āI think itās much more interesting to live not knowing than to have answers which might be wrongā¦ when we know that we actually do live in uncertainty, then we ought to admit it; it is of great value to realize that we do not know the answers to different questions.ā*ā¦
The immense complexity of the climate makes it impossible to model accurately. Instead, David Stainforth argues, we must use uncertainty to our advantageā¦
Todayās complex climate models arenāt equivalent to reality. In fact, computer models of Earth are very different to reality ā particularly on regional, national and local scales. They donāt represent many aspects of the physical processes that we know are important for climate change, which means we canāt rely on them to provide detailed local predictions. This is a concern because human-induced climate change is all about our understanding of the future. This understanding empowers us. It enables us to make informed decisions by telling us about the consequences of our actions. It helps us consider what the future will be like if we act strongly to reduce greenhouse gas emissions, if we act only half-heartedly, or if we take no action at all. Such information enables us to assess the level of investment that we believe is worthwhile as individuals, communities and nations. It enables us to balance action on climate change against other demands on our finances such as health, education, security and culture.
For many of us, these issues are approached through the lens of personal experience and personal cares: we want to know what changes to expect where we live, in the places we know, and in the regions where we have our roots. We want local climate predictions ā predictions conditioned on the choices that our societies make.
So, where do we get them? Well, nowadays most of these predictions originate from complicated computer models of the climate system ā so-called Earth System Models (ESMs). These models are ubiquitous in climate change science. And for good reason. The increasing greenhouse gases in the atmosphere are driving the climate system into a never-before-seen state. That means the past cannot be a good guide to the future, and predictions based simply on historic observations canāt be reliable: the information isnāt in the observational data, so no amount of processing can extract it. Climate prediction is therefore about our understanding of the physical processes of climate, not about data-processing. And since there are so many physical processes involved ā everything from the movement of heat and moisture around the atmosphere to the interaction of oceans with ice-sheets ā this naturally leads to the use of computer models.
But thereās a problem: models arenāt equivalent to reality.
So, what can we do? One option is to make the models better. Make them more detailed and more complicated. That, though, raises an important question: when is a model sufficiently realistic to predict something as complex as climate change? When will the models be good enough? We donāt have an answer to this question. Indeed, scientists have hardly begun to study this problem, and some argue that these models might never be sufficiently accurate to make multi-decadal, local climate predictions.
Nevertheless, changing the way we use ESMs could provide a different and better way to generate the local climate information we seek. Doing so involves embracing uncertainty as a key part of our knowledge about climate change. It involves stepping back and accepting that what we want is not precise predictions but robust predictions, even if robustness involves accepting large uncertainties in what we can know about the futureā¦
[Stainforth explains the current state of modeling, efforts to make them better, and the problems those efforts encounterā¦]
ā¦ focusing on high-resolution modelling is dangerous not only because we have no answer to the question of when a model is sufficiently realistic. Investing in this approach also means we donāt have the capacity to explore the uncertainties, which inevitably encourages overconfidence in the predictions that models make. This is a particular concern because Earth System Models are increasingly being used to guide decisions and investments across our societies. Overconfidence in model-based predictions therefore risks encouraging bad decisions: decisions that are optimised for the futures in our models rather than what we understand about the range of possible futures for reality.
By contrast, perturbed physics ensembles and storyline approaches focus on exploring and describing our uncertainties. Placing uncertainty front and centre is important. When we make an investment or a gamble, we donāt just base it on what we think is the most likely result. We consider the range of outcomes that we think are possible ā ideally these are characterised by probabilities, although this isnāt always achievable. Itās the same with climate change. We should not only make plans based solely on our best estimate of what might happen. We should also consider the range of plausible outcomes we foresee. Our knowledge of uncertainty is also part of what we know about climate change. We should embrace this knowledge, expand it and use it.
If we understand the uncertainties well, we can bring our values to bear on the risks we are willing to take. Uncertainty therefore needs to be at the core of adaptation planning while also being the lens through which we judge the value of climate policy and the energy transition. In my view, climate researchers and modellers wanting to support society should focus on understanding, characterising and quantifying uncertainty, and avoid the trap of seeking climate models that make reliable predictions. They may well never existā¦
A more practical approach to preparing for climate change: āThe model of catastrophe,ā from @aeon.co
* Richard Feynman
###
As we preference plausibility (over predictability), we might send never-ending birthday greetings to August Mƶbius; he was born on this date in 1790. An astronomer and mathematician, he studied under mathematician Carl Friedrich Gauss while Gauss was the director of the Gƶttingen Observatory. From there, he went on to study with Carl Gaussās instructor, Johann Pfaff, at the University of Halle, where he completed his doctoral thesis The occultation of fixed stars in 1815. In 1816, he became Extraordinary Professor in the āchair of astronomy and higher mechanicsā at the University of Leipzig, where he remained for the rest of his career. Mƶbius made many contributions to both astronomy and the math that underlay it: he was among the first to conceive the possibility of geometry in more than three dimensions; he introduced homogeneous coordinates into projective geometry; and he pioneered the barycentric coordinate systemā¦ all parts of the intellectual foundation of the complex system modeling described above.
But while he was an influential scholar and professor, he is best remembered for his creation of the āMƶbius strip.ā
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#astronomy #augustMobius #augustMobius2 #complexSystemModels #complexSystems #culture #history #mathematics #mobiusStrip #mobius #mobiusStrip2 #models #planning #plausibility #predicition #science #technology #uncertainty
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