Climate Modeling
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Climate Modeling. Inez Fung University of California, Berkeley. Weather Prediction by Numerical Process Lewis Fry Richardson 1922. Weather Prediction by Numerical Process Lewis Fry Richardson 1922. Grid over domain Predict pressure, temperature, wind. Temperature -->density Pressure

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Climate modeling

Climate Modeling

Inez Fung

University of California, Berkeley


Weather prediction by numerical process lewis fry richardson 1922

Weather Prediction by Numerical ProcessLewis Fry Richardson 1922


Weather prediction by numerical process lewis fry richardson 19221

Weather Prediction by Numerical ProcessLewis Fry Richardson 1922

  • Grid over domain

  • Predict pressure, temperature, wind

  • Temperature

  • -->density

  • Pressure

    Pressure gradient

  • Wind

  • temperature


Weather prediction by numerical process lewis fry richardson 19222

Weather Prediction by Numerical ProcessLewis Fry Richardson 1922

  • Predicted:

  • 145 mb/ 6 hrs

  • Observed:

  • -1.0 mb / 6 hs


First successful numerical weather forecast march 1950

First Successful Numerical Weather Forecast: March 1950

  • Grid over US

  • 24 hour, 48 hour forecast

  • 33 days to debug code and do the forecast

  • Led by J. Charney (far left) who figured out the quasi-geostrophic equations


Eniac 10 words of read write memory

ENIAC: <10 words of read/write memory

Function tables

(read memory)


16 operations in each time step

16 operations in each time step

Platzman, Bull. Am Meteorol. Soc. 1979


Reasons for success in 1950

Reasons for success in 1950

  • More & better observationsafter WWII--> initial conditions + assessment

  • Faster computers(24 hour forecast in 24 hours)

  • Improved physics -

    • Atm flow is quasi 2-D (Ro<<1) and is baroclinically unstable

    • quasi-geostrophic vorticity equations

    • filtered out gravity waves

    • Initial C: pressure (no need for u,v)

    • t ~30 minutes (instead of 5-10 minutes)


Climate modeling

2007

Nobel Peace Prize toVP Al Gore andUN Intergovt Panel for Climate Change

Bert Bolin

5/15/1925 - 12/30/2007

Founding Chairman of the IPCC

[student at 1950 ENIAC calculation]


Atmosphere

Atmosphere

momentum

mass

energy

water vapor

convective mixing


Ocean

Ocean

momentum

mass

energy

salinity


Numerical weather prediction days

Numerical Weather Prediction ( ~ days)

Initial

Conditions

t = 0 hr

Prediction

t = 6 hr

12

18

24

  • Predict evolution of state of atmosphere (t)

  • Error grows w time --> limit to weather prediction


Seasonal climate prediction el nino southern oscillation

Seasonal Climate Prediction ( El – Nino Southern Oscillation )

{Prediction}

t = 1 month

2

3

{ Initial

Conditions}

Atm + Ocn

t = 0

  • Coupled atmosphere-ocean instability

  • Require obs of initial states of both atm & ocean,

  • esp. Equatorial Pacific

  • {Ensemble} of forecasts

  • Forecast statistics (mean & variance) – probability

  • Now – experimental forecasts (model testing in ~months)


Continued success since 1950

Continued Success Since 1950

  • More & better observations

  • Faster computers

  • Improved physics


Modern climate models

Modern climate models

  • Forcing:solar irradiance, volanic aerosols, greenhouse gases, …

  • Predict:T, p, wind, clouds, water vapor, soil moisture, ocean current, salinity, sea ice, …

  • Very high spatial resolution:

    • <1 deg lat/lon resolution

    • ~50 atm, ~30 ocn, ~10 soil layers

  • ==> 6.5 million grid boxes

  • Very small time steps(~minutes)

  • Ensemble runsmultiple experiments)

  • Model experiments (e.g. 1800-2100) take weeks to months on supercomputers


Continued success since 19501

Continued Success Since 1950

  • More & better observations

  • Faster computers

  • Improved physics


Earth s energy balance with ghg

Earth’s Energy Balance, with GHG

Sun

Earth

100

70

30

20absorbed by atm

23

7

114

95

CO2, H2O, GHG

50 absorbed by sfc


Climate processes

Climate Processes

  • Radiative transfer: solar & terrestrial

  • phase transition of water

  • Convective mixing

  • cloud microphysics

  • Evapotranspirat’n

  • Movement of heat and water in soils


Climate forcing

Climate Forcing

CO2

change in radiative heating (W/m2) at surface for a given change in trace gas composition or other change external to the climate system

CH4

N2O

10,000 years ago


Climate feedbacks

Climate Feedbacks

Decrease snow cover;

Decrease reflectivity of surface

Increase absorption of solar energy

Evaporation from ocean,

Increase water vapor in atm

Enhance greenhouse effect

Increase cloud cover;

Decrease absorption of solar energy

Warming


Urgency rapid melting of glaciers accelerate warming

Urgency: Rapid Melting of Glaciers --> accelerate warming

J. Zwally

Moulin

Greenland


Climate modeling

Will cloud cover increase or decrease with warming? [models: decrease; warm air can hold more moisture; +ve feedback]

Saturation Vapor Pressure (mb)

Temperature (K)

C

A  B

+ water vapor

+ longwave abs

Warming

liquid

B

A  C

+ water vapor

+ cloud cover

+ longwave abs

- shortwave abs

A

vapor

275 280 285 290 295 300


Attribution

Attribution

Observations

  • are observed changes consistent with

  • expected responses to forcings

  • inconsistent with alternative explanations

Climate model: All forcing

Climate model: Solar+volcanic only

IPCC AR4 (2007)


Oceans bottleneck to warming long memory of climate system

Oceans: Bottleneck to warminglong memory of climate system

  • 4000 meters of water, heated from above

  • Stably stratified

    • Very slow diffusion of chemicals and heat to deep ocean

  • Fossil fuel CO2:

    • 200 years emission,

    • penetrated to upper 500-1000 m

    • Slow warming of oceans --> continue evaporation, continue warming


21 st c warming depends on rate of co 2 increase

21stC warming depends on rate of CO2 increase

21thC “Business as usual”:

CO2 increasing 380 to 680 ppmv

20thC stabilizn:

CO2 constant at 380 ppmv for the 21stC

Meehl et al. (Science 2005)


Model predicted change in recurrence of 100 year drought

Model predicted change in recurrence of “100 year drought”

2020s

2070s

years

Changes in the probability distribution as well the mean


Outlook

Outlook

  • More & better observations

  • Faster computers

  • Improved physics + Biogeochemistry: include atmospheric chemistry, land and ocean biology to predict climate forcing and surface boundary conditions


Atmosphere1

Atmosphere

momentum

mass

energy

water vapor

convective mixing


Climate modeling

Ship Tracks:- more cloud condensation nuclei- smaller drops- more drops- more reflective- D energy balance


Climate model s view of the global c cycle

Climate Model’s View of the Global C Cycle

Atmosphere

CO2 = 280 ppmv (560 PgC) + …

90±

60±

Turnover

Time of C

102-103 yr

Turnover

time of C

101yr

Ocean Circ.

+ BGC

2000 Pg C

37400 Pg C

Biophysics

+ BGC

FF


Prognostic carbon cycle

Prognostic Carbon Cycle

Atm

Ocean

Land-live

Land-dead


21st c carbon climate feedback coupled minus uncoupled

21st C Carbon-Climate Feedback:  = Coupled minus Uncoupled

{dT, Soil Moisture Index}

Warm-wet

Warm-dry

Regression of NPP

vs T

Photosynthesis decreases with carbon-climate coupling

Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005


Changing carbon sink capacity

Changing Carbon Sink Capacity

CO2 Airborne fraction

=atm increase /

Fossil fuel emission

  • With SRES A2 (fast FF emission): as CO2 increases

  • Capacity of land and ocean to store carbon decreases (slowing of photosyn; reduce soil C turnover time; slower thermocline mixing …)

  • Airborne fraction increases --> more warming

Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005


Continued success since 19502

Continued Success Since 1950

  • More & better observations:

    • initial conditions,

    • Analysis --> improve physics

    • assessment of model results

  • Faster computers

  • Improved physics


Initial condition numerical weather prediction

Initial Condition: Numerical Weather Prediction

Challenge

Diverse, asynchronous obs of atm

Find the current state of the atm at tn

Model --> forecast for tn+1

Practice

Ensemble forecast -->

mean state,

uncertainty in forecast

Kalnay 2003


Approach data assimilation

Approach: Data Assimilation

obs yo

yo

xa

Model: xbn= M(xan-1)

xb

tn-1

tn

Find best estimate of x (xan) given

imperfect model (xbn) and incomplete obs (yo)

x=[T, p, u,v, q, s, … model parameters]

yo


Approaches to merge data model

Approaches to Merge Data + Model

  • Optimal analysis

  • 3D variational data assimilation

  • 4D var

  • Kalman Filter

  • Ensemble Kalman Filter

  • Local Ensemble Transform Kalman Filter


Observations the a train

Observations: The A-Train

4/28/2006

Coordinated Observations

5/4/2002

12/18/2004

1:26

CloudSat – 3-D cloud climatology

CALIPSO – 3-D aerosol climatology

7/15/2004

aerosols, polarization

AIRS – T, P, H2O,

CO2, CH4

MODIS – cloud, aerosols, albedo

TES – T, P, H2O, O3, CH4, CO

MLS – O3, H2O, CO

HIRDLS – T, O3, H2O, CO2, CH4

OMI – O3, aerosol climatology

OCO - - CO2

O2 A-band

ps, clouds,

aerosols

Challenge: assimilating ALL data simultaneously in high-resolution climate model to understand interactions


Outlook research challenges

Outlook: Research challenges

  • Climate Change Science:

  • High resolution climate projections 1800-2030:

  • Project impact on water availability, ecosystems, agriculture, at a resolution useful to inform policy and strategies for adaptation and carbon management

  • Articulation of uncertainties and risks


Outlook research challenges1

Outlook: Research challenges

Adaptation and Mitigation

  • Production and consumption energy efficiency

  • Alternative energy

  • Carbon capture & sequestrat’n - scalable?

  • Geo-engineering - potential harm vs benefits

Maturity

Need a new generation of models where climate interacts with adaptation and mitigation strategies to guide, prioritize policy decisions


Climate modeling

http://www.ipcc.ch

4th Assessment

Report 2007

WGI: Science

WGII: Impacts

WGIII: Adaptation

and Mitigation


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