1. Climate Modeling
Inez Fung
University of California, Berkeley

2. Weather Prediction by Numerical Process Lewis Fry Richardson 1922

3. Weather Prediction by Numerical Process Lewis Fry Richardson 1922
Grid over domain
Predict pressure, temperature, wind
Temperature
-->density
Pressure
Pressure gradient
Wind
temperature

4. Weather Prediction by Numerical Process Lewis Fry Richardson 1922
Predicted:
145 mb/ 6 hrs
Observed:
-1.0 mb / 6 hs

5. 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

6. ENIAC: <10 words of read/write memory
Function tables
(read memory)

7. 16 operations in each time step
Platzman, Bull. Am Meteorol. Soc. 1979

8. 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)

9. 2007
Nobel Peace Prize to VP Al Gore and UN Intergovt Panel for Climate Change
Bert Bolin
5/15/ /30/2007
Founding Chairman of the IPCC …
[student at 1950 ENIAC calculation]

10. Atmosphere
mass
energy
water vapor
momentum
convective mixing

11. Ocean
momentum
mass
energy
salinity

12. 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

13. 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)

14. Continued Success Since 1950
More & better observations
Faster computers
Improved physics

15. 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 multiple experiments)
Model experiments (e.g ) take weeks to months on supercomputers

16. Continued Success Since 1950
More & better observations
Faster computers
Improved physics

17. Earth’s Energy Balance, with GHG
Sun 30
20 absorbed by atm
100
Earth 70 95
114 23 7
CO2, H2O, GHG
50 absorbed by sfc

18. Climate Processes Radiative transfer: solar & terrestrial
phase transition of water
Convective mixing
cloud microphysics
Evapotranspirat’n
Movement of heat and water in soils

19. 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

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

21. Urgency: Rapid Melting of Glaciers --> accelerate warming
J. Zwally
Moulin
Urgency: Rapid Melting of Glaciers --> accelerate warming
Greenland

22. Will cloud cover increase or decrease with warming
Will cloud cover increase or decrease with warming? [models: decrease; warm air can hold more moisture; +ve feedback]
Temperature (K)
Saturation Vapor Pressure (mb) C
A  B
+ water vapor
+ longwave abs
Warming
liquid B
A  C
+ water vapor
+ cloud cover
+ longwave abs
- shortwave abs A
vapor

23. Attribution are observed changes consistent with
Observations
are observed changes consistent with
expected responses to forcings
inconsistent with alternative explanations
Climate model: All forcing
Climate model: Solar+volcanic only
Attribution of climate change to causes involves READ
Climate models are important tools for attributing and understanding climate change. Understanding observed changes is based on our best understanding of climate physics, as contained in simple to complex climate models. For the 4rth assessment report, we had a new and very comprehensive archive of 20th century simulations available. This has greatly helped.
This figure gives an example.
You see observed global and annual mean temperature in black over the 20th century compared to that simulated by a wide range of these models. On the top, in red, are individual model simulations and their overall mean shown fat, that are driven by external influences including increases in greenhouse gases, in aerosols, in changes in solar radiation and by volcanic eruptions. The observations rarely leave the range of model simulations. The trends and individual events like cooling in response to volcanic eruptions (POINT) are well reproduced. The fuzzy range gives an idea of uncertainty with variability in the climate system.
IPCC AR4 (2007)

24. 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 m
Slow warming of oceans --> continue evaporation, continue warming

25. 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)

26. Model predicted change in recurrence of “100 year drought”
2020s
2070s
years
Changes in the probability distribution as well the mean

27. Outlook More & better observations Faster computers
Improved physics + Biogeochemistry: include atmospheric chemistry, land and ocean biology to predict climate forcing and surface boundary conditions

28. Atmosphere
mass
energy
water vapor
momentum
convective mixing

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

30. Climate Model’s View of the Global C CycleFF
Biophysics
+ BGC
Atmosphere
CO2 = 280 ppmv (560 PgC) + …
Ocean Circ.
37400 Pg C
2000 Pg C
90±
60±
Turnover
Time of C yr
time of C
101 yr

31. Prognostic Carbon Cycle
Atm
Ocean
Land-live
Land-dead

32. 21st C Carbon-Climate Feedback:  = Coupled minus Uncoupled
Warm-wet
Warm-dry
{dT, 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

33. 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

34. Continued Success Since 1950
More & better observations:
initial conditions,
Analysis --> improve physics
assessment of model results
Faster computers
Improved physics

35. 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

36. Approach: Data Assimilation
x=[T, p, u,v, q, s, … model parameters]
obs yo 
tn-1 tn  yo xa
Find best estimate of x (xan) given
imperfect model (xbn) and incomplete obs (yo) xb
Model: xbn = M(xan-1)  yo

37. Approaches to Merge Data + Model
Optimal analysis
3D variational data assimilation
4D var
Kalman Filter
Ensemble Kalman Filter
Local Ensemble Transform Kalman Filter …

38. Observations: The A-Train
1:26
TES – T, P, H2O, O3, CH4, CO
MLS – O3, H2O, CO
HIRDLS – T, O3, H2O, CO2, CH4
OMI – O3, aerosol climatology
aerosols, polarization
CloudSat – 3-D cloud climatology
CALIPSO – 3-D aerosol climatology
AIRS – T, P, H2O,
CO2, CH4
MODIS – cloud, aerosols, albedo
OCO - - CO2
O2 A-band
ps, clouds,
aerosols
Coordinated Observations
5/4/2002
4/28/2006
7/15/2004
12/18/2004
Challenge: assimilating ALL data simultaneously in high-resolution climate model to understand interactions

39. Outlook: Research challenges
Climate Change Science:
High resolution climate projections :
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

40. 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

41. 
4th Assessment
Report 2007
WGI: Science
WGII: Impacts
WGIII: Adaptation
and Mitigation