Climate: Past, Present & Future


How earthworms can help us understand past climates?

How earthworms can help us understand past climates?
Name of proxy

Earthworm calcite granules (ECG)

Type of record

Paleotemperature and paleoprecipitation reconstruction; radiocarbon dating


Continental environments – loess/paleosol sequences

Period of time investigated

Mostly Last full Glacial cycle – from 112,000-15,000 years Before Present (BP) (or older depending on the preservation of the granules).

How does it work?

Earthworms are commonly found living in soil and feeding on organic matter at the soil surface. In carbonate soil, some of them secrete small granules (0.1 to 2 mm) within 20 cm of the soil surface (Fig. 1). These granules, composed of crystalline calcite, are formed in the calciferous glands of the common earthworm species Lumbricus (Fig. 1).

Figure 1. Formation and structure of earthworm calcite granules: A) Schema of the calciferous glands of Lumbricus terrestris (Canti, 1998; Darwin, 1881), B) Scanning Electron Microscopy of a fossil granule, modified from CoDEM/BATLAB C) Distribution of granules through present day experimental soil (Canti and Piearce, 2003), D) Thin section of a fossil granule (photo P. Antoine).

Fossil earthworm calcite granules (ECG) are common in various carbonate-rich Quaternary deposits and have been identified in loess-paleosol sequences in western Europe. Aeolian loess (i.e. accumulation of silt size sediment formed by the deposition of wind-blown dust) preserves evidence for climatic fluctuations in the past: generally, primary loess representing periglacial conditions coeval with expanded ice sheets alternates with tundra permafrost horizons and arctic soils representing milder climates.

Over the last glacial cycle (between 112-15 ky BP), the climate of the Earth varied on millennial timescales between cold (stadial) and temperate (interstadial) periods. This climate variability is reflected in the character of the loess sediment. These short-term climatic changes had a strong influence on landscapes, ecosystems, including human beings. However, loess sediment analysis only give us information on the relative changes of climates. We lack quantitative temperature and precipitation data to precisely reconstruct past conditions.

The granules of earthworms living in past loess environments provide a quantitative tool. The granule concentrations correlate with the nature of the loess sediment; paleosols preserve the highest concentrations while primary loess the lowest. These observations highlight a rapid response of the earthworm population to climatic variations suggesting milder climatic conditions during the formation of paleosol. ECG can be considered as a new paleoenvironmental proxy, capable of detecting rapid climatic events within the Last Glacial loess sequence. Furthermore, the chemistry of these ancient earthworms’ diets can be calibrated to the temperature and precipitation of the climate prevailing at the time.

What are the key findings that have been done using this proxy?

We developed a new method to calculate past temperatures and precipitations based on oxygen and carbon stable isotope compositions of earthworm granules from loess at the Nussloch site in the Rhine Valley, Germany. Our results provide the first quantitative past climate data from loess sediments.

Figure 2: First quantification of paleoclimate data in a loess sequence: Comparison between radiocarbon dating (Moine et al., 2017), granule concentration (Prud’homme et al., 2018b) and quantitative paleoclimate parameters (Tair and MAP, Prud’homme et al., 2016, 2018a) of the Nussloch loess sequence (Antoine et al., 2009) with the δ18O of Greenland ice core (NGRIP, Rasmussen et al., 2014).

Figure 2 shows the results stable isotope geochemistry of earthworm granules from selected strata within the loess sequence at Nussloch. Temperatures for the warmest months were estimated between 10 to 12°C and the mean annual precipitation was estimated between 250 and 400 mm during the formation of palaeosols. Our results suggest that the climate at Nussloch during the temperate periods (interstadials) were most likely subarctic with cool summers and very cold winters.

Earthworm granules can also be directly dated by radiocarbon methods (Moine et al., 2017). Since the nature of loess sediments reflects climatic variations over short (millennial) timescales, the lack of precise chronologies in loess can be a problem when trying to correlate with global climatic events. Our new approach, combining precise radiocarbon dating with quantitative climate reconstruction, represents a major advance for understanding climate in terrestrial regions.

Moreover, the radiocarbon chronology facilitates precise correlation between terrestrial sequences and ice core records. This is fundamental for understanding teleconnections between mid- and high-latitude climate changes, as well as the spatial and temporal impact on prehistoric populations in Europe.

This post has been reviewed by the editor.


Antoine, P., Rousseau, D.D., Moine, O., Kunesch, S., Hatté, C., Lang, A., Tissoux, H. & Zöller, L. (2009) Rapid and cyclic aeolian deposition during the Last Glacial in European loess: a high-resolution record from Nussloch, Germany. Quaternary Science Reviews 28, 2955–2973.

Canti, M.G. (1998) Origin of calcium carbonate granules found in buried soils and Quaternary deposits. Boreas 27, 275–288.

Canti, M.G. & Piearce, T.G. (2003) Morphology and dynamics of calcium carbonate granules produced by different earthworm species. Pedobiologia 47, 511–521.

Darwin, C. (1881) The Formation of Vegetable Mould through the action of Worms, with Observations on their Habits. Murray, London.

Moine, O., Antoine, P., Hatté, C., Landais, A., Mathieu, J., Prud’homme, C. & Rousseau, D.D. (2017) The impact of Last Glacial climate variability in west-European loess revealed by radiocarbon dating of fossil earthworm granules. Proceedings of the National Academy of Sciences of the United States of America, 1–6.

Prud’homme, C., Lécuyer, C., Antoine, P., Moine, O., Hatté, C., Fourel, F., Martineau, F. & Rousseau, D.D. (2016) Palaeotemperature reconstruction during the Last Glacial from δ18O of earthworm calcite granules from Nussloch loess sequence, Germany. Earth and Planetary Science Letters 442, 13–20.

Prud’homme, C., Lécuyer, C., Antoine, P., Moine, O., Hatté, C., Fourel, F., Amiot, R., Martineau, F. & Rousseau, D.D. (2018) δ13C signal of earthworm calcite granules: a new proxy for palaeoprecipitation reconstructions during the Last Glacial in Western Europe. Quaternary Science Reviews 179, 158–166.

Prud’homme, C., Moine, O., Mathieu, J., Saulnier-Copard, S. & Antoine, P. High-resolution quantification of earthworm calcite granules from western European loess sequences reveals stadial–interstadial climatic variability during the Last Glacial. Boreas. Accepted 1st October 2018

Rasmussen, S.O., Bigler, M., Blockley, S.P., Blunier, T., Buchardt, S.L., Clausen, H.B., Cvijanovic, I., Dahl-Jensen, D., Johnsen, S.J., Fischer, H., Gkinis, V., Guillevic, M., Hoek, W.Z., Lowe, J.J., Pedro, J.B., et al. (2014) A stratigraphic framework for abrupt climatic changes during the Last Glacial period based on three synchronized Greenland ice-core records: Refining and extending the INTIMATE event stratigraphy. Quaternary Science Reviews 106, 14–28.

God does not play DICE – but Bill Nordhaus does! What can models tell us about the economics of climate change?

Climate change has been described as “the biggest market failure in human history”[1]. Although fuel is costly, emitting the by-product CO2 is for free; yet it causes damages to society. In other words, those who benefit, by using the atmosphere as waste dump, do not pay the full costs, i.e. the adverse effects climate change has on societies on a global scale. Can this market failure be cured? Should humankind sacrifice some of its present welfare to prevent future climate damages? William Nordhaus was jointly awarded the Nobel Prize for economy for providing a framework to answer these questions.

DICE– the Dynamic Integrated model of Climate and the Economy [2] – combines a simple economic model with a simple climate model. The aim is not to fully cover all details of economic and climate processes, but rather to provide a model that is sufficiently simple to be used by non-specialists, including policy makers. Figure 1 shows a simplified structure of the DICE model.

Figure. 1: Schematic illustration of the DICE model. The dark blue arrows correspond to the purely economic component of the model. The yellow and green arrows indicate how the economy impacts climate and vice versa. The light blue arrows illustrate the effect of climate policy.

The economy of the DICE model

The heart of DICE is an economic growth model (dark blue arrows in fig. 1). Economic production occurs when labour and capital is available. Labour is proportional to the world population, which is homogenous and grows according to externally prescribed data. Part of the economic production is invested to create capital for the next time step, while the remaining part is consumed. It is assumed that the “happiness” (called utility in the jargon) of the population depends exclusively on consumption, in a sublinear fashion: The more you consume, the happier you are. However, if you are already rich, then one extra Euro will not increase your happiness as much as when you are poor.

In this purely economic model, the only decision the world population has to take is to determine the saving rate – the fraction of economic production to invest for the next period. If we invest too much, we reduce our current happiness; if we invest too little, we have too little to consume next period. Therefore, the aim is to find an optimal pathway to be reasonably happy now and in the future. However, there is a twist: observations suggest that we, humans, value the present more than the future. E.g. if we are offered 1 Euro either now or next year, we would prefer to be paid now, even in absence of inflation or increasing income. However, if offered 1 Euro now or 1.03 Euro next year, we might begin to prefer the delayed, but larger payment. The extra amount needed to make later payment acceptable is called “Rate of Pure Time Preference”; in our example, it is 3% [3, p.28]. A high Rate of Pure Time Preference basically means that we care much less about future welfare than about the present one. If there is an economic growth (which is the case in DICE), there is an additional reason to prefer being paid now rather than later: In the future, you will be richer, so one additional Euro will mean less to you than now while you are still relatively poor. This effect means that the total “discount rate”, defined as the extra payment needed to make delayed payment attractive, is even higher than the rate of pure time preference [3, chapter 1] .


The impact of climate change

To bring climate change into play, Nordhaus assumed that apart from labour and capital, economic production also requires energy. However, energy production causes CO2 emissions. Part of the CO2 ends up in the biosphere or in the ocean, but another part remains in the atmosphere, leading to global warming.

Practically, everyone agrees that substantial warming will have damaging effects on the economy. Although there may not be “good” or “bad” temperatures a priori, ecosystems and human societies are adapted to the current climate conditions, and any (rapid) change away from what we are accustomed to will cause severe stress. For example, there may not be an “ideal” sea level, but strong sea level rise – or fall – will cause severe strain on coastal communities who are adapted to the current level[4].

These damages are extremely hard to quantify. First, we obviously have no reliable empirical data – we simply have not yet experienced the economic damages associated with rapid warming by several degrees. Second, there could be “low chance, high impact” events [5], e.g. events that even under climate change are deemed unlikely to our current knowledge, but would have dramatic consequences if they occur – for example, a collapse of large parts of the Antarctic ice sheet. Third, there are damages, like the loss of a beautiful glacial landscape or the human suffering inflicted by famine, which cannot be quantified in terms of money.

When formulating his Nobel prize-winning DICE model, William Nordhaus tried to solve the first problem by performing an extensive review of the (scarce) existing studies on climate-induced damages and greatly extrapolating the results. E.g. if data was available on reduced wheat production in the Eastern US during a heat wave, Nordhaus might assume that damage for all food crops in Africa is, say, twice as big (as Africa is more dependent on agriculture than the US). This may still be quite ad-hoc, but one might argue that even rough data is better than no data at all. The second and third of the above points where largely circumvented with the “willingness-to-pay” approach [2]: people were asked how much they would pay to prevent the extinction of polar bears or the collapse of the Antarctic ice sheet, for example, and the price they names was used as substitute for damages associated to these events.

Finally, Nordhaus came up with an estimate for climate damage:

D=k1T + k2T2

where D is the damage in % of the GDP, T is the global mean temperature change, and k are constants (k1 = -0.0035 K-1 and k2=+0.0045 K-1) [2, p. 207]. Note that the k1<0 implies that for small T, global warming is actually beneficial. 2.5 degree and 5 degree warming yield damages of 1.1% and 6.5% of the GDP, respectively. Later versions of DICE have k1=0.


To reduce global warming, humanity can reduce their carbon emissions. In other words, part of the global economic production is sacrificed to pay for greener energy. This will leave less money to spend on consumption and/or investment in capital, but it also diminishes future climate damages. Therefore, in order to maximise the “happiness”, two control variables must now be chosen at each time step: the saving rate and the emission reduction fraction. We want to reduce carbon emissions enough to avoid very dangerous climate change, but also avoid unnecessary costs.



Figure. 2 Results of the DICE model. The optimal policy (i.e. maximising “happiness”) in the 2013 version of DICE. The blue lines indicate the optimal policy, while yellow lines indicate no climate policy (i.e. zero emission reduction). The first plot shows the emission reduction fraction or “abatement”, i.e. the fraction of carbon emissions that are prevented. 1 means that no CO2 is emitted. The second plot shows the atmospheric CO2 concentrations in ppmv. For the optimal policy, CO2 concentrations peak at 770ppmv, whereas in absence of a policy, they rise beyond 2000ppmv. The pre-industrial value is 280ppmv. The third plot shows the global mean temperature change. For the optimal policy, it peaks at about 3.2K, i.e. above the limit of 2K or even 1.5K agreed by the Paris agreement.


Results and Criticism

The results in fig. 2 show that under the “optimal” policy, i.e. the policy which maximises “happiness”, the Paris agreement will not be met. This result suggests that the costs required for keeping global warming below 1.5 or 2ºC warming are too high compared to the benefit, namely strong reduction in climate damages. However, some researchers
criticise that DICE severely underestimates the risks of climate change. For example, the damage function might be too low and does not explicitly take into account the risk of “low chance, high impact events”. Including such events, occurring at low probability but causing high damages if they occur, will lead to more stringent climate action [6].

The rate of pure time preference has given rise to even fiercer discussions [7,8,9]. As explained above, a society’s discount rate can be estimated from market interest rates [3]. Knowing the economic growth, we can infer the rate of pure time preference used in market decisions. Many economists argue that the rate of pure time preference in models like DICE should be chosen consistent with observations[8]. Nordhaus followed this approach. However, one can argue that even if individuals care less for the future than the present, this does not mean that such an approach is ethically defendable in the context of climate change. Individuals are mortal and may choose to consume their own savings before they die. But climate change is a global and intergenerational problem, and it has been argued [7,9] that we should care for future generations as much as for ourselves. Therefore the rate of pure time preference must be (nearly) zero. Note that this still allows for some discounting, arguing that if future generations are richer, they might be able to deal better with climate change.

Another reason for the relatively weak carbon emission reduction in DICE’s optimal policy may be that it is too pessimistic concerning future costs of emission reduction. For example, DICE does not include the learning-by-doing effect: The more we reduce emissions, the more efficient technologies we discover, and the cheaper it gets. In addition, the costs for green energy are partly one-time investments, e.g. restructuring the energy distribution grids, which are now adapted for few, central energy providers, to a more decentralised structure with smaller providers (e.g. households with solar panels). Once these (large) efforts have been made, the costs for green energy will decrease. But if DICE overestimates the costs of carbon emission reduction, it will be biased towards recommending low reductions.

Due to the above, and many more, issues some researchers criticise that models like DICE are “close to useless”, and even harmful, as they pretend to give precise instructions to policy makers while in fact they struggle with huge uncertainties [10]. In my opinion, models like DICE should not be used for precise policy recommendations like fixing the carbon tax, but are still useful for a somewhat qualitative scenario exploration. For example, it can be fruitful to add “low chance, high impact events” or the learning-by-doing effect and investigate the qualitative effect on the optimal abatement.

Many more economy-climate models have been written in the last decades, some of which are much more sophisticated than DICE. Moreover, there are many models focussing only on specific aspects of the problem, for example, the details of the energy sector. This is still a very active field of research. So, however limited DICE may be, it has laid the foundations for a highly relevant scientific and societal discussion. And even if one should take its precise output with a lump of salt, it is a valuable tool to help policy makers to qualitatively grasp the essence of climate economy.

This post has been edited by the editorial board.


[1] Nicholas Stern: “The Economics of Climate Change” ( RICHARD T. ELY LECTURE )

[2] A thorough description of the model is given by William Nordhaus and Jospeh Boyer, “Warming the World. Economic Models of global warming” ( There are newer model versions available, but the underlying concepts remain the same.

[3] A thorough introduction to discounting is given in this book: Christian Gollier, “Pricing the Future: The economics of discounting and sustainable development” (, especially chapter 1.

[4] see e.g. Wong, P.P., I.J. Losada, J.-P. Gattuso, J. Hinkel, A. Khattabi, K.L. McInnes, Y. Saito, and A. Sallenger, 2014: Coastal systems and low-lying areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A:
Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

[5] e.g. Lenton et al. “Tipping elements in the Earth’s climate system”,

[6] Cai et al., “Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction”,
[7] The Stern Review on the Economics of Climate Change (

[8] Peter Lilley: “What’s wrong with Stern?” (

[9] Frank Ackermann: “Debating Climate Economics: The Stern Review vs. Its Critics” (

[10] Robert Pindyck, “The Use and Misuse of Models for Climate Policy”,


What can artificial intelligence do for climate science?

What can artificial intelligence do for climate science?

What is machine learning?

Artificial Intelligence, and its subfield of machine learning, is a very trending topic as it plays an increasing role in our daily life. Examples are: translation programs, speech recognition software in mobile phones and automatic completion of search queries. However, what value do these new techniques have for climate science? And how complicated is it to use them?

The idea behind machine learning is simple: a computer is not explicitly programmed to perform a particular task, but rather learns to perform a task based on some input data. There are various ways to do this, and machine learning is usually separated into three different domains: supervised learning, unsupervised learning and reinforcement learning. Reinforcement learning is of less interest to climate science, and will therefore not be touched upon here.

In supervised learning, the computer is provided both with the data and some information about the data: i.e. the data is labeled. This means that each chunk of data (usually called one sample) has a label. This label can be a string (e.g. a piece of text), a number or, in principle, any other kind of data. The data samples could be for example images of animals, and the labels the names of the species. The machine learning program then learns to connect images with labels and, when successfully trained, can correctly label new images of animals that it has not seen yet. This principle idea is sketched in Figure 1.

Figure 1: A schematic of supervised machine learning in climate science.

In a climate context, the “image” might be a rough global representation of for example surface pressure, and the label some local phenomenon like strong rainfall in a small region. This is sketched in Figure 2. Some contemporary machine learning methods can decide which features of a picture are related to its label with very little or no prior information. This is well comparable to certain types of human learning. Imagine being taught how to distinguish between different tree species by being shown several images of trees, each labeled with a tree name. After seeing enough pictures, you will be able to identify the tree species shown in images you had not seen before. How you managed to learn this may not be clear to you, but your brain manages to translate the visual input reaching your retina into information you can use to interpret and categorize successive visual inputs. This is exactly the idea of supervised machine learning: one presents the computer with some data and a description of the data, and then lets the computer figure out how to connect the two.


FIgure 2: example of using machine learning for predicting local rainfall.


In unsupervised learning, on the other hand, the machine learning program is presented with some data, without any additional information on the data itself (such as labels). The idea is that the program searches autonomously for structure or connections in the data This might be for example certain weather phenomena that usually occur together (e.g. very humid conditions in one place A, and strong rainfall in place B). Another example are “typical” patterns of the surface temperature of the ocean. These temperature patterns look slightly different every day, but with machine learning we can find a small number of “typical” configurations – which then can help in understanding the climate.

How difficult is it to implement machine learning techniques?

Machine learning techniques often sound complicated and forbidding. However, due to the widespread use of many machine learning techniques both in research and in commercial applications, there are many publicly available user-ready implementations. A good example is the popular python library scikit-learn1. With this library, classification or regression models based on a wide range of techniques can be constructed with a few lines of code. It is not necessary to know how the algorithm works exactly. If one has a basic understanding of how to apply and evaluate machine learning models, the methods themselves can to a large extent be treated as black-boxes. One simply uses them as tools to address a specific problem, and checks whether they work.

What can machine learning do for climate science?

By now you are hopefully convinced that machine learning methods are: 1) widely used and 2) quite easy to apply in practice. This however still leaves the most important question open: can we actually use them in climate science? And even more importantly: can they help us in actually understanding the climate system? In most climate science applications, machine learning tools can be seen as engineering tools. Take for example statistical downscaling of precipitation. Machine learning algorithms are trained on rainfall data from reanalyses and in-situ observations, and thus learn how to connect large-scale fields and local precipitation. This “knowledge” can then be applied to low-resolution climate simulations, allowing to get an estimate of the local precipitation values that was not available in the original data. A similar engineering approach is “short-cutting” expensive computations in climate models, for example in the radiation schemes. If trained on a set of calculations performed with a complex radiation scheme, a machine learning algorithm can then provide approximate solutions for new climatic conditions and thus prevent the need to re-run the scheme at every time-step in the real model simulation, making it computationally very effective.

However, next to this engineering approach, there are also ways to use machine learning methods in order to actually gain new understanding. For example, in systematically changing the extent of input data, one can try to find out which part of the data is relevant for a specific task. For example “which parts of the atmosphere provide the information necessary to predict precipitation/windspeeds above a specific city and at a specific height?

As final point, artificial intelligence and machine learning techniques are widely used in research and industry, and will evolve in the future independent of their use in climate research. Therefore, they provide an opportunity of getting new techniques “for free”: an opportunity which can and should be used.



This article has been edited by Gabriele Messori and Célia Sapart.

Pollen, more than forests’ story-tellers

Pollen, more than forests’ story-tellers
Name of proxy

Sporomorphs (pollen grains and fern spores)

Type of record

Biostratigraphy and Geochronology markers, Vegetation dynamics


Terrestrial environment

Period of time investigated

Present to 360 million years

How does it work?

The sporomorphs (pollen grains and fern spores) are cells produced by plants involved in the reproduction. They are microscopic (less than a fifth of a millimeter) and contain a molecule called sporopollenin in their cell wall, which is very resistant to degradation. The sporopollenin molecule allows sporomorphs to be preserved in sedimentary archives such as lake sediment or peat deposits.

These reproductive structures appeared during the Paleozoic (570 million years ago) but the first spores looked rather similar and were indistinguishable among species. Later speciation of plants promoted the diversification of the reproductive cells between species and brought the opportunity to relate the fossil sporomorphs found in the sedimentary archives to the parental plant that produced them.

Figure 1. Plant communities are different depending on a wide range of environmental conditions. Above: Andean grasslands (páramo) in Ecuador. Below: Swamp forest in Orinoco Delta (Venezuela).

Plants are immobile organisms, and each species has its own tolerance range to the existing environmental conditions. The occurrence of certain plant communities in a specific environment depends on their different tolerance ranges. For instance, we do not observe today the same plants growing in the tropical rainforests of South America than in the polar tundra (Figure 1). Paleopalynology is the discipline that helps characterizing which plant species have occurred at a specific location during a particular time period. This provides information on the environmental conditions of the studied region. To identify the different species, palynologists have to analyze under the optical microscope the specific features of the sporomorphs’ cell walls. They look at e.g. the presence of spines or air sacs, or the number of apertures that the pollen grain has (Figure 2). These features are specific for each plant, which allows relating the pollen grain found in the sedimentary archive to the plant that produced it at the study location at a particular period of time.

Figure 2. Pollen grains have very different morphologies that allow identification of the plant that produce them. A: Byttneria asterotricha (Sterculiaceae); B: Triplaris americana (Polygonaceae); and C: Calyptranthes nervata (Myrtaceae). Bar scales in the pictures represent 25 micrometers.

What are the key findings made using this proxy?

Paleopalynology has a wide range of applications in geoscience. For instance, the presence of specific sporomorphs has been used as chronological markers to pinpoint several geological periods, especially in the far past biostratigraphy (million years ago)  (Salard-Cheboldaeff 1990).

In palaeoecology (the ecology of past ecosystems), the analyses of fossil sporomorphs help in specifying the dynamics of vegetation communities through time. This type of work started a century ago by Lennart van Post (1916) and provided the opportunity to study plants population and community natural trends within the appropriate temporal frame for long-lived species (i.e. tree species such as pines or oaks can live several centuries). Moreover, it provides a unique empirical evidence of the actual responses of vegetation to disturbances that occurred in the past, e.g. natural hazards, human populations land use and other anthropogenic impacts, or climatic shifts.

For instance, regarding past climates, paleopalynology allowed us to:

i)               understand the independent behavior of the species during glacial cycles (i.e., when a single species responded to changes, but the plant community as a unity did not respond) in forming new plant communities each time (Davis 1981; Williams and Jackson 2007);

ii)             map the re-colonization events and the assemblages formed during the last deglaciation until the vegetation communities we observe today (Giesecke et al. 2017).

In addition, in some characteristic environments, such as mountain regions, the occurrence and disappearance of specific species can allow the estimation of the temperature change with respect to present-day conditions. Another example has been developed in the last decade: the study of organic compounds contained in the sporopollenin of the sporomorphs’ walls. It has been identified as an accurate proxy that registers UV-B rays’ signals (Fraser et al. 2014). As UV-B rays’ are related to solar irradiation trends through time, reconstructing the organic compound variations in the sporomorphs’ walls allows reconstituting past solar irradiation trends in continuous archives such as lake and peat deposits.

This all shows that despite being a tiny structure, pollen grains are the story tellers of how the planet has been changing through history and can provide a wide range of outcomes essential for geosciences.


Davis, M.B. (1981). Quaternary history and the stability of forest communities. In: West, D.C., Shugart, H.H., Botkin D.B. (Eds.) Forest succession. New York, NY: Springer-Verlag.

Fraser, W.T., Lomax, B.H., Jardine, P.E., Gosling, W.D., Sephton, M.A. (2014). Pollen and spores as a passive monitor of ultraviolet radiation. Frontiers in Ecology & Evolution 2: 12.

Giesecke, T., Brewer, S., Finsinger, W., Leydet, M., Bradshaw, R.H.W. (2017). Patterns and dynamics of European vegetation change over the last 15,000 years. Journal of Biogeography 44: 1441-1456.

Salard-Cheboldaeff, M. (1990). Interptropical African palynostratigraphy from Cretaceous to late Quaternary times. Journal of African Earth Sciences 11: 1-24.

Von Post, L. (1916). Om skogsträdpollen i sydsvenska torfmosslagerföljder. Geol.   Fören. Stockh. Förhandlingar 38, 384–390.

Williams, J.W., Jackson, S.T. (2007). Novel climates, no-analog communities, and ecologica lsurprises. Frontiers in Ecology and the Environment 5: 475-482.

                                                                                                                           Edited by Célia Sapart and Carole Nehme