by Judith Curry
I have been asked to write an Expert Report on climate models.
No, I can’t tell you the context for this request (at this time, anyways). But the audience is lawyers.
Here are the specific questions I have been asked to respond to:
- What is a Global Climate Model (GCM)?
- What is the reliability of climate models?
- What are the failings of climate models?
- Are GCM’s are a reliable tool for predicting climate change?
I’ve appended my draft Report below. I tried to avoid giving a ‘science lesson’, and focus on what climate models can and can’t do, focusing on policy relevant applications of climate models. I’ve tried write an essay that would be approved by most climate modelers; at the same time, it has to be understandable by lawyers. I would greatly appreciate your feedback on:
- whether you think lawyers will understand this
- whether the arguments I’ve made are the appropriate ones
- whether I’m missing anything
- anything that could be left out (its a bit long).
What is a Global Climate Model (GCM)?
Global climate models (GCMs) simulate the Earth’s climate system, with modules that simulate the atmosphere, ocean, land surface, sea ice and glaciers. The atmospheric module simulates evolution of the winds, temperature, humidity and atmospheric pressure using complex mathematical equations that can only be solved using computers. These equations are based on fundamental physical principles, such as Newton’s Laws of Motion and the First Law of Thermodynamics.
GCMs also include mathematical equations describing the three-dimensional oceanic circulation, how it transports heat, and how the ocean exchanges heat and moisture with the atmosphere. Climate models include a land surface model that describes how vegetation, soil, and snow or ice cover exchange energy and moisture with the atmosphere. GCMs also include models of sea ice and glacier ice.
To solve these equations on a computer, GCMs divide the atmosphere, oceans, and land into a 3-dimensional grid system (see Figure 1). The equations and are then calculated for each cell in the grid repeatedly for successive time steps that march forward in time throughout the simulation period.
Figure 1. Schematic of a global climate model.
The number of cells in the grid system determines the model ‘resolution.’ Common resolutions for a GCM include a horizontal resolution of about 100-200 km, a vertical resolution of about 1 km, and a time stepping resolution that is typically about 30 minutes. While GCMs represent processes more realistically at higher resolution, the computing time required to do the calculations increases substantially at higher resolutions. The coarseness of the model resolution is driven by the available computer resources, and tradeoffs between model resolution, model complexity and the length and number of simulations to be conducted.
Because of the relatively coarse spatial and temporal resolutions of the models, there are many important processes that occur on scales that are smaller than the model resolution (such as clouds and rainfall; see inset in Figure 1). These subgrid-scale processes are represented using ‘parameterizations.’ Parameterizations of subgrid-scale processes are simple formulas based on observations or derivations from more detailed process models. These parameterizations are ‘calibrated’ or ‘tuned’ so that the climate models perform adequately when compared with historical observations.
The actual equations used in the GCM computer codes are only approximations of the physical processes that occur in the climate system. While some of these approximations are highly accurate, others are unavoidably crude. This is because the real processes they represent are either poorly understood or too complex to include in the model given the constraints of the computer system. Of the processes that are most important for climate change, parameterizations related to clouds and precipitation remain the most challenging, and are the greatest source of disagreement among different GCMs.
GCMs are used for the following purposes:
- Simulation of present and past climate states to understand planetary energetics and other complex interactions
- Numerical experiments to understand how the climate system works. Sensitivity experiments are used to turn off, constrain or enhance certain physical processes or external forcings (e.g. CO2, volcanoes, solar output) to see how the system responds.
- Understanding the causes of past climate variability and change (e.g. how much of the change can be attributed to human causes such as CO2, versus natural causes such as solar variations, volcanic eruptions, and slow circulations in the ocean).
- Simulation of future climate states, from decades to centuries, e.g. simulations of future climate states under different emissions scenarios.
- Prediction and attribution of the statistics extreme weather events (e.g. heat waves, droughts, hurricanes)
- Projections of future regional climate variations to support decision making related adaptation to climate change
- Guidance for emissions reduction policies
- Projections of future risks of black swan events (e.g. climate surprises)
The specific objectives of a GCM vary with purpose of the simulation. Generally, when simulating the past climate using a GCM, the objective is to correctly simulate the spatial variation of climate conditions in some average sense. When predicting future climate, the aim is not to simulate conditions in the climate system on any particular day, but to simulate conditions over a longer period—typically decades or more—in such a way that the statistics of the simulated climate will match the statistics of the actual future climate.
There are more than 20 climate modeling groups internationally, that contribute climate model simulations to the IPCC Assessment Reports. Further, many of the individual climate modeling groups contribute simulations from multiple different models. Why are there so many different climate models? Is it possible to pick a ‘best’ climate model?
There are literally thousands of different choices made in the construction of a climate model (e.g. resolution, complexity of the submodels, parameterizations). Each different set of choices produces a different model having different sensitivities. Further, different modeling groups have different focal interests, e.g. long paleoclimate simulations, details of ocean circulations, nuances of the interactions between aerosol particles and clouds, the carbon cycle. These different interests focus computational resources on a particular aspect of simulating the climate system, at the expense of others.
Is it possible to select a ‘best’ model? Well, several models generally show a poorer performance overall when compared with observations. However, the best model depends on how you define ‘best’, and no single model is the best at everything. The more germane issue is to assess model’s ‘fitness for purpose’, which is addressed in Sections 2-4.
The reliability of climate models ……