To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Climate change is increasing global-mean tropospheric temperatures, but the localised trends are uneven, including cooling the lower stratosphere and lifting the tropopause. The wind speeds are also being modified, both at the surface and aloft. A further effect, additional to wind and temperature alone, is of increasing fluctuations and severity of extreme weather. These are impacting air transport, and this will continue. The effects are known to include increased take-off distances where excess runway lengths exist and reduced payloads where they do not, increased en-route flight times, increased frequency and severity of encounters with clear air turbulence in some regions, changed patterns of wildlife — particularly bird — activity in some regions (potentially also for other anthropogenic reasons) are shifting locations of flight safety hazards, and increased burdens upon airport and associated infrastructure. There is increasing understanding and acknowledgment by companies and authorities of these effects and the importance of mitigating them, although this is not universal and there are as yet no universally understood best practices for air transport climate change mitigation.
Climate change has affected the geographical distributions of most species worldwide; in particular, insects of economic importance inhabiting tropical regions have been impacted. Current and future predictions of change in geographic distribution are frequently included in species distribution models (SDMs). The potential spatial distributions of the fruit fly Anastrepha striata Schiner, the main species of agricultural importance in guava crops, under current and possible future scenarios in Colombia were modeled, and the establishment risk was assessed for each guava-producing municipality in the country. SDMs were developed using 221 geographical records in conjunction with nine scenopoetic variables. The model for current climate conditions indicated an extensive suitable area for the establishment of A. striata in the Andean region, smaller areas in the Caribbean and Pacific, and almost no areas in the Orinoquia and Amazonian regions. A brief discussion regarding the area's suitability for the fly is offered. According to the results, altitude is one of the main factors that direct the distribution of A. striata in the tropics. The Colombian guava-producing municipalities were classified according to the degree of vulnerability to fly establishment as follows: 42 were high risk, 16 were intermediate risk, and 17 were low risk. The implementation of future integrated management plans must include optimal spatial data and must consider environmental aspects, such as those suggested by the models presented here. Control decisions should aim to mitigate the positive relationship between global warming and the increase in the dispersal area of the fruit fly.
Nature writing has been parodied for what Richard Kerridge identifies as ‘purple prose’. Given the remarkable resurgence of the popularity of nature writing in the first decades of this century, this chapter considers how nature writers now can develop a prose style that avoids the excesses traditionally associated with the genre and that will face up to and not shrink from the threats to nature, including ‘global warming and the huge loss of wildlife populations’, that demand perspectival shifts between the local and the global, the personal and the planetary.
Old climate models are often evaluated on whether they made correct predictions of global warming. But, if the old models were missing processes that we know now to be important, any correctness of their predictions would have to be attributed to a fortuitous compensation of errors, creating a paradoxical situation. Climate models are also tested for falsifiability by using them to predict the impact of short-term events like volcanic eruptions. But climate models do not exhibit the numeric convergence to a unique solution characteristic of small-scale computational fluid dynamics (CFD) models, like the ones that simulate flow over a wing. Compensating errors may obscure the convergence of individual components of a climate model. Lack of convergence suggests that climate modeling is facing a reducibility barrier, or perhaps even a reducibility limit.
Machine learning (ML) is a data-driven modeling approach that has become popular in recent years, thanks to major advances in software and hardware. Given enough data about a complex system, ML allows a computer model to imitate that system and predict its behavior. Unlike a deductive modeling approach, which requires some understanding of a system to be able to predict its behavior, the inductive approach of ML can predict the behavior of a system without ever understanding it in a traditional sense. Climate is a complex system, but there is not enough observed data describing an unprecedented event like global warming on which a computer model can be trained. Instead, it may be more fruitful to use ML to imitate a climate model, or a component of it, to greatly speed up computations. This will allow the parameter space of climate models to be explored more efficiently.
The Rumsfeld knowledge matrix – which spans the knowledge categories “known knowns,” “known unknowns,” and “unknown unknowns” – is used to illustrate the process of model improvement. Two new knowledge subcategories – “poorly known unknowns” and “well-known unknowns” – are introduced to distinguish between accuracy of parameterizations. A distinction is made between “downstream benefits” of parameterizations, which improve prediction skill, and “upstream benefits,” which improve understanding of the phenomenon being parameterized but not necessarily the prediction skill. Since new or improved parameterizations add to the complexity of models, it may be important to distinguish between essential and nonessential complexity. The fourth knowledge category in the Rumsfeld matrix is “unknown knowns” or willful ignorance, which can be used to describe contrarian views on climate change. Contrarians dismiss climate models for their limitations, but typically only offer alternatives born of unconstrained ideation.
Geoengineering describes a range of technologies that attempt to mitigate the effects of global warming caused by increasing greenhouse gas concentrations. Some geoengineering approaches remove carbon dioxide from the atmosphere. These are not controversial, but they are currently too expensive to serve as a viable option. The most cost-effective technique, called solar radiation management, aims to reflect sunlight by continuously dumping large quantities of sulfate aerosols into the stratosphere, much as a volcanic eruption would. But geoengineering attempts to address the symptoms of the disease of global warming rather than the disease itself, which will persist as long as carbon emissions continue. Computer models of climate are essential to assess the efficacy of any geoengineering approach, because large-scale physical experimentation would be dangerous. However, the information that is most crucial for us to know – the impact geoengineering would have on regional climates – is something models have trouble predicting.
Communicating the strengths and limitations of climate modeling to those outside the field of climate science is a formidable challenge. The nuances of scientific language can be lost in the translation to natural language when climate predictions are presented to a general audience. This loss in translation can lead to misinformation and disinformation that hampers a rational response to the climate crisis. Even simple terms like “model,” “data,” and “prediction” have many different meanings depending on the context. Anytime we talk about the future, we are using a model. In climate science, we might think we are dealing with data from the past, but often this is processed data that is produced by analysis models applied to raw data. The word “prediction” can mean a range of things, from unconditional prophecies to conditional projections.
Global warming became a growing public concern following Jim Hansen’s US Senate testimony in 1988 asserting that the warming was happening. The Intergovernmental Panel on Climate Change (IPCC) was formed in response to this concern. The IPCC issues periodic assessments summarizing recent scientific developments relating to climate change. Climate models were used to attribute global warming to increasing concentrations of carbon dioxide and other greenhouse gases. Certain types of extreme weather can also be probabilistically attributed to these causes. The effect of aerosols and stochastic variability on the past global warming signal is described. The IPCC projects the global warming signal into the future using a range of carbon dioxide emission scenarios, resulting in different degrees of predicted warming. The importance of regional climate change and the difficulty of predicting it are discussed.
The fundamental difference between weather prediction and climate prediction is explained, using a “nature versus nurture” analogy. To predict weather, we start from initial conditions of the atmosphere and run the weather forecast model. To predict climate, the initial conditions matter less, but we need boundary conditions, such as the angle of the sun or the concentration of carbon dioxide in the atmosphere, which control the greenhouse effect. Charles David Keeling began measuring carbon dioxide in the late 1950s, and found that its concentration was steadily increasing. Carbon dioxide concentrations for the past 800,000 years can also be measured using ice cores that contain trapped air. These ice core data show that the rise in carbon dioxide concentrations measured by Keeling was unprecedented. Manabe, and another scientist, Jim Hansen, used climate models to predict that increasing carbon dioxide could cause global warming.
The Aymara people use the metaphor that the future is behind us and the past is in front of us. Imagine that we are driving a car in reverse into our climate future. The past is in front of us; climate models act as the rearview mirror, showing what lies behind us in the future. The view is blurry because there is uncertainty, and the car is moving fast as we continue to emit greenhouse gases. We need to brake quickly – reduce emissions – because we know that the braking distance is very long. The Paris Agreement to reduce worldwide emissions is like a potluck dinner: Each guest decides how much food to bring. If the guests don’t bring enough food for everyone, then some will leave hungry. Similarly, emission reductions pledged in the Paris Agreement are voluntary and may be not be sufficient to strongly mitigate the warming.
The Geophysical Fluid Dynamics Laboratory (GFDL) is a pioneering institution in the field of climate modeling. Its founding director, Joseph Smagorinsky, was a member of the Princeton Meteorology Group. He hired a Japanese scientist, Syukuro Manabe, who formulated a one-dimensional model of climate, known as the radiative–convective model, that was able to calculate the amplifying climate feedback due to water vapor. This model provided one of the first reliable estimates of global warming. Manabe worked with other scientists to build three-dimensional climate models, including the first model that coupled an atmospheric model to an ocean model. The concepts of reductionism and emergentism, which provide the philosophical context for these scientific developments, are introduced.
Global warming and some climate change policies pose additional social risks that necessitate novel responses from the welfare state. Eco-social policies have significant potential to address these challenges, but their wide-scale adoption will depend, among other factors, on public support. In the current article, we theorise how public opinion about eco-social policies is likely to be influenced by a set of contextual and individual-level factors, as well as the perceived welfare deservingness of the target groups. Alongside contributing to the emerging body of literature on eco-social policies, this theoretical framework could help policymakers to anticipate the social groups that will support or oppose eco-social policy agendas and how some of the contradictions could be reduced through policy design.
Tossa (Corchorus olitorius L.) is a significant cash crop, cultivated commercially in the lower flood plain of Bangladesh. The climatic regimes in Bangladesh are changing as well as the world does. However, this species is threatened by climate change. Occurrences of data on threatened and endangered species are frequently sparse which makes it difficult to analyse the species suitable habitat distribution using various modelling approaches. The current paper used maximum entropy (Maxent) and educational global climate model (EdGCM) modelling to predict and conserve the suitable habitat distributions for Tossa species in Bangladesh to the year 2100. Nine environmental variables, 239 occurrence data and two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) were used for the Maxent modelling to project the impact of climate change on the Tossa distributions. Furthermore, the EdGCM was used to study the climatic space suitability for the Tossa species in the context of Bangladesh. Both of the climatic scenarios were used for the prediction to the year 2100. The Maxent model performed better than random for the Tossa species with a high AUC value of 0.86. Under the RCP scenarios, the Maxent model predicted habitat reduction for RCP4.5 is 2%, RCP8.5 is 9% and EdGCM is 10.2% from the current localities. The predictive modelling approach presented here is promising and can be applied to other important species for conservation planning, monitoring and management, especially those under the threat of extinction due to climate change.
This chapter invites us to understand New Orleans not only as an historic rim city and a great world port but also as a poetic ecosphere filled with resonant sounds, wavelike rhythms, and a buzzing profusion of messages. Such a poetics takes shape within the city’s understudied lyric traditions, formed and informed by three centuries of multicultural confluence and botanical excess. Desire unfolds not simply in terms of human longing, but as the comingled energies of plant, animal, and wetland life-forms that drive myriad currents and subtle undercurrents of imagination. Now facing submersion, New Orleans maintains as both a great port city and a port of entry into poems. The surrounding Mississippi River and Gulf waters saturate consciousness. Images and sound meld within a rich linguistic and ecological alluvium. We seek messages within sedimentations and intonations. The metamorphic work of poetry becomes dead serious in this tenuous rim of a city, where a desire for meaning unfolds in the thick of things –– stirred on by water, water everywhere.
The novel since the nineteenth century has displayed a thorny ambivalence toward the question of having children. In its representation of human vitality it can seem to promote the giving of life, but again and again it betrays a nagging doubt about the moral implications of procreation. The Novel and the Problem of New Life identifies this tension as a defining quality of the modern British and European novel. Beginning with the procreative-skeptical writings of Flaubert, Butler, and Hardy, then turning to the high modernist work of Lawrence, Woolf, and Huxley, and culminating in the postwar fiction of Lessing and others, this book chronicles the history of the novel as it came to accommodate greater misgivings about the morality of reproduction. This is the first study to examine in literature a problem that has long troubled philosophers, environmental thinkers, and so many people in everyday life.
Climate hazards arise through interactions of weather-related shocks, vulnerability, and exposure. The atmosphere is warming and population growth is increasing, setting the stage for potentially explosive increases in impacts. Of all weather hazards, heat waves tend to be the most immediate, and often the most deadly. Unfortunately, relatively small changes in air temperatures can lead to large increases in the frequency of extreme heat waves. This chapter uses 1880–2019 monthly and 1983–2016 daily temperature estimates to explore observed increase in extreme temperatures. Exceptional warmth, over more than 20 perent of the Earth's surface, has become the new norm. Warmer-than-ever conditions prevailed in 2015 through 2019. Over this same time period 71 extreme-temperature disasters affected 4.5 million people, resulting in 9,916 deaths, 90,014 injuries, and $1.8 billion losses. These exceptional temperatures threaten the Earth's basic ecosystem services: fisheries, coral reefs, and CO2-absorbing rainforests. Analysis based on a new very high-resolution data set identifies very large increases in the number of people exposed to very warm heat waves. Between 2000 and 2016, the number of heat wave exposure events has increased by approximately 15 billion people-days. Climate change projections for 2050 indicate further increases of ~70 billion. A sidebar describes a climate attribution study on Hyderabad, India, in 2015.
Linking a warming atmosphere, droughts, and more extreme precipitation is our thin, thin atmosphere. If the Earth were a basketball, the atmosphere would be 0.03-inch or 0.8-mm thick, literally whisker deep. The amount of water vapor contained in this air is strongly controlled by temperature. Warmer air holds more atmospheric water vapor, resulting in more extreme precipitation. Rainfall observations indicate that global precipitation extremes have already increased by more than 8 percent. If the observed trend continues, a similar-magnitude increase is likely over the next thirty years. This is very concerning, because extreme precipitation events are already extremely dangerous and costly. Between 1998 and 2017, floods, storms, and hurricanes affected more people than any other type of disaster, impacting 2.7 billion people overall resulting in $1.99 trillion of recorded economic losses. 2015–2019 disaster data suggests that the most dangerous non-cyclone storms affected 223 million people, led to more than 9,000 deaths, and resulted in $80 billion in damages. There is solid observational and model-based evidence supporting the link between a warming atmosphere and more intense precipitation extremes, and clear evidence that these extremes are having deadly and costly impacts today.
The Earth's climate is in a complex state of change as a result of human activity. The interface between climate change and physical health has received significant attention, but its effects on mental health and illness are less understood. This article provides an insight into the psychiatric sequelae of climate change, suggests strategies that psychiatrists can use to take action, and argues that it is their responsibility to do so.