An Introduction to Climate Modeling

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Oct 24, 2013 (3 years and 10 months ago)

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Gettelman: November 2006

An Introduction to

Climate Modeling


Andrew Gettelman

National Center for Atmospheric Research

Boulder, Colorado USA

Assistance from:
J. J. Hack (NCAR)




Gettelman: November 2006


A. Gettelman& J. Hack

Real NCAR Scientists


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Outline


What is Climate


Why is climate different from weather and forecasting



Hierarchy of atmospheric modeling strategies


Focus on 3D General Circulation models (GCMs)



Conceptual Framework for General Circulation Models



Parameterization of physical processes


concept of resolvable and unresolvable scales of motion


approaches rooted in budgets of conserved variables


Model Validation and Model Solutions


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Question 1: What is Climate?

A.
Average/Expected ‘Weather’

B.
The temperature & precipitation range

C.
Distribution of all possible weather

D.
Record of Extreme events


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Climate change

and its manifestation

in terms of weather

(climate extremes)

(1) What is Climate?


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Climate change

and its manifestation

in terms of weather

(climate extremes)


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Climate change

and its manifestation

in terms of weather

(climate extremes)


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Impacts of Climate Change

Mote et al 2005

Observed Change 1950
-
1997



Snowpack




Temperature

(
-

+
)

(
-

+
)


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Observed Temperature Records

IPCC, 3rd Assessment, Summary For Policymakers


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‘Anthropogenic’ Changes

Radiative Forcing (Wm
-
2
)

1000

1200

1400

1600

1800

2000


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‘Anthropogenic’ Changes (2)

560ppmv CO
2

~2060


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Question 2


What is the difference between Numerical Weather
Prediction and Climate prediction?


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Climate v. Numerical Weather Prediction


NWP:


Initial state is CRITICAL


Don’t really care about whole PDF, just probable phase space


Non
-
conservation of mass/energy to match observed state



Climate


Get rid of any dependence on initial state


Conservation of mass & energy critical


Want to know the PDF of all possible states


Don’t really care where we are on the PDF


Really want to know tails (extreme events)



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Question 3

How can we predict Climate (50 yrs)

if we can’t predict Weather (10 days)?







Statistics!


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Conceptual Framework for Modeling


Can’t resolve all scales, so have to represent them



Energy Balance / Reduced Models


Mean State of the System


Energy Budget, conservation, Radiative transfer



Dynamical Models


Finite element representation of system


Fluid Dynamics on a rotating sphere


Basic equations of motion


Advection of mass, trace species


Physical Parameterizations for moving energy



Scales: Cloud Resolving/Mesoscale/Regional/Global


Global= General Circulation Models (GCM’s)


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Physical processes regulating climate


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2000


2005

Earth System Model ‘Evolution’


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Modeling the Atmospheric General Circulation

?Requires
understanding of :


atmospheric predictability/basic fluid dynamics


physics/dynamics of phase change


radiative transfer (aerosols, chemical constituents, etc.)


interactions between the atmosphere and ocean (El Nino, etc.)


solar physics (solar
-
terrestrial interactions, solar dynamics, etc.)


impacts of anthropogenic and other biological activity



Basic Process:


iterate finite element versions of dynamics on a rotating sphere


Incorporate representation of physical processes




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Meteorological Primitive Equations


Applicable to wide scale of motions; > 1hour, >100km


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Global Climate Model Physics

Terms
F, Q,

and
S
q
represent physical processes



Equations of motion,
F


turbulent transport, generation, and dissipation of momentum



Thermodynamic energy equation,
Q


convective
-
scale transport of heat


convective
-
scale sources/sinks of heat (phase change)


radiative sources/sinks of heat



Water vapor mass continuity equation


convective
-
scale transport of water substance


convective
-
scale water sources/sinks (phase change)


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Grid Discretizations

Equations are distributed on a sphere



Different grid approaches:


Rectilinear (lat
-
lon)


Reduced grids


‘equal area grids’: icosahedral, cubed sphere


Spectral transforms



Different numerical methods for solution:


Spectral Transforms


Finite element


Lagrangian (semi
-
lagrangian)



Vertical Discretization


Terrain following (sigma)


Pressure


Isentropic


Hybrid Sigma
-
pressure (most common)


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Model Physical Parameterizations

Physical processes breakdown:



Moist Processes


Moist convection, shallow convection, large scale condensation



Radiation and Clouds


Cloud parameterization, radiation



Surface Fluxes


Fluxes from land, ocean and sea ice (from data or models)



Turbulent mixing


Planetary boundary layer parameterization, vertical diffusion,
gravity wave drag


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Basic Logic in a GCM (Time
-
step Loop)

For a grid of atmospheric columns:

1.
‘Dynamics’: Iterate Basic Equations

Horizontal momentum, Thermodynamic energy,

Mass conservation, Hydrostatic equilibrium,

Water vapor mass conservation

2.
Transport ‘constituents’ (water vapor, aerosol, etc)

3.
Calculate forcing terms (“Physics”) for each column

Clouds & Precipitation, Radiation, etc

4.
Update dynamics fields with physics forcings

5.
Gravity Waves, Diffusion (fastest last)

6.
Next time step (repeat)


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Physical Parameterization


Physical parameterization


express unresolved physical processes in terms of resolved processes


generally empirical techniques



Examples of parameterized physics


dry and moist convection


cloud amount/cloud optical properties


radiative transfer


planetary boundary layer transports


surface energy exchanges


horizontal and vertical dissipation processes


...

To close the governing equations, it is necessary to incorporate
the effects of physical processes that occur on scales below the
numerical truncation limit


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Q

F

F

S
q

S
q


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Atmospheric Energy Transport

Synoptic
-
scale mechanisms



hurricanes



extratropical storms

http://www.earth.nasa.gov


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Process Models and Parameterization


Boundary Layer


Clouds

Stratiform

Convective


Microphysics



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Radiation


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Other Energy Budget Impacts From Clouds

http://www.earth.nasa.gov


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Energy Budget Impacts of Atmospheric Aerosol

http://www.earth.nasa.gov


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Scales of Atmospheric Motions/Processes

Anthes et al. (1975)

Resolved Scales

Global Models

Future Global Models

Cloud/Mesoscale/Turbulence Models

Cloud Drops

Microphysics

CHEMISTRY


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Global Modeling and Horizontal Resolution


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Examples of Global Model Resolution

Typical Climate Application

Next Generation Climate
Applications

~300km





50
-
100km


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High Resolution Art Global Model Simulation

100km x 100km Global Model Precipitation

NCAR CCM3 run on Earth Simulator, Japan


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Key Uncertainties for Climate (1):

1.
Low Clouds over the ocean:

Reflect Sunlight (cool) : Dominant Effect

Trap heat (warm)

More Clouds=Cooling


Fewer Clouds=Warming


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Marine Stratus: Low Clouds over the Ocean


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Parameterization of Clouds

Weare and Mokhov (1995)

Cloud amount (fraction) as simulated by 25 atmospheric GCMs


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Low Clouds Over the Ocean

Change in low cloud


with 2xCO2


2 Models: Changes

are OPPOSITE!


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Key Uncertainties for Climate (2):

2.

High Clouds:

Dominant effect is that they Trap heat (warm)

More Clouds=Warming


Fewer Clouds=Cooling


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Key Uncertainties for Climate (3):

3.
Water Vapor: largest greenhouse gas

Increasing Temp=Increasing water Vapor (more greenhouse)

Effect is expected to ‘amplify’ warming through a ‘feedback’


1D Radiative
-
Convective Model:

Higher humidity=>warmer surface


Gettelman: November 2006

Summary


Global Climate Modeling


complex and evolving scientific problem


parameterization of physical processes pacing progress


observational limitations pacing process understanding


Parameterization of physical processes


opportunities to explore alternative formulations


exploit higher
-
order statistical relationships?


exploration of scale interactions using modeling and
observation


high
-
resolution process modeling to supplement observations


e.g., identify optimal truncation strategies for capturing major scale
interactions


better characterize statistical relationships between resolved and
unresolved scales


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How can we evaluate simulation quality?



Compare long term mean climatology


average mass, energy, and momentum balances


tells you where the physical approximations take you


but you don’t necessarily know how you get there!


Consider dominant modes of variability


provides the opportunity to evaluate
climate sensitivity


response of the climate system to a specific forcing factor


exploit natural forcing factors to test model response


diurnal and seasonal cycles, El Niño Southern Oscillation
(ENSO), solar variability





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Comparison of Mean Simulation Properties 1

Observed

Precipitation

Simulated

Precipitation


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Comparison of Mean Simulation Properties 1

Difference:

Sim
-

Observed

Simulated

Precipitation


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Comparison of Mean Simulation Properties 2

Observed

Land Temp

Simulated

Land Temp


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Comparison of Mean Simulation Properties 2

Simulated

Land Temp

Difference:

Sim
-

Observed


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Testing AGCM Sensitivity

Cloud (OLR) Anomalies and ENSO

Hack (1998)

Observed

Simulated

More Cloud





Less Cloud


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Turning The Crank: Results


Simulations of Atmospheric Model Coupled to Ocean


Present Day Climate


Simulations into the future with ‘Scenarios’


Different Models=Different ‘Sensitivity’


Potential Changes in Temp, Precip


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Kicking the System: Radiative Forcing


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Observations: 20
th

Century Warming


Model Solutions
with Human Forcing


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Surface Temperature Variations 1000
-
2100



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CCSM Past: Last Millennium to 2100


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Atmospheric CO
2

(input)


Temperature (output)

CCSM Future: Next 100+ years


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CMIP 2001: Temperature and Precipitation

Covey et al. (2001)


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Impacts of Climate Change

Mote et al 2005

Observed Change 1950
-
1997



Snowpack




Temperature

(
-

+
)

(
-

+
)


Gettelman: November 2006

The Future

Regardless of Scale: Still need parameterizations for most things

Resolved Scales

Global Models

Future Global Models

Cloud/Mesoscale/Turbulence Models

Goal: get interactions right (Mesoscale). Also extreme events


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Example of State of the Art Global Model Simulation

10 X 10 km Global Model Precipitation

NEIS AGCM for the Earth Simulator, Japan


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Example of State of the Art Global Model Simulation

10 X 10 km Global Model Precipitation: Mid Latitude Cyclone over Japan


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‘Nested’ Models inside a GCM

Another Approach: Nested Modeling (GCM forces Cloud or Mesoscale Model)

NCAR NRCM: Outgoing Longwave Radiation, Jan1: 36km

Recall Scales: Still need parameterizations for most things


(Radiation, Convection, Microphysics).

Goal is to do small scale interactions better


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The End