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 Pressure gradient



Wind



temperature

Weather Prediction by Numerical Process Lewis Fry Richardson 1922



p s



t

•

Predicted: 145 mb/ 6 hrs

 •

Observed: -1.0

mb / 6 hs

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

Function tables (read memory)

16 operations in each time step

Platzman, Bull. Am Meteorol. Soc. 1979

Reasons for success in 1950

•

More & better observations initial conditions + assessment after WWII-->

•

Faster computers hours) (24 hour forecast in 24

•

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)

2007

Nobel Peace Prize to VP Al Gore and UN Intergovt Panel for Climate Change Bert Bolin 5/15/1925 - 12/30/2007 Founding Chairman of the IPCC … [student at 1950 ENIAC calculation]

momentum mass energy

 

u



t



Atmosphere



u

  

u



p

2

  

u

 

1

 

p



g k

ˆ

   

t

  

RT

;

 

(

 

u

)



f



0 (

T

,

q

)



T



t

 

u

 

T



SW

 

LW

 

SH

  

F LH

 

(



u

)

 

(

T

)

SW



f

(

clouds

,

aerosols

,...)

LW



f

(

T

,

q

,

CO

2 ,

GHG

...) water vapor

 

q



t

 

u

 

q



convective mixing

Evap



Condensati on

 

(

q

)

momentum mass energy salinity

Ocean



u

2



t



u

2

 

u

2



2

 

u

2

 

1



0



p



F

 

0

wind stress

 

u

2

 

w



z



0



T



t

0

  

p



z



u

3

  

T

 

g;

 

Q

0

f (T, s ) surface heating

 

(T )



s



t



u

3

 

s



s

0



0



z (E



P )

0

 

(s ) freshwater flux



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 ) { Initial Conditions} {Prediction} Atm + Ocn t = 0 t = 1 month 2 3

•

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

•

More & better observations

•

Faster computers

•

Improved physics

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 runs experiments) multiple Model experiments (e.g. 1800 2100) take weeks to months on supercomputers

Continued Success Since 1950

•

More & better observations

•

Faster computers

•

Improved physics

Earth’s Energy Balance, with GHG

Sun Earth 100 70 30 20 absorbed by atm CO 2 , H 2 O, GHG 7 23 114 95 50 absorbed by sfc

Climate Processes

•

Radiative transfer: solar & terrestrial

•

phase transition of water

•

Convective mixing

•

cloud microphysics

•

Evapotranspirat’n

•

Movement of heat and water in soils

CO2 CH4 N2O 10,000 years ago

Climate Forcing

change in radiative heating (W/m 2 ) at surface for a given change in trace gas composition or other change external to the climate system

Warming

Climate Feedbacks

Evaporation from ocean, Increase water vapor in atm Enhance greenhouse effect Increase cloud cover; Decrease absorption of solar energy Decrease snow cover; Decrease reflectivity of surface Increase absorption of solar energy

Greenland

Moulin

J. Zwally

Urgency: Rapid Melting of Glaciers --> accelerate warming

Will cloud cover increase or decrease with warming? [models: decrease; warm air can hold more moisture; +ve feedback] 40 35 30 25 20

liquid C

A



B + water vapor + longwave abs Warming

B

15 10 5

vapor A

0

1 2 3 4 5 6 275 280 285 290 295 300

Temperature (K) A



C + water vapor + cloud cover + longwave abs - shortwave abs

Attribution

•

are observed changes consistent with



expected responses to forcings



inconsistent with alternative explanations IPCC AR4 (2007) Observations Climate model: All forcing Climate model: Solar+volcanic only

Oceans: Bottleneck to warming long 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 CO 2 :

•

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 21 th C “Business as usual”:

CO 2 increasing 380 to 680 ppmv

20 th C stabilizn:

CO 2 constant at 380 ppmv for the 21 st C Meehl et al. (Science 2005)

2020s

Model predicted change in recurrence of “100 year drought”

2070s years

Changes in the probability distribution as well the mean

Outlook

•

More & better observations

•

Faster computers

•

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

momentum mass energy

 

u



t



Atmosphere



u

  

u



p

2

  

u

 

1

 

p



g k

ˆ

   

t

  

RT

;

 

(

 

u

)



f



0 (

T

,

q

)



T



t

 

u

 

T



SW

 

LW

 

SH

  

F LH

 

(



u

)

 

(

T

)

SW



f

(

clouds

,

aerosols

,...)

LW



f

(

T

,

q

,

CO

2 ,

GHG

...) water vapor

 

q



t

 

u

 

q



convective mixing

Evap



Condensati on

 

(

q

)

Ship Tracks: more cloud condensation nuclei - smaller drops - more drops - more reflective



energy balance

Climate Model’s View of the Global C Cycle

FF

Turnover Time of C 10 2 -10 3 yr Atmosphere CO 2 = 280 ppmv (560 PgC) + …

90

±

60

±

Biophysics Ocean Circ.

+ BGC + BGC 37400 Pg C 2000 Pg C Turnover time of C 10 1 yr

Prognostic Carbon Cycle Atm Ocean

DC a Dt



(FF



Def



F oa air



sea_flux



F ba ) atm



land _ flux 0

 

(C a ) DC o Dt

 

F oa

0



P



L

 

(C o ) biology

Land-live



C k b _ live



t

  

k F ab



photosynthesis 0



k C b _ live



live k mortality

Land-dead



C k b _ dead



t



k C b _ live



live k

 

F jk j



k C b _ dead



dead k mortality decomposition



21st C Carbon-Climate Feedback:

d

= Coupled minus Uncoupled

{ d

T

, d

Soil Moisture Index Warm-wet Warm-dry Regression of

d

NPP vs

d

T Photosynthesis decreases with carbon-climate changing climate. PNAS 2005 coupling

Changing Carbon Sink Capacity

CO2 Airborne fraction =atm increase / Fossil fuel emission With SRES A2 (fast FF emission): as CO 2 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 1950

•

More & better observations:

–

initial conditions,

–

Analysis --> improve physics

–

assessment of model results

•

Faster computers

•

Improved physics

Initial Condition: Numerical Weather Prediction

• • •

Challenge Diverse, asynchronous obs of atm Find the current state of the atm at t n Model --> forecast for t n+1

Kalnay 2003 •

Practice Ensemble forecast -->

–

mean state,

–

uncertainty in forecast

Approach: Data Assimilation

obs y

o



Model: x

o

b n

x a

=

M

(x a x

b

n-1 )



y

o

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

t

n-1

t

n

Find best estimate of x (x a n ) given imperfect model (x b n ) and incomplete obs (y o )

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

Coordinated Observations

4/28/2006 5/4/2002 12/18/2004

1:26

7/15/2004 aerosols, polarization

CloudSat CALIPSO

– 3-D cloud climatology – 3-D aerosol climatology

TES MLS

– T, P, H 2 O, O 3 , CH 4 , CO – O 3 , H 2 O, CO

HIRDLS OMI

– T, O 3 , H 2 O, CO 2 , CH 4 – O 3 , aerosol climatology

AIRS

– T, P, H 2 O,

MODIS

CO 2 , CH 4 – cloud, aerosols, albedo

OCO

- - CO 2 O 2 A-band p s , clouds, aerosols

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

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 challenges

Maturity

• • • •

Adaptation and Mitigation Production and consumption energy efficiency Alternative energy Carbon capture & sequestrat’n - scalable?

Geo-engineering - potential harm vs benefits Need a new generation of models where climate interacts with adaptation and mitigation strategies to guide, prioritize policy decisions

http://www.ipcc.ch

4th Assessment Report 2007 WGI: Science WGII: Impacts WGIII: Adaptation and Mitigation