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CST-323: Introduction to Earth System Modelling/CAP-465: Modelling and Simulation of Earth Systems (INPE Course 2024)

  • Professors: Pedro R. Andrade, Gilberto Câmara
  • Lectures: Wednesdays and Thursdays, 14h00-17h00


Earth System Science is an interdisciplinary area that deals with the different aspects of interaction between society and nature. At the broadest level, Earth System Science models deal with natural systems (Climate, Ecosystems, Biogeochemical cycles, Hydrology) and its interaction with society (Economics, Sociology, Energy, Agriculture, Urbanisation, Demographics). Since Earth System Science covers a broad area of expertise, this course covers the basic fundamentals of nature-society interactions, by describing some of the foundational models in the area.

The course covers three main areas of expertise: (a) System Dynamics; (b) Environmental spatially-explicit models; © Social simulation. In the first part, we cover the basis of systems dynamics, following the Donella Meadows book, which is a good introduction to the field. In the second, we draw on some examples from the book of Andrew Ford (“Modelling the Environment”). In the third part, we focus on agent-based modelling, taking some examples from the literature (such as the Sugarscape model).

Considering the broad nature of the field, the course does not require a background on Natural Sciences. It tries to present the basics of modelling through examples taken from the literature.


“The biggest problem with models is the fact that they are made by humans who tend to shape or use their models in ways that mirror their own notion of what a desirable outcome would be.” (John Firor, formed director of NCAR, cited in Myanna Lahsen's paper “Seductive Simulations”.

There are certain similarities between a work of fiction and a model: Just as we may wonder how much the characters in a novel are drawn from real life and how much is artifice, we might ask the same of a model; How much is based on observation and measurement of accessible phenomena, how much is based on informed judgment, and how much is convenience? (Naomi Oreskes, professor of History of Science, also cited by Myanna Lahsen).

“A model is clear, decisive, and positive, but it is believed by no one but the man who created it. Observations, on the other hand, are messy, inexact things, which are believed by everyone except the man who did that work”. Harlow Shapley, American astronomer

Conclusion: to understand what models are, a scientist needs to be able to develop models himself. He needs to master computer programs that allow him to grasp the basics of modelling activity. He needs to be understand the different techniques used in modelling and their limitations.




Title Models Scenarios Concepts Exercises
1 Lógica de Programação
2 Lua for TerraME nil, number, boolean, string, table, function Lua exercises
3 Systems Dynamics Tub (sysdyn) tub-scenarios (sysdyn) Model, Event, Timer, Chart
4 Feedbacks Coffee, PopulationGrowth (sysdyn) coffee-scenarios, population-scenarios-1, population-scenarios-2 (sysdyn) Environment, instance of Model Water in the Dam Deadline: 18 July 23h59
5 Epidemics SIR (sysdyn) infection-scenarios-1, infection-scenarios-2, infection-scenarios-3 (sysdyn)
6 Chaos ChaoticGrowth (sysdyn), Lorenz (sysdyn)
7 Predator-Prey PredatorPrey (sysdyn)
8 Daisyworld Daisyworld (sysdyn) daisy (calibration) MultipleRuns (calibration)
9 Cellular Automata Life (ca) Cell, CellularSpace, Neighborhood, Map, Choice

Final Project

Deadline: 02 Sep 23h59 BRT

The final project consists of an implementation and discussion of a model described in a scientific paper that uses one of the paradigms presented during the course. There are some suggestions below. In case of replicating a paper, it is possible to submit the results to ReScience C.

System Dynamics

Cellular Automata

Agent-based Modeling

cst-317/classes2024.txt · Last modified: 2024/07/18 16:55 by pedro