1,721,279 research outputs found
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
No full textThis *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the E-forth ensemble generator (de Melo Viríssimo and Stainforth, in preparation), which is a MATLAB toolbox that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023). These model outputs are presented and discussed in the Preprint "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective". The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013) and de Melo Viríssimo et al. (2024). All files uploaded were generated from simulations run by the lead author. For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the preprint above. If you have any questions, please feel free to contact me.
References: Van Veen et al. (2001): https://onlinelibrary.wiley.com/doi/abs/10.1034/j.1600-0870.2001.00241.x Daron and Stainforth (2013): https://dx.doi.org/10.1088/1748-9326/8/3/034021 de Melo Viríssimo and Stainforth (2023): https://doi.org/10.5194/egusphere-egu23-14755 de Melo Viríssimo et al. (2024): https://doi.org/10.1063/5.0180870 de Melo Viríssimo and Stainforth (in preparation): to appea
Q and A with David Stainforth on Predicting our climate future: what we know, what we don’t know, and what we can’t know
No full textWe speak to David Stainforth about his new book, Predicting Our Climate Future: What We Know, What We Don’t Know, and What We Can’t Know, which argues for a re-evaluation of how we go about the study of climate change in the physical sciences, the social sciences, economics and policy
Mixing Memory and Desire: Exploring Utopian Currents in Heritage
No full textThere is some precedent for utopian thinking around cultural heritage, and a number of writers have commented on the utopian ideal of museums to house and preserve intact cultural memory. However, this article focuses on another, distinct utopian strain relevant to cultural heritage, which can be traced through the influence of William Morris on the formation of conservation methods in the nineteenth century. While such figures have been linked to the critique of ‘monumental heritage’ in recent years, a central message in Morris’s writings was that the guiding principle for conservation should not be stasis but change. Equally, for him, knowledge of the past was important for recovering the hopes of former generations, a theme he explored in his utopian fiction. Morris’s utopianism presents a challenge to the logic of inheritance, whereby the past is figured as a legacy to be maintained and the future, in turn, is extracted confidently from the present. Instead, it involves a mixture of memory and desire, which signals a way into thinking about alternative experiences and expectations. Here, I discuss how the utopian currents in Morris’s work shed light on contemporary heritage debates and the different kinds of futures implicit in heritage-making
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
No full textThis *.rar file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the E-forth ensemble generator (de Melo Viríssimo and Stainforth, in preparation), which is a MATLAB toolboox that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023).
These model outputs are presented and discussed in the Preprint "The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example". The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013).
All files uploaded were generated from simulations run by the authors.
For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.
References:
Van Veen et al. (2003): https://doi.org/10.1034/j.1600-0870.2001.00241.x
Daron and Stainforth (2013): https://dx.doi.org/10.1088/1748-9326/8/3/034021
de Melo Viríssimo and Stainforth (2023): https://doi.org/10.5194/egusphere-egu23-14755
de Melo Viríssimo and Stainforth (2023): in preparation
Note: This version (v1.1) is the same version as v1.0 but with the correct README file.Version (v1.1) is the same version as v1.0 but with the correct README file
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
No full textThis *.rar file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the E-forth ensemble generator (de Melo Viríssimo and Stainforth, in preparation), which is a MATLAB toolbox that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023). These model outputs are presented and discussed in the Preprint "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example". The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013). All files uploaded were generated from simulations run by the authors. For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.
References:
Van Veen et al. (2003): https://doi.org/10.1034/j.1600-0870.2001.00241.x
Daron and Stainforth (2013): https://dx.doi.org/10.1088/1748-9326/8/3/034021
de Melo Viríssimo and Stainforth (2023): https://doi.org/10.5194/egusphere-egu23-14755
de Melo Viríssimo and Stainforth (2023): in preparation Note: This version (v1.1) is the same version as v1.0 but with the correct README file
Model output used in the manuscript "The evolution of a non-autonomous chaotic system under non-periodic forcing: a climate change example"
No full text<p>This *.rar file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (Van Veen et al, 2001; Daron and Stainforth, 2013). To run these simulations, we used the E-forth ensemble generator (de Melo Viríssimo and Stainforth, <em>in preparation</em>), which is a MATLAB toolboox that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (de Melo Viríssimo and Stainforth, 2023).</p>
<p>These model outputs are presented and discussed in the Preprint "<em>The evolution of a non-autonomouys chaotic system under non-periodic forcing: a climate change example</em>". The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to Daron and Stainforth (2013).</p>
<p>All files uploaded were generated from simulations run by the authors.</p>
<p>For specific information about each file uploaded, please refer to the README file. If you have any questions, please feel free to contact me.</p>
<p><strong>References:</strong></p>
<ul>
<li>Van Veen et al. (2003): https://doi.org/10.1034/j.1600-0870.2001.00241.x</li>
<li>Daron and Stainforth (2013): https://dx.doi.org/10.1088/1748-9326/8/3/034021</li>
<li>de Melo Viríssimo and Stainforth (2023): https://doi.org/10.5194/egusphere-egu23-14755</li>
<li>de Melo Viríssimo and Stainforth (2023): in preparation</li>
</ul>
MoDA Podcast Season 2, Episode 7, Staging home
No full textIn this episode MoDA's curator, Ana Baeza, discusses with Elizabeth Stainforth (University of Leeds) how people have staged their homes for public view, from analogue photography to digital imaging on social media. They also talk about the enigmatic Location Finder collection at MoDA, which captures domestic scenes in London from the 1980s to the 2000s
On estimating local long-term climate trends
Get PDFClimate sensitivity is commonly taken to refer to the equilibrium change in the annual mean global surface temperature following a doubling of the atmospheric carbon dioxide concentration. Evaluating this variable remains of significant scientific interest, but its global nature makes it largely irrelevant to many areas of climate science, such as impact assessments, and also to policy in terms of vulnerability assessments and adaptation planning. Here, we focus on local changes and on the way observational data can be analysed to inform us about how local climate has changed since the middle of the nineteenth century. Taking the perspective of climate as a constantly changing distribution, we evaluate the relative changes between different quantiles of such distributions and between different geographical locations for the same quantiles. We show how the observational data can provide guidance on trends in local climate at the specific thresholds relevant to particular impact or policy endeavours. This also quantifies the level of detail needed from climate models if they are to be used as tools to assess climate change impact. The mathematical basis is presented for two methods of extracting these local trends from the data. The two methods are compared first using surrogate data, to clarify the methods and their uncertainties, and then using observational surface temperature time series from four locations across Europe
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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