Download Weather Prediction by Numerical Process Lewis Fry Richardson 1922

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 quasigeostrophic 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 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)
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]
Atmosphere
momentum
mass
energy
water vapor


u 
1



ˆ
 u  u  2  u   p  gk  F  ( u)
t



   ( u)  0
t
p  RT ;   f (T , q )
T 
 u  T  SW   LW   SH  LH  (T )
t
SW  f (clouds , aerosols ,...)
LW  f (T , q , CO2 , GHG ...)
q 
 u  q  Evap  Condensati on  (q )
t
 convective mixing
Ocean
u 2
1
 u 2  u 2  2  u 2   p  F  0
0
momentum t
wind stress
mass
energy
salinity
w
  u2 
0
z
p
0    g;   f (T, s )
z
T
 u 3  T 
t
Q0
 (T )
surface heating
s
s0
 u 3  s 
(E  P )  (s)
t
 0z
0
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}
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 1950
• More & better observations
• Faster computers
• Improved physics
• Forcing: solar irradiance, volanic
Modern climate
models
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. 18002100) 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
CO2, H2O, GHG
23
7
114
50 absorbed by sfc
95
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
CO2
CH4
N2O
10,000 years ago
change in radiative
heating (W/m2) at
surface for a given
change in trace gas
composition or other
change external to
the climate system
Climate Feedbacks
Evaporation from ocean,
Increase water vapor in atm
Enhance greenhouse effect
Warming
Increase cloud cover;
Decrease absorption of solar
energy
Decrease snow cover;
Decrease reflectivity of
surface
Increase absorption of solar
energy
Moulin
J. Zwally
Greenland
Urgency: Rapid Melting
of Glaciers --> accelerate
warming
Saturation Vapor Pressure (mb)
Will cloud cover increase or decrease with
warming? [models: decrease; warm air can
hold more moisture; +ve feedback]
40
35
C
liquid
AB
+ water vapor
+ longwave abs
Warming
30
25
20
B
15
10
5
A
vapor
0
1
275
2
280
3
285
4
290
5
295
Temperature (K)
6
300
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 CO2:
• 200 years emission,
• penetrated to upper 5001000 m
Slow warming of oceans -> continue evaporation,
continue warming
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)
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
Atmosphere
momentum
mass
energy
water vapor


u 
1



ˆ
 u  u  2  u   p  gk  F  ( u)
t



   ( u)  0
t
p  RT ;   f (T , q )
T 
 u  T  SW   LW   SH  LH  (T )
t
SW  f (clouds , aerosols ,...)
LW  f (T , q , CO2 , GHG ...)
q 
 u  q  Evap  Condensati on  (q )
t
 convective mixing
Ship Tracks:
- more cloud
condensation nuclei
- smaller drops
- more drops
- more reflective
-  energy balance
Climate Model’s View of
the Global C Cycle
FF
Atmosphere
CO2 = 280 ppmv (560 PgC) + …
90
60±
±
Turnover Ocean Circ.
Time of C + BGC
102-103 yr 37400 Pg C
Biophysics Turnover
+ BGC
time of C
2000 Pg C 101 yr
Prognostic Carbon Cycle
Atm
DCa
 (FF  Def  Foa
 Fba )
 (Ca )
Dt
air sea_flux
atm land _ flux
0
Ocean
DCo
 Foa
Dt
0
 P  L  (Co )
biology
k
Cbk _ live
C
k
b _ live



F


ab
k
Land-live
t

live
photosynthesis
0
mortality
Cbk _ dead Cbk _ live
Cbk _ dead
Land-dead

k   Fjk 
k
t
 live

dead
j
mortality

decomposition
21st C Carbon-Climate Feedback:
d = Coupled minus Uncoupled
{dT,
dSoil Moisture Inde
Warm-wet
Warm-dry
Regression of dNPP
vs dT
Photosynthesis
decreases with
Fung
et al. Evolution of carbon sinks in a
carbon-climate
changing climate. PNAS 2005
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 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 tn
• Model --> forecast for
tn+1
Practice
• Ensemble forecast -->
– mean state,
Kalnay 2003
– uncertainty in forecast
Approach: Data Assimilation
obs yo


xa
yo
x=[T, p, u,v, q, s, …
model
parameters]

Model:
xbn = M(xxan-1)
y
o
b
tn-1
tn
Find best estimate of x
(xan) given
imperfect model (xbn) and
incomplete obs (yo)
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
12/18/2004
5/4/2002
1:26
CloudSat – 3-D cloud climatology
CALIPSO – 3-D aerosol climatology
7/15/2004
aerosols,
polarization
TES – T, P, H2O, O3, CH4, CO
MLS – O3, H2O, CO
HIRDLS – T, O3, H2O, CO2, CH4
OMI – O3, aerosol climatology
AIRS – T, P, H2O,
CO2, CH4
MODIS – cloud,
aerosols, albedo
OCO - - CO2
O2 A-band
ps, clouds,
aerosols
Challenge: assimilating ALL data simultaneously in highresolution 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
Adaptation and Mitigation
• Production and consumption energy
efficiency
• Alternative energy
• Carbon capture & sequestrat’n - scalable?
• Geo-engineering - potential harm vs
Maturity
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