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2020 vision

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A meeting of smoke and storms (NASA Earth Observatory)

No-one needs another litany of all the terrible things that happened this year, but there are three areas relevant to climate science that are worth thinking about:

  • What actually happened in climate/weather (and how they can be teased apart). There is a good summary on the BBC radio Discover program covering wildfires, heat waves, Arctic sea ice, the hurricane season, etc. featuring Mike Mann, Nerlie Abram, Sarah Perkins-Kilpatrick, Steve Vavrus and others. In particular, there were also some new analyses of hurricanes (their rapid intensification, slowing, greater precipitation levels etc.), as well as the expanding season for tropical storms that may have climate change components. Yale Climate Connections also has a good summary.
  • The accumulation of CMIP6 results. We discussed some aspects of these results extensively – notably the increased spread in Equilibrium Climate Sensitivity, but there is a lot more work to be done on analyzing the still-growing database that will dominate the discussion of climate projections for the next few years. Of particular note will be the need for more sophisticated analyses of these model simulations that take into account observational constraints on ECS and a wider range of future scenarios (beyond just the SSP marker scenarios that were used in CMIP). These issues will be key for the upcoming IPCC 6th Assessment Report and the next National Climate Assessment.
  • The intersection of climate and Covid-19.
    • The direct connections are clear – massive changes in emissions of aerosols, short-lived polluting gases (like NOx) and CO2 – mainly from reductions in transportation. Initial results demonstrated a clear connection between cleaner air and the pandemic-related restrictions and behavioural changes, but so far the impacts on temperature or other climate variables appear to be too small to detect (Freidlingstein et al, 2020). The impact on global CO2 emissions (LeQuere et al, 2020) has been large (about 10% globally) – but not enough to stop CO2 concentrations from continuing to rise (that would need a reduction of more like 70-80%). Since the impact from CO2 is cumulative this won’t make a big difference in future temperatures unless it is sustained through post-pandemic changes.
    • The metaphorical connections are also clear. The instant rise of corona virus-denialism, the propagation of fringe viewpoints from once notable scientists, petitions to undermine mainstream epidemiology, politicized science communications, and the difficulty in matching policy to science (even for politicians who want to just ‘follow the science’), all seem instantly recognizable from a climate change perspective. The notion that climate change was a uniquely wicked problem (because of it’s long term and global nature) has evaporated as quickly as John Ioannidis’ credibility.

I need to take time to note that there has been human toll of Covid-19 on climate science, ranging from the famous (John Houghton) to the families of people you never hear about in the press but whose work underpins the data collection, analysis and understanding we all rely on. This was/is a singular tragedy.

With the La Niña now peaking in the tropical Pacific, we can expect a slightly cooler year in 2021 and perhaps a different character of weather events, though the long-term trends will persist. My hope is that the cracks in the system that 2020 has revealed (across a swathe of issues) can serve as an motivation to improve resilience, equity and planning, across the board. That might well be the most important climate impact of all.

A happier new year to you all.

References

  1. P.M. Forster, H.I. Forster, M.J. Evans, M.J. Gidden, C.D. Jones, C.A. Keller, R.D. Lamboll, C.L. Quéré, J. Rogelj, D. Rosen, C. Schleussner, T.B. Richardson, C.J. Smith, and S.T. Turnock, "Current and future global climate impacts resulting from COVID-19", Nature Climate Change, vol. 10, pp. 913-919, 2020. http://dx.doi.org/10.1038/s41558-020-0883-0
  2. C. Le Quéré, R.B. Jackson, M.W. Jones, A.J.P. Smith, S. Abernethy, R.M. Andrew, A.J. De-Gol, D.R. Willis, Y. Shan, J.G. Canadell, P. Friedlingstein, F. Creutzig, and G.P. Peters, "Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement", Nature Climate Change, vol. 10, pp. 647-653, 2020. http://dx.doi.org/10.1038/s41558-020-0797-x

The number of tropical cyclones in the North Atlantic

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2020 has been an unusual and challenging year in many ways. One was the record-breaking number of named tropical cyclones in the North Atlantic (and the Carribean Sea). There has been 30 named North Atlantic tropical cyclones in 2020, beating the previous record of 28 from 2005 by two.

A natural question then is whether we can expect this high number in the future or if the number of tropical storms will continue to increase. A high number of such events is equivalent to a high frequency of tropical cyclones.

But we should expect fewer tropical cyclones generally in a warmer world according to the IPCC “SREX” report from 2012, and those that form may become even more powerful than the ones that we have observed to date:

There is generally low confidence in projections of changes in extreme winds because of the relatively few studies of projected extreme winds, and shortcomings in the simulation of these events. An exception is mean tropical cyclone maximum wind speed, which is likely to increase, although increases may not occur in all ocean basins. It is likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged…

So how does this conclusion relate to the number of tropical cyclones in the North Atlantic with a new record this season? One reason to look in more detail at the North Atlantic is because its observational record is believed to be more complete and more reliable than for other regions around the world.

The observational record may also suggest that the number of tropical cyclones in the North Atlantic has increased slowly over the 50 years in addition to year-to-year fluctuations around this trend (black symbols in Fig 1).

We know that the number of cyclones is sensitive to the time of the year (hence, hurricane seasons), phenomena such as El Niño Southern Oscillation (ENSO) and the Madden Julian Oscillation (MJO), and geography (the ocean basin shape and the latitude). We also know that the sea surface needs to be warmer than 26.5°C for them to form.

The role of sea surface temperature is indeed an important factor, and from physical reasoning, one would think that the number of tropical cyclones depends on the area of warm sea surface A (sea surface temperature exceeding 26.5°C).

One explanation for why the area is a key factor may be that the probability of finding favourable conditions with right ‘seed’ for organised convection (e.g. easterly waves) and no wind shear increases when there is a greater region with sufficient sea surface temperatures.

The area of warm sea surface is mentioned in the IPCC SREX that dismisses the expectation that an increase in the area extent of the region of 26°C sea surface temperature should lead to increases in tropical cyclone frequency. Specifically it says that there is

a growing body of evidence that the minimum SST [sea surface temperature] threshold for tropical cyclogenesis increases at about the same rate as the SST increase due solely to greenhouse gas forcing.

On the other hand, there has also been some indication that the number of tropical cyclones does seem to be proportional to the area to the power of 5: n \propto A^5 (Benestad, 2008). When this relationship is extended to recent years, as shown with the green and blue curves in Fig 1, we see an increase that this crude estimate more or less follows the observed number of evens.

Global warming implies a greater area with sea surface exceeding the threshold of 26.5°C for tropical cyclone genesis. Also, the nonlinear dependency to A implies few events and little trend as long as A is below a critical size. The combination of a nonlinear relationship and a critical threshold area could explain why it is difficult to detect a trend in the historical data.

There is some good news in that A is limited by the geometry of the ocean basin. Nevertheless, a potential nonlinear connection between the number of tropical cyclones and A is a concern. If this cannot be falsified, then the tropical cyclones represent a more potent danger than anticipated by the IPCC SREX conclusions. So let’s hope that somebody is able to show that the analysis presented in (Benestad, 2008) is wrong.

Fig 1. Observed (black symbols) and estimated (green and blue curves) number of named tropical cyclones in the North Atlantic and the Caribbean Sea after (Benestad, 2008). Source: “demo(tropicalcyclones)”.

References

  1. R.E. Benestad, "On tropical cyclone frequency and the warm pool area", Natural Hazards and Earth System Sciences, vol. 9, pp. 635-645, 2009. http://dx.doi.org/10.5194/nhess-9-635-2009

An ever more perfect dataset?

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Do you remember when global warming was small enough for people to care about the details of how climate scientists put together records of global temperature history? Seems like a long time ago…

Nonetheless, it’s worth a quick post to discuss the latest updates in HadCRUT (the data product put together by the UK’s Hadley Centre and the Climatic Research Unit at the University of East Anglia). They have recently released HadCRUT5 (Morice et al., 2020), which marks a big increase in the amount of source data used (similarly now to the upgrades from GHCN3 to GHCN4 used by NASA GISS and NOAA NCEI, and comparable to the data sources used by Berkeley Earth). Additionally, they have improved their analysis of the sea surface temperature anomalies (a perennial issue) which leads to an increase in the recent trends. Finally, they have started to produce an infilled data set which uses an extrapolation to fill in data-poor areas (like the Arctic – first analysed by us in 2008…) that were left blank in HadCRUT4 (so similar to GISTEMP, Berkeley Earth and the work by Cowtan and Way). Because the Arctic is warming faster than the global mean, the new procedure corrects a bias that existing in the previous global means (by about 0.16ºC in 2018 using a 1951-1980 baseline). Combined, the new changes give a result that is much closer to the other products:

Differences persist around 1940, or in earlier decades, mostly due to the treatment of ocean temperatures in HadSST4 vs. ERSST5.

In conclusion, this update further solidifies the robustness of the surface temperature record, though there are still questions to be addressed, and there remain mountains of old paper records to be digitized.

The implications of these updates for anything important (such as the climate sensitivity or the carbon budget) will however be minor because all sensible analyses would have been using a range of surface temperature products already.

With 2020 drawing to a close, the next annual update and intense comparison of all these records, including the various satellite-derived global products (UAH, RSS, AIRS) will occur in January. Hopefully, HadCRUT5 will be extended beyond 2018 by then.

In writing this post, I noticed that we had written up a detailed post on the last HadCRUT update (in 2012). Oddly enough the issues raised were more or less the same, and the most important conclusion remains true today:

First and foremost is the realisation that data synthesis is a continuous process. Single measurements are generally a one-time deal. Something is measured, and the measurement is recorded. However, comparing multiple measurements requires more work – were the measuring devices calibrated to the same standard? Were there biases in the devices? Did the result get recorded correctly? Over what time and space scales were the measurements representative? These questions are continually being revisited – as new data come in, as old data is digitized, as new issues are explored, and as old issues are reconsidered. Thus for any data synthesis – whether it is for the global mean temperature anomaly, ocean heat content or a paleo-reconstruction – revisions over time are both inevitable and necessary.

References

  1. , 2021. https://www.metoffice.gov.uk/hadobs/hadcrut5/HadCRUT5_accepted.pdf

Thinking, small and big

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The point that climate downscaling must pay attention to the law of small numbers is no joke.

The World Climate Research Programme (WCRP) will become a ‘new’ WCRP with a “soft launch” in 2021. This is quite a big story since it coordinates much of the research and the substance on which the Intergovernmental Panel on Climate Change (IPCC) builds.  

 

Until now, the COordinated Regional Downscaling EXperiment (CORDEX) has been a major project sponsored by the WRCP. CORDEX has involved regional modelling and downscaling with a focus on the models and methods rather than providing climate services. In its new form, the activities that used to be carried out within CORDEX will belong to the WCRP community called ‘Regional information for society’ (RifS). This implies a slight shift in emphasis.

 

With this change, the WCRP signals a desire for the regional modelling results to become more useful and relevant for decision-makers. The change will also introduce a set of new requirements, and hence the law of small numbers.

 

The law of small numbers is described in Daniel Kahneman’s book ‘Thinking, fast and slow‘ and is a condition that can be explained by statistical theory. It says that you are likely to draw a misleading conclusion if your sample is small.  

 

I’m no statistician, but a physicist who experienced a “statistical revelation” about a decade ago. Physics-based disciplines, such as meteorology, often approach a problem from a different angle to the statisticians, and there are often some gaps in the understanding and appreciation between the two communities.

 

A physicist would say that if we know one side of an equation, then we also know the other side. The statistician, on the other hand, would use data to prove there is an equation in the first place.

 

One of the key pillars of statistics is that we have a random sample that represents what we want to study. We have no such statistical samples for future climate outlooks, but we do have ensembles of simulations representing future projections.

 

We also have to keep in mind that regional climate behaves differently to global climate. There are pronounced stochastic variations on regional and decadal scales that may swamp the long-term trends due to greenhouse gases (Deser et al., 2012). These variations are subdued on a global scale since opposite variations over different regions tend to cancel each other.

 

CORDEX has in the past produced ensembles that can be considered as small, and Mezghani et al., (2019) demonstrated that the Euro-CORDEX ensemble is affected by the law of small numbers.

Even if you have a perfect global climate model and perfect downscaling, you risk getting misleading results with a small ensemble, thanks to the law of small numbers. The regional variations are non-deterministic due to the chaotic nature of the atmospheric circulation.

 

My take-home-message is that there is a need for sufficiently large ensembles of downscaled results. Furthermore, it is the number of different simulations with global climate models that is key since they provide boundary conditions for the downscaling.

 

Hence, there is a need for a strong and continued coordination between the downscaling groups so that more scientists contribute to building such ensembles.

 

Also, while CORDEX has been strong on regional climate modelling, the new RifS community needs additional new expertise. Perhaps a stronger presence of statisticians is a good thing. And while the downscaled results from large ensembles can provide a basis for a risk analysis, there is also another way to provide regional information for society: stress-testing.

References

  1. C. Deser, R. Knutti, S. Solomon, and A.S. Phillips, "Communication of the role of natural variability in future North American climate", Nature Climate Change, vol. 2, pp. 775-779, 2012. http://dx.doi.org/10.1038/nclimate1562
  2. A. Mezghani, A. Dobler, R. Benestad, J.E. Haugen, K.M. Parding, M. Piniewski, and Z.W. Kundzewicz, "Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling", Journal of Applied Meteorology and Climatology, vol. 58, pp. 1061-1078, 2019. http://dx.doi.org/10.1175/JAMC-D-18-0179.1

Unforced Variations: Nov 2020

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This month’s open thread for climate science. As if there wasn’t enough going on, we have still more hurricanes in the Atlantic, temperature records tumbling despite La Niña, Arctic sea ice that doesn’t want to reform, bushfire season kicking off in the Southern Hemisphere while we are barely done with it in the North…

Welcome to the new normal, folks.

New studies confirm weakening of the Gulf Stream circulation (AMOC)

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Many of the earlier predictions of climate research have now become reality. The world is getting warmer, sea levels are rising faster and faster, and more frequent heat waves, extreme rainfall, devastating wildfires and more severe tropical storms are affecting many millions of people. Now there is growing evidence that another climate forecast is already coming true: the Gulf Stream system in the Atlantic is apparently weakening, with consequences for Europe too.

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Denial and Alarmism in the Near-Term Extinction and Collapse Debate

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Guest article by Alastair McIntosh,  honorary professor in the College of Social Sciences at the University of Glasgow in Scotland. This is an excerpt from his new book, Riders on the Storm: The Climate Crisis and the Survival of Being

cover art for Riders on the StormMostly, we only know what we think we know about climate science because of the climate science. I have had many run-ins with denialists, contrarians or climate change dismissives as they are variously called. Over the past two years especially, concern has also moved to the other end of the spectrum, to alarmism. Both ends, while the latter has been more thinly tapered, can represent forms of denial. In this abridged adaptation I will start with denialism, but round on the more recent friendly fire on science that has emerged in alarmism.
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