Review: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming By Paul N. Edwards Reviewed by Susan Maret San Jose State University, California, USA Edwards, Paul N. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming.Cambridge, MA: The MIT Press, 2010. 528pp. ISBN 0262013924. US$32.95, cloth. Global climate is one of the most pressing and misunderstood issues facing civil society in the 21 st century. In a recent poll of Americans, “majorities... incorrectly believe that the climate often changes from year to year or that 'weather' means the average climate conditions in a region, suggesting that many people continue to confuse weather and climate” (Leiserowitz 2010). In addition, many Americans believe “computer models are too unreliable to predict the climate of the future (37%)” (Leiserowitz 2010). Perhaps in part due to poor scientific and technical grasp of the evolution of climate modeling and data interpretation, the subject is also linked to conspiracy theory and skepticism over global warming (Hickman and Randerson 2009; BBC News 2010). A Vast Machine, written by University of Michigan iSchool Associate Professor Paul N. Edwards, is a significant text that contributes to understanding how we make climate knowledge (p. xiv). Edwards' book not only offers an historical account of the evolution of the global weather information infrastructure, scientific acceptance of interactions between data and models, and includes discussion of “global atmospheric politics” and current controversies over global warming. Using John Ruskin's (1839, p. 210) idea of the “moving power of a vast machine,” a “global weather observing system” (Edwards 2004), Edwards recounts the complex history of data collecting initiatives that contributed to what we know about climate “as the history of weather” (Edwards 2010, p. xiv). In a sense, the book is a case study in the philosophy of science in terms of how models and simulations are constructed, data interpreted, and standards adopted by epistemic communities. In writing this history, Edwards devises fresh concepts that serve as a conceptual framework. For example, data friction is tied to “making global data”; metadata friction reflects the “difficulty in recovering contextual knowledge about records” (Edwards 2010, p. xvi-xvii); data analysis models (used to “process historical weather and climate records”) and climate knowledge infrastructure (those national and international entities that observe, share, and link data historically to the present) show how communities construct climate knowledge in the form of environmental history and interpreting contemporary data. Of note, Chapters 8 through 10 relate to the “weather information infrastructure between 1950 and 1980, the years the U.S. military took interest in weather data, especially in attempts to model global nuclear fallout patterns that “could be used to trace flows back in time to an origin point, thus determining the approximate locations of secret test sites” (Edwards 2010, p.

A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming

Abstract Castanea henryi, with edible nuts and timber value, is a key tree species playing essential roles in China's subtropical forest ecosystems. However, natural and human perturbations have nearly depleted its wild populations. The study identified the dominant environmental variables enabling and limiting its distribution and predicted its suitable habitats and distribution. The 212 occurrence records covering the whole distribution range of C. henryi in China and nine main bioclimatic variables were selected for detailed analysis. We applied the maximum entropy model (MaxEnt) and QGIS to predict potentially suitable habitats under the current and four future climate‐change scenarios. The limiting factors for distribution were accessed by Jackknife, percent contribution, and permutation importance. We found that the current distribution areas were concentrated in the typical subtropical zone, mainly Central and South China provinces. The modeling results indicated temperature as the critical determinant of distribution patterns, including mean temperature of the coldest quarter, isothermality, and mean diurnal range. Winter low temperature imposed an effective constraint on its spread. Moisture served as a secondary factor in species distribution, involving precipitation seasonality and annual precipitation. Under future climate‐change scenarios, excellent habitats would expand and shift northwards, whereas range contraction would occur on the southern edge. Extreme climate change could bring notable range shrinkage. This study provided a basis for protecting the species' germplasm resources. The findings could guide the management, cultivation, and conservation of C. henryi, assisted by a proposed three‐domain operation framework: preservation areas, loss areas, and new areas, each to be implemented using tailor‐made strategies.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.9597

Effects of climate‐change scenarios on the distribution patterns of Castanea henryi

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.

The influence of scale-dependent geodiversity on species distribution models in a biodiversity hotspot

https://doi.org/10.1016/j.ecoinf.2022.101675

Habitat distribution change of commercial species in the Adriatic Sea during the COVID-19 pandemic

Forecasting the future suitable growth areas and constructing ecological corridors for the vulnerable species Ephedra sinica in China

MaxEnt is an important aid in understanding the influence of climate change on species distributions. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this work. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data in MaxEnt modeling has been limited by the large size of climate model output collections and the fact that MaxEnt can only operate on a relatively small set of predictors stored in a computer's main memory. In this study, we demonstrate the feasibility of a Monte Carlo method that overcomes this limitation by finding a useful subset of predictors in a larger, externally-stored collection of environmental variables in a reasonable amount of time. Our proposed solution takes an ensemble approach wherein many MaxEnt runs, each drawing on a small random subset of variables, converges on a global estimate of the top contributing subset of variables in the larger collection. In preliminary tests, the Monte Carlo approach selected a consistent set of top six variables within 540 runs, with the four most contributory variables of the top six accounting for approximately 93% of overall permutation importance in a final model. These results suggest that a Monte Carlo approach could offer a viable means of screening environmental predictors prior to final model construction that is amenable to parallelization and scalable to very large data sets. This points to the possibility of near-real-time multiprocessor implementations that could enable broader and more exploratory use of global climate model outputs in environmental niche modeling and aid in the discovery of viable predictors.

/pdf/toward-a-monte-carlo-approach-to-selecting-climate-variables-3x5gthf6c5.pdf

Toward a Monte Carlo approach to selecting climate variables in MaxEnt.

MaxEnt is an important aid in understanding the influence of climate change on species distributions and abundance. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this work. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data in MaxEnt modeling has been limited due to the large size of climate model output collections. In this study, we investigated the potential of a Monte Carlo method to find a useful subset of predictors in a larger collection of environmental variables in a reasonable amount of time. Our proposed solution takes an ensemble approach wherein many MaxEnt runs, each drawing on a small random subset of variables, converges on a global estimate of the top contributing subset of variables in the larger collection. The Monte Carlo approach resulted in a consistent set of top six variables within 540 runs, and the four most contributory variables of the top six accounted for approximately 93% of overall permutation importance in the final model. These preliminary results suggest that a Monte Carlo approach could offer a viable means of selecting environmental predictors for MaxEnt models that is amenable to parallelization and scalable to large data sets, including externally-stored collections. This points to the possibility of near-real-time multiprocessor implementations that could enable broader and more exploratory use of global climate model outputs in environmental niche modeling and aid in the discovery of viable predictors.

/pdf/toward-a-monte-carlo-approach-to-selecting-climate-variables-38vklhu0c0.pdf

Toward a Monte Carlo Approach to Selecting Climate Variables in MaxEnt: A Case Study Using Cassin’s Sparrow (Peucaea cassinii)

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jian Li

Author Tools

Chat about Author

Papers

Toward a Monte Carlo approach to selecting climate variables in MaxEnt.

Toward a Monte Carlo Approach to Selecting Climate Variables in MaxEnt: A Case Study Using Cassin’s Sparrow (Peucaea cassinii)