Articles | Volume 13, issue 2
https://doi.org/10.5194/esd-13-993-2022
© 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/esd-13-993-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2022
Research article |
| 14 Jun 2022
| 14 Jun 2022
A methodology for the spatiotemporal identification of compound hazards: wind and precipitation extremes in Great Britain (1979–2019)
Department of Geography, King's College London, London WC2B 4BG,
United Kingdom
European Commission, Joint Research Centre, Ispra, Italy
Bruce D. Malamud
Department of Geography, King's College London, London WC2B 4BG,
United Kingdom
Amélie Joly-Laugel
EDF Energy R & D UK Centre, Croydon CR0 2AJ, United Kingdom
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Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, and Luc Feyen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-41, https://doi.org/10.5194/essd-2024-41, 2024
Revised manuscript accepted for ESSD
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This article presents a reanalysis of Europe's rivers streamflow for the period 1950–2020, using a state-of-the-art hydrological simulation framework. The dataset, called HERA (Hydrological European ReAnalysis), uses detailed information about the landscape, climate, and human activities to estimate river flow. HERA can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources, flood and drought risks.
Tiberiu-Eugen Antofie, Stefano Luoni, Alois Tilloy, Andrea Sibilia, Sandro Salari, Gustav Eklund, Davide Rodomonti, Christos Bountzouklis, and Christina Corbane
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-220, https://doi.org/10.5194/nhess-2023-220, 2024
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This is the first study that uses spatial patterns (clusters/hot-spots) and meta-analysis in order to identify the regions at European level at risk to multi-hazards. The findings point out the socio-economic dimension as determinant factor for the risk potential to multi-hazard. The outcome provides valuable input for the Disaster Risk Management policy support and will assist national authorities on the implementation of a multi-hazard approach in the National Risk Assessments preparation.
Aloïs Tilloy, Bruce D. Malamud, Hugo Winter, and Amélie Joly-Laugel
Nat. Hazards Earth Syst. Sci., 20, 2091–2117, https://doi.org/10.5194/nhess-20-2091-2020, https://doi.org/10.5194/nhess-20-2091-2020, 2020
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Estimating risks induced by interacting natural hazards remains a challenge for practitioners. An approach to tackle this challenge is to use multivariate statistical models. Here we evaluate the efficacy of six models. The models are compared against synthetic data which are comparable to time series of environmental variables. We find which models are more appropriate to estimate relations between hazards in a range of cases. We highlight the benefits of this approach with two examples.
Lakshmi S. Gopal, Rekha Prabha, Hemalatha Thirugnanam, Maneesha Vinodini Ramesh, and Bruce D. Malamud
EGUsphere, https://doi.org/10.5194/egusphere-2024-1536, https://doi.org/10.5194/egusphere-2024-1536, 2024
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We critically reviewed 250 articles from 2010 to 2023, analysed how social media is used to manage disasters, and developed the Social Media Literature Database. We summarise the methods used for data collection and filtering. Key findings include the widespread use of the latest technologies to handle data, proficiency in spatiotemporal analysis, and gaps in community interaction and resource identification. We also propose best practices for using social media to enhance disaster management.
Harriet E. Thompson, Joel C. Gill, Robert Šakić Trogrlić, Faith E. Taylor, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-101, https://doi.org/10.5194/nhess-2024-101, 2024
Revised manuscript accepted for NHESS
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We describe a methodology to systematically gather evidence of the breadth of single natural hazards and their multi-hazard interrelationships in data-scarce urban settings. We apply this methodology to Kathmandu Valley, Nepal, where we find evidence of 21 single hazard types, and 83 multi-hazard interrelationships. This evidence is supplemented with multi-hazard scenarios developed by practitioner stakeholders engaged in disaster risk reduction research and practice in Kathmandu Valley.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, and Luc Feyen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-41, https://doi.org/10.5194/essd-2024-41, 2024
Revised manuscript accepted for ESSD
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This article presents a reanalysis of Europe's rivers streamflow for the period 1950–2020, using a state-of-the-art hydrological simulation framework. The dataset, called HERA (Hydrological European ReAnalysis), uses detailed information about the landscape, climate, and human activities to estimate river flow. HERA can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources, flood and drought risks.
Tiberiu-Eugen Antofie, Stefano Luoni, Alois Tilloy, Andrea Sibilia, Sandro Salari, Gustav Eklund, Davide Rodomonti, Christos Bountzouklis, and Christina Corbane
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-220, https://doi.org/10.5194/nhess-2023-220, 2024
Revised manuscript accepted for NHESS
Short summary
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This is the first study that uses spatial patterns (clusters/hot-spots) and meta-analysis in order to identify the regions at European level at risk to multi-hazards. The findings point out the socio-economic dimension as determinant factor for the risk potential to multi-hazard. The outcome provides valuable input for the Disaster Risk Management policy support and will assist national authorities on the implementation of a multi-hazard approach in the National Risk Assessments preparation.
Robert Šakić Trogrlić, Amy Donovan, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci., 22, 2771–2790, https://doi.org/10.5194/nhess-22-2771-2022, https://doi.org/10.5194/nhess-22-2771-2022, 2022
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Here we present survey responses of 350 natural hazard community members to key challenges in natural hazards research and step changes to achieve the Sustainable Development Goals. Challenges identified range from technical (e.g. model development, early warning) to governance (e.g. co-production with community members). Step changes needed are equally broad; however, the majority of answers showed a need for wider stakeholder engagement, increased risk management and interdisciplinary work.
Faith E. Taylor, Paolo Tarolli, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci., 20, 2585–2590, https://doi.org/10.5194/nhess-20-2585-2020, https://doi.org/10.5194/nhess-20-2585-2020, 2020
Aloïs Tilloy, Bruce D. Malamud, Hugo Winter, and Amélie Joly-Laugel
Nat. Hazards Earth Syst. Sci., 20, 2091–2117, https://doi.org/10.5194/nhess-20-2091-2020, https://doi.org/10.5194/nhess-20-2091-2020, 2020
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Estimating risks induced by interacting natural hazards remains a challenge for practitioners. An approach to tackle this challenge is to use multivariate statistical models. Here we evaluate the efficacy of six models. The models are compared against synthetic data which are comparable to time series of environmental variables. We find which models are more appropriate to estimate relations between hazards in a range of cases. We highlight the benefits of this approach with two examples.
Joel C. Gill, Bruce D. Malamud, Edy Manolo Barillas, and Alex Guerra Noriega
Nat. Hazards Earth Syst. Sci., 20, 149–180, https://doi.org/10.5194/nhess-20-149-2020, https://doi.org/10.5194/nhess-20-149-2020, 2020
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This paper describes a replicable approach for characterising interactions between natural hazards. Guatemala is exposed to multiple natural hazards, which do not always occur independently. There can be interactions between natural hazards. For example, one hazard may trigger multiple secondary hazards, which can subsequently trigger further hazards. Here we use diverse evidence of such interactions to construct matrices of hazard interactions in Guatemala at national and sub-national scales.
Annette Witt, Bruce D. Malamud, Clara Mangili, and Achim Brauer
Hydrol. Earth Syst. Sci., 21, 5547–5581, https://doi.org/10.5194/hess-21-5547-2017, https://doi.org/10.5194/hess-21-5547-2017, 2017
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Here we present a unique 9.5 m palaeo-lacustrine record of 771 palaeofloods which occurred over a period of 10 000 years in the Piànico–Sèllere basin (southern Alps) during an interglacial period in the Pleistocene (sometime between 400 000 and 800 000 years ago). We analyse the palaeoflood series correlation, clustering, and cyclicity properties, finding a long-range cyclicity with a period of about 2030 years superimposed onto a fractional noise.
Bruce D. Malamud, Donald L. Turcotte, and Harold E. Brooks
Nat. Hazards Earth Syst. Sci., 16, 2823–2834, https://doi.org/10.5194/nhess-16-2823-2016, https://doi.org/10.5194/nhess-16-2823-2016, 2016
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We introduce a novel method for the spatial–temporal cluster analysis of severe tornado touchdowns that are part of tornado outbreaks. Tornado outbreaks, groups of tornadoes occurring close to each other in time and space, constitute a severe hazard that has few quantitative measures. Our new approach, which we illustrate using three USA severe tornado outbreaks and models, differentiates between types of tornado outbreaks and, within outbreaks, identifies clusters in both time and space.
Joel C. Gill and Bruce D. Malamud
Earth Syst. Dynam., 7, 659–679, https://doi.org/10.5194/esd-7-659-2016, https://doi.org/10.5194/esd-7-659-2016, 2016
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Understanding interactions between hazards and other processes can help us to better understand the complex environment in which disasters occur. This enhanced understanding may help us to better manage hazards and reduce the risk of disasters occurring. Interactions (e.g. one hazard triggering another hazard) are noted between (i) natural hazards, such as earthquakes; (ii) human activity, such as groundwater abstraction; and (iii) technological hazards/disasters, such as building collapse.
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The sensitivity of the El Niño–Southern Oscillation to volcanic aerosol spatial distribution in the MPI Grand Ensemble
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The 18.6-year lunar nodal cycle arises from variations in the angle of the Moon's orbital plane and affects ocean tides. In this work we use a climate model to examine the effect of this cycle on the ocean, surface, and atmosphere. The timing of anomalies is consistent with the so-called slowdown in global warming and has implications for when global temperatures will exceed 1.5 ℃ above pre-industrial levels. Regional anomalies have implications for seasonal climate areas such as Europe.
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Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
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This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
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In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
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Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Han Qiu, Dalei Hao, Yelu Zeng, Xuesong Zhang, and Min Chen
Earth Syst. Dynam., 14, 1–16, https://doi.org/10.5194/esd-14-1-2023, https://doi.org/10.5194/esd-14-1-2023, 2023
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The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the global and the northern-high-latitude (NHL) ecosystems will continue to have positive net ecosystem production (NEP) in the next few decades under four global change scenarios but with large uncertainties. NHL ecosystems will experience faster climate warming but steadily contribute a small fraction of the global NEP. However, the relative uncertainty of NHL NEP is much larger than the global values.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
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Equilibrium climate sensitivity (ECS) is a measure of how much long-term warming should be expected in response to a change in greenhouse gas concentrations. It is generally calculated in climate models by extrapolating global average temperatures to a point of where the planet is no longer a net absorber of energy. Here we show that some climate models experience energy leaks which change as the planet warms, undermining the standard approach and biasing some existing model estimates of ECS.
Jun Wang, John C. Moore, Liyun Zhao, Chao Yue, and Zhenhua Di
Earth Syst. Dynam., 13, 1625–1640, https://doi.org/10.5194/esd-13-1625-2022, https://doi.org/10.5194/esd-13-1625-2022, 2022
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We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the greater Beijing region containing over 50 million people. Climate models have too coarse resolutions to resolve regional variations well, so we compare two workarounds for this – an expensive physical model and a cheaper statistical method. The statistical method generally gives a reasonable representation of climate and has limited resolution and a different seasonality from the physical model.
Nidheesh Gangadharan, Hugues Goosse, David Parkes, Heiko Goelzer, Fabien Maussion, and Ben Marzeion
Earth Syst. Dynam., 13, 1417–1435, https://doi.org/10.5194/esd-13-1417-2022, https://doi.org/10.5194/esd-13-1417-2022, 2022
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We describe the contributions of ocean thermal expansion and land-ice melting (ice sheets and glaciers) to global-mean sea-level (GMSL) changes in the Common Era. The mass contributions are the major sources of GMSL changes in the pre-industrial Common Era and glaciers are the largest contributor. The paper also describes the current state of climate modelling, uncertainties and knowledge gaps along with the potential implications of the past variabilities in the contemporary sea-level rise.
Changgui Lin, Erik Kjellström, Renate Anna Irma Wilcke, and Deliang Chen
Earth Syst. Dynam., 13, 1197–1214, https://doi.org/10.5194/esd-13-1197-2022, https://doi.org/10.5194/esd-13-1197-2022, 2022
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This study endorses RCMs' added value on the driving GCMs in representing observed heat wave magnitudes. The future increase of heat wave magnitudes projected by GCMs is attenuated when downscaled by RCMs. Within the downscaling, uncertainties can be attributed almost equally to choice of RCMs and to the driving data associated with different GCMs. Uncertainties of GCMs in simulating heat wave magnitudes are transformed by RCMs in a complex manner rather than simply inherited.
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 13, 1157–1165, https://doi.org/10.5194/esd-13-1157-2022, https://doi.org/10.5194/esd-13-1157-2022, 2022
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The Madden–Julian Oscillation (MJO) has important socioeconomic impacts due to its influence on both tropical and extratropical weather extremes. In this study, we use machine learning (ML) to correct the predictions of the weather model holding the best performance, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
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We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
Amber Boot, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 13, 1041–1058, https://doi.org/10.5194/esd-13-1041-2022, https://doi.org/10.5194/esd-13-1041-2022, 2022
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Atmospheric pCO2 of the past shows large variability on different timescales. We focus on the effect of the strength of Atlantic Meridional Overturning Circulation (AMOC) on this variability and on the AMOC–pCO2 relationship. We find that climatic boundary conditions and the representation of biology in our model are most important for this relationship. Under certain conditions, we find internal oscillations, which can be relevant for atmospheric pCO2 variability during glacial cycles.
Shruti Nath, Quentin Lejeune, Lea Beusch, Sonia I. Seneviratne, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, https://doi.org/10.5194/esd-13-851-2022, 2022
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Uncertainty within climate model projections on inter-annual timescales is largely affected by natural climate variability. Emulators are valuable tools for approximating climate model runs, allowing for easy exploration of such uncertainty spaces. This study takes a first step at building a spatially resolved, monthly temperature emulator that takes local yearly temperatures as the sole input, thus providing monthly temperature distributions which are of critical value to impact assessments.
Linh N. Luu, Robert Vautard, Pascal Yiou, and Jean-Michel Soubeyroux
Earth Syst. Dynam., 13, 687–702, https://doi.org/10.5194/esd-13-687-2022, https://doi.org/10.5194/esd-13-687-2022, 2022
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This study downscales climate information from EURO-CORDEX (approx. 12 km) output to a higher horizontal resolution (approx. 3 km) for the south of France. We also propose a matrix of different indices to evaluate the high-resolution precipitation output. We find that a higher resolution reproduces more realistic extreme precipitation events at both daily and sub-daily timescales. Our results and approach are promising to apply to other Mediterranean regions and climate impact studies.
Aine M. Gormley-Gallagher, Sebastian Sterl, Annette L. Hirsch, Sonia I. Seneviratne, Edouard L. Davin, and Wim Thiery
Earth Syst. Dynam., 13, 419–438, https://doi.org/10.5194/esd-13-419-2022, https://doi.org/10.5194/esd-13-419-2022, 2022
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Our results show that agricultural management can impact the local climate and highlight the need to evaluate land management in climate models. We use regression analysis on climate simulations and observations to assess irrigation and conservation agriculture impacts on warming trends. This allowed us to distinguish between the effects of land management and large-scale climate forcings such as rising CO2 concentrations and thus gain insight into the impacts under different climate regimes.
Koffi Worou, Hugues Goosse, Thierry Fichefet, and Fred Kucharski
Earth Syst. Dynam., 13, 231–249, https://doi.org/10.5194/esd-13-231-2022, https://doi.org/10.5194/esd-13-231-2022, 2022
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Over the Guinea Coast, the increased rainfall associated with warm phases of the Atlantic Niño is reasonably well simulated by 24 climate models out of 31, for the present-day conditions. In a warmer climate, general circulation models project a gradual decrease with time of the rainfall magnitude associated with the Atlantic Niño for the 2015–2039, 2040–2069 and 2070–2099 periods. There is a higher confidence in these changes over the equatorial Atlantic than over the Guinea Coast.
Roman Procyk, Shaun Lovejoy, and Raphael Hébert
Earth Syst. Dynam., 13, 81–107, https://doi.org/10.5194/esd-13-81-2022, https://doi.org/10.5194/esd-13-81-2022, 2022
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This paper presents a new class of energy balance model that accounts for the long memory within the Earth's energy storage. The model is calibrated on instrumental temperature records and the historical energy budget of the Earth using an error model predicted by the model itself. Our equilibrium climate sensitivity and future temperature projection estimates are consistent with those estimated by complex climate models.
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Kathrin Naegeli, and Stefan Wunderle
Earth Syst. Dynam., 12, 1061–1098, https://doi.org/10.5194/esd-12-1061-2021, https://doi.org/10.5194/esd-12-1061-2021, 2021
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Climate change over High Mountain Asia is investigated with CMIP6 climate models. A general cold bias is found in this area, often related to a snow cover overestimation in the models. Ensemble experiments generally encompass the past observed trends, suggesting that even biased models can reproduce the trends. Depending on the future scenario, a warming from 1.9 to 6.5 °C, associated with a snow cover decrease and precipitation increase, is expected at the end of the 21st century.
Benjamin Ward, Francesco S. R. Pausata, and Nicola Maher
Earth Syst. Dynam., 12, 975–996, https://doi.org/10.5194/esd-12-975-2021, https://doi.org/10.5194/esd-12-975-2021, 2021
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Using the largest ensemble of a climate model currently available, the Max Planck Institute Grand Ensemble (MPI-GE), we investigated the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. By selecting three eruptions with different aerosol distributions, we found that the shift of the Intertropical Convergence Zone (ITCZ) is the main driver of the ENSO response, while other mechanisms commonly invoked seem less important in our model.
Matthias Gröger, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, H. E. Markus Meier, Paul A. Miller, Anna Rutgersson, and Lichuan Wu
Earth Syst. Dynam., 12, 939–973, https://doi.org/10.5194/esd-12-939-2021, https://doi.org/10.5194/esd-12-939-2021, 2021
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Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Halima Usman, Thomas A. M. Pugh, Anders Ahlström, and Sofia Baig
Earth Syst. Dynam., 12, 857–870, https://doi.org/10.5194/esd-12-857-2021, https://doi.org/10.5194/esd-12-857-2021, 2021
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The study assesses the impacts of climate change on forest productivity in the Hindu Kush Himalayan region. LPJ-GUESS was simulated from 1851 to 2100. In first approach, the model was compared with observational estimates. The comparison showed a moderate agreement. In the second approach, the model was assessed for the temporal and spatial trends of net biome productivity and its components along with carbon pool. Increases in both variables were predicted in 2100.
Kerstin Hartung, Ana Bastos, Louise Chini, Raphael Ganzenmüller, Felix Havermann, George C. Hurtt, Tammas Loughran, Julia E. M. S. Nabel, Tobias Nützel, Wolfgang A. Obermeier, and Julia Pongratz
Earth Syst. Dynam., 12, 763–782, https://doi.org/10.5194/esd-12-763-2021, https://doi.org/10.5194/esd-12-763-2021, 2021
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In this study, we model the relative importance of several contributors to the land-use and land-cover change (LULCC) flux based on a LULCC dataset including uncertainty estimates. The uncertainty of LULCC is as relevant as applying wood harvest and gross transitions for the cumulative LULCC flux over the industrial period. However, LULCC uncertainty matters less than the other two factors for the LULCC flux in 2014; historical LULCC uncertainty is negligible for estimates of future scenarios.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, and Andrea Alessandri
Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, https://doi.org/10.5194/esd-12-725-2021, 2021
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The roots of vegetation largely control the Earth's water cycle by transporting water from the subsurface to the atmosphere but are not adequately represented in land surface models, causing uncertainties in modeled water fluxes. We replaced the root parameters in an existing model with more realistic ones that account for a climate control on root development and found improved timing of modeled river discharge. Further extension of our approach could improve modeled water fluxes globally.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
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Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Francisco José Cuesta-Valero, Almudena García-García, Hugo Beltrami, and Joel Finnis
Earth Syst. Dynam., 12, 581–600, https://doi.org/10.5194/esd-12-581-2021, https://doi.org/10.5194/esd-12-581-2021, 2021
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The current radiative imbalance at the top of the atmosphere is increasing the heat stored in the oceans, atmosphere, continental subsurface and cryosphere, with consequences for societies and ecosystems (e.g. sea level rise). We performed the first assessment of the ability of global climate models to represent such heat storage in the climate subsystems. Models are able to reproduce the observed atmosphere heat content, with biases in the simulation of heat content in the rest of components.
Francesco Piccioni, Céline Casenave, Bruno Jacques Lemaire, Patrick Le Moigne, Philippe Dubois, and Brigitte Vinçon-Leite
Earth Syst. Dynam., 12, 439–456, https://doi.org/10.5194/esd-12-439-2021, https://doi.org/10.5194/esd-12-439-2021, 2021
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Small lakes are ecosystems highly impacted by climate change. Here, the thermal regime of a small, shallow lake over the past six decades was reconstructed via 3D modelling. Significant changes were found: strong water warming in spring and summer (0.7 °C/decade) as well as increased stratification and thermal energy for cyanobacteria growth, especially in spring. The strong spatial patterns detected for stratification might create local conditions particularly favourable to cyanobacteria bloom.
Pablo Ortega, Jon I. Robson, Matthew Menary, Rowan T. Sutton, Adam Blaker, Agathe Germe, Jöel J.-M. Hirschi, Bablu Sinha, Leon Hermanson, and Stephen Yeager
Earth Syst. Dynam., 12, 419–438, https://doi.org/10.5194/esd-12-419-2021, https://doi.org/10.5194/esd-12-419-2021, 2021
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Deep Labrador Sea densities are receiving increasing attention because of their link to many of the processes that govern decadal climate oscillations in the North Atlantic and their potential use as a precursor of those changes. This article explores those links and how they are represented in global climate models, documenting the main differences across models. Models are finally compared with observational products to identify the ones that reproduce the links more realistically.
Calum Brown, Ian Holman, and Mark Rounsevell
Earth Syst. Dynam., 12, 211–231, https://doi.org/10.5194/esd-12-211-2021, https://doi.org/10.5194/esd-12-211-2021, 2021
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The variety of human and natural processes in the land system can be modelled in many different ways. However, little is known about how and why basic model assumptions affect model results. We compared two models that represent land use in completely distinct ways and found several results that differed greatly. We identify the main assumptions that caused these differences and therefore key issues that need to be addressed for more robust model development.
Johannes Vogel, Pauline Rivoire, Cristina Deidda, Leila Rahimi, Christoph A. Sauter, Elisabeth Tschumi, Karin van der Wiel, Tianyi Zhang, and Jakob Zscheischler
Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, https://doi.org/10.5194/esd-12-151-2021, 2021
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We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on LASSO regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
Peter Pfleiderer, Aglaé Jézéquel, Juliette Legrand, Natacha Legrix, Iason Markantonis, Edoardo Vignotto, and Pascal Yiou
Earth Syst. Dynam., 12, 103–120, https://doi.org/10.5194/esd-12-103-2021, https://doi.org/10.5194/esd-12-103-2021, 2021
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In 2016, northern France experienced an unprecedented wheat crop loss. This crop loss was likely due to an extremely warm December 2015 and abnormally high precipitation during the following spring season. Using stochastic weather generators we investigate how severe the metrological conditions leading to the crop loss could be in current climate conditions. We find that December temperatures were close to the plausible maximum but that considerably wetter springs would be possible.
Jelle van den Berk, Sybren Drijfhout, and Wilco Hazeleger
Earth Syst. Dynam., 12, 69–81, https://doi.org/10.5194/esd-12-69-2021, https://doi.org/10.5194/esd-12-69-2021, 2021
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A collapse of the Atlantic Meridional Overturning Circulation can be described by six parameters and Langevin dynamics. These parameters can be determined from collapses seen in climate models of intermediate complexity. With this parameterisation, it might be possible to estimate how much fresh water is needed to observe a collapse in more complicated models and reality.
Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, and Christoph C. Raible
Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, https://doi.org/10.5194/esd-12-1-2021, 2021
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Compound extremes such as heavy precipitation and extreme winds can lead to large damage. To date it is unclear how well climate models represent such compound extremes. Here we present a new measure to assess differences in the dependence structure of bivariate extremes. This measure is applied to assess differences in the dependence of compound precipitation and wind extremes between three model simulations and one reanalysis dataset in a domain in central Europe.
Christian B. Rodehacke, Madlene Pfeiffer, Tido Semmler, Özgür Gurses, and Thomas Kleiner
Earth Syst. Dynam., 11, 1153–1194, https://doi.org/10.5194/esd-11-1153-2020, https://doi.org/10.5194/esd-11-1153-2020, 2020
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In the warmer future, Antarctica's ice sheet will lose more ice due to enhanced iceberg calving and a warming ocean that melts more floating ice from below. However, the hydrological cycle is also stronger in a warmer world. Hence, more snowfall will precipitate on Antarctica and may balance the amplified ice loss. We have used future climate scenarios from various global climate models to perform numerous ice sheet simulations to show that precipitation may counteract mass loss.
Renate Anna Irma Wilcke, Erik Kjellström, Changgui Lin, Daniela Matei, Anders Moberg, and Evangelos Tyrlis
Earth Syst. Dynam., 11, 1107–1121, https://doi.org/10.5194/esd-11-1107-2020, https://doi.org/10.5194/esd-11-1107-2020, 2020
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Two long-lasting high-pressure systems in summer 2018 led to heat waves over Scandinavia and an extended summer period with devastating impacts on both agriculture and human life. Using five climate model ensembles, the unique 263-year Stockholm temperature time series and a composite 150-year time series for the whole of Sweden, we found that anthropogenic climate change has strongly increased the probability of a warm summer, such as the one observed in 2018, occurring in Sweden.
Jeemijn Scheen and Thomas F. Stocker
Earth Syst. Dynam., 11, 925–951, https://doi.org/10.5194/esd-11-925-2020, https://doi.org/10.5194/esd-11-925-2020, 2020
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Variability of sea surface temperatures (SST) in 1200–2000 CE is quite well-known, but the history of deep ocean temperatures is not. Forcing an ocean model with these SSTs, we simulate temperatures in the ocean interior. The circulation changes alter the amplitude and timing of deep ocean temperature fluctuations below 2 km depth, e.g. delaying the atmospheric signal by ~ 200 years in the deep Atlantic. Thus ocean circulation changes are shown to be as important as SST changes at these depths.
Sebastian Milinski, Nicola Maher, and Dirk Olonscheck
Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, https://doi.org/10.5194/esd-11-885-2020, 2020
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Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system, but there is no established method to determine the required ensemble size for a given problem. We propose a new framework that can be used to estimate the required ensemble size from a model's control run or an existing large ensemble.
Yu Huang, Lichao Yang, and Zuntao Fu
Earth Syst. Dynam., 11, 835–853, https://doi.org/10.5194/esd-11-835-2020, https://doi.org/10.5194/esd-11-835-2020, 2020
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We investigate the applicability of machine learning (ML) on time series reconstruction and find that the dynamical coupling relation and nonlinear causality are crucial for the application of ML. Our results could provide insights into causality and ML approaches for paleoclimate reconstruction, parameterization schemes, and prediction in climate studies.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti
Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, https://doi.org/10.5194/esd-11-807-2020, 2020
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Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
James D. Annan, Julia C. Hargreaves, Thorsten Mauritsen, and Bjorn Stevens
Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, https://doi.org/10.5194/esd-11-709-2020, 2020
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In this paper we explore the potential of variability for constraining the equilibrium response of the climate system to external forcing. We show that the constraint is inherently skewed, with a long tail to high sensitivity, and that while the variability may contain some useful information, it is unlikely to generate a tight constraint.
Andrea Böhnisch, Ralf Ludwig, and Martin Leduc
Earth Syst. Dynam., 11, 617–640, https://doi.org/10.5194/esd-11-617-2020, https://doi.org/10.5194/esd-11-617-2020, 2020
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North Atlantic air pressure variations influencing European climate variables are simulated in coarse-resolution global climate models (GCMs). As single-model runs do not sufficiently describe variations of their patterns, several model runs with slightly diverging initial conditions are analyzed. The study shows that GCM and regional climate model (RCM) patterns vary in a similar range over the same domain, while RCMs add consistent fine-scale information due to their higher spatial resolution.
György Károlyi, Rudolf Dániel Prokaj, István Scheuring, and Tamás Tél
Earth Syst. Dynam., 11, 603–615, https://doi.org/10.5194/esd-11-603-2020, https://doi.org/10.5194/esd-11-603-2020, 2020
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We construct a conceptual model to understand the interplay between the atmosphere and the ocean biosphere in a climate change framework, including couplings between extraction of carbon dioxide by phytoplankton and climate change, temperature and carrying capacity of phytoplankton, and wind energy and phytoplankton production. We find that sufficiently strong mixing can result in decaying global phytoplankton content.
Kira Rehfeld, Raphaël Hébert, Juan M. Lora, Marcus Lofverstrom, and Chris M. Brierley
Earth Syst. Dynam., 11, 447–468, https://doi.org/10.5194/esd-11-447-2020, https://doi.org/10.5194/esd-11-447-2020, 2020
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Under continued anthropogenic greenhouse gas emissions, it is likely that global mean surface temperature will continue to increase. Little is known about changes in climate variability. We analyze surface climate variability and compare it to mean change in colder- and warmer-than-present climate model simulations. In most locations, but not on subtropical land, simulated temperature variability up to decadal timescales decreases with mean temperature, and precipitation variability increases.
Eirik Myrvoll-Nilsen, Sigrunn Holbek Sørbye, Hege-Beate Fredriksen, Håvard Rue, and Martin Rypdal
Earth Syst. Dynam., 11, 329–345, https://doi.org/10.5194/esd-11-329-2020, https://doi.org/10.5194/esd-11-329-2020, 2020
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This paper presents efficient Bayesian methods for linear response models of global mean surface temperature that take into account long-range dependence. We apply the methods to the instrumental temperature record and historical model runs in the CMIP5 ensemble to provide estimates of the transient climate response and temperature projections under the Representative Concentration Pathways.
Lea Beusch, Lukas Gudmundsson, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, https://doi.org/10.5194/esd-11-139-2020, 2020
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Earth system models (ESMs) are invaluable to study the climate system but expensive to run. Here, we present a statistical tool which emulates ESMs at a negligible computational cost by creating stochastic realizations of yearly land temperature field time series. Thereby, 40 ESMs are considered, and for each ESM, a single simulation is required to train the tool. The resulting ESM-specific realizations closely resemble ESM simulations not employed during training at point to regional scales.
Yu Sun and Riccardo E. M. Riva
Earth Syst. Dynam., 11, 129–137, https://doi.org/10.5194/esd-11-129-2020, https://doi.org/10.5194/esd-11-129-2020, 2020
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The solid Earth is still deforming because of the effect of past ice sheets through glacial isostatic adjustment (GIA). Satellite gravity observations by the Gravity Recovery and Climate Experiment (GRACE) mission are sensitive to those signals but are superimposed on the redistribution effect of water masses by the hydrological cycle. We propose a method separating the two signals, providing new constraints for forward GIA models and estimating the global water cycle's patterns and magnitude.
Mareike Schuster, Jens Grieger, Andy Richling, Thomas Schartner, Sebastian Illing, Christopher Kadow, Wolfgang A. Müller, Holger Pohlmann, Stephan Pfahl, and Uwe Ulbrich
Earth Syst. Dynam., 10, 901–917, https://doi.org/10.5194/esd-10-901-2019, https://doi.org/10.5194/esd-10-901-2019, 2019
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Decadal climate predictions are valuable to society as they allow us to estimate climate conditions several years in advance. We analyze the latest version of the German MiKlip prediction system (https://www.fona-miklip.de) and assess the effect of the model resolution on the skill of the system. The increase in the resolution of the system reduces the bias and significantly improves the forecast skill for North Atlantic extratropical winter dynamics for lead times of two to five winters.
Calum Brown, Bumsuk Seo, and Mark Rounsevell
Earth Syst. Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, https://doi.org/10.5194/esd-10-809-2019, 2019
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Concerns are growing that human activity will lead to social and environmental breakdown, but it is hard to anticipate when and where such breakdowns might occur. We developed a new model of land management decisions in Europe to explore possible future changes and found that decision-making that takes into account social and environmental conditions can produce unexpected outcomes that include societal breakdown in challenging conditions.
Francine Schevenhoven, Frank Selten, Alberto Carrassi, and Noel Keenlyside
Earth Syst. Dynam., 10, 789–807, https://doi.org/10.5194/esd-10-789-2019, https://doi.org/10.5194/esd-10-789-2019, 2019
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Weather and climate predictions potentially improve by dynamically combining different models into a
supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
Cited articles
AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I.,
Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.:
Climate extremes and compound hazards in a warming world, Annu. Rev. Earth
Planet. Sci., 48, 519–548, https://doi.org/10.1146/annurev-earth-071719-055228, 2020.
Agrawal, K. P., Garg, S., Sharma, S., and Patel, P.: Development and
validation of OPTICS based spatio-temporal clustering technique, Inf. Sci.,
369, 388–401, https://doi.org/10.1016/j.ins.2016.06.048, 2016.
Angélil, O., Perkins-Kirkpatrick, S., Alexander, L. V., Stone, D., Donat, M. G., Wehner, M., Shiogama, H., Ciavarella, A., and Christidis, N.: Comparing regional precipitation and temperature extremes in climate model and reanalysis products, Weather Clim. Extrem., 13, 35–43,
https://doi.org/10.1016/j.wace.2016.07.001, 2016.
Ansari, M. Y., Ahmad, A., Khan, S. S., Bhushan, G., and Mainuddin:
Spatiotemporal clustering: a review, Artif. Intell. Rev., 53, 2381–2423,
https://doi.org/10.1007/s10462-019-09736-1, 2020.
Bach, B., Dragicevic, P., Archambault, D., Hurter, C., and Carpendale, S.: A
review of temporal data visualizations based on space-time cube operations, in Eurographics Conference on Visualization (EuroVis), June 2014, Swansea, Wales, UK, 23–41, https://doi.org/10.2312/eurovisstar.20141171, 2014.
Balch, J. K., Iglesias, V., Braswell, A. E., Rossi, M. W., Joseph, M. B.,
Mahood, A. L., Shrum, T. R., White, C. T., Scholl, V. M., McGuire, B., Karban, C., Buckland, M., and Travis, W. R.: Social-environmental extremes:
Rethinking extraordinary events as outcomes of interacting biophysical and
social systems, Earth's Future, 8, e2019EF001319,
https://doi.org/10.1029/2019EF001319, 2020.
Barton, Y., Giannakaki, P., von Waldow, H., Chevalier, C., Pfahl, S., and
Martius, O.: Clustering of regional-scale extreme precipitation events in
southern Switzerland, Mon. Weather Rev., 144, 347–369,
https://doi.org/10.1175/MWR-D-15-0205.1, 2016.
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and
Wood, E. F.: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Sci. Data, 5, 1–12, https://doi.org/10.1038/sdata.2018.214, 2018.
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A.
I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of
26 precipitation datasets using Stage-IV gauge-radar data for the CONUS,
Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019.
Birant, D. and Kut, A.: ST-DBSCAN: An algorithm for clustering spatial-temporal data, Data Knowl. Eng., 60, 208–221, https://doi.org/10.1016/j.datak.2006.01.013, 2007.
Blenkinsop, S., Lewis, E., Chan, S. C., and Fowler, H. J.: Quality-control of
an hourly rainfall dataset and climatology of extremes for the UK, Int. J.
Climatol., 37, 722–740, https://doi.org/10.1002/joc.4735, 2017.
Blöschl, G. and Sivapalan, M.: Scale issues in hydrological modelling: A
review, Hydrol. Process., 9, 251–290, https://doi.org/10.1002/hyp.3360090305, 1995.
Brakenridge, G. R.: Global Active Archive of Large Flood Events. Dartmouth
Flood Observatory, University of Colorado, USA,
http://floodobservatory.colorado.edu/Archives/, last access: 29 June 2021.
Brönnimann, S., Allan, R., Atkinson, C., Buizza, R., Bulygina, O., Dahlgren, P., Dee, D., Dunn, R., Gomes, P., John, V. O., Jourdain, S., Haimberger, L., Hersbsbach, H., Kennedy, J., Poli, P., Pulliainen, J., Rayner, N., Saunders, R., Schulz, J., Sterin, A., Stickler, A., Titchner, H., Valente, M. A., Ventura, C., and Wilkinson, C.: Observations for reanalyses, B. Am. Meteorol. Soc., 99, 1851–1866, https://doi.org/10.1175/BAMS-D-17-0229.1, 2018.
Catto, J. L. and Dowdy, A.: Understanding compound hazards from a weather
system perspective, Weather Clim. Extrem., 32, 100313,
https://doi.org/10.1016/j.wace.2021.100313, 2021.
Chazette, P., Flamant, C., Raut, J., Totems, J., and Shang, X.: Tropical
moisture enriched storm tracks over the Mediterranean and their link with
intense rainfall in the Cevennes-Vivarais area during HyMeX, Q. J. Roy.
Meteorol. Soc., 142, 320–334, https://doi.org/10.1002/qj.2674, 2016.
Copernicus Climate Data Store: ERA5-Land hourly data, Copernicus Climate Data Store [data set], https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land (last access: 18 March 2022), 2020.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones,
P. D.: An Ensemble Version of the E-OBS temperature and precipitation data
sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
CRED – Centre for Research on the Epidemiology of Disasters: EM-DAT – The
Emergency Events Database, Univ. Catholique Louvain, https://www.emdat.be/, last access: 22 December 2020.
Cressie, N. A. C.: Statistics for Spatial Data, John Wiley & Sons, Inc.,
New York, https://doi.org/10.1002/9781119115151, 1993.
De Angeli, S., Malamud, B. D., Rossi, L., Taylor, F. E., Trasforini, E., and
Rudari, R.: A multi-hazard framework for spatial-temporal impact analysis,
Int. J. Disast. Risk Reduct., 73, 102829, https://doi.org/10.1016/j.ijdrr.2022.102829, 2022.
Deroche, M. S., Choux, M., Codron, F., and Yiou, P.: Three variables are
better than one: Detection of european winter windstorms causing important
damages, Nat. Hazards Earth Syst. Sci., 14, 981–993,
https://doi.org/10.5194/nhess-14-981-2014, 2014.
Diederen, D., Liu, Y., Gouldby, B., Diermanse, F., and Vorogushyn, S.:
Stochastic generation of spatially coherent river discharge peaks for large-scale, event-based flood risk assessment, Nat. Hazards Earth Syst. Sci., 19, 1041–1053, https://doi.org/10.5194/nhess-19-1041-2019, 2019.
Donat, M. G., Sillmann, J., Wild, S., Alexander, L. V., Lippmann, T., and
Zwiers, F. W.: Consistency of temperature and precipitation extremes across
various global gridded in situ and reanalysis datasets, J. Climate, 27,
5019–5035, https://doi.org/10.1175/JCLI-D-13-00405.1, 2014.
Dowdy, A. J. and Catto, J. L.: Extreme weather caused by concurrent cyclone,
front and thunderstorm occurrences, Sci. Rep., 7, 1–8, https://doi.org/10.1038/srep40359, 2017.
Eden, P.: Great British Weather Disasters, Continuum, London, ISBN 10 1441145915, ISBN 13 978-1441145918, 2008.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X.: A density-based algorithm
for discovering clusters in large spatial databases with noise, in: KDD-1996
Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining, 2–4 August 1996, Portland, Oregon, 226–231, https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf (last access: 1 May 2022), 1996.
Fan, X., Scaringi, G., Korup, O., West, A. J., van Westen, C. J., Tanyas, H., Hovius, N., Hales, T. C., Jibson, R. W., Allstadt, K. E., Zhang, L., Evans, S. G., Xu, C., Li, G., Pei, X., Xu, Q., and Huang, R.: Earthquake-induced chains of geologic hazards: Patterns, mechanisms, and impacts, Rev. Geophys., 57, 421–503, https://doi.org/10.1029/2018RG000626, 2019.
Frame, T., Harrisonm, G., Hewson, T., and Roberts, N.: Meteorological Risk:
extra-tropical cyclones, tropical cyclones and convective storms, in: Science
for Disaster Risk Management 2017: Knowing Better and Losing Less, Publications Office of the European Union, Luxembourg, 246–256, https://doi.org/10.2788/842809, 2017.
García-Herrera, R., Garrido-Perez, J. M., Barriopedro, D., Ordóñez, C., Vicente-Serrano, S. M., Nieto, R., Gimeno, L., Sorí, R., and Yiou, P.: The European 2016/17 drought, J. Climate, 32, 3169–3187, https://doi.org/10.1175/JCLI-D-18-0331.1, 2019.
Georgoulas, G., Konstantaras, A., Katsifarakis, E., Stylios, C. D., Maravelakis, E., and Vachtsevanos, G. J.: “Seismic-mass” density-based
algorithm for spatio-temporal clustering, Expert Syst. Appl., 40, 4183–4189, https://doi.org/10.1016/j.eswa.2013.01.028, 2013.
Gill, J. C. and Malamud, B. D.: Reviewing and visualizing the interactions
of natural hazards, Rev. Geophys., 52, 680–722, https://doi.org/10.1002/2013RG000445, 2014.
Gleixner, S., Demissie, T., and Diro, G. T.: Did ERA5 improve temperature and
precipitation reanalysis over East Africa?, Atmosphere (Basel), 11, 996,
https://doi.org/10.3390/atmos11090996, 2020.
Hahsler, M., Piekenbrock, M., and Doran, D.: dbscan: Fast density-based
clustering with R, J. Stat. Softw., 91, 1–30, https://doi.org/10.18637/jss.v091.i01, 2019.
Hawcroft, M. K., Shaffrey, L. C., Hodges, K. I., and Dacre, H. F.: How much
Northern Hemisphere precipitation is associated with extratropical cyclones?, Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL053866, 2012.
Hendry, A., Haigh, I. D., Nicholls, R. J., Winter, H. C., Neal, R., Wahl, T., Joly-Laugel, A., and Darby, S. E.: Assessing the characteristics and drivers of compound flooding events around the UK coast, Hydrol. Earth Syst. Sci., 23, 3117–3139, https://doi.org/10.5194/hess-23-3117-2019, 2019.
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 1979 to present. Copernicus Climate Change
Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.adbb2d47, 2018.
Hersbach, H., Bell, B., Berrisford, P., Horányi, A., Sabater, J. M.,
Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.:
Global reanalysis: goodbye ERA-Interim, hello ERA5, ECMWF Newslett., 159,
17–24, https://doi.org/10.21957/vf291hehd7, 2019.
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., 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., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hillier, J. K., Matthews, T., Wilby, R. L., and Murphy, C.: Multi-hazard
dependencies can increase or decrease risk, Nat. Clim. Change, 10, 595–598, https://doi.org/10.1038/s41558-020-0832-y, 2020.
Holley, D. M., Dorling, S. R., Steele, C. J., and Earl, N.: A climatology of
convective available potential energy in Great Britain, Int. J. Climatol., 34, 3811–3824, https://doi.org/10.1002/joc.3976, 2014.
Huang, W., Xu, S., Yan, Y., and Zipf, A.: An exploration of the interaction
between urban human activities and daily traffic conditions: A case study of
Toronto, Canada, Cities, 84, 8–22, https://doi.org/10.1016/j.cities.2018.07.001, 2019.
Hulme, M. and Barrow, E.: Climates of the British Isles, Routledge, London,
https://doi.org/10.4324/9781315870793, 1997.
Huntingford, C., Marsh, T., Scaife, A. A., Kendon, E. J., Hannaford, J., Kay, A. L., Lockwood, M., Prudhomme, C., Reynard, N. S., Parry, S., Lowe, J. A., Screen, J. A., Ward, H. C., Roberts, M., Stott, P. A., Bell, V. A., Bailey, M., Jenkins, A., Legg, T., Otto, F. E. L., Massey, N., Schaller, N., Slingo, J., and Allen, M. R.: Potential influences on the United Kingdom's floods of winter 2013/14, Nat. Clim. Change, 4, 769–777, https://doi.org/10.1038/nclimate2314, 2014.
Iordanidou, V., Koutroulis, A. G., and Tsanis, I. K.: Investigating the
relationship of lightning activity and rainfall: A case study for Crete Island, Atmos. Res., 172–173, 16–27, https://doi.org/10.1016/j.atmosres.2015.12.021, 2016.
Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and
infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487–503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
Kassambara, A.: Practical Guide to Cluster Analysis in R: Unsupervised machine learning, STHDA, Poland, ISBN 10 1542462703, ISBN 13 978-1542462709, 2017.
Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan,
S. C., Evans, J. P., Fosser, G., and Wilkinson, J. M.: Do convection-permitting regional climate models improve projections of future
precipitation change?, B. Am. Meteorol. Soc., 98, 79–93, https://doi.org/10.1175/BAMS-D-15-0004.1, 2017.
Kholodovsky, V. and Liang, X.-Z.: A generalized spatio-temporal threshold
clustering method for identification of extreme event patterns, Adv. Stat.
Clim. Meteorol. Oceanogr., 7, 35–52, https://doi.org/10.5194/ascmo-7-35-2021, 2021.
Klerk, W. J., Winsemius, H. C., van Verseveld, W. J., Bakker, A. M. R., and
Diermanse, F. L. M.: The co-incidence of storm surges and extreme discharges
within the Rhine–Meuse Delta, Environ. Res. Lett., 10, 035005,
https://doi.org/10.1088/1748-9326/10/3/035005, 2015.
Lancaster, J.: What is the right scale? Encouraging fruitful engagement for
ecology with ecohydraulics, J. Ecohydraulics, 3, 63–76,
https://doi.org/10.1080/24705357.2018.1535260, 2018.
Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B. J., McInnes, K., Risbey, J., Schuster, S., Jakob, D., and Stafford-Smith, M.: A
compound event framework for understanding extreme impacts, Wiley Interdisciplin. Rev. Clim. Change, 5, 113–128, https://doi.org/10.1002/wcc.252, 2014.
Mahto, S. S. and Mishra, V.: Does ERA-5 Outperform Other Reanalysis Products
for Hydrologic Applications in India?, J. Geophys. Res.-Atmos., 124, 9423–9441, https://doi.org/10.1029/2019JD031155, 2019.
Mailier, P. J., Stephenson, D. B., Ferro, C. A. T., and Hodges, K. I.: Serial
clustering of extratropical cyclones, Mon. Weather Rev., 134, 2224–2240,
https://doi.org/10.1175/MWR3160.1, 2006.
Manning, C., Widmann, M., Bevacqua, E., Van Loon, A. F., Maraun, D., and
Vrac, M.: Increased probability of compound long-duration dry and hot events in Europe during summer (1950–2013), Environ. Res. Lett., 14, 094006,
https://doi.org/10.1088/1748-9326/ab23bf, 2019.
Martius, O., Pfahl, S., and Chevalier, C.: A global quantification of compound precipitation and wind extremes, Geophys. Res. Lett., 43, 7709–7717, https://doi.org/10.1002/2016GL070017, 2016.
Mastrantonas, N., Herrera-Lormendez, P., Magnusson, L., Pappenberger, F., and
Matschullat, J.: Extreme precipitation events in the Mediterranean:
Spatiotemporal characteristics and connection to large-scale atmospheric flow patterns, Int. J. Climatol., 41, 2710–2728, https://doi.org/10.1002/joc.6985, 2020.
Met Office: Past weather events, https://www.metoffice.gov.uk/weather/learn-about/past-uk-weather-events, last access: 1 October 2020.
Milligan, G. W. and Cooper, M. C.: Methodology review: Clustering methods,
Appl. Psychol. Meas., 11, 329–354, https://doi.org/10.1177/014662168701100401, 1987.
Minola, L., Zhang, F., Azorin-Molina, C., Pirooz, A. A. S., Flay, R. G. J.,
Hersbach, H., and Chen, D.: Near-surface mean and gust wind speeds in ERA5
across Sweden: towards an improved gust parametrization, Clim. Dynam., 55, 887–907, https://doi.org/10.1007/s00382-020-05302-6, 2020.
Molina, M. O., Gutiérrez, C., and Sánchez, E.: Comparison of ERA5
surface wind speed climatologies over Europe with observations from the HadISD dataset, Int. J. Climatol. 41, 4864–4878, https://doi.org/10.1002/joc.7103, 2021.
Naylor, L. A., Spencer, T., Lane, S. N., Darby, S. E., Magilligan, F. J.,
Macklin, M. G., and Möller, I.: Stormy geomorphology: geomorphic contributions in an age of climate extremes, Earth Surf. Proc. Land., 42, 166–190, https://doi.org/10.1002/esp.4062, 2017.
Nitschke, M., Tucker, G. R., Hansen, A. L., Williams, S., Zhang, Y., and Bi, P.: Impact of two recent extreme heat episodes on morbidity and mortality in
Adelaide, South Australia: a case-series analysis, Environ. Health, 10, 42, https://doi.org/10.1186/1476-069X-10-42, 2011.
ONS – Office for National Statistics: NUTS1 Regions,
https://www.ons.gov.uk/methodology/geography/ukgeographies/eurostat, last access: 29 June 2021.
Orlanski, I.: A rational subdivision of scales for atmospheric processes, Am. Meteorol. Soc., 56, 527–530, 1975.
Pfahl, S. and Wernli, H.: Quantifying the relevance of cyclones for precipitation extremes, J. Climate, 25, 6770–6780, https://doi.org/10.1175/JCLI-D-11-00705.1, 2012.
Pinto, J. G., Karremann, M. K., Born, K., Della-Marta, P. M., and Klawa, M.:
Loss potentials associated with European windstorms under future climate
conditions, Clim. Res., 54, 1–20, https://doi.org/10.3354/cr01111, 2012.
Rahman, M. S., Yang, R., and Di, L.: Clustering Indian Ocean tropical cyclone
tracks by the standard deviational ellipse, Climate, 6, 39, https://doi.org/10.3390/cli6020039, 2018.
Ramsay, H. A., Camargo, S. J., and Kim, D.: Cluster analysis of tropical
cyclone tracks in the Southern Hemisphere, Clim. Dynam., 39, 897–917,
https://doi.org/10.1007/s00382-011-1225-8, 2012.
Raveh-Rubin, S. and Wernli, H.: Large-scale wind and precipitation extremes
in the Mediterranean: A climatological analysis for 1979–2012, Q. J. Roy.
Meteorol. Soc., 141, 2404–2417, https://doi.org/10.1002/qj.2531, 2015.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/, last access: 1 May 2022.
Ridder, N. N., Pitman, A. J., Westra, S., Ukkola, A., Do, H. X., Bador, M.,
Hirsch, A. L., Evans, J. P., Di Luca, A., and Zscheischler, J.: Global hotspots for the occurrence of compound events, Nat. Commun., 11, 5956,
https://doi.org/10.1038/s41467-020-19639-3, 2020.
Rigo, T., Berenguer, M., and del Llasat, M. C.: An improved analysis of
mesoscale convective systems in the western Mediterranean using weather radar, Atmos. Res., 227, 147–156, https://doi.org/10.1016/j.atmosres.2019.05.001, 2019.
Rivoire, P., Martius, O., and Naveau, P.: A comparison of moderate and extreme ERA-5 Daily precipitation with two observational data sets, Earth
Space Sci., 8, 1–23, https://doi.org/10.1029/2020EA001633, 2021.
Sadegh, M., Moftakhari, H. R., Gupta, H. V., Ragno, E., Mazdiyasni, O., Sanders, B. F., Matthew, R. A., and AghaKouchak, A.: Multihazard scenarios
for analysis of compound extreme events, Geophys. Res. Lett., 45, 5470–5480, https://doi.org/10.1029/2018GL077317, 2018.
Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y. T., Chuang, H. Y., Juang, H. M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J. K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C. Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1057, https://doi.org/10.1175/2010BAMS3001.1, 2010.
Schneider, D. C.: Quantitative Ecology: Spatial and Temporal Scaling, Elsevier Inc., Oxford, UK, https://doi.org/10.1016/C2009-0-02445-3, 1994.
Sedlmeier, K., Feldmann, H., and Schädler, G.: Compound summer temperature and precipitation extremes over central Europe, Theor. Appl.
Climatol., 131, 1493–1501, https://doi.org/10.1007/s00704-017-2061-5, 2018.
Sharifi, E., Eitzinger, J., and Dorigo, W.: Performance of the state-of-the-art gridded precipitation products over mountainous terrain: A
regional study over Austria, Remote Sens., 11, 1–20, https://doi.org/10.3390/rs11172018, 2019.
Shi, Z. and Pun-Cheng, L.: Spatiotemporal Data Clustering: A survey of methods, ISPRS Int. J. Geo-Inform., 8, 112, https://doi.org/10.3390/ijgi8030112, 2019.
Skøien, J. O., Blöschl, G., and Western, A. W.: Characteristic space
scales and timescales in hydrology, Water Resour. Res., 39, 1304,
https://doi.org/10.1029/2002WR001736, 2003.
Skok, G., Žagar, N., Honzak, L., Žabkar, R., Rakovec, J., and Ceglar,
A.: Precipitation intercomparison of a set of satellite- and raingauge-derived datasets, ERA Interim reanalysis, and a single WRF regional climate simulation over Europe and the North Atlantic, Theor. Appl. Climatol., 123, 217–232, https://doi.org/10.1007/s00704-014-1350-5, 2016.
Spinoni, J., Barbosa, P., Bucchignani, E., Cassano, J., Cavazos, T., Cescatti, A., Christensen, J. H., Christensen, O. B., Coppola, E., Evans, J.
P., Forzieri, G., Geyer, B., Giorgi, F., Jacob, D., Katzfey, J., Koenigk, T., Laprise, R., Lennard, C. J., Kurnaz, M. L., Li, D., Llopart, M., McCormick, N., Naumann, G., Nikulin, G., Ozturk, T., Panitz, H., Rocha, R. P., Solman, S. A., Syktus, J., Tangang, F., Teichmann, C., Vautard, R., Vogt, J. V., Winger, K., Zittis, G., and Dosio, A.: Global exposure of population and land-use to meteorological droughts under different warming levels and SSPs: a CORDEX-based study, Int. J. Climatol., 41, 6825–6853, https://doi.org/10.1002/joc.7302, 2021.
Strauss, C., Rosa, M. B., and Stephany, S.: Spatio-temporal clustering and
density estimation of lightning data for the tracking of convective events,
Atmos. Res., 134, 87–99, https://doi.org/10.1016/j.atmosres.2013.07.008, 2013.
Sutanto, S. J., Vitolo, C., Di Napoli, C., D'Andrea, M., and Van Lanen, H. A.
J.: Heatwaves, droughts, and fires: Exploring compound and cascading dry
hazards at the pan-European scale, Environ. Int., 134, 105276,
https://doi.org/10.1016/j.envint.2019.105276, 2020.
Tarek, M., Brissette, F. P., and Arsenault, R.: Evaluation of the ERA5
reanalysis as a potential reference dataset for hydrological modelling over
North America, Hydrol. Earth Syst. Sci., 24, 2527–2544,
https://doi.org/10.5194/hess-24-2527-2020, 2020.
Temme, A., Guzzetti, F., Samia, J., and Mirus, B. B.: The future of
landslides' past – a framework for assessing consecutive landsliding
systems, Landslides, 17, 1519–1528, https://doi.org/10.1007/s10346-020-01405-7, 2020.
Tencer, B., Weaver, A., and Zwiers, F.: Joint occurrence of daily temperature
and precipitation extreme events over Canada, J. Appl. Meteorol. Clim., 53, 2148–2162, https://doi.org/10.1175/JAMC-D-13-0361.1, 2014.
Tencer, B., Bettolli, M. L., and Rusticucci, M.: Compound temperature and
precipitation extreme events in southern South America: Associated atmospheric circulation, and simulations by a multi-RCM ensemble, Clim. Res., 68, 183–199, https://doi.org/10.3354/cr01396, 2016.
Tilloy, A.: Wind and Precipitation Extremes in Great Britain (1979–2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards, Zenodo [data set], https://doi.org/10.5281/zenodo.4906264, 2021.
Tilloy, A.: Alowis/SI-CH: A methodology for the spatiotemporal identification of compound hazards, Zenodo [code], https://doi.org/10.5281/zenodo.6555825, 2022.
Tilloy, A., Malamud, B. D., Winter, H., and Joly-Laugel, A.: A review of
quantification methodologies for multi-hazard interrelationships, Earth-Sci. Rev., 196, 102881, https://doi.org/10.1016/j.earscirev.2019.102881, 2019.
Ulbrich, U., Leckebusch, G. C., and Pinto, J. G.: Extra-tropical cyclones in
the present and future climate: A review, Theor. Appl. Climatol., 96, 117–131, https://doi.org/10.1007/s00704-008-0083-8, 2009.
Visser-Quinn, A., Beevers, L., Collet, L., Formetta, G., Smith, K., Wanders,
N., Thober, S., Pan, M., and Kumar, R.: Spatio-temporal analysis of compound
hydro-hazard extremes across the UK, Adv. Water Resour., 130, 77–90,
https://doi.org/10.1016/j.advwatres.2019.05.019, 2019.
Vogel, M. M., Zscheischler, J., Fischer, E. M., and Seneviratne, S. I.:
Development of future heatwaves for different hazard thresholds, J. Geophys.
Res.-Atmos., 125, e2019JD032070, https://doi.org/10.1029/2019JD032070, 2020.
Vorogushyn, S., Merz, B., Lindenschmidt, K.-E., and Apel, H.: A new
methodology for flood hazard assessment considering dike breaches, Water Resour. Res., 46, W08541, https://doi.org/10.1029/2009WR008475, 2010.
Widmann, M. and Bretherton, C. S.: Validation of mesoscale precipitation in
the NCEP reanalysis using a new gridcell dataset for the Northwestern United
States, J. Climate, 13, 1936–1950, https://doi.org/10.1175/1520-0442(2000)013<1936:VOMPIT>2.0.CO;2, 2000.
WMO – World Meteorological Organization: List of all Variables, https://space.oscar.wmo.int/variables, last access: 17 May 2022.
Xu, D. and Tian, Y.: A comprehensive survey of clustering algorithms, Ann.
Data Sci., 2, 165–193, https://doi.org/10.1007/s40745-015-0040-1, 2015.
Yuan, G., Sun, P., Zhao, J., Li, D., and Wang, C.: A review of moving object
trajectory clustering algorithms, Artif. Intell. Rev., 47, 123–144,
https://doi.org/10.1007/s10462-016-9477-7, 2017.
Yue, S.: The Gumbel logistic model for representing a multivariate storm
event, Adv. Water Resour., 24, 179–185, https://doi.org/10.1016/s0309-1708(00)00039-7, 2000.
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P. D., Tank, A. K., Peterson, T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in extremes based on daily temperature and precipitation data, Wiley Interdisciplin. Rev. Clim. Change, 2, 851–870, 2011.
Zhang, Y., Sun, X., and Chen, C.: Characteristics of concurrent precipitation
and wind speed extremes in China, Weather Clim. Extrem., 32, 100322,
https://doi.org/10.1016/j.wace.2021.100322, 2021.
Zscheischler, J. and Seneviratne, S. I.: Dependence of drivers affects risks
associated with compound events, Sci. Adv., 3, e1700263,
https://doi.org/10.1126/sciadv.1700263, 2017.
Zscheischler, J., Mahecha, M. D., Harmeling, S., and Reichstein, M.: Detection and attribution of large spatiotemporal extreme events in Earth
observation data, Ecol. Inform., 15, 66–73, https://doi.org/10.1016/j.ecoinf.2013.03.004, 2013.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of compound weather and climate events, Nat. Rev. Earth Environ., 1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020.
Zscheischler, J., Naveau, P., Martius, O., Engelke, S., and Raible, C. C.: Evaluating the dependence structure of compound precipitation and wind speed extremes, Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, 2021.
Short summary
Compound hazards occur when two different natural hazards impact the same time period and spatial area. This article presents a methodology for the spatiotemporal identification of compound hazards (SI–CH). The methodology is applied to compound precipitation and wind extremes in Great Britain for the period 1979–2019. The study finds that the SI–CH approach can accurately identify single and compound hazard events and represent their spatial and temporal properties.
Compound hazards occur when two different natural hazards impact the same time period and...
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