96 research outputs found
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
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>
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
Mapping climate change in European temperature distributions
Get PDFClimate change poses challenges for decision makers across society, not just in preparing for the climate of the future but even when planning for the climate of the present day. When making climate sensitive decisions, policy makers and adaptation planners would benefit from information on local scales and for user-specific quantiles (e.g. the hottest/coldest 5% of days) and thresholds (e.g. days above 28 ° C), not just mean changes. Here, we translate observations of weather into observations of climate change, providing maps of the changing shape of climatic temperature distributions across Europe since 1950. The provision of such information from observations is valuable to support decisions designed to be robust in today's climate, while also providing data against which climate forecasting methods can be judged and interpreted. The general statement that the hottest summer days are warming faster than the coolest is made decision relevant by exposing how the regions of greatest warming are quantile and threshold dependent. In a band from Northern France to Denmark, where the response is greatest, the hottest days in the temperature distribution have seen changes of at least 2 ° C, over four times the global mean change over the same period. In winter the coldest nights are warming fastest, particularly in Scandinavia
Testing climate assumptions
No full textStudies often assume that climate is equally sensitive to emissions of warming greenhouse gases and cooling sulphate aerosols. Now, research illustrates that this is not true in models and that without this assumption recent assessments would have produced higher estimates of future temperatures
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Climate nonlinearities: selection, uncertainty, projections, and damages
Get PDFClimate 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
New priorities for climate science and climate economics in the 2020s
No full textClimate science and climate economics are critical sources of expertise in our pursuit of the Sustainable Development Goals. Effective use of this expertise requires a strengthening of its epistemic foundations and a renewed focus on more practical policy problems
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