Introduction to Climate Sciences#
My name is Carlos D. Hoyos, and I am a professor at Universidad Nacional de Colombia, Facultad de Minas, Sede Medellín. I have a Ph.D. in Earth and Atmospheric Sciences from Georgia Institute of Technology, along with a Masters in Oceanic and Atmospheric Sciences, a Masters in Water Resources, and a Civil Engineering degree. My work has taken me through diverse fields, studying the behavior of atmosphere and the ocean and using data science to solve problems related to agriculture, energy, risk management, and climate.
In this course, we will explore the science of climatology, examining the atmospheric processes that shape our climate and drive its variability. This course will provide you with a foundation in climate systems, including how they are influenced by human activities and natural factors, as well as the impacts of climate on our world. The goal is to be able to connect scientific principles with practical applications, empowering students to understand and address real-world environmental issues.
Why Study Climatology?#
Climatology is the science of understanding climate and its patterns, which are essential for addressing critical issues like climate variability and change, natural disaster preparedness, and resource management. By studying climatology, we can better understand natural phenomena such as monsoons, droughts, the greenhouse effect, interannual variability patterns, as well as how climate shapes ecosystems, water resources, and human societies.
This course emphasizes the importance of climatology for addressing practical environmental issues:
Disaster Preparedness: Understanding climate helps predict extreme weather and develop early warning systems for natural disasters.
Water Resource Management: Climatology aids in managing water supplies, especially in areas affected by droughts and seasonal variability.
Agriculture and Food Security: Climate information is essential for agriculture, helping farmers plan for planting, irrigation, and pest control.
Energy Planning: Renewable energy sources like solar and wind depend on weather patterns, making climate data crucial for sustainable energy planning.
1. The Climate System#
The climate system comprises the atmosphere, hydrosphere, cryosphere, lithosphere, and biosphere, all interacting to regulate Earth’s climate. Understanding these components and their interactions is crucial for explaining climate variability and predicting long-term changes.
2. The Importance of Climate Data#
In climatology, data collection and analysis are foundational. We use data from weather stations, satellites, and ocean buoys to observe climate patterns over time. The data helps us study climate variability, extreme events, and long-term trends, from droughts to heatwaves.
3. Seasonal and Long-term Climate Variability#
Climate is not static; it changes with natural cycles like the El Niño–Southern Oscillation (ENSO), which affects global weather patterns. Studying these cycles helps us predict seasonal changes and understand long-term shifts in the climate.
4. Extreme Weather Events and Climate Change#
Climate change is increasing the frequency and intensity of extreme events such as hurricanes, heatwaves, and floods. Climatology provides the tools to analyze these changes and their potential impacts on societies, economies, and ecosystems.
Course Format#
Throughout this course, we will use several books and papers as guides, supplemented by hands-on exercises using Python to analyze real climate datasets. These activities will give students insights into how we can understand climate patterns, variability, and long-term changes.
The course runs over 16 weeks, divided into eight 2-week cycles, each built around a core theme and hands-on work:
Cycle 1 (Weeks 1–2): Tropical Thermodynamics & Static Stability
Composition of dry air & water vapor
Potential temperature (θ) & equivalent potential temperature (θₑ)
Static stability & buoyancy; Brunt–Väisälä frequency
Moist adiabatic lapse rates & convective triggers
Cycle 2 (Weeks 5–6): Radiative Transfer & Energy Balance
Solar (SW) vs. terrestrial (LW) radiation spectra
Radiative transfer equation & two-stream approximation
Grey-atmosphere models
Earth’s top-of-atmosphere budget & OLR diagnostics
Cycle 3 (Weeks 3–4): Fluid Dynamics, Geostrophic Balance & Atmospheric Circulation
Forces on air parcels: pressure-gradient & Coriolis
Geostrophic & gradient winds
Horizontal divergence & vertical vorticity
Ageostrophic motion & tropical dynamical regimes
Atmospheric Dynamics & Circulation
Hadley, Ferrel & Polar cells
Jet streams & monsoon circulations
Cycle 4 (Weeks 7–8): Moist Processes & Cloud Microphysics
Moisture variables: specific humidity & saturation
LCL (lifting condensation level) & LFC (level of free convection)
CAPE (convective available potential energy) & CIN (convective inhibition)
Droplet nucleation & condensational growth equations
Cycle 5 (Weeks 9–10): Waves, Teleconnections & Boundary-Layer Turbulence
Gravity-wave theory & spectral identification
Rossby waves & upper-troposphere streamfunction
Climate Variability & Teleconnections
ENSO, MJO, NAO, PDO
Boundary-layer structure: Monin–Obukhov similarity
Sensible vs. latent heat fluxes & diurnal cycles
Cycle 6 (Weeks 11–12): Simple Climate Models, Large-Scale Circulation & Extremes
1D energy-balance models (EBM)
Tropical Hadley cell & mass-streamfunction diagnostics
Meridional heat transport
Extreme Events & Climate Change
Heatwaves, storms, droughts, floods
Detection & attribution frameworks
Trends in extremes under warming
Cycle 7 (Weeks 13–14): Instabilities, QG Modelling, Convection Schemes & Projections
Baroclinic instability & Eady growth rates
Quasi-geostrophic potential vorticity equation
2D QG model integration in Python
Convective parameterizations: CAPE-based vs. Tokioka
Climate Modeling & Projections
Conceptual vs. numerical models
Global Climate Models (GCMs) & downscaling
Scenario analysis (SSPs/RCPs)
Cycle 8 (Weeks 15–16): Applications & Attribution in Water, Agriculture & Energy under Climate Change
Seasonal reservoir inflow forecasting
Crop-yield & pest-disease risk modeling using seasonal forecasts
Solar & wind resource climatology analysis
RCP4.5 & RCP8.5 scenario response simulations
Multivariate regression for detection & attribution
Spatial mapping of forced vs. internal variability
Structure per cycle:
Lectures: 3 sessions covering theory & examples
Python: hands-on exercises
Homework: State-of-the-art mini-project (choose 1 of 3 topics; work in pairs for the first 4 deliverables)
This eight-cycle framework blends rigorous theory, interactive coding, and real-world case studies to build both conceptual understanding and practical skills in climate science.
Why in English?#
While classes are in Spanish, course materials are in English to familiarize you with the technical terminology in atmospheric sciences. Engaging with the content in English will also prepare you for global collaboration in research and industry, opening doors to international conferences, publications, and partnerships. It’s an investment that will expand your opportunities both academically and professionally. Many of the key resources—such as academic papers, research articles, and advanced textbooks—are published in English, and mastering this language will help you stay connected with the global scientific community.
Why Jupyter Books?#
Jupyter Books provide a unique platform for integrating live code, dynamic visualizations, and interactive examples directly into our course materials. This allows us to break down complex concepts and equations in real time, giving you the opportunity to not only read but also engage with the material. As we progress through the course, I encourage you to experiment with the code and simulations provided in these materials to deepen your learning. You can learn about Jupyter Books at jupyterbook.org.
Why GitHub?#
By hosting these materials on GitHub, I’m inviting you into a collaborative learning environment. GitHub offers an opportunity for you to suggest improvements, raise issues, and even contribute to new content. You will submit pull requests to update our Jupyter notebooks, open issues to suggest data examples or corrections, and work in teams on mini-project branches. These contributions will be tracked and factored into your participation grade and overall course assessment, reinforcing both collaboration and version-control skills essential for research and industry.
If you don’t already have a GitHub account, I recommend creating one, as it will be useful not only for this course but also for future projects and collaborations in your career.