Guest post by Heleen van Soest
In April, the annual European Geosciences Union conference was held in Vienna, Austria. Heleen van Soest, MSc student Climate Studies at Wageningen University, attended the conference, and shares some thoughts and tweets (
The opening reception, April 7, reveals that geoscientists are fond of beer. I get to talk to some nice people and hand out my first business cards. Yay! I talk with Walter Schmidt, President of the Division on Geosciences Instrumentation and Data Systems, about observations and data. Lesson learned: data are important, but never take them for granted. Especially from satellites: they basically measure counts and voltages. To interpret the numbers and get something useful, we already need models, i.e. algorithms. Usually, model skill is tested against data. Disagreement between them is often blamed on model errors, assumptions, etc. Keep in mind that data might be wrong, too. Fortunately, raw data is increasingly archived as such, together with the algorithms used to interpret them. In that way, data can still be used if the algorithms are updated. I dedicate my first #egu2013 tweet to this conversation and go home. I am happy to find a Va Piano (Italian restaurant) in ‘my’ street. Together with Sherlock Holmes (the book, that is), I eat my pasta.
At #egu2013 opening reception, interesting conversation about models and data: “important, but never take them for granted” (Walter Schmidt)
Monday, 8 April
Permafrost day. An important issue, as permafrost contains about half of the world’s soil carbon. If permafrost thaws, the organic carbon becomes available for microbes to degrade. Greenhouse gas (methane) emissions are a result, further increasing temperatures. This positive feedback is sometimes compared to a time bomb. Modelling studies of permafrost do show it will degrade under further warming. For example, Greenland permafrost south of 76°N will disintegrate this century. However, see RealClimate before you start to worry that this bomb is about to explode.
But today is not only permafrost; I’ve also got something on ice observations.
For ice mass balance estimates, in situ data have sparse spatial coverage, while remote sensing is not very detailed and cannot provide temperature profiles. So both methods will be needed to get a grasp of the system. Many ground or field campaigns are concerned with validation of satellite products. For sea ice thickness, exotic techniques like ultrasound and upward looking sonar can be used. The latter actually measures sea ice draft (the height of the ice above the water), which can be translated to total thickness. New techniques are being developed as well, such as sea ice thickness monitoring by dog sleds with a conductivity probe (using the difference between ice and water). Sounds cool, doesn’t it?
Tuesday, 9 April
There is one session dealing with feedbacks in the Arctic, which I think is very interesting. Many feedbacks play a role, but independent presentations and posters show that some are more important than others (“All animals are equal, but some animals are more equal than others” to quote George Orwell’s Animal Farm). Some of the feedback processes with the most dominant role in determining Arctic temperature change are the ice albedo feedback, the water vapour feedback, and the lapse rate feedback. The first is well known: with (sea) ice loss, more open water or land is exposed to the sun, absorbing more heat and resulting in further ice loss (a positive, or self-amplifying, feedback). The water vapour feedback is not restricted to the Arctic, but a globally important one. With higher temperatures, the air can hold more water vapour, which is a greenhouse gas and as such contributes to further warming (thus also a positive feedback).
The lapse rate feedback is maybe less well-known (see for a good introduction also this “Introduction to climate dynamics and climate modelling“). In the lower part of the atmosphere, called the troposphere, temperature decreases with height (with about 6.5°C per km, the lapse rate). This temperature change is important, as more long wave radiation is emitted at higher temperatures. With global warming, the upper troposphere in the tropics will warm more than the surface as a result of moist convection, giving a smaller lapse rate. The higher temperatures in the upper troposphere enhance the outgoing radiation to space: a negative lapse rate feedback. At higher latitudes such as the Arctic, however, the surface will warm more than the upper atmosphere. There, more long wave radiation is sent to the surface. So the lapse rate feedback is positive in the Arctic. The global average effect thus depends on a balance between a negative lapse rate feedback in the tropics and a positive lapse rate feedback at high latitudes. For the Arctic itself, all of this means that the lapse rate feedback contributes to Arctic amplification. Arctic amplification is the higher rate of temperature rise in the Arctic compared to the global average (a factor 2). So, if the lapse rate feedback dampens warming in the tropics and amplifies warming in the Arctic, taking the ratio of Arctic over global warming results in a number at least higher than one.
Writing that up refreshed my memory a bit. I could write more about Arctic feedbacks, but let’s just skip to tipping points for now. An important question regarding Arctic sea ice involves tipping points and bifurcations. Some say sea ice has passed a tipping point (see, for example, Livina & Lenton, 2013). Others say that the bifurcation found is just a result of data analysis. Still, amplitude of the seasonal cycle increased while the mean ice extent and the summer minimum decreased. Besides, the Arctic sea ice annual cycle has got into a different regime since 2007, according to Peter Ditlevsen. What does that mean for the future? François Massonnet presents an interesting (sorry, again) modelling study. The five CMIP5 models that best simulated Arctic sea ice predicted a summer ice-free Arctic between 2040 and 2060. This opens up opportunities for exploration and exploitation, and possibly more interesting feedbacks.
Wednesday, 10 April
“Prediction is very difficult, especially about the future.” (Niels Bohr)
Moving from sea ice to land, many presentations deal with the Greenland ice sheet. An important concept here is ice sheet mass balance, which compares input (precipitation, snow) with output (e.g. melt water run-off). Until the 90s, Greenland’s ice sheet mass balance was zero, meaning mass loss equalled mass gain. Currently, mass balance is negative due to increasing discharge, so Greenland is losing mass and contributing to sea level rise. Model projections show a further decrease in Greenland’s surface mass balance and significant melt along the ice sheet margins in the future, giving a contribution to sea level rise of 2–20 cm. Although an increase in precipitation might raise the interior of the ice sheet, a decrease in mass balance along the margins also linearly relates to higher temperatures. Linear relations make modelling a little bit easier, but non-linear processes also play a role and should be taken into account. One such non-linear process is the relation between ice sheet topography or height and surface mass balance, the so-called elevation feedback. As temperatures decrease with height in the atmosphere, melting brings the Greenland ice sheet to lower elevations with higher temperatures. The higher temperatures enhance melting, giving rise to the positive elevation feedback. This process can give an additional mass loss of around 10%, compared to projections that do not take the elevation feedback into account. To capture this process well, a regional climate model should be fully coupled with an ice sheet model. And just a small note: Greenland is not the only ice contributing to sea level rise. Over the last decade, the contribution of Northern hemisphere glaciers to the observed sea level rise increased by 30–40%.
Thursday, 11 April
Today a bit more about Greenland, but first something about snow cover. Snow cover plays a role in boreal forests and permafrost regions, for example. Models can reproduce the mean snow extent, but the variability proves more difficult to capture. Sounds like there are more processes I should take into account in my own model. Maybe I should build a supermodel… Oh wait, such a project even exists! It is called the SUMO project: Super Modelling by combining imperfect models.
Well then, more on Greenland and sea level rise. Model projections using RCP8.5 (the highest emissions scenario) give a contribution by Greenland of 22.3 cm to sea level rise over the next 100 years (Antarctica contributes 7.3 cm). More than 50% of Greenland’s contribution is due to the change in surface mass balance caused by climate change. Atmospheric circulation might still play a role, especially in record events like the widespread ice sheet melt over Greenland in 2012. Antarctica’s contribution to sea level rise, on the other hand, is mostly due to ice shelf basal melting. According to Robert Bindschadler, feedbacks will not be a major factor determining sea level rise during the coming 200 years. Besides, he found that the ice sheet response is surprisingly linear. Going way back in time, mass loss from southern Greenland contributed 17 mm to sea level rise from the little ice age through 2010, which is 9-10% of total sea level rise. Increased thermal expansion also played a role (see e.g. Weather Underground), contributing to the steadily accelerating sea level rise (Jevrejeva et al., 2008).
T.Bolch: sign. contribution (~20%) of Greenland glaciers&ice caps to overall Greenland mass loss Abstract: http://meetingorganizer.copernicus.org/EGU2013/EGU2013-9136.pdf
Friday, 12 April
Found some interesting sessions last-minute. A good way to end my week, with presentations on a topic I would like to look into somewhere in the near future: black carbon, or soot.
Black carbon (BC) is a tiny particle (aerosol) that, when emitted to the air, can absorb solar radiation and warm the atmosphere. When incorporated in more hygroscopic aerosol (e.g. together with sulphate), it can also contribute to cloud formation and affect cloud properties by acting as so-called Cloud Condensation Nuclei (CCN). Clouds can either warm or cool the surface, depending, among other things, on their height in the atmosphere. Black carbon could result in more low clouds in autumn, which warm the surface and could thus contribute to ice melt. The dark BC can also influence snow and ice directly, by lowering the albedo (reflectivity) of these bright surfaces. Including black carbon in models results in snow and ice melt, increased atmospheric absorption of solar UV and more low clouds, contributing to further warming.
But where does that black carbon come from? In summer, the dominant source is biomass burning. Diesel engines also emit black carbon, so filters could help reducing these emissions (see this UNEP report). Another important source turns out to be gas flaring by the oil and petroleum industries. The highest black carbon concentrations in the Arctic occur over Russia and China and are linked to gas flaring there. Globally, flaring contributes only 3% to BC emissions, but the contribution in the Arctic is way larger. One estimate says flaring contributes 42% to Arctic mean BC, another says 2/3 (at 66°N). Either way, black carbon makes a big difference in the Arctic, and flaring in the region will probably increase due to higher industrial activity with sea ice loss. The location of emissions is also important. Emitting black carbon within the Arctic gives a five-fold increase in Arctic surface temperature compared to the same amount of emissions at mid-latitudes (0.24 versus 0.05 K/Tg BC/year). A large part of the difference can be explained by deposition of BC on snow and ice, after emissions within the Arctic.
So far, models have had difficulties with simulating the magnitude and seasonality of Arctic haze, which is formed by high aerosol concentrations in winter and early spring. Including black carbon from domestic combustion and flaring improves the simulations. Including flaring helps simulating the magnitude of the haze (which is usually underestimated) and better explains the seasonality of the haze, because there is also a strong seasonal cycle in flaring. What might further improve model simulations of (high) black carbon concentrations is a monthly or daily resolution.
Sea level: how much can it rise this century? Anders Levermann argues 1.5 m (upper limit, not projection) http://meetingorganizer.copernicus.org/EGU2013/EGU2013-1805.pdf