1km 1000km100km

Convection

Explicitly

resolved

Statistical ensemble mean

Deterministic convection

parameterization

Stochastic Convection

That takes into account fluctuations so that the ensemble mean is not satisfied each timestep but more in a canonical sense Microcanonical limit

New Pathways

Resolved

Scales

3.5 km

turbulence

convection

clouds

radiation

Pathway 1: Global Cloud Resolving Modelling (Brute Force)

NICAM simulation: MJO DEC2006 NICAM simulation: MJO DEC2006 ExperimentExperiment

MTSAT-1R NICAM 3.5km

Miura et al. (2007, Science)

3.5km run: 7 days from 25 Dec 2006

•Short timeslices

•Testbed for interactions:

deep convection and the large scale•Boundary clouds, turbulence, radiation still unresolved

Pathway 2: Superparameterization

2D

CRM

turbulence

(b)

convection

clouds

radiation

5 km 250 km

Resolved

Scales

Pathway 2: Superparameterization

What do we get? •Explicit deep convection

•Explicit fractional cloudiness

•Explicit cloud overlap and possible 3d cloud effects

•Convectively generated gravity waves

But…..

A GCM using a super-parameterization is 2 to 3 orders of magnitude more expensive than a GCM that uses conventional parameterizations.

On the other hand super-parameterizations provide a way to utilize more processors for a given GCM resolution

Boundary Layer Clouds, Microphysics and Turbulence still needs to be parameterized

Remarks:

Resolved

Scales

turbulence

convection

clouds

radiation

~100 km Large scalesUnresolved scales

Resolved

Scales

vuqv ,,,

Pathway 3: Consistent based parameterizations

Increase consistency between the parameterizations!

How?

Topic of the coming week.

Single Column

Models

Climate Models

NWP ModelsDirect Numerical Simulation Large Eddy Simulation

Use the full range of observations and simulation hierarchy....

GEWEX Cloud Systems Study (GCSS) Strategy.

Field CampaignsInstrumented

Sites

Global Observational

Data sets

Scale Hierarchy

High Low

Direct

Numerical

Simulation

Large

Eddy

Simulations

Global

Climate

SimulationsSimulations

Laboratory

experiments

Atmospheric

Profiling

stations

Field

campaignsSatellite

data

Observations

resolution

i

titemt

i

radlileml

ei

z

qVqw

dt

dq

z

FVw

dt

d

wwdt

dz

0,,,

0,,,

lqt

zi

wel,iweqt,i

Vqt,0Vl,0

Minimal Mixed layer Model

w

Plus closure:

radibasetltle Fzzqqwwfw ,,,,,'','' 00

Example: Bulk Model (“parameterization”) of Scu topped PBL

Scale Hierarchy

High Low

low

high

Direct

Numerical

Simulation

Level of

“understanding”

or

conceptualisation Large

Eddy

Simulations

Global

Climate


Laboratory

experiments

Atmospheric

Profiling

stations

Field

campaignsSatellite

data

Observations

Models/Parameterizations

resolution

Mixed layer

modelsmicrophysical

models

Scale Hierarchy

High Low

low

high

Direct

Numerical

Simulation

Conceptual models

Statistical Mechanics

Self-Organised Criticality

Level of

“understanding”

or


Eddy

Simulations

Global

Climate


Laboratory

experiments

Atmospheric

Profiling

stations

Field

campaignsSatellite

data

Observations


resolution

Mixed layer

models

Interface &

microphysical

models

Complexity

P.W. Anderson

“More is Different”, Science 1977

“the emerge of collective properties

with a large number of interacting elements”

Interacting elements:

•Atoms in Physics

•Macromolecules in Biology

•People in economics

•Convective cloud elements in atmospheric Science?

Dielectric Breakdown

Galaxy distribution

Internet structure

Phase Transitions in Statistical Mechanics

Toy Example: Percolation p=probability of having a conductive bond

1-p = probability of having a insulating bond

p=0.3 pc=0.5 p=0.8

insulating conductingTransition point

At the transistion point: scaling laws, fractal geometry, renormalization group theory

But……This exotic behaviour is only achieved if the order parameter (p) is exactly set to the critical point.

Self Organized Criticality

• In many non-equilibrium systems self-similarity and fractal behaviour emerges spontaneously (including atmosperic turbulence, convection and clouds)

• Toy model that displays this features: Sand Pile Model.

Add randomly grains of sand: 1,, jiji zz

Redistribute grains if a local threshold slope is reached:

1

1

4

1,1,

,1,1

,,

jiji

jiji

jijic

zz

zz

zzthenzzif

Causing avalanches of any scale (power law).

Scale Hierarchy

High Low

low

high

Direct

Numerical

Simulation

Conceptual models

Statistical Mechanics

Self-Organised Criticality

Level of

“understanding”

or


Eddy

Simulations

Global

Climate


Laboratory

experiments

Atmospheric

Profiling

stations

Field

campaignsSatellite

data

Observations


resolution

Mixed layer

models

Interface &

microphysical

models

7 oktober 2009 Challenge the future Delft University of Technology ‘Simulation and Modeling...

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Transcript of 7 oktober 2009 Challenge the future Delft University of Technology ‘Simulation and Modeling...