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"Extremes"
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The Effect of a Short Observational Record on the Statistics of Temperature Extremes
In June 2021, the Pacific Northwest experienced a heatwave that broke all previous records. Estimated return levels based on observations up to the year before the event suggested that reaching such high temperatures is not possible in today's climate. We here assess the suitability of the prevalent statistical approach by analyzing extreme temperature events in climate model large ensemble and synthetic extreme value data. We demonstrate that the method is subject to biases, as high return levels are generally underestimated and, correspondingly, the return period of low‐likelihood heatwave events is overestimated, if the underlying extreme value distribution is derived from a short historical record. These biases have even increased in recent decades due to the emergence of a pronounced climate change signal. Furthermore, if the analysis is triggered by an extreme event, the implicit selection bias affects the likelihood assessment depending on whether the event is included in the modeling. Plain Language Summary In June 2021, the Pacific Northwest experienced a record‐breaking heatwave event. Based on historical data, the scientific community has applied statistical models to understand how likely this event was to occur. However, due to the record‐shattering nature of this particular heatwave, the model suggested that reaching such high temperatures should not have been possible. In this study, we evaluate the accuracy of these statistical models in describing the occurrence probability of extreme events. We find that the current models tend to underestimate the occurrence probability and that the bias has become more pronounced in recent years due to climate change. Finally, we assess how the way extreme events are included in the model can also affect the accuracy of estimates. Key Points Standard return period estimates of temperature extremes are systematically overestimated in short records under non‐stationary conditions The small‐sample bias in maximum likelihood estimates is found both for extremes in climate model data and in synthetic data experiments Future analysis should account for the statistical implications of the selection bias if the analysis is triggered by an extreme event
Journal Article
Catastrophic weather
Explores the causes and effects of catastrophic weather and asks the question: Are we prepared to adapt our lifestyles to prevent climate change from spiraling out of control?
Book
2023: Weather and Climate Extremes Hitting the Globe with Emerging Features
Globally, 2023 was the warmest observed year on record since at least 1850 and, according to proxy evidence, possibly of the past 100 000 years. As in recent years, the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world. Here, we provide an overview of those of 2023, with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change. We also highlight emerging features associated with some of these extreme events. Hot extremes are occurring earlier in the year, and increasingly simultaneously in differing parts of the world (e.g., the concurrent hot extremes in the Northern Hemisphere in July 2023). Intense cyclones are exacerbating precipitation extremes (e.g., the North China flooding in July and the Libya flooding in September). Droughts in some regions (e.g., California and the Horn of Africa) have transitioned into flood conditions. Climate extremes also show increasing interactions with ecosystems via wildfires (e.g., those in Hawaii in August and in Canada from spring to autumn 2023) and sandstorms (e.g., those in Mongolia in April 2023). Finally, we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.
Journal Article
North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends
The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.
Journal Article
The hottest and the coldest
\"Learn all about the hottest and coldest places on Earth and find out what it takes for life to survive in these extreme locations\"-- Provided by publisher.
Book
The impacts of climate extremes on the terrestrial carbon cycle: A review
The increased frequency of climate extremes in recent years has profoundly affected terrestrial ecosystem functions and the welfare of human society. The carbon cycle is a key process of terrestrial ecosystem changes. Therefore, a better understanding and assessment of the impacts of climate extremes on the terrestrial carbon cycle could provide an important scientific basis to facilitate the mitigation and adaption of our society to climate change. In this paper, we systematically review the impacts of climate extremes (e.g. drought, extreme precipitation, extreme hot and extreme cold) on terrestrial ecosystems and their mechanisms. Existing studies have suggested that drought is one of the most important stressors on the terrestrial carbon sink, and that it can inhibit both ecosystem productivity and respiration. Because ecosystem productivity is usually more sensitive to drought than respiration, drought can significantly reduce the strength of terrestrial ecosystem carbon sinks and even turn them into carbon sources. Large inter-model variations have been found in the simulations of drought-induced changes in the carbon cycle, suggesting the existence of a large gap in current understanding of the mechanisms behind the responses of ecosystem carbon balance to drought, especially for tropical vegetation. The effects of extreme precipitation on the carbon cycle vary across different regions. In general, extreme precipitation enhances carbon accumulation in arid ecosystems, but restrains carbon sequestration in moist ecosystems. However, current knowledge on the indirect effects of extreme precipitation on the carbon cycle through regulating processes such as soil carbon lateral transportation and nutrient loss is still limited. This knowledge gap has caused large uncertainties in assessing the total carbon cycle impact of extreme precipitation. Extreme hot and extreme cold can affect the terrestrial carbon cycle through various ecosystem processes. Note that the severity of such climate extremes depends greatly on their timing, which needs to be investigated thoroughly in future studies. In light of current knowledge and gaps in the understanding of how extreme climates affect the terrestrial carbon cycle, we strongly recommend that future studies should place more attention on the long-term impacts and on the driving mechanisms at different time scales. Studies based on multi-source data, methods and across multiple spatial-temporal scales, are also necessary to better characterize the response of terrestrial ecosystems to climate extremes.
Journal Article
Hottest places on the planet
\"Simple text and full-color photographs describe the hottest places on the planet\"-- Provided by publisher.
Book
Changes in Annual Extremes of Daily Temperature and Precipitation in CMIP6 Models
This study presents an analysis of daily temperature and precipitation extremes with return periods ranging from 2 to 50 years in phase 6 of the Coupled Model Intercomparison Project (CMIP6) multimodel ensemble of simulations. Judged by similarity with reanalyses, the new-generation models simulate the present-day temperature and precipitation extremes reasonably well. In line with previous CMIP simulations, the new simulations continue to project a large-scale picture of more frequent and more intense hot temperature extremes and precipitation extremes and vanishing cold extremes under continued global warming. Changes in temperature extremes outpace changes in global annual mean surface air temperature (GSAT) over most landmasses, while changes in precipitation extremes follow changes in GSAT globally at roughly the Clausius–Clapeyron rate of ∼7% °C−1. Changes in temperature and precipitation extremes normalized with respect to GSAT do not depend strongly on the choice of forcing scenario or model climate sensitivity, and do not vary strongly over time, but with notable regional variations. Over the majority of land regions, the projected intensity increases and relative frequency increases tend to be larger for more extreme hot temperature and precipitation events than for weaker events. To obtain robust estimates of these changes at local scales, large initial-condition ensemble simulations are needed. Appropriate spatial pooling of data from neighboring grid cells within individual simulations can, to some extent, reduce the needed ensemble size.
Journal Article