Dataset Information

Trends of extreme US weather events in the changing climate.

ABSTRACT: Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in [Formula: see text] geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.

SUBMITTER: Shenoy S

PROVIDER: S-EPMC9704732 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Trends of extreme US weather events in the changing climate.

Shenoy Saahil S Gorinevsky Dimitry D Trenberth Kevin E KE Chu Steven S

Proceedings of the National Academy of Sciences of the United States of America 20221114 47

Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each mont ...[more]

PMID: 36375064

Similar Datasets

Project description:The increasing intensity and frequency of extreme weather events resulting from climate change have led to grid outages and other negative consequences. To ensure the resilience of buildings which serve as primary shelters for occupants, resilient strategies are being developed to improve their ability to withstand these extreme events (e.g., building upgrades and renewable energy generators and storage). However, a crucial step towards creating a resilient built environment is accurately estimating building performance during such conditions using historical extreme climate change-induced weather events. To conduct Building Performance Simulation (BPS) in extreme conditions, such as weather events induced by climate change, it is essential to utilize Actual Meteorological Year (AMY) weather files instead of Typical Meteorological Year (TMY) files. AMY files capture the precise climatic conditions during extreme weather events, enabling accurate simulation of such scenarios. These weather files provide valuable data that can be used to assess the vulnerabilities and resilience of buildings to extreme weather events. By analyzing past events and their impacts using BPS tools, we can gain insights into the specific weaknesses and areas that require improvement. This approach applies to both existing buildings needing climate change-resilient retrofits and new building designs that must be compatible with future climatic conditions. Moreover, the intensification and frequency increase of these extreme weather events makes developing adaptation and resilient-building measures imperative. This involves understanding the potential losses that households may experience due to the intensification of extreme events and developing farsighted coping strategies and climate-proof resilient-building initiatives. However, addressing the knowledge gap caused by the absence of an AMY weather file dataset of extreme events is essential. This will allow for accurate BPS during past extreme climate change-induced weather events. To fill this gap, this article introduces a comprehensive .epw format weather file dataset focusing on historical extreme weather events in Canada. This collection encompasses a diverse array of past extreme climate change occurrences in various locations, with potential for future expansion to include additional locations and countries. This dataset enables energy simulations for different types of buildings and considers a diverse range of historical weather conditions, allowing for better estimation of thermal performance.

Project description:Bangladesh is susceptible to climate change, thus a detailed study, including the analyses of trends, sub-trends, extreme events and indices was conducted to obtain a complete picture of the climate change pattern in Bangladesh utilizing daily rainfall, maximum, minimum and average temperature data of 26 stations from 1975 to 2019 using R 4.0.2 software. For the trend analysis Mann Kendal (MK), modified Mann Kendall (mMK), Innovative Trend Analysis (ITA) and Sen's slope methods were used. Sub-trend analysis was conducted using ITA. Standard Anomaly Index (SAI) has been used to identify the frequency and severity of extreme events. ClimPACT2 software was used to check the homogeneity and calculate the extremes of temperature and rainfall data. Our analysis showed that during the last four decades, climate variables changed their patterns and trend heterogeneously over Bangladesh. Most stations showed decremental rainfall trend when central part of the country showed a substantial decrease. The northern and central parts of the country showed significant growth of trend for annual average temperature. The temperature in the monsoon season increased, whereas those in dry season decreased. The rainfall and maximum temperature were inversely related during monsoon whereas during dry season both of them decrease. The pre-monsoon and post-monsoonal rainfall also showed decreasing trends, indicating prevailing drought conditions especially in northern and central parts of the country. The SAI analysis showed alternating drought and wet years in almost all the stations. In the past 20 years, the country's western region experienced more drought years than before whereas the coastal region experienced more wet years. The analysis of climate extreme indices suggests that, Consecutive Dry Days (CDD), Hot Days (TX90P) and Hot Nights (TN90P) show significant increasing trend throughout the country. The agricultural productivity, water resource management and food security are anticipated to benefit from this study.

Project description:At a global level, climate change is expected to result in more frequent and higher-intensity weather events, with impacts ranging from inconvenient to catastrophic. The potential for disasters to act as "focusing events" for policy change, including adaptation to climate change risk, is well known. Moreover, local action is an important element of climate change adaptation and related risk management efforts. As such, there is a good reason to expect local communities to mobilize in response to disaster events, both with immediate response and recovery-focused activities, as well as longer-term preparedness and adaptation-focused public policy changes. However, scholars also note that the experience of disaster does not always yield policy change; indeed, disasters can also result in policy inertia and failure, perhaps as often or more often than major policy change. This study poses two key research questions. First, we ask to what degree policy change occurs in communities impacted by an extreme weather event. Second, we seek to understand the conditions that lead to adaptation-oriented policy adoption in response to an extreme weather event. Our results suggest two main recipes for future-oriented policy adoption in the wake of an extreme weather event. For both recipes, a high-impact event is a necessary condition for future-oriented policy adoption. In the first recipe for change, policy adoption occurs in Democratic communities with highly focused media attention. The second, less expected recipe for change involves Republican communities that have experienced other uncommon weather events in the recent past. We use a comparative case approach with 15 cases and fuzzy set qualitative comparative analysis methods. Our approach adds to the existing literature on policy change and local adaptation by selecting a mid-N range of cases where extreme weather events have the potential to act as focusing events, thereby sidestepping selection on the dependent variable. Our approach also takes advantage of a novel method for measuring attention, the latent Dirichlet allocation approach.

Dataset Information

Trends of extreme US weather events in the changing climate.

Publications

Trends of extreme US weather events in the changing climate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets