Malcolm Turnbull, how should Australia adapt to climate change without science?

Adapting to climate change needs information on local changes in the mean, weather variability and extremes. Observed changes in the means are not enough.

If you don't like what #climate science is telling you, just fire all the climate scientists

Miles Grant

The "conservative" government of Australia plans to gut its climate research and kill the groups doing climate research at Australia's main research institute, CSIRO. Australia's opposition leader rightly said the Prime Minister Malcolm Turnbull should "hang his head in shame".

The destruction is not for lack of quality of the research. CSIRO's new chief Larry Marshall send an email to its employees stating:

"CSIRO pioneered climate research ... Our climate models are among the best in the world and our measurements honed those models to prove global climate change.

From this the strange conclusion is drawn:

That question has been answered, and the new question is what do we do about it, and how can we find solutions for the climate we will be living with?"

[UPDATE. Judith Curry agrees with this strange sentiment: "Now that the UN’s community of nations has accepted consensus climate science to drive international energy and carbon policy, what is the point of heavy government funding of climate research, particularly global ­climate modelling?"]

Just because we know climate change is real, does not mean that we understand everything. Projecting increases in the global mean temperature is easy. Saying something about the changes in the hydrological cycle is much harder. We know how much the global mean precipitation will increase because we can estimate the additional evaporation and what goes up must go down, but say where and how it goes down is hard. These assessments naturally have their uncertainties and it certainly pays to reduce them to make better political decisions.

Much more important than the uncertainties in the changes in the global means, for "solutions for the climate we will be living with" (adapting to climate change) we will need local predictions. That is a lot harder and very uncertain. Locally the changes can be very different from the global change. As Roger Pielke Jr. writes about storms on the US East Coast: "So those who argue for a simple relationship between increasing water content of the atmosphere and storm strength, data do not support such a claim over this multi-decadal period, in this region." (my emphasis)



Much more important than the uncertainties in the changes in the global means, for adaptation we need information on changes in weather variability and extremes. Especially for a country like Australia that knows very large variations due, for example, to El Nino.

One of the strategies of the mitigation skeptics is to pretend that adaptation is straightforward and cheap. When the sea level goes up 1 mm, just make the dikes 1 mm higher. However, the sea dikes will break during spring tide and a strong storm. Thus we also need to understand the storms to know how much stronger the dikes need to be. They will break during a once in a century storm. Or at least during what used to be a once in a century storm. Try to estimate from observations during a changing climate whether the 100-year storms are getting worse.

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"With climate change, we can’t drive by looking in the rear view mirror. We’re in a new normal."
Climate scientists Berrien Moore and Katharine Hayhoe

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Was the flooding of New Orleans due to [[Hurricane Katrina]] a unique event or the "new normal"? During the flooding last year in South Carolina in some locations the rain amounted to a 1,000-year event (in a given year there is a 1 in 1,000 chance of observing rainfall of that magnitude of more). Does South Carolina have to adapt to this because this will happen more often or will this remain an outlier? Parts of the United Kingdom were hit three times by 100 year rain events the last few years. How often will they have to suffer this before we know from waiting and seeing that the weather has changed, people will have to move and the infrastructure needs to be more more robust?

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"There's no point putting in flood defenses that respond to mean climate change if you haven't thought of what a one-in-a-hundred-year event will look like in a warmer world... They don't want to know what the climate will be like, they want to know what the weather will be like in 20, 30, 50 years time."

Chief Scientist UK MetOffice Dame Julia Slingo on adaptation

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The same goes less visibly for droughts. When your farm takes a hit due to a drought, do you build it up again when the rain comes back or is your land no longer profitable. Do you want to do this blindly? Or do you prefer some scientific guidance? For planning crops and managing reservoirs during droughts, seasonal and decadal climate predictions reduce costs and hardship. For planning new reservoirs and desalination plants long-term climate projects give guidance.

Meteorologists and climatologists are building seamless prediction systems. Going from short term weather predictions and nowcasting using observations during severe weather, to long-term weather predictions to prepare for bad weather, to seasonal and decadal predictions for planning and climate projections for adaptation. In many wealthy countries governments are setting up national climate service centers to help their societies adapt. The World Meteorological Organization is building a Global Framework for Climate Services (GFCS) to coordinate such efforts and help poorer countries understand the changes their region will see. While Australia sticks its head in the sand.

We will need very good science, a very good understanding of the coming climatic changes to adapt. The Australian government destroys climatology at a moment people, communities and companies need it most to adapt to the climatic changes that we have set in motion. This is about as stupid as the US states where the civil servants are no longer allowed to talk about climate change, which will mean that these communities will suffer the consequences without being prepared for the changes.

The same is true for (nearly?) every impact of climate change. In the past we could use long-term observations to determine what kind of extremes we could expect. Now, after all the delays to solve the problem, humanity is becoming more and more dependent on climate science and climate models, the models the mitigation skeptics who campaign for more global warming claim not to trust.

If you do not know which climatic changes you need to adapt to, you need to adapt to everything. Preparing for the worst case scenario in every direction is very expensive.

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Never attribute to maladaptation that which can be adequately explained by stupidity.

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When Australia notices what a blunder they are making it will easily take over a decade until Australia's climate research is again where it started. It takes years until you understand a climate model or a data set well and start to be productive. Science is a social profession and once you are proficient you can start building your network. Then you notice the kinds of expertise still missing in your freshly build up institute. Unfortunately, like trust losing scientific expertise goes much faster than building it up.



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Related reading

CSIRO boss’s failed logic over climate science could waste billions in taxes by Andy Pitman, Director of the Centre for Climate System Science.

The CSIRO and farming in a changing climate

'Misleading, inaccurate and in breach of Paris': CSIRO scientist criticises cuts. Stefan Rahmstorf​: "Closing down climate research capacity at a time of rapid global warming is not just short-sighted, it borders on the insane."

The Sydney Morning Herald: Climate science to be gutted as CSIRO swings jobs axe

Australia's CSIRO dims the lights on climate and environment

Thomas Peterson chair of WMO Commission on Climate explains the need for climate research by example how to deal with a drought.


Top photo. Severe suburban flooding in New Orleans, USA. Aftermath of Hurricane Katrina. Photo by ark Moran, NOAA Corps, NMAO/AOC (CC BY 2.0)
Second photo. Flames burn out of control at Possum Kingdom Lake near Pickwick, TX, on April 15, 2011. Photo by Texas Military Forces, available through a CC license.
Last photo. Flash flooding stalls traffic on I-45 in Houston on May 26, 2015. Photo by Bill Shirley, available through a CC license.

Be careful with the new daily temperature dataset from Berkeley

The Berkeley Earth Surface Temperature project now also provides daily temperature data. On the one hand this is an important improvement, that we now have a global dataset with homogenized daily data. On the other hand, there was a reason that climatologists did not publish a global daily dataset yet. Homogenization of daily data is difficult and the data provided by Berkeley is likely better than analyzing raw data, but still insufficient for robust conclusions about changes in extreme weather and weather variability.

The new dataset is introuduced by Zeke Hausfather and Robert Rohde on Real Climate:

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.

The post explains in more detail how the BEST daily method works and presents some beautiful visualizations and videos of the data. Worth reading in detail.

Daily homogenization

When I understand the homogenization procedure of BEST right, it is based on their methods for the monthly mean temperature and this only accounts for non-climatic changes (inhomogeneities) in the mean temperature.

The example of a move from black roof in a city to an airport is also a good example that not only the mean can change. The black roof will show more variability because on hot sunny days the warm bias is larger than on windy cloudy days. Thus part of this variability is variability in solar insolation and wind.

Also the urban heat island could be a source of variability, the UHI is strongest on wind and cloud free days. Thus part of the variability in observed temperature will be due to variability in wind and clouds.

A nice illustration of the problem can be found in a recent article by Blair Trewin. He compares the distribution of two stations, one in a city near the coast and one at an airport more inland. In the past the station was in the city, nowadays it is at the airport. The modern measurements in the city that are shown below have been made to study the influence of this change.

For this plot he computed the 0th to the 100th percentile. The 50th percentile is the median, 50% of the data has a lower value. The 10th percentile is the value where 10% of the data is smaller, and so on. The 0th and 100th percentile in this plot are the minimum and maximum. What is displayed is the temperature difference between these percentiles. On average the difference is about 2°C, the airport is warmer. However, for the higher percentiles (95th) the difference is much larger. Trewin explains this by cooling of the city station by a land-sea circulation (sea breeze) often seen on hot summer days. For the highest percentiles (99th), the difference becomes smaller again because offshore wind override the sea breeze.



Clearly if you would homogenize this time series for the transition from the coast to the inland by only correcting the mean, you would still have a large inhomogeneity in the higher percentiles, which would still lead to non-climatic spurious trends in hot weather.

Thus we would need a bias correction of the complete probability distribution and not just its mean.

Or we should homogenize the indices we are interested in, for example percentiles or the number of days above 40°C. etc. The BEST algorithm being fully automatic could be well suited for such an approach.

On the importance of changes in weather variability for changes in extremes

This is part 2 of the series on weather variability.

A more extreme climate is often interpreted in terms of weather variability. In the media weather variability and extreme weather are typically even used as synonyms. However, extremes may also change due to changes in the mean state of the atmosphere (Rhines and Huybers, 2013) and it is in general difficult to decipher the true cause.

Katz and Brown theorem

Changes in mean and variability are dislike quantities. Thus comparing them is like comparing apples and oranges. Still Katz and Brown (1992) found one interesting general result: the more extreme the event, the more important a change in the variability is relative to the mean (Figure 1). Thus if there is a change in variability, it is most important for the most extreme events. If the change is small, these extreme events may have to be extremely extreme.

Given this importance of variability they state:

"[Changes in the variability of climate] need to be addressed before impact assessments for greenhouse gas-induced climate change can be expected to gain much credibility."

The relative sensitivity of an extreme to changes in the mean (dashed line) and in the standard deviation (solid line) for a certain temperature threshold (x-axis). The relative sensitivity of the mean (standard deviation) is the change in probability of an extreme event to a change in the mean (or standard deviation) divided by its probability. From Katz and Brown (1992). It is common in the climatological literature to also denote events that happen relatively regularly with the term extreme. For example, the 90 and 99 percentiles are often called extremes even if such exceedances will occur a few times a month or year. Following the common parlance, we will denote such distribution descriptions as moderate extremes, to distinguish them from extreme extremes. (Also the terms soft and hard extremes are used.) Based on the theory of Katz and Brown, the rest of this section will be ordered from moderate to extreme extremes.

Examples from scientific literature

We start with the variance, which is a direct measure of variability and strongly related to the bulk of the distribution. Della-Marta et al. (2007) studied trends in station data over the last century of the daily summer maximum temperature (DSMT). They found that the increase in DSMT variance over Western Europe and central Western Europe is, respectively, responsible for approximately 25% and 40% of the increase in hot days in these regions.

They also studied trends in the 90th, 95th and 98th percentiles. For these trends variability was found to be important: If only changes in the mean had been taken into account these estimates would have been between 14 and 60% lower.

Also in climate projections for Europe, variability is considered to be important. Fischer and Schär (2009) found in the PRUDENCE dataset (a European downscaling project) that for the coming century the strongest increases in the 95th percentile are in regions where variability increases most (France) and not in regions where the mean warming is largest (Iberian Peninsula).

The 2003 heat wave is a clear example of an extreme extreme, where one would thus expect that variability is important. Schär et al. (2004) indeed report that the 2003 heat wave is extremely unlikely given a change in the mean only. They show that a recent increase in variability would be able to explain the heat wave. An alternative explanation could also be that the temperature does not follow the normal distribution.

Introduction to series on weather variability and extreme events

This is the introduction to a series on changes in the daily weather and extreme weather. The series discusses how much we know about whether and to what extent the climate system experiences changes in the variability of the weather. Variability here denotes the the changes of the shape of probability distribution around the mean. The most basic variable to denote variability would be the variance, but many other measures could be used.

Dimensions of variability

Studying weather variability adds more dimensions to our apprehension of climate change and also complexities. This series is mainly aimed at other scientists, but I hope it will be clear enough for everyone interested. If not, just complain and I will try to explain it better. At least if that is possible, we do not have much solid results on changes in the weather variability yet.

The quantification of weather variability requires the specification of the length of periods and the size of regions considered (extent, the scope or domain of the data). Different from studying averages is that the consideration of variability adds the dimension of the spatial and temporal averaging scale (grain, the minimum spatial resolution of the data); thus variability requires the definition of an upper and lower scale. This is important in climate and weather as specific climatic mechanisms may influence variability at certain scale ranges. For instance, observations suggest that near-surface temperature variability is decreasing in the range between 1 year and decades, while its variability in the range of days to months is likely increasing.

Similar to extremes, which can be studied on a range from moderate (soft) extremes to extreme (hard) extremes, variability can be analysed by measures which range from describing the bulk of the probability distribution to ones that focus more on the tails. Considering the complete probability distribution adds another dimension to anthropogenic climate change. Such a soft measure of variability could be the variance, or the interquartile range. A harder measure of variability could be the kurtosis (4th moment) or the distance between the first and the 99th percentile. A hard variability measure would be the difference between the maximum and minimum 10-year return periods.

Another complexity to the problem is added by the data: climate models and observations typically have very different averaging scales. Thus any comparisons require upscaling (averaging) or downscaling, which in turn needs a thorough understanding of variability at all involved scales.

A final complexity is added by the need to distinguish between the variability of the weather and the variability added due to measurement and modelling uncertainties, sampling and errors. This can even affect trend estimates of the observed weather variability because improvements in climate observations have likely caused apparent, but non-climatic, reductions in the weather variability. As a consequence, data homogenization is central in the analysis of observed changes in weather variability.