Climate Modeling: MEA-719 DATE OF EXAM: MAY 05, 2003 TIME OF EXAM: 9-11am

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Climate Modeling MEA-719 DATE OF EXAM MAY 05,
2003TIME OF EXAM 9-11am

• REVIEW FOR FINAL EXAM

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Grading scheme

• Homework assignments 20
• Mid-term test 20
• Final exam 30
• Term paper 30 (10o/20w)

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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)

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Main Topics Covered

• TOPIC 1 International organization of climate
  research and applications programs
• TOPIC 2 SESONAL-TO-INTERANNUAL VARIABILITY
  PREDICTABILITY OF THE GLOBAL OCEAN-ATMOSPHERE-LAND
  SYSTEM (GOALS)observations-diagnosis-models-appl
  ications
• 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)
• NORTH ATLANTIC OSCILLATION (D1)
• TROPICAL ATLANTIC VARIABILITY(D2)
• ATLANTIC THERMOHALINE CIRCULATION (D3)
• TOPIC 4 ANTHROPOGENIC CLIMATE CHANGE

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Chronology of Lectures for MEA-719

• 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

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Guiding Questions

• Should be familiar with all the guiding
  questions given at the beginning of the class
  notes for each major course topic

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• International organization of climate programs
• Basic structure of the CLIVAR- World Climate
  Research Program - WCRP (see schematic diagram
• Scientific functions of each principal component

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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

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Methods of Model and Observational Data Analyses

• 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)

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EOF Method

• Need to be familiar with the primary steps for
  implementing the EOF method

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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 doesnt always work more
   sophisticated criteria exist.

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Main Steps (continued)

• 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

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Construction of E0F Time series
Correlation Matrix
Data
data map at tk(k Column)
Patterns
.
E0Fi , amp 1 Var?1
tn
amp (E0Fi , tk)
E0Fi , ampi Var?i
tk
t1
tn
E0Fi , ampp Var?p
t1
tn
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Decision Models

• (i) Derivation of simple decision model
• (ii) Main assumptions (concept of ensemble
  forecasting)
• (iii) Interpretation extreme conditions

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Palmers Decision Model
USER SECTOR
MODEL HINDCAST
MET. OBS
Define (E) Identity C L
Forecast (E) Specify (Pt)
Obs (E) Compute
Region 1
OCCURANCE Fst No ? ? Yes ? ? No
Yes
Region 2
Region 9
ROC
H
F
Perfect
Climatology
See fig.
See fig.
DECISION IF IS IMPORTANT TO SECTOR
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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)

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difference between Spectral Finite Difference
Methods

• Finite Difference Method
• Local such that represents the value of
  
• at a particular point in space
• Finite difference equations determine the
  evolution

• Spectral
• Method
• Based on global functions
• Basis functions determine the amplitudes and
  phases such that when summed up determine spatial
  distribution of dependant variables

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Mathematical forms and main properties of basic
numerical schemes - eulerial-
semi-Lagrangian- explicit- semi-implicit
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Building blocks of simple spectral barotropic
model and main components of typical prediction
cycle
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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

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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.

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Current performance of models for the AA-Monsoon

• - 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)
• 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

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Different strategies of using models to
understand climate variability

• Africa
• South America
• Asia

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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 _at_ step

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Summary of IPCC Assessment Activities Familiarit
y with Sequence of activities
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KEY FINDINGS

• 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

Observations vs Observations
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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

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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.

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KEY FINDINGS
Observations vs. Models (with external forcing)

• Anthropogenic factors do provide an explanation
  of 20th century temperature change.

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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

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MEA-719 Term Paper Assignment May/01/2003

• Write a report on the following climate aspects
  for the country assigned to you.
• 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/cli
  var_conf/clivar_conf.htmNAT, (ii) publications
  and websites referenced in the course, and (iii)
  other sources.

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Country Assignments

• (1) Chenjie Huang Tanzania
• (2) Ryan Boyles India
• (3) Katie Robertson Canada
• (4) Shu-Yun Chen Argentina

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Schedule for Term Paper Oral Presentation15
minutes _at_ 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