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