Will GCMs really tell us everything we need to know about climate change?

In a previous blog, I discussed General Circulation Models (GCMs) at varying resolutions.

Here, I’ll highlight a few limitations, especially when looking at tropical cyclones.

Even though GCMs are able to capture tropical cyclone tracks and storm formation to provide hugely valuable forecasts for public safety concerns, we should be aware of the limitations in looking at climate scale variability and change. For example, looking seasons or years ahead into a climate projection, GCMs have less ability to say how many and how intense the storms might be. Hurricane season forecasts are put together using a variety of statistical and GCM-based techniques and we can get a lot of value from both approaches. But there is only so much that we can say.

However, papers by Deser et al 2012 and Done et al 2014 are useful in determining what can be explained on a seasonal or decadal time-scale. James Done found that based on one season, his regional climate model experiments shows that around 40% of the variability in tropical cyclone frequency in the North Atlantic is simply natural variability, and not associated with forcing from greenhouse gases, volcanoes, aerosols or solar variability (external forcing). He notes that from Deser et al. 2012, regional scales can see internal variability becoming greater than externally forced variability. This also highlights the difficulty in assigning a single regional event to changes in climate on a global scale.

To sum up, GCMs

• as numerical weather prediction models, offer great ability to provide operational forecasts and warnings on a day-to-day basis, 
• as global/regional climate models, to experiment with the atmosphere and explore sensitivities in the processes that bring about extremes of climate, global climate variability or climate change. 

When looking at seasonal or longer timescales, GCMs run at lower resolution and so lose the ability to capture small scale features that drive tropical cyclones, and so we have to model the large scale influences to look at more general shifts in probabilities of single or seasonal phenomena (e.g. hurricanes or droughts).

Deser et al. 2012 also calls for greater dialogue between science and policy/decision-makers to improve communication and avoid raising expectations of regional climate predictions. I totally agree. Better communication between scientists and stakeholders is important because talking about storms and climate change is highly political. Poor communication can lead to gross misrepresentations by those aiming to mitigate and adapt to climate change, as well as those who do not accept that climate change is a concern.

Future for GCMs?

I can see how GCMs have great ability in helping us understand the sensitivities of the climate system, and as they improve and as computing power increases (along with big data solutions), then so too should our understanding of various climate processes. In fact, growth of the GCM capabilities may well increase the level of uncertainty as we start to model more and more complexity. I do wonder where the next big step will be though. Between CMIP3 and CMIP5 (two rounds of climate model comparison projects – see previous blog) Bellenger et al. (2015) showed some progress, but also commented that overall, there were limited improvements of how ENSO (a dominant mode of climate variability)  is characterised.

An interesting article here by Shackley et al. back in 1998 called; “Uncertainty, Complexity and Concepts of Good Science in Climate ChangeModeling: Are GCMs the Best Tools?”, shows a range of interesting discussion points asking whether GCM-based climate science is actually the best approach from a number of perspectives. Are there alternative types of models that could allow us to better engage with the public, with policy makers or with the private sector? There are certainly alternatives that show promise as discussed on Judith Curry’s blog, who is of the opinion that climate modelling is in a “big expensive rut.” I hope I can find time to expand on this interesting topic in my blog here.

Personally, I am a big fan of GCMs. It's amazing that they can represent the atmosphere with such high fidelity, but it's good to ask these questions and not to forget alternative approaches which may be much more practical and 'fit-for-purpose' in particular situations.. 

In a future blog, I’ll discuss a little about how we talk about probability of future events, and then follow on with a blog on how we currently stand on tropical cyclones and climate change. 

A Model Family

Many of my recent blogs have been quite focussed on the past. It seems clear that we have a few useful methods that can help us understand storm frequency, with less certainty on how severe they have been. As powerful as palaeotempestology might be, it is sadly unlikely to be able to provide enough data for us to truly compare the climate proxy outputs at the fidelity with which we have been observing storms in the last 100 or so years, especially since we began to use satellites to observe the weather.

However, as an ex-professional in the world of weather forecasting, I often get asked about the chances of a certain intensity of storm occurring, such as, could we see another Hurricane Katrina, or will the Philippines see another Typhoon Haiyan, or closer to home (UK), when will we see another Great Storm of 1987 (aka 87J). Of course, these questions are difficult to answer, unless a storm of similar characteristics is starting to form and picked up in numerical weather prediction models such as the UK Met Office’s Unified Model (UM), or the U.S. NOAA’s Global Forecast System (GFS) (there are many more).

This blog will talk a little about what I know of the types of models that are based on physical laws at work in the atmosphere and oceans, and take super computers bigger than my flat (not saying much) to run.

General Circulation Modelling – the granddaddy of physical modelling

General Circulation Models (GCMs) focus on the actual physical dynamics of the atmosphere and model them by building a system of grid cells (lego-like blocks) which talk to each other regarding momentum and heat exchanges. The size of these grid cells defines the scale of the weather phenomena that can be modelled.

However, there is a trade-off between three facets of a GCM configuration. With limited computing resources, a balance must be struck between complexity (the physics that are included in the model in the actual lines of code), resolution (size of grid-cells) and run-length (how much time does the model  represent i.e. into the future or a period in the past perhaps). Basically climate models use Duplo bricks, and high resolution models use normal Lego bricks. The analogy also works as the can fit together nicely (Figure 1).

Figure 1: Larger Duplo (climate models) bricks and smaller Lego (weather forecasting models) bricks working together. Source: Wiki Commons Contributor: Kalsbricks

I wonder what type of modelling is analogous to mechano? Thoughts on a postcard, please, or in the comments section below?

In case you were wondering, the Lego analogy came about since that's what I bought my three year old nephew, Harry, for Christmas. The present that keeps on giving! Merry Christmas by the way!

Lego Bricks

High-resolution model configurations of some of the big GCMs have been built that can, for example, capture the small-scale eddies around the headlands of the Isle of Wight in the UK (by the Met Office during their involvement London Olympics 2012). Models of grid-scale, in the order of a few hundred metres, are used for this detailed work and are run over a very small region.

Another example of high resolution modelling: A regional model was employed to reanalyse Cyclone Megi from 2010 which had one of lowest central pressures ever recorded. The comparison shows satellite imagery alongside a model run (by Stuart Webster at the Met Office) with amazing detail of the eye-structure and outer bands of convection. Because of the presentation of the model data, the two are difficult to distinguish for the untrained eye (Figure 2).

Figure 2: Cyclone Megi simulation (top) showing eye- wall and convective bands, compared to similar locations and overall size of the real storm in a satellite image from MT-SAT 2. Source: Met Office.

Duplo bricks

GCMs traditionally struggle to match the intensity of storms in climate model configurations, as described in the IPCC AR5 chapter on evaluation of climate models (IPCC WG1 AR5: 9.5.4.3), but example such as the Met Office’s Cyclone Megi, and others models with resolutions of 100km or so show that the science is still able to model many features of tropical cyclone evolution.

They are also used to model the large scale planetary interactions that govern phenomena such as ENSO, and are captured well according to the selection of models used in the Coupled Model Inter-comparison Project (CMIP). CMIP is currently on its fifth incarnation, CMIP5, which is used by the IPCC to understand future climate change. This paper by Bellenger et al. (2015) shows some of the progress made in recent years, between CMIP version, however, due to similra ability to represent large scale features when examining ENSO, both CMIP3 and CMIP4 models can be used in conjunction as a broader comparison

Assembling the ensemble

The “ensemble” is also a technique used to run a model multiple times with slightly different starting conditions to capture a range of uncertainty in the outputs. No model is perfect so their products shouldn’t be believed on face value, but ensembles can help us by showing the range of possibilities as we try to represent what we don’t know in the input data.

This addresses some of the observational uncertainty. GCMs starting points are based on the network of observations that are connected up throughout the world, and standardised by the World Meteorological Organisation (WMO) for weather forecasting. These observations include ground-based observations (manual and automatic), radar imagery of precipitation, satellite images, aircraft reconnaissance (with tropical cyclones), sea surface readings, and weather balloon ascents (and more) which are all assimilated into an initial condition, and gradually step forward in time by the gridded global model. The starting point is also called ‘the initialisation’ in a forecasting model. For climate models the starting point can be current climate, or whatever version of the climate is relevant to experimental design.

Regardless of how a mode is started on it time-stepping through a defined period, ensembles provide an idea of the range of possible outcomes through minor perturbations in observing conditions, or even how certain physical processes are handled (i.e. through different paramaterisation schemes for features too small to be represented at a given resolution). In my forecasting days at the Met Office, looking at the solutions from a variety of the world’s big weather modelling organisations (NOAA, Met Office, ECMWF, JMA) was colloquially termed ‘a poor man’s ensemble’ as normally an ensemble will consistent of many tens of solutions. A similar concept, although not using GCMs, is found in risk modelling applications such as catastrophe loss modelling, many tens of thousands of simulations are performed to try to statistically represent extreme events, but using extreme value theory and statistical fits to the rare events on a probability distribution. A useful paper reviewing methods in loss modelling for hurricanes can be found by Watson et al. in 2004.

And the weather today...

So numerical weather prediction models used for day-to-day forecasting are run at high resolution, high complexity, but can only go a week or so into the future. Their accuracy has improved greatly in the last few decades. A forecasting for three days ahead now is as accurate as a forecast for one day ahead in the 1980s, according to the Met Office. And below (Figure 3) is a picture of the European Centre for Medium Range Forecasting’s (ECMWF) verification of different ranges over the decades

Figure 3: ECMWF’s verification scores for a range of forecast ranges. Source: ECMWF.

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Climate models on the other hand are run with lower complexity and lower resolution, allowing them to be run out to represent decades. Since large scale climate modes such as ENSO (or the AMO, or MJO, or many others) can influence storm activity, intensity and track, GCMs are invaluable tools in helping us understand the broader climate, as well as the small-scale processes.

Basically, GCMs can be run at different resolutions with different input data depending on the application (e.g. weather forecasting or climate experimentation). The computing power dictates how these model configurations perform and the range at which they can produce outputs in a reasonable run time. They have developed into the key tool for understanding our weather and climate and interactions with the Earth’s surface (via other modelling approaches such as land surface models  or ocean circulation models. 

A Vanishing Sea of Toxic Dust Storms

In the last Climate Change MSc lecture of 2015, a case study was presented regarding the changes that have happened in a relatively short space of time in the Aral Sea, on the border between Kazakhstan and Uzbekistan. Figure 1 below clearly shows the reduction in the area covered by water in the sequence which runs from 2000 to 2015.

Figure 1: Aral Sea satellite image sequence from 2000 to 2015 (looping). The black outline is the approximate lake shoreline in 1960. Source: Constructed animating gif from NASA Earth Observatory images.

Otherwise known as the ‘Sea of Islands’, this endorheic sea was once the fourth largest inland sea in the world, and allowed fishing communities and agriculture to sustain themselves for decades in the early half of the 20^th century. As an endorheic sea (meaning no outflow to the ocean) it acts as a terminus for surrounding hydrological systems, also termed as a terminal lake. Terminal seas and lakes such as this are very sensitive to changes in climate, for example through changes in evaporation rates. In fact, the Aral Sea has undergone a cycle of drying out and filling up over the past 10 thousand years (Micklin 2007).

Another picture, Figure 2 (sourced cited by an article on the Aral Sea Crisis by Columbia University) shows some older images than Figure 1, which highlight the longer term reduction.

Figure 2: Clear reduction in Aral Sea. When combined with Figure 1 we see the extremes of the reduction in water surface area. Source: http:/www.envis.maharashtra.gov.in and cited by Thompson 2008

The main cause for this reduction was the development of the Karakum Canal, built for agricultural irrigation, shipping and fisheries allow for economic development of Turkmenistan. It was started in 1954 and completed in 1988. It has enabled huge areas of Turkmenistan to be committed to high intensity agriculture, essentially draining the Aral Sea of water.

The reason for this huge engineering endeavour was the farming of cotton. The cotton, nicknamed ‘white gold’, requires a huge amount of water. To make matters worse the engineering practices used to construct the canal allow around 50% of the water to be lost into the ground and to evaporation.

Impact

Micklin noted the reduction in water surface area to be around 75%, and the lake level reduction to be around 23 to 30 meters (Glantz 2007), which led to a volume reduction of 90% and an increase in salinity of over an order of magnitude, from 10 g/l to over 100 g/l. This lead to tragic and severe impacts to the local ecosystems, mainly fish species, as well as enhancing the frequency of dust storms to roughly ten per year (Glantz 1999, cited by an article by Thompson in 2008 on the Columbia University website). These impacts deveastated local communities and made the area extremely inhospitable. 

Knock on impacts on local communities and industries are numerous. Obviously, the fishing industry in the sea has been decimated due to increasing salinity and agricultural practices are now hampered by the loss of water resources. Mammals and birds have also seen sharp decline in species diversity: from 1960 to 2007, the area lost roughly half of the number of species (Micklin 2007).

The other major impact of over 36,000 km² (Wiggs et al. 2003) of dusty seabed being created is that there is now a large source of extra dust available to be picked up by the winds and on occasion whipped into dust storms (Figure 3). Roughly ten dust storms occur in the region per year (Glantz 1999, cited by an article by Thompson in 2008 on the Columbia University website).

Figure 3: Dust storms on the coast of the Aral Sea in May 2007 (Source: NASA)

Agricultural waste products containing pesticides, insecticides, herbicides and fertilisers have drained into the sea, accumulated over time, and then once the sea dried, they became baked into the exposed sediments. The desiccated land surface also potentially contains remnants from Soviet Union's biological warfare testing in the 1950’s, including Antrax, which is just waiting to be transported around by the aeolian processes. Vozrozhdeniye island, also known as Resurrection island, remained a controversial subject as it was one of the chief locations for such testing.

Wiggs et al. (2003) studied the link between aoelian dust and child health in the populations close to the Aral Sea, and found some associations to local respiratory illness in local populations, although there are significant long-distance sources of dust in the region too. Micklin (2007) also confirmed this negative impact on human health and agriculture in the wider area from dust storms that can grow to be 500km in size.

Climate perspective

Although the case of the Aral Sea’s reduction is an extreme example, it seems fair to assume that endorheic lakes will see pressure due to global climate change (Timms 2005), whether there is significant human influence or not. The Aral Sea has suffered from a two-pronged attack as the region undergoes warming, and agricultural exploitation and over-use. Strategies to preserve the remaining water in the North Aral Sea through damming projects after the sea split in to two basins in 1987, seem to be successful, which will enable the communities in the area to hold on to their way of life to an extent.

The former majesty of the larger portion of Aral Sea (the Big Aral), now seems to resemble no more than a salty (and toxic) dust bowl, with former islands now parched monuments to the impact of cotton farming and climate change, although to a lesser but still significant extent, (Aus Der Beek et al. 2011). The region will only come under more pressure if water resources become scarcer in the area linked to global warming and high evapotranspiration rates. 

Small et al. (2001) examined how the desiccation of such a large area through excessive irrigation has modified the sea surface temperatures, precipitation regimes and the hydrological cycle in the area. I wonder if the original plans to build the Karakum Canal took any of these knock-on effects in to consideration.

To end, I’ll post this interactive storymap hosted by Esri which highlights some human induced change since 1990 using the Landsat satellite imagery from NASA. The first example is the Aral Sea and, you can see again, by swiping the dividing line, how the lake has undergone a dramatic and rapid drying out in the last 25 years. The other pages of the map, also show cases of anthropogenic land use change from urban expansion, damming, land reclamation, and agricultural uses.

*UPDATE*
My brother's comment below makes a very good point regarding the fact that such a sad story, now serves as a an evocative reminder of the impact of human over exploitation of the environment. This reminder should be documented as it happens, not only in scientific literature, but in art too. We are both keen photographers, and so I thought I'd add this link to herwigphoto.com's Aral Sea project. Some amazing and poignant images.

Palaeotempestology: Tree rings

In my last blog, I explored how the layers of calcium carbonate, which build up as a coral skeleton grows, can be used as a climate proxy. We can find a similar process by looking at tree rings. One of the more established practices in palaeoclimatology is dendroclimatology (the use of tree rings to study the past climates). Like other palaeoclimatological proxies, it allows us to extend the range of our observational record beyond that of conventional weather recording instrumentation.

Just as corals live for hundreds of years (sometimes over a thousand years), trees can keep on recording the composition of the atmosphere in their layers of cellulose for many hundreds of years, and beyond when fossilised. Figure 1 below shows an example of Huon pine samples ready for analysis, each dark line denoting a season of growth.

Figure 1: Huon Pine ready for analysis. Source: Edward Cook, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY

Isotopic differences

Ancient pines are often the favoured study subjects due to their longevity. They can give annual or seasonal information on atmospheric composition. To extend the record beyond a single sample, a variety of sources can be combined together using distinctive signatures as shown in Figure 2 below.

Figure 2: Sources of tree ring data showing how various samples can be linked together. Source: Laboratory of Tree-Ring Research, The University of Arizona

The main process that allow us to look at past storms is the fractionation of stable oxygen isotopes through condensation and evaporation. I touch upon this in my previous blog about corals, it is the difference atomic weight between the heavier oxygen-18 isotope and oxygen-16 isotope that allows us to glean clues about past climate events from tree cores.

The difference in atomic weight of oxygen isotopes is derived from the number of neutrons in the atomic structure. The most common natural isotope is oxygen-16 (over 99% of atmospheric oxygen) which has 8 protons and 8 neutrons (electrons are virtually weightless by comparison), but stable oxygen atoms can also have 9 or 10 neutrons to make up the different isotopes that we find useful for palaeoclimatology. As mentioned before, the water molecules with the lighter oxygen isotopes (oxygen-16) are preferentially evaporated in warm temperatures, while conversely the water molecules with heavier isotopic values (oxygen-18) tend to condense and form clouds or precipitation more easily. It is this property that allows us to identify different sources of precipitation in tree ring samples.

In extreme precipitation events associated with tropical cyclones, the level of oxygen-18 depletion in the rain water is high due to the highly efficient process of forming precipitation via condensation in the core of a tropical cyclone (Lawrence in 1998, Monksgaard et al. 2015). In Lawrence’s paper, five tropical cyclones that made landfall in Texas, U.S, were studied. They showed much lower oxygen-18 to oxygen-16 ratios (or δ¹⁸O) from tropical cyclones than normal summer convective storms.

This finding was further corroborated by a study of Hurricane Olivia by Lawrence et al. in 2002. Tropical cyclones are also large and long-lived and create vast areas of precipitation that can stay in the water system for weeks, giving different isotopic characteristics associated with the location of the heaviest rain bands and storm centre (Monksgarrd et al 2015). Deep soil water can remain unaffected by normal summer rainfall, and in the absence of further heavy rain events, it is allowed to be taken up by trees (Tang and Feng, 2001).

It seems clear that oxygen isotope analysis seems to be the favoured form of tree ring analysis for palaeotempestology.

Tapping the potential

Upon learning about these methods it also seems reasonable to assume that different intensities and characters of storms will result in different levels of oxygen-18 depletion. It seems likely that there would be much uncertainty in making assumptions of a storm’s intensity based on isotope fractionation (but I’ll keep looking for more research on this). At the moment, it seems that the uncertainty may preclude a reliable intensity measure of past storms using this approach.

The oxygen isotopes uptake into the tree’s structure will depend on many factors, including biological processes that are dependent on species, tree age, exposure to the storm, soil composition. Growth cycles are also taken into account. By doing so we can try to limit the degree to which uncertainty derived from the mismatch between growth season and storm season, can cloud useful information.

In the North Atlantic basin for example, hurricane season runs from early June to late November and as such overlaps mainly with latewood (as opposed to earlywood) growing phase. Therefore it is these sections of the layers of tree rings which are focussed upon for palaeotempestological studies.

Miller et al. 2006 presented the emerging case for using oxygen isotopes more widely after the devastation left behind by the busy 2004 and 2005 hurricane seasons, by building a 220-year record to identify past storms from unusually low oxygen-18 isotopes in pine forests. This is potentially very useful for engineering and loss modelling concerns.

“Can’t see the wood for the trees”

There are many uncertainties in the application of tree ring data to palaeoclimatology, let alone palaeotempestology, as summarized in the review paper by Sternberg et al. in 2009, including complex cellulose uptake biology, changes in isotopic composition of soil water, assumptions based on the relationship between leaf temperature and ambient temperature.

However, every study adds to the wealth of information and since each site represents a single location slice through time, it seems as though the science of dendroclimatology will only continually benefit from new data. And there still seems to be push to collect and analyse more data. The National Climatic Data Center, hosted by NOAA, is a font of old and recent tree ring datasets.

A recent review of the data by Schubert and Jahren published in October this year (2015) takes a wide view. It aims to unify tree ring data sets, to bring together a global picture of past extreme precipitation events based on low oxygen-18 isotope records. They conducted 5 new surveys and used 28 sites from the literature to create a relationship using seasonal temperature and precipitation, which can explain most of the isotopic oxygen ratio in tree cellulose. This seems to be a step up in resolution, as looking at seasonal variations rather than annual cycles may provide a step closer to identifying individual storms or storm clusters using tree ring data. It is interesting to see a comment in the conclusion of this paper about the fact that much of the uncertainty that still remains in this link, is derived from disturbances, such as storms.

Figure 3: Comparison between measured δ¹⁸O in the cellulose of studies trees and the calculated δ¹⁸O using the model developed by Schubert and Jahren which uses known climate characteristics. It shows a good correlation on relating seasonal temperature and precipitation to oxygen-18 isotope ratios. Source: Schubert and Jahren, 2015

It seems clear that it would be much more difficult to develop a simple equation to explain the extremes of the isotopic ratio chronologies to identify extreme storms. However, Schubert and Jahren seem to have taken a step forward while remaining focussed on average seasonal conditions. Nevertheless, I can’t help but wonder if there is a way for extreme events to be linked in to somehow.

Alternatives to isotopes

When looking specifically at past storms in trees rings, I did find a couple of other approaches to using tree ring data that may also be worth a mention.  

Firstly, an interesting couple of papers by Akachuka in 1991  and another in 1993, used a method where trees that have been forced to lean after a hurricane. This phenomenon is examined for any extra clues that it may provide by assessing how these trees recover from such disturbances. Although the papers do not look specifically at characterising the storms themselves (i.e. there is no wind speed to bole displacement relationship), I couldn’t help but wonder if there is some extra information to gather from these trees and whether we could build a relationship to specific storms or storm seasons.

Another paper by Sheppard et al. in 2005 looks at the effect of a tornado in 1992 on a specific dendrochronology and re-evaluates the pre-historical records from wood samples retrieved from an 11^th century ruin in Arizona. He looks for similar patterns in wood growth (see Figure 2 for conceptualisation). Unfortunately, the patterns found in the tree rings which were caused by the tornado in 1992 were not replicated in the ring patterns of the 11^th century sample. This is certainly interesting work, but I imagine that finding enough data for trees that are damaged but still survive tornadoes is not easy, especially when comparing to single older samples.

Conclusions

Although individual studies using tree lean or damage from specific events like tornados, are interesting and worthwhile academic endeavours to help us understand the ways in which storms of various scales impact certain tree growth, they do seem somewhat less applicable to thinking about climate change and how frequency and severity of storms are changing over a wide area.

With so many subtleties based on factors such as tree species or topography of a study site, I feel that the broader synthesis approaches (as per Schubert and Jahren above) using stable oxygen isotopes offer greater immediate potential for aiding our understanding of past changes in storm activity with possibility for application to risk assessments and projecting impacts of future climate change. 

Palaeotempestology: Beach ridges, corals and sclerosponges

After looking in depth at lake sediment layers as a proxy for hurricane activity, I’ll now turn my attention to the marine environment, as we head the seaside in our investigation. As we move off shore into the ocean, there are some other proxies as we broaden our options for looking at past tropical cyclones. For example, large scale storm surge or precipitation events can lead to rapid erosion or landslides which may become trapped in sediment records in the ocean. Corals can be smashed and broken in a storm and deposited or trapped in mud substrates.

Rubble Ridges

A ridge that is largely made up of broken coral or shell in mud layers is called a chenier. The subtle differences between a beach ridge and a chenier are described in the introduction of a paper by Taylor and Stone in 1996. Basically, it describes a chenier as having muddy swales in between ridges of sand, coral and shell deposits over the layers of sediment from the normal active geomorphological processes. Beach ridges however, are long ridges aligned with the general approach of the waves and confined by the limits of tidal depositing. 

Taylor and Stone (1996) describe how beach ridges and cheniers have formative processes during normal tidal and swell events, but ridge formation above the high tide level is likely to be due to extreme tropical cyclone action via extra deposition of sand, coral or shell. It is also likely that in the tropics, most or even all, cheniers are built by tropical cyclones (or the rare tsunami). It is this fact that allows them to add to the jigsaw of palaetempestological data, when appropriate examples are found.

Coral rubble ridges can also provide eveidence of storm history. If the bathymetry allows, we can see ridges of left behind by storms, which will likely contains larger proportions of coral debris. An excellent example of this is again found in Taylor and Stone 1996 where they have examined Curacao island in the Great Barrier Reef in Austalia (Figures 1 and 2). Figure 1 is a view to show the sheltered side of the island which suffers less wave action (and so mainatains a historical record) and therefore can accumulate beach and coral ridges during storm surge events. These ridges can be radiocarbon dated to provide the chronology in Figure 2 (labelled as Figure 3 in Figure 1).

Figure 1: Curacoa Island in the Great Barrier Reef, Australia showing coral rubble ridges. Source: Taylor and Stone 1996

Figure 2: Coral rubble ridges from transect denoted in Figure 1, showing radiocarbon dates of each mound.Source: Taylor and Stone 1996

These data can be misleading however, and although provide a clear demonstration of large surge events, in periods of high storm frequency, multiple storms will be superimposed on top of one another as it takes time for the sediments to become resistant to further storm erosion. This resistance is through carbonate cementation via weathering of coral material. This is an example of the one of the sources on uncertainty in using this data.

Frequency is studied by looking at the interval between ridges, but Nott and Hayne in 2003, also developed a proxy for intensity of storms. It links the height of the ridge with the minimum flood depth due to storm surge, which is above the highest tide level. The paper suggests their identified ‘super cyclones’ are much more frequent than previously considered along the Great Barrier Reef.

However, a key and more stable marine proxies in the near-coasts zones affected by tropical cyclone landfalls, is found through drilling cores both in corals and sclerosponges. A combination of coral core data and examination of beach and coastal zone sediments can be a powerful duo when assessing coastal impacts.

Correlating with corals

Normally, these cores are taken from old specimens in the areas most frequently affected by tropical cyclones. Many studies have been conducted on corals and sclerosponges in the Caribbean and across the North Atlantic coast of the U.S. prone to tropical cyclone activity, as well as pacific basins also prone to tropical cyclones. A list of datasets on coral and sclerosponges is compiled by NOAA’s National Climate Data Center and shows the range of study undertaken.

Layers of growth can be examined for stable isotopes or metal deposits which can tell us much about the past characteristics of the uppermost levels of the marine environment. One of the key pieces of information relevant to tropical cyclone formation that we can gain from coral cores, is the proxies for sea surface temperature (SST). SST is one of the main near-coast environmental components for generating storms that make landfall i.e. if the waters are warmer in the North Atlantic, perhaps during a La Nina phase of the El Nino-Southern Oscillation, then conditions are more favourable for cyclogenesis (birth of a cyclone) which gives a higher likelihood of landfall if occurring near to the coast. Information from corals is highly relevant and adds to the gamut of data that are used to build palaeotempestological records.

Diving for data

This has to be one of the more appealing ways to gather climate data: Dive in to the warm tropical waters, search around the ocean floor looking for suitable corals or sponges, retrieve your sample (Figure 3) followed by some lab analysis over a rum cocktail – sign me up! Of course, as with any worthwhile endeavour, there is only a very small amount of time spent in the field doing the fun stuff, compared to the lab work and analysis to follow.

Figure 3:  Coring large Porites coral, Rowley Shoals, Western Australia (Photo credit: Eric Matson, AIMS)

The equipment used (as shown in Figure 3) is custom built and after finding a suitable specimen, based on age, size, shape and species. It is often difficult to find multiple samples for verification purposes - a particularly tricky element of this type of study.

Due to limitations in the field, it is difficult to know whether a sample is of high or low quality, as it is not always obvious where interruptions in growth cycles, infestations, or damage from marine life are present. Samples are returned to the lab to have X-ray images taken and for chemical analysis. Two main factors are derived from corals. Firstly, there is the growth rate based on samples with clear banding, and secondly the information via the geochemical composition of  various layers of the coral skeleton which represent different times in its life cycle.

Figure 4 shows a slice of a Porites coral illuminated by ultraviolet light to show luminescent banding associated with freshwater inputs from heavy precipitation events which lead to local flooding and therefore more terrestrial-based organic material being made available.

Figure 4: Coral slice illuminated by UV light showing luminescent banding which indicated freshwater input after flood events. Source: Lough, 2010 John Wiley & Son s, Ltd

A good NOAA summary of how sclerosponges are used to reconstruct past climate can be found when the practice was still quite young in 1998 can be found here in the proceedings from a workshop held in Miami.

Another method used with sponges and corals is to analyse stable oxygen and carbon isotopes. Rather than luminescence, this examines the stable oxygen isotopes within their carbonate skeletons, which are formed according to their surroundings and 'locked-in' as a record. The ratio between Oxygen-18 (heavy and abundant in sea water) and Oxygen-16 (lighter and abundant in clouds and water vapour), can tell us a lot about sea surface temperatures. This ratio depends on temperatures since the fractionation between the isotopes occurs via evaporation and condensation. Since most evaporation occurs in the tropics, we see less and less oxygen-18 in the atmosphere as we head towards the poles as the heavier isotopes tends to condense out first. 

Using coral cores, one of the most important factors to consider is that the process by which corals and sponges incorporate oxygen into their skeletons (as Calcium Carbonate mainly) also prefers the heavier oxygen-18 isotope. This is corrected for using other biochemical characteristics. Once calibrated, the coral core can reveal information about past temperatures as corals  tend to preferentially utilise the heavier isotope in colder water. This therefore allows us to infer sea surface temperature from coral cores and link our data to past events such as strong La Nina conditions or fluctuations in the Pacific Decadal Oscillation (PDO). For more detail, this NASA Earth Observatory educational web page on Oxygen isotopes.

So after coral and sponge samples have been retrieved and analysed how do we actually gain some useful information?

How do corals tell us about tropical cyclones?

There are a number of studies that use coral luminescence. This sounds like a fairly abstract concept at first but the rationale is fairly straight forward so let me try to explain:

Luminescence can be a strong indicator of flooding in rivers near to the sample sites. It is this property of coral records that becomes very useful for palaeotempestology in that flood events that affects a large area, are likely to be caused by severe storms, monsoonal changes. This begins to tell us about the extremes of any given climatic conditions in the coral’s history. Climate variability is known to shift rainfall patterns, such as during ENSO phases or monsoon rains, and so this can lead to modulation of rainfall amounts over land, which correlate well with coral luminescence. These luminescent lines also act as ground for comparison for other coral records. 

Work by Johan Nyberg (2002) and Barnes et al. (2003) are examples of using coral luminescence to infer tropical cyclone activity in certain parts of the world. These papers explain the process of measuring luminescence and how the data can be applied to various fields of study surrounding past climates and climate variability.

The method of using UV light to identify terrestrial run off events in coral was first identified by Peter Isdale in 1984, Isdale identified that strong banding did not occur in corals greater that 20km from the coast and that the brighter bands correlated with periods of high precipitation and therefore enhanced riverine outflow to the sea.

Coral information combined with studies of tree rings can provide good cross validation and increase confidence in building past chronologies of climatic events such as monsoon droughts as studied by D’Arrigo et al. 2008, and so I wonder if there is anything that can help us find out about past wet seasons, or climate modes, such as the PDO, as in Rodriguez-Ramirez et al. 2014,or ENSO as in D’Arrigo et al. 2006, and therefore link to storm activity.

In my next blog, I will explore the use of tree rings to find out about past storms.