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O Say Can You CO2…

Filed under: — group @ 12 October 2017

Guest Commentary by Scott Denning

The Orbiting Carbon Observatory (OCO-2) was launched in 2014 to make fine-scale measurements of the total column concentration of CO2 in the atmosphere. As luck would have it, the initial couple of years of data from OCO-2 documented a period with the fastest rate of CO2 increase ever measured, more than 3 ppm per year (Jacobson et al, 2016;Wang et al, 2017) during a huge El Niño event that also saw global temperatures spike to record levels.

As part of a series of OCO-2 papers being published this week, a new Science paper by Junjie Liu and colleagues used NASA’s comprehensive Carbon Monitoring System to analyze millions of measurements from OCO-2 and other satellites to map the impact of the 2015-16 El Niño on sources and sinks of CO2, providing insight into the mechanisms controlling carbon-climate feedback.

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References

  1. J. Wang, N. Zeng, M. Wang, F. Jiang, H. Wang, and Z. Jiang, "Contrasting terrestrial carbon cycle responses to the two strongest El Niño events: 1997–98 and 2015–16 El Niños", Earth System Dynamics Discussions, pp. 1-32, 2017. http://dx.doi.org/10.5194/esd-2017-46
  2. J. Liu, K.W. Bowman, D.S. Schimel, N.C. Parazoo, Z. Jiang, M. Lee, A.A. Bloom, D. Wunch, C. Frankenberg, Y. Sun, C.W. O’Dell, K.R. Gurney, D. Menemenlis, M. Gierach, D. Crisp, and A. Eldering, "Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño", Science, vol. 358, pp. eaam5690, 2017. http://dx.doi.org/10.1126/science.aam5690

1.5ºC: Geophysically impossible or not?

Filed under: — group @ 4 October 2017

Guest commentary by Ben Sanderson

Millar et al’s recent paper in Nature Geoscience has provoked a lot of lively discussion, with the authors of the original paper releasing a statement to clarify that their paper did not suggest that “action to reduce greenhouse gas emissions is no longer urgent“, rather that 1.5ºC (above the pre-industrial) is not “geophysically impossible”.

The range of post-2014 allowable emissions for a 66% chance of not passing 1.5ºC in Millar et al of 200-240GtC implies that the planet would exceed the threshold after 2030 at current emissions levels, compared with the AR5 analysis which would imply most likely exceedance before 2020. Assuming the Millar numbers are correct changes 1.5ºC from fantasy to merely very difficult.

But is this statement overconfident? Last week’s post on Realclimate raised a couple of issues which imply that both the choice of observational dataset and the chosen pre-industrial baseline period can influence the conclusion of how much warming the Earth has experienced to date. Here, I consider three aspects of the analysis – and assess how they influence the conclusions of the study.
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Sensible Questions on Climate Sensitivity

Filed under: — group @ 15 August 2017

Guest Commentary by Cristian Proistosescu, Peter Huybers and Kyle Armour

tl;dr 

Two recent papers help bridge a seeming gap between estimates of climate sensitivity from models and from observations of the global energy budget. Recognizing that equilibrium climate sensitivity cannot be directly observed because Earth’s energy balance is a long way from equilibrium, the studies instead focus on what can be inferred about climate sensitivity from historical trends. Calculating a climate sensitivity from the simulations that is directly comparable with that observed shows both are consistent. Crucial questions remain, however, regarding how climate sensitivity will evolve in the future.

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Observations, Reanalyses and the Elusive Absolute Global Mean Temperature

One of the most common questions that arises from analyses of the global surface temperature data sets is why they are almost always plotted as anomalies and not as absolute temperatures.

There are two very basic answers: First, looking at changes in data gets rid of biases at individual stations that don’t change in time (such as station location), and second, for surface temperatures at least, the correlation scale for anomalies is much larger (100’s km) than for absolute temperatures. The combination of these factors means it’s much easier to interpolate anomalies and estimate the global mean, than it would be if you were averaging absolute temperatures. This was explained many years ago (and again here).

Of course, the absolute temperature does matter in many situations (the freezing point of ice, emitted radiation, convection, health and ecosystem impacts, etc.) and so it’s worth calculating as well – even at the global scale. However, and this is important, because of the biases and the difficulty in interpolating, the estimates of the global mean absolute temperature are not as accurate as the year to year changes.

This means we need to very careful in combining these two analyses – and unfortunately, historically, we haven’t been and that is a continuing problem.

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Climate Sensitivity Estimates and Corrections

You need to be careful in inferring climate sensitivity from observations.

Two climate sensitivity stories this week – both related to how careful you need to be before you can infer constraints from observational data. (You can brush up on the background and definitions here). Both cases – a “Brief Comment Arising” in Nature (that I led) and a new paper from Proistosescu and Huybers (2017) – examine basic assumptions underlying previously published estimates of climate sensitivity and find them wanting.

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References

  1. C. Proistosescu, and P.J. Huybers, "Slow climate mode reconciles historical and model-based estimates of climate sensitivity", Science Advances, vol. 3, pp. e1602821, 2017. http://dx.doi.org/10.1126/sciadv.1602821