climate modeling mea 719 date of exam may 05 2003 time of exam 9 11am n.
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Climate Modeling: MEA-719 DATE OF EXAM: MAY 05, 2003 TIME OF EXAM: 9-11am PowerPoint Presentation
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Climate Modeling: MEA-719 DATE OF EXAM: MAY 05, 2003 TIME OF EXAM: 9-11am

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, 2003 TIME OF EXAM: 9-11am

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  1. Climate Modeling: MEA-719DATE OF EXAM: MAY 05, 2003TIME OF EXAM: 9-11am REVIEW FOR FINAL EXAM

  2. Grading scheme • Homework assignments: 20% • Mid-term test: 20% • Final exam: 30% • Term paper: 30% (10%o/20%w)

  3. 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)


  5. 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

  6. Guiding Questions Should be familiar with all the guiding questions given at the beginning of the class notes for each major course topic

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

  8. 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

  9. 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)

  10. EOF Method Need to be familiar with the primary steps for implementing the EOF method

  11. Main Steps for Implementing EOF Method Construction of standardized data matrix Construction of covariance or correlation matrix (R) Solve characteristic equation for the covariance/correlation matrix to obtain eigen value/eigen vector pairs 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.

  12. 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

  13. Construction of E0F Time series Correlation = Matrix =Data data map at t=k(k Column) Patterns . E0Fi , amp 1 Var=1 amp (E0Fi , t=k) t:=n E0Fi , ampi Var=i t:=k t:=1 t:=n E0Fi , ampp Var=p t:=1 t:=n

  14. Decision Models (i) Derivation of simple decision model (ii) Main assumptions (concept of ensemble forecasting) (iii) Interpretation extreme conditions

  15. Palmer’s 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

  16. 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)

  17. 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 …difference between Spectral & Finite Difference Methods…

  18. Mathematical forms and main properties of basic numerical schemes- eulerial- semi-Lagrangian- explicit- semi-implicit

  19. Building blocks of simple spectral barotropic model and main components of typical prediction cycle

  20. 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

  21. 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.

  22. 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

  23. Different strategies of using models to understand climate variability • Africa • South America • Asia

  24. 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

  25. Summary of IPCC Assessment Activities Familiarity with Sequence of activities

  26. 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

  27. KEY FINDINGS 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 Observations vs Models (natural variability)

  28. KEY FINDINGS 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. Observations vs Models (with external forcing)

  29. KEY FINDINGS Anthropogenic factors do provide an explanation of 20th century temperature change. Observations vs. Models (with external forcing)

  30. 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 …”

  31. 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 [], (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.

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

  33. Schedule for Term Paper Oral Presentation15 minutes @ presentationMay 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