There is an interesting news article ($) in Science this week by Paul Voosen on the increasing amount of transparency on climate model tuning. (Full disclosure, I spoke to him a couple of times for this article and I’m working on tuning description paper for the US climate modeling centers). The main points of the article are worth highlighting here, even if a few of the characterizations are slightly off.
Scientists getting organized to help readers sort fact from fiction in climate change media coverage
Guest post by Emmanuel Vincent
While 2016 is on track to easily surpass 2015 as the warmest year on record, some headlines, in otherwise prestigious news outlets, are still claiming that “2015 Was Not Even Close To Hottest Year On Record” (Forbes, Jan 2016) or that the “Planet is not overheating…” (The Times of London, Feb 2016). Media misrepresentation confuses the public and prevents our policy makers from developing a well-informed perspective, and making evidence-based decisions.
Professor Lord Krebs recently argued in an opinion piece in The Conversation that “accurate reporting of science matters” and that it is part of scientists’ professional duty to “challenge poor media reporting on climate change”. He concluded that “if enough [scientists] do so regularly, [science reporting] will improve – to the benefit of scientists, the public and indeed journalism itself.”
This is precisely what a new project called Climate Feedback is doing: giving hundreds of scientists around the world the opportunity to not only challenge unscientific reporting of climate change, but also to highlight and support accurate science journalism.
Global climate models (GCM) are designed to simulate earth’s climate over the entire planet, but they have a limitation when it comes to describing local details due to heavy computational demands. There is a nice TED talk by Gavin that explains how climate models work.
We need to apply downscaling to compute the local details. Downscaling may be done through empirical-statistical downscaling (ESD) or regional climate models (RCMs) with a much finer grid. Both take the crude (low-resolution) solution provided by the GCMs and include finer topographical details (boundary conditions) to calculate more detailed information. However, does more details translate to a better representation of the world?
The question of “added value” was an important topic at the International Conference on Regional Climate conference hosted by CORDEX of the World Climate Research Programme (WCRP). The take-home message was mixed on whether RCMs provide a better description of local climatic conditions than the coarser GCMs.
Guest commentary by Tony Patt, ETH Zürich
This morning I was doing my standard reading of the New York Times, which is generally on the good side with climate reporting, and saw the same old thing: an article about a potential solution, which just got the story wrong, at least incomplete. The particular article was about new technologies for converting CO2 into liquid fuels. These could be important if they are coupled with air capture of CO2, and if the energy that fuels them is renewable: this could be the only realistic way of producing large quantities of liquid fuel with no net CO2 emissions, large enough (for example) to supply the aviation sector. But the article suggested that this technology could make coal-fired power plants sustainable, because it would recycle the carbon. Of course that is wrong: to achieve the 2°C target we need to reduce the carbon intensity of the energy system by 100% in about 50 years, and yet the absolute best that a one-time recycling of carbon can do is to reduce the carbon intensity of the associated systems by 50%.
The fact is, there is a huge amount of uncritical, often misleading media coverage of the technological pathways and government policies for climate mitigation. As with the above story, the most common are those suggesting that approaches that result in a marginal reduction of emissions will solve the problem, and fail to ask whether those approaches also help us on the pathway towards 100% emissions reduction, or whether they take us down a dead-end that stops well short of 100%. There are also countless articles suggesting that the one key policy instrument that we need to solve the problem is a carbon tax or cap-and-trade market. We know, from two decades of social-science research, that these instruments do work to bring about marginal reductions in emissions, largely by stimulating improvements in efficiency. We also know that, at least so far, they have done virtually nothing to stimulate investment in the more sweeping changes in energy infrastructure that are needed to eliminate reliance on fossil fuels as the backbone of our system, and hence reduce emissions by 100%. We also know that other policy instruments have worked to stimulate these kinds of changes, at least to a limited extent. One thing we don’t know is what combination of policies could work to bring about the changes fast enough in the future. That is why this is an area of vigorous social science research. Just as there are large uncertainties in the climate system, there are large uncertainties in the climate solution system, and misreporting on these uncertainties can easily mislead us.
It’s fantastic that web sites like Real Climate and Climate Feedback re out there to clear some of the popular misconceptions about how the climate system functions. But if we care about actually solving the problem of climate change, then we also need to work continuously to clear the misconceptions, arising every day, about the strategies to take us there.
Anthony Patt is professor at the ETH in Zurich; his research focuses on climate policy