Articles | Volume 8, issue 2
https://doi.org/10.5194/ascmo-8-249-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-8-249-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
14 Dec 2022
| 14 Dec 2022
Evaluation of simulated responses to climate forcings: a flexible statistical framework using confirmatory factor analysis and structural equation modelling – Part 2: Numerical experiment
Katarina Lashgari
CORRESPONDING AUTHOR
Department of Mathematics, Division of Mathematical Statistics, Stockholm University, 106 91 Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden
previously published under the name Ekaterina Fetisova
Anders Moberg
Department of Physical Geography, Stockholm University, 106 91 Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden
Gudrun Brattström
Department of Mathematics, Division of Mathematical Statistics, Stockholm University, 106 91 Stockholm, Sweden
Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden
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Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, https://doi.org/10.5194/ascmo-8-225-2022, 2022
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This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, https://doi.org/10.5194/ascmo-8-225-2022, 2022
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This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
Renate Anna Irma Wilcke, Erik Kjellström, Changgui Lin, Daniela Matei, Anders Moberg, and Evangelos Tyrlis
Earth Syst. Dynam., 11, 1107–1121, https://doi.org/10.5194/esd-11-1107-2020, https://doi.org/10.5194/esd-11-1107-2020, 2020
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Two long-lasting high-pressure systems in summer 2018 led to heat waves over Scandinavia and an extended summer period with devastating impacts on both agriculture and human life. Using five climate model ensembles, the unique 263-year Stockholm temperature time series and a composite 150-year time series for the whole of Sweden, we found that anthropogenic climate change has strongly increased the probability of a warm summer, such as the one observed in 2018, occurring in Sweden.
Johann H. Jungclaus, Edouard Bard, Mélanie Baroni, Pascale Braconnot, Jian Cao, Louise P. Chini, Tania Egorova, Michael Evans, J. Fidel González-Rouco, Hugues Goosse, George C. Hurtt, Fortunat Joos, Jed O. Kaplan, Myriam Khodri, Kees Klein Goldewijk, Natalie Krivova, Allegra N. LeGrande, Stephan J. Lorenz, Jürg Luterbacher, Wenmin Man, Amanda C. Maycock, Malte Meinshausen, Anders Moberg, Raimund Muscheler, Christoph Nehrbass-Ahles, Bette I. Otto-Bliesner, Steven J. Phipps, Julia Pongratz, Eugene Rozanov, Gavin A. Schmidt, Hauke Schmidt, Werner Schmutz, Andrew Schurer, Alexander I. Shapiro, Michael Sigl, Jason E. Smerdon, Sami K. Solanki, Claudia Timmreck, Matthew Toohey, Ilya G. Usoskin, Sebastian Wagner, Chi-Ju Wu, Kok Leng Yeo, Davide Zanchettin, Qiong Zhang, and Eduardo Zorita
Geosci. Model Dev., 10, 4005–4033, https://doi.org/10.5194/gmd-10-4005-2017, https://doi.org/10.5194/gmd-10-4005-2017, 2017
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Climate model simulations covering the last millennium provide context for the evolution of the modern climate and for the expected changes during the coming centuries. They can help identify plausible mechanisms underlying palaeoclimatic reconstructions. Here, we describe the forcing boundary conditions and the experimental protocol for simulations covering the pre-industrial millennium. We describe the PMIP4 past1000 simulations as contributions to CMIP6 and additional sensitivity experiments.
Y. Brugnara, R. Auchmann, S. Brönnimann, R. J. Allan, I. Auer, M. Barriendos, H. Bergström, J. Bhend, R. Brázdil, G. P. Compo, R. C. Cornes, F. Dominguez-Castro, A. F. V. van Engelen, J. Filipiak, J. Holopainen, S. Jourdain, M. Kunz, J. Luterbacher, M. Maugeri, L. Mercalli, A. Moberg, C. J. Mock, G. Pichard, L. Řezníčková, G. van der Schrier, V. Slonosky, Z. Ustrnul, M. A. Valente, A. Wypych, and X. Yin
Clim. Past, 11, 1027–1047, https://doi.org/10.5194/cp-11-1027-2015, https://doi.org/10.5194/cp-11-1027-2015, 2015
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A data set of instrumental pressure and temperature observations for the early instrumental period (before ca. 1850) is described. This is the result of a digitisation effort involving the period immediately after the eruption of Mount Tambora in 1815, combined with the collection of already available sub-daily time series. The highest data availability is therefore for the years 1815 to 1817. An analysis of pressure variability and of case studies in Europe is performed for that period.
A. Moberg, R. Sundberg, H. Grudd, and A. Hind
Clim. Past, 11, 425–448, https://doi.org/10.5194/cp-11-425-2015, https://doi.org/10.5194/cp-11-425-2015, 2015
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Experiments with climate models can help to understand causes of past climate changes. We develop a statistical framework for comparing data from simulation experiments with temperature reconstructions for the last millennium. A combination of several external factors is found to explain a significant part of the observed variations, but our selection of data cannot tell which of two alternative choices of past solar forcing gives the best fit between simulations and reconstructions.
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Comparing climate time series – Part 2: A multivariate test
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Benjamin James Washington, Lynne Seymour, and Thomas L. Mote
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 1–28, https://doi.org/10.5194/ascmo-9-1-2023, https://doi.org/10.5194/ascmo-9-1-2023, 2023
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We develop new methodology to statistically model known bias in general atmospheric circulation models. We focus on Puerto Rico specifically because of other important ongoing and long-term ecological and environmental research taking place there. Our methods work even in the presence of Puerto Rico's broken climate record. With our methods, we find that climate change will not only favor a warmer and wetter climate in Puerto Rico, but also increase the frequency of extreme rainfall events.
Katarina Lashgari, Gudrun Brattström, Anders Moberg, and Rolf Sundberg
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, https://doi.org/10.5194/ascmo-8-225-2022, 2022
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This work theoretically motivates an extension of the statistical model used in so-called detection and attribution studies to structural equation modelling. The application of one of the models suggested is exemplified in a small numerical study, whose aim was to check the assumptions typically placed on ensembles of climate model simulations when constructing mean sequences. he result of this study indicated that some ensembles for some regions may not satisfy the assumptions in question.
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
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Timothy DelSole and Michael K. Tippett
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Julie Bessac and Philippe Naveau
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Eric Gilleland
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Verifying high-resolution weather forecasts has become increasingly complicated,
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Thomas Patrick Leahy
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 1–11, https://doi.org/10.5194/ascmo-7-1-2021, https://doi.org/10.5194/ascmo-7-1-2021, 2021
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Yoann Robin and Aurélien Ribes
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221, https://doi.org/10.5194/ascmo-6-205-2020, https://doi.org/10.5194/ascmo-6-205-2020, 2020
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Timothy DelSole and Michael K. Tippett
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Scientists often are confronted with the question of whether two time series are statistically distinguishable. This paper proposes a test for answering this question. The basic idea is to fit each time series to a time series model and then test whether the parameters in that model are equal. If a difference is detected, then new ways of visualizing those differences are proposed, including a clustering technique and a method based on optimal initial conditions.
Joshua North, Zofia Stanley, William Kleiber, Wiebke Deierling, Eric Gilleland, and Matthias Steiner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 79–90, https://doi.org/10.5194/ascmo-6-79-2020, https://doi.org/10.5194/ascmo-6-79-2020, 2020
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Very short-term forecasting, called nowcasting, is used to monitor storms that pose a significant threat to people and infrastructure. These threats could include lightning strikes, hail, heavy precipitation, strong winds, and possible tornados. This paper proposes a fast approach to nowcasting lightning threats using simple statistical methods. The proposed model results in fast nowcasts that are more accurate than a competitive, computationally expensive, approach.
Ola Haug, Thordis L. Thorarinsdottir, Sigrunn H. Sørbye, and Christian L. E. Franzke
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 1–12, https://doi.org/10.5194/ascmo-6-1-2020, https://doi.org/10.5194/ascmo-6-1-2020, 2020
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Trends in gridded temperature data are commonly assessed independently for each grid cell, ignoring spatial coherencies. This may severely affect the interpretation of the results. This article proposes a space–time model for temperatures that allows for joint assessments of the trend across locations. In a case study of summer season trends in Europe, it is found that the region with a significant trend under spatial coherency is vastly different from that under independent assessments.
Alexis Hannart
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 161–171, https://doi.org/10.5194/ascmo-5-161-2019, https://doi.org/10.5194/ascmo-5-161-2019, 2019
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In climate change attribution studies, one often seeks to maximize a signal-to-noise ratio, where the
signalis the anthropogenic response and the
noiseis climate variability. A solution commonly used in D&A studies thus far consists of projecting the signal on the subspace spanned by the leading eigenvectors of climate variability. Here I show that this approach is vastly suboptimal – in fact, it leads instead to maximizing the noise-to-signal ratio. I then describe an improved solution.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
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Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
X. Joey Wang, John R. J. Thompson, W. John Braun, and Douglas G. Woolford
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 57–66, https://doi.org/10.5194/ascmo-5-57-2019, https://doi.org/10.5194/ascmo-5-57-2019, 2019
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This paper presents the analysis of data from small-scale laboratory experimental smouldering fires that were digitally video-recorded. The video images of these fires bear a resemblance to remotely sensed images of wildfires and provide an opportunity to fit and assess a spatial model for fire spread that attempts to account for uncertainty in fire growth. We found that the fitting method is feasible, and the spatial model provides a suitable mathematical for the fire spread process.
Robin Tokmakian and Peter Challenor
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 17–35, https://doi.org/10.5194/ascmo-5-17-2019, https://doi.org/10.5194/ascmo-5-17-2019, 2019
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As an example of how to robustly determine climate model uncertainty, the paper describes an experiment that perturbs the initial conditions for the ocean's temperature of a climate model. A total of 30 perturbed simulations are used (via an emulator) to estimate spatial uncertainties for temperature and precipitation fields. We also examined (using maximum covariance analysis) how ocean temperatures affect air temperatures and precipitation over land and the importance of feedback processes.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
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Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Tony E. Wong
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 53–63, https://doi.org/10.5194/ascmo-4-53-2018, https://doi.org/10.5194/ascmo-4-53-2018, 2018
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Millions of people worldwide are at a risk of coastal flooding, and this number will increase as the climate continues to change. This study analyzes how climate change affects future flood hazards. A new model that uses multiple climate variables for flood hazard is developed. For the case study of Norfolk, Virginia, the model predicts 23 cm higher flood levels relative to previous work. This work shows the importance of accounting for climate change in effectively managing coastal risks.
Amy Braverman, Snigdhansu Chatterjee, Megan Heyman, and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 93–105, https://doi.org/10.5194/ascmo-3-93-2017, https://doi.org/10.5194/ascmo-3-93-2017, 2017
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In this paper, we introduce a method for expressing the agreement between climate model output time series and time series of observational data as a probability value. Our metric is an estimate of the probability that one would obtain two time series as similar as the ones under consideration, if the climate model and the observed series actually shared the same underlying climate signal.
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67–92, https://doi.org/10.5194/ascmo-3-67-2017, https://doi.org/10.5194/ascmo-3-67-2017, 2017
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We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55–66, https://doi.org/10.5194/ascmo-3-55-2017, https://doi.org/10.5194/ascmo-3-55-2017, 2017
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This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33–53, https://doi.org/10.5194/ascmo-3-33-2017, https://doi.org/10.5194/ascmo-3-33-2017, 2017
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We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1–16, https://doi.org/10.5194/ascmo-3-1-2017, https://doi.org/10.5194/ascmo-3-1-2017, 2017
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We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155–169, https://doi.org/10.5194/ascmo-2-155-2016, https://doi.org/10.5194/ascmo-2-155-2016, 2016
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Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, https://doi.org/10.5194/ascmo-2-137-2016, 2016
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Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
Whitney K. Huang, Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, https://doi.org/10.5194/ascmo-2-79-2016, 2016
Sergei N. Rodionov
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 63–78, https://doi.org/10.5194/ascmo-2-63-2016, https://doi.org/10.5194/ascmo-2-63-2016, 2016
David Bolin, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39–47, https://doi.org/10.5194/ascmo-2-39-2016, https://doi.org/10.5194/ascmo-2-39-2016, 2016
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1–16, https://doi.org/10.5194/ascmo-2-1-2016, https://doi.org/10.5194/ascmo-2-1-2016, 2016
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Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59–74, https://doi.org/10.5194/ascmo-1-59-2015, https://doi.org/10.5194/ascmo-1-59-2015, 2015
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There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
R. Philbin and M. Jun
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, https://doi.org/10.5194/ascmo-1-29-2015, 2015
T. K. Doan, J. Haslett, and A. C. Parnell
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 15–27, https://doi.org/10.5194/ascmo-1-15-2015, https://doi.org/10.5194/ascmo-1-15-2015, 2015
W. B. Leeds, E. J. Moyer, and M. L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 1–14, https://doi.org/10.5194/ascmo-1-1-2015, https://doi.org/10.5194/ascmo-1-1-2015, 2015
Cited articles
Allen, M. R. and Stott, P. A.: Estimating signal amplitudes in optimal fingerprinting, part I: theory,
Clim. Dynam., 21, 477–491, https://doi.org/10.1007/s00382-003-0313-9, 2003. a
Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillet, N., Gutzler, D., Hansingo, K., Hegerl, G., Hu, Y.,
Jain, S., Mokhov, I. I., Overland, J., Perlwitz, J., Sebbari, R., and Zhang, X.:
Detection and Attribution of Climate Change: from Global to Regional, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the Intergovermental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex V., and Medgley,
P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 867–952, 2013. a
Bollen, K. A.: Structural equations with latent variables, Wiley, ISBN 0471011711, 1989.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte, V., Abe-Ouchi, A.,
Otto-Bliesner, B., and Zhao, Y.: Evaluation of climate models using palaeoclimatic data, Nat. Clim. Change,
2, 417–424, https://doi.org/10.1038/nclimate1456, 2012. a
Brewer, S., Guiot, J., and Torre, F.: Mid-Holocene climate change in Europe: a data-model comparison, Clim. Past, 3, 499–512, https://doi.org/10.5194/cp-3-499-2007, 2007a. a
Brewer, S., Alleaume, S., Guiot, J., and Nicault, A.: Historical droughts in Mediterranean regions during the last 500 years: a data/model approach, Clim. Past, 3, 355–366, https://doi.org/10.5194/cp-3-355-2007, 2007b. a
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré C.,
Myneni, R. B., Piao, S., and Thornton, P.: Carbon and Other Biogeochemical Cyc-les, in: Climate Change 2013: The Physical Science Basis.
Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by:
Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013. a
DelSole, T., Trenary, L., Yan, X., and Tippett, M. K.: Confidence intervals in optimal fingerprinting,
Clim. Dynam., 52, 4111–4126, https://doi.org/10.1007/s00382-018-4356-3, 2019. a, b
Fetisova, E.: Evaluation of climate model simulations by means of statistical methods, licentiate thesis, Department of
Mathematics, Stockholm University, http://www.math.su.se/polopoly_fs/1.260101.1449662582!/menu/standard/file/LicUppsats_KatarinaF.pdf (last access: 11 November 2022),
2015. a
Fetisova, E.: Towards a flexible statistical modelling by latent factors for evaluation
of simulated climate forcing effects, doctoral thesis, Department of Mathematics, Stockholm
University, http://su.diva-portal.org/smash/record.jsf?pid=diva2:1150197&dswid=9303 (last access: 11 November 2022), 2017. a, b
Fetisova, E., Moberg, A., and Brattström, G.: Towards a flexible statistical modelling by latent factors for evaluation of simulated
responses to climate forcings: Part III (Supplement) in doctoral thesis,
http://su.diva-portal.org/smash/get/diva2:1150165/FULLTEXT02.pdf (last access: 11 November 2022), 2017. a, b, c, d, e, f, g, h, i, j, k
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., Cox, P.,
Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and
Rummukainen, M.: Evaluation of Climate Models, in: Climate Change 2013:
The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment
Report of the Intergovermental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K.,
Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a, b
Fox, J.: TEACHER'S CORNER: Structural Equation Modeling with the sem package in R, Struct. Equ.
Modeling,
13, 465–486, https://doi.org/10.1207/s15328007sem1303_7, 2006. a
Fuller, W. A.: Measurement Error Models, Wiley, ISBMN 0-471-86187-1, 1987. a
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 Intergovermental Panel on Climate Change, edited by:
Solomon, S. Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. K.,
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007. a
Hind, A. and Moberg, A.: Past millennial solar forcing magnitude.
A statistical hemispheric-scale climate model versus proxy data comparison,
Clim. Dynam., 41, 2527–2537, https://doi.org/10.1007/s00382-012-1526-6, 2013. a
Hind, A., Moberg, A., and Sundberg, R.: Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 2: A pseudo-proxy study addressing the amplitude of solar forcing, Clim. Past, 8, 1355–1365, https://doi.org/10.5194/cp-8-1355-2012, 2012. a
Jones, P. D., Briffa, K. R., Osborn, T. J., Lough, J. M., van Ommen, T. D., Vinther, B. M., Luterbacher, J., Wahl, E. R.,
Zwiers, F. W., Mann, M. E., Schmidt, G. A., Ammann, C. M., Buckley, B. M., Cobb, K. M., Esper, J., Goosse, H., and Graham, N.:
High-resolution palaeoclimatology of the last millennium: a review of current status and future prospects,
Holocene, 19, 3–49, https://doi.org/10.1177/0959683608098952, 2009. a, b
Jungclaus, J. H., Bard, E., Baroni, M., Braconnot, P., Cao, J., Chini, L. P., Egorova, T., Evans, M., González-Rouco, J. F., Goosse, H., Hurtt, G. C., Joos, F., Kaplan, J. O., Khodri, M., Klein Goldewijk, K., Krivova, N., LeGrande, A. N., Lorenz, S. J., Luterbacher, J., Man, W., Maycock, A. C., Meinshausen, M., Moberg, A., Muscheler, R., Nehrbass-Ahles, C., Otto-Bliesner, B. I., Phipps, S. J., Pongratz, J., Rozanov, E., Schmidt, G. A., Schmidt, H., Schmutz, W., Schurer, A., Shapiro, A. I., Sigl, M., Smerdon, J. E., Solanki, S. K., Timmreck, C., Toohey, M., Usoskin, I. G., Wagner, S., Wu, C.-J., Yeo, K. L., Zanchettin, D., Zhang, Q., and Zorita, E.: The PMIP4 contribution to CMIP6 – Part 3: The last millennium, scientific objective, and experimental design for the PMIP4 past1000 simulations, Geosci. Model Dev., 10, 4005–4033, https://doi.org/10.5194/gmd-10-4005-2017, 2017. a
Kimball, J. and Njoku, E. G. (Ed.): Vegetation phenology,
in: Encyclopedia of Remote Sensing, Part of the series Encyclopedia of Earth Sciences
Series, Springer New York, New York, NY, 886–890, https://doi.org/10.1007/978-0-387-36699-9_188, 2014. a
Kutzbach, J. E.: The nature of climate and climatic variations, Quatern. Res., 6, 471–480,
https://doi.org/10.1016/0033-5894(76)90020-X, 1976. a
Lashgari, K., Brattström, G., Moberg, A., and Sundberg, R.: Evaluation of simulated responses to climate forcings:
a flexible statistical framework using confirmatory factor
analysis and structural equation modelling
– Part 1: Theory, Adv. Stat. Clim. Meteorol. Oceanogr., 8, 225–248, https://doi.org/10.5194/ascmo-8-225-2022, 2022. a
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Fletcher, C. G., Lawrence, P. J., Levis, S., Swenson, S. C., and Bonan, G. B.:
The CCSM4 Land Simulation, 1850–2005: Assessment of Surface Climate and New Capabilities, J. Climate, 25, 2240–2260,
https://doi.org/10.1175/JCLI-D-11-00103.1, 2012. a
Liepert B. G.: The physical concept of climate forcing, WIREs Clim. Change 2010, 1, 786–802, https://doi.org/10.1002/wcc.75, 2010. a
Marvel, K., Schmidt, G. A., Shindell, D., Bonfils, C., LeGrande, A. N. Nazarenko, L., and Tsigaridis, K.:
Do responses to different anthropogenic forcings add linearly in climate models?, Environ. Res. Lett.,
10, 104010, https://doi.org/10.1088/1748-9326/10/10/104010, 2015. a
Moberg, A. and Hind, A.: Simulated seasonal temperatures 850–2005 for the seven PAGES 2k regions derived from the CESM last millennium ensemble. Dataset version 1, Bolin Centre Database, https://doi.org/10.17043/moberg-2019-cesm-1, 2019. a, b
Moberg, A., Sundberg, R., Grudd, H., and Hind, A.: Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 3: Practical considerations, relaxed assumptions, and using tree-ring data to address the amplitude of solar forcing, Clim. Past, 11, 425–448, https://doi.org/10.5194/cp-11-425-2015, 2015. a
Otto-Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L., Stevenson, S., Rosenbloom, N.,
Mai, A., and Strand, G.: Climate variability and changes since 850 CE: An Ensemble Approach with the Community Earth
System Model, B. Am. Meteorol. Soc., 97, 735–754, https://doi.org/10.1175/BAMS-D-14-00233.1, 2016. a
PAGES 2k Consortium: Continental-scale temperature variability during the past two millennia,
Nat. Geosci., 6, 339–346, https://doi.org/10.1038/NGEO1797, 2013. a, b, c
PAGES 2k-PMIP3 group: Continental-scale temperature variability in PMIP3 simulations and PAGES 2k regional temperature reconstructions over the past millennium, Clim. Past, 11, 1673–1699, https://doi.org/10.5194/cp-11-1673-2015, 2015. a, b
Philbin, R. and Jun, M.: Bivariate spatial analysis of temperature and precipitation from general circulation models and observation proxies, Adv. Stat. Clim. Meteorol. Oceanogr., 1, 29–44, https://doi.org/10.5194/ascmo-1-29-2015, 2015. a
Servonnat, J., Yiou, P., Khodri, M., Swingedouw, D., and Denvil, S.: Influence of solar variability, CO2 and orbital forcing between 1000 and 1850 AD in the IPSLCM4 model, Clim. Past, 6, 445–460, https://doi.org/10.5194/cp-6-445-2010, 2010. a, b
Schmidt, G. A., Jungclaus, J. H., Ammann, C. M., Bard, E., Braconnot, P., Crowley, T. J., Delaygue, G., Joos, F., Krivova, N. A., Muscheler, R., Otto-Bliesner, B. L., Pongratz, J., Shindell, D. T., Solanki, S. K., Steinhilber, F., and Vieira, L. E. A.: Climate forcing reconstructions for use in PMIP simulations of the last millennium (v1.0), Geosci. Model Dev., 4, 33–45, https://doi.org/10.5194/gmd-4-33-2011, 2011. a
Schurer, A. P., Tett S. F., and Hegerl G. C.: Small influence of solar variability on climate over the past
millennium, Nat. Geosci., 7, 104–108, https://doi.org/10.1038/NGEO2040, 2014. a
Shapiro, S. S. and Wilk, M. B.: An analysis of variance test for normality (complete samples), Biometrika, 52, 591–611,
https://doi.org/10.1093/biomet/52.3-4.591, 1965. a
Smerdon, J. E.: Climate models as a test bed for climate reconstruction methods: pseudoproxy experiments, WIREs Clim. Change,
3, 63–77, https://doi.org/10.1002/wcc.149, 2012. a
Sundberg, R., Moberg, A., and Hind, A.: Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 1: Theory, Clim. Past, 8, 1339–1353, https://doi.org/10.5194/cp-8-1339-2012, 2012. a
Sörbom, D.: Model Modification, Psychometrika, 54, 371–384, https://doi.org/10.1007/BF02294623, 1989.
Texier, D., de Noblet, N., Harrison, S., Haxeltine, A., Jolly, D., Joussaume, S., Laarif, F., Prentice, I., and Tarasov, P.:
Quantifying the role of biosphere-atmosphere feedbacks in climate change: coupled model
simulations for 6000 years BP and comparison with palaeodata for northern Eurasia and northern Africa,
Clim. Dynam., 13, 865–881, https://doi.org/10.1007/s003820050202, 1997. a
Thorarinsdottir, T. L., Gneiting, T., and Gissibl, N.: Using Proper Divergence Functions to Evaluate Climate Models,
Soc. Indust. Appl. Math., 1, 522–534, https://doi.org/10.1137/130907550, 2013.
a
Westland, J. C.: Structural Equation Models: from paths to networks, Springer, https://doi.org/10.1007/978-3-319-16507-3, 2015. a
Short summary
The performance of a new statistical framework containing various structural equation modelling (SEM) models is evaluated in a pseudo-proxy experiment in comparison with the performance of statistical models used in many detection and attribution studies. Each statistical model was fitted to seven continental-scale regional temperature data sets. The results indicated the SEM specification is the most appropriate for describing the underlying latent structure of the simulated data analysed.
The performance of a new statistical framework containing various structural equation modelling...
