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REVIEW FOR FINAL EXAM
Climate Modeling: MEA-719
DATE OF EXAM: MAY 05, 2003
TIME OF EXAM: 9-11am
Grading scheme
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Homework assignments:
Mid-term test:
Final exam:
Term paper:
20%
20%
30%
30%
(10%o/20%w)
Organization of the Course
Course divided into the following components ~
• International climate research organizational
structure
• Climate models
• Climate model predictions (SIP; Paleo climate; CC
projections)
• Climate modeling (observational)
• Climate modeling (prediction)
• Climate modeling applications (end-user)
Main Topics Covered
TOPIC 1: International organization of climate research and applications programs
TOPIC 2: SESONAL-TO-INTERANNUAL VARIABILITY & PREDICTABILITY OF THE GLOBAL OCEANATMOSPHERE-LAND SYSTEM (GOALS)~observations-diagnosis-models-applications
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ENSO (G1)
VARIABILITY OF THE ASIAN-AUSTRALIAN MONSOON SYSTEM (G2)
VARIABILITY OF THE AMERICAN MONSOON SYSTEM (VAMOS-G3)
VARIABILITY OF THE AFRICAN CLIMATE SYSTEM (VACS-G4)
TOPIC 3: DECADAL TO CENTENNIAL TIME SCALES (DecCen)
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NORTH ATLANTIC OSCILLATION (D1)
TROPICAL ATLANTIC VARIABILITY(D2)
ATLANTIC THERMOHALINE CIRCULATION (D3)
TOPIC 4: ANTHROPOGENIC CLIMATE CHANGE
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CLIMATE CHANGE PREDICTION (A1)
CLIMATE CHANGE DETECTION AND ATTRIBUTION (A2)
Chronology of Lectures for MEA-719
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Lecture Notes for Lecture 1: Introduction
Lecture Notes for Lecture 2: International organization of global climate research
programs
Lecture Notes for Lectures 3 & 4: Climate models (global and regional)
Lecture Notes for Lecture 4: Methods for solving Model Equations
Lecture Notes for Lecture 5: Spectral Method for solving Model Equations
Lecture Notes for Lecture 6: Semi-Lagrangian Method for solving Model Equations
Lecture Notes for Lecture 7: Model Skill in Predicting ENSO
Lecture Notes for Lecture 8: Value and Skill of Climate Prediction Models
Lecture Notes for Lecture 9: African and European Climate Variability
Mid-term exam
Lecture Notes for Lecture 10: EOF Method
Lecture Notes for Lecture 11: Asian Summer Monsoon
Lecture Notes for Lecture 12: Variability of the American Monsoon System (VAMOS)
Lecture Notes for Lecture 12: Variability of the American Monsoon System (VAMOS)supplement
Lecture Notes for Lecture 13: Anthropogenic Climate Change (ACC)
Lecture Notes for Lecture 14: Review for MEA-719
Guiding Questions
Should be familiar with all the
guiding questions given at the
beginning of the class notes for
each major course topic
International organization of
climate programs
• Basic structure of the CLIVARWorld Climate Research
Program - WCRP (see
schematic diagram
• Scientific functions of each
principal component
Organization
• WCRP – oversees coordination of several
key areas of climate variability
• CLIVAR – oversees co-ordination of the
physical component of climate variability
• Components (or Panels) – each has an
agenda, typically about 12 experts from all
around the World, provide guidance to the
international climate community in its
particular area
Methods of Model and
Observational Data Analyses
(i)
(ii)
(iii)
(iv)
(v)
(vi)
Time evolution of the anomalies
EOF method
Time series & pattern correlation analysis
Root mean square error analysis
Hit/false alarm rates (& ROC)
Decision modeling (added value)
EOF Method
Need to be familiar with the
primary steps for
implementing the EOF
method
Main Steps for Implementing EOF Method
1. Construction of standardized data matrix
2. Construction of covariance or correlation matrix (R)
3. Solve characteristic equation for the covariance/correlation
matrix to obtain eigen value/eigen vector pairs
4. Determine cutoff for “noise” & signal E0Fs. A rule of
thumb is to retain only those components with variance ()
greater than one or that explain at least a proportion 1/p of
the total variance. This rule doesn’t always work & more
sophisticated criteria exist.
Main Steps (continued)
5. Plot
(i) Histogram for eigen values & separation
between ‘noise’ & ‘signal’ modes may
show
(ii) E0F patterns for dominant modes
(iii) E0F time series for dominant modes
6. If needed reconstruct data matrix by combining contribution
of a subset of eigen modes. This is one way of filtering the
original data set by ignoring the ‘noise’ modes
Construction of E0F Time series
Correlation =
Matrix
1



rp1
rp1 


1 

Z11


Z p1
i H eigen vector
of R Matrix
ei1, ei2 ,...,eip 

Z1k 
 
Z 
 p k 
e i 1 Z1k e i 2 Z 2k  ei 3 Z 3k 
.
E0Fi , amp 1
Var=1
E0Fi , ampi
Var=i
t:=1
amp (E0Fi , t=k)
t:=k
Z1k
Z pk
Z1n 
 =Data

Z pn 
k ie
data map at t=k(k
Column)
= amp(E0Fi t  k)
ei p Z pk
Patterns
t:=n
t:=n
E0Fi , ampp
Var=p
t:=1
t:=n
Decision Models
(i) Derivation of simple decision model
(ii) Main assumptions (concept of
ensemble forecasting)
(iii) Interpretation extreme conditions
Palmer’s Decision Model
USER SECTOR
MODEL HINDCAST
Define (E)
Identity C & L
Forecast (E)
Specify (Pt)
MET. OBS
Obs (E)
Compute
Region 1
OCCURANCE
Fst No  
Yes  
No Yes
Region 2
Region 9
ROC
Hit rate H   /   
False alarm F     
Perfect
C
M p er  o
L
M F
H
C
1  o   Ho 1  C   o
L
L

Vpt  
MCli  M
MCli  M per
Vop t  max V 

o (E)

Savings$  MCli  Mopt  NL
DECISION IF $ IS
IMPORTANT TO SECTOR
See fig.
See fig.
F
Climatology
C 
MCli  min  , o 
L 
Models
- AGCMs/OGCMS/AOGCMS
- Vorticity equation model
(i) Basic assumptions, (ii) terms in governing
equations, and (iii) simple numerical schemes
(in class reviewed centered differencing
scheme)
…difference between Spectral &
Finite Difference Methods…
Finite Difference
Method
Spectral
Method
• Local such that m,n
represents the value of
  ,   at a particular
point in space
• Finite difference
equations determine
the evolution
• Based on global functions
• Basis functions determine
the amplitudes and phases
such that when summed up
determine spatial
distribution of dependant
variables
Mathematical forms and main
properties of basic numerical
schemes
- eulerial
- semi-Lagrangian
- explicit
- semi-implicit
Building blocks of simple spectral barotropic
model and main components of typical
prediction cycle
  j ,  k 
  j ,  k 
4
  ,  , t  
   2 


  

A ,   
   2 


   
1
   t Y  ,  
  t  t 
  t  t 

A  j , k
2
3

  t   i  t   nn  11 A
t
A
1
A 
4

2 1
  Y A,  dd
*
0 1
Interannual & Decadal Variability
• Climatology - Global annual cycle (e.g., rainfall)
• Variability (& mechanism where known) for all primary regions location of dominant signal
• Model capabilities and deficiencies based on model vs
observations) with emphasis on the following:
: ENSO
: AA-Monsoon
: VAMOS (North America)
: Europe
: Africa
Current performance of models
for ENSO
• (i) Both statistical and dynamical models produce useful tropical
SSTA forecasts for the peak phase of ENSO up to two seasons in
advance.
• (ii) A consensus forecast (i.e. an ensemble across prediction
systems) is remarkably skillful, whereas an ensemble of realizations
of a single prediction system improves the skill only marginally.
• (iii) The periods of retrospective forecasting are too short in terms of
distinguishing between the skill scores of the various prediction
systems.
• (iv) Models predict the sign of extreme events well, but sometimes
predict warm or cold events when the observations call for normal
conditions.
• (v) Consistency among forecasts initialized one month apart is not a
good a priori measure of forecast skill.
Current performance of models
for the AA-Monsoon
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Models have smaller pattern correlations and larger rmsd
relative to the observational uncertainty
The errors among the models are larger than the
uncertanity in the observations
EOF-1: associated with the northward shift of the Tropical
Convergence Zone (TCZ)
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EOF-2: associated with the southward shift of the Tropical
Convergence Zone (TCZ)
Models are realistic in their representation of EOF-2
Discrepancies east of 100E
Models fail to capture extension of enhanced rainfall to the
South China Sea where the EOF-2 mode is deficient
Different strategies of using models
to understand climate variability
• Africa
• South America
• Asia
Climate Change
• Should be familiar with the main steps
involved in the assessment of the
understanding of climate change, including
how scenarios of human activities can cause
such changes, future projections
•Current state of understanding for @ step
Summary of IPCC
Assessment
Activities
Familiarity with
Sequence of
activities
KEY FINDINGS
Observations vs Observations
Palaeoclimatic reconstructions for the last
1,000 years indicate that the 20th century
warming is highly unusual, even taking
into account the large uncertainties in
these reconstructions
KEY FINDINGS
Observations vs Models (natural variability)
The observed warming is inconsistent with
model estimates of natural internal climate
variability. It is therefore unlikely (bordering
on very unlikely) that natural internal
variability alone can explain the changes
in global climate over the 20th century
KEY FINDINGS
Observations vs Models (with external forcing)
The observed warming in the latter half of
the 20th century appears to be
inconsistent with natural external (solar
and volcanic) forcing of the climate
system.
KEY FINDINGS
Observations vs. Models (with external forcing)
Anthropogenic factors do provide an
explanation of 20th century temperature
change.
KEY RESULTS
• SAR-1995: Concluded that, “… The
balance of evidence suggests that there is
discernable human influence on global
climate…”
• TAR-2001: Concluded that, “… There is
new & stronger evidence that most of the
warming observed over the last 50 years
is attributable to human activities …”
MEA-719 Term Paper Assignment
May/01/2003
Write a report on the following climate aspects for the country assigned to you.
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Geographical location and features of the country
National meteorological observational network
Main characteristics of the mean climatic conditions
Dominant modes and sources of climate variability
Performance of current dynamical models in simulating and predicting the climate?
Deficiencies of dynamical models that account for inadequacies in the simulation of climate?
How well the climatic impacts of the 2002/2003 ENSO were predicted for your country
National climate research programs
Involvement in international climate programs
The report should not exceed 6 pages of text and 2 pages of diagrams. The report should have a one paragraph
summary, an introduction, main body of the text, conclusions, and references. The deadline for submitting the
reports is May/01/2003. You will be expected to give a power point presentation on May/01/2003. The countries will
be assigned in a ballot.
Give all references and sources of your information (not part of page limit)
Your search for information may include (i) the CLIVAR WebPages for country summaries
[http://www.clivar.org/publications/other_pubs/clivar_conf/clivar_conf.htm#NAT], (ii) publications and websites
referenced in the course, and (iii) other sources.
Find 2 examples of previous years power point presentations generated by graduate students, at the course
webpage. Note that the specific of the example assignments were different. These examples are meant only to give
you some appreciation of the scope and quality of power point presentation that is expected.
Country Assignments
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(1) Chenjie Huang: Tanzania
(2) Ryan Boyles: India
(3) Katie Robertson: Canada
(4) Shu-Yun Chen: Argentina
Schedule for Term Paper Oral
Presentation
15 minutes @ presentation
May 01, 2003, 11.20-12.35
(1) Chenjie Huang: 11.20-11.35
Break: 5 minutes
(2) Ryan Boyles: 11.40-11.55
Break: 5 minutes
(3) Katie Robertson: 12.00-12.15
Break: 5 minutes
(4) Shu-Yun Chen: 12.20-12.35
Note: The deadline for submitting the term paper write-up reports is
May/01/2003