Climate Modeling

1
Climate Modeling
2
Need for Models

• Laboratory to perform experiments
• Way to understand complex connections, including
  feedbacks
• Way to predict what will happen in the future

3
Building a Model

• What is needed
• Framework (time space)
• One or more specific questions to be addressed
• Appropriate type of model
• Mathematics describing the model
• Powerful computer
• Validation data set

4
Types of Climate Models

• Conceptual models
• Diagnostic models
• Prognostic models

5
Conceptual Model
6
Diagnostic Models

• Quantitative model
• Use measured data (input) to determine another
  quantity (output)
• No future prediction involved
• Example use observed temperature, wind,
  humidity to determine current or past
  precipitation

7
Diagnostic Model
8
Prognostic Models

• Quantitative model
• Use measured data to determine another quantity
  AND make a prediction
• Examples
• Atmospheric GCMs
• Coupled GCMs
• Regional climate models

9
Time and Space

• Spatial domain
• Global
• Regional
• Spatial resolution
• Grid
• Temporal predictions
• Depends on response time of system
• Years to 1000s years
• Time scale of integration
• How long conditions act on the system until new
  conditions are calculated
• Minutes to days

10
(No Transcript)
11
Temporal Scales of Predictions

• Paleo-climatological predictions
• 1,000 to 100,000 years
• Pangea
• Long-term predictions
• Decades to centuries
• Global warming
• NAO
• Medium-term predictions
• Years
• ENSO

12
Concept of Numerical Modeling

• Mathematical representation of processes
• Equations describing evolution with time
• Integration of equations with time

13
Models are a Simplification

• Climate system is complex
• Climatic processes limited by
• Understanding
• Computational ability
• Temporal spatial resolution limited by
• Data availability
• Computational ability

14
Parameterization

• Representing processes in a simpler way
• Ignoring processes that have little impact on
  climate at the chosen scale
• Avoids unnecessary computing time
• Mutual consistency is required
• If two processes produce feedbacks with opposite
  signs, one should not be considered without the
  other

15
Say youre building a climate model you want to
include clouds
Model needs to include the effect of clouds on
both LW SW
LW
SW
-

16
Uncertainty of Results

• Results have inherent uncertainty
• Initial conditions based on observational data
• Observational data may be
• Incomplete
• Inaccurate
• Results are only as good as model initial data

17
GCMs

• General Circulation Models or Global Climate
  Models
• More elaborate than other climate models
• Models three-dimensional characteristics of
  climate
• Physical geography plays important role
• Involves labor-intensive calculations

18
Coupled GCM

• Models the coupling of ocean-atmosphere
  interactions
• Ocean-atmosphere interface exchanges
• Heat
• Water
• Momentum

19
Coupled Ocean-Atmosphere
20
Coupled Ocean-Atmosphere

• Wind drives ocean circulation
• Heat moisture from ocean influence atmospheric
  circulation weather
• Rain alters ocean salinity, therefore ocean
  circulation

21
Areas of Research

• GCMs are being used to explore
• Increasing atmospheric CO2 concentration
• Reoccurrence of a Little Ice Age
• Future climates impact on marine life
• Air pollution the Southeast Asian monsoon
• Rain season prediction for crop planting in the
  Amazon basin

22
Expectations for Future GCMs

• Finer-scale to include
• Large-scale eddies in ocean
• River flow detail
• Surface feature detail
• More extensive modeling of natural processes
• carbon cycling between land, ocean, atmosphere

23
Educational Global Climate Model (EdGCM)

• Prognostic model
• Models 3-D features of climate system
• Global spatial domain
• Coupled ocean-atmosphere

24
How EdGCM Works

• Numerically solves equations for. . .
• Physical conservation of
• Energy
• Mass
• Momentum
• Moisture
• Equation of State (pVnRT)
• Billions of calculations for each year

25
EdGCM Calculations

• Calculations take into account
• Seasonal diurnal cycles
• Aerosols
• Relevant gases
• Large-scale convective cloud cover
• Precipitation
• Snow depth
• SST

26
EdGCM Spatial Resolution

• Horizontal 8 latitude by 10
  longitude
• Vertical 9 layers in atmosphere

27
EdGCM Temporal Resolution

• Calculations performed for every 30 min
• Output may be analyzed by
• Month
• Season
• Year(s)
• Lab explores global warming
• long-term prediction
• 50 100 year runs

28
EdGCM Input

• Land surface condition
• Start end dates of run
• SST mode (observed or predicted SST)
• Solar luminosity
• GHG concentrations
• Solar and/or GHG trend (optional)

29
EdGCM Output

• Global data for 400 climate variables,
• including
• Temperature
• Albedo
• Wind
• Snow cover
• Cloud cover

30
Analysis Options

• Summary tables
• Time series plots
• Maps
• Vertical profiles
• Anomaly maps profiles
• User chooses temporal scale
• Interpolation option

31
(No Transcript)
32
(No Transcript)
33
(No Transcript)
34
(No Transcript)
35
(No Transcript)
36
(No Transcript)
37
(No Transcript)