162,119 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
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>
[Report to Chief J. E. Curry, by an unknown author #1]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
[Report to Chief J. E. Curry, by an unknown author #2]
No full textReport to Chief J. E. Curry, by an unknown author. The report contains a list of officers who gave depositions to the United States Attorney
On estimating local long-term climate trends
No full textClimate 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
Murder on the mountain: author talk with Peter J. Wosh
No full textAuthor talk by Peter J. Wosh on May 5th, 2022, on his book, "Murder on the Mountain: crime, passion, and punishment in gilded age New Jersey.
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Climate nonlinearities: selection, uncertainty, projections, and damages
No full textClimate projections are uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?), structural (what equations do we use in the first place?), and due to internal variability (natural variability intrinsic to the climate system). The former and latter are straightforward to characterise in principle, though may be computationally intensive for complex climate models. The second is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energy-balance model representations of climate physics that underpin many economists' integrated assessment models (IAMs) (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a model with a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperature-dependence is opposite of what Earth system models suggest. This difference of sign is likely due to the assumption tha the recent pattern effect can be represented as a temperature dependence. Moreover, models other than the most likely one contain a majority of the posterior probability, indicating that structural uncertainty is important for climate projections. Indeed, in projections using shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for IAMs in particular, and for climate projections in general
Mr. Melvin J. Collier, RWWL AUC, June 2011
No full textThis video is a conversation with Mr. Melvin J. Collier. Mr. Collier talks about his book, "From Mississippi to Africa: A Journey of Discovery". Daniel Le, AUC Woodruff Library, is the interviewer
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