Articles | Volume 30, issue 1
https://doi.org/10.5194/npg-30-63-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-30-63-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
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15 Feb 2023
Research article | Highlight paper |
| 15 Feb 2023
| 15 Feb 2023
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Clara Deser
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, CO, USA
Adam S. Phillips
National Center for Atmospheric Research, Boulder, CO, USA
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Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager
Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, https://doi.org/10.5194/esd-12-1393-2021, 2021
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A large ensemble of simulations with 100 members has been conducted with the state-of-the-art CESM2 Earth system model, using historical and SSP3-7.0 forcing. Our main finding is that there are significant changes in the variance of the Earth system in response to anthropogenic forcing, with these changes spanning a broad range of variables important to impacts for human populations and ecosystems.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
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
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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.
Doug M. Smith, James A. Screen, Clara Deser, Judah Cohen, John C. Fyfe, Javier García-Serrano, Thomas Jung, Vladimir Kattsov, Daniela Matei, Rym Msadek, Yannick Peings, Michael Sigmond, Jinro Ukita, Jin-Ho Yoon, and Xiangdong Zhang
Geosci. Model Dev., 12, 1139–1164, https://doi.org/10.5194/gmd-12-1139-2019, https://doi.org/10.5194/gmd-12-1139-2019, 2019
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The Polar Amplification Model Intercomparison Project (PAMIP) is an endorsed contribution to the sixth Coupled Model Intercomparison Project (CMIP6). It will investigate the causes and global consequences of polar amplification through coordinated multi-model numerical experiments. This paper documents the experimental protocol.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
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A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
Nicola Maher, Adam S. Phillips, Clara Deser, Robert C. Jnglin Wills, Flavio Lehner, John Fasullo, Julie M. Caron, Lukas Brunner, and Urs Beyerle
EGUsphere, https://doi.org/10.5194/egusphere-2024-3684, https://doi.org/10.5194/egusphere-2024-3684, 2024
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We present a new multi-model large ensemble archive (MMLEAv2) and introduce the newly updated Climate Variability Diagnostics Package version 6 (CVDPv6), which is designed specifically for use with large ensembles. For highly variable quantities, we demonstrate that a model might evaluate poorly or favourably compared to the single realisation of the world that the observations represent, highlighting the need for large ensembles for model evaluation.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, https://doi.org/10.5194/esd-14-309-2023, 2023
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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.
Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager
Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, https://doi.org/10.5194/esd-12-1393-2021, 2021
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A large ensemble of simulations with 100 members has been conducted with the state-of-the-art CESM2 Earth system model, using historical and SSP3-7.0 forcing. Our main finding is that there are significant changes in the variance of the Earth system in response to anthropogenic forcing, with these changes spanning a broad range of variables important to impacts for human populations and ecosystems.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
Short summary
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
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.
Doug M. Smith, James A. Screen, Clara Deser, Judah Cohen, John C. Fyfe, Javier García-Serrano, Thomas Jung, Vladimir Kattsov, Daniela Matei, Rym Msadek, Yannick Peings, Michael Sigmond, Jinro Ukita, Jin-Ho Yoon, and Xiangdong Zhang
Geosci. Model Dev., 12, 1139–1164, https://doi.org/10.5194/gmd-12-1139-2019, https://doi.org/10.5194/gmd-12-1139-2019, 2019
Short summary
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The Polar Amplification Model Intercomparison Project (PAMIP) is an endorsed contribution to the sixth Coupled Model Intercomparison Project (CMIP6). It will investigate the causes and global consequences of polar amplification through coordinated multi-model numerical experiments. This paper documents the experimental protocol.
Veronika Eyring, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, Oliver Andrews, Irene Cionni, Edouard L. Davin, Clara Deser, Carsten Ehbrecht, Pierre Friedlingstein, Peter Gleckler, Klaus-Dirk Gottschaldt, Stefan Hagemann, Martin Juckes, Stephan Kindermann, John Krasting, Dominik Kunert, Richard Levine, Alexander Loew, Jarmo Mäkelä, Gill Martin, Erik Mason, Adam S. Phillips, Simon Read, Catherine Rio, Romain Roehrig, Daniel Senftleben, Andreas Sterl, Lambertus H. van Ulft, Jeremy Walton, Shiyu Wang, and Keith D. Williams
Geosci. Model Dev., 9, 1747–1802, https://doi.org/10.5194/gmd-9-1747-2016, https://doi.org/10.5194/gmd-9-1747-2016, 2016
Short summary
Short summary
A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) in CMIP has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
A comparison of two nonlinear data assimilation methods
Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation
Quantum data assimilation: a new approach to solving data assimilation on quantum annealers
Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model
Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model
Control simulation experiments of extreme events with the Lorenz-96 model
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Control simulation experiment with Lorenz's butterfly attractor
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
A study of capturing Atlantic meridional overturning circulation (AMOC) regime transition through observation-constrained model parameters
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Generalization properties of feed-forward neural networks trained on Lorenz systems
Vivian A. Montiforte, Hans E. Ngodock, and Innocent Souopgui
Nonlin. Processes Geophys., 31, 463–476, https://doi.org/10.5194/npg-31-463-2024, https://doi.org/10.5194/npg-31-463-2024, 2024
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Advanced data assimilation methods are complex and computationally expensive. We compare two simpler methods, diffusive back-and-forth nudging and concave–convex nonlinearity, which account for change over time with the potential of providing accurate results with a reduced computational cost. We evaluate the accuracy of the two methods by implementing them within simple chaotic models. We conclude that the length and frequency of observations impact which method is better suited for a problem.
Fumitoshi Kawasaki and Shunji Kotsuki
Nonlin. Processes Geophys., 31, 319–333, https://doi.org/10.5194/npg-31-319-2024, https://doi.org/10.5194/npg-31-319-2024, 2024
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Recently, scientists have been looking into ways to control the weather to lead to a desirable direction for mitigating weather-induced disasters caused by torrential rainfall and typhoons. This study proposes using the model predictive control (MPC), an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrate that the system can be successfully controlled with shorter forecasts compared to previous studies.
Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi
Nonlin. Processes Geophys., 31, 237–245, https://doi.org/10.5194/npg-31-237-2024, https://doi.org/10.5194/npg-31-237-2024, 2024
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In Earth science, data assimilation plays an important role in integrating real-world observations with numerical simulations for improving subsequent predictions. To overcome the time-consuming computations of conventional data assimilation methods, this paper proposes using quantum annealing machines. Using the D-Wave quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches and significantly reduced computational time.
Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023, https://doi.org/10.5194/npg-30-457-2023, 2023
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This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data into a global weather model. By assimilating atmospheric observations and soil moisture data, the accuracy of forecasts was improved, and certain biases were reduced. The method was found to be particularly beneficial in areas like the Sahel and equatorial Africa, where precipitation patterns vary seasonally. This new approach has the potential to improve the precision of weather predictions.
Mao Ouyang, Keita Tokuda, and Shunji Kotsuki
Nonlin. Processes Geophys., 30, 183–193, https://doi.org/10.5194/npg-30-183-2023, https://doi.org/10.5194/npg-30-183-2023, 2023
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This research found that weather control would change the chaotic behavior of an atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.
Qiwen Sun, Takemasa Miyoshi, and Serge Richard
Nonlin. Processes Geophys., 30, 117–128, https://doi.org/10.5194/npg-30-117-2023, https://doi.org/10.5194/npg-30-117-2023, 2023
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This paper is a follow-up of a work by Miyoshi and Sun which was published in NPG Letters in 2022. The control simulation experiment is applied to the Lorenz-96 model for avoiding extreme events. The results show that extreme events of this partially and imperfectly observed chaotic system can be avoided by applying pre-designed small perturbations. These investigations may be extended to more realistic numerical weather prediction models.
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022, https://doi.org/10.5194/npg-29-329-2022, 2022
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This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022, https://doi.org/10.5194/npg-29-133-2022, 2022
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The weather is chaotic and hard to predict, but the chaos implies an effective control where a small control signal grows rapidly to make a big difference. This study proposes a control simulation experiment where we apply a small signal to control
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626, https://doi.org/10.5194/npg-28-615-2021, https://doi.org/10.5194/npg-28-615-2021, 2021
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Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
Zhao Liu, Shaoqing Zhang, Yang Shen, Yuping Guan, and Xiong Deng
Nonlin. Processes Geophys., 28, 481–500, https://doi.org/10.5194/npg-28-481-2021, https://doi.org/10.5194/npg-28-481-2021, 2021
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A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021, https://doi.org/10.5194/npg-28-23-2021, 2021
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To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data are sparse and the model can be non-linear. We propose a novel approach to address these challenges.
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207, https://doi.org/10.5194/npg-27-187-2020, https://doi.org/10.5194/npg-27-187-2020, 2020
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A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020, https://doi.org/10.5194/npg-27-209-2020, 2020
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Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019, https://doi.org/10.5194/npg-26-381-2019, 2019
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Neural networks are a technique that is widely used to predict the time evolution of physical systems. For this the past evolution of the system is shown to the neural network – it is
trained– and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.
Cited articles
Andrews, T., Bodas-Salcedo, A., Gregory, J. M., Dong, Y., Armour, K.
C., Paynter, D., Lin, P., Modak, A., Mauritsen, T., Cole, J. N. S.,
Medeiros, B., Benedict, J. J., Douville, H., Roehrig, R., Koshiro, T.,
Kawai, H., Ogura, T., Dufresne, J.-L., Allan, R. P., and Liu, C.: On the
effect of historical SST patterns on radiative feedback, J. Geophys.
Res.-Atmos., 127, e2022JD036675, https://doi.org/10.1029/2022JD036675, 2022.
Barnes, E. A., Hurrell, J. W., and Uphoff, I. E.: Viewing forced climate
patterns through an AI lens, Geophys. Res. Lett., 46, 13389–13398,
https://doi.org/10.1029/2019GL084944, 2019.
Berkeley Earth: Berkeley Earth's Global Temperature Report for 2022, Berkeley Earth [data set], http://berkeleyearth.org/data/, last access: 10 January 2022.
Beusch, L., Gudmundsson, L., and Seneviratne, S. I.: Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land, Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, 2020.
Bódai, T., Drótos, G., Herein, M., Lunkeit, F., and Lucarini, V.: The
Forced Response of the El Niño–Southern Oscillation–Indian Monsoon
Teleconnection in Ensembles of Earth System Models, J. Climate, 33,
2163–2182, https://doi.org/10.1175/JCLI-D-19-0341.1, 2020.
Bódai, T., Lee, J.-Y., and Sundaresan, A.: Sources of Nonergodicity
for Teleconnections as Cross-Correlations, Geophys. Res. Lett., 49,
e2021GL096587, https://doi.org/10.1029/2021GL096587, 2022.
Bonfils, C. J. W., Santer, B. D., Fyfe, J. C., Marvel, K., Phillips, T. J.,
and Zimmerman, S. R. H.: Human influence on joint changes in temperature,
rainfall and continental aridity, Nat. Clim. Change, 10, 726–731,
https://doi.org/10.1038/s41558-020-0821-1, 2020.
Capotondi, A., Deser, C., Phillips, A., Okumura, Y., and Larson, S.: ENSO and
Pacific DecadalVvariability in the Community Earth System Model Version 2,
J. Adv. Model. Earth Sy., 12, e2019MS002022,
https://doi.org/10.1029/2019MS002022, 2020.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman,
P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M.,
Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff,
S. D., and Worley, S. J.: The twentieth century reanalysis project, Q. J. Roy.
Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011.
Danabasoglu, G., Deser, C., Rodgers, K., and Timmermann, A.: CESM2 Large Ensemble, Climate Data Gateway at NCAR [data set], https://doi.org/10.26024/kgmp-c556, 2020.
Davenport, F. V. and Diffenbaugh, N. S.: Using machine learning to analyze
physical causes of climate change: A case study of U.S. Midwest extreme
precipitation, Geophys. Res. Lett., 48,
e2021GL093787, https://doi.org/10.1029/2021GL093787, 2021.
Deser, C.: Certain uncertainty: The role of internal climate variability in
projections of regional climate change and risk management, Earths
Future, 8, e2020EF001854, https://doi.org/10.1029/2020EF001854, 2020.
Deser, C. and Phillips, A. S.: Defining the internal component of Atlantic
Multidecadal Variability in a changing climate, Geophys. Res. Lett., 48,
e2021GL095023, https://doi.org/10.1029/2021GL095023, 2021.
Deser, C., Phillips, A., Bourdette, V., and Teng, H. Y.: Uncertainty in
climate change projections: The role of internal variability, Clim. Dynam.,
38, 527–546. https://doi.org/10.1007/s00382-010-0977-x, 2012.
Deser, C., Phillips, A., Alexander, M. A., and Smoliak, B. V.: Projecting
North American climate over the next 50 years: Uncertainty due to internal
variability, J. Climate, 27, 2271–2296,
https://doi.org/10.1175/JCLI-D-13-00451.1, 2014.
Deser, C., Terray, L., and Phillips, A. S.: Forced and internal components
of winter air temperature trends over North America during the past 50
years: Mechanisms and implications, J. Climate, 29, 2237–2258,
https://doi.org/10.1175/JCLI-D-15-0304.1, 2016.
Deser, C., Hurrell, J. W., and Phillips, A. S.: The role of the North
Atlantic Oscillation in European Climate Projections, Clim. Dynam., 49,
3141–3157, https://doi.org/10.1007/s00382-016-3502-z, 2017a.
Deser, C., Simpson, I. R., McKinnon K. A., and Phillips, A. S.: The Northern
Hemisphere extra-tropical atmospheric circulation response to ENSO: How well
do we know it and how do we evaluate models accordingly?, J. Climate, 30,
5059–5082, https://doi.org/10.1175/JCLI-D-16-0844.1, 2017b.
Deser, C., Simpson, I. R., Phillips, A. S., and McKinnon, K. A.: How well do
we know ENSO's climate impacts over North America, and how do we evaluate
models accordingly?, J. Climate, 30, 4991–5014,
https://doi.org/10.1175/JCLI-D-17-0783.1, 2018.
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio,
P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E.,
Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S.,
Randerson, J., Screen, J. A, Simpson, I. R., and Ting, M.: Insights from
earth system model initial-condition large ensembles and future
prospects, Nat. Clim. Change, 10, 277–286,
https://doi.org/10.1038/s41558-020-0731-2, 2020a.
Deser, C., Phillips, A. S., Simpson, I. R., Rosenbloom, N., Coleman, D.,
Lehner, F., Pendergrass, A., DiNezio, P., and Stevenson, S.: Isolating the
Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New
CESM1 Large Ensemble Community Resource, J. Climate, 33, 7835–7858,
https://doi.org/10.1175/JCLI-D-20-0123.1, 2020b.
Deutscher Wetterdienst: Global Precipitation Climatology Centre (GPCC) precipitation, Deutscher Wetterdienst [data set], https://www.dwd.de/EN/ourservices/gpcc/gpcc.html, last access: 10 January 2022.
DiNezio, P. N., Deser, C., Okumura, Y., and Karspeck, A.: Predictability of
2-year La Niña events in a coupled general circulation model, Clim. Dynam.,
49, 4237–4261, 2017.
Dong, Y., Armour, K. C., Zelinka, M., Proistosescu, C., Battisti, D., Zhou,
C., and Andrews, T.: Inter-model spread in the pattern effect and its
contribution to climate sensitivity in CMIP5 and CMIP6 models, J.
Climate, 33, 7755–7775, https://doi.org/10.1175/JCLI-D-19-1011.1, 2020.
Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L.,
and Robinson, N.: Do seasonal-to-decadal climate predictions underestimate
the predictability of the real world?, Geophys. Res. Lett., 41, 5620–5628,
https://doi.org/10.1002/2014GL061146, 2014.
ECMWF: ECMWF Reanalysis v5 (ERA5), ECMWF [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, last access: 10 January 2022.
Fasullo, J. T. and Nerem, R. S.: Altimeter-era emergence of the patterns of
forced sea-level rise in climate models and implications for the
future, P. Natl. Acad. Sci. USA, 115, 12944–12949, https://doi.org/10.1073/pnas.1813233115, 2018.
Fasullo, J., 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.
Gordon, E. M. and Barnes, E. A.: Incorporating uncertainty into a regression
neural network enables identification of decadal state-dependent
predictability, Geophys. Res. Lett., 49,
e2022GL098635, https://doi.org/10.1029/2022GL098635, 2022.
Gould, S. J.: Wonderful Life: The burgess shale and the nature of history,
W. W. Norton & Co., ISBN 978-0-393-30700-9, 1989.
Griffies, S. M. and Bryan, K.: Predictability of North Atlantic multidecadal
climate variability, Science, 275, 181–184,
https://doi.org/10.1126/science.275.5297.181, 1997.
Guo, R. X., Deser, C., Terray, L., and Lehner, F.: Human influence on
terrestrial precipitation trends revealed by dynamical adjustment, Geophys.
Res. Lett., 46, 3426–3434, https://doi.org/10.1029/2018GL081316, 2019.
Hegerl, G. C., Zwiers, F. W., Braconnot, P., Gillett, N. P., Luo, Y., Marengo Orsini, J. A., Nicholls, N., Penner, J. E., and Stott, P. A.: Understanding and Attributing Climate Change, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter9-1.pdf (last access: 23 March 2022), 2007.
Hurrell J. W., Kushnir, Y., Ottersen G., and Visbeck M. (Eds.): The North
Atlantic Oscillation: climate significance and environmental impact,
Geophys. Monogr. Ser, 134, AGU, Washington, D.C., 2003.
James, I. N. and James, P. M.: Spatial structure of ultra-low-frequency
variability of the flow in a simple atmospheric circulation model, Q. J.
Roy. Meteor. Soc., 118, 1211–1233, https://doi.org/10.1002/qj.49711850810,
1992.
Jin, E. K., Kinter, J. L., and Wang, B.: Current status of ENSO prediction
skill in coupled ocean–atmosphere models, Clim. Dynam., 31, 647–664,
https://doi.org/10.1007/s00382-008-0397-3, 2008.
Kay, J., 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.
Klavans, J. M., Cane, M. A., Clement, A. C., and Murphy, L. N.: NAO
predictability from external forcing in the late 20th century, Npj Clim.
Atmos. Sci., 4, 22, https://doi.org/10.1038/s41612-021-00177-8, 2021.
Lehner, F., Schurer, A. P., Hegerl, G. C., Deser, C., and Frölicher, T.
L.: The importance of ENSO phase during volcanic eruptions for detection and
attribution, Geophys. Res. Lett. 43, 2851–2858,
https://doi.org/10.1002/2016GL067935, 2016.
Lehner, F., Deser, C., and Terray, L.: Towards 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,
https://doi.org/10.1175/JCLI-D-16-0792.1, 2017.
Lehner, F., Deser, C., Simpson, I. R., and Terray, L.: Attributing the US
Southwest's recent shift into drier conditions, Geophys. Res. Lett., 45,
6251–6261, https://doi.org/10.1029/2018GL078312, 2018.
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.
Leith, C. E.: The standard error of time-average estimates of climatic
means, J. Appl. Meteorol. Clim., 12, 1066–1069,
https://doi.org/10.1175/1520-0450(1973)012<1066:TSEOTA>2.0.CO;2,
1973.
Lorenz, E. N.: Deterministic nonperiodic flow, J. Atmos. Sci., 20, 130–141,
https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2, 1963.
Madden, R. A.: Estimates of the natural variability of time-averaged
sea-level pressure, Mon. Weather Rev., 104, 942–952,
https://doi.org/10.1175/1520-0493(1976)104<0942:EOTNVO>2.0.CO;2,
1975.
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.
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.
McGraw, M. C., Barnes, E. A., and Deser, C.: Reconciling the observed and
modeled southern hemisphere circulation response to volcanic
eruptions, Geophys. Res. Lett., 43, 7259–7266,
https://doi.org/10.1002/2016GL069835, 2016.
McKenna, C. M. and Maycock, A. C.: Sources of uncertainty in multimodel
large ensemble projections of the winter North Atlantic
Oscillation, Geophys. Res. Lett., 48,
e2021GL093258, https://doi.org/10.1029/2021GL093258, 2021.
McKinnon, K.: Observational Large Ensemble, GitHub [code], https://github.com/karenamckinnon/observational_large_ensemble, last access: 21 January 2022.
McKinnon, K.: karenamckinnon/observational_large_ensemble: v1 (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.7636551, 2023.
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, 90, 7585–7598, https://doi.org/10.1175/JCLI-D-16-0905.1, 2017.
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.
McKinnon, K. A. and Deser, C.: The inherent uncertainty of precipitation
variability, trends, and extremes due to internal variability, with
implications for Western US water resources, J. Climate, 34, 9605–9622,
https://doi.org/10.1175/JCLI-D-21-0251.1, 2021.
Meehl, G., Hu, A., and Teng, H: Initialized decadal prediction for transition
to positive phase of the Interdecadal Pacific Oscillation, Nat. Commun., 7,
11718, https://doi.org/10.1038/ncomms11718, 2016.
Merrifield, A., Lehner, F., Xie, S.-P., and Deser, C.: Removing circulation
effects to assess Central US land-atmosphere interactions in the CESM Large
Ensemble, Geophys. Res. Lett., 44, 9938–9946,
https://doi.org/10.1002/2017GL074831, 2017.
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.
Newman, M.: Interannual to decadal predictability of tropical and North
Pacific sea surface temperatures, J. Climate, 20, 2333–2356,
https://doi.org/10.1175/JCLI4165.1, 2007.
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Di
Lorenzo, E., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H.,
Schneider, N., Vimont, D. J., Phillips, A. S., Scott, J. D., and Smith, C.
A.: The Pacific decadal oscillation, revisited, J. Climate, 29, 4399–4427,
https://doi.org/10.1175/JCLI-D-15-0508.1, 2016.
O'Brien, J. P. and Deser, C.: Quantifying and understanding forced changes
to unforced modes of atmospheric circulation variability over the North
Pacific in a coupled model large ensemble, J. Climate, 36, 17–35, https://doi.org/10.1175/JCLI-D-22-0101.1, 2023.
Olivarez, H. C., Lovenduski, N. S., Brady, R. X., Fay, A. R., Gehlen,
M., Gregor, L., Landschützer, P., McKinley, G. A., McKinnon, K. A., and
Munro, D. R.: Alternate histories: Synthetic large ensembles of sea-air
CO2 flux, Global Biogeochem. Cy., 36,
e2021GB007174, https://doi.org/10.1029/2021GB007174, 2022.
Persad, G. G. and Caldeira, K.: Divergent global-scale temperature effects
from identical aerosols emitted in different regions, Nat. Commun., 9, 3289,
https://doi.org/10.1038/s41467-018-05838-6, 2018.
Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., Bódai, T., Chung, E.-S., Huang, L., Kim, W. M., Lamarque, J.-F., Lombardozzi, D. L., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, 2021.
Rohde, R., Muller, R., Jacobsen, R., Perlmutter, S., Rosenfeld, A., Wurtele,
J., Curry, J., Wickham, C., and Mosher, S.: Berkeley Earth temperature
averaging process, Geoinf. Geostat. Overview, 1, 2,
https://doi.org/10.4172/2327-4581.1000103, 2013.
Santer, B., 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.
Scaife, A. A. and Smith, D.: A signal-to-noise paradox in climate
science, Npj Clim. Atmos. Sci., 1, 28,
https://doi.org/10.1038/s41612-018-0038-4, 2018.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T.,
Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., Hermanson,
L., Knight, J. R., Lea, D. J., MacLachlan, C., Maidens, A., Martin, M.,
Peterson, A. K., Smith, D., Vellinga, M., Wallace, E., Waters, J., and
Williams, A.: Skillful long-range prediction of European and North American
winters, Geophys. Res. Lett., 41, 2514–2519,
https://doi.org/10.1002/2014GL059637, 2014.
Schneider, D. P., Deser, C., and Fan, T.: Comparing the impacts of tropical
SST variability and polar stratospheric ozone loss on the Southern Ocean
westerly winds, J. Climate, 28, 9350–9372,
https://doi.org/10.1175/JCLI-D-15-0090.1, 2015.
Schneider, U., Fuchs, T., Meyer-Christoffer, A., and Rudolf, B.: GPCC's new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle, Theor. Appl. Climatol., 115, 15–40, https://doi.org/10.1007/s00704-013-0860-x, 2014.
Shepherd, T.: Atmospheric circulation as a source of uncertainty in climate
change projections, Nat. Geosci., 7, 703–708,
https://doi.org/10.1038/ngeo2253, 2014.
Sippel, S. Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G.,
Fischer, E., and Knutti, R.: Uncovering the forced climate response from a
single ensemble member using statistical learning, J. Climate, 32,
5677–5699, https://doi.org/10.1175/JCLI-D-18-0882.1, 2019.
Smith, D. M., Scaife, A. A., Eade, R., Athanasiadis, P., Bellucci, A.,
Bethke, I., Bilbao, R., Borchert, L. F., Caron, L. P., Counillon, F.,
Danabasoglu, G., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J.,
Estella-Perez, V., Flavoni, S., Hermanson, L., Keenlyside, N., Kharin, V.,
Kimoto, M., Merryfield, W. J., Mignot, J., Mochizuki, T., Modali, K.,
Monerie, P. A., Müller, W. A., Nicolí, D., Ortega, P., Pankatz, K.,
Pohlmann, H., Robson, J., Ruggieri, P., Sospedra-Alfonso, R., Swingedouw,
D., Wang, Y., Wild, S., Yeager, S., Yang, X., and Zhang, L.: North Atlantic
climate far more predictable than models imply, Nature, 583, 796–800,
https://doi.org/10.1038/s41586-020-2525-0, 2020.
Smoliak, B. V., Wallace, J. M., Lin, P., and Fu, Q.: Dynamical adjustment of
the Northern Hemisphere surface air temperature field: Methodology and
application to observations, J. Climate, 28, 1613–1629,
https://doi.org/10.1175/JCLI-D-14-00111.1, 2015.
Stevenson, S., Fox-Kemper, B., Jochum, M., Neale, R., Deser, C., and Meehl,
G.: Will there be a significant change to El Nino in the 21st Century?, J.
Climate, 25, 2129–2145, https://doi.org/10.1175/JCLI-D-11-00252.1, 2012.
Strommen, K., Juricke, S., and Cooper, F.: Improved teleconnection between Arctic sea ice and the North Atlantic Oscillation through stochastic process representation, Weather Clim. Dynam., 3, 951–975, https://doi.org/10.5194/wcd-3-951-2022, 2022.
Suarez-Gutierrez, L., Milinski, S., and Maher, N.: Exploiting large
ensembles for a better yet simpler climate model evaluation, Clim.
Dynam., 57, 2557–2580, https://doi.org/10.1007/s00382-021-05821-w,
2021.
Swart, N. C., Fyfe, J. C., Hawkins, E., Kay, J. E., and Jahn A.: Influence
of internal variability on Arctic sea-ice trends, Nat. Clim. Change, 5,
86–89, https://doi.org/10.1038/nclimate2483, 2015.
Tebaldi, C., Dorheim, K., Wehner, M., and Leung, R.: Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates, Earth Syst. Dynam., 12, 1427–1501, https://doi.org/10.5194/esd-12-1427-2021, 2021.
Tél, T., Bódai, T., Drótos, G., Haszpra, T., Herein, M.,
Kaszás, B., and Vincze, M.: The theory of parallel climate realizations,
J. Stat, Phys., 179, 1496–1530, https://doi.org/10.1007/s10955-019-02445-7,
2020.
Teng, H. and Branstator, G.: Initial-value predictability of prominent modes of North Pacific subsurface temperature in a CGCM, Clim. Dynam., 36, 1813–1834, https://doi.org/10.1007/s00382-010-0749-7, 2011.
Terray, L.: A dynamical adjustment perspective on extreme event attribution, Weather Clim. Dynam., 2, 971–989, https://doi.org/10.5194/wcd-2-971-2021, 2021a.
Terray, L.: terrayl/Dynamico: Dynamico version v1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.5584777, 2021b.
Thompson, D. W. J., Barnes, E. A., Deser, C., Foust, W. E., and Phillips, A.
S.: Quantifying the role of internal climate variability in future climate
trends, J. Climate, 28, 6443–6456,
https://doi.org/10.1175/JCLI-D-14-00830.1, 2015.
Trenary, L. and DelSole, T.: Does the Atlantic Multidecadal Oscillation Get
Its Predictability from the Atlantic Meridional Overturning Circulation?, J.
Climate, 29, 5267–5280, https://doi.org/10.1175/JCLI-D-16-0030.1, 2016.
Wallace, J. M., Deser, C., Smoliak, B. V., and Phillips, A. S.: Attribution
of climate change in the presence of internal variability, in: Climate
Change: Multidecadal and Beyond, edited by: Chang, C. P., Ghil, M., Latif, M., and Wallace, J.
M., World Scientific Series on Asia-Pacific Weather and Climate, 6,
1–29, https://doi.org/10.1142/9789814579933_0001, 2013.
Wang, C., Deser, C., Yu, J. -Y., DiNezio, P., and Clement, A.: El Nino and
Southern Oscillation (ENSO): A Review, in: Coral Reefs of the Eastern Pacific, edited by: Glymn,
P., Manzello, D. and Enochs, I., Springer Science Publisher, 4,
85–106, https://doi.org/10.1007/978-94-017-7499-4_4, 2017.
Wills, R. C. J., Battisti, D. S., Armour, K. C., Schneider, T., and Deser,
C.: Pattern recognition methods to separate forced responses from internal
variability in climate model ensembles and observations, J. Climate, 33,
8693–8719, https://doi.org/10.1175/JCLI-D-19-0855.1, 2020.
Wittenberg, A. T.: Are historical records sufficient to constrain ENSO
simulations?, Geophys. Res. Lett., 36, L12702,
https://doi.org/10.1029/2009GL038710, 2009.
Wu, X., Okumura, Y. M., Deser, C., and DiNezio, P. N.: Two-year dynamical
predictions of ENSO event duration during 1954–2015, J. Climate, 34,
4069–4087, https://doi.org/10.1175/JCLI-D-20-0619.1, 2021.
Yeager, S. Danabasoglu, D., Rosenbloom, N. A., Strand, W., Bates, S. C.,
Meehl, G. A., Karspeck, A. R., Lindsay, K., Long, M. C., Teng, H., and
Lovenduski, N. S.: Predicting near-term changes in the Earth System: A large
ensemble of initialized decadal prediction simulations using the Community
Earth System Model, B. Am. Meteorol. Soc., 99, 1867–1886,
https://doi.org/10.1175/BAMS-D-17-0098.1, 2018.
Zhang, R., Sutton, R., Danabasoglu, G., Kwon, Y.-O., Marsh, R., Yeager, S.
G., Amrhein, D. E., and Little, C. M.: A review of the role of the Atlantic
Meridional Overturning Circulation in Atlantic Multidecadal Variability and
associated climate impacts, Rev. Geophys., 57, 316–375,
https://doi.org/10.1029/2019RG000644, 2019.
Executive editor
The paper presents a valuable review of state-of-the-art large ensemble methodology, comparing observational-based analogues and model-generated perturbations with the aim of studying the impact of long-term variability on European past, present and future climate. This work is helpful as a theoretical and methodological benchmark for a number of open issues in ensemble modeling, that are currently the object of intense discussions in the research community. Given that this topic very much relates to the work done within the Coupled Model Intercomparison Project, and ultimately to the redaction of the IPCC Assessment Reports, the paper is of major interest for the research community as well as for the broader public.
The paper presents a valuable review of state-of-the-art large ensemble methodology, comparing...
Short summary
Past and future climate change at regional scales is a result of both human influences and natural (internal) variability. Here, we provide an overview of recent advances in climate modeling and physical understanding that has led to new insights into their respective roles, illustrated with original results for the European climate. Our findings highlight the confounding role of internal variability in attribution, climate model evaluation, and accuracy of future projections.
Past and future climate change at regional scales is a result of both human influences and...
