This press release via Eurekalert reads more like an advertisement than it does some serious science. But then,we are dealing with a science that in some cases has lost all sense of seriousness, such as the bonkers claim that “climate change” will start killing off felis catus en masse in just a few years.
Within a mere nine years, global warning could produce temperature spikes so elevated as to generate massive cat mortality? The idea is so ludicrous that I hardly know where to begin.
Source: Geocurrents. Eco-Authoritarian Catastrophism: The Dismal and Deluded Vision of Naomi Oreskes and Erik M. Conway
h/t to Bishop Hill for that one.The abstract of the sales pitch paper they are citing starts out like this:
Global climate change and its impact on human life has become one of our era’s greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story.
As a result, big data–induced progress within climate science has been slower compared with big data’s success in other fields such as biology or advertising. The slow progress has been vexing given that climate science has become one of the most data-rich domains in terms of data volume, velocity, and variety.
Of course they are assuming the climate data is all valid, like so many people assume Mann’s interpretations of tree ring data is valid.
So please excuse me if I think that “big data” analysis might only lead to big ludicrous Oreskian style claims, especially when it is packaged as a sales pitch like this one.
New Rochelle, October 14, 2014 –Big Data analytics are helping to provide answers to many complex problems in science and society, but they have not contributed to a better understanding climate science, despite an abundance of climate data. When it comes to analyzing the climate system, Big Data methods alone are not enough and sound scientific theory must guide data modeling techniques and results interpretation, according to an insightful article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available free on the Big Data website.
In “A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science,” James Faghmous, PhD and Vipin Kumar, PhD, The University of Minnesota–Twin Cities, explore the challenges and opportunities for mining large climate datasets and the subtle differences that are needed compared to traditional Big Data methods if accurate conclusions are to be drawn. The authors discuss the importance of combining scientific theory and First Principles with Big Data analytics and use examples from existing research to illustrate their novel approach.
“This paper is a great example of leveraging the abundance of climate data with powerful analytical methods, scientific theory, and solid data engineering to explain and predict important climate change phenomena,” says Big Data Editor-in-Chief Vasant Dhar, Co-Director, Center for Business Analytics, Stern School of Business, New York University.
About the Journal
Big Data , published quarterly in print and online, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website.