Consider three possible ways that climate change could exact an economic cost:. A once-fertile agricultural area experiences hotter weather and drought, causing its crop yields to decrease. A road destroyed by flooding because of rising seas and more frequent hurricanes must be rebuilt.
An electrical utility spends hundreds of millions of dollars to build a more efficient power grid because the old one could not withstand extreme weather. Usually when factories sit idle during a recession, there is a reasonable expectation that they will start cranking again once the economy returns to health. The road rebuilding might be expensive, but at least that money is going to pay people and businesses to do their work. The cost for society over all is that the resources that go to rebuilding the road are not available for something else that might be more valuable.
And in a recession, it might even be a net positive, under the same logic that fiscal stimulus can be beneficial in a downturn. By contrast, new investment in the power grid could yield long-term benefits in energy efficiency and greater reliability. But some of that spending also created long-term benefits for society, like the innovations that led to the internet and to reliable commercial jet aircraft travel.
Certain types of efforts to reduce carbon emissions or adapt to climate impacts are likely to generate similar benefits, says Nicholas Stern, chair of the Grantham Research Institute on Climate Change and the Environment at the London School of Economics. Stern said.
There is more fertile ground in areas like transportation and infrastructure, he said. Electric cars, instead of those with internal combustion engines, would mean less air pollution in cities, for example.
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At the core of the project were sophisticated efforts to model how a hotter earth will affect thousands of different places. Michael Greenstone, who is now director of the Becker Friedman Institute at the University of Chicago and of the Energy Policy Institute there, as well as a contributor to The Upshot, was part of those efforts. Greenstone said. But even once you have an estimate of the cost of a hotter climate in future decades, some seemingly small assumptions can drastically alter the social cost of carbon today.
Finance uses something called the discount rate to compare future value with present value. Likewise, the cost of carbon emissions varies greatly depending on how you value the well-being of people in future decades — many not born yet, and who may benefit from technologies and wealth we cannot imagine — versus our well-being today.
Knutti et al. For uncertainty exploration, simple models also can be attractive, since their low computational cost allows exploration of a wide range of hypotheses about the climate system see e. Many authors, though by no means all those who advocate a pluralist approach, also recommend further supplementing the existing hierarchy of physics-based climate models with data-driven and semi-empirical models Kravtsov et al. The hope is that insights from Earth-system dynamics, techniques drawn from computer science and formal learning theory, and the availability of increasing quantities of climatic data will allow data-driven and semi-empirical models to contribute in substantial ways to predicting and understanding climate.
With respect to understanding climate, empirical and quasi-empirical modeling may reveal clues about the drivers and sensitivities of emergent climatic phenomena, including regional climate phenomena and phenomena that physics-based models do not yet adequately simulate; for example, they may do so by revealing correlations between largely internally driven modes of climate variability and temperature patterns see, e.
On a practical level, the pluralist approach, like the hierarchy approach, faces the challenge of institutional inertia. A scientific challenge for the pluralist approach concerns the sampling of structural uncertainty. Current knowledge gives no clear picture of the space of model structures that should be sampled, nor of what it would mean to adequately or systematically sample that space Smith ; Murphy et al.
For instance, is it more important to more thoroughly sample uncertainty associated with already-included processes or to expand the range of processes and feedbacks included? How should such decisions be approached? This challenge does not apply, however, for pluralists who call for exploration of hypotheses about the climate system that are suggested by empirical data or by physical reasoning about incompletely understood climatic mechanisms. Yet one should not exaggerate the differences between standard climate models and those that are considered semi-empirical or even data-driven.
GCMs and ESMs are themselves substantially empirical because they incorporate a number of parameters whose values are set in part by tuning to empirical data. The use of optimal fingerprinting to quantify the causes of recent climate change is a salient example. In any case, the role of data-driven and semi-empirical climate models can be thought of as supplementary to the roles of other climate models. For instance, it may be relatively straightforward to modify a semi-empirical model so that it embodies a new hypothesis about processes contributing to a pattern of variability, whereas modifying the physics of a high-end model so that it does so may be quite challenging.
Plausible costs and scientific gains of the unified, hierarchy and pluralist approaches relative to a business-as-usual baseline, assuming each approach is pursued independently. Potential for significant improvement, but difficult to discern beyond some improvement at seasonal lead times. In an ideal world with unlimited funding and expertise, perhaps all three approaches could be pursued alongside current modeling practices. Synergies among the approaches could be expected: for example, increased understanding would likely facilitate more reliable predictions, at least for some quantities.
And all of this would occur without sacrificing the relative security provided by current practice. But the actual world is not an ideal one, leaving difficult questions about what the future of climate modeling should look like and how desired changes could be effected in practice. We cannot hope to answer these questions fully here, but we can offer a few remarks. It is noteworthy that only one of the proposals — the unified approach — seems to require huge increases in funding.
But for many climate-related decisions, we cannot afford to wait. This is not to deny that developing very high resolution climate models or pursuing a seamless prediction strategy has value; it is merely to cast doubt on the idea that accelerating efforts in this direction can make much difference to climate decision making in the near term. We note also that some ways of improving the practice of climate modeling seem to be within easier reach. This includes the piecemeal pursuit of the hierarchy approach as well as increased attention to empirical modeling, especially empirical modeling undertaken with the aim of advancing understanding of climate phenomena.
These activities are ones that can be undertaken locally, by individual researchers or modeling groups, and at relatively little additional cost beyond business as usual. Moreover, they can be expected to yield gains even if other researchers maintain the business-as-usual modeling approach. Doing so is not yet the explicit target of major modeling efforts, despite its potential. This would free up resources that could be invested in the pursuit of any of the approaches identified above. Admittedly, this course of action would involve significant challenges, both scientific and institutional.
Chapter 4: Climate Models, Scenarios, and Projections
On the scientific side, for instance, there are still questions about how model independence is best conceptualized and assessed. Still, further attention to this course of actions seems warranted. Finally, while the present discussion has focused on proposals for changing the practice of climate modeling, it points to a larger question: that of how resources can best be directed to advance climate science. It may be that alternatives to climate modeling — such as theorizing that is not model-driven, efforts to expand or update observing systems, more careful empirical investigation of poorly represented or omitted feedbacks, or development of much more detailed and careful process models — are at least as important.
Skip to main content Skip to sections. Advertisement Hide. Download PDF. The future of climate modeling. Open Access. First Online: 18 June This process is experimental and the keywords may be updated as the learning algorithm improves. As Held notes, the importance of model hierarchies has long been recognized see e. On the contrary, the tendency has been continually to add processes and detail to models at the complex end of the spectrum, even as the behavior of existing models remains relatively poorly understood see also Jakob Costs are also estimated in a qualitative way.
All are relative to a business-as-usual baseline, i.
Admittedly, it is not easy to fill in this table; we welcome alternative analyses that prompt further discussion of the benefits and costs of the different strategies. Table 1 Plausible costs and scientific gains of the unified, hierarchy and pluralist approaches relative to a business-as-usual baseline, assuming each approach is pursued independently.
Understanding Prediction Uncertainty assessment Costs Unified approach Difficult to discern Potential for significant improvement, but difficult to discern beyond some improvement at seasonal lead times Difficult to discern Increased very significantly Hierarchy approach Significant improvement Some improvement as a consequence of increased understanding Some improvement as a consequence of increased understanding Limited Impact Pluralist approach Some improvement Some improvement Some improvement Increased.
Curry J A 21st century perspective on climate models from a climate scientist. Accessed July 26, Eos 90 13 — CrossRef Google Scholar. Edwards P History of climate modeling. Science — CrossRef Google Scholar. Harrison S, Stainforth D Predicting climate change: lessons from reductionism, emergence and the past. Held I The gap between simulation and understanding in climate modeling. Held I Simplicity amid complexity.
Hoskins BJ Dynamical processes in the atmosphere and the use of models. In the future, it will prove important that next-generation global reanalyses are coordinated and, if possible, staggered to ensure that the basic observational data record is improved before each reanalysis, so that there is time to analyse and hence learn from the output of past efforts. Further improvements to reanalyses, including expansion to encompass key trace constituents and the ocean, land and sea-ice domains, hold promise for extending their use in climate-change studies, research and applications.
While global mean metrics of temperature, precipitation and sea-level rise are convenient for tracking global climate change, many sectors of society require actionable information on considerably finer spatial scales. The increased confidence in attribution of global-scale climate change to human-induced greenhouse-gas emissions, and the expectation that such changes will increase in future, has lead to an increased demand in predictions of regional climate change to guide adaptation.
Although there is some confidence in the large-scale patterns of changes in some parameters, the skill in regional prediction is much more limited and indeed difficult to assess, given that we do not have data for a selection of different climates against which to test models. Much research is being done to improve model predictions but progress is likely to be slow. In the meantime, WCRP recognizes that governments and businesses are faced with making decisions now and require the best available climate advice today.
Despite their limitations, climate models provide the most promising means of providing information on climate change and WCRP has encouraged making data available from climate predictions to guide decisions, provided the limitations of such predictions are made clear. This will include assessments of the ability of the models used to predict current climate, and the range of predictions from as large a number of different models as possible.
Toward this end, WCRP has begun to develop a framework to evaluate regional climate downscaling RCD techniques for use in downscaling global climate projections Giorgi et al. Such a framework would be conceptually similar to the successful coupled model intercomparisons undertaken by WGCM and would have the goal of quantifying the performance of regional climate modelling techniques and assessing their relative merits.