This chapter addresses changes in weather and climate events relevant to extreme impacts and disasters. An extreme (weather or climate) event is generally defined as the occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends (‘tails’) of the range of observed values of the variable. Some climate extremes (e.g., droughts, floods) may be the result of an accumulation of weather or climate events that are, individually, not extreme themselves (though their accumulation is extreme). As well, weather or climate events, even if not extreme in a statistical sense, can still lead to extreme conditions or impacts, either by crossing a critical threshold in a social, ecological, or physical system, or by occurring simultaneously with other events. A weather system such as a tropical cyclone can have an extreme impact, depending on where and when it approaches landfall, even if the specific cyclone is not extreme relative to other tropical cyclones. Conversely, not all extremes necessarily lead to serious impacts. [3.1] Many weather and climate extremes are the result of natural climate variability (including phenomena such as El Nino), and natural decadal or multi-decadal variations in the climate provide the backdrop for anthropogenic climate changes. Even if there were no anthropogenic changes in climate, a wide variety of natural weather and climate extremes would still occur. [3.1] A changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of weather and climate extremes, and can result in unprecedented extremes. Changes in extremes can also be directly related to changes in mean climate, because mean future conditions in some variables are projected to lie within the tails of present-day conditions. Nevertheless, changes in extremes of a climate or weather variable are not always related in a simple way to changes in the mean of the same variable, and in some cases can be of opposite sign to a change in the mean of the variable. Changes in phenomena such as the El Nino-Southern Oscillation or monsoons could affect the frequency and intensity of extremes in several regions simultaneously.

/pdf/changes-in-climate-extremes-and-their-impacts-on-the-natural-1fikyoq21o.pdf

Changes in climate extremes and their impacts on the natural physical environment.

Changes in climate extremes and their impacts on the natural physical environment: An overview of the IPCC SREX report

Compound weather and climate events describe combinations of multiple climate drivers and/or hazards that contribute to societal or environmental risk. Although many climate-related disasters are caused by compound events, the understanding, analysis, quantification and prediction of such events is still in its infancy. In this Review, we propose a typology of compound events and suggest analytical and modelling approaches to aid in their investigation. We organize the highly diverse compound event types according to four themes: preconditioned, where a weather-driven or climate-driven precondition aggravates the impacts of a hazard; multivariate, where multiple drivers and/or hazards lead to an impact; temporally compounding, where a succession of hazards leads to an impact; and spatially compounding, where hazards in multiple connected locations cause an aggregated impact. Through structuring compound events and their respective analysis tools, the typology offers an opportunity for deeper insight into their mechanisms and impacts, benefiting the development of effective adaptation strategies. However, the complex nature of compound events results in some cases inevitably fitting into more than one class, necessitating soft boundaries within the typology. Future work must homogenize the available analytical approaches into a robust toolset for compound-event analysis under present and future climate conditions. Research on compound events has increased vastly in the last several years, yet, a typology was absent. This Review proposes a comprehensive classification scheme, incorporating compound events that are preconditioned, multivariate, temporally compounding and spatially compounding events.

A typology of compound weather and climate events

We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of climate, atmosphere, ocean, and environment modeling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of DA, and of the methods thereof, stands on its interdisciplinary nature across statistics, dynamical systems, and numerical optimization, when applied to geosciences, an additional difficulty arises by the continually increasing sophistication of the environmental models. Thus, in spite of DA being nowadays ubiquitous in geosciences, it has so far remained a topic mostly reserved to experts. We aim this overview article at geoscientists with a background in mathematical and physical modeling, who are interested in the rapid development of DA and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities.

Data assimilation in the geosciences: An overview of methods, issues, and perspectives

Selecting and estimating regular vine copulae and application to financial returns

. Probability distributions of multivariate random variables are generally more complex compared to their univariate counterparts which is due to a possible nonlinear dependence between the random variables. One approach to this problem is the use of copulas, which have become popular over recent years, especially in fields like econometrics, finance, risk management, or insurance. Since this newly emerging field includes various practices, a controversial discussion, and vast field of literature, it is difficult to get an overview. The aim of this paper is therefore to provide an brief overview of copulas for application in meteorology and climate research. We examine the advantages and disadvantages compared to alternative approaches like e.g. mixture models, summarize the current problem of goodness-of-fit (GOF) tests for copulas, and discuss the connection with multivariate extremes. An application to station data shows the simplicity and the capabilities as well as the limitations of this approach. Observations of daily precipitation and temperature are fitted to a bivariate model and demonstrate, that copulas are valuable complement to the commonly used methods.

/pdf/multivariate-non-normally-distributed-random-variables-in-rzsh2fps10.pdf

Multivariate non-normally distributed random variables in climate research - introduction to the copula approach

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Multivariate non-normally distributed random variables in climate research - introduction to the copula approach