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Model Independence Day

Filed under: — gavin @ 4 July 2018

We hold these truths to be self-evident, that all models are created equal, that they are endowed by their Creators with certain unalienable Rights, that among these are a DOI, Runability and Inclusion in the CMIP ensemble mean.

Well, not quite. But it is Independence Day in the US, and coincidentally there is a new discussion paper (Abramowitz et al) (direct link) posted on model independence just posted at Earth System Dynamics.

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References

  1. G. Abramowitz, N. Herger, E. Gutmann, D. Hammerling, R. Knutti, M. Leduc, R. Lorenz, R. Pincus, and G.A. Schmidt, "Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing", Earth System Dynamics Discussions, pp. 1-20, 2018. http://dx.doi.org/10.5194/esd-2018-51

Will climate change bring benefits from reduced cold-related mortality? Insights from the latest epidemiological research

Filed under: — stefan @ 11 June 2018

Guest post by Veronika Huber

Climate skeptics sometimes like to claim that although global warming will lead to more deaths from heat, it will overall save lives due to fewer deaths from cold. But is this true? Epidemiological studies suggest the opposite.

Mortality statistics generally show a distinct seasonality. More people die in the colder winter months than in the warmer summer months. In European countries, for example, the difference between the average number of deaths in winter (December – March) and in the remaining months of the year is 10% to 30%. Only a proportion of these winter excess deaths are directly related to low ambient temperatures (rather than other seasonal factors). Yet, it is reasonable to suspect that fewer people will die from cold as winters are getting milder with climate change. On the other hand, excess mortality from heat may also be high, with, for example, up to 70,000 additional deaths attributed to the 2003 summer heat wave in Europe. So, will the expected reduction in cold-related mortality be large enough to compensate for the equally anticipated increase in heat-related mortality under climate change? More »

Climate indicators

The climate system is complex, and a complete description of its state would require huge amounts of data. However, it is possible to keep track of its conditions through summary statistics.

There are some nice resources which give an overview of a number for climate indicators. Some examples include NASA and The Climate Reality Project.

The most common indicator is the atmospheric background CO2 concentration, the global mean temperature, the global mean sea level, and the area with snow or Arctic sea ice. Other indicators include rainfall statistics, drought indices, or other hydrological aspects. The EPA provides some examples.   

One challenge has been that the state of the hydrological cycle is not as easily summarised by one single index in the same way as the global mean temperature or the global mean sea level height. However, Giorgi et al. (2011) suggested a measure of hydro-climatic intensity (HY-INT) which is an integrated metric that captures the precipitation intensity as well as dry spell length.  

There are also global datasets of indices representing the more extreme aspects of climate called CLIMDEX, providing a list of 27 core climate extremes indices (so-called the ‘ETCCDI’ indices, referring to the ‘CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices’).

In addition, there is a website hosted by the NOAA that presents various U.S. Climate Extremes Index (CEI) in an interactive way.

So there are quite a few indicators for various aspects of the climate. One question we should ask, however, is whether they capture all the important and relevant aspects of the climate. I think that they don’t, and that there are still some gaps.

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References

  1. F. Giorgi, E. Im, E. Coppola, N.S. Diffenbaugh, X.J. Gao, L. Mariotti, and Y. Shi, "Higher Hydroclimatic Intensity with Global Warming", Journal of Climate, vol. 24, pp. 5309-5324, 2011. http://dx.doi.org/10.1175/2011JCLI3979.1

Why extremes are expected to change with a global warming

Filed under: — rasmus @ 5 September 2017

Joanna Walters links extreme weather events with climate change in a recent article in the Guardian, however, some  reservations have been expressed about such links in past discussions.

For example, we discussed the connection between single storms and global warming in the post Hurricanes and Global Warming – Is there a connection?, the World Meteorological Organization (WMO) has issued a statement, and Mike has recently explained the connection in the Guardian.

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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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Predicting annual temperatures a year ahead

Filed under: — gavin @ 16 September 2016

I have a post at Nate Silver’s 538 site on how we can predict annual surface temperature anomalies based on El Niño and persistence – including a (by now unsurprising) prediction for a new record in 2016 and a slightly cooler, but still very warm, 2017.

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Do regional climate models add value compared to global models?

2016-05-20 10.08.17

Global climate models (GCM) are designed to simulate earth’s climate over the entire planet, but they have a limitation when it comes to describing local details due to heavy computational demands. There is a nice TED talk by Gavin that explains how climate models work.

We need to apply downscaling to compute the local details. Downscaling may be done through empirical-statistical downscaling (ESD) or regional climate models (RCMs) with a much finer grid. Both take the crude (low-resolution) solution provided by the GCMs and include finer topographical details (boundary conditions) to calculate more detailed information. However, does more details translate to a better representation of the world?

The question of “added value” was an important topic at the International Conference on Regional Climate conference hosted by CORDEX of the World Climate Research Programme (WCRP). The take-home message was mixed on whether RCMs provide a better description of local climatic conditions than the coarser GCMs.

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Comparing models to the satellite datasets

How should one make graphics that appropriately compare models and observations? There are basically two key points (explored in more depth here) – comparisons should be ‘like with like’, and different sources of uncertainty should be clear, whether uncertainties are related to ‘weather’ and/or structural uncertainty in either the observations or the models. There are unfortunately many graphics going around that fail to do this properly, and some prominent ones are associated with satellite temperatures made by John Christy. This post explains exactly why these graphs are misleading and how more honest presentations of the comparison allow for more informed discussions of why and how these records are changing and differ from models.
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Nenana Ice Classic 2016

Filed under: — gavin @ 23 April 2016

Just a quick note since I’ve been tracking this statistic for a few years, but the Nenana Ice Classic tripod went down this afternoon (Apr 23, 3:39 Alaska Standard Time). See the earlier post for what this is and why it says something about the climate (see posts on 2014 and 2015 results).

With this unofficial time, this year places 4th earliest for the breakup of ice in the Tanana river. It is unsurprising that it was early given the exceptional warmth in Alaska this year.

The exact ranking of years depends a little on how one accounts for leap-year and other calendrical effects. The raw date is the 4th earliest, but given that this year is a leap year, it would be the 5th earliest counting Julian days from the start of the year. Tying the season to the vernal equinox is more stable, which again leads to the 4th earliest. But regardless of that detail, and consistent with local climate warming, the ice break-up date have advanced about 7 days over the last century.

As a side bet, I predict (based on previous years) that despite enormous attention in the skeptic-osphere given the Nenana result in 2013 (when it was remarkably late), it won’t be mentioned there this year.

Anti-scientists

Filed under: — rasmus @ 9 February 2016

Ross McKitrick was so upset about a paper ‘Learning from mistakes in climate research(Benestad et al., 2015) that he has written a letter of complaint and asked for immediate retraction of the pages discussing his work.

This is an unusual step in science, as most disagreements and debate involve a comment or a response to the original article. The exchange of views, then, provides perspectives from different angles and may enhance the understanding of the problem. This is part of a learning process.

Responding to McKitrick’s letter, however, is a new opportunity to explain some basic statistics, and it’s excellent to have some real and clear-cut examples for this purpose.

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References

  1. R.E. Benestad, D. Nuccitelli, S. Lewandowsky, K. Hayhoe, H.O. Hygen, R. van Dorland, and J. Cook, "Learning from mistakes in climate research", Theoretical and Applied Climatology, vol. 126, pp. 699-703, 2015. http://dx.doi.org/10.1007/s00704-015-1597-5