Articles | Volume 12, issue 2
https://doi.org/10.5194/esd-12-401-2021
© Author(s) 2021. 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-12-401-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2021
Research article |
| 22 Apr 2021
| 22 Apr 2021
Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble
Max Planck Institute for Meteorology, Hamburg, Germany
Sebastian Milinski
Max Planck Institute for Meteorology, Hamburg, Germany
Ralf Ludwig
Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
Related authors
Andrew D. King, Tilo Ziehn, Matthew Chamberlain, Alexander R. Borowiak, Josephine R. Brown, Liam Cassidy, Andrea J. Dittus, Michael Grose, Nicola Maher, Seungmok Paik, Sarah E. Perkins-Kirkpatrick, and Aditya Sengupta
EGUsphere, https://doi.org/10.5194/egusphere-2023-2961, https://doi.org/10.5194/egusphere-2023-2961, 2024
Short summary
Short summary
Governments are targeting net-zero emissions later this century with the aim of limiting global warming in line with the Paris Agreement. However, few studies explore the long-term consequences of reaching net-zero emissions and the effects of delay in reaching net-zero. We use the Australian Earth System Model to examine climate evolution under net-zero emissions. We find substantial changes which differ regionally, including continued Southern Ocean warming and Antarctic sea ice reduction.
Víctor Malagón-Santos, Aimée B. A. Slangen, Tim H. J. Hermans, Sönke Dangendorf, Marta Marcos, and Nicola Maher
Ocean Sci., 19, 499–515, https://doi.org/10.5194/os-19-499-2023, https://doi.org/10.5194/os-19-499-2023, 2023
Short summary
Short summary
Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive global climate models. Statistical models are a cheaper alternative for projecting DSL but may contain significant errors. Here, we partly remove those errors (driven by internal climate variability) by using pattern recognition techniques.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
Short summary
Short summary
Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
Nicola Maher, Thibault P. Tabarin, and Sebastian Milinski
Earth Syst. Dynam., 13, 1289–1304, https://doi.org/10.5194/esd-13-1289-2022, https://doi.org/10.5194/esd-13-1289-2022, 2022
Short summary
Short summary
El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
Short summary
Short summary
Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Alexei Koldunov, Tobias Kölling, Josh Kousal, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Domokos Sármány, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
EGUsphere, https://doi.org/10.5194/egusphere-2024-913, https://doi.org/10.5194/egusphere-2024-913, 2024
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale"), and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Julia Miller, Andrea Böhnisch, Ralf Ludwig, and Manuela I. Brunner
Nat. Hazards Earth Syst. Sci., 24, 411–428, https://doi.org/10.5194/nhess-24-411-2024, https://doi.org/10.5194/nhess-24-411-2024, 2024
Short summary
Short summary
We assess the impacts of climate change on fire danger for 1980–2099 in different landscapes of central Europe, using the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by 2099. High fire danger (FWI > 21.3) becomes the mean condition by 2099 under an RCP8.5 scenario. This study highlights the potential for severe fire events in central Europe from a meteorological perspective.
Andrew D. King, Tilo Ziehn, Matthew Chamberlain, Alexander R. Borowiak, Josephine R. Brown, Liam Cassidy, Andrea J. Dittus, Michael Grose, Nicola Maher, Seungmok Paik, Sarah E. Perkins-Kirkpatrick, and Aditya Sengupta
EGUsphere, https://doi.org/10.5194/egusphere-2023-2961, https://doi.org/10.5194/egusphere-2023-2961, 2024
Short summary
Short summary
Governments are targeting net-zero emissions later this century with the aim of limiting global warming in line with the Paris Agreement. However, few studies explore the long-term consequences of reaching net-zero emissions and the effects of delay in reaching net-zero. We use the Australian Earth System Model to examine climate evolution under net-zero emissions. We find substantial changes which differ regionally, including continued Southern Ocean warming and Antarctic sea ice reduction.
Florian Willkofer, Raul Roger Wood, and Ralf Ludwig
EGUsphere, https://doi.org/10.5194/egusphere-2023-2019, https://doi.org/10.5194/egusphere-2023-2019, 2023
Short summary
Short summary
Severe flood events pose threat to riverine areas, yet robust estimates about the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses and benefits from data from a RCM SMILE to drive a high-resolution hydrological model for 98 catchments of the Hydrological Bavaria to exploit the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Víctor Malagón-Santos, Aimée B. A. Slangen, Tim H. J. Hermans, Sönke Dangendorf, Marta Marcos, and Nicola Maher
Ocean Sci., 19, 499–515, https://doi.org/10.5194/os-19-499-2023, https://doi.org/10.5194/os-19-499-2023, 2023
Short summary
Short summary
Climate change will alter heat and freshwater fluxes as well as ocean circulation, driving local changes in sea level. This sea-level change component is known as ocean dynamic sea level (DSL), and it is usually projected using computationally expensive global climate models. Statistical models are a cheaper alternative for projecting DSL but may contain significant errors. Here, we partly remove those errors (driven by internal climate variability) by using pattern recognition techniques.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
Short summary
Short summary
Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
Nicola Maher, Thibault P. Tabarin, and Sebastian Milinski
Earth Syst. Dynam., 13, 1289–1304, https://doi.org/10.5194/esd-13-1289-2022, https://doi.org/10.5194/esd-13-1289-2022, 2022
Short summary
Short summary
El Niño events occur as two broad types: eastern Pacific (EP) and central Pacific (CP). EP and CP events differ in strength, evolution, and in their impacts. In this study we create a new machine learning classifier to identify the two types of El Niño events using observed sea surface temperature data. We apply our new classifier to climate models and show that CP events are unlikely to change in frequency or strength under a warming climate, with model disagreement for EP events.
Elizaveta Felsche and Ralf Ludwig
Nat. Hazards Earth Syst. Sci., 21, 3679–3691, https://doi.org/10.5194/nhess-21-3679-2021, https://doi.org/10.5194/nhess-21-3679-2021, 2021
Short summary
Short summary
This study applies artificial neural networks to predict drought occurrence in Munich and Lisbon, with a lead time of 1 month. An analysis of the variables that have the highest impact on the prediction is performed. The study shows that the North Atlantic Oscillation index and air pressure 1 month before the event have the highest importance for the prediction. Moreover, it shows that seasonality strongly influences the goodness of prediction for the Lisbon domain.
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
Short summary
Short summary
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.
Benjamin Poschlod, Ralf Ludwig, and Jana Sillmann
Earth Syst. Sci. Data, 13, 983–1003, https://doi.org/10.5194/essd-13-983-2021, https://doi.org/10.5194/essd-13-983-2021, 2021
Short summary
Short summary
This study provides a homogeneous data set of 10-year rainfall return levels based on 50 simulations of the Canadian Regional Climate Model v5 (CRCM5). In order to evaluate its quality, the return levels are compared to those of observation-based rainfall of 16 European countries from 32 different sources. The CRCM5 is able to capture the general spatial pattern of observed extreme precipitation, and also the intensity is reproduced in 77 % of the area for rainfall durations of 3 h and longer.
Fabian von Trentini, Emma E. Aalbers, Erich M. Fischer, and Ralf Ludwig
Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020, https://doi.org/10.5194/esd-11-1013-2020, 2020
Short summary
Short summary
We compare the inter-annual variability of three single-model initial-condition large ensembles (SMILEs), downscaled with three regional climate models over Europe for seasonal temperature and precipitation, the number of heatwaves, and maximum length of dry periods. They all show good consistency with observational data. The magnitude of variability and the future development are similar in many cases. In general, variability increases for summer indicators and decreases for winter indicators.
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
Short summary
Short summary
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.
Fabian Willibald, Sven Kotlarski, Adrienne Grêt-Regamey, and Ralf Ludwig
The Cryosphere, 14, 2909–2924, https://doi.org/10.5194/tc-14-2909-2020, https://doi.org/10.5194/tc-14-2909-2020, 2020
Short summary
Short summary
Climate change will significantly reduce snow cover, but the extent remains disputed. We use regional climate model data as a driver for a snow model to investigate the impacts of climate change and climate variability on snow. We show that natural climate variability is a dominant source of uncertainty in future snow trends. We show that anthropogenic climate change will change the interannual variability of snow. Those factors will increase the vulnerabilities of snow-dependent economies.
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
Short summary
Short summary
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.
Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins
Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, https://doi.org/10.5194/esd-11-491-2020, 2020
Short summary
Short summary
Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
Winfried Hoke, Tina Swierczynski, Peter Braesicke, Karin Lochte, Len Shaffrey, Martin Drews, Hilppa Gregow, Ralf Ludwig, Jan Even Øie Nilsen, Elisa Palazzi, Gianmaria Sannino, Lars Henrik Smedsrud, and ECRA network
Adv. Geosci., 46, 1–10, https://doi.org/10.5194/adgeo-46-1-2019, https://doi.org/10.5194/adgeo-46-1-2019, 2019
Short summary
Short summary
The European Climate Research Alliance is a bottom-up association of European research institutions helping to facilitate the development of climate change research, combining the capacities of national research institutions and inducing closer ties between existing national research initiatives, projects and infrastructures. This article briefly introduces the network's structure and organisation, as well as project management issues and prospects.
Enrica Perra, Monica Piras, Roberto Deidda, Claudio Paniconi, Giuseppe Mascaro, Enrique R. Vivoni, Pierluigi Cau, Pier Andrea Marras, Ralf Ludwig, and Swen Meyer
Hydrol. Earth Syst. Sci., 22, 4125–4143, https://doi.org/10.5194/hess-22-4125-2018, https://doi.org/10.5194/hess-22-4125-2018, 2018
Erwin Isaac Polanco, Amr Fleifle, Ralf Ludwig, and Markus Disse
Hydrol. Earth Syst. Sci., 21, 4907–4926, https://doi.org/10.5194/hess-21-4907-2017, https://doi.org/10.5194/hess-21-4907-2017, 2017
Short summary
Short summary
In this research, SWAT was used to model the upper Blue Nile Basin where comparisons between ground and CFSR data were done. Furthermore, this paper introduced the SWAT error index (SEI), an additional tool to measure the level of error of hydrological models. This work proposed an approach or methodology that can effectively be followed to create better and more efficient hydrological models.
I. Beck, R. Ludwig, M. Bernier, T. Strozzi, and J. Boike
Earth Surf. Dynam., 3, 409–421, https://doi.org/10.5194/esurf-3-409-2015, https://doi.org/10.5194/esurf-3-409-2015, 2015
M. J. Muerth, B. Gauvin St-Denis, S. Ricard, J. A. Velázquez, J. Schmid, M. Minville, D. Caya, D. Chaumont, R. Ludwig, and R. Turcotte
Hydrol. Earth Syst. Sci., 17, 1189–1204, https://doi.org/10.5194/hess-17-1189-2013, https://doi.org/10.5194/hess-17-1189-2013, 2013
J. A. Velázquez, J. Schmid, S. Ricard, M. J. Muerth, B. Gauvin St-Denis, M. Minville, D. Chaumont, D. Caya, R. Ludwig, and R. Turcotte
Hydrol. Earth Syst. Sci., 17, 565–578, https://doi.org/10.5194/hess-17-565-2013, https://doi.org/10.5194/hess-17-565-2013, 2013
Cited articles
Aalbers, E., Lenderink, G., van Meijgaard, E., and van den Hurk, B.:
Local-scale changes in mean and heavy precipitation in Western Europe,
climate change or internal variability?, Clim. Dynam., 40, 4745–4766,
https://doi.org/10.1007/s00382-017-3901-9, 2018. a, b, c
Allen, M., Dube, O., Solecki, W., Aragón-Durand, F., Cramer, W., Humphreys,
S., Kainuma, M., Kala, J., Mahowald, N., Mulugetta, Y., Perez, R., Wairiu,
M., and Zickfeld, K.: Framing and Context, in: Global Warming of 1.5 ∘C.
An IPCC Special Report on the impacts of global warming of 1.5 ∘C above
pre-industrial levels and related global greenhouse gas emission pathways, in
the context of strengthening the global response to the threat of climate
change, sustainable development, and efforts to eradicate poverty, edited by:
Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J.,
Shukla, P., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors,
S., Matthews, J., Chen, Y., Zhou, X., Gomis, M., Lonnoy, E., Maycock, T.,
Tignor, M., and Waterfield, T., in press, 2018. a
Barnett, T. P., Arpe, K., Bengtsson, L., Ji, M., and Kumar, A.: Potential
Predictability and AMIP Implications of Midlatitude Climate Variability in
Two General Circulation Models, J. Climate, 10, 2321–2329,
https://doi.org/10.1175/1520-0442(1997)010<2321:PPAAIO>2.0.CO;2, 1997. a
Bittner, M., Schmidt, H., Timmreck, C., and Sienz, F.: Using a large ensemble
of simulations to assess the Northern Hemisphere stratospheric dynamical
response to tropical volcanic eruptions and its uncertainty, Geophys.
Res. Lett., 43, 9324–9332, https://doi.org/10.1002/2016GL070587, 2016. a
Böhnisch, A., Ludwig, R., and Leduc, M.: Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe, Earth Syst. Dynam., 11, 617–640, https://doi.org/10.5194/esd-11-617-2020, 2020. a, b, c, d, e, f, g
Branstator, G. and Selten, F.: “Modes of Variability” and Climate Change,
J. Climate, 22, 2639–2658, https://doi.org/10.1175/2008JCLI2517.1, 2009. a
Christensen, O. and Kjellström, E.: Partitioning uncertainty components of
mean climate and climate change in a large ensemble of European regional
climate model projections, Clim. Dynam., 54, 4293–4308,
https://doi.org/10.1007/s00382-020-05229-y, 2020. a, b
Dai, A. and Bloecker, C. E.: Impacts of internal variability on temperature and
precipitation trends in large ensemble simulations by two climate models,
Clim. Dynam., 52, 289–306, https://doi.org/10.1007/s00382-018-4132-4, 2019. a
Deser, C., Laurent, T., and Phillips, A.: Forced and Internal Components of
Winter Air Temperature Trends over North America during the past 50 Years:
Mechanisms and Implications, J. Climate, 29, 2237–2258,
https://doi.org/10.1175/JCLI-D-15-0304.1, 2016. a
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T., DiNezio, P.,
Fiore, A., Frankignoul, C., Fyfe, J., Horton, D., Kay, J. E., Knutti, R.,
Lovenduski, N., Marotzke, J., McKinnon, K., Minobe, S., Randerson, J.,
Screen, J., Simpson, I., and Ting, A.: Strength in Numbers: The Utility of
Large Ensembles with Multiple Earth System Models, Nat. Clim. Change,
https://doi.org/10.1038/s41558-020-0731-2, 2020 (data available at: http://www.cesm.ucar.edu/projects/community-projects/MMLEA/, last access: 14 April 2020). a, b, c, d, e
Di Luca, A., de Elía, R., and Laprise, R.: Potential for small scale added
value of RCM's downscaled climate change signal, Clim. Dynam., 40,
601–618, https://doi.org/10.1007/s00382-012-1415-z, 2013. a
Diffenbaugh, N. S., Swain, D. L., and Touma, D.: Anthropogenic warming has
increased drought risk in California, P. Natl. Acad.
Sci. USA, 112, 3931–3936, https://doi.org/10.1073/pnas.1422385112, 2015. a
Dittus, A., Hawkins, E., Wilcox, L., Sutton, R., Smith, C., Andrews, M., and
Forster, P.: Sensitivity of historical climate simulations to uncertain
aerosol forcing, Geophys. Res. Lett., 47, e2019GL085806,
https://doi.org/10.1029/2019GL085806, 2020. a
Dudhia, J.: A history of mesoscale model development, Asia-Pac. J.
Atmos. Sci., 50, 121–131, https://doi.org/10.1007/s13143-014-0031-8, 2014. a
Díaz, L. B., Saurral, R. I., and Vera, C. S.: Assessment of South America
summer rainfall climatology and trends in a set of global climate models
large ensembles, Int. J. Climatol., 41, E59–E77,
https://doi.org/10.1002/joc.6643, 2021. a
Evans, J. and McCabe, M.: Effect of model resolution on a regional climate
model simulation over southeast Australia, Clim. Res., 56, 131–145,
https://doi.org/10.3354/cr01151, 2013. a
Fasullo, J. T. and Nerem, R. S.: Interannual Variability in Global Mean Sea
Level Estimated from the CESM Large and Last Millennium Ensembles, Water, 8, 491,
https://doi.org/10.3390/w8110491, 2016. a
Fasullo, J. T., Phillips, A. S., and Deser, C.: Evaluation of Leading Modes of
Climate Variability in the CMIP Archives, J. Climate, 33,
5527–5545, https://doi.org/10.1175/JCLI-D-19-1024.1, 2020. a
Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., and Zahn, M.: Regional
Climate Models Add Value to Global Model Data: A Review and Selected
Examples:, B. Am. Meteorol. Soc., 92, 1181–1192,
https://doi.org/10.1175/2011BAMS3061.1, 2011. a, b
Fischer, E., Beyerle, U., and Knutti, R.: Robust spatially aggregated
projections of climate extremes, Nat. Clim. Change, 3, 1033–1038, 2013. a
Fischer, E. M., Luterbacher, J., Zorita, E., Tett, S. F. B., Casty, C., and
Wanner, H.: European climate response to tropical volcanic eruptions over the
last half millennium, Geophys. Res. Lett., 34, L05707,
https://doi.org/10.1029/2006GL027992, 2007. a, b, c
Frankignoul, C., Gastineau, G., and Kwon, Y.-O.: Estimation of the SST
Response to Anthropogenic and External Forcing and Its Impact on the Atlantic
Multidecadal Oscillation and the Pacific Decadal Oscillation, J.
Climate, 30, 9871–9895, https://doi.org/10.1175/JCLI-D-17-0009.1, 2017. a, b
Gagné, M.-È., Fyfe, J. C., Gillett, N. P., Polyakov, I. V., and Flato, G. M.:
Aerosol-driven increase in Arctic sea ice over the middle of the twentieth
century, Geophys. Res. Lett., 44, 7338–7346,
https://doi.org/10.1002/2016GL071941, 2017a. a, b
Gagné, M.-È., Kirchmeier-Young, M. C., Gillett, N. P., and Fyfe, J. C.: Arctic
sea ice response to the eruptions of Agung, El Chichón, and Pinatubo,
J. Geophys. Res.-Atmos., 122, 8071–8078,
https://doi.org/10.1002/2017JD027038, 2017b. a, b
Gates, W. L.: AN AMS CONTINUING SERIES: GLOBAL CHANGE–AMIP: The Atmospheric
Model Intercomparison Project, B. Am. Meteorol.
Soc., 73, 1962–1970,
https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2, 1992. a
Gibson, P., Perkins-Kirkpatrick, S., Alexander, L., and Fischer, E.: Comparing
Australian heat waves in the CMIP5 models through cluster analysis, J. Geophys. Res.-Atmos., 122, 3266–3281, 2017. a
Haszpra, T., Topál, D., and Herein, M.: On the time evolution of the Arctic
Oscillation and related wintertime phenomena under different forcing
scenarios in an ensemble approach, J. Climate, 33, 3107–3124,
https://doi.org/10.1175/JCLI-D-19-0004.1, 2020b. a
Haugen, M. A., Stein, M. L., Moyer, E. J., and Sriver, R. L.: Estimating
Changes in Temperature Distributions in a Large Ensemble of Climate
Simulations Using Quantile Regression, J. Climate, 31, 8573–8588,
https://doi.org/10.1175/JCLI-D-17-0782.1, 2018. a
Hawkins, E. and Sutton, R.: Decadal predictability of the Atlantic Ocean in a
coupled GCM: forecast skill and optimal perturbations using linear inverse
modeling, J. Climate, 22, 3960–3978, https://doi.org/10.1175/2009JCLI2720.1,
2009. a
Herein, M., Drótos, G., Haszpra, T., Márfy, and Tél, T.: The
theory of parallel climate realizations as a new framework for teleconnection
analysis, Sci. Rep.-UK, 7, 44529, https://doi.org/10.1038/srep44529, 2017. a
Jacob, D., Kotova, L., Teichmann, C., Sobolowski, S. P., Vautard, R., Donnelly,
C., Koutroulis, A. G., Grillakis, M. G., Tsanis, I. K., Damm, A., Sakalli,
A., and van Vliet, M. T. H.: Climate Impacts in Europe Under +1.5 ∘C Global
Warming, Earth's Future, 6, 264–285,
https://doi.org/10.1002/2017EF000710, 2018. a
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M.,
Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A.,
Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin,
E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A.,
García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro,
J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I.,
Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G.,
Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van
Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G.,
de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli,
E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel,
B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec,
L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi,
K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives
from the EURO-CORDEX community, Reg. Environ. Change, 20, 51,
https://doi.org/10.1007/s10113-020-01606-9, 2020. a
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) Large Ensemble Project: A Community
Resource for Studying Climate Change in the Presence of Internal Climate
Variability, B. Am. Meteorol. Soc., 96, 1333–1349,
https://doi.org/10.1175/BAMS-D-13-00255.1, 2015. a, b, c, d
Kirchmeier-Young, M., Zwiers, F., and Gillett, N.: Attribution of Extreme
Events in Arctic Sea Ice Extent, J. Climate, 30, 553–571,
https://doi.org/10.1175/JCLI-D-16-0412.1, 2017 (data available at: https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c, last access: 14 April 2020). a, b, c, d, e, f, g, h, i
Kirchmeier-Young, M. C., Gillett, N. P., Zwiers, F. W., Cannon, A. J., and
Anslow, F. S.: Attribution of the Influence of Human-Induced Climate Change
on an Extreme Fire Season, Earth's Future, 7, 2–10,
https://doi.org/10.1029/2018EF001050, 2019. a, b
Krumhardt, K. M., Lovenduski, N. S., Long, M. C., and Lindsay, K.: Avoidable
impacts of ocean warming on marine primary production: Insights from the CESM
ensembles, Global Biogeochem. Cy., 31, 114–133,
https://doi.org/10.1002/2016GB005528, 2017. a
Kushner, P. J., Mudryk, L. R., Merryfield, W., Ambadan, J. T., Berg, A., Bichet, A., Brown, R., Derksen, C., Déry, S. J., Dirkson, A., Flato, G., Fletcher, C. G., Fyfe, J. C., Gillett, N., Haas, C., Howell, S., Laliberté, F., McCusker, K., Sigmond, M., Sospedra-Alfonso, R., Tandon, N. F., Thackeray, C., Tremblay, B., and Zwiers, F. W.: Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system, The Cryosphere, 12, 1137–1156, https://doi.org/10.5194/tc-12-1137-2018, 2018 (data available at: https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c, last access: 14 April 2020). a, b, c
Landrum, L. and Holland, M.: Extremes become routine in an emerging new Arctic,
Nat. Clim. Change, 10, 1108–1115, https://doi.org/10.1038/s41558-020-0892-z, 2020. a
Lang, A. and Mikolajewicz, U.: Rising extreme sea levels in the German Bight
under enhanced CO2 levels: a regionalized large ensemble approach for the
North Sea, Clim. Dynam., 55, 1829–1842, https://doi.org/10.1007/s00382-020-05357-5, 2020. a
Leduc, M., Laprise, R., de Elía, R., and Šeparović, L.: Is Institutional
Democracy a Good Proxy for Model Independence?, J. Climate, 29,
8301–8316, https://doi.org/10.1175/JCLI-D-15-0761.1, 2016. a
Leduc, M., Mailhot, A., Frigon, A., Martel, J.-L., Ludwig, R., Brietzke, G. B.,
Giguère, M., Brissette, F., Turcotte, R., Braun, M., and Scinocca, J.: The
ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km
Resolution over Europe and Northeastern North America with the Canadian
Regional Climate Model (CRCM5), J. Appl. Meteorol.
Clim., 58, 663–693, https://doi.org/10.1175/JAMC-D-18-0021.1, 2019 (data available at: https://www.climex-project.org/en/data-access, last access: 14 April 2020). a, b, c, d, e, f, g
Lehner, F., Deser, C., and Sanderson, B.: Future risk of record-breaking
summer temperatures and its mitigation, Climatic Change, 145, 363–375,
2016. a
Lehner, F., Deser, C., and Terray, L.: Toward a New Estimate of ”Time of
Emergence” of Anthropogenic Warming: Insights from Dynamical Adjustment and a
Large Initial-Condition Model Ensemble, J. Climate, 30, 7739–7756,
2017. a
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020. a, b, c, d
Li, H. and Ilyina, T.: Current and Future Decadal Trends in the Oceanic Carbon
Uptake Are Dominated by Internal Variability, Geophys. Res. Lett.,
45, 916–925, https://doi.org/10.1002/2017GL075370, 2018. a
Liguori, G., McGregor, S., Arblaster, J. M., Singh, M. S., and Meehl, G. A.: A joint role for forced and
internally-driven variability in the decadal modulation of global warming,
Nat. Commun., 11, 3827, https://doi.org/10.1038/s41467-020-17683-7, 2020. a
Liu, F., Li, J., Wang, B., Liu, J., Li, T., Huang, G., and Wang, Z.: Divergent
El Niño responses to volcanic eruptions at different latitudes over the
past millennium, Clim. Dynam., 50, 3799–3812, https://doi.org/10.1007/s00382-017-3846-z, 2018. a
Lovenduski, N. S., McKinley, G. A., Fay, A. R., Lindsay, K., and Long, M. C.:
Partitioning uncertainty in ocean carbon uptake projections: Internal
variability, emission scenario, and model structure, Global Biogeochem.
Cy., 30, 1276–1287, https://doi.org/10.1002/2016GB005426, 2016. a
Lucas-Picher, P., Laprise, R., and Winger, K.: Evidence of added value in
North American regional climate model hindcast simulations using
ever-increasing horizontal resolutions, Clim. Dynam., 48, 2611–2633,
https://doi.org/10.1007/s00382-016-3227-z, 2017. a, b
Maher, N., Matei, D., Milinski, S., and Marotzke, J.: ENSO Change in Climate
Projections: Forced Response or Internal Variability?, Geophys. Res.
Lett., 45, 11390–11398, https://doi.org/10.1029/2018GL079764, 2018. a, b
Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Dobrynin, M.,
Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C., Li,
H., Manzini, E., Notz, D., Putrasahan, D., Boysen, L., Claussen, M., Ilyina,
T., Olonscheck, D., Raddatz, T., Stevens, B., and Marotzke, J.: The Max
Planck Institute Grand Ensemble: Enabling the Exploration of Climate System
Variability, J. Adv. Model. Earth Sy., 11, 2050–2069,
https://doi.org/10.1029/2019MS001639, 2019. a, b, c, d
Maher, N., Lehner, F., and Marotzke, J.: Quantifying the role of internal
variability in the temperature we expect to observe in the coming decades,
Environ. Res. Lett., 15, 054014, https://doi.org/10.1088/1748-9326/ab7d02, 2020. a
Maher, N., Power, S., and Marotzke, J.: More accurate quantification of
model-to-model agreement in externally forced climatic responses over the
coming century, Nat. Commun., 12, 788, https://doi.org/10.1038/s41467-020-20635-w,
2021. a
Mankin, J. S., Lehner, F., Coats, S., and McKinnon, K. A.: The Value of Initial
Condition Large Ensembles to Robust Adaptation Decision-Making, Earth's
Future, 8, e2012EF001610, https://doi.org/10.1029/2020EF001610,
2020. a
Marotzke, J.: Quantifying the irreducible uncertainty in near-term climate
projections, WIREs Climate Change, 10, e563, https://doi.org/10.1002/wcc.563, 2019. a
Martynov, A., Laprise, R., Sushama, L., Winger, K., Šeparović, L., and Dugas,
B.: Reanalysis-driven climate simulation over CORDEX North America domain
using the Canadian Regional Climate Model, version 5: model performance
evaluation, Clim. Dynam., 41, 2073–3005,
https://doi.org/10.1007/s00382-013-1778-9, 2013. a, b
McKinley, G. A., Pilcher, D. J., Fay, A. R., Lindsay, K., Long, M. C., and Lovenduski, N. S.: Timescales for detection of
trends in the ocean carbon sink, Nature, 530, 469–472, https://doi.org/10.1038/nature16958, 2016. a
McKinnon, K. A. and Deser, C.: Internal Variability and Regional Climate
Trends in an Observational Large Ensemble, J. Climate, 31,
6783–6802, https://doi.org/10.1175/JCLI-D-17-0901.1, 2018. a
McKinnon, K. A., Poppick, A., Dunn-Sigouin, E., and Deser, C.: An
“Observational Large Ensemble” to Compare Observed and Modeled Temperature
Trend Uncertainty due to Internal Variability, J. Climate, 30,
7585–7598, https://doi.org/10.1175/JCLI-D-16-0905.1, 2017. a
Mearns, L. O., Sain, S., Leung, L. R., Bukovsky, M. S., McGinnis, S., Biner,
S., Caya, D., Arritt, R. W., Gutowski, W., Takle, E., Snyder, M., Jones,
R. G., Nunes, A. M. B., Tucker, S., Herzmann, D., McDaniel, L., and Sloan,
L.: Climate change projections of the North American Regional Climate Change
Assessment Program (NARCCAP), Climatic Change, 120, 965–975,
https://doi.org/10.1007/s10584-013-0831-3, 2013. a
Merrifield, A. L., Brunner, L., Lorenz, R., Medhaug, I., and Knutti, R.: An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles, Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, 2020. a, b, c
Milinski, S., Maher, N., and Olonscheck, D.: How large does a large ensemble need to be?, Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, 2020. a, b, c
Mittermeier, M., Braun, M., Hofstätter, M., Wang, Y., and Ludwig, R.: Detecting
Climate Change Effects on Vb-cyclones in a 50-Member Single-Model Ensemble
using Machine Learning., Geophys. Res. Lett., 46, 14653–14661,
2019. a
Olonscheck, D., Rugenstein, M., and Marotzke, J.: Broad Consistency Between
Observed and Simulated Trends in Sea Surface Temperature Patterns,
Geophys. Res. Lett., 47, e2019GL086773, https://doi.org/10.1029/2019GL086773, 2020. a
Pausata, F. S. R., Grini, A., Caballero, R., Hannachi, A., and Seland, Ø.:
High-latitude volcanic eruptions in the Norwegian Earth System Model: the
effect of different initial conditions and of the ensemble size, Tellus B, 67, 26728,
https://doi.org/10.3402/tellusb.v67.26728, 2015. a
Pendergrass, A. G., Coleman, D. B., Deser, C., Lehner, F., Rosenbloom, N., and
Simpson, I. R.: Nonlinear Response of Extreme Precipitation to Warming in
CESM1, Geophys. Res. Lett., 46, 10551–10560,
https://doi.org/10.1029/2019GL084826, 2019. a, b, c, d
Penduff, T., Barnier, B., Terray, L., Sérazin, G., Gregorio, S., Brankart,
J.-M., Moine, M.-P., Molines, J.-M., and Brasseur, P.: Ensembles of eddying ocean simulations for climate, Vol. 65, InternationalCLIVAR Project Office, Southampton, United Kingdom, 19–22, 2014. a
Poschlod, B., Willkofer, F., and Ludwig, R.: Impact of climate change on the
hydrological regimes in Bavaria, Water, 12, 1599, https://doi.org/10.3390/w12061599, 2020a. a
Poschlod, B., Zscheischler, J., Wood, R., Sillmann, J., and Ludwig, R.:
Climate Change Effects on hydrometeorological compound events over Southern
Norway, Weather and Climate Extremes, 28, https://doi.org/10.1016/j.wace.2020.100253, 2020b. a
Rodgers, K. B., Lin, J., and Frölicher, T. L.: Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model, Biogeosciences, 12, 3301–3320, https://doi.org/10.5194/bg-12-3301-2015, 2015. a
Rummukainen, M.: State-of-the-art with regional climate models, WIREs Clim.
Change, 1, 82–96, https://doi.org/10.1002/wcc.8, 2010. a
Rummukainen, M.: Added value in regional climate modeling, WIREs Clim.
Change, 7, 145–159, https://doi.org/10.1002/wcc.378, 2016. a, b, c
Sanderson, B. M., Oleson, K. W., Strand, W. G., Lehner, F., and O'Neill, B. C.:
A new ensemble of GCM simulations to assess avoided impacts in a climate
mitigation scenario, Climatic Change, 146, 303–318,
https://doi.org/10.1007/s10584-015-1567-z, 2018. a
Santer, B. D., Fyfe, J. C., Solomon, S., Painter, J. F., Bonfils, C., Pallotta,
G., and Zelinka, M. D.: Quantifying stochastic uncertainty in detection time
of human-caused climate signals, P. Natl. Acad.
Sci. USA, 116, 19821–19827, https://doi.org/10.1073/pnas.1904586116, 2019. a
Schlunegger, S., Rodgers, K., and Sarmiento, J.: Emergence of anthropogenic
signals in the ocean carbon cycle, Nat. Clim. Change, 9, 719–725,
https://doi.org/10.1038/s41558-019-0553-2, 2019. a
Schlunegger, S., Rodgers, K. B., Sarmiento, J. L., Ilyina, T., Dunne, J.,
Takano, Y., Christian, J., Long, M., Frölicher, T. L., Slater, R., and
Lehner, F.: Time of Emergence & Large Ensemble intercomparison for ocean
biogeochemical trends, Global Biogeochem. Cy., 34, e2019GB006453,
https://doi.org/10.1029/2019GB006453, 2020. a, b
Shiogama, H., Hirata, R., Hasegawa, T., Fujimori, S., Ishizaki, N. N., Chatani, S., Watanabe, M., Mitchell, D., and Lo, Y. T. E.: Historical and future anthropogenic warming effects on droughts, fires and fire emissions of CO2 and PM2.5 in equatorial Asia when 2015-like El Niño events occur, Earth Syst. Dynam., 11, 435–445, https://doi.org/10.5194/esd-11-435-2020, 2020. a, b, c, d, e
Sippel, S., Meinshausen, N., Fischer, E., 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
Smith, A. and Jahn, A.: Definition differences and internal variability affect the simulated Arctic sea ice melt season, The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, 2019. a
Spring, A. and Ilyina, T.: Predictability horizons in the global carbon cycle
inferred from a perfect-model framework, Geophys. Res. Lett., 47, e2019GL085311,
https://doi.org/10.1029/2019GL085311, 2020. a
Stolpe, M. B., Medhaug, I., Sedláček, J., and Knutti, R.: Multidecadal
Variability in Global Surface Temperatures Related to the Atlantic Meridional
Overturning Circulation, J. Climate, 31, 2889–2906,
https://doi.org/10.1175/JCLI-D-17-0444.1, 2018. a
Suarez-Gutierrez, L., Li, C., Müller, W. A., and Marotzke, J.: Internal
variability in European summer temperatures at 1.5 ∘C and 2 ∘C of
global warming, Environ. Res. Lett., 13, 064026, https://doi.org/10.1088/1748-9326/aaba58, 2018. a
Tebaldi, C. and Wehner, M.: Benefits of mitigation for future heat extremes
under RCP4.5 compared to RCP8.5, Climatic Change, 146, 349–361,
https://doi.org/10.1007/s10584-016-1605-5, 2018. a, b
Topál, D., Hatvani, I., and Kern, Z.: Refining projected multidecadal
hydroclimate uncertainty in East-Central Europe using CMIP5 and single-model
large ensemble simulations, Theor. Appl. Climatol., 142,
1147–1167, https://doi.org/10.1007/s00704-020-03361-7, 2020. a
van der Wiel, K., Wanders, N., Selten, F. M., and Bierkens, M. F. P.: Added
Value of Large Ensemble Simulations for Assessing Extreme River Discharge in
a 2∘ C Warmer World, Geophys. Res. Lett., 46, 2093–2102,
https://doi.org/10.1029/2019GL081967, 2019. a
von Trentini, F., Leduc, M., and Ludwig, R.: Assessing natural variability
in RCM signals: comparison of a multi model EURO-CORDEX ensemble with a
50-member single model large ensemble, Clim. Dynam., 53, 1963–1979,
https://doi.org/10.1007/s00382-019-04755-8, 2019. a
von Trentini, F., Aalbers, E. E., Fischer, E. M., and Ludwig, R.: Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe, Earth Syst. Dynam., 11, 1013–1031, https://doi.org/10.5194/esd-11-1013-2020, 2020. a, b, c, d
Wang, S.-Y. S., Zhao, L., Yoon, J.-H., Klotzbach, P., and Gillies, R. R.:
Quantitative attribution of climate effects on Hurricane Harvey's extreme
rainfall in Texas, Environ. Res. Lett., 13, 054014,
https://doi.org/10.1088/1748-9326/aabb85, 2018. a
Ward, B., Pausata, F. S. R., and Maher, N.: The sensitivity of the ENSO to volcanic aerosol spatial distribution in the MPI large ensemble, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2020-63, in review, 2020. a
Watanabe, M., Suzuki, T., O'ishi, R., Komuro, Y., Watanabe, S., Emori, S.,
Takemura, T., Chikira, M., Ogura, T., Sekiguchi, M., Takata, K., Yamazaki,
D., Yokohata, T., Nozawa, T., Hasumi, H., Tatebe, H., and Kimoto, M.:
Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate
Sensitivity, J. Climate, 23, 6312–6335,
https://doi.org/10.1175/2010JCLI3679.1, 2010.
a
Willibald, F., Kotlarski, S., Grêt-Regamey, A., and Ludwig, R.: Anthropogenic climate change versus internal climate variability: impacts on snow cover in the Swiss Alps, The Cryosphere, 14, 2909–2924, https://doi.org/10.5194/tc-14-2909-2020, 2020. a
Willkofer, F., Wood, R. R., von Trentini, F., Weismüller, J., Poschlod, B., and Ludwig, R.: A Holistic Modelling Approach for the Estimation of Return Levels of Peak Flows in Bavaria, Water, 12, 2349, https://doi.org/10.3390/w12092349, 2020. a, b
Wills, R., Battisti, D., and Armour, K.: Pattern recognition methods to
separate forced responses from internal variability in climate model
ensembles and observations, J. Climate, 33, 8693–8719, 2020. a
Wood, R. and Ludwig, R.: Analyzing Internal Variability and Forced Response of
Sub-daily and Daily Extreme Precipitation over Europe, Geophys. Res.
Lett., 47, e2020GL089300, https://doi.org/10.1029/2020GL089300, 2020. a
Zelle, H., Jan van Oldenborgh, G., Burgers, G., and Dijkstra, H.: El Niño
and Greenhouse Warming: Results from Ensemble Simulations with the NCAR
CCSM, J. Climate, 18, 4669–4683, https://doi.org/10.1175/JCLI3574.1, 2005. a
Zhou, T., Lu, J., Zhang, W., and Chen, Z.: The Sources of Uncertainty in the
Projection of Global Land Monsoon Precipitation, Geophys. Res.
Lett., 47, e2020GL088415, https://doi.org/10.1029/2020GL088415, 2020. a, b
Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., 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, 2018. a
Zuo, M., Man, W., and Zhou, T., and Guo, Z.: Different impacts of Northern,
tropical, and Southern volcanic eruptions on the tropical pacific SST in the
Last Millennium, J. Climate, 31, 6729–6744, https://doi.org/10.1175/JCLI-D-17-0571.1, 2018. a
Šeparović, L., Alexandru, A., Laprise, R., Martynov, A., Sushama, L., Winger,
K., Tete, K., and Valin, M.: Present climate and climate change over North
America as simulated by the fifth-generation Canadian regional climate
model, Clim. Dynam., 41, 3167–3201, https://doi.org/10.1007/s00382-013-1737-5,
2013. a
Altmetrics