Understanding Climate Prediction and Modeling: Approaches and Challenges | Study notes Ecology and Environment

Connexions module: m31704 1

Climate Prediction

∗

Andy Kerr

This work is produced by The Connexions Project and licensed under the

Creative Commons Attribution License

†

Abstract

This chapter provides an introduction to the use of advanced information technologies in climate

research.

Key Concepts

•

Climate prediction

•

Climate modeling

1 Introduction

Over 100 years ago Svante Arrhenius, who would go on to win the Nobel Prize for Chemistry, postulated

that changes in levels of carbon dioxide in the atmosphere could aect global temperatures. We now know

that a number of natural and industrial chemicals, including water vapour and carbon dioxide, aect the

properties of the atmosphere. Levels of carbon dioxide, methane and nitrous oxide have increased markedly

as a result of fossil fuel use, agriculture and land use change since the start of the industrial revolution.

This past and projected future rise in emissions, coupled with the observed rise in global mean temperatures

over the past three decades, has led to considerable concern about future climate change. For geoscientists

seeking to understand how the global climate system operates, the challenge has been how to represent a

system that is not fully accessible, because of the time and space constraints of experiments conducted on,

and observations of, environmental systems. The solution has involved the development of numerical models

to represent physical processes.

Climate is the statistical average of the weather over long (30 year) periods of time. An old saying goes:

Climate is what you expect; weather is what you get. Climate models have evolved over four decades from

simple energy balance models to the massively complex global system models of today, which are largely

extensions of models used for weather forecasting. This evolution has been enabled by the extraordinary

development of computational power, largely in supercomputers but increasingly through dispersed appli-

cations, and the management of the correspondingly massive data sets. Not only have these developments

provided far greater scope for more complex numerical models, the technology has fundamentally changed

the scientic questions that can be posed.

At the heart of such climate models are the mathematical equations representing geophysical processes.

Such relationships are non-linear, necessitating a range of numerical methods to provide approximate itera-

tive solutions to the equations. There is no one best climate model. Model components and subcomponents

are combined to answer specic questions and at a time and space scale of interest to the user. For example,

∗

Version 1.1: Aug 27, 2009 5:55 am GMT-5

†

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http://cnx.org/content/m31704/1.1/

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Climate Prediction

Andy Kerr

This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License †

Abstract This chapter provides an introduction to the use of advanced information technologies in climate research.

Key Concepts

Climate prediction
Climate modeling

1 Introduction

Over 100 years ago Svante Arrhenius, who would go on to win the Nobel Prize for Chemistry, postulated that changes in levels of carbon dioxide in the atmosphere could aect global temperatures. We now know that a number of natural and industrial chemicals, including water vapour and carbon dioxide, aect the properties of the atmosphere. Levels of carbon dioxide, methane and nitrous oxide have increased markedly as a result of fossil fuel use, agriculture and land use change since the start of the industrial revolution. This past and projected future rise in emissions, coupled with the observed rise in global mean temperatures over the past three decades, has led to considerable concern about future climate change. For geoscientists seeking to understand how the global climate system operates, the challenge has been how to represent a system that is not fully accessible, because of the time and space constraints of experiments conducted on, and observations of, environmental systems. The solution has involved the development of numerical models to represent physical processes. Climate is the statistical average of the weather over long (30 year) periods of time. An old saying goes: Climate is what you expect; weather is what you get. Climate models have evolved over four decades from simple energy balance models to the massively complex global system models of today, which are largely extensions of models used for weather forecasting. This evolution has been enabled by the extraordinary development of computational power, largely in supercomputers but increasingly through dispersed appli- cations, and the management of the correspondingly massive data sets. Not only have these developments provided far greater scope for more complex numerical models, the technology has fundamentally changed the scientic questions that can be posed. At the heart of such climate models are the mathematical equations representing geophysical processes. Such relationships are non-linear, necessitating a range of numerical methods to provide approximate itera- tive solutions to the equations. There is no one best climate model. Model components and subcomponents are combined to answer specic questions and at a time and space scale of interest to the user. For example,

∗Version 1.1: Aug 27, 2009 5:55 am GMT- †http://creativecommons.org/licenses/by/3.0/

a global system model might include ocean, atmosphere, ecosystem and ice sheet components at coarse res- olutions. A regional model might use ner numerical grids to resolve small-scale meteorological phenomena, but will need to use the outputs of the global model as a boundary condition to its more detailed study. The trade-o between model components and scale has typically reected the computational eciency of the model and its ability to include as much of the detailed physical processes as possible. However, even for models which incorporate detailed physical processes, the non-linear nature of the problem means that equations can only be solved approximately. There is inherent loss of information at scales below the averaging (grid) scales of models and through the process of parameterizing physical relationships within the model. As a result, conrmation that a complex climate model actually represents the underlying physical processes of the global climate is rather challenging; instead, the onus is on the modeller to establish a sucient degree of condence in the model through its ability to recreate observed data to a reasonable accuracy. This chapter rst introduces the dierent approaches used by modellers for climate prediction, detailing the complexities of this endeavour. It concludes by considering the importance of collaborative working in development of predictive models and the future challenges facing climate science.

2 Predicting the future? Model Ensembles

Attempting to predict the future has profound implications for model development and application. Until very recently, information from climate models about possible future climates has been presented as a scenario or projection, without specied probabilities. This has reected the diculty of managing the core uncertainties associated with climate modelling:

Changing boundary conditions: these are the factors aecting the climate system that are treated as separate from, or outside, the climate system. These include changes in solar output, volcanic eruptions and human factors, such as levels of emissions of greenhouse gases
The natural internal variability of the global climate system: the global climate system is chaotic, which means that very small changes in one location and at one point in time can lead to large dierences in other locations at a future point in time
The extent to which the models accurately represent (parameterize) the physical processes of the climate system; in other words our understanding of the component parts of the global climate system and how they respond to change

With increasing demands from the public and private sectors for information to manage future changes in climate, and with enhanced computational power, climate modellers can now begin to explore this range of uncertainty. Dierent approaches exist for developing probabilistic climate predictions. One relies on brute force, based on large ensembles of simulations from computationally ecient models. This approach carries out large numbers of model runs in which model parameters are varied within their current range of uncertainty. Model parameterizations which fail to replicate existing climate observations are rejected, with the remainder used to explore future climate scenarios. This approach is complemented by continuous improvement in model representations of physical processes and higher resolution data, which improves the parameterizations the model representation of physical processes. The second approach for developing probabilistic predictions relies on expert judgement, drawn from small ensembles of state-of-the-art models. An ensemble consists of many simulations run with a specic climate model, each one slightly dierent from the rest. The uncertainty associated with natural climate variability is studied using initial condition ensembles, which vary the distribution of temperature, wind, humidity and other factors at the beginning of the simulation. The uncertainty associated with the model boundary conditions is studied using ensembles with dierent scenarios for human-induced or natural greenhouse gas emissions. These seek to examine the full range of possible boundary conditions of, for example, future global greenhouse gas emissions from society under dierent economic futures. The nal source of uncertainty reects the quality of the model representation of the climate; this is studied by using ensembles of dierent climate models. This approach as- sumes that the available models from climate modelling centres capture the full range of plausible behaviour,

captures the learning developed over many years by dierent modelling groups across the UK. A new doctoral graduate student who is exploring science questions about ice sheets is likely to use this as the starting point, perhaps with the aim of improving physical processes within the model, or using the model to answer new science questions. These community model developments, enabled by distributed access to computer models and more eective model and data management, have changed the way modellers work together. Instead of a largely individual approach, collegiate approaches are now the norm. e-Research methods have had a fundamental impact on the way in which climate science is undertaken. These methods have changed how individuals and modelling groups work together. They have changed the very science questions that can be posed. However, the very success of high performance or distributed computing to produce colourful ensemble model outputs has also disguised critical questions about what models can usefully oer and how the outputs are used by decision makers and politicians. To those outside the modelling community, probabilistic predictions might well be assumed to be objective probabilities of future events, rather than subjective assessments based on incomplete information. Such a perception will aect the decisions that are taken about managing future climate impacts. Yet, climate models are not truth machines; they are inherently partial. In practice, there is an asymmetry between explanation and prediction of complex systems. Satisfactory explanation of the future is possible even when absolute prediction is impossible. Separately, extensive work by behavioural economists has shown that humans are inherently poor at calculating and managing probabilities when making monetary decisions. Yet the output of these ensemble model runs shows a wide range of probabilistic outcomes, from futures with little change to futures with catastrophic change. Taking the next step and enabling more eective decision making on the basis of these model outputs remains challenging. While computer-enabled methods of research may not be able to address these problems of human decision making, they have enhanced climate science through the development of models that expand our knowledge of a range of possible future climates that could occur based on dierent variables.

Understanding Climate Prediction and Modeling: Approaches and Challenges, Study notes of Ecology and Environment

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Partial preview of the text

Download Understanding Climate Prediction and Modeling: Approaches and Challenges and more Study notes Ecology and Environment in PDF only on Docsity!

Climate Prediction

Andy Kerr

1 Introduction

2 Predicting the future? Model Ensembles