CL
Climate: Past, Present & Future

# Climate of the Present

## 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.

## Defrosting the freezer. Climate change and glacial meltwater

Why are glaciers important?

Glaciers cover around 10% of the global land surface. This includes the large ice sheets (e.g. in Greenland and Antarctica) as well as smaller ice caps and valley glaciers (e.g. in Iceland, Norway and New Zealand). Figure 1 shows the current distribution of glaciers around the world.

Figure 1 – The global distribution of glaciers around the world from the GLIMS glacier database. Source: https://nsidc.org/glims/

Glaciers play an important role in moderating global and local climate, but they are very sensitive to changes in climatic conditions. Currently, around 90% of the world’s glaciers are retreating. Under current IPCC predictions of future global warming and climatic changes, many glaciers will have disappeared by 2100. Figure 2 shows the temperature for different parts of the globe in 20167 relative to average (‘normal’) values. Red and yellow colours mean that temperatures are hotter than usual, and it is clear that most of the world is warming. The Arctic is warming especially quickly, and is several degrees (°C) warmer than normal. Glaciers here will therefore be especially sensitive to climate change.

Figure 2 – Global average (mean) surface temperature January-June 2016 relative to long-term conditions. Red and yellow colours indicate higher temperatures than normal. Source: https://svs.gsfc.nasa.gov/12305

Glaciers contain around 75% of the world’s freshwater. Many of the world’s rivers are fed by meltwater from glaciers and mountain snowpacks. These include major rivers such as the Ganges and Brahmaputra, where meltwater from Himalayan glaciers and snow makes its way downstream and, together with river water from other sources such as monsoon rains, eventually supplies over 1 billion people.

#### What are the key issues?

As climate change continues, and global air temperature rise leads to enhanced glacier melt, there are a number of key considerations:

How will glaciers respond to climate change? – Will they disappear?

How will glacier melt affect water flow downstream?

How quickly might these changes happen?

#### How will glacier melt affect river systems?

Here we consider some of the impacts of glacier retreat on river flow, but there are also many other impacts, including: changes to river water chemistry, and impacts on ecosystems – the plants and animals living in and around the rivers

1. Turning on the tap

Increased glacier melt produces more meltwater, which means that rivers will have a higher flow and more water will be transported downstream. However, this situation is likely to last only temporarily, because…

2. Turning off the tap

Eventually (usually over several decades or longer), if a glacier melts fully, there will be no meltwater feeding into rivers downstream. Some rivers, that are fed by water from multiple sources (such as rainfall) do not rely on glacial meltwater and will not be greatly impacted by the disappearance of glaciers in their headwaters. Other rivers, especially those in mountain catchments, are supplied only by snow and ice melt. The disappearance of glaciers would therefore have major impacts on their water supply – the equivalent of turning off a tap. We know that many glaciers are melting rapidly, and some are predicted to have disappeared over the next few decades.

3. Changing lanes

In some places, as a glacier retreats, the meltwater streams may change course entirely and flow in a different direction. This has been seen recently in Alaska, where meltwater from the Kaskawulsh glacier has undergone a major transformation in its drainage pathway in the space of only four days. Meltwater previously flowed northwards, supplying the Slims River, but recent glacier retreat has caused a shift in the drainage pathway, and it is no longer favourable for the water to flow north, and the Slims has almost entirely disappeared. Instead, meltwater has been diverted towards the south to the Alsek river. This event has highlighted that major transformations in glaciers and river systems, in response to climate change, can happen in the blink of an eye. See a full news report on the changes here and the full research article here.

4. The four seasons

Climate change can also affect seasonality – the timing and duration of the seasons in a year. For example, with increased global warming, we might expect some parts of the planet to experience a longer warm season. Climate change might also affect the duration and intensity of precipitation (e.g. rain and snowfall) events and storminess. Changes in seasonality are already being felt in some parts of the world. In some parts of the Arctic, the Spring melt season, and therefore the onset of river flow, is starting earlier than it has done in the past. Such changes will influence when and in what quantities meltwater is transported downstream. Continued monitoring of climatic conditions, glacier and river behaviour will allow us to more fully understand the changes that are occurring in glacial environments in response to global temperature rise.

#### In summary

• We know that global climate change is influencing glacier behaviour. Some glaciers are responding rapidly to climate change – over years and decades – and many will have melted completely by 2100.
• As glaciers melt they produce more meltwater, which increases the flow of river systems downstream.
• But if glaciers melt entirely, the meltwater ‘tap’ will be switched off. This may have major impacts on river systems that rely on meltwater inputs – such as in high mountain regions where meltwater is the dominant source of river water.
• We have seen recently in Alaska, that glacier retreat can cause meltwater drainage to change direction in a matter of days.
• Understanding glacier and river response to climate change is therefore key for our ability to prepare for future scenarios.

The following links provide information, data, graphics, and videos about glaciers, glacier melt, meltwater, and climate change. There is something suitable for all age groups.

National Snow and Ice Data Centre https://nsidc.org/

INTERACT Arctic Monitoring programmes http://www.eu-interact.org/

NASA Climate https://climate.nasa.gov/

## Hot towns, summer in the city!

Cities obviously experience a different climate than natural landscapes. Already in 1810 the British meteorologist Luke Howard documented that the air temperature in the city of London was several degrees higher than in its surroundings. This so called urban heat island has several causes. In general the relatively dark surfaces of asphalt and roofs absorb solar radiation very efficiently and this heat is stored in building material during the day. At night this heat is released to the atmosphere, which keeps the city warm. Moreover, heat produced by air conditioning, traffic and industry contributes substantially to a city’s heat load. With a decreased amount of vegetation, cities also lose the shade and cooling effect of trees (Figure 1).

Currently already over 50% of the world’s population is residing in urban areas and this number is foreseen to increase even further in the future. Moreover, climate model projections indicate that heat waves will occur more often in the future. Together these developments will make many citizens potentially vulnerable to  urban heat. Although a slight temperature increase might look appreciable at first glance, elevated temperatures affect human health, since hospital visits and mortality are enhanced in warm conditions (about 2% per degree Celsius, e.g. Hajat et al., 2002). In addition, labour productivity in warm periods is reduced, resulting in economic losses. E.g. for Australia this was estimated to be about \$650 per capita (Zander et al, 2015) which is a substantial contribution to the national income. Also, the urban energy demand needed for heating purposes in winter and cooling in summer is governed by urban weather. Finally cities are vulnerable to flooding in case of extreme precipitation by peak showers, when the sewage system capacity is hampered. Hence how do cities manage urban heat and keep dry feet?

Behind the general picture on the urban heat island, several scientific questions do remain. E.g. what is the temperature variability within a city, and how can we monitor temperatures? Also, can we make special weather forecasts for cities? Monitoring urban weather and climate is challenging since traditional weather stations are not suitable for urban areas since they require undisturbed terrain. Crowdsourcing, i.e. the collection of weather data by citizens has now become popular. Many hobby meteorologists have installed weather stations at home, and distribute their data directly via several websites as www.wunderground.com, www.netatmo.com, and www.wow.metoffice.gov.uk. These crowdsourced observations were crucial in estimation of the urban heat island effect in the Netherlands, a west-European country with a mild maritime climate were little attention was paid to urban climate. On hot summer days the urban heat island was over 6 degrees (Steeneveld et al., 2011, Heusinkveld et al., 2014)!

The urban climate can be efficiently monitored by tricycle traverse measurements (Figure 2). Bikes are excellent modules to measure the urban climate, since a wide variety of urban morphology (vegetation cover, building design and material) can be explored, especially outside the main roads. And it is carbon free, all driven by electricity generated by solar panels. Moreover, the bike is appealing for the general public.

Figure 2: A cargo tricycle equipped with a weather station to measure temperature, humidity, wind speed, solar and thermal radiation (source: Wageningen University; design Bert Heusinkveld).

What do we learn from such bike traverses? Figure 3 shows the variety of temperatures observed during a heat wave in a mid-size town in the Netherlands. Obviously, local temperature differences at the end of the afternoon may reach up to 3.5 ºC in this case. In general the town centre is relatively warm, though more surprisingly the relatively young neighbourhoods at the city edges appear to be warm too. In these neighbourhoods the vegetation is young, resulting in limited shadowing and therefore efficient heat absorption in roads and building walls. Local cool spots appear in parks and at small lakes. Luckily, enough room to escape from the heat!

Figure 3: Air temperature observations in the mid-size town Wageningen (the Netherlands, August 2nd 2013, 17.00 local time) obtained from two bike traverses. Source land cover maps: googlemaps.com.

No weather station in your garden? We still catch you via your smartphone! Smartphone users with the OpenSignal App that is intended to monitor wireless network capacity provide as a by product the smartphone battery temperature. Recently it was discovered that the temperature of your smartphone battery follows the air temperature outdoor (Overeem et al., 2013). Inversely this means that if we know the smartphone battery temperature, we can estimated the outdoor temperature. This insight offers a high potential for recording urban temperatures in areas where observations are rather scarce. The open question remains whether spatial and temporal scales these observations are applicable. Is it possible to get a reliable temperature record for your neighbourhood via available smartphones. Also, will these modern Big Data techniques change the paradigm on performing traditional measurements?

Weather forecasts on TV and radio never focus on the detailed weather for cities, which is somewhat surprising since many human activities, human health and critical infrastructures depend on the city temperature. Since computer power has rapidly grown the last decades, and still is, weather forecast models have refined their grid spacing. With this refinement urban areas “become visible” for these models. On one hand this offers a great potential for city specific forecasts. On the other hand information about the urban morphology is needed to feed these weather forecast models. For example they need to know whether urban districts contain skyscrapers or just three-story residential areas, and for example how much vegetation is present. Thereto the World Urban Database and Portal Tool is set up in which local experts document their city. You are welcome to join to describe your city and inform our weather forecast models how your city looks like!

##### References
• Heusinkveld, B.G., G.J. Steeneveld, L.W.A. van Hove, C.M.J. Jacobs, and A.A.M. Holtslag 2014: Spatial variability of the Rotterdam urban heat island as influenced by urban land use, J. Geophys. Res, 119, 677–692.
• Overeem, A.,  J. C. R. Robinson,  H. Leijnse,  G. J. Steeneveld,  B. K. P. Horn, and  R. Uijlenhoet (2013), Crowdsourcing urban air temperatures from smartphone battery temperatures, Geophys. Res. Lett., 40, 4081–4085, doi:10.1002/grl.50786
• Steeneveld, G.J., S. Koopmans, B.G. Heusinkveld, L.W.A. van Hove, and A.A.M. Holtslag, 2011: Quantifying urban heat island effects and human comfort for cities of variable size and urban morphology in The Netherlands, J. Geophys. Res., 116, D20129, doi:10.1029/2011JD015988.
• Zander, K.K., W.J.W. Botzen, E. Oppermann, T. Kjellstrom, S.T. Garnett, 2015: Heat stress causes substantial labour productivity loss in Australia, Nature Climate Change  5, 647–651.

Written by Gert-Jan Steeneveld, associate Professor at Wageningen University, The Netherlands

Edited by Célia Sapart and Caroline Jacques

## The Climate Tango of ENSO and CO2

In 1904, the Swedish chemist Svante Arrhenius suggested that the burning of fossil fuels to satiate our hunger for energy would increase the percentage of carbon dioxide (CO2) in the atmosphere, which would change the Earth’s temperature. Regular measurements of atmospheric CO2, started in the late 1950’s at remote locations such as Mauna Loa in Hawaii and the South Pole, confirmed his hypothesis about increasing CO2, with one important caveat. The rate at which CO2 was accumulating in the atmosphere did not match the rate at which it was being produced by fossil fuel burning. In fact, the atmosphere was apparently retaining only half of what was pouring in. Figure 1 shows the observed atmospheric mole fraction of CO2 – moles of CO2 per mole of dry air, usually expressed in “parts per million” – changing with time, along with the change we would have expected if all our fossil fuel emissions would have stayed in the atmosphere.

Figure 1: Measured (CO2) mole fraction (moles of (CO2) per mole of dry air) at Mauna Loa, Hawaii in blue, which is a good approximation to the global average atmospheric (CO2). In red is what the global average mole fraction would have been if all the fossil fuel and land use change (mostly biomass burning) emissions since preindustrial times had accumulated in the atmosphere. The green shaded area, therefore, is (CO2) that was emitted due to human activity but did not stay in the atmosphere. Compared to the preindustrial baseline of 280 parts per million (ppm), the (CO2) accumulated in the atmosphere is roughly half of what was emitted. Monthly mean Mauna Loa (CO2) measurements from the Scripps Institute of Oceanography (SIO, http://scrippsco2.ucsd.edu/) and National Oceanic and Atmospheric Administration (NOAA, https://www.esrl.noaa.gov/gmd/ccgg/trends/), fossil fuel emissions from the Carbon Dioxide Information Analysis Center (CDIAC, http://cdiac.ornl.gov/), and land use change emissions from the Global Carbon Project.

Today, we know that the other half – the fossil fuel CO2 emission “missing” from the atmosphere – is taken up by the land biosphere and oceans. The land biosphere accumulates carbon from the atmosphere by increasing plant mass, while the oceans dissolve atmospheric CO2 as the accumulating fossil fuel CO2 in the atmosphere drives the ocean-atmosphere carbon equilibrium out of balance. Beyond this large-scale picture, however, much still remains uncertain. Which part of the land biosphere – the tropics, the temperate latitudes, or the boreal forests and grasslands – takes up the most carbon? How does that uptake change from, say, a drought year to a rainy year? Do old growth forests and young forests take up carbon differently? To what extent do the land and ocean uptakes respond to natural climate cycles such as El Niño or the Pacific Decadal Oscillation?

Answering these questions accurately will not only tell us how the carbon cycle works today, but also how it might respond to a changing climate in the future. Almost all climate models used today to predict future climate contain a model to simulate the response of the carbon cycle (such as the land uptake of CO2) to future climate forcings such as droughts, floods, and elevated temperatures. The prediction skill of a climate model depends crucially on the fidelity of these carbon cycle responses built into the model. However, for the future land carbon uptake these models do not even agree on the sign of the response. Natural climate variations such as the El Niño Southern Oscillations (ENSO) provide us with experiments to evaluate and improve our understanding of these carbon cycle responses.

ENSO is a climate pattern that involves periodic oscillation in winds and sea surface temperatures over the tropical eastern Pacific Ocean. It represents a major control on the year-to-year variation in temperature and precipitation in the Tropics, going through its El Niño and La Niña phases in an irregular fashion that is still difficult to predict but repeating in roughly four-year cycles on average. Less known is the control of ENSO on the atmospheric chemical composition. The global abundance of several gases including greenhouse gases of the natural atmosphere, such as CO2 and CH4, show a clear relation with ENSO. In the case of CO2, both the land and the ocean contribute to this variation in ways that are not well quantified yet, making ENSO an excellent test for models. Closest to the imagination are probably pictures with vague contours of Indonesian farmland covered in thick smoke during El Nino. Indeed, fire is an important mechanism connecting precipitation variability to CO2 variability.

Figure 2 shows the carbon cycle response to the ENSO cycle, as manifest in the atmospheric mole fraction of CO2. Atmospheric CO2 (as well as other greenhouse gases such as CH4) is measured cooperatively by multiple laboratories at a global network of sampling sites, an effort that began more than fifty years ago with Mauna Loa and South Pole. Our knowledge of the carbon cycle response to ENSO – such as the amount of additional carbon in the atmosphere during a strong El Niño, or the partitioning of that signal into contributing factors such as fires in Tropical Asia versus drought in Amazonia – derives to a large degree from these measurements.

Figure 2: The (CO2) growth rate as measured by the increment of one month over the same month in the previous year. The inter-annual variations in the (CO2) growth rate show a clear imprint of ENSO. Large El Niño events such as the 1997-98 and 2015-16 ones show up as anomalously large spikes in the growth rate. Most of the additional (CO2) in the atmosphere during and after an El Niño comes from the Tropics, and therefore the response measured at Mauna Loa (red) is usually larger than the global average response across a network of background sites (blue). (CO2) data taken from the NOAA (CO2) trends page at https://www.esrl.noaa.gov/gmd/ccgg/trends/.

The atmospheric growth rate of the CO2 mole fraction spikes right after a big El Niño event, such as after 1997-98 and 2015-2016 in Figure 2. Since we know the total mass of air in the atmosphere, we can translate between CO2 mole fraction spikes of Figure 2 and mass of carbon added to the atmosphere. The 1997-98 El Niño added ~2 Petagrams carbon (PgC) to the atmosphere, while preliminary estimates suggest that the more recent 2015-16 event injected ~3 PgC (for comparison, our current global fossil fuel emission is ~10 PgC/year). Having a global network of sites – instead of just background sites such as Mauna Loa – allows us to drill down into the mechanisms behind each of these CO2 increments. For example, we know now that most of the additional 2 PgC carbon from the 1997-98 El Niño was injected from extended fires in the Tropics. Due to the sheer magnitude of the carbon cycle response to El Nino, with the year 2015 setting the record in global CO2 growth to just above 3 ppm/yr, ENSO events present natural experiments against which we can verify our understanding of the interaction between climate and the carbon cycle.

Even for the large changes in CO2 as observed during strong El Nino’s the attribution to specific processes remains a challenge, because of the various coupled responses in the Earth system. For example, the ocean-atmosphere exchange of CO2 is also influenced by ENSO, as shifting patterns in tropical sea surface temperature change the mixing rate of deep and surface waters, influencing gas exchange. Therefore, to get the process-attribution correct, scientist try to disentangle the various influences on atmospheric CO2, which requires a lot of measurements.

Over the past couple of decades, it has become clear that our cooperative network of atmospheric measurements has large gaps over areas that play very important roles in determining the climate impact on the carbon cycle. The gaps are usually due to logistical reasons, such as the difficulty of maintaining measurement sites in Tropical forests, or the expense of making regular shipboard measurements to cover the oceans. To fill this data gap, space based measurements have emerged as a promising yet challenging alternative.

Carbon cycle gases such as CO2 and CH4 (and to a lesser extent CO), by virtue of being greenhouse gases, absorb and emit electromagnetic radiation at certain specific frequencies, usually in the infrared (IR). In theory, a downward-looking IR sensor at the top of the atmosphere should be able to estimate the amount of these gases by measuring the strength of IR radiation at those frequencies. Figure 3 shows the average CO2 mole fraction between the surface and the top of the atmosphere (“column average CO2”) retrieved from the Orbiting Carbon Observatory 2 (OCO2) satellite over Equatorial Africa, showing elevated values due to biomass burning.

Figure 3: Column average CO2 over Central Africa in 2015 from the Orbiting Carbon Observatory 2 (OCO2) satellite. The bright red band over Equatorial Africa due to biomass burning is visible in contrast to lower (CO2) elsewhere. Column average CO2 from the ACOS algorithm available at https://co2.jpl.nasa.gov/#mission=OCO-2

In practice, IR measured from space is sensitive to many interfering species other than CO2 or CH4, such as the amount of water vapor and dust particles, complicating the estimation of CO2 and CH4 from space. If these complications can be resolved in the near future, space-based measurements could potentially fill the data gap between surface measurement sites (such as Equatorial Africa, which has almost no surface measurements) and enrich our knowledge of the carbon cycle. With such improvements to our measurement capabilities, we hope to better understand today’s carbon cycle and its response to climate. The clearer we see this carbon-climate tango, the better we will be able to predict its imprint on tomorrow’s climate. The most recent climax was one of the most well observed in history. Data are pouring in from multiple sources, and the coming few years promises a lot of interesting analysis as we try to decipher the steps of this complicated dance. Keep your eyes peeled for updates on this blog!

This post has been written by:

Dr Sourish Basu,  NOAA Earth System Research Laboratory, USA
Dr Sander Houweling, SRON Netherlands Institute for Space Research, NL

and edited by the new editor of this blog Célia Julia Sapart, Université Libre de Bruxelles, B.

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