1. Intro to Climate Modeling

2. Climate Model Types Box Model – ecosystems/studies of ocean circulation Zero-dimensional – effect of changes in solar output without addressing temperature distribution on earth Radiative-determine effects of varying greenhouse gas concentrations Higher dimension – expanded from zero to account for temperature distribution on earth

3. Basic Concepts to Cover GCM Resolution (Horizontal, Vertical) Ocean Coupling Time-Step Parameterization Ensemble

4. What is a GCM? Global Climate Model Rather than a simple model of the climate system, a GCM tries to account for nearly everything. (Brainstorm what features this might mean…) – Radiation in/out, gases, precipitation, cloud cover, vegetation, ocean, snow/ice cover

5. Factors in a GCM

6. Resolution Resolution is how “finely” your model runs. It describes how big your “boxes” of air and water are, both horizontally and vertically. Analogy: Think of resolution on a TV or computer monitor. What do you suppose is the resolution of a typical GCM?

7. Resolution ctd.

8. Ocean Coupling The climate is not just about the air. The atmosphere passes material and energy back and forth between it and the ocean. Therefore, the GCM is often “coupled” to an ocean model with its own resolution and equations. – Usu. run the air calculations, then run the water calculations, then the air calculations, etc.

9. Ocean Coupling Visual

10. Time-Step Time-step is like the resolution of your modeled time. If you want to model every second of the weather, it’s going to take the computer way too long. Typical time-step is on the order of a day.

11. Parameterization Problem: You need clouds in a model of the weather, but CLOUDS ARE NOT AS BIG AS YOUR GRID BOX. That’s why the model uses “parameterization”. – Mathematically treating a box as “50% cloudy” without specifying exactly where the clouds might be.

12. Ensemble “Ensemble” just means a large set of trials. Often, climatologists run those trials with slightly different starting conditions. – Small changes at the start can lead to big changes by the end. (See ‘The Butterfly Effect’, described here: http://www.realclimate.org/index.php/archives/2 005/11/chaos-and-climate/ ) http://www.realclimate.org/index.php/archives/2 005/11/chaos-and-climate/ But if most of your ensemble ends up predicting warmer temperatures…