Some of you might have read about the lawsuit by a number of municipalities (including San Francisco and Oakland) against the major oil companies for damages (related primarily to sea level rise) caused by anthropogenic climate change. The legal details on standing, jurisdiction, etc. are all very interesting (follow @ColumbiaClimate for those details), but somewhat uniquely, the judge (William Alsup) has asked for a tutorial on climate science (2 hours of evidence from the plaintiffs and the defendents). Furthermore, he has posted a list of eight questions that he’d like the teams to answer.
Instrumental Record
More ice-out and skating day data sets
The responses to the last post on the Rideau Canal Skateway season changes were interesting, and led to a few pointers to additional data sets that show similar trends and some rather odd counter-points from the usual suspects.
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2017 temperature summary
This is a thread to discuss the surface temperature records that were all released yesterday (Jan 18). There is far too much data-vizualization on this to link to, but feel free to do so in the comments. Bottom line? It’s still getting warmer.
[Update: the page of model/observational data comparisons has now been updated too.]
O Say can you See Ice…
Some concerns about continued monitoring of sea ice by remote sensing were raised this week in Nature News an article in the (UK) Observer: Donald Trump accused of obstructing satellite research into climate change. The last headline is not really correct, but the underlying issues are real.
1.5ºC: Geophysically impossible or not?
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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Data rescue projects
It’s often been said that while we can only gather new data about the planet at the rate of one year per year, rescuing old data can add far more data more quickly. Data rescue is however extremely labor intensive. Nonetheless there are multiple data rescue projects and citizen science efforts ongoing, some of which we have highlighted here before. For those looking for an intro into the subject, this 2014 article is an great introduction.
Weather diary from the the Observatoire de Paris, written by Giovanni Cassini on 18th January 1789.
I was asked this week whether there was a list of these projects, and with a bit of help from Twitter, we came up with the following:
- Old Weather (@oldweather)
- Weather Detective (closing soon)
- Weather Rescue
- NOAA Climate Database Modernization Program
- New Zealand (@DeepSouth_NZ)
- The International Environmental Data Rescue Organization (IEDRO)
- Atmospheric Circulation Reconstruction over the Earth (@met_acre)
- The International Data Rescue Portal (i-Dare)
- Met Éirann (poster)
- Historical Climatology (list of more databases)
- Data Rescue at home
- Historical Canadian data
- SE Australia Recent Climate History (no longer active?)
- Congo basin eco-climatological data recovery and valorisation (COBECORE)
- The climate and environmental history collaborative research environment (Tambora)
(If you know of any more, please add them in the comments, and I’ll try and keep this list up to date).
Sensible Questions on Climate Sensitivity
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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Joy plots for climate change
This is joy as in ‘Joy Division’, not as in actual fun.
Many of you will be familiar with the iconic cover of Joy Division’s Unknown Pleasures album, but maybe fewer will know that it’s a plot of signals from a pulsar (check out this Scientific American article on the history). The length of the line is matched to the frequency of the pulsing so that successive pulses are plotted almost on top of each other. For many years this kind of plot did not have a well-known designation until, in fact, April this year:
I hereby propose that we call these "joy plots" #rstats https://t.co/uuLGpQLAwY
— Jenny Bryan (@JennyBryan) April 25, 2017
So “joy plots” it is.
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
- C. Proistosescu, and P.J. Huybers, "Slow climate mode reconciles historical and model-based estimates of climate sensitivity", Science Advances, vol. 3, 2017. http://dx.doi.org/10.1126/sciadv.1602821