Download dwaliser_climate101_07_21_06

Removing the Mystery of
Predicting Climate Change
Duane Waliser
JPL
101 Lecture Series
July 19, 2006
Your connections to climate change predictions
Today’s Lecture
Boo
- Includes Physics/Math
•Bad News
•Good News - Only ONE Test Question!!
_____________________________________________________________________________
Boo
1. What does he mean by “climate change”?
• Observations
2. Build a Simple Climate Model
• Greenhouse Gases
• Climate Feedback; “What If”?
• Model Predictions
3. State of the Art Climate Models
• Computation Challenges
• Do They Work?
4. Reducing the Uncertainties
• Faster Computers
• More/Better Satellite Data
What Kind of Climate Change
Are We talking about?
Natural Variations
Geological Changes - Ice Ages - No
El Nino <-> La Nina - No
Volcanic Induced - No…
Solar Variations - No…
The Kind in “Day After Tomorrow” - Definitely Not
Anthropogenic = Man-Made
CFCs & Ozone Destruction - No
Enhanced Greenhouse Gases (e.g., CO2) - Yes
Why All
The
Concern?
Rarely If Ever
So Warm
Rarely if Ever
So Fast
“Hockey Stick”
What MIGHT be causing this warming?
Industrial Age
Extended
Using
Far and
Proxy
Away
Data
One of
e.g.Most
The
Ice
Cores
Important
Climate
Data
Sets
So, How is the Warming & CO2 Connected?
What about the Future?
Past Experience Suggests
Starting with a Simple Model
Yes, this is
where the
physics and Math
come in
Simple Climate Model
Observations
Stefan-Boltzmann
Law:
1884
Physics
+ Math
Solar “constant”
4
TET
4
~335 W/m2 Emitted Radiation
Total Solar Radiation
Radiation
101=- Total
TakeTerrestrial
Away Message
Temperature
↑
(aka Infrared, Thermal)
Emitted Radiation ↑↑↑↑
Earth Emits Radiation to Cool
Sun Heats the Earth
Simple Climate Model
 TE4
Earth
Surface
Solve For TE => 277 K = 4 C ~ 40 F
Real Global Average
Temperature = 288 K = 15 C ~ 59 F
Not Bad - But We Missed Two
Really Important Things
Clouds, Ice, Snow,
Desert and Dust
Reflects
Sunlight
Back to
Space
Improve Our Simple Climate Model
Ice/Snow
Clouds
Deserts
  TE4
A = “Albedo” ~ 0.30
Earth
Surface
Now Solve For TE => 254 K = -19 C ~ -2 F
Freezing Cold!!!
Pretty Bad - But We Still Have
Something Very Important to Include
Greenhouse Earth
Gases such as
H2O, CO2, CH4
Are Known As
Greenhouse
Gases
Greenhouse
Analogy
More Improvements to our Climate Model
90% Solar
20% Terrestrial Atmosphere
Passes
Passes Thru; Rest
Cools As
TA4
Thru
Heats the Atmosphere
Ice/Snow
Clouds
Deserts
Greenhouse Gases, H2O, CO2
Atmosphere,
TA
Surface, TE
Back
the Math
& Physics
Now,
Weto
Balance
Energy
(i.e.  & )
at the Top of the Atmosphere and at the
Surface - 2 Equations & 2 Unknowns.
Lets Spare the Details…..
Now Solve For T => 286 K = 13 C ~ 55 F
Pretty Good! .
The GH’effect Changes This
To This .
So Why The “Global Warming”?
Ice/Snow
Clouds
Deserts
Greenhouse Gases, H2O, CO2
Atmosphere,
TA
Surface, TE
Recall, CO2 Has Been Increasing
So Why The “Global Warming”?
Ice/Snow
Clouds
Deserts
Greenhouse
Greenhouse Gases,
Gases, H
H22O,
O, CO
CO22
Atmosphere,
TA
TE 
Surface, TE
More GHGs, More Trapping, Higher Temperatures
This Part is Well Established
So Why Are We Uncertain?
Climate Feedbacks!!
Ice/Snow-Albedo Feedback
Water Vapor
Feedback
Cloud
Feedback
Climate Feedbacks
Positive or Negative
Ice/Snow
Clouds
Deserts
Greenhouse Gases, H2O, CO2
Atmosphere,
TA
Surface, TE
Te  : Ice/Snow Melt : Reflection  : Te 
+
Te  : Water Vapor  : GHG  : Te 
+
Water Vapor  : ?Clouds  : Reflection  : Te  Depends on the type of cloud, its height, ice/water, etc.!
The balance of these
feedbacks, and MANY
others, have to be
properly represented
In Climate Models
Ice/Snow-Albedo Feedback
Water Vapor
Feedback
Cloud
Feedback
How do We Do this in State-ofthe-Art Climate Modeling?
_____________________________________________________________________________
•Divide the Atmosphere Into Boxes (How
many - as many as possible)
•Do the type of calculations for each
Box like we did in our simple model.
•Use Conservation
of Mass, Energy, and
Test Question
Momentum
and the
Gas Law.
Did our model
useIdeal
Conservation
of
1) Mass
2) Energy
3) Momentum
Climate Model Computer “Grid”
Similar for Ocean,
Land & Ice Systems
Scope of numerical problem in Excel terms
Temperature
<- Longitude ->
Other files for q,
U, V, W, P, etc.
<- Latitude ->
<- Height ->
360 Longitude * 180 Latitude * 30 in Height * 20+ Variables (e.g., Temp, Water
Vapor, Wind, Clouds, Radiation, etc) = ~40 Million; Then make a calculation of
these to step forward in time for 20 minutes until you get to 100 years.
That’s Why We Need
Super-Computers
JPL Dell Xeon Cluster
cosmos.jpl.nasa.gov
What Can These Climate Models Do?
Natural
&
Man-Made
Induced
Changes
Volcanoes
Solar
Greenhouse
Ozone
Model
Hindsight
Pretty
Good
Predicting the Future
Science, Politics & Society
Plausible
“Scenarios”
For CO2
Emissions
Climate Model Projections
Intergovernmental Panel on Climate Change (IPCC, 2001)
While there is
considerable
disagreement, ALL
models predict
WARMING for ALL
plausible scenarios.
Where does the warming occur?
IPCC, 4th (newest) Assessment Report
Projected
Temperature
Change
In 2100
2099-2070
Minus
1999-1970
How About Our Backyard?
IPCC, 4th (newest) Assessment Report
Systematic Warming
1.5 - 3.0 C
2.7 - 5.4 F
Relatively Agreeable
+/- 20%
Much Less Certain
Why do the Model
Predictions Differ?
Estimating “Unresolved”
and Complex Processes
Difficulty with Clouds, Climate and Computer Grids
Consider drawing a picture of this cloud
We would like to
Have a sharp pencil
For most clouds we
have a BIG CRAYON
Clouds - and other
features - have
very fine scales
How do you
Realistically
Represent
this with ONE
number?
1) Get More Numbers
2) Make Sure it is a Good Number
10km Grid
Our Excel ~ 100 km
1000 X More Work
Longest
Simulations
A Few
Months
1) Get More Numbers
“Nesting”
Or
“Downscaling”
Get More
Numbers
Where You
Most Need
Them
2) Make Sure it is a Good Number
That’s where satellite data are crucial
ICE
ICE
ICE
SNOW
MIXED
LIQUID
LIQUID
RAIN
Cloud
Feedback
cccc bbc
ccmm cccr
r
cccc aat t
4
ccmm 477
aat t
663
ccnn 3
rm
rm
ccss
i
ggff iroro
ddl l
2
ggff 200
d
dl l
ggi i 2211
ssss
ee
ggi i hh
ssss
eer r
inin iaiapp
mm
ccmm
0.09
0.08
0.20
0.07
0.15
0.06
0.05
0.10
0.04
0.03
0.05
0.02
0.01
0.00
0.00
Model
m ipipss
m
irir l l
m oocchh
m
iriroo r r
ccmm
rr
mm
ppi i
mm
rr
uukk nn i i
m ccaar r
uukk m
oo
m
m ccmm
oogg 33
eemm
11
Cloud
Path (kg/m^2)
(kg/m^2)
Cloud Ice
Ice Path
Cloud Ice
Strong Influence on Climate
IPCC
Models:
GlobalAverage
Average Ice
Water
PathIce
IPCC
Models:
Global
Total
Cloud
0.10
0.25
Factor of ~20 Difference
Factor of ~7 Difference
Cloud Ice: Models vs Observations
Li et al. 2005
Observations?
AURA/MLS provides the first vertical profiles of
Cloud Ice in the upper troposphere -> Extremely
Valuable Information to Improve Climate Models.
Climate Models
And The MJO
Day 0
Day 10
Influence Weather
Hurricanes,Monsoons
& El Nino
Day 20
Day 30
Models Do Poorly
Simulating & Predicting
the MJO
Day 40
NCEP/NCAR ~ Observations
Tian et al. 2006
AIRS
Tropical Thunderstorms / Convective Clouds
• Produce The Cloud Ice
• Big Temperature Variations
• Very Important for Water & Energy Cycles
• Hardest to Get Right in Climate Models
• Need more information on composition & Structure
ICE
ICE
ICE
SNOW
MIXED
LIQUID
LIQUID
RAIN
CloudSat : Fabulous!
In Summary
• Warming is Evident in the Observations
• The Result of Incorporating our Scientific
Knowledge (Theory+Data), and in some cases our
Intuition, into Climate “Models”, Unequivocally
Indicates the Warming is Anthropogenic in Nature
and Likely to Continue
• How Much?
1. Depends on Interplay of Society, Economics and
Politics (Highly Uncertain).
2. Model Predictions Are Our Most Objective
Guide (Better Means to Establish & Reduce
Uncertainty).
Reducing Remaining Uncertainties
•Better/More
Measurements
•Faster/Better
Computers &
Infrastructure
•Continued Focus