Articles | Volume 8, issue 1
https://doi.org/10.5194/ascmo-8-135-2022
© Author(s) 2022. 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-8-135-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 Jun 2022
| 13 Jun 2022
A multi-method framework for global real-time climate attribution
Daniel M. Gilford
CORRESPONDING AUTHOR
Climate Central, Princeton, NJ, USA
Andrew Pershing
Climate Central, Princeton, NJ, USA
Benjamin H. Strauss
Climate Central, Princeton, NJ, USA
Karsten Haustein
Institute for Meteorology, Leipzig University, Leipzig, Germany
Friederike E. L. Otto
Grantham Institute of Climate Change, Imperial College London, UK
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Daniel M. Gilford
Geosci. Model Dev., 14, 2351–2369, https://doi.org/10.5194/gmd-14-2351-2021, https://doi.org/10.5194/gmd-14-2351-2021, 2021
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Potential intensity (PI) is a tropical cyclone's maximum speed limit given by modeling the storm as a thermal heat engine. pyPI is the first software package fully documenting the PI algorithm and translating it to Python. This study details/validates the underlying PI model and demonstrates its use in tropical cyclone intensity research. pyPI supports open science and transparency in the tropical meteorological community and is ideally suited for ongoing community development and improvement.
Rosa Pietroiusti, Inne Vanderkelen, Friederike E. L. Otto, Clair Barnes, Lucy Temple, Mary Akurut, Philippe Bally, Nicole P. M. van Lipzig, and Wim Thiery
Earth Syst. Dynam., 15, 225–264, https://doi.org/10.5194/esd-15-225-2024, https://doi.org/10.5194/esd-15-225-2024, 2024
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Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides and drove the levels of Lake Victoria to a record-breaking maximum in May 2020. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and investigate how human-induced climate change influenced the probability and intensity of the record-breaking lake levels and flooding by applying a multi-model extreme event attribution methodology.
Dominik L. Schumacher, Mariam Zachariah, Friederike Otto, Clair Barnes, Sjoukje Philip, Sarah Kew, Maja Vahlberg, Roop Singh, Dorothy Heinrich, Julie Arrighi, Maarten van Aalst, Mathias Hauser, Martin Hirschi, Verena Bessenbacher, Lukas Gudmundsson, Hiroko K. Beaudoing, Matthew Rodell, Sihan Li, Wenchang Yang, Gabriel A. Vecchi, Luke J. Harrington, Flavio Lehner, Gianpaolo Balsamo, and Sonia I. Seneviratne
Earth Syst. Dynam., 15, 131–154, https://doi.org/10.5194/esd-15-131-2024, https://doi.org/10.5194/esd-15-131-2024, 2024
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The 2022 summer was accompanied by widespread soil moisture deficits, including an unprecedented drought in Europe. Combining several observation-based estimates and models, we find that such an event has become at least 5 and 20 times more likely due to human-induced climate change in western Europe and the northern extratropics, respectively. Strong regional warming fuels soil desiccation; hence, projections indicate even more potent future droughts as we progress towards a 2 °C warmer world.
Robert Vautard, Geert Jan van Oldenborgh, Rémy Bonnet, Sihan Li, Yoann Robin, Sarah Kew, Sjoukje Philip, Jean-Michel Soubeyroux, Brigitte Dubuisson, Nicolas Viovy, Markus Reichstein, Friederike Otto, and Iñaki Garcia de Cortazar-Atauri
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
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A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
Sjoukje Y. Philip, Sarah F. Kew, Geert Jan van Oldenborgh, Faron S. Anslow, Sonia I. Seneviratne, Robert Vautard, Dim Coumou, Kristie L. Ebi, Julie Arrighi, Roop Singh, Maarten van Aalst, Carolina Pereira Marghidan, Michael Wehner, Wenchang Yang, Sihan Li, Dominik L. Schumacher, Mathias Hauser, Rémy Bonnet, Linh N. Luu, Flavio Lehner, Nathan Gillett, Jordis S. Tradowsky, Gabriel A. Vecchi, Chris Rodell, Roland B. Stull, Rosie Howard, and Friederike E. L. Otto
Earth Syst. Dynam., 13, 1689–1713, https://doi.org/10.5194/esd-13-1689-2022, https://doi.org/10.5194/esd-13-1689-2022, 2022
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In June 2021, the Pacific Northwest of the US and Canada saw record temperatures far exceeding those previously observed. This attribution study found such a severe heat wave would have been virtually impossible without human-induced climate change. Assuming no nonlinear interactions, such events have become at least 150 times more common, are about 2 °C hotter and will become even more common as warming continues. Therefore, adaptation and mitigation are urgently needed to prepare society.
Daniel M. Gilford
Geosci. Model Dev., 14, 2351–2369, https://doi.org/10.5194/gmd-14-2351-2021, https://doi.org/10.5194/gmd-14-2351-2021, 2021
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Potential intensity (PI) is a tropical cyclone's maximum speed limit given by modeling the storm as a thermal heat engine. pyPI is the first software package fully documenting the PI algorithm and translating it to Python. This study details/validates the underlying PI model and demonstrates its use in tropical cyclone intensity research. pyPI supports open science and transparency in the tropical meteorological community and is ideally suited for ongoing community development and improvement.
Geert Jan van Oldenborgh, Folmer Krikken, Sophie Lewis, Nicholas J. Leach, Flavio Lehner, Kate R. Saunders, Michiel van Weele, Karsten Haustein, Sihan Li, David Wallom, Sarah Sparrow, Julie Arrighi, Roop K. Singh, Maarten K. van Aalst, Sjoukje Y. Philip, Robert Vautard, and Friederike E. L. Otto
Nat. Hazards Earth Syst. Sci., 21, 941–960, https://doi.org/10.5194/nhess-21-941-2021, https://doi.org/10.5194/nhess-21-941-2021, 2021
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Southeastern Australia suffered from disastrous bushfires during the 2019/20 fire season, raising the question whether these have become more likely due to climate change. We found no attributable trend in extreme annual or monthly low precipitation but a clear shift towards more extreme heat. However, this shift is underestimated by the models. Analysing fire weather directly, we found that the chance has increased by at least 30 %, but due to the underestimation it could well be higher.
Sarah F. Kew, Sjoukje Y. Philip, Mathias Hauser, Mike Hobbins, Niko Wanders, Geert Jan van Oldenborgh, Karin van der Wiel, Ted I. E. Veldkamp, Joyce Kimutai, Chris Funk, and Friederike E. L. Otto
Earth Syst. Dynam., 12, 17–35, https://doi.org/10.5194/esd-12-17-2021, https://doi.org/10.5194/esd-12-17-2021, 2021
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Motivated by the possible influence of rising temperatures, this study synthesises results from observations and climate models to explore trends (1900–2018) in eastern African (EA) drought measures. However, no discernible trends are found in annual soil moisture or precipitation. Positive trends in potential evaporation indicate that for irrigated regions more water is now required to counteract increased evaporation. Precipitation deficit is, however, the most useful indicator of EA drought.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
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Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Robert Vautard, Geert Jan van Oldenborgh, Friederike E. L. Otto, Pascal Yiou, Hylke de Vries, Erik van Meijgaard, Andrew Stepek, Jean-Michel Soubeyroux, Sjoukje Philip, Sarah F. Kew, Cecilia Costella, Roop Singh, and Claudia Tebaldi
Earth Syst. Dynam., 10, 271–286, https://doi.org/10.5194/esd-10-271-2019, https://doi.org/10.5194/esd-10-271-2019, 2019
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The effect of human activities on the probability of winter wind storms like the ones that occurred in Western Europe in January 2018 is analysed using multiple model ensembles. Despite a significant probability decline in observations, we find no significant change in probabilities due to human influence on climate so far. However, such extreme events are likely to be slightly more frequent in the future. The observed decrease in storminess is likely to be due to increasing roughness.
Sjoukje Philip, Sarah Sparrow, Sarah F. Kew, Karin van der Wiel, Niko Wanders, Roop Singh, Ahmadul Hassan, Khaled Mohammed, Hammad Javid, Karsten Haustein, Friederike E. L. Otto, Feyera Hirpa, Ruksana H. Rimi, A. K. M. Saiful Islam, David C. H. Wallom, and Geert Jan van Oldenborgh
Hydrol. Earth Syst. Sci., 23, 1409–1429, https://doi.org/10.5194/hess-23-1409-2019, https://doi.org/10.5194/hess-23-1409-2019, 2019
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In August 2017 Bangladesh faced one of its worst river flooding events in recent history. For the large Brahmaputra basin, using precipitation alone as a proxy for flooding might not be appropriate. In this paper we explicitly test this assumption by performing an attribution of both precipitation and discharge as a flooding-related measure to climate change. We find the change in risk to be of similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach.
Geert Jan van Oldenborgh, Sjoukje Philip, Sarah Kew, Michiel van Weele, Peter Uhe, Friederike Otto, Roop Singh, Indrani Pai, Heidi Cullen, and Krishna AchutaRao
Nat. Hazards Earth Syst. Sci., 18, 365–381, https://doi.org/10.5194/nhess-18-365-2018, https://doi.org/10.5194/nhess-18-365-2018, 2018
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On 19 May 2016 a temperature of 51.0 °C in Phalodi (northwest India) set a new Indian record. In 2015 a very lethal heat wave had occurred in the southeast. We find that in India the trend in extreme temperatures due to greenhouse gases is largely cancelled by increasing air pollution and irrigation. The health impacts of heat waves do increase due to higher humidity and air pollution. This implies that we expect heat waves to become much hotter as soon as air pollution is brought under control.
Benoit P. Guillod, Richard G. Jones, Andy Bowery, Karsten Haustein, Neil R. Massey, Daniel M. Mitchell, Friederike E. L. Otto, Sarah N. Sparrow, Peter Uhe, David C. H. Wallom, Simon Wilson, and Myles R. Allen
Geosci. Model Dev., 10, 1849–1872, https://doi.org/10.5194/gmd-10-1849-2017, https://doi.org/10.5194/gmd-10-1849-2017, 2017
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The weather@home climate modelling system uses the computing power of volunteers around the world to generate a very large number of climate model simulations. This is particularly useful when investigating extreme weather events, notably for the attribution of these events to anthropogenic climate change. A new version of weather@home is presented and evaluated, which includes an improved representation of the land surface and increased horizontal resolution over Europe.
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, https://doi.org/10.5194/ascmo-3-17-2017, 2017
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The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
Sebastian Sippel, Jakob Zscheischler, Martin Heimann, Holger Lange, Miguel D. Mahecha, Geert Jan van Oldenborgh, Friederike E. L. Otto, and Markus Reichstein
Hydrol. Earth Syst. Sci., 21, 441–458, https://doi.org/10.5194/hess-21-441-2017, https://doi.org/10.5194/hess-21-441-2017, 2017
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The paper re-investigates the question whether observed precipitation extremes and annual totals have been increasing in the world's dry regions over the last 60 years. Despite recently postulated increasing trends, we demonstrate that large uncertainties prevail due to (1) the choice of dryness definition and (2) statistical data processing. In fact, we find only minor (and only some significant) increases if (1) dryness is based on aridity and (2) statistical artefacts are accounted for.
Mitchell T. Black, David J. Karoly, Suzanne M. Rosier, Sam M. Dean, Andrew D. King, Neil R. Massey, Sarah N. Sparrow, Andy Bowery, David Wallom, Richard G. Jones, Friederike E. L. Otto, and Myles R. Allen
Geosci. Model Dev., 9, 3161–3176, https://doi.org/10.5194/gmd-9-3161-2016, https://doi.org/10.5194/gmd-9-3161-2016, 2016
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This study presents a citizen science computing project, known as weather@home Australia–New Zealand, which runs climate models on thousands of home computers. By harnessing the power of volunteers' computers, this project is capable of simulating extreme weather events over Australia and New Zealand under different climate scenarios.
Geert Jan van Oldenborgh, Sjoukje Philip, Emma Aalbers, Robert Vautard, Friederike Otto, Karsten Haustein, Florence Habets, Roop Singh, and Heidi Cullen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-308, https://doi.org/10.5194/hess-2016-308, 2016
Manuscript not accepted for further review
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Extreme rain caused flooding in France and Germany at the end of May 2016. After such an event the question is always posed to what extent it can be attributed to anthropogenic climate change. Using observations and five model ensembles we give a first answer. For the 3-day precipitation extremes over the Seine and Loire basins that caused the flooding all methods agree that the probability has increased by a factor of about two. For 1-day precipitation extremes in Germany the methods disagree.
S. Sippel, F. E. L. Otto, M. Forkel, M. R. Allen, B. P. Guillod, M. Heimann, M. Reichstein, S. I. Seneviratne, K. Thonicke, and M. D. Mahecha
Earth Syst. Dynam., 7, 71–88, https://doi.org/10.5194/esd-7-71-2016, https://doi.org/10.5194/esd-7-71-2016, 2016
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We introduce a novel technique to bias correct climate model output for impact simulations that preserves its physical consistency and multivariate structure. The methodology considerably improves the representation of extremes in climatic variables relative to conventional bias correction strategies. Illustrative simulations of biosphere–atmosphere carbon and water fluxes with a biosphere model (LPJmL) show that the novel technique can be usefully applied to drive climate impact models.
G. J. van Oldenborgh, F. E. L. Otto, K. Haustein, and H. Cullen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-13197-2015, https://doi.org/10.5194/hessd-12-13197-2015, 2015
Revised manuscript not accepted
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On 4–6 December 2015, the storm 'Desmond' caused very heavy rainfall in northern England and Scotland, which led to widespread flooding. We provide an initial assessment of the influence of anthropogenic climate change on the likelihood of precipitation events like this. We use three independent methods of extreme event attribution based on observations and two climate models. All methods agree that the effect of climate change is positive, making events like this about 40% (5–80%) more likely.
Related subject area
Climate research
Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency
Changes in the distribution of annual maximum temperatures in Europe
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
Comparing climate time series – Part 4: Annual cycles
Statistical reconstruction of European winter snowfall in reanalysis and climate models based on air temperature and total precipitation
Analysis of the evolution of parametric drivers of high-end sea-level hazards
Comparing climate time series – Part 3: Discriminant analysis
Spatial heterogeneity in rain-bearing winds, seasonality and rainfall variability in southern Africa's winter rainfall zone
Spatial heterogeneity of 2015–2017 drought intensity in South Africa's winter rainfall zone
A statistical framework for integrating nonparametric proxy distributions into geological reconstructions of relative sea level
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
A protocol for probabilistic extreme event attribution analyses
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Possible impacts of climate change on fog in the Arctic and subpolar North Atlantic
Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models
Comparison and assessment of large-scale surface temperature in climate model simulations
Future climate emulations using quantile regressions on large ensembles
Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures
Estimates of climate system properties incorporating recent climate change
The joint influence of break and noise variance on the break detection capability in time series homogenization
A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Building a traceable climate model hierarchy with multi-level emulators
Svenja Szemkus and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 10, 29–49, https://doi.org/10.5194/ascmo-10-29-2024, https://doi.org/10.5194/ascmo-10-29-2024, 2024
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This paper uses the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019), which we extend to the description of common extremes in two variables. We develop an extreme pattern index (EPI), a pattern-based aggregation to describe spatially extended weather extremes. Our results show that the EPI is suitable for describing heat waves. We extend the EPI to describe extremes in two variables and obtain an index to describe compound precipitation deficits and heat waves.
Graeme Auld, Gabriele C. Hegerl, and Ioannis Papastathopoulos
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 45–66, https://doi.org/10.5194/ascmo-9-45-2023, https://doi.org/10.5194/ascmo-9-45-2023, 2023
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In this paper we consider the problem of detecting changes in the distribution of the annual maximum temperature, during the years 1950–2018, across Europe.
We find that, on average, the temperature that would be expected to be exceeded
approximately once every 100 years in the 1950 climate is expected to be exceeded once every 6 years in the 2018 climate. This is of concern due to the devastating effects on humans and natural systems that are caused by extreme temperatures.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 29–44, https://doi.org/10.5194/ascmo-9-29-2023, https://doi.org/10.5194/ascmo-9-29-2023, 2023
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Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 187–203, https://doi.org/10.5194/ascmo-8-187-2022, https://doi.org/10.5194/ascmo-8-187-2022, 2022
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Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
Flavio Maria Emanuele Pons and Davide Faranda
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 155–186, https://doi.org/10.5194/ascmo-8-155-2022, https://doi.org/10.5194/ascmo-8-155-2022, 2022
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The objective motivating this study is the assessment of the impacts of winter climate extremes, which requires accurate simulation of snowfall. However, climate simulation models contain physical approximations, which result in biases that must be corrected using past data as a reference. We show how to exploit simulated temperature and precipitation to estimate snowfall from already bias-corrected variables, without requiring the elaboration of complex, multivariate bias adjustment techniques.
Alana Hough and Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 117–134, https://doi.org/10.5194/ascmo-8-117-2022, https://doi.org/10.5194/ascmo-8-117-2022, 2022
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We use machine learning to assess how different geophysical uncertainties relate to the severity of future sea-level rise. We show how the contributions to coastal hazard from different sea-level processes evolve over time and find that near-term sea-level hazards are driven by thermal expansion and the melting of glaciers and ice caps, while long-term hazards are driven by ice loss from the major ice sheets.
Timothy DelSole and Michael K. Tippett
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 97–115, https://doi.org/10.5194/ascmo-8-97-2022, https://doi.org/10.5194/ascmo-8-97-2022, 2022
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A common problem in climate studies is to decide whether a climate model is realistic. Such decisions are not straightforward because the time series are serially correlated and multivariate. Part II derived a test for deciding wether a simulation is statistically distinguishable from observations. However, the test itself provides no information about the nature of those differences. This paper develops a systematic and optimal approach to diagnosing differences between stochastic processes.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 31–62, https://doi.org/10.5194/ascmo-8-31-2022, https://doi.org/10.5194/ascmo-8-31-2022, 2022
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Cape Town is situated in a small but ecologically and climatically highly diverse and vulnerable pocket of South Africa uniquely receiving its rain mostly in winter. We show complex structures in the spatial patterns of rainfall seasonality and year-to-year changes in rainfall within this domain, tied to spatial differences in the rain-bearing winds. This allows us to develop a new spatial subdivision of the region to help future studies distinguish spatially distinct climate change responses.
Willem Stefaan Conradie, Piotr Wolski, and Bruce Charles Hewitson
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 63–81, https://doi.org/10.5194/ascmo-8-63-2022, https://doi.org/10.5194/ascmo-8-63-2022, 2022
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The
Day Zerowater crisis affecting Cape Town after the severe 2015–2017 drought motivated renewed research interest into causes and projections of rainfall variability and change in this water-stressed region. Unusually few wet months and very wet days characterised the Day Zero Drought. Its extent expanded as it shifted gradually north-eastward, concurrent with changes in the weather systems driving drought. Our results emphasise the need to consider the interplay between drought drivers.
Erica L. Ashe, Nicole S. Khan, Lauren T. Toth, Andrea Dutton, and Robert E. Kopp
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 1–29, https://doi.org/10.5194/ascmo-8-1-2022, https://doi.org/10.5194/ascmo-8-1-2022, 2022
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We develop a new technique to integrate realistic uncertainties in probabilistic models of past sea-level change. The new framework performs better than past methods (in precision, accuracy, bias, and model fit) because it enables the incorporation of previously unused data and exploits correlations in the data. This method has the potential to assess the validity of past estimates of extreme sea-level rise and highstands providing better context in which to place current sea-level change.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020, https://doi.org/10.5194/ascmo-6-177-2020, 2020
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Event attribution studies can now be performed at short notice. We document a protocol developed by the World Weather Attribution group. It includes choices of which events to analyse, the event definition, observational analysis, model evaluation, multi-model multi-method attribution, hazard synthesis, vulnerability and exposure analysis, and communication procedures. The protocol will be useful for future event attribution studies and as a basis for an operational attribution service.
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020, https://doi.org/10.5194/ascmo-6-115-2020, 2020
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Evaluation of modern high-resolution global climate models often does not account for the geographic location of the underlying weather station data. In this paper, we quantify the impact of geographic sampling on the relative performance of climate model representations of precipitation extremes over the United States. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance.
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020, https://doi.org/10.5194/ascmo-6-91-2020, 2020
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We have developed a novel and fast statistical method for diagnosing effective radiative forcing (ERF), a measure of the net effect of greenhouse gas emissions on Earth's energy budget. Our method works by inverting a recursive digital filter energy balance representation of global climate models and has been successfully validated using simulated data from UK Met Office climate models. We have estimated time series of historical ERF by applying our method to the global temperature record.
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020, https://doi.org/10.5194/ascmo-6-61-2020, 2020
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Extremes in weather can have lasting effects on human health and resource consumption. Studying the recurrence of these events on a regional scale can improve response times and provide insight into a changing climate. We introduce a set of clustering tools that allow for regional clustering of weather recordings from stations across Germany. We use these clusters to form regional models of summer temperature extremes and find an increase in the mean from 1960 to 2018.
Richard E. Danielson, Minghong Zhang, and William A. Perrie
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 31–43, https://doi.org/10.5194/ascmo-6-31-2020, https://doi.org/10.5194/ascmo-6-31-2020, 2020
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Visibility is estimated for the 21st century using global and regional climate model output. A baseline decrease in visibility in the Arctic (10 %) is more notable than in the North Atlantic (< 5 %). We develop an adjustment that yields greater consistency among models and explore the justification of our ad hoc adjustment toward ship observations during the historical period. Baseline estimates are found to be sensitive to the representation of temperature and humidity.
Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 133–146, https://doi.org/10.5194/ascmo-5-133-2019, https://doi.org/10.5194/ascmo-5-133-2019, 2019
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Extreme temperature and precipitation events in Australia have caused significant socio-economic and environmental impacts. Determining the factors contributing to these extremes is an active area of research. This paper describes a set of studies that have examined the causes of extreme climate events in recent years in Australia. Ideally, this review will be useful for the application of these extreme event attribution approaches to climate and weather extremes occurring elsewhere.
Raquel Barata, Raquel Prado, and Bruno Sansó
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 67–85, https://doi.org/10.5194/ascmo-5-67-2019, https://doi.org/10.5194/ascmo-5-67-2019, 2019
Matz A. Haugen, Michael L. Stein, Ryan L. Sriver, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 37–55, https://doi.org/10.5194/ascmo-5-37-2019, https://doi.org/10.5194/ascmo-5-37-2019, 2019
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This work uses current temperature observations combined with climate models to project future temperature estimates, e.g., 100 years into the future. We accomplish this by modeling temperature as a smooth function of time both in the seasonal variation as well as in the annual trend. These smooth functions are estimated at multiple quantiles that are all projected into the future. We hope that this work can be used as a template for how other climate variables can be projected into the future.
Rasmus E. Benestad, Bob van Oort, Flavio Justino, Frode Stordal, Kajsa M. Parding, Abdelkader Mezghani, Helene B. Erlandsen, Jana Sillmann, and Milton E. Pereira-Flores
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 37–52, https://doi.org/10.5194/ascmo-4-37-2018, https://doi.org/10.5194/ascmo-4-37-2018, 2018
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A new study indicates that heatwaves in India will become more frequent and last longer with global warming. Its results were derived from a large number of global climate models, and the calculations differed from previous studies in the way they included advanced statistical theory. The projected changes in the Indian heatwaves will have a negative consequence for wheat crops in India.
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, https://doi.org/10.5194/ascmo-4-19-2018, https://doi.org/10.5194/ascmo-4-19-2018, 2018
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We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Ralf Lindau and Victor Karel Christiaan Venema
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 1–18, https://doi.org/10.5194/ascmo-4-1-2018, https://doi.org/10.5194/ascmo-4-1-2018, 2018
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Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105–114, https://doi.org/10.5194/ascmo-2-105-2016, https://doi.org/10.5194/ascmo-2-105-2016, 2016
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Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
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In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Cited articles
Abatzoglou, J. T. and Williams, A. P.: Impact of anthropogenic climate change
on wildfire across western US forests, P. Natl. Acad. Sci. USA, 113, 11770–11775, https://doi.org/10.1073/pnas.1607171113, 2016. a
Baynton, H. W., Bidwell, J. M., and Beran, D. W.: correspondence: To coin a word, B. Am. Meteorol. Soc., 45, 393–393, https://doi.org/10.1175/1520-0477-45.7.393, 1964. a
Berry, H. L., Bowen, K., and Kjellstrom, T.: Climate change and mental health:
A causal pathways framework, Int. J. Public Health, 55,
123–132, https://doi.org/10.1007/s00038-009-0112-0, 2010. a
Byrne, M. P.: Amplified warming of extreme temperatures over tropical land,
Nat. Geosci., 14, 837–841, https://doi.org/10.1038/s41561-021-00828-8, 2021. a
Callaghan, M., Schleussner, C.-F., Nath, S., Lejeune, Qu., Knutson, T. R.,
Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha,
R. J., Hegarty, M., Jones, C., Lee, K., Lucas, A., van Maane, N., Menk,
I., Pfleiderer, P., Yesil, B., and Minx, J. C.: Machine learning-based
evidence and attribution mapping of 100 000 climate impact studies, Nat. Clim. Change, 11, 966–972, https://doi.org/10.1038/s41558-021-01168-6, 2021. a, b, c
Chen, D., Rojas, M., Samset, B., Cobb, K., Diongue Niang, A., Edwards, P.,
Emori, S., Faria, S., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M.,
Mustafa, S., Plattner, G.-K., and Tréguier, A.-M.: Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 1–215, https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (last access: 3 June 2022),
2021. a
Chen, Y., Zhou, B., Zhai, P., and Moufouma-Okia, W.: Half-a-Degree Matters for
Reducing and Delaying Global Land Exposure to Combined Daytime-Nighttime Hot
Extremes, Earths Future, 7, 953–966, https://doi.org/10.1029/2019EF001202, 2019. a, b
Coles, S.: An introduction to statistical modeling of extreme values, Springer
Series in Statistics, Springer-Verlag, London, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a, b
Cowtan, K. and Way, R. G.: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends, Q. J. Roy.
Meteor. Soc., 140, 1935–1944, https://doi.org/10.1002/qj.2297, 2014. a
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the
role of natural variability in future North American climate, Nat. Clim. Change, 2, 775–779, https://doi.org/10.1038/nclimate1562, 2012. a
Diffenbaugh, N. S. and Ashfaq, M.: Intensification of hot extremes in the
United States, Geophys. Res. Lett., 37, 1–5,
https://doi.org/10.1029/2010GL043888, 2010. a
Diffenbaugh, N. S., Singh, D., Mankin, J. S., Horton, D. E., Swain, D. L.,
Touma, D., Charland, A., Liu, Y., Haugen, M., Tsiang, M., and Rajaratnam, B.:
Quantifying the influence of global warming on unprecedented extreme climate
events, P. Natl. Acad. Sci. USA, 114, 4881–4886, https://doi.org/10.1073/pnas.1618082114, 2017. a, b
Donat, M. G. and Alexander, L. V.: The shifting probability distribution of
global daytime and night-time temperatures, Geophys. Res. Lett.,
39, L14707, https://doi.org/10.1029/2012GL052459, 2012. a
Eden, J. M., Wolter, K., Otto, F. E., and Van Oldenborgh, G. J.:
Multi-method attribution analysis of extreme precipitation in Boulder,
Colorado, Environ. Res. Lett., 11, 124009,
https://doi.org/10.1088/1748-9326/11/12/124009, 2016. a
Efron, B. and Gong, G.: A Leisurely Look at the Bootstrap, the Jackknife, and
Cross-Validation, The American Statistician, 37, 36, https://doi.org/10.2307/2685844, 1983. a
Emanuel, K. A., Neelin, J. D., and Bretherton, C. S.: On large‐scale
circulations in convecting atmospheres, Q. J. Roy. Meteor. Soc., 120, 1111–1143, https://doi.org/10.1002/qj.49712051902, 1994. 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
Eyring, V., Gillet, N., Achuta Rao, K., Barimalala, R., Barreiro Parrillo, M.,
Bellouin, N., Cassou, C., Durack, P., Kosaka, Y., McGregor, S., Min, S.,
Morgenstern, O., and Sun, Y.: Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (last access: 3 June 2022),
2021. a
Findell, K. L., Berg, A., Gentine, P., Krasting, J. P., Lintner, B. R.,
Malyshev, S., Santanello, J. A., and Shevliakova, E.: The impact of
anthropogenic land use and land cover change on regional climate extremes,
Nat. Commun., 8, 1–9, https://doi.org/10.1038/s41467-017-01038-w, 2017. a
Fischer, E. M. and Knutti, R.: Anthropogenic contribution to global occurrence
of heavy-precipitation and high-temperature extremes, Nat. Clim. Change,
5, 560–564, https://doi.org/10.1038/nclimate2617, 2015. a
Frame, D., Joshi, M., Hawkins, E., Harrington, L. J., and De Roiste, M.:
Population-based emergence of unfamiliar climates, Nat. Clim. Change,
7, 407–411, https://doi.org/10.1038/nclimate3297, 2017. a, b
Gilford, D.: climatecentral/gilford22_attframework: Release supporting the publication of “A multi-method framework for global real-time climate attribution” (v0.1), Zenodo [code], https://doi.org/10.5281/zenodo.6624712, 2022. a
Hansen, J., Johnson, D., Lacis, A., Lebedeff, S., Lee, P., Rind, D., and
Russell, G.: Climate impact of increasing atmospheric carbon dioxide,
Science, 213, 957–966, https://doi.org/10.1126/science.213.4511.957, 1981. a
Hansen, J., Fung, I., Lacis, A., Rind, D., Lebedeff, S., Ruedy, R., Russell,
G., and Stone, P.: Global climate changes as forecast by Goddard Institute
for Space Studies three-dimensional model, J. Geophys. Res., 93, 9341–9364, https://doi.org/10.1029/JD093iD08p09341, 1988. a
Hansen, J., Sato, M., and Ruedy, R.: Perception of climate change,
P. Natl. Acad. Sci. USA, 109, E2415–E2423,
https://doi.org/10.1073/pnas.1205276109, 2012. a
Hawkins, E. and Sutton, R.: Time of emergence of climate signals, Geophys. Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087, 2012. 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
Huber, M. and Knutti, R.: Anthropogenic and natural warming inferred from
changes in Earth's energy balance, Nat. Geosci., 5, 31–36,
https://doi.org/10.1038/ngeo1327, 2012. a
Jézéquel, A., Dépoues, V., Guillemot, H., Trolliet, M.,
Vanderlinden, J.-P., and Yiou, P.: Behind the veil of extreme event
attribution, Clim. Change, 149, 367–383, https://doi.org/10.1007/s10584-018-2252-9,
2018. a, b
Jones, G. S., Lockwood, M., and Stott, P. A.: What influence will future solar
activity changes over the 21st century have on projected global near-surface
temperature changes?, J. Geophys. Res.-Atmos., 117,
1–13, https://doi.org/10.1029/2011JD017013, 2012. a
Kaufman, D., McKay, N., Routson, C., Erb, M., Dätwyler, C., Sommer,
P. S., Heiri, O., and Davis, B.: Holocene global mean surface temperature, a multi-method reconstruction approach, Scientific Data, 7, 1–13,
https://doi.org/10.1038/s41597-020-0530-7, 2020. a
Kew, S. F., Philip, S. Y., Van Oldenborgh, G. J., Otto, F. E., Vautard, R.,
and Van Der Schrier, G.: 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, b
King, A. D. and Harrington, L. J.: The Inequality of Climate Change From 1.5
to 2 ∘C of Global Warming, Geophys. Res. Lett., 45, 5030–5033,
https://doi.org/10.1029/2018GL078430, 2018. a
Klein, C., Jackson, L. S., Parker, D. J., Marsham, J. H., Taylor, C. M.,
Rowell, D. P., Guichard, F., Vischel, T., Famien, A. M., and Diedhiou, A.:
Combining CMIP data with a regional convection-permitting model and
observations to project extreme rainfall under climate change, Environ. Res. Lett., 16, 104023, https://doi.org/10.1088/1748-9326/ac26f1, 2021. a
Knutson, T., Camargo, S. J., Chan, J. C., Emanuel, K., Ho, C. H., Kossin, J.,
Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical cyclones
and climate change assessment, B. Am. Meteorol. Soc., 100, 1987–2007, https://doi.org/10.1175/BAMS-D-18-0189.1, 2019. a
Lange, S.: ISIMIP2b Bias-Correction Code, Zenodo [code], https://doi.org/10.5281/zenodo.1069050, 2017. a
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019. a, b, c
Lewis, S. C. and King, A. D.: Evolution of mean, variance and extremes in 21st
century temperatures, Weather and Climate Extremes, 15, 1–10,
https://doi.org/10.1016/j.wace.2016.11.002, 2017. a
Mahlstein, I., Knutti, R., Solomon, S., and Portmann, R. W.: Early onset of
significant local warming in low latitude countries, Environ. Res.
Lett., 6, 034009, https://doi.org/10.1088/1748-9326/6/3/034009, 2011. a
Mahlstein, I., Hegerl, G., and Solomon, S.: Emerging local warming signals in
observational data, Geophys. Res. Lett., 39, L21711,
https://doi.org/10.1029/2012GL053952, 2012. a
Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J., Shukla,
P., Pirani, A. P., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S.,
Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock,
T., Tignor, M., and Waterfield, T.: Cambridge, United Kingdom and New York,
NY, USA, https://www.ipcc.ch/sr15/ (last access: 3 June 2022), 2018. a, b
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, https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (last access: 3 June 2022),
2021. a, b
Mitchell, D.: Climate attribution of heat mortality, Nat. Clim. Change,
11, 467–468, https://doi.org/10.1038/s41558-021-01049-y, 2021. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick,
R. E., Dunn, R. J., Osborn, T. J., Jones, P. D., and Simpson, I. R.: An
Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5
Data Set, J. Geophys. Res.-Atmos., 126, 1–28,
https://doi.org/10.1029/2019JD032361, 2021. a, b
Mueller, N. D., Butler, E. E., Mckinnon, K. A., Rhines, A., Tingley, M.,
Holbrook, N. M., and Huybers, P.: Cooling of US Midwest summer temperature
extremes from cropland intensification, Nat. Clim. Change, 6, 317–322,
https://doi.org/10.1038/nclimate2825, 2016a. a
Mueller, N. D., Butler, E. E., Mckinnon, K. A., Rhines, A., Tingley, M.,
Holbrook, N. M., and Huybers, P.: Cooling of US Midwest summer temperature
extremes from cropland intensification, Nat. Clim. Change, 6, 317–322,
https://doi.org/10.1038/nclimate2825, 2016b. a
Muller, R. A., Rohde, R., Jacobsen, R., Muller, E., and Wickham, C.: A New
Estimate of the Average Earth Surface Land Temperature Spanning 1753 to
2011, Geoinformatics and Geostatistics: An Overview, 01, 1–7,
https://doi.org/10.4172/2327-4581.1000101, 2013. a
Organization, W. M.: Guidelines on the Calculation of Climate Normals, Tech. Rep. 1203, World Meteorological Organization,
https://library.wmo.int/doc_num.php?explnum_id=4166 (last access: 3 June 2022), 2017. a
Otto, F. E.: Attribution of Weather and Climate Events, Annu. Rev. Env. Resour., 42, 627–646,
https://doi.org/10.1146/annurev-environ-102016-060847, 2017. a
Otto, F. E., Philip, S., Kew, S., Li, S., King, A., and Cullen, H.:
Attributing high-impact extreme events across timescales – a case study of four different types of events, Clim. Change, 149, 399–412,
https://doi.org/10.1007/s10584-018-2258-3, 2018. a, b, c, d
Otto, F. E., Harrington, L., Schmitt, K., Philip, S., Kew, S., van Oldenborgh,
G. J., Singh, R., Kimutai, J., and Wolski, P.: Challenges to understanding
extreme weather changes in lower income countries, B. Am.
Meteorol. Soc., 101, E1851–E1860, https://doi.org/10.1175/BAMS-D-19-0317.1,
2020. a, b, c
Perkins-Kirkpatrick, S. E., Stone, D. A., Mitchell, D. M., Rosier, S., King,
A. D., Lo, Y. T., Pastor-Paz, J., Frame, D., and Wehner, M.: On the
attribution of the impacts of extreme weather events to anthropogenic climate
change, Environ. Res. Lett., 17, 024009, https://doi.org/10.1088/1748-9326/ac44c8,
2022. a
Pfahl, S., O'Gorman, P. A., and Fischer, E. M.: Understanding the regional
pattern of projected future changes in extreme precipitation, Nat. Clim.
Change, 7, 423–427, https://doi.org/10.1038/nclimate3287, 2017. a, b
Philip, S., Kew, S. F., van Oldenborgh, G. J., Aalbers, E., Vautard, R., Otto,
F., Haustein, K., Habets, F., and Singh, R.: Validation of a rapid
attribution of the May/June 2016 flood-inducing precipitation in France to
climate change, J. Hydrometeorol., 19, 1881–1898,
https://doi.org/10.1175/JHM-D-18-0074.1, 2018a. a
Philip, S., Kew, S. F., van Oldenborgh, G. J., Otto, F., O'Keefe, S., Haustein,
K., King, A., Zegeye, A., Eshetu, Z., Hailemariam, K., Singh, R., Jjemba, E.,
Funk, C., and Cullen, H.: Attribution Analysis of the Ethiopian Drought of
2015, J. Climate, 31, 2465–2486, https://doi.org/10.1175/JCLI-D-17-0274.1,
2018b. a
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, c
Philip, S. Y., Kew, S. F., Oldenborgh, G. J. V., Yang, W., Vecchi, G. A.,
Anslow, F. S., Li, S., Seneviratne, S. I., Luu, L. N., Arrighi, J., Singh,
R., Aalst, V., Hauser, M., Schumacher, D. L., Marghidan, C. P., Ebi, K. L.,
Vautard, R., Tradowsky, J., Coumou, D., Lehner, F., Rodell, C., Stull, R.,
Howard, R., Gillett, N., and Otto, F. E. L.: Rapid attribution analysis of
the extraordinary heatwave on the Pacific Coast of the US and Canada June
2021, Tech. Rep. June, World Weather Attribution, 37 pp., https://www.worldweatherattribution.org/wp-content/uploads/NW-US-extreme-heat-2021-scientific-report-WWA.pdf (last access: 3 June 2022),
2021. a, b, c, d, e
Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and
Haerter, J.: Statistical Bias Correction of Global Simulated Daily
Precipitation and Temperature for the Application of Hydrological Models,
J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024,
2010. a
Rahmstorf, S. and Coumou, D.: Increase of extreme events in a warming world, P. Natl. Acad. Sci., 108, 17905–17909, https://doi.org/10.1073/pnas.1101766108, 2011. a
Ranasinghe, R., Ruane, A., Vautard, R., Arnell, N., Coppola, E., Crus, F. A.,
Dessai, S., Islam, A. S., Rahimi, M., Ruiz Carrascal, D., Sillman, J., Sylla,
M. B., Tebaldi, C., Wang, W., and Zaaboul, R.: Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1767–1926, https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (last access: 3 June 2022),
2021. a
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann,
G., Nakicenovic, N., and Rafaj, P.: RCP 8.5-A scenario of comparatively high
greenhouse gas emissions, Clim. Change, 109, 33–57,
https://doi.org/10.1007/s10584-011-0149-y, 2011. a
Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020. a
Rohde, R., Muller, R., Jacobsen, R., Perlmutter, S., and Mosher, S.: Berkeley
Earth Temperature Averaging Process, Geoinformatics and Geostatistics: An
Overview, 1, 2, https://doi.org/10.4172/2327-4581.1000103, 2013. a
Santer, B. D., Po-Chedley, S., Zelinka, M. D., Cvijanovic, I., Bonfils, C.,
Durack, P. J., Fu, Q., Kiehl, J., Mears, C., Painter, J., Pallotta, G.,
Solomon, S., Wentz, F. J., and Zou, C.-Z.: Human influence on the seasonal
cycle of tropospheric temperature, Science, 361, eaas8806,
https://doi.org/10.1126/science.aas8806, 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
Seneviratne, S., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A.,
Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satho,
M., Vicente-Serrano, S., Wehner, M., and Zhou, B.: Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 1513–1766, https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (last access: 3 June 2022),
2021. a, b, c
Seneviratne, S. I., Wilhelm, M., Stanelle, T., Van Den Hurk, B., Hagemann,
S., Berg, A., Cheruy, F., Higgins, M. E., Meier, A., Brovkin, V., Claussen,
M., Ducharne, A., Dufresne, J. L., Findell, K. L., Ghattas, J., Lawrence,
D. M., Malyshev, S., Rummukainen, M., and Smith, B.: Impact of soil
moisture-climate feedbacks on CMIP5 projections: First results from the
GLACE-CMIP5 experiment, Geophys. Res. Lett., 40, 5212–5217,
https://doi.org/10.1002/grl.50956, 2013. a
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R., and Wilby, R. L.:
Allowable CO2 emissions based on regional and impact-related climate
targets, Nature, 529, 477–483, https://doi.org/10.1038/nature16542, 2016. a, b
Shen, S. S., Gurung, A. B., Oh, H. S., Shu, T., and Easterling, D. R.: The
twentieth century contiguous US temperature changes indicated by daily data
and higher statistical moments, Clim. Change, 109, 287–317,
https://doi.org/10.1007/s10584-011-0033-9, 2011. a
Shepherd, T. G.: Atmospheric circulation as a source of uncertainty in climate
change projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/NGEO2253,
2014. a
Sherwood, S. C. and Huber, M.: An adaptability limit to climate change due to
heat stress, P. Natl. Acad. Sci. USA, 107, 9552–9555, https://doi.org/10.1073/pnas.0913352107, 2010. a
Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., and Bronaugh, D.:
Climate extremes indices in the CMIP5 multimodel ensemble: Part 2, Future
climate projections, J. Geophys. Res.-Atmos., 118,
2473–2493, https://doi.org/10.1002/jgrd.50188, 2013. a
Sippel, S., Otto, F. E., Flach, M., and Van Oldenborgh, G. J.: 11. The role
of anthropogenic warming in 2015 central European heat waves, B. Am. Meteorol. Soc., 97, S51–S56, https://doi.org/10.1175/BAMS-D-16-0150.1, 2016. a
Sippel, S., Meinshausen, N., Fischer, E. M., Székely, E., and Knutti, R.: Climate change now detectable from any single day of weather at global
scale, Nat. Clim. Change, 10, 35–41, https://doi.org/10.1038/s41558-019-0666-7,
2020. a, b
Sippel, S., Meinshausen, N., Székely, E., Fischer, E., Pendergrass, A. G.,
Lehner, F., and Knutti, R.: Robust detection of forced warming in the
presence of potentially large climate variability, Sci. Adv., 7,
eabh4429, https://doi.org/10.1126/sciadv.abh4429, 2021. a, b
Stone, D. A., Rosier, S. M., and Frame, D. J.: The question of life, the
universe and event attribution, Nat. Clim. Change, 11, 276–278,
https://doi.org/10.1038/s41558-021-01012-x, 2021. 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
Strauss, B. H., Orton, P. M., Bittermann, K., Buchanan, M. K., Gilford, D. M.,
Kopp, R. E., Kulp, S., Massey, C., de Moel, H., and Vinogradov, S.: Economic
damages from Hurricane Sandy attributable to sea level rise caused by
anthropogenic climate change, Nat. Commun., 12, 15,
https://doi.org/10.1038/s41467-021-22838-1, 2021. a, b, c
Sullivan, A. and White, D. D.: An assessment of public perceptions of climate
change risk in three western U.S. Cities, Weather Clim. Soc., 11, 449–463, https://doi.org/10.1175/WCAS-D-18-0068.1, 2019. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a, b
Tebaldi, C. and Wehner, M. F.: Benefits of mitigation for future heat extremes
under RCP4.5 compared to RCP8.5, Clim. Change, 146, 349–361,
https://doi.org/10.1007/s10584-016-1605-5, 2018. a
Thiery, W., Davin, E. L., Lawrence, D. M., Hirsch, A. L., Hauser, M., and
Seneviratne, S. I.: Present-day irrigation mitigates heat extremes, J. Geophys. Res., 122, 1403–1422, https://doi.org/10.1002/2016JD025740, 2017. a
Thiery, W., Visser, A. J., Fischer, E. M., Hauser, M., Hirsch, A. L., Lawrence,
D. M., Lejeune, Q., Davin, E. L., and Seneviratne, S. I.: Warming of hot
extremes alleviated by expanding irrigation, Nat. Commun., 11,
1–7, https://doi.org/10.1038/s41467-019-14075-4, 2020. a
Trenberth, K. E., Fasullo, J. T., and Shepherd, T. G.: Attribution of climate
extreme events, Nat. Clim. Change, 5, 725–730,
https://doi.org/10.1038/nclimate2657, 2015. a, b, c
van der Wiel, K., Kapnick, S. B., van Oldenborgh, G. J., Whan, K., Philip, S., Vecchi, G. A., Singh, R. K., Arrighi, J., and Cullen, H.: Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change, Hydrol. Earth Syst. Sci., 21, 897–921, https://doi.org/10.5194/hess-21-897-2017, 2017. a
Van Oldenborgh, G. J., Van Der Wiel, K., Sebastian, A., Singh, R., Arrighi,
J., Otto, F., Haustein, K., Li, S., Vecchi, G., and Cullen, H.: Attribution
of extreme rainfall from Hurricane Harvey, August 2017, Environ. Res. Lett., 12, 124009, https://doi.org/10.1088/1748-9326/aa9ef2, 2017. a, b, c
van Oldenborgh, G. J., Philip, S., Kew, S., van Weele, M., Uhe, P., Otto, F., Singh, R., Pai, I., Cullen, H., and AchutaRao, K.: Extreme heat in India and anthropogenic climate change, Nat. Hazards Earth Syst. Sci., 18, 365–381, https://doi.org/10.5194/nhess-18-365-2018, 2018. a
Vargas Zeppetello, L. R. and Battisti, D. S.: Projected Increases in Monthly
Midlatitude Summertime Temperature Variance Over Land Are Driven by Local
Thermodynamics, Geophys. Res. Lett., 47, e2020GL090197,
https://doi.org/10.1029/2020GL090197, 2020. a
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. N., Lavigne, E., Correa,
P. M., Ortega, N. V., Kan, H., Osorio, S., Kyselý, J., Urban, A., Orru,
H., Indermitte, E., Jaakkola, J. J. K., 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. 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
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, b
Wang, J., Guan, Y., Wu, L., Guan, X., Cai, W., Huang, J., Dong, W., and Zhang,
B.: Changing Lengths of the Four Seasons by Global Warming, Geophys. Res. Lett., 48, e2020GL091753,
https://doi.org/10.1029/2020GL091753, 2021. a
Wehner, M., Stone, D., Shiogama, H., Wolski, P., Ciavarella, A., Christidis,
N., and Krishnan, H.: Early 21st century anthropogenic changes in extremely
hot days as simulated by the C20C+ detection and attribution multi-model
ensemble, Weather and Climate Extremes, 20, 1–8,
https://doi.org/10.1016/j.wace.2018.03.001, 2018. a
Williams, I. N., Pierrehumbert, R. T., and Huber, M.: Global warming,
convective threshold and false thermostats, Geophys. Res. Lett.,
36, L21805, https://doi.org/10.1029/2009GL039849, 2009. a
Zscheischler, J., Westra, S., Van Den Hurk, B. J., Seneviratne, S. I., Ward,
P. J., Pitman, A., Aghakouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and
Zhang, X.: Future climate risk from compound events, Nat. Clim. Change,
8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018. 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
We developed a framework to produce global real-time estimates of how human-caused climate change affects the likelihood of daily weather events. A multi-method approach provides ensemble attribution estimates accompanied by confidence intervals, creating new opportunities for climate change communication. Methodological efficiency permits daily analysis using forecasts or observations. Applications with daily maximum temperature highlight the framework's capacity on daily and global scales.
We developed a framework to produce global real-time estimates of how human-caused climate...
