Climate Models

(Mt/Ag/EnSc/EnSt 404/504 - Global Change)(Mt/Ag/EnSc/EnSt 404/504 - Global Change) Climate Models (from IPCC WG-I, Chapter 8)Climate Models (from IPCC WG-I, Chapter 8)

Climate ModelsClimate Models

Primary Source: Primary Source:

IPCC WG-I Chapter 8 - Climate Models and Their EvaluationIPCC WG-I Chapter 8 - Climate Models and Their Evaluation


Part 1: Model StructurePart 1: Model Structure


The Climate SystemThe Climate System

How do we simulate this?How do we simulate this?


Starting Point: Fundamental Laws of PhysicsStarting Point: Fundamental Laws of Physics

1. Conservation of Mass1. Conservation of Mass

2. First Law of Thermodynamics2. First Law of Thermodynamics

3. Newton’s Second Law3. Newton’s Second Law

Plus conservation of water vapor, chemical species, …Plus conservation of water vapor, chemical species, …

But - these are complex differential equations!

How can we use them?

By solving them on a grid.


Global Climate Models: StructureGlobal Climate Models: Structure

(Bradley, 1999)


Resolution Resolution Increases over Increases over

TimeTime

Increased computing power has allowed increased resolution

Computing demand Computing demand increases inversely with increases inversely with cubecube of horizontal of horizontal resolution.resolution.


Development of Development of Global Climate Global Climate Models (GCMs)Models (GCMs)

… and increasing complexity.

Which should be Which should be favored?favored?


Differing scales:

distributed surface properties

PBL

ATMOS

SVAT

Export throughStreamflow

SW/GW

External Forcing

Global Climate Models: Land-Atmosphere LinkGlobal Climate Models: Land-Atmosphere Link


Global Climate Models: Development of Ocean ModelsGlobal Climate Models: Development of Ocean Models

(Bradley, 1999)


Global Climate Models: ParameterizationGlobal Climate Models: Parameterization

What’s a model to do?What’s a model to do?

Important processes Important processes smaller than a grid box:smaller than a grid box:

e.g., thunderstorms e.g., thunderstorms (atmospheric convection)(atmospheric convection) few km

(www.physicalgeography.net)

(www.physicalgeography.net)

ParameterizationParameterization: Represent the : Represent the effects of the unresolved processes effects of the unresolved processes on the grid. Assume that on the grid. Assume that unresolved processes are at least unresolved processes are at least partly driven by the resolved partly driven by the resolved climate.climate.


Higher Resolution Can HelpHigher Resolution Can Help

Regional (limited-area) Climate ModelRegional (limited-area) Climate Model

~ 0.5˚ (lat) x ~ 0.5˚ (lon)~ 0.5˚ (lat) x ~ 0.5˚ (lon)

Part of a Global Climate ModelPart of a Global Climate Model

2.5˚ (lat) x 3.75˚ (lon)2.5˚ (lat) x 3.75˚ (lon)


Higher Resolution Can HelpHigher Resolution Can Help

Regional (limited-area) Climate ModelRegional (limited-area) Climate Model

~ 0.5˚ (lat) x ~ 0.5˚ (lon)~ 0.5˚ (lat) x ~ 0.5˚ (lon)

Part of a Global Climate ModelPart of a Global Climate Model

2.5˚ (lat) x 3.75˚ (lon)2.5˚ (lat) x 3.75˚ (lon)


EndEndPart 1: Model StructurePart 1: Model Structure


Part 2: Model EvaluationPart 2: Model Evaluation


How Are Models Evaluated?How Are Models Evaluated?

• Testing against observations (present and past)Testing against observations (present and past)

• Comparison with other modelsComparison with other models

• Metrics of reliabilityMetrics of reliability

• Comparison with numerical weather prediction Comparison with numerical weather prediction


What Limits Evaluation?What Limits Evaluation?

• Unforced (internal) variability Unforced (internal) variability

• Availability of ObservationsAvailability of Observations

• Accuracy of ObservationsAccuracy of Observations

• Accuracy of Boundary Conditions (Forcing) Accuracy of Boundary Conditions (Forcing)

These help determine what is “good simulation”.These help determine what is “good simulation”.


GCM Simulations of Global TGCM Simulations of Global T

~ 5-95% confidence limits (obs)

58 simulations, 14 GCMs

Ensemble Average


Time Average Surface Temperature (1980-1999)Time Average Surface Temperature (1980-1999)

Mean Model: Average of 23 GCMsMean Model: Average of 23 GCMs

˚C


Errors in Simulated Errors in Simulated Surface Temperature Surface Temperature

(1980-1999)(1980-1999)

Lines: Observed meanLines: Observed mean

Colors (top): Colors (top): Ensemble mean - obs.Ensemble mean - obs.

Colors (bottom):Colors (bottom): RMS RMS differences in simulated-differences in simulated-observed time series (i.e., observed time series (i.e., typical error) typical error)

Spatial pattern correlation: Spatial pattern correlation: ~ 98% (individual models)~ 98% (individual models)


Annual Variability (Seasons)Annual Variability (Seasons)Lines: Observed Standard Deviation (of monthly means)Lines: Observed Standard Deviation (of monthly means)

Colors: Colors: Ensemble mean - observationsEnsemble mean - observations


Diurnal Temperature Range (1980-1999)Diurnal Temperature Range (1980-1999)


˚C

Tendency to be smaller than observedTendency to be smaller than observed

Problems with clouds? Boundary layer?Problems with clouds? Boundary layer?


Atmospheric Zonal Average (1980-1999)Atmospheric Zonal Average (1980-1999)


Vertical Axes: Vertical Axes:

Left - Pressure (millibars)Left - Pressure (millibars)

Right - Elevation (kilometers)Right - Elevation (kilometers)

Tendency for cool polar Tendency for cool polar tropopause. Persistent feature tropopause. Persistent feature of GCMs, though now smallerof GCMs, though now smaller

K


Mean Reflected Solar RadiationMean Reflected Solar Radiation

Satellite Observations(solid)

Average of 23 GCMs(dashed)

Colors: Individual Models

(1985-1989)


Mean Emitted Infrared RadiationMean Emitted Infrared Radiation

Satellite Observations(solid)

(1985-1989)




Zonal Average PrecipitationZonal Average Precipitation

Observations(solid)

(1980-1999)




Annual Mean Annual Mean PrecipitationPrecipitation(1980-1999)(1980-1999)

ObservationsObservations

Average of 23 GCMsAverage of 23 GCMs


Atmospheric Specific Humidity (1980-1999)Atmospheric Specific Humidity (1980-1999)


Moist bias in tropical troposphereMoist bias in tropical troposphere

g/kg

Vertical Axes: Vertical Axes: Left - Pressure (millibars)Left - Pressure (millibars)

Right - Elevation (km)Right - Elevation (km)(bias)

- 40%- 40% + 40%+ 40%


Ocean (Potential) Temperature (1957-1990)Ocean (Potential) Temperature (1957-1990)



Ocean Salinity (1957-1990)Ocean Salinity (1957-1990)


PSU

Vertical Axes: Depth (m)Vertical Axes: Depth (m)

PSU = “practical salinity units”PSU = “practical salinity units”

• based on conductivity of electricity in waterbased on conductivity of electricity in water

• PSU = 35 PSU = 35 water is 3.5% salt water is 3.5% salt


Ocean Heat TransportOcean Heat Transport

(Feb 85 - Apr 89)

Models: 1980-1999


Sea Ice Sea Ice SimulationSimulation

MarchMarch SeptemberSeptember

14 GCMs14 GCMs

(1980-1999)(1980-1999)

““Number of Models” = models with Number of Models” = models with ice cover > 15% in the 2.5˚ x 2.5˚ ice cover > 15% in the 2.5˚ x 2.5˚ region.region.

Red lines: Observed 15% Red lines: Observed 15% concentration boundariesconcentration boundaries


El NiEl Niño - ño - Southern Southern

Oscillation Oscillation (ENSO)(ENSO)

““Power” = amount of variability Power” = amount of variability occurring for a cycle length (period)occurring for a cycle length (period)

Recent GCMs Recent GCMs

(~ 2000-2005)(~ 2000-2005)

Previous generation GCMs Previous generation GCMs

(~ 1995-2000)(~ 1995-2000)


Are Models Improving? - 1Are Models Improving? - 1

““Normalized” = RMS error / observed space-time variabilityNormalized” = RMS error / observed space-time variability


Are Models Improving? - 2Are Models Improving? - 2

““Performance Index” combines error estimates of Performance Index” combines error estimates of

Sea level pressureSea level pressure TemperatureTemperature WindsWinds

HumidityHumidity PrecipitationPrecipitation Snow/IceSnow/Ice

Ocean salinityOcean salinity Heat fluxHeat flux

(Reichler and Kim, 2007)(Reichler and Kim, 2007)


EndEnd Part 2: Model Evaluation Part 2: Model Evaluation


Part 3: Model FeedbacksPart 3: Model Feedbacks


How does Earth’s temperature get established How does Earth’s temperature get established and maintained?and maintained?

Positive Feedback: ExamplePositive Feedback: Example


IR radiation absorbed & re-emitted, partially toward surface

Solar radiation penetrates

Greenhouse Effect - 1Greenhouse Effect - 1



IR radiation absorbed & re-emitted, partially toward surface

Emitted IR: ~200-500 W-mEmitted IR: ~200-500 W-m

Net IR: ~25-100 W-mNet IR: ~25-100 W-m


Cooler atmosphere: - Less water vapor - Less IR radiation absorbed & re-emitted




Cooler atmosphere: - thus less surface warming - cooler surface temperature




1.1. Perturb climate systemPerturb climate system

2.2. Positive feedback moves climate away Positive feedback moves climate away from starting pointfrom starting point

3.3. A A destabilizingdestabilizing factor factor

Other examples:Other examples:

- ice-albedo feedback- ice-albedo feedback

- CO- CO22-ocean temperature feedback-ocean temperature feedback

Positive FeedbackPositive Feedback


1.1. Perturb climate systemPerturb climate system

2.2. Negative feedback moves climate back Negative feedback moves climate back toward starting pointtoward starting point

3.3. A A stabilizingstabilizing factor factor

Example:Example:

1.1. Decrease Earth’s temperatureDecrease Earth’s temperature

2.2. Cooler Earth emits less radiation (energy)Cooler Earth emits less radiation (energy)

3.3. Outgoing radiation < solar inputOutgoing radiation < solar input

4.4. Net positive energy inputNet positive energy input

5.5. Earth warms up from net energy inputEarth warms up from net energy input

Negative FeedbackNegative Feedback


Key Feedbacks - 1Key Feedbacks - 1

1.1. Water Vapor:Water Vapor:

• Warmer atmosphere can contain more water vaporWarmer atmosphere can contain more water vapor

• Increased water vapor increases greenhouse effectIncreased water vapor increases greenhouse effect

• Atmosphere warms furtherAtmosphere warms further

2. Clouds:2. Clouds:

• Clouds cool the climate (reflect sunlight) and warm the Clouds cool the climate (reflect sunlight) and warm the climate (block outgoing infrared radiation)climate (block outgoing infrared radiation)

• Changes in cloud distribution can thus amplify or reduce the Changes in cloud distribution can thus amplify or reduce the warmingwarming


Key Feedbacks - 2Key Feedbacks - 2

1.1. Snow-ice albedo:Snow-ice albedo:

• Warmer climate has reduced snow and iceWarmer climate has reduced snow and ice

• Surface reflects less and absorbs more solar radiationSurface reflects less and absorbs more solar radiation

• Climate warms furtherClimate warms further

2. Lapse rate (decrease of T with height):2. Lapse rate (decrease of T with height):

• In warmer climate, especially tropics, temperature In warmer climate, especially tropics, temperature decreases less with heightdecreases less with height

• Upper troposphere warms more than surfaceUpper troposphere warms more than surface

• Upper troposphere emits energy to space (infrared Upper troposphere emits energy to space (infrared radiation) more effectively than surface, countering the radiation) more effectively than surface, countering the greenhouse effect.greenhouse effect.


Feedback StrengthsFeedback Strengths


EndEnd Part 3: Model Feedbacks Part 3: Model Feedbacks


ENDEND

Climate ModelsClimate Models

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