The boiled frog story is a cautionary fable that warns us of the risks that arise when we choose to ignore or deny change. Like the ostrich, we can choose to bury our heads in the sand, ignore change, deny the evidence, and thereby place ourselves in peril.
"If you drop a frog in a pot of boiling water, it will of course frantically try to clamber out. But if you place it gently in a pot of tepid water and turn the heat on low, it will float there quite placidly. As the water gradually heats up, the frog will sink into a tranquil stupor, exactly like one of us in a hot bath, and before long, with a smile on its face, it will unresistingly allow itself to be boiled to death."
Historically, climate change, has been largely characterized as a slow and incremental process. We might be excused, therefore, for ignoring or disputing climatic changes such as temperature and CO2 elevations over the past 250 years. Can we afford, however, to disregard the virtual consensus of our subject matter experts (climate scientists)?
The international scientific consensus that humans are causing recent global warming is shared by 90% – 100% of publishing climate scientists according to six independent studies. This is consistent with the 97% consensus reported by Cook et al based on 11,944 abstracts of research papers, of which 4,014 took a position on the cause of recent global warming.
In fact, the consensus is growing and is likely much higher. A more recent publication by James Lawrence Powell which surveyed 54,195 peer-reviewed articles from 1991 to 2015 had a an average consensus of 99.94%.
But is consensus a good basis for scientific decision making? A strong skeptic of global warming, author Michael Crichton offered the following opinion taken from his environmental and global warming essays written in 2009:
If consensus is irrelevant, what should be the foundation for our decision making? To answer this let's revisit the scientific method:
- Make an observation or observations.
- Ask questions about the observations and gather information.
- Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
- Test the hypothesis and predictions in an experiment that can be reproduced.
- Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
- Reproduce the experiment until there are no discrepancies between observations and theory.
Predictability is the ability to accurately define trends and future conditions such as atmospheric and oceanic temperature. Climate models are inherently uncertain given that initial conditions can't be fully defined and that the model is an imperfect representation of the climate and its underlying physics. So just how accurate are the climate model predictions that are the foundation of the scientific publications above? Climate models published since 1973 have generally been quite skillful in projecting future warming. Climate model predictive accuracy (both past and future) is outlined on the Skeptical Science website. While some temperature predictions were too low and some too high, they all show outcomes reasonably close to what has actually occurred.
Reproducibility is the ability to test a result using independent (different) methods and data processing. This term should not be confused with replicability which is the ability to rerun and validate the analysis using the same methods and data processing.
Climate model variability isn't surprising given the enormous number of variables and interdependencies. Traditional reductionism can't be applied to climate modelling (see Gavin Schmidt's Ted talk), instead holistic climate models must contend with fourteen orders of spatial and time magnitude. Models require massive and ever growing data sets that can limit reproducibility. Even with good data transparency, resource and tool constraints (e.g., bandwidth and computational restrictions) may preclude or hinder reproducing and verifying model results. Fortunately, there are a number of initiatives to enable climate scientists to better share, compare and analyze climate model results. One such initiative is the the coupled intercomparison project (CMIP).
Climate model predictive certainty will continue to improve, although most climate scientists acknowledge that there are predictive limits. No matter how sophisticated our models become, there will always be an irreducible element of chaos in the earth’s climate system that no supercomputer can eliminate.
There is much more evidence of global warming, of course, than that predicted by climate models. Examples include GISS Surface Temperature Analysis which is the ongoing tracking of global surface temperature changes (both replicable and reproducible) and paleo-climate records such as those collected in projects like PAGES2K.
This site does not attempt to address the full body of evidence for anthropogenic climate change. There are many sites providing compelling scientific evidence and expert testimony that man is driving and accelerating climate change. Instead, this site moves beyond the argument and targets individuals and organizations seeking strategies and actions to hold our educators, politicians, corporations and governments accountable for current and future climate policy. To hold today's decision-makers accountable for the growing climate consequences that will be borne by our children and grandchildren.