I’m writing this post to see if our audience can help out with a challenge: Can we collectively produce some coherent, properly referenced, open-source, scalable graphics of global temperature history that will be accessible and clear enough that we can effectively out-compete the myriad inaccurate and misleading pictures that continually do the rounds on social media?
Technical Note: Sorry for any recent performance issues. We are working on it.
I am always interested in non-traditional data sets that can shed some light on climate changes. Ones that I’ve discussed previously are the frequency of closing of the Thames Barrier and the number of vineyards in England. With the exceptional warmth in Alaska last month (which of course was coupled with colder temperatures elsewhere), I was reminded of another one, the Nenana Ice Classic.
Guest commentary from Zeke Hausfather and Robert Rohde
Daily temperature data is an important tool to help measure changes in extremes like heat waves and cold spells. To date, only raw quality controlled (but not homogenized) daily temperature data has been available through GHCN-Daily and similar sources. Using this data is problematic when looking at long-term trends, as localized biases like station moves, time of observation changes, and instrument changes can introduce significant biases.
For example, if you were studying the history of extreme heat in Chicago, you would find a slew of days in the late 1930s and early 1940s where the station currently at the Chicago O’Hare airport reported daily max temperatures above 45 degrees C (113 F). It turns out that, prior to the airport’s construction, the station now associated with the airport was on the top of a black roofed building closer to the city. This is a common occurrence for stations in the U.S., where many stations were moved from city cores to newly constructed airports or wastewater treatment plants in the 1940s. Using the raw data without correcting for these sorts of bias would not be particularly helpful in understanding changes in extremes.
There has been a veritable deluge of new papers this month related to recent trends in surface temperature. There are analyses of the CMIP5 ensemble, new model runs, analyses of complementary observational data, attempts at reconciliation all the way to commentaries on how the topic has been covered in the media and on twitter. We will attempt to bring the highlights together here. As background, it is worth reading our previous discussions, along with pieces by Simon Donner and Tamino to help put in context what is being discussed here.
A new paper in Nature Climate Change out this week by England and others joins a number of other recent papers seeking to understand the climate dynamics that have led to the so-called “slowdown” in global warming. As we and others have pointed out previously (e.g. here), the fact that global average temperatures can deviate for a decade or longer from the long term trend comes as no surprise. Moreover, it’s not even clear that the deviation has been as large as is commonly assumed (as discussed e.g. in the Cowtan and Way study earlier this year), and has little statistical significance in any case. Nevertheless, it’s still interesting, and there is much to be learned about the climate system from studying the details.
Several studies have shown that much of the excess heating of the planet due to the radiative imbalance from ever-increasing greenhouses gases has gone into the ocean, rather than the atmosphere (see e.g. Foster and Rahmstorf and Balmaseda et al.). In their new paper, England et al. show that this increased ocean heat uptake — which has occurred mostly in the tropical Pacific — is associated with an anomalous strengthening of the trade winds. Stronger trade winds push warm surface water towards the west, and bring cold deeper waters to the surface to replace them. This raises the thermocline (boundary between warm surface water and cold deep water), and increases the amount of heat stored in the upper few hundred meters of the ocean. Indeed, this is what happens every time there is a major La Niña event, which is why it is globally cooler during La Niña years. One could think of the last ~15 years or so as a long term “La-Niña-like” anomaly (punctuated, of course, by actual El Niño (like the exceptionally warm years 1998, 2005) and La Niña events (like the relatively cool 2011).
A very consistent understanding is thus emerging of the coupled ocean and atmosphere dynamics that have caused the recent decadal-scale departure from the longer-term global warming trend. That understanding suggests that the “slowdown” in warming is unlikely to continue, as England explains in his guest post, below. –Eric Steig
Guest commentary by Matthew England (UNSW)
For a long time now climatologists have been tracking the global average air temperature as a measure of planetary climate variability and trends, even though this metric reflects just a tiny fraction of Earth’s net energy or heat content. But it’s used widely because it’s the metric that enjoys the densest array of in situ observations. The problem of course is that this quantity has so many bumps and kinks, pauses and accelerations that predicting its year-to-year path is a big challenge. Over the last century, no single forcing agent is clearer than anthropogenic greenhouse gases, yet zooming into years or decades, modes of variability become the signal, not the noise. Yet despite these basics of climate physics, any slowdown in the overall temperature trend sees lobby groups falsely claim that global warming is over. Never mind that the globe – our planet – spans the oceans, atmosphere, land and ice systems in their entirety.
This was one of the motivations for our study out this week in Nature Climate Change (England et al., 2014) With the global-average surface air temperature (SAT) more-or-less steady since 2001, scientists have been seeking to explain the climate mechanics of the slowdown in warming seen in the observations during 2001-2013. One simple way to address this is to examine what is different about the recent decade compared to the preceding decade when the global-mean SAT metric accelerated. This can be quantified via decade-mean differences, or via multi-decadal trends, which are roughly equivalent if the trends are more-or-less linear, or if the focus is on the low frequency changes.
- G. Foster, and S. Rahmstorf, "Global temperature evolution 1979–2010", Environ. Res. Lett., vol. 6, pp. 044022, 2011. http://dx.doi.org/10.1088/1748-9326/6/4/044022
- M.A. Balmaseda, K.E. Trenberth, and E. Källén, "Distinctive climate signals in reanalysis of global ocean heat content", Geophysical Research Letters, vol. 40, pp. 1754-1759, 2013. http://dx.doi.org/10.1002/grl.50382
- M.H. England, S. McGregor, P. Spence, G.A. Meehl, A. Timmermann, W. Cai, A.S. Gupta, M.J. McPhaden, A. Purich, and A. Santoso, "Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus", Nature Climate change, vol. 4, pp. 222-227, 2014. http://dx.doi.org/10.1038/nclimate2106