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Climate ModelingClimate Modeling
Inez FungUniversity of California, Berkeley
Weather Prediction by Numerical ProcessWeather Prediction by Numerical ProcessLewis Fry Richardson 1922Lewis Fry Richardson 1922
Weather Prediction by Numerical ProcessWeather Prediction by Numerical ProcessLewis Fry Richardson 1922Lewis Fry Richardson 1922
• Grid over domain • Predict pressure,
temperature, wind
Temperature -->density Pressure
Pressure gradient Wind temperature
Weather Prediction by Numerical ProcessWeather Prediction by Numerical ProcessLewis Fry Richardson 1922Lewis Fry Richardson 1922
• Predicted: 145 mb/ 6 hrs
• Observed: -1.0 mb / 6 hs€
∂ps
∂t
First Successful Numerical Weather First Successful Numerical Weather Forecast: March 1950Forecast: 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: ENIAC: <10 words of read/write memory<10 words of read/write memory
Function tables(read memory)
16 operations in each time step16 operations in each time step
Platzman, Bull. Am Meteorol. Soc. 1979
Reasons for success in 1950Reasons for success in 1950• More & better observations after 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)
20072007
Bert BolinBert Bolin 5/15/1925 - 12/30/2007Founding Chairman of the IPCC…[student at 1950 ENIAC calculation]
Nobel Peace PrizeNobel Peace Prize to toVP Al Gore andVP Al Gore andUN Intergovt Panel for Climate ChangeUN Intergovt Panel for Climate Change
mass
energy
water vapor
momentum
)(
...),,,(
,...),(
)(
),(;
0)(
)(ˆ12
2
qonCondensatiEvapqutq
GHGCOqTfLW
aerosolscloudsfSW
TLHSHLWSWTutT
qTfRTp
ut
uFkgpuuutu
ℑ+−=∇•+∂∂
==
ℑ++++=∇•+∂∂
==
=•∇+∂∂
ℑ+++∇−=×Ω+∇•+∂∂
r
bbr
r
rrrrrr
ρρ
ρρ
ρ
AtmosphereAtmosphere
ℑ convective mixing
OceanOcean
momentum
mass
energy
salinity
€
∂ r
u 2∂t+
r u 2 • ∇
r u 2 + 2Ω×
r u 2 = −
1
ρ 0∇p +
r F +
r τ 0
wind stress{
∇ •r u 2 +
∂w
∂z= 0
0 = −∂p
∂z+ ρg; ρ = f (T, s )
∂T
∂t+
r u 3 • ∇T = Q
0
surface heating{
+ ℑ (T )
∂s
∂t+
r u 3 • ∇s =
s0ρ 0Δz
(E − P )0
freshwater flux1 2 4 4 3 4 4
+ ℑ (s)
Numerical Weather Prediction Numerical Weather Prediction ( ~ days)( ~ 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 Seasonal Climate Prediction ( El – Nino Southern Oscillation )( El – Nino Southern Oscillation )
{ Initial Conditions}
Atm + Ocn t = 0
{Prediction}
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 1950Continued Success Since 1950
• More & better observations
• Faster computers
• Improved physics
Modern climate Modern climate modelsmodels
• 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 multiple experiments)
Model experiments (e.g. 1800-2100) take weeks to months on supercomputers
Continued Success Since 1950Continued Success Since 1950
• More & better observations
• Faster computers
• Improved physics
Earth’s Energy Balance, with GHGEarth’s Energy Balance, with GHG
COCO22, H, H22O, GHGO, GHG
Earth
70
95114
23
7
50 absorbed by sfc
Sun
30
20 absorbed by atm
100
Climate ProcessesClimate Processes• Radiative transfer:
solar & terrestrial
• phase transition of water
• Convective mixing
• cloud microphysics
• Evapotranspirat’n
• Movement of heat and water in soils
Climate ForcingClimate Forcing
change in radiative heating (W/m2) at surface for a given change in trace gas composition or other change external to the climate system
CO2
CH4
N2O
10,000 years ago
Climate FeedbacksClimate Feedbacks
Warming
Decrease snow cover;Decrease reflectivity of surfaceIncrease absorption of solar energy
Increase cloud cover;Decrease absorption of solar energy
Evaporation from ocean,Increase water vapor in atmEnhance greenhouse effect
J. Zwally
Greenland
Urgency: Rapid Melting Urgency: Rapid Melting of Glaciers --> accelerate of Glaciers --> accelerate
warmingwarming
Moulin
Will cloud cover increase or decrease with Will cloud cover increase or decrease with warming? warming? [models: decrease; warm air can [models: decrease; warm air can hold more moisture; +ve feedback]hold more moisture; +ve feedback]
A B + water vapor + longwave abs Warming
A C + water vapor + cloud cover + longwave abs - shortwave abs
275 280 285 290 295 3000
5
10
15
20
25
30
35
40
1 2 3 4 5 6
Temperature (K)
Sat
ura
tio
n V
apo
r P
ress
ure
(m
b)
A
B
Cliquid
vapor
AttributionAttribution
• are observed changes consistent with
expected responses to forcings
inconsistent with alternative explanations
Observations
Climate model: All forcing
Climate model: Solar+volcanic only
IPCC AR4 (2007)
Oceans: Bottleneck to warmingOceans: Bottleneck to warminglong memory of climate systemlong 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
2121ststC warming depends on rate of COC warming depends on rate of CO2 2 increaseincrease
20thC stabilizn:CO2 constant at 380 ppmv for the 21stC
21thC “Business as usual”:CO2 increasing 380 to 680 ppmv
Meehl et al. (Science 2005)
Model Model predicted predicted change in change in
recurrence of recurrence of “100 year “100 year drought”drought”
years
2020s
2070s
Changes in the probability distribution Changes in the probability distribution as well the meanas well the mean
OutlookOutlook• More & better observations
• Faster computers
• Improved physics + Biogeochemistry: include atmospheric chemistry, land and ocean biology to predict climate forcing and surface climate forcing and surface boundary conditionsboundary conditions
mass
energy
water vapor
momentum
)(
...),,,(
,...),(
)(
),(;
0)(
)(ˆ12
2
qonCondensatiEvapqutq
GHGCOqTfLW
aerosolscloudsfSW
TLHSHLWSWTutT
qTfRTp
ut
uFkgpuuutu
ℑ+−=∇•+∂∂
==
ℑ++++=∇•+∂∂
==
=•∇+∂∂
ℑ+++∇−=×Ω+∇•+∂∂
r
bbr
r
rrrrrr
ρρ
ρρ
ρ
AtmosphereAtmosphere
ℑ convective mixing
Ship Tracks:Ship Tracks:- - more cloud more cloud condensation nucleicondensation nuclei- smaller drops- smaller drops- more drops- more drops- more reflective- more reflective- - energy balance energy balance
Climate Model’s View of Climate Model’s View of the Global C Cyclethe Global C Cycle
Biophysics+ BGC
AtmosphereCO2 = 280 ppmv (560 PgC) + …
Ocean Circ.+ BGC
37400 Pg C 2000 Pg C
90± 60±
TurnoverTurnoverTime of CTime of C101022-10-103 3 yryr
TurnoverTurnovertime of Ctime of C101011 yryr
FFFF
Prognostic Carbon CyclePrognostic Carbon Cycle
€
DCa
Dt= (FF + Def + Foa c
air− sea_flux1 2 3 + Fba c )
atm− land _ flux1 2 3
0
+ ℑ (Ca )
DCo
Dt= −Foa c
0+ P − L
biology1 2 3 + ℑ (Co )
∂Cb _ livek
∂t= −α k Fab ↓
photosynthesis{
0
−Cb _ live
k
τ livek
mortality1 2 3
∂Cb _ deadk
∂t=
Cb _ livek
τ livek
mortality1 2 3
+ Fjk
j
∑ −Cb _ dead
k
τ deadk
decomposition1 2 3
Atm
Ocean
Land-live
Land-dead
Warm-wet
Warm-dry
T, Soil Moisture Index}
Regression of NPP vs T
Photosynthesis decreases with carbon-climate coupling
Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005
21st C Carbon-Climate Feedback: 21st C Carbon-Climate Feedback: = Coupled minus Uncoupled = Coupled minus Uncoupled
Changing Carbon Sink CapacityChanging Carbon Sink Capacity
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
CO2 Airborne fractionCO2 Airborne fraction=atm increase /=atm increase /Fossil fuel emissionFossil fuel emission
Continued Success Since 1950Continued Success Since 1950
• More & better observations: –initial conditions, –Analysis --> improve physics–assessment of model results
• Faster computers
• Improved physics
Initial Condition: Initial Condition: Numerical Weather PredictionNumerical 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 AssimilationApproach: Data Assimilation
yo
x=[T, p, u,v, q, s, … model parameters]
obs yo
tn-1 tn
yo
xbModel: xb
n = M(xa
n-1)xa
Find best estimate of x (xa
n) given imperfect model (xb
n) and incomplete obs (yo)
Approaches to Merge Data + ModelApproaches 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-TrainObservations: The A-Train
1:26
TES – T, P, H2O, O3, CH4, COMLS – O3, H2O, COHIRDLS – T, O3, H2O, CO2, CH4
OMI – O3, aerosol climatology
aerosols, polarization
CloudSat – 3-D cloud climatologyCALIPSO – 3-D aerosol climatology
AIRS – T, P, H2O, CO2, CH4
MODIS – cloud, aerosols, albedo
OCO - - CO2
O2 A-band ps, clouds, aerosols
Coordinated Observations
5/4/20024/28/2006
7/15/2004
12/18/2004
Challenge: assimilating ALL data simultaneously in high-resolution climate model to understand interactions
Outlook: Research challengesOutlook: Research challenges
Climate Change ScienceClimate 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 challengesOutlook: Research challenges
Adaptation and Mitigation
• Production and consumption energy efficiency
• Alternative energy• Carbon capture & sequestrat’n - scalable?• Geo-engineering - potential harm vs
benefitsMaturity
Need a new generation of models where climate interacts with adaptation and mitigation strategies to guide, prioritize policy decisions
http://www.ipcc.ch
4th Assessment4th AssessmentReport 2007Report 2007
WGI: ScienceWGI: Science
WGII: ImpactsWGII: Impacts
WGIII: Adaptation WGIII: Adaptation and Mitigationand Mitigation