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Climate: Past, Present & Future

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Decomposing algae have not said their last word yet!

Decomposing algae have not said their last word yet!
Name of proxy

Phytane, a compound resulting from the degradation of Chlorophyll-a (Chl a), a green pigment in plants and algae that is involved in photosynthesis

Type of record

Atmospheric carbon dioxide concentrations

Paleoenvironment

Marine sediments and oils

Period of time investigated

Phanerozoic (last 540 million years)

How it works

Before we can start predicting the potential impact of human activities on climate change, we first need to study the behavior of atmospheric CO2 concentration (pCO2) in the past. This represents a challenge given that continuous atmospheric measurements started only about 60 years ago.

To reconstruct past pCO2, a single well-constrained, ubiquitous proxy would be ideal. Unfortunately, it does not exist and we have to combine the estimates from many different types of proxies, each with their own advantages and weaknesses.

Different potential compounds that can give information on the pCO2 over the last hundreds of millions of years have been explored. The idea is based on the fact that algae consume CO2 and use that carbon to build organic compounds. But carbon atoms are not all the same. They can have different masses depending on the composition of their nucleus. We therefore differentiate between heavy carbon (13C) and light carbon (12C). Algae rather use the lighter, more common carbon (12C), but when less CO2 is available, they must use both 12C and 13C– or go hungry.

Therefore, it is considered that the more abundant is the CO2, the smaller is the 13C/12C ratio of the algae. Likewise, when CO2 is lower, the 13C/12C ratio in algae is higher. Long after an algae died, the 13C/12C ratio of its organic compounds will remain preserved in ancient sediments and oils and reflect the environment in which they were first produced.

Consequently, this ratio is compared to the ratio in CO2 consumed by the algae in order to calculate the fractionation factor (Ɛp) due to CO2-fixation. The fractionation factor is a measure of the discrimination occurring between heavy and light carbon. It can therefore be used to estimate the abundance of CO2 at the time the organic compounds were first produced.

Key Findings

This pCO2 estimation method via Ɛp is generally applied to organic compounds that come from specific species of algae. However, this has limitations – a single species is limited by its evolutionary history and its global abundance.

Here instead, we decided to explore the possibilities of using a general biomarker – an organic compound that all algae have and that contributes to the ocean record. We have explored naturally occurring CO2 vents, pockets of bubbling CO2 caused by volcanic activity, to test several different general biomarkers using this Ɛp method.

Collecting water samples, plankton nets, and sediment at a CO2 vent in Japan

One of the biomarkers that seems to work well are phytol and phytane, which are byproducts of Chl – a green pigment in plants and algae involved in photosynthesis. Phytane was used in specific case studies (e.g. Bice et al., 2006; Damste et al., 2008) but has not been tested extensively. We are now calibrating this potential proxy, by comparing it with other well-established pCO2 proxies over different timescales, and forming a compilation that extends the entire Phanerozoic (the past 540 million years!).

This proxy could significantly increase how far we can reconstruct pCO2 – Chl a and its products have been found in samples over 2 billion years old and is found everywhere (both spatially and temporally) throughout the geologic record.

Edited by Caroline Jacques and Célia Sapart

Further readings

Bice, K. L., Birgel, D., Meyers, P. A., Dahl, K. A., Hinrichs, K. U., & Norris, R. D. (2006). A multiple proxy and model study of Cretaceous upper ocean temperatures and atmospheric CO2 concentrations. Paleoceanography21(2).

Damsté, J. S. S., Kuypers, M. M., Pancost, R. D., & Schouten, S. (2008). The carbon isotopic response of algae,(cyano) bacteria, archaea and higher plants to the late Cenomanian perturbation of the global carbon cycle: Insights from biomarkers in black shales from the Cape Verde Basin (DSDP Site 367). Organic Geochemistry39(12), 1703-1718.

What speleothems can tell about the past climates !

What speleothems can tell about the past climates !
Name of the proxy:

Stable isotope ratios of carbonates in speleothems

Type of proxy:

Precipitation, atmospheric circulation, CO2 availability in soil, soil productivity

Paleoenvironment:

Continental environments

Period of time investigated:

Present day to 10 million years

Figure 1: Cut face of the Jeita cave stalagmite covering the last 12,000 years and showing  how the stratigraphy of a speleothem can be determined (Lebanon, central-Levant) (Modified after Verheyden et al., 2008)

How does it work?

Speleothems are inorganic carbonate deposits growing in caves that form from super-saturated cave waters (with respect to CaCO3) (Figure 1). Their analysis allows recovering aspects of past changes of the cave drip water geochemical composition, which provides information on climate and environmental variations above the cave (Fairchild and Baker, 2012). Different types of speleothems (e.g. flowstone, stalagmites) are widespread in karstic cave environments, but stalagmites as well as flowstones are used mainly to reconstruct past climates, because of a well-defined stratigraphic order. The major strengths of speleothems include their suitability for accurate age determinations (U/Th for ages up to c. 500,000 years; U/Pb for ages older than 500,000 years). Moreover, the preservation of multiple quasi-independent climate and environmental proxies enables the investigation of past climate changes on orbital to seasonal scale worldwide. Some of the most used proxies of speleothem carbonates are the ratios between oxygen-18 and oxygen-16 (δ18O) and carbon-13 and carbon-12 (δ13C), which are stated as a relative deviation to the Vienna Pee Dee Belemnite (VPDB) standard.

 

Figure 2: A diagram illustrating the primary processes related to δ18O variations relevant to paleoclimatology using speleothem records. Variations in temperature and relative humidity affect δ18O values through various processes in the atmosphere, in in the hydrosphere, in the soil and epikarst zones, and finally in the speleothem CaCO3. Modified after Lachniet, 2009.

 

The δ18O values of speleothem carbonates are determined mostly by two variables: the δ18O value of the cave drip water, which in turn is related to the δ18O value of the precipitation and in-cave fractionation processes (Lachniet, 2009). The δ18O value of the precipitation is determined by the atmospheric circulation, the trajectory of the precipitation, the amount effect (describing the negative relationship between precipitation δ18O and precipitation amount) and/or the seasons. The δ13C values of speleothem carbonates are locally controlled by biogenic soil productivity associated with the vegetation type (C4- or C3-type) and density, which regulates the soil CO2 content. Furthermore, it can reflect the availability of CO2 in the soil during the dissolution of limestone, which is a function of the water level in the karst and thus of the local precipitation amounts.

 

Figure 3: Different types of speleothem laminas. (A) Fluorescent laminas excited by UV light. (B) Visible laminas observed under reflected-light microscopy. (C) Calcite (C) and aragonite (A) couplets, observed under transmitted-light microscopy (Modified from Tan et al. (2006) and Johnson et al. (2006)) and (D) δ18O and δ13C variations measured in different laminas, reported in permil VPDB, from the Proserpine stalagmite (Han-sur-Lesse cave, Belgium). Note that dark and compacted layers become whiter due to the translucent light of the scan while the white and porous layers become dark. Modified from Van Rambelbergh et al. (2013).

What are the key findings that have been done using speleothems?

Speleothems are growing in caves worldwide and complement marine and polar climate archives, revealing unique views onto past climates. Speleothem δ18O records were employed to study the timing and climate of glacial/interglacial transitions (Lachniet et al., 2014; Winograd et al., 1992) as well as Heinrich events of the late Pleistocene. Several speleothem δ18O records from Western Europe (Genty et al., 2003) and the Eastern Mediterranean (Unal-Imer et al., 2015) revealed Dansgaard-Oeschger (rapid climate fluctuations) oscillations and were used to precisely date these climate events (Fleitmann et al., 2009). Furthermore, speleothem δ18O records allow studying past changes of global Monsoon systems (Cruz et al., 2005; Partin et al., 2007; Wang et al., 2005) as far back as 640 thousand years (Cheng et al., 2016). Lately new efforts are undertaken by the speleothem community to map the speleothem landscape in space and time to identify the current status of speleothem-based paleoclimate reconstructions globally.

You can learn more here: http://www.pages-igbp.org/ini/wg/sisal/intro

Edited by Célia Sapart

References
 • Cheng et al., 2016, Nature 534, 640-646.
 • Cruz et al., 2005, Nature 434, 63-66.
 • Fairchild & Baker, 2012,  John Wiley & Sons, Ltd, Chichester, UK.
 • Fleitmann et al., 2009, Geophysical Research Letters 36.
 • Lachniet et al., 2009, Quaternary Science Reviews, 28(5), 412-432.
 • Lachniet et al., 2014, Nature Communications 5, 8.
 • Genty et al., 2003, Nature, 421(6925), 833.
 • Partin et al., 2014, Pages Magazine, 22(1), 22-23.
 • Ünal-İmer et al., 2015, Scientific Reports, 5, 13560.
 • Wang et al, 2005, Science 308, 854-857.
 • Winograd et al., 1992, Science (New York, N.Y.) 258, 255-260.

Of butterflies and climate: how mathematics helps us to better understand the atmosphere

Applied mathematics is often seen as an obscure field, which the general public has no hope of ever understanding. In the context of climate science, this is far from the truth. In fact, many mathematical concepts and ideas applied to the study of the climate system stem from intuitive arguments. While their implementation can be very complex, understanding the basic ideas behind them does not require a PhD in Science.

The Lorenz 1963 attractor, often known as the “Lorenz Butterfly”. Author: Paul Bourke (http://paulbourke.net/fractals/lorenz/)

For example, this is the case of some recent developments in the field of dynamical systems analysis applied to atmospheric data. The atmosphere changes continuously and in many ways: for example, winds become stronger or die down, temperatures rise or fall and rain comes and goes. Understanding this evolution is important in many domains, from weather forecasting to air traffic management to catastrophe response services. The basic idea of the dynamical systems approach is to visualize the evolution of the atmosphere as a series of points connected with a line, which form a trajectory. The figure above shows a well-known example of such a trajectory: the so-called “Lorenz butterfly” (Lorenz, 1963). Now imagine focusing on a specific variable – for example daily surface temperature – and a specific region – let’s say Europe. We can build a trajectory, similar to the one shown above, describing the day-by-day properties of this two-dimensional (latitude by longitude, just as in a geographical map) temperature field. From day to day, the temperature varies therefore each day corresponds to a different point along the trajectory. In the case where two days are very similar to each other, they will correspond to two points very close together. On the contrary, if they show very different temperatures, the points will be further apart. If the similar days are well separated in time, for example occurring during different years, the trajectory representing surface temperature over our chosen region will therefore return close to a point it had previously visited, meaning that the closeness of the points and their distance in time do not always correlate. In the figure below, for example, the three turquoise dots are close to each other and also correspond to successive days along the atmospheric trajectory. The two red dots correspond to temperature configurations similar to those of the turquoise dots, but are separated from the latter by several days.

The continuous black line represents an idealized trajectory, while the circles correspond to successive days along the trajectory. The arrows indicate the direction the time goes.

This way of visualizing the atmosphere might seem bizarre, but it can give us some very powerful insights on how the climate system works. Consider, for example, summer heatwaves in Europe. The most severe ones can persist for several days and can have major impacts on human health, the environment and the economy. As can be intuitively understood, their persistence is due to the fact that the large-scale atmospheric conditions causing them are also persistent. If we return to our atmospheric trajectory, this will mean that we have a large number of points which are close to each other and successive in time – such as is the case for the three turquoise dots in the figure above. Namely, the trajectory moves very slowly and for several days the large-scale circulation only changes very slightly. In mathematical terms, this is a “sticky” state, and again the name is very intuitive! Analyzing the stickiness of the atmospheric states help us to predict how long a given circulation configuration is likely to last, thus providing useful information for weather forecasts.

The next natural step is to try to predict what the atmosphere will do once it has left a sticky state. Dynamical systems theory can again help us. It is in fact possible to define another quantity called “local dimension”, which tells us how complex the state of the atmosphere is. Once again the word “complex” here means exactly what you imagine: a complex temperature state will be one with lots of small, complicated spatial patterns. A simple state will be one with only a small number of large-scale features: for example, a day with high temperatures across the Mediterranean region and cold temperatures over most of Continental and Eastern Europe. Returning to our trajectory, these complex (or high-dimensional) and simple (or low-dimensional) states can be interpreted as follows. In the simple case, it is easy to predict the direction the trajectory will take in the future. This is the same as saying that all similar states evolve in a similar way. So if we want to forecast tomorrow’s temperature and we know that today is a “simple” state, we can look for states similar to today in the past years and we know the evolution of today’s state will be similar to that of these past states. In the complex case, on the contrary, it is very difficult to predict what the trajectory will do in the future. This means that similar atmospheric states will evolve in very different ways, and looking at past days with similar temperatures to today will not help us to forecast tomorrow’s temperature. A complex, high-dimensional state will therefore be more challenging for weather forecasters than a simple, low-dimensional one.

Now imagine looking at a very long climate dataset, for example covering the last century. If the climate system is always the same, one would expect the trajectories for the first and second half of the century to be indistinguishable. If, however, the climate is changing, then one would expect the trajectories representing it to also change. To make an analogy, imagine taking your heart rate. If you measure it on two different days while you are at rest, the number of heart beats per minute will probably be equal. In this case the system – which is here your body – is always in the same state. However, if one day you take your heart rate at rest and the following day you take it while you are running, the results will be very different. In this case something in the system has changed. In just the same way, if the climate system is changing, its “pulse” – namely the trajectory – will change with it. The trajectories of the two half-centuries in the dataset will therefore look different, and their local dimensions and stickiness will display different properties – for example a different mean value. The same two indicators that can help us improve weather predictions at daily to weekly timescales can therefore also help us to understand how climate varies across the centuries.

The dynamical systems approach can be applied to a wide range of scientific problems beyond the examples discussed above. These range from turbulence in fluids to the analysis of financial datasets. Picturing such a complex system as the atmosphere as a “spaghetti plot” is therefore an excellent example of an intuitive mathematical approach that can help us advance our knowledge of the world around us.

Edited by Célia J. Sapart.

Reference: Lorenz, E. N. (1963). Deterministic nonperiodic flow. J. Atmos. Sci., 20(2), 130-141.

Corals, the thermometers of the past!

Corals, the thermometers of the past!
Name of proxy:

Coral

Type of record:

Oceanic variability

Paleoenvironment:

Fringing reefs, barrier reefs, or atoll

Period of time investigated:

Mainly the last 200 years

How does it works ?

What we usually picture as a coral is actually a colony of tiny living animals called coral polyps, which are closely related to jellyfish or anemones. They live in symbiosis with photosynthetic algae called Zooxanthellae (Figure 1).

Figure 1: Schematic of a coral with its individual parts (modified from Veron, 1986).

Each polyp secretes a skeleton made of aragonite -a form of calcium carbonate- whose chemical composition depends on ambient oceanic and climatic conditions. Coral skeletons can therefore serve as monitors of the past oceanic and climatic variability through time (Figure 2).

Figure 2: X-radiographs and coral images (modified from DeLong et al., 2011).

Corals are distributed in the tropical belt mostly in the central and western Pacific, the Indian Ocean, and the Caribbean. These areas are also the most affected by climate variability such as the El Niño Southern Oscillation (ENSO) phenomenon. At interannual time scale, this phenomenon influences worldwide patterns of sea surface temperature (SST). Our present understanding of ENSO variability is limited by the short duration of instrumental records. In the current context of climate change, we need to understand the past variability of this phenomenon to be able to predict its future evolution. A proxy for past SST changes in the tropical oceans is therefore highly desirable to extend the length of the instrumental record.

Key Findings

Coral skeletal Sr/Ca have been shown to be an accurate tracer (“proxy”) of SST at many sites (Corrège, 2006). There is an inverse relationship between coral Sr/Ca values and SST conditions, with low Sr/Ca values corresponding to high SST environments and vice versa. Regression of coral Sr/Ca to instrumental SST (Figure 3) leads to a calibration equation that allows reconstruction of SST variability further back in time. SST records that span at least the last 200 years allow to differentiate the contributions of natural climate variability from those that are anthropogenically forced (Solomon et al., 2011). These results place coral as a perfect tool to reconstruct past oceanic variability which leads to a better understanding of past climate variability and a tremendously useful record to help predict future changes.

Figure 3: Time series of Sr/Ca from a living coral from New Caledonia and local SST (left). Calibration of Sr/Ca vs. SST. Sr/Ca appears to be a robust SST tracer (right).

Further readings

Corrège, T. (2006), Sea surface temperature and salinity reconstruction from coral geochemical tracers, Palaeogeography, Palaeoclimatology, Palaeoecology, 232(2-4), 408-428, doi:10.1016/j.palaeo.2005.10.014.

DeLong, K. L., J. A. Flannery, C. R. Maupin, R. Z. Poore, and T. M. Quinn (2011), A coral Sr/Ca calibration and replication study of two massive corals from the Gulf of Mexico, Palaeogeography, Palaeoclimatology, Palaeoecology, 307, 117–128, doi:10.1016/j.palaeo.2011.05.005.

Solomon A, et al. (2011), Distinguishing the roles of natural and anthropogenically forced decadal climate variability: Implications for prediction. Bull Am Meteorol Soc, 92:141–156.

Veron, J.E.N. (1986), Corals of Australia and the Indo-Pacific. Angus and Robertson:London/Sidney.

 

Edited by Caroline Jacques and Célia Sapart