Webpage of the course: Climate Modeling                                                                                    (LMAST) - A.A. 2019/20 - (I Sem.)

                                          G. Redaelli (gianluca.redaelli@aquila.infn.it)

                                                                           L. Sangelantoni

    

     TEXTBOOKS:

       J. Wallace and P. Hobbs, Atmospheric Science: An introductory survey, Academic Press

  
     Lessons
1
 Tu 01
October
 GR
 Introduction to the course
2
 Tu 15
October 		GR Present day climate. Temperature, pressure, winds. Surface winds and SST. The climate system.
3
 Tu 22
October 		GR Black Body, spectra and radiative properties of the atmosphere. Calculation of the Effective Emission Temperature of the Earth. Greenhouse effect.
4
 Tu 29
October 		GR Radiative Forcing. Climate Equilibria, Sensitivity and Feedback. Climate feedback factors.
5
 Tu 5
November 		GR Transient versus equilibrium response. Daisyworld.
6
 Tu 12
November 		GR GHGs and GWP. Evidences of the buiding of GHGs and of global warming. Anthropogenic forcings. 
7



8
 Tu 19
November 		GR




LS
Climate sensitivity (TCR, ECS and ESS) from model and experimental data


Components and phenomena in the climate system. Modeling the climate system and providing climate information at different temporal scale: Weather-Seasona-Climate projections
9
 We 20
November 		LS
 From global to regional scale, dynamical downscaling. RegCM. Run a seasonal climate forecast
10 Th 21
November 		LS
 Statistical methods applied to atmospheric sciences. Background of the Empirical Distributions and Exploratory Data Analysis; graphical summary techniques, empirical cumulative distribution functions. Reexpression, standardized anomalies
11




12
 Tu 26
November 		GR





LS

Climate tipping points. Basic types of climate models.


Exploratory Techniques for Paired Data:Scatterplots, Pearson Correlation, Spearman Rank Correlation and Kendall’s τ Exploratory Techniques for Higher-Dimensional Data: the Glyph Scatter-plot, Bi-variate histograms, The Correlation Matrix. MatLab laboratory: empirical distributions representation techniques applied to ensemble seasonal forecasting. 
13 We 27
November 		GR 1D Energy Balance Model (EBM) for the calculation of Ts: numerical solution.
14 Th 28
November 		LS MatLab laboratory: Ensemble seasonal forecasting: how to derive a probabilistic forecast regarding mean and distribution tails anomalies
15 Tu 3
December		 LS Parametric Distribution Models. Introduction to climate extremes and expected changes due to the anthropogenic forcing.
16

17
We 4
December 		LS


AR
 		Computational laboratory exercises: computing climate extremes with CDO programming language in Unix environment (shell scripting)

Oceanic General Circulation
18 Th 5
December 		GR
 Model's design and application. Hierarchy and evolution of climate models.
19

20
 		Tu 10
December 		GR

LS
 		 Finite grid vs spectral formulation for AGCMs.

 Climate extreme indices' computation. Practical session
21

22
 		We 11
December 		LS

AR
  Climate extreme indices' computation. Practical session

Oceanic General Circulation
23 Th 12
December 		GR
 From primitive equations to general circulation in an Aquaplanet atmosphere. Patterns and indices of Climate Variability.
24 Tu 17
December 		LS
Climate extreme indices' computation. Practical session 
25
We 18
December
 BT
 ChyM Hydrological Model
26 Th 19
December 		GR Climate modelling activities of CETEMPS



           Additional material