Executive Summary

The U.S. National Assessment of the Potential Consequences of Climate Variability and Change for the Nation intends to “provide a detailed understanding of the consequences of climate change for the nation." This report argues that the National Assessment will not be able to provide policymakers and the public with useful information on climate change because of its reliance on flawed computer climate models. These models, which are intended to describe climate only on a very large scale, are currently used by the National Assessment to describe possible scenarios of regional climate change in the U.S. Because current models cannot accurately represent the existing climate without manipulation, they are unlikely to render reliable global climate scenarios or provide useful forecasts of future climate changes in regions of the United States as small as the Midwest, West or South.

The Guide explains how General Circulation Models (GCMs) describe changes in the complex factors that make up our climate, such as atmospheric changes, interaction of the land, sea, and air, and the role of clouds in climate. The strengths and weaknesses of climate models are discussed and the report shows how researchers attempt to answer the important questions about global warming as they refine their use of GCMs.

The two climate models used in the U.S. National Assessment are then described with reference to their similarities and differences. The limitations of these models the Canadian Global Coupled Model and the Hadley Climate Model from Great Britain- are outlined with special emphasis on their inability to provide useful regional scenarios of climate change. The report concludes with an analysis of how well these two models reproduce the present-day climate as a benchmark for their ability to reproduce future climate.

Key findings in this report include:

The utility of current GCMs is limited by our incomplete understanding of the climate system and by our ability to transform this incomplete understanding into mathematical representations. It is common practice to "tune" GCMs to make them represent current conditions more accurately, but the need for this manipulation casts serious doubt on their

ability to predict future conditions. Because all factors are interconnected in climate modeling, an error in one field will adversely affect the simulation of every other variable.

• To reduce complexity and computational time, GCMs treat surfaces as uniform and average the flows of moisture and energy between the land surface and the atmosphere over large areas. But the extensive variability of the land surface and the effects that even small-scale changes can have make modeling land-surface interactions quite difficult.

• The National Assessment itself recognized that both the models that it selected provide a more extreme climate change scenario than other models that were available and that had been developed in the U.S.

Both models offer incomplete modeling of the effects of individual greenhouse gases, including water vapor and atmospheric sulfates. The CGCMI in particular fails to model sea ice dynamics and offers a simplistic treatment of land-surface hydrology. Predicted temperature increases over various regions of the United States differ considerably between the two models; these predictions fail to correspond with observed precipitation variability and contradict each other.

In general, the Hadley model simulation is closer to the observed climate in the United States than the Canadian simulation, although both models produced considerable differences from observations. This, again, cast serious doubt on the models' ability to simulate future climate change.

Conclusion: Given these uncertainties, using the available GCMs to assess the potential for climate change in specific regions is not likely to yield valid and consistent results. GCMs can provide possible scenarios for climate change, but at the present level of sophistication, they are not reliable enough to be used as the basis for public policy. Using GCMs to make predictions about local climate change in the United States is not legitimate.

A Layman's Guide to the General Circulation Models
Used in the National Assessment

INTRODUCTION

What is a General Circulation Model (GCM)?

The word "model" usually conjures up images of a miniature replica of a real object. Model trains, automobiles, and airplanes, for example, are intended to be scale-reduced versions of the original. Models are judged by their attention to detail, and sometimes functionality, with respect to their real counterparts and are quite distinct from "toys", which also are intended to resemble the original but lack the attention to detail and functionality.

In science, the word "model" has a similar, but broader, meaning. Models can be physical replicas; for example, a model may be a smaller version of a larger habitat for a given animal or plant species. A model also, however, can be a working representation of a difficult concept, such as a model of an atom, for example. In this case, the model is simply a more useful way to describe and analyze a portion of nature that is only partially understood and observable. Usually, such models can be described by a set of mathematical equations - some from fundamental laws, and some empirical – rather than being a true physical replica.

General circulation models (or GCMs) are a further example of the latter definition. They are not physical reproductions of the earth and its climate system but instead are mathematical representations of the physical laws and processes that govern and dictate the climate of the earth. As such, they are computer models - computer programs that are able to solve the complex interactions among these mathematical equations to derive fields of air temperature, humidity, winds, precipitation, and other variables that define the earth's climate. General circulation models are limited both by our understanding of what drives, shapes, and affects the climate of the earth as well as how the earth's climate responds to a variety of external forces in addition to the speed and capabilities of modern-day computers.

The Concept of Space in GCMs

If we were to build a GCM, our first and fundamental decision would be the selection of the model's concept of space – how we choose to physically describe the three-dimensions of the atmosphere. Here we have two fundamental choices: the model can either be a Cartesian grid model or it can be a spectral model.

Conceptually, the Cartesian grid climate model is easier to understand and grasp, although it is less flexible and recently seems to be the less desirable choice among climate modelers. Consider a set of building blocks that might be toys for a young child. We could arrange the blocks in the form of a regular lattice where the face of every block is flush against another block. We could make this wall of blocks several blocks high and several blocks wide. Thus, each block in the center of the wall is adjacent to six other blocks - one above, one below, and four adjacent to each horizontal face.

In a Cartesian grid model, we extend the concept of these building blocks to represent hypothetical "blocks" of atmosphere, stacked adjacent to and on top of each other in the same manner we stacked the child's building blocks (Figure 1). Since the earth's surface is a sphere, however, we extend these blocks around the globe until they reach the blocks on the other end. Thus, in our climate model, every block has an adjacent partner on each of its four horizontal faces - our "wall" of blocks extends around the globe and covers the entire earth's surface. The only edges that exist are the blocks on the bottom and those on the top. Here, however, the blocks on the bottom are in contact with the earth's surface and can be used to describe the interactions between the atmosphere and the land surface. Although the atmosphere really has no "top" (air simply becomes thinner with height until its density approaches zero), the blocks on top of our stack can be used to represent the vertical extent of the atmosphere.

Since each block has six faces, we will simply describe (mathematically) the flows of energy, mass, and other physical quantities between one of our atmospheric boxes and the six adjacent boxes. We assume that each box is homogeneous; temperature, humidity, and other atmospheric variables can only vary between boxes and not within a box. Each of these variables is associated with the location (both horizontally and vertically) of the center of the box. As the box centers form a lattice or a grid around the earth's surface, the name "Cartesian grid model" is justified.

A typical Cartesian grid model will employ a lattice of approximately 72 boxes by 90 boxes (21⁄2° of latitude by 4o of longitude) stacked about 15 boxes high. The more boxes that are employed, more spatial resolution is obtained but at the expense of increased computer time. This choice of resolution is usually appropriate to allow sufficient spatial variability within a reasonable amount of computer run time.

By contrast, the spectral model does not use the concept of "boxes" at all but relies on a framework that is harder to grasp. Imagine a tabletop covered by several sheets of paper stacked on top of one another. Each sheet represents a different atmospheric layer. Vertically, the interaction between the layers is similar to the vertical interaction between the boxes that we saw with the Cartesian grid model. However, the horizontal representation of the field is not described by interactions among boxes; but rather, it is presented and manipulated in the form of waves. Just as energy is carried through the ocean in the form of oceanic waves, we can represent flows of energy and mass along each atmospheric layer using a series of waves having different amplitudes and frequencies (called spherical harmonics). Although these waves are difficult to describe, one can think of them as a series of sine and cosine curves (true really only in the east-west direction) that, when taken together, can be used to represent the spatial variability of any field (Figure 2). Grid values, akin to the representation of the Cartesian grid model, are computed from these waves and the horizontal and vertical resolutions become commensurate with those of Cartesian grid models.

At the same spatial resolution, spectral models have the advantage in that they can more easily (or compactly) describe a field than a Cartesian grid model. Thus, computation times are reduced. Moreover, spatial resolutions can be changed more easily with a spectral model, which allows for more flexibility and adaptability. Some have argued that Cartesian grid GCMs are

Representation of three-dimensional space in general circulation models (GCMs). Cartesian GCMs (left) use a concept similar to a series of stacked boxes, while spectral GCMs (right) use a series of waves and smoothly varying functions. Both representations, however, use the Cartesian analog (ie., stacked boxes or stacked waves in their representation of the vertical dimension. (Figure taken from Henderson-Sellers and McGuffie, 1987).