Climate Modeling MEA 719 cr:3'0 Lecture Set1

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Climate Modeling MEA 719cr3.0Lecture Set-1

• Instructor Professor Fredrick Semazzi
• Days M W
• Time 0300-0415PM
• Bldg Jordan rm 1109
• Office Hours Friday, 1200-100 (Jordan 4144)

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Course Overview
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Course Objectives

• Course covers atmospheric behavior on time scales
  of weeks to centuries, including interactions
  between the atmosphere and other climate system
  components
• Review of present capacity to make accurate
  predictions/projections
• Review of observational limitations
  opportunities
• Understanding of requirements for applications
• No "required text" but readings will be assigned
  (or suggested) each week from the texts below and
  possibly others.

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References on Library Reserve

• Climate change (The IPCC Assessment The science
  of climate change, 2007)
• Climate System Modeling , edited by K. E.
  Trenberth, (1992 ISBN 0521432316 Call NRL
  QC981.c65).
• An introduction to three-dimensional climate
  modeling (Oxford University Press), by W.
  Washington and C. Parkinson (1986 Call NRL
  QC981.w32)
• WCRP, 1995 CLIVAR - A Study on Climate
  Variability and Predictability - Science Plan.
  World Climate Research Programme, WCRP No. 89,
  WMO/TD No. 690, Geneva, 172pp.
• WCRP, 1998 CLIVAR Initial Implementation Plan.
  World Climate Research Programme, WCRP No. 103,
  WMO/TD No. 869, ICPO No. 14, Geneva, 314pp.

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Pre-requisites or Co-requisites(recommended but
not necessary)

• MEA 705 - Dynamic Meteorology 3(3-0-0) - If you
  do not have MEA 705 please see instructor
• Brief review of classical and physical
  hydrodynamics scale analysis of dynamic
  equations atmospheric instabilities dynamics of
  tropical convections perturbation theory and
  approximations for atmospheric wave motions

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Grading scheme

• HW assignments 15
• Class presentations 10
• Mid-term exam 25
• Final Exam 50

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Grading scheme (continued)

• A 97-100
• A 93-96
• A- 89-92
• B 85-88
• B 81-84
• B- 77-80
• C 73-76
• C 69-72
• C- 65-68
• D 61-64
• D 57-60
• D- 53-56
• F 52 below

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Topics of the Course

• Course consists of the following topics
• INTERNATIONAL CLIMATE RESEARCH ORGANIZATIONAL
  STRUCTURE
• GLOBAL SEASONAL CLIMATE PREDICTION SYSTEM
• METHODS OF ANALYSIS OF CLIMATE VARIABILITY
• TROPICAL INTRA-SEASONAL MODES
• MONSOONS DYNAMICS
• ENSO THEORY THE DELAYED OSCILLATOR
• DECADAL VARIABILITY
• CLIMATE CHANGE
• PREDICTABILITY-CHAOS-LORENZ ATTRACTOR
• EVALUATION OF ADDED VALUE OF CLIMATE PREDICTION
• MODEL DEFICIENCIES
• DECISION MODELING
• OBSERVATIONAL LIMITATIONS OPPORTUNITIES
• .

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International climate research organizational
structure
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Climate model prediction (SIP Paleo climate
CC projections)

• SESONAL-TO-INTERANNUAL VARIABILITY
  PREDICTABILITY OF THE GLOBAL OCEAN-ATMOSPHERE-LAND
  SYSTEM (GOALS)
• 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)
• DECADAL TO CENTENNIAL TIME SCALES (DecCen)
• NORTH ATLANTIC OSCILLATION (D1)
• TROPICAL ATLANTIC VARIABILITY(D2)
• ATLANTIC THERMOHALINE CIRCULATION (D3)
• PACIFIC INDIAN OCEAN DECADAL VARIABILITY (D4)
• SOUTHERN OCEAN THERMOCLINE CIRCULATION (D5)
• ANTHROPOGENIC CLIMATE CHANGE ( PALEO CLIMATE)
• CLIMATE CHANGE PREDICTION MODELING (A1)
• CLIMATE CHANGE DETECTION AND ATTRIBUTION
  MODELING ISSUES (A2)

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Global Seasonal Climate Prediction System
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METHODS OF ANALYSIS OF CLIMATE VARIABILITY Example
of Atlantic EOFs
Largest loading Over Northern tropical Atlantic
Largest loading Over Southern tropical Atlantic
ENSO signal (largest loading over the Pacific)
Semazzi Sud (1986)
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TROPICAL INTRA-SEASONAL MODES
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MONSOONS DYNAMICS
East Africa
S.A Monsoon
North Australia
MONSOONS J A N U A R Y
http//www.sandiego.edu/weather/N-N/O16.jpg
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MONSOONS DYNAMICS
NA Monsoon
SE Asia
India monsoon
West Africa
MONSOONS JULY
http//www.sandiego.edu/weather/N-N/O16.jpg
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MONSOONS DYNAMICS
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Time-Zero
ENSO Theory The Delayed Oscillator
25 days
125 days
50 days
175 days
75 days
225 days
100 days
275 days
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DECADAL VARIABILITY
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CLIMATE CHANGE
increase since 1861
1976-00 large increase
1910-45 large increase
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Predictability-Chaos-Lorenz Attractor
Slight change in ICs ends up in a same wing
Are these conditions predictable?
target Prediction event
Lorenz Attractor
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EVALUATION OF ADDED VALUE OF CLIMATE PREDICTION
(Palmer et al, 1999)
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MODEL DEFICIENCIES
Performance of the RegCM3 regional climate model
(OND total rainfall in 1988) over homogeneous
climate regions of eastern Africa TF-Tropical
Forest, KH-Kenya Highlands, SC-Southern Coastal,
TZ-Tanzania, SA-Semi Arid, EA-East Africa (entire
region average). Rainfall in mm.
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CLIMATE MODEL DEFICIENCIES
Nearly all tropical modes are poorly handled by
current climate models. This impacts everything
from tropical cyclones to ENSO and interactions
with the extratropics (Greg Holland, personal
communication)
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GLOBAL MODEL DEFICIENCIES
The Role of AIR-Sea Coupling Land
Interactions Misra_JLI_Dec_2008
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DECISION MODELINGEXAMPLE OF INFLUENCE DIAGRAM
FOR MENINGITIS MANAGEMENT
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Distribution of rain-gauge station over the Horn
of Africa. The size of the circles is
proportional to the amount of total annual
rainfall at the station. Over the arid and
semi-arid regions of Kenya rain-gauge data
coverage is very marginal.
Observational Limitations Opportunities
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Example of uncertainties associated with existing
gridded observational rainfall climate data over
the arid and semi-arid region of Kenya. The top
figure shows average annual rainfall based on
Willmott dataset. The bottom figure shows the
difference between Willmott and CRU datasets.
Although both datasets have the same spatial
resolution there are significant differences
between them, roughly 10 of the actual rainfall.
Over regions where the differences are large, is
an indication of uncertainty in the
characterization of the climate based on these
datasets. Nevertheless, the actual climate
variability over these regions (see Fig.2) is
generally larger than the uncertainty and we can
still infer useful information from the
variability analysis based on these datasets.
Observational Limitations
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Oceanic Monitoring Improvements