Articles | Volume 9, issue 2
https://doi.org/10.5194/ascmo-9-83-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-83-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Jul 2023
| 14 Jul 2023
Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change
Institute for Atmospheric and Climate Science,
ETH Zurich, 8092 Zurich, Switzerland
Erich M. Fischer
Institute for Atmospheric and Climate Science,
ETH Zurich, 8092 Zurich, Switzerland
Related authors
No articles found.
Sebastian Sippel, Clair Barnes, Camille Cadiou, Erich Fischer, Sarah Kew, Marlene Kretschmer, Sjoukje Philip, Theodore G. Shepherd, Jitendra Singh, Robert Vautard, and Pascal Yiou
EGUsphere, https://doi.org/10.5194/egusphere-2023-2523, https://doi.org/10.5194/egusphere-2023-2523, 2023
Short summary
Short summary
Winter temperature in Central Europe have warmed. But cold winters can still cause problems for energy systems, infrastructure, or human health. Here we tested whether a record-cold winter, such as the one observed in 1963 over Central Europe, could still occur despite climate change. The answer is yes, it’s possible, but it’s very unlikely. Our results rely on climate model simulations and statistical rare event analysis. In conclusion, society must be prepared for such cold winters.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatimah Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-176, https://doi.org/10.5194/gmd-2023-176, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
For the next generation of Earth System Model runs to project future climate change, the scientific community considers new scenarios to succeed RCPs and SSPs. As a contribution to that debate, we reflect on relevant policy and scientific research questions and suggest categories for Representative Emission Pathways (REP). These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Fabian von Trentini, Emma E. Aalbers, Erich M. Fischer, and Ralf Ludwig
Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020, https://doi.org/10.5194/esd-11-1013-2020, 2020
Short summary
Short summary
We compare the inter-annual variability of three single-model initial-condition large ensembles (SMILEs), downscaled with three regional climate models over Europe for seasonal temperature and precipitation, the number of heatwaves, and maximum length of dry periods. They all show good consistency with observational data. The magnitude of variability and the future development are similar in many cases. In general, variability increases for summer indicators and decreases for winter indicators.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
Short summary
Short summary
Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
Jakob Zscheischler, Erich M. Fischer, and Stefan Lange
Earth Syst. Dynam., 10, 31–43, https://doi.org/10.5194/esd-10-31-2019, https://doi.org/10.5194/esd-10-31-2019, 2019
Short summary
Short summary
Many climate models have biases in different variables throughout the world. Adjusting these biases is necessary for estimating climate impacts. Here we demonstrate that widely used univariate bias adjustment methods do not work well for multivariate impacts. We illustrate this problem using fire risk and heat stress as impact indicators. Using an approach that adjusts not only biases in the individual climate variables but also biases in the correlation between them can resolve these problems.
Stefan Brönnimann, Jan Rajczak, Erich M. Fischer, Christoph C. Raible, Marco Rohrer, and Christoph Schär
Nat. Hazards Earth Syst. Sci., 18, 2047–2056, https://doi.org/10.5194/nhess-18-2047-2018, https://doi.org/10.5194/nhess-18-2047-2018, 2018
Short summary
Short summary
Heavy precipitation events in Switzerland are expected to become more intense, but the seasonality also changes. Analysing a large set of model simulations, we find that annual maximum rainfall events become less frequent in late summer and more frequent in early summer and early autumn. The seasonality shift is arguably related to summer drying. Results suggest that changes in the seasonal cycle need to be accounted for when preparing for moderately extreme precipitation events.
Camille Li, Clio Michel, Lise Seland Graff, Ingo Bethke, Giuseppe Zappa, Thomas J. Bracegirdle, Erich Fischer, Ben J. Harvey, Trond Iversen, Martin P. King, Harinarayan Krishnan, Ludwig Lierhammer, Daniel Mitchell, John Scinocca, Hideo Shiogama, Dáithí A. Stone, and Justin J. Wettstein
Earth Syst. Dynam., 9, 359–382, https://doi.org/10.5194/esd-9-359-2018, https://doi.org/10.5194/esd-9-359-2018, 2018
Short summary
Short summary
This study investigates the midlatitude atmospheric circulation response to 1.5°C and 2.0°C of warming using modelling experiments run for the HAPPI project (Half a degree Additional warming, Prognosis & Projected Impacts). While the chaotic nature of the atmospheric flow dominates in these low-end warming scenarios, some local changes emerge. Case studies explore precipitation impacts both for regions that dry (Mediterranean) and regions that get wetter (Europe, North American west coast).
Michael Wehner, Dáithí Stone, Dann Mitchell, Hideo Shiogama, Erich Fischer, Lise S. Graff, Viatcheslav V. Kharin, Ludwig Lierhammer, Benjamin Sanderson, and Harinarayan Krishnan
Earth Syst. Dynam., 9, 299–311, https://doi.org/10.5194/esd-9-299-2018, https://doi.org/10.5194/esd-9-299-2018, 2018
Short summary
Short summary
The United Nations Framework Convention on Climate Change challenged the scientific community to describe the impacts of stabilizing the global temperature at its 21st Conference of Parties. A specific target of 1.5 °C above preindustrial levels had not been seriously considered by the climate modeling community prior to the Paris Agreement. This paper analyzes heat waves in simulations designed for this target. We find there are reductions in extreme temperature compared to a 2 °C target.
Daniel Mitchell, Krishna AchutaRao, Myles Allen, Ingo Bethke, Urs Beyerle, Andrew Ciavarella, Piers M. Forster, Jan Fuglestvedt, Nathan Gillett, Karsten Haustein, William Ingram, Trond Iversen, Viatcheslav Kharin, Nicholas Klingaman, Neil Massey, Erich Fischer, Carl-Friedrich Schleussner, John Scinocca, Øyvind Seland, Hideo Shiogama, Emily Shuckburgh, Sarah Sparrow, Dáithí Stone, Peter Uhe, David Wallom, Michael Wehner, and Rashyd Zaaboul
Geosci. Model Dev., 10, 571–583, https://doi.org/10.5194/gmd-10-571-2017, https://doi.org/10.5194/gmd-10-571-2017, 2017
Short summary
Short summary
This paper provides an experimental design to assess impacts of a world that is 1.5 °C warmer than at pre-industrial levels. The design is a new way to approach impacts from the climate community, and aims to answer questions related to the recent Paris Agreement. In particular the paper provides a method for studying extreme events under relatively high mitigation scenarios.
Carl-Friedrich Schleussner, Tabea K. Lissner, Erich M. Fischer, Jan Wohland, Mahé Perrette, Antonius Golly, Joeri Rogelj, Katelin Childers, Jacob Schewe, Katja Frieler, Matthias Mengel, William Hare, and Michiel Schaeffer
Earth Syst. Dynam., 7, 327–351, https://doi.org/10.5194/esd-7-327-2016, https://doi.org/10.5194/esd-7-327-2016, 2016
Short summary
Short summary
We present for the first time a comprehensive assessment of key climate impacts for the policy relevant warming levels of 1.5 °C and 2 °C above pre-industrial levels. We report substantial impact differences in intensity and frequency of extreme weather events, regional water availability and agricultural yields, sea-level rise and risk of coral reef loss. The increase in climate impacts is particularly pronounced in tropical and sub-tropical regions.
Related subject area
Statistics
Applying different methods to model dry and wet spells at daily scale in a large range of rainfall regimes across Europe
Comparison of climate time series – Part 5: Multivariate annual cycles
Regridding uncertainty for statistical downscaling of solar radiation
Statistical modeling of the space–time relation between wind and significant wave height
Modeling general circulation model bias via a combination of localized regression and quantile mapping methods
Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 1: Theory
Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 2: Numerical experiment
A conditional approach for joint estimation of wind speed and direction under future climates
Comparing climate time series – Part 2: A multivariate test
Forecast score distributions with imperfect observations
Novel measures for summarizing high-resolution forecast performance
Copula approach for simulated damages caused by landfalling US hurricanes
Nonstationary extreme value analysis for event attribution combining climate models and observations
Comparing climate time series – Part 1: Univariate test
A statistical approach to fast nowcasting of lightning potential fields
Spatial trend analysis of gridded temperature data at varying spatial scales
An improved projection of climate observations for detection and attribution
Bivariate Gaussian models for wind vectors in a distributional regression framework
Fitting a stochastic fire spread model to data
Influence of initial ocean conditions on temperature and precipitation in a coupled climate model's solution
NWP-based lightning prediction using flexible count data regression
An integration and assessment of multiple covariates of nonstationary storm surge statistical behavior by Bayesian model averaging
Probabilistic evaluation of competing climate models
Assessing NARCCAP climate model effects using spatial confidence regions
Generalised block bootstrap and its use in meteorology
Estimating trends in the global mean temperature record
Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression
Analysis of variability of tropical Pacific sea surface temperatures
Evaluating NARCCAP model performance for frequencies of severe-storm environments
Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions
A comparison of two methods for detecting abrupt changes in the variance of climatic time series
Calibrating regionally downscaled precipitation over Norway through quantile-based approaches
Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic
Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT
Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies
Joint inference of misaligned irregular time series with application to Greenland ice core data
Simulation of future climate under changing temporal covariance structures
Giorgio Baiamonte, Carmelo Agnese, Carmelo Cammalleri, Elvira Di Nardo, Stefano Ferraris, and Tommaso Martini
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 51–67, https://doi.org/10.5194/ascmo-10-51-2024, https://doi.org/10.5194/ascmo-10-51-2024, 2024
Short summary
Short summary
In hydrology, the probability distributions are used to determine the probability of occurrence of rainfall events. In this study, two different methods for modeling rainfall time characteristics have been applied: a direct method and an indirect method that make it possible to relax the assumptions of the renewal process. The analysis was extended to two additional time variables that may be of great interest for practical hydrological applications: wet chains and dry chains.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 1–27, https://doi.org/10.5194/ascmo-10-1-2024, https://doi.org/10.5194/ascmo-10-1-2024, 2024
Short summary
Short summary
This paper introduces a method to assess whether two data sets come from the same source. Current methods do not adequately consider spatial and temporal correlations and their annual cycles in a comprehensive test. This method addresses that gap, thereby providing a new and rigorous tool for evaluating the realism of climate simulations and measuring changes in variability over time.
Maggie D. Bailey, Douglas Nychka, Manajit Sengupta, Aron Habte, Yu Xie, and Soutir Bandyopadhyay
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 103–120, https://doi.org/10.5194/ascmo-9-103-2023, https://doi.org/10.5194/ascmo-9-103-2023, 2023
Short summary
Short summary
To ensure photovoltaic (PV) plants last, we need to understand how climate change affects solar radiation. Climate models help predict future radiation for PV plants, but there is often uncertainty. We explore this uncertainty and its impact on building PV plants. We highlight the importance of considering uncertainties for accurate planning and management. A California case study shows a practical application for the solar industry.
Said Obakrim, Pierre Ailliot, Valérie Monbet, and Nicolas Raillard
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 67–81, https://doi.org/10.5194/ascmo-9-67-2023, https://doi.org/10.5194/ascmo-9-67-2023, 2023
Short summary
Short summary
Ocean wave climate has a significant impact on human activities, and its understanding is of socioeconomic and environmental importance. In this study, we propose a statistical model that predicts wave heights in a location in the Bay of Biscay. The proposed method allows us to understand the spatiotemporal relationship between wind and waves and predicts well both wind seas and swells.
Benjamin James Washington, Lynne Seymour, and Thomas L. Mote
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 1–28, https://doi.org/10.5194/ascmo-9-1-2023, https://doi.org/10.5194/ascmo-9-1-2023, 2023
Short summary
Short summary
We develop new methodology to statistically model known bias in general atmospheric circulation models. We focus on Puerto Rico specifically because of other important ongoing and long-term ecological and environmental research taking place there. Our methods work even in the presence of Puerto Rico's broken climate record. With our methods, we find that climate change will not only favor a warmer and wetter climate in Puerto Rico, but also increase the frequency of extreme rainfall events.
Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, https://doi.org/10.5194/ascmo-8-225-2022, 2022
Short summary
Short summary
This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
Katarina Lashgari, Anders Moberg, and Gudrun Brattström
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 249–271, https://doi.org/10.5194/ascmo-8-249-2022, https://doi.org/10.5194/ascmo-8-249-2022, 2022
Short summary
Short summary
The performance of a new statistical framework containing various structural equation modelling (SEM) models is evaluated in a pseudo-proxy experiment in comparison with the performance of statistical models used in many detection and attribution studies. Each statistical model was fitted to seven continental-scale regional temperature data sets. The results indicated the SEM specification is the most appropriate for describing the underlying latent structure of the simulated data analysed.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022, https://doi.org/10.5194/ascmo-8-205-2022, 2022
Short summary
Short summary
We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 73–85, https://doi.org/10.5194/ascmo-7-73-2021, https://doi.org/10.5194/ascmo-7-73-2021, 2021
Short summary
Short summary
After a new climate model is constructed, a natural question is whether it generates realistic simulations. Here,
realisticdoes not mean that the detailed patterns on a particular day are correct, but rather that the statistics over many years are realistic. Past approaches to answering this question often neglect correlations in space and time. This paper proposes a method for answering this question that accounts for correlations in space and time.
Julie Bessac and Philippe Naveau
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 53–71, https://doi.org/10.5194/ascmo-7-53-2021, https://doi.org/10.5194/ascmo-7-53-2021, 2021
Short summary
Short summary
We propose a new forecast evaluation scheme in the context of models that incorporate errors of the verification data. We rely on existing scoring rules and incorporate uncertainty and error of the verification data through a hidden variable and the conditional expectation of scores. By considering scores to be random variables, one can access the entire range of their distribution and illustrate that the commonly used mean score can be a misleading representative of the distribution.
Eric Gilleland
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 13–34, https://doi.org/10.5194/ascmo-7-13-2021, https://doi.org/10.5194/ascmo-7-13-2021, 2021
Short summary
Short summary
Verifying high-resolution weather forecasts has become increasingly complicated,
and simple, easy-to-understand summary measures are a good alternative. Recent work has demonstrated some common pitfalls with many such summaries. Here, new summary measures are introduced that do not suffer from these drawbacks, while still providing meaningful information.
Thomas Patrick Leahy
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 1–11, https://doi.org/10.5194/ascmo-7-1-2021, https://doi.org/10.5194/ascmo-7-1-2021, 2021
Short summary
Short summary
This study looked at estimating damages caused by hurricanes in the United States. It assessed the relationship between the maximum wind speed at landfall and the resulting damage caused. The study found that the complex processes that determine the size of the damages inflicted could be estimated using this simple relationship. This work could be used to examine how often extreme damage events are likely to occur and the impact of stronger hurricane winds on the US Atlantic and Gulf coasts.
Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
Short summary
Short summary
We have developed a new statistical method to describe how a severe weather event, such as a heat wave, may have been influenced by climate change. Our method incorporates both observations and data from various climate models to reflect climate model uncertainty. Our results show that both the probability and the intensity of the French July 2019 heatwave have increased significantly in response to human influence. We find that this heat wave might not have been possible without climate change.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 159–175, https://doi.org/10.5194/ascmo-6-159-2020, https://doi.org/10.5194/ascmo-6-159-2020, 2020
Short summary
Short summary
Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
Short summary
Short summary
Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
Short summary
Short summary
Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Alexis Hannart
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 161–171, https://doi.org/10.5194/ascmo-5-161-2019, https://doi.org/10.5194/ascmo-5-161-2019, 2019
Short summary
Short summary
In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the
signalis the anthropogenic response and the
noiseis climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
Short summary
Short summary
Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
X. Joey Wang, John R. J. Thompson, W. John Braun, and Douglas G. Woolford
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 57–66, https://doi.org/10.5194/ascmo-5-57-2019, https://doi.org/10.5194/ascmo-5-57-2019, 2019
Short summary
Short summary
This paper presents the analysis of data from small-scale laboratory experimental smouldering fires that were digitally video-recorded. The video images of these fires bear a resemblance to remotely sensed images of wildfires and provide an opportunity to fit and assess a spatial model for fire spread that attempts to account for uncertainty in fire growth. We found that the fitting method is feasible, and the spatial model provides a suitable mathematical for the fire spread process.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
Short summary
Short summary
As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
Short summary
Short summary
Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
Short summary
Short summary
Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, https://doi.org/10.5194/ascmo-3-93-2017, 2017
Short summary
Short summary
In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67–92, https://doi.org/10.5194/ascmo-3-67-2017, https://doi.org/10.5194/ascmo-3-67-2017, 2017
Short summary
Short summary
We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55–66, https://doi.org/10.5194/ascmo-3-55-2017, https://doi.org/10.5194/ascmo-3-55-2017, 2017
Short summary
Short summary
This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
Short summary
Short summary
We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1–16, https://doi.org/10.5194/ascmo-3-1-2017, https://doi.org/10.5194/ascmo-3-1-2017, 2017
Short summary
Short summary
We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155–169, https://doi.org/10.5194/ascmo-2-155-2016, https://doi.org/10.5194/ascmo-2-155-2016, 2016
Short summary
Short summary
Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
Short summary
Short summary
Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Sergei N. Rodionov
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 63–78, https://doi.org/10.5194/ascmo-2-63-2016, https://doi.org/10.5194/ascmo-2-63-2016, 2016
David Bolin, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39–47, https://doi.org/10.5194/ascmo-2-39-2016, https://doi.org/10.5194/ascmo-2-39-2016, 2016
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
Short summary
Short summary
Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59–74, https://doi.org/10.5194/ascmo-1-59-2015, https://doi.org/10.5194/ascmo-1-59-2015, 2015
Short summary
Short summary
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
R. Philbin and M. Jun
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, https://doi.org/10.5194/ascmo-1-29-2015, 2015
T. K. Doan, J. Haslett, and A. C. Parnell
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 15–27, https://doi.org/10.5194/ascmo-1-15-2015, https://doi.org/10.5194/ascmo-1-15-2015, 2015
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
Cited articles
Akhtar, R.: Introduction: Extreme Weather Events and Human Health: A Global
Perspective, in: Extreme Weather Events and Human Health, edited by Akhtar,
R., Springer International Publishing, Cham, 3–11,
https://doi.org/10.1007/978-3-030-23773-8_1, 2020. a
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., and
García-Herrera, R.: The Hot Summer of 2010: Redrawing the Temperature
Record Map of Europe, Science, 332, 220–224, https://doi.org/10.1126/science.1201224,
2011. a
Bartusek, S., Kornhuber, K., and Ting, M.: 2021 North American heatwave
amplified by climate change-driven nonlinear interactions, Nat. Clim.
Change, 12, 1143–1150, https://doi.org/10.1038/s41558-022-01520-4, 2022. a, b
Beillouin, D., Schauberger, B., Bastos, A., Ciais, P., and Makowski, D.:
Impact of extreme weather conditions on European crop production in 2018,
Philos. T. R. Soc., 375,
20190510, https://doi.org/10.1098/rstb.2019.0510, 2020. a
Bercos‐Hickey, E., O’Brien, T. A., Wehner, M. F., Zhang, L., Patricola,
C. M., Huang, H., and Risser, M. D.: Anthropogenic Contributions to the 2021
Pacific Northwest Heatwave, Geophys. Res. Lett., 49, e2022GL099396,
https://doi.org/10.1029/2022GL099396, 2022. a
Bieli, M., Pfahl, S., and Wernli, H.: A lagrangian investigation of hot and
cold temperature extremes in europe, Q. J. Roy.
Meteor. Soc., 141, 98–108, https://doi.org/10.1002/qj.2339, 2015. a
Brunner, L., Hauser, M., Lorenz, R., and Beyerle, U.: The ETH Zurich CMIP6
next generation archive: technical documentation, Tech. Rep., ETH Zurich,
Institute for Atmospheric and Climate Science, Zurich, Switzerland, Zenodo,
https://doi.org/10.5281/zenodo.3734128, 2020. a
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I.: STL: A
Seasonal-Trend Decomposition Procedure Based on Loess, J. Off.
Stat., 6, 3–73, 1990. a
Coles, S.: An introduction to statistical modeling of extreme values,
Springer series in statistics, 3rd print edn., Springer, London, ISBN 978-1-85233-459-8, https://doi.org/10.1007/978-1-4471-3675-0,
2001. a
Cooley, D.: Return Periods and Return Levels Under Climate Change, in:
Extremes in a Changing Climate: Detection, Analysis and Uncertainty, edited
by: AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian,
S., chap. 4, Springer Netherlands, Dordrecht, Netherlands, 97–114,
https://doi.org/10.1007/978-94-007-4479-0, 2013. a
Cooley, D., Hunter, B. D., and Smith, R. L.: Univariate and Multivariate
Extremes for the Environmental Sciences, in: Handbook of Environmental and
Ecological Statistics, edited by: Gelfand, A., Fuentes, M., Hoeting, J. A.,
and Smith, R. L., chap. 8, Chapman and Hall/CRC,
Taylor & Francis, Boca Raton, 153–180, ISBN 9781315152509, https://doi.org/10.1201/9781315152509-9, 2019. a, b
Deb, P., Moradkhani, H., Abbaszadeh, P., Kiem, A. S., Engström, J.,
Keellings, D., and Sharma, A.: Causes of the Widespread 2019–2020
Australian Bushfire Season, Earth's Future, 8, e2020EF001671, https://doi.org/10.1029/2020EF001671,
2020. a
de Haan, L. and Ferreira, A.: Extreme Value Theory: An Introduction, Springer
Science & Business Media, New York, NY, United States, ISBN 978-0-387-29959-4, https://doi.org/10.1007/0-387-34471-3, 2006. a
Della-Marta, P. M., Haylock, M. R., Luterbacher, J., and Wanner, H.: Doubled
length of western European summer heat waves since 1880, J.
Geophys. Res., 112, D15103, https://doi.org/10.1029/2007JD008510,
2007a. a
Della-Marta, P. M., Luterbacher, J., von Weissenfluh, H., Xoplaki, E., Brunet,
M., and Wanner, H.: Summer heat waves over western Europe 1880–2003, their
relationship to large-scale forcings and predictability, Clim. Dynam.,
29, 251–275, https://doi.org/10.1007/s00382-007-0233-1, 2007b. a, b, c
Deser, C., Terray, L., and Phillips, A. S.: Forced and internal components of
winter air temperature trends over North America during the past 50 years:
Mechanisms and implications, J. Climate, 29, 2237–2258,
https://doi.org/10.1175/JCLI-D-15-0304.1, 2016. a
Detring, C., Müller, A., Schielicke, L., Névir, P., and Rust, H. W.: Occurrence and transition probabilities of omega and high-over-low blocking in the Euro-Atlantic region, Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, 2021. a
Domeisen, D. I. V., Eltahir, E. A. B., Fischer, E. M., Knutti, R.,
Perkins-Kirkpatrick, S. E., Schär, C., Seneviratne, S. I., Weisheimer,
A., and Wernli, H.: Prediction and projection of heatwaves, Nature Reviews
Earth & Environment, 4, 36–50, https://doi.org/10.1038/s43017-022-00371-z, 2022. a
Eastoe, E. F. and Tawn, J. A.: Modelling Non-Stationary Extremes with
Application to Surface Level Ozone, J. R. Stat. Soc.
C-Appl., 58, 25–45,
https://doi.org/10.1111/j.1467-9876.2008.00638.x, 2009. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Feudale, L. and Shukla, J.: Influence of sea surface temperature on the
European heat wave of 2003 summer. Part I: an observational study, Climate
Dynamics, 36, 1691–1703, https://doi.org/10.1007/s00382-010-0788-0, 2011. a
Fischer, E. M. and Schär, C.: Future changes in daily summer temperature
variability: driving processes and role for temperature extremes, Clim.
Dynam., 33, 917–935, https://doi.org/10.1007/s00382-008-0473-8, 2009. a
Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D., and
Schär, C.: Soil moisture-atmosphere interactions during the 2003
European summer heat wave, J. Climate, 20, 5081–5099,
https://doi.org/10.1175/JCLI4288.1, 2007. a, b
Fischer, E. M., Sippel, S., and Knutti, R.: Increasing probability of
record-shattering climate extremes, Nat. Clim. Change, 11, 689–695,
https://doi.org/10.1038/s41558-021-01092-9, 2021. a, b
Fischer, P. H., Brunekreef, B., and Lebret, E.: Air pollution related deaths
during the 2003 heat wave in the Netherlands, Atmos. Environ., 38,
1083–1085, https://doi.org/10.1016/j.atmosenv.2003.11.010, 2004. a
Friederichs, P.: Statistical downscaling of extreme precipitation events using
extreme value theory, Extremes, 13, 109–132,
https://doi.org/10.1007/s10687-010-0107-5, 2010. a
Friederichs, P., Göber, M., Bentzien, S., Lenz, A., and Krampitz, R.: A
probabilistic analysis of wind gusts using extreme value statistics,
Meteorol. Z., 18, 615–629, https://doi.org/10.1127/0941-2948/2009/0413,
2009. a
Gabda, D., Tawn, J., and Brown, S.: A step towards efficient inference for
trends in UK extreme temperatures through distributional linkage between
observations and climate model data, Nat. Hazards, 98, 1135–1154,
https://doi.org/10.1007/s11069-018-3504-8, 2019. a
Gessner, C., Fischer, E. M., Beyerle, U., and Knutti, R.: Very rare heat
extremes: quantifying and understanding using ensemble re-initialization,
J. Climate, 34, 6619–6634, https://doi.org/10.1175/JCLI-D-20-0916.1, 2021. a
Ghil, M., Yiou, P., Hallegatte, S., Malamud, B. D., Naveau, P., Soloviev, A., Friederichs, P., Keilis-Borok, V., Kondrashov, D., Kossobokov, V., Mestre, O., Nicolis, C., Rust, H. W., Shebalin, P., Vrac, M., Witt, A., and Zaliapin, I.: Extreme events: dynamics, statistics and prediction, Nonlin. Processes Geophys., 18, 295–350, https://doi.org/10.5194/npg-18-295-2011, 2011. a
Gilleland, E.: Bootstrap Methods for Statistical Inference. Part I:
Comparative Forecast Verification for Continuous Variables, J.
Atmos. Ocean. Tech., 37, 2117–2134,
https://doi.org/10.1175/JTECH-D-20-0069.1, 2020. a
Gilleland, E. and Katz, R. W.: ExtRemes 2.0: An extreme value analysis package
in R, J. Stat. Softw., 72, 1–39, https://doi.org/10.18637/jss.v072.i08,
2016. a
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning, Springer Series in Statistics, Springer New York, New York, NY,
https://doi.org/10.1007/b94608, 2009. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Horton, D. E., Johnson, N. C., Singh, D., Swain, D. L., Rajaratnam, B., and
Diffenbaugh, N. S.: Contribution of changes in atmospheric circulation
patterns to extreme temperature trends, Nature, 522, 465–469,
https://doi.org/10.1038/nature14550, 2015. a
Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E., and Raymond, C.: A Review
of Recent Advances in Research on Extreme Heat Events, Current Climate
Change Reports, 2, 242–259, https://doi.org/10.1007/s40641-016-0042-x, 2016. a
Huang, W. K., Stein, M. L., McInerney, D. J., Sun, S., and Moyer, E. J.: Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions, Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, 2016. a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J. F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb,
W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P.,
Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl,
J., and Marshall, S.: The community earth system model: A framework for
collaborative research, B. Am. Meteorol. Soc., 94,
1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013. a
IPCC: Summary for Policymakers, in: Climate Change 2021: The Physical
Science Basis. Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte,
V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S., Caud, N.,
Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy, E.,
Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and
Zhou, B., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, 3–32, https://doi.org/10.1017/9781009157896.001, 2021. a
Jahn, M.: Economics of extreme weather events: Terminology and regional impact
models, Weather and Climate Extremes, 10, 29–39,
https://doi.org/10.1016/j.wace.2015.08.005, 2015. a
Jézéquel, A., Yiou, P., and Radanovics, S.: Role of circulation in
European heatwaves using flow analogues, Clim. Dynam., 50, 1145–1159,
https://doi.org/10.1007/s00382-017-3667-0, 2018. a, b, c, d
Katz, R. W.: Statistical Methods for Nonstationary Extremes, in: Extremes in
a Changing Climate: Detection, Analysis and Uncertainty, edited by:
AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S.,
chap. 2, Springer Netherlands, Dordrecht, Netherlands, 15–37,
https://doi.org/10.1007/978-94-007-4479-0, 2013. a
Kew, S. f., Philip, S. Y., Jan van Oldenborgh, G., van der Schrier, G., Otto,
F. E. L., and Vautard, R.: The Exceptional Summer Heat Wave in Southern
Europe 2017, B. Am. Meteorol. Soc., 100, S49–S53,
https://doi.org/10.1175/BAMS-D-18-0109.1, 2019. a
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma,
M. J.: On the Nature of Soil Moisture in Land Surface Models, J.
Climate, 22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1, 2009. a
Koutsoyiannis, D. and Montanari, A.: Negligent killing of scientific concepts:
the stationarity case, Hydrolog. Sci. J., 60, 1174–1183,
https://doi.org/10.1080/02626667.2014.959959, 2015. a
Kröner, N., Kotlarski, S., Fischer, E., Lüthi, D., Zubler, E., and
Schär, C.: Separating climate change signals into thermodynamic,
lapse-rate and circulation effects: theory and application to the European
summer climate, Clim. Dynam., 48, 3425–3440,
https://doi.org/10.1007/s00382-016-3276-3, 2017. a
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson,
S. C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K.,
Bonan, G. B., and Slater, A. G.: Parameterization improvements and
functional and structural advances in Version 4 of the Community Land Model,
J. Adv. Model. Earth Sy., 3, M03001,
https://doi.org/10.1029/2011MS00045, 2011. a
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020. a
Lorenz, R., Stalhandske, Z., and Fischer, E. M.: Detection of a Climate Change
Signal in Extreme Heat, Heat Stress, and Cold in Europe From Observations,
Geophys. Res. Lett., 46, 8363–8374, https://doi.org/10.1029/2019GL082062,
2019. a
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C., and
Vilà-Guerau de Arellano, J.: Mega-heatwave temperatures due to
combined soil desiccation and atmospheric heat accumulation, Nat.
Geosci., 7, 345–349, https://doi.org/10.1038/ngeo2141, 2014. a, b
Mo, R., Lin, H., and Vitart, F.: An anomalous warm-season trans-Pacific
atmospheric river linked to the 2021 western North America heatwave,
Communications Earth & Environment, 3, 127,
https://doi.org/10.1038/s43247-022-00459-w, 2022. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Naveau, P., Hannart, A., and Ribes, A.: Statistical methods for extreme event
attribution in climate science, Annu. Rev. Stat.
Appl., 7, 89–110, https://doi.org/10.1146/annurev-statistics-031219-041314,
2020. a
Neal, E., Huang, C. S. Y., and Nakamura, N.: The 2021 Pacific Northwest Heat
Wave and Associated Blocking: Meteorology and the Role of an Upstream Cyclone
as a Diabatic Source of Wave Activity, Geophys. Res. Lett., 49, e2021GL097699,
https://doi.org/10.1029/2021GL097699, 2022. a
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S.,
Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J.-F.,
Marsh, D., Mills, M., Smith, A. K., Tilmes, S., Vitt, F., Morrison, H.,
Cameron-Smith, P., Collins, W. D., Iacono, M. J., Easter, R. C., Ghan, S. J.,
Liu, X., Rasch, P. J., and Taylor, M. A.: Description of the NCAR Community
Atmosphere Model (CAM 5.0), Tech. Rep. November, National Center For
Atmospheric Research, Boulder, Colorado, United States,
https://doi.org/10.5065/wgtk-4g06, 2012. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Overland, J. E.: Causes of the Record-Breaking Pacific Northwest Heatwave,
Late June 2021, Atmosphere, 12, 1434, https://doi.org/10.3390/atmos12111434, 2021. a
Parente, J., Pereira, M., Amraoui, M., and Fischer, E.: Heat waves in
Portugal: Current regime, changes in future climate and impacts on extreme
wildfires, Sci. Total Environ., 631–632, 534–549,
https://doi.org/10.1016/j.scitotenv.2018.03.044, 2018. a
Perkins, S. E.: A review on the scientific understanding of heatwaves-Their
measurement, driving mechanisms, and changes at the global scale,
Atmos. Res., 164–165, 242–267,
https://doi.org/10.1016/j.atmosres.2015.05.014, 2015. a
Pfahl, S. and Wernli, H.: Quantifying the relevance of atmospheric blocking
for co-located temperature extremes in the Northern Hemisphere on (sub-)daily
time scales, Geophys. Res. Lett., 39, L12807,
https://doi.org/10.1029/2012GL052261, 2012. a, b
Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, 2020. a, b
Philip, S. Y., Kew, S. F., van Oldenborgh, G. J., Anslow, F. S., Seneviratne, S. I., Vautard, R., Coumou, D., Ebi, K. L., Arrighi, J., Singh, R., van Aalst, M., Pereira Marghidan, C., Wehner, M., Yang, W., Li, S., Schumacher, D. L., Hauser, M., Bonnet, R., Luu, L. N., Lehner, F., Gillett, N., Tradowsky, J. S., Vecchi, G. A., Rodell, C., Stull, R. B., Howard, R., and Otto, F. E. L.: Rapid attribution analysis of the extraordinary heat wave on the Pacific coast of the US and Canada in June 2021, Earth Syst. Dynam., 13, 1689–1713, https://doi.org/10.5194/esd-13-1689-2022, 2022. a, b, c, d
Rácz, Z. and Smith, R. K.: The dynamics of heat lows, Q. J.
Roy. Meteor. Soc., 125, 225–252,
https://doi.org/10.1002/qj.49712555313, 1999. a
R Core Team: R: A Language and Environment for Statistical Computing,
https://www.r-project.org/ (last access: 9 July 2023), 2022. a
Rex, D. F.: Blocking Action in the Middle Troposphere and its Effect upon
Regional Climate, Tellus, 2, 275–301, https://doi.org/10.3402/tellusa.v2i4.8603,
1950. a
Ribeiro, A. F. S., Russo, A., Gouveia, C. M., Páscoa, P., and Zscheischler, J.: Risk of crop failure due to compound dry and hot extremes estimated with nested copulas, Biogeosciences, 17, 4815–4830, https://doi.org/10.5194/bg-17-4815-2020, 2020. a
Risser, M. D. and Wehner, M. F.: Attributable Human-Induced Changes in the
Likelihood and Magnitude of the Observed Extreme Precipitation during
Hurricane Harvey, Geophys. Res. Lett., 44, 12457–12464,
https://doi.org/10.1002/2017GL075888, 2017. a
Robin, Y. and Ribes, A.: Nonstationary extreme value analysis for event attribution combining climate models and observations, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, 2020. a, b
Russell, B. T., Cooley, D. S., Porter, W. C., Reich, B. J., and Heald, C. L.:
Data mining to investigate the meteorological drivers for extreme ground
level ozone events, Ann. Appl. Stat., 10, 1673–1698,
https://doi.org/10.1214/16-AOAS954, 2016. a
Schaller, N., Sillmann, J., Anstey, J., Fischer, E. M., Grams, C. M., and
Russo, S.: Influence of blocking on Northern European and Western Russian
heatwaves in large climate model ensembles, Environ. Res. Lett.,
13, 054015, https://doi.org/10.1088/1748-9326/aaba55, 2018. a
Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C.,
Liniger, M. A., and Appenzeller, C.: The role of increasing temperature
variability in European summer heatwaves, Nature, 427, 332–336,
https://doi.org/10.1038/nature02300, 2004. a
Schumacher, D. L., Hauser, M., and Seneviratne, S. I.: Drivers and Mechanisms
of the 2021 Pacific Northwest Heatwave, Earth's Future, 10, 9156,
https://doi.org/10.1029/2022EF002967, 2022. a, b, c, d
Seneviratne, S. I. and Hauser, M.: Regional Climate Sensitivity of Climate
Extremes in CMIP6 Versus CMIP5 Multimodel Ensembles, Earth's Future, 8,
e2019EF001474, https://doi.org/10.1029/2019EF001474, 2020. a, b, c
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture-climate interactions in a changing climate: A review, Earth-Sci.
Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010. a
Serinaldi, F. and Kilsby, C. G.: Stationarity is undead: Uncertainty dominates
the distribution of extremes, Adv. Water Resour., 77, 17–36,
https://doi.org/10.1016/j.advwatres.2014.12.013, 2015. a
Shaposhnikov, D., Revich, B., Bellander, T., Bedada, G. B., Bottai, M.,
Kharkova, T., Kvasha, E., Lezina, E., Lind, T., Semutnikova, E., and
Pershagen, G.: Mortality related to air pollution with the Moscow heat wave
and wildfire of 2010, Epidemiology, 25, 359–364,
https://doi.org/10.1097/EDE.0000000000000090, 2014. a
Shepherd, T. G.: A Common Framework for Approaches to Extreme Event
Attribution, Current Climate Change Reports, 2, 28–38,
https://doi.org/10.1007/s40641-016-0033-y, 2016. a
Sippel, S., Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G.,
Fischer, E., and Knutti, R.: Uncovering the forced climate response from a
single ensemble member using statistical learning, J. Climate, 32,
5677–5699, https://doi.org/10.1175/jcli-d-18-0882.1, 2019. a
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J.,
Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S.,
Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S.,
Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) reference
manual: Ocean component of the Community Climate System Model (CCSM) and
Community Earth System Model (CESM), Tech. Rep., National Center for
Atmospheric Research NCAR, Boulder, Colorado, USA,
http://nldr.library.ucar.edu/repository/collections/OSGC-000-000-000-954 (last access: 9 July 2023),
2010. a
Stillman, J. H.: Heat Waves, the New Normal: Summertime Temperature Extremes
Will Impact Animals, Ecosystems, and Human Communities, Physiology, 34,
86–100, https://doi.org/10.1152/physiol.00040.2018, 2019. a
Stott, P. A., Stone, D. A., and Allen, M. R.: Human contribution to the
European heatwave of 2003, Nature, 432, 610–614, https://doi.org/10.1038/nature03089,
2004. a
Stott, P. A., Christidis, N., Otto, F. E. L., Sun, Y., Vanderlinden, J., van
Oldenborgh, G. J., Vautard, R., von Storch, H., Walton, P., Yiou, P., and
Zwiers, F. W.: Attribution of extreme weather and climate‐related events,
WIREs Clim. Change, 7, 23–41, https://doi.org/10.1002/wcc.380, 2016. a
Suarez-Gutierrez, L., Müller, W. A., Li, C., and Marotzke, J.: Dynamical
and thermodynamical drivers of variability in European summer heat extremes,
Clim. Dynam., 54, 4351–4366, https://doi.org/10.1007/s00382-020-05233-2, 2020. a, b
Sutton, R. T., Dong, B., and Gregory, J. M.: Land/sea warming ratio in
response to climate change: IPCC AR4 model results and comparison with
observations, Geophys. Res. Lett., 34, L02701,
https://doi.org/10.1029/2006GL028164, 2007. a
Terray, L.: A dynamical adjustment perspective on extreme event attribution, Weather Clim. Dynam., 2, 971–989, https://doi.org/10.5194/wcd-2-971-2021, 2021. a, b
Toda, M., Watanabe, M., and Yoshimori, M.: An energy budget framework to
understand mechanisms of land–ocean warming contrast induced by increasing
greenhouse gases Part I: Near-equilibrium state, J. Climate, 34,
9279–9292, https://doi.org/10.1175/JCLI-D-21-0302.1, 2021. a
Vautard, R., Yiou, P., Otto, F., Stott, P., Christidis, N., Van Oldenborgh,
G. J., and Schaller, N.: Attribution of human-induced dynamical and
thermodynamical contributions in extreme weather events, Environ.
Res. Lett., 11, 114009, https://doi.org/10.1088/1748-9326/11/11/114009, 2016. a
Vautard, R., van Aalst, M., Boucher, O., Drouin, A., Haustein, K., Kreienkamp,
F., van Oldenborgh, G. J., Otto, F. E., Ribes, A., Robin, Y., Schneider, M.,
Soubeyroux, J.-M., Stott, P., Seneviratne, S. I., Vogel, M. M., and Wehner,
M.: Human contribution to the record-breaking June and July 2019 heat waves
in Western Europe, Environ. Res. Lett., 15, 094077,
https://doi.org/10.1088/1748-9326/aba3d4, 2020. a, b
Vicedo-Cabrera, A. M., Scovronick, N., Sera, F., Royé, D., Schneider, R.,
Tobias, A., Astrom, C., Guo, Y., Honda, Y., Hondula, D. M., Abrutzky, R.,
Tong, S., Coelho, M. d. S. Z. S., Saldiva, P. H., Lavigne, E., Correa, P. M.,
Ortega, N. V., Kan, H., Osorio, S., Kyselý, J., Urban, A., Orru, H.,
Indermitte, E., Jaakkola, J. J., Ryti, N., Pascal, M., Schneider, A.,
Katsouyanni, K., Samoli, E., Mayvaneh, F., Entezari, A., Goodman, P., Zeka,
A., Michelozzi, P., De’Donato, F., Hashizume, M., Alahmad, B., Diaz, M. H.,
Valencia, C. D. L. C., Overcenco, A., Houthuijs, D., Ameling, C., Rao, S.,
Di Ruscio, F., Carrasco-Escobar, G., Seposo, X., Silva, S., Madureira, J.,
Holobaca, I. H., Fratianni, S., Acquaotta, F., Kim, H., Lee, W., Iniguez, C.,
Forsberg, B., Ragettli, M. S., Guo, Y. L., Chen, B. Y., Li, S., Armstrong,
B., Aleman, A., Zanobetti, A., Schwartz, J., Dang, T. N., Dung, D. V.,
Gillett, N., Haines, A., Mengel, M., Huber, V., and Gasparrini, A.: The
burden of heat-related mortality attributable to recent human-induced climate
change, Nat. Clim. Change, 11, 492–500,
https://doi.org/10.1038/s41558-021-01058-x, 2021. a
Vignotto, E., Sippel, S., Lehner, F., and Fischer, E.: Towards dynamical
adjustment of the full temperature distribution, in: Proceedings of the 10th
International Conference on Climate Informatics, online, 22–25 September 2020, ACM, New York,
NY, USA, 52–59, https://doi.org/10.1145/3429309.3429317, 2020. a
Vogel, M. M., Orth, R., Cheruy, F., Hagemann, S., Lorenz, R., van den Hurk,
B. J., and Seneviratne, S. I.: Regional amplification of projected changes
in extreme temperatures strongly controlled by soil moisture-temperature
feedbacks, Geophys. Res. Lett., 44, 1511–1519,
https://doi.org/10.1002/2016GL071235, 2017.
a
WCRP: WCRP Coupled Model Intercomparison Project (Phase 6), WCRP [data set], https://esgf-node.llnl.gov/projects/cmip6/, last access: 11 July 2023. a
Wehner, M. F.: Characterization of long period return values of extreme daily
temperature and precipitation in the CMIP6 models: Part 2, projections of
future change, Weather and Climate Extremes, 30, 100284,
https://doi.org/10.1016/j.wace.2020.100284, 2020. a, b
Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M., and Seneviratne, S. I.:
Identifying Key Driving Processes of Major Recent Heat Waves, J.
Geophys. Res.-Atmos., 124, 11746–11765,
https://doi.org/10.1029/2019JD030635, 2019. a
Wehrli, K., Luo, F., Hauser, M., Shiogama, H., Tokuda, D., Kim, H., Coumou, D., May, W., Le Sager, P., Selten, F., Martius, O., Vautard, R., and Seneviratne, S. I.: The ExtremeX global climate model experiment: investigating thermodynamic and dynamic processes contributing to weather and climate extremes, Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, 2022. a
White, R. H., Anderson, S., Booth, J. F., Braich, G., Draeger, C., Fei, C.,
Harley, C. D. G., Henderson, S. B., Jakob, M., Lau, C.-A., Mareshet Admasu,
L., Narinesingh, V., Rodell, C., Roocroft, E., Weinberger, K. R., and West,
G.: The unprecedented Pacific Northwest heatwave of June 2021, Nat.
Commun., 14, 727, https://doi.org/10.1038/s41467-023-36289-3, 2023. a
Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014. a
Yiou, P., Jézéquel, A., Naveau, P., Otto, F. E. L., Vautard, R., and Vrac, M.: A statistical framework for conditional extreme event attribution, Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, 2017. a
Youngman, B. D.: evgam : An R Package for Generalized Additive Extreme Value
Models, J. Stat. Softw., 103, 1–26, https://doi.org/10.18637/jss.v103.i03,
2022. a
Zeder, J. and Fischer, E.: Quantifying the statistical dependence of mid-latitude heatwave intensity and likelihood on prevalent physical drivers and climate change (Dataset), ETH Library [data set], https://doi.org/10.3929/ethz-b-000615056, 2023. a
Zschenderlein, P., Fink, A. H., Pfahl, S., and Wernli, H.: Processes
determining heat waves across different European climates, Q. J. Roy. Meteor. Soc., 145, 2973–2989, https://doi.org/10.1002/qj.3599,
2019. a
Zwiers, F. W., Zhang, X., and Feng, Y.: Anthropogenic Influence on Long Return
Period Daily Temperature Extremes at Regional Scales, J. Climate,
24, 881–892, https://doi.org/10.1175/2010JCLI3908.1, 2011. a
Short summary
The intensities of recent heatwave events, such as the record-breaking heatwave in early June 2021 in the Pacific Northwest area, are substantially altered by climate change. We further quantify the contribution of the local weather situation and the land surface conditions with a statistical model suited for extreme data. Based on this method, we can answer
what ifquestions, such as estimating the change in the 2021 heatwave temperature if it happened in a world without climate change.
The intensities of recent heatwave events, such as the record-breaking heatwave in early June...
