This blue coral specimen was collected during the U.S. Navy expedition to Bikini Atoll, in advance of the famous hydrogen bomb tests and forced evacuation of the Bikinians.
After years essentially collecting dust in someone’s basement, a group of corals from that expedition were donated to the California Academy of Sciences in San Francisco.
I took the photo during a recent visit to give a talk and to tour their incredible collections.
There is an ongoing feud about value in communicating the scientific consensus on climate change to the public. One side argues that we need to talk about the consensus in order to raise public awareness about climate change. A new review article by John Cook and Peter Jacobs (also described in the Guardian) reviews the evidence for “consensus messaging”. The counterargument, proposed by Dan Kahan and others, is that talking about consensus will increase political polarization about climate change.
A recent paper by Kahan called “Climate Science Communication and the Measurement Problem” suggests the disagreement among communications researchers is related to the “contamination of education and politics with forms of cultural status competition”. It is a fascinating paper with a lot of important findings. But I wonder if, deep in the data, there may be evidence that the drumbeat of climate change news and outreach campaigns has actually been effective.
The core result of the paper is described by the following figure:
If people were rationally assessing scientific information, higher science comprehension would translate into higher perceived risk from climate change (left panel). Kahan’s experiments find the opposite for people on the right of the political spectrum (red, right panel). That’s been the headline: for conservatives, better knowledge of climate science might mean less concern about climate change.
In other words, when people go beyond the basics, opinions become polarized. That is not very surprising, given that someone on the right of the political spectrum with greater interest and/or ability in science who looks for information about climate change may head to right-wing media and blogs, which often house an alternate universe of “facts” about climate change.
What is more surprising are the results for people with low “science comprehension”.
Why are people with low science comprehension on both the left and the right of the political spectrum perceiving moderate to high risk from climate change? If people’s views on climate change are defined more by their cultural identity than by the facts of the case, why would people on the right of the spectrum with low science comprehension have even moderate concern about climate change?
This opinion about the risk from climate change must derive from something. It isn’t a detailed knowledge of the science, or the problem. Otherwise, the people would fall elsewhere on the graph. It also isn’t their community. Their community, if defined correctly, generally believes the risk from climate change to be low.
What’s happening? Perhaps there has been enough mention of climate change in the public domain, whether on the news, in private conversations, etc., that even those who pay scant little attention to science have been able to develop some level of concern about climate change.
It may be that, current polarization aside, the much-maligned information deficit model has actually worked, at least with very basic information, and in the way political messaging works. Years of repeating the general facts of the case – climate change is real, caused by humans, and poses a risk to the future – appears to have created a basic public consciousness about climate change.
The proposed climate agreement between the U.S. and China will put the Canadian government’s promise to harmonize with U.S. policy to the test.
Under the new agreement, the U.S. would limit greenhouse gas emissions to 26-28% below 2005 levels by the year 2025. That is a step beyond the existing U.S. target, shared by Canada, of 17% below 2005 levels by the year 2020.
The U.S. is roughly on pace to meet its 2020 target, with emissions for 2012, the most recent year available, only 8% above the target and further reductions possible due to automobile regulation, coal regulations and the shift from coal to natural gas for energy.
Canada is not; emissions were 13% above the 2020 target in 2012 and are projected to increase over the next two decades largely because of activities in the oil sands sector. According to the Environment Canada projections, Canada’s emissions are projected to reach 762 Mt of CO2e by the year 2020, 9% above the 2012 level and 25% above the 2020 target.
The new agreement will increase the gap between U.S. and Canadian ambitions. Canadian greenhouse gas emissions are projected to be one third above the new target for the year 2025 agreed to by President Obama (assuming the Environment Canada projection for 2025 is halfway between the 2020 – 762 Mt – and 2030 – 815 Mt).
The core of the gap is the oil sands. This is not environmental rhetoric. This is math, based on the government’s own projections, submitted to the United Nations according to international reporting agreements. Roughly three-quarters of the projected growth in Canadian emissions by 2030 comes from the oil sands sector, with the remaining growth in industries like rail and heavy-duty trucks that can be related to the oil and gas activity.
Without addressing the expected growth in greenhouse gas emissions from the oil sands, Canada will not come close to even stabilizing emissions in the next couple decades, let alone keeping up with action by the U.S. and the rest of the developed world. At some point, our leaders need to address the elephant in the room.
The word “data” is misused a lot in conversations and publications about climate change. How many times have you heard or read the phrase “future climate data”?
Data, according to the Webster-Miriam dictionary, is factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation. It is something that was measured. Numbers, words, sounds, emotional responses can all be data, so long as they were observed and recorded. Without a time machine, there is no “data” about the future.
Future projections from climate models are not, strictly-speaking, data. A numerical model, whether a complicated computer model of the atmosphere or a Newtonian physics equation from high school, may rely on actual data as initial inputs. The numbers produced by that model are not data. They were not directly measured. They are predictions derived from inputting the measurements into a simplified representation of the system being studied.
To clearly distinguish between actual data and numbers produced by models, climate scientists usually refer to the numbers produced by a model as “model output”, or sometimes “model results”.
The line between data and output can sometimes be blurry. For example, the ocean temperatures from a remote sensing group like NOAA Coral Reef Watch are the result of an algorithm integrating a series of point satellite observations over a region or “grid cell”, and controlling for factors like clouds. It is therefore the output of a simple model, which is why it is common to refer to those values as “satellite-derived data” rather than “satellite data”.
And if you really decompose the way much “data” is physically measured, you will find the line between data and output disappear entirely. Often the instrument, whether a simple thermometer or a complicated fluorometer, used to make measurements itself relies on some embedded empirical relationship that translates a raw independent observation into the desired variable.
Regardless, the rhetorical confusion between climate “data” and climate model “output” is not just semantics. In some settings, people’s choice of wording reflects a real confusion about what climate science can do for the world.
One of the central challenges in helping the developing world adapt to climate change is the gap between the desire for precise information about the future and the uncertain, probabilistic projections that science can deliver. During workshops, research interviews and private conversations in the Pacific Islands, I often here laments that “we need the data”. The use of the word data to describe future climate projections exemplifies that gap. People want precise, down-scaled climate predictions for their island or village, not a probabilistic range for an entire region. People want science to deliver something – precise answers – that is not possible.
Explaining and demonstrating the difference between data and output can go a long way to bridging the gap between the ability and the demands of climate science.
Bleached corals, Fiji, April 2014
This week, the U.S. government announced it would be listing 20 coral species found in American waters as “threatened” under the Endangered Species Act. Part of the rationale is the threat posed by climate change and ocean acidification, a potentially groundbreaking policy move. What may be missed in this announcement is that the original proposal included a longer list of 66 coral species.
The decision begs a broad question. If we are considering the research on climate change and ocean acidification in the decision, then why not list all coral species as threatened?
The Coral Specialist Group of the IUCN, of which I’ve been a part, submitted a detailed comment to the U.S. proceedings. My meager contribution to the group’s terrific dissection of coral ecology and physiology was the argument that committed climate warming may alone be sufficient evidence for all coral species to be listed as threatened. Here is the excerpt, with wording vastly improved by my colleagues:
The projected increase in sea surface temperatures due to the physical commitment from the present accumulation of greenhouse gases due to anthropogenic activity, as well as the socioeconomic commitment (i.e. it is logistically impossible to instantly eliminate anthropogenic emissions, regardless of policy decisions, because of inertia to the existing energy system), is sufficient to cause frequent and higher magnitude heat stress for the majority of the world’s coral reefs by 2050 (Donner, 2009). The primary source of uncertainty in this forecast is the ability of the coral holobiont to acclimate and/or adapt to heat stress. The fact that the future abundance of coral species depends on a rate of adjustment to heat stress that is unprecedented in geological history should be sufficient to warrant a minimum status of threatened for all coral species.
The problem of coping with commitment, the title of that cited 2009 paper, was highlighted by a terrific new paper that happened to be released almost simultaneously with the U.S. coral decision. In “Commitment Accounting of CO2 emissions”, Stephen Davis and Rob Socolow calculate the committed emissions from the operation of new energy investments, like coal plants, over the expected lifetime of those investments (see Dot Earth for a lengthy discussion). They conclude that it would be sensible to use committed emissions, rather than the annual emissions, to inform public policy.
The socioeconomic commitment or capital “lock-in” to future emissions has implications for everything from these species listings to oil pipeline decisions. We can’t perfectly project the future of each coral species, but we can say that the oceans are committed to physical and chemical changes which may be dangerous or fatal to corals. These changes do not guarantee widespread extinction or endangerment, given potential adaptability of many species and the potential refuges in the ocean, but certainly could classify as threatening.