New Forecast Model Provides First Global Scale Seasonal River Flow Forecasts

new_web_figure2_rivernetwork

Over the past ~decade, extended-range forecasts of river flow have begun to emerge around the globe, combining meteorological forecasts with hydrological models to provide seasonal hydro-meteorological outlooks. Seasonal forecasts of river flow could be useful in providing early indications of potential floods and droughts; information that could be of benefit for disaster risk reduction, resilience and humanitarian aid, alongside applications in agriculture and water resource management.

While seasonal river flow forecasting systems exist for some regions around the world, such as the U.S., Australia, Africa and Europe, the forecasts are not always accessible, and forecasts in other regions and at the global scale are few and far between.  In order to gain a global overview of the upcoming hydrological situation, other information tends to be used – for example historical probabilities based on past conditions, or seasonal forecasts of precipitation. However, precipitation forecasts may not be the best indicator of floodiness, as the link between precipitation and floodiness is non-linear. A recent paper by Coughlan-de-Perez et al (2017), “should seasonal rainfall forecasts be used for flood preparedness?”, states:

“Ultimately, the most informative forecasts of flood hazard at the seasonal scale are streamflow forecasts using hydrological models calibrated for individual river basins. While this is more computationally and resource intensive, better forecasts of seasonal flood risk could be of immense use to the disaster preparedness community.”

twitter_screenshotOver the past months, researchers in the Water@Reading* research group have been working with the European Centre for Medium-Range Weather Forecasts (ECMWF), to set up a new global scale hydro-meteorological seasonal forecasting system. Last week, on 10th November 2017, the new forecasting system was officially launched as an addition to the Global Flood Awareness System (GloFAS). GloFAS is co-developed by ECMWF and the European Commission’s Joint Research Centre (JRC), as part of the Copernicus Emergency Management Services, and provides flood forecasts for the entire globe up to 30 days in advance. Now, GloFAS also provides seasonal river flow outlooks for the global river network, out to 4 months ahead – meaning that for the first time, operational seasonal river flow forecasts exist at the global scale – providing globally consistent forecasts, and forecasts for countries and regions where no other forecasts are available.

The new seasonal outlook is produced by forcing the Lisflood hydrological river routing model with surface and sub-surface runoff from SEAS5, the latest version of ECMWF’s seasonal forecasting system, (also launched last week), which consists of 51 ensemble members at ~35km horizontal resolution. Lisflood simulates the groundwater and routing processes, producing a probabilistic forecast of river flow at 0.1o horizontal resolution (~10km, the resolution of Lisflood) out to four months, initialised using the latest ERA-5 model reanalysis.

The seasonal outlook is displayed as three new layers in the GloFAS web interface, which is publicly (and freely) available at www.globalfloods.eu. The first of these gives a global overview of the maximum probability of unusually high or low river flow (defined as flow exceeding the 80th or falling below the 20th percentile of the model climatology), during the 4-month forecast horizon, in each of the 306 major world river basins used in GloFAS-Seasonal.

new_web_figure1_basins
The new GloFAS Seasonal Outlook Basin Overview and River Network Layers.

The second layer provides further sub-basin-scale detail, by displaying the global river network (all pixels with an upstream area >1500km2), again coloured according to the maximum probability of unusually high or low river flow during the 4-month forecast horizon. In the third layer, reporting points with global coverage are displayed, where more forecast information is available. At these points, an ensemble hydrograph is provided showing the 4-month forecast of river flow, with thresholds for comparison of the forecast to typical or extreme conditions based on the model climatology. Also displayed is a persistence diagram showing the weekly probability of exceedance for the current and previous three forecasts.

blog_screenshot
The new GloFAS Seasonal Outlook showing the river network and reporting points providing hydrographs and persistence diagrams.

Over the coming months, an evaluation of the system will be completed – for now, users are advised to evaluate the forecasts for their particular application. We welcome any feedback on the forecast visualisations and skill – feel free to contact me at the email address below!

To find out more, you can see the University’s press release here, further information on SEAS5 here, and the user information on the seasonal outlook GloFAS layers here.

*Water@Reading is “a vibrant cross-faculty centre of research excellence at the University of Reading, delivering world class knowledge in water science, policy and societal impacts for the UK and internationally.”

Full list of collaborators: 

Rebecca Emerton1,2, Ervin Zsoter1,2, Louise Arnal1,2, Prof. Hannah Cloke1, Dr. Liz Stephens1, Dr. Florian Pappenberger2, Prof. Christel Prudhomme2, Dr Peter Salamon3, Dr. Davide Muraro3, Dr. Gabriele Mantovani3

1 University of Reading
2 ECMWF
3 European Commission JRC

Contact: r.e.emerton@pgr.reading.ac.uk

Sea ice is complicated, but do sea ice models need to be?

email: r.frew@pgr.reading.ac.uk

Sea ice is complex…

When sea water freezes it forms sea ice, a composite of ice and brine. Sea ice exhibits varying structural, thermodynamic and mechanical properties across a range of length- and time-scales. It can be subcategorised into numerous different types of sea ice depending on where is grows and how old it is.

 

 

ice_formation
Different sea ice growth processes and types 1.

However, climate models do not simulate the evolution of floes (they model floes as cylindrical) or the floe size distribution, which has implications for ice melt rates and exchange of heat with the atmosphere and ocean. Sea ice also hosts algae and small organisms within brine channels in the ice, which can be important for nutrient cycles. This is a developing area of earth system modelling.

sympagic_web
Schematic of life within brine channels in sea ice 2.

How much complexity do global climate models need to sufficiently model the interactions of sea ice with the ocean and atmosphere?
The representation of sea ice in global climate models is actually very simple, with minimal sea ice types and thickness categories. The main important feature of sea ice for global climate models is its albedo, which is much greater than that of open water, making it important for the surface energy balance. So, it is important to get the correct area of sea ice. Global climate models need sea ice:

  • to get the correct heat exchange with the atmosphere and ocean
  • to get a realistic overturning circulation in the ocean.
  • because salt release during sea ice growth is important for the ocean salinity structure, and therefore important to get the correct amount of sea in/near deep water formation sites.
  • sea ice is not important for sea level projections.

So, do the complex features of sea ice matter, or are simple parameterisations sufficient?

Sea_ice_Drawing_General_features.svg Schematic showing some dynamic features of sea ice 3.

Which leads to a lot more questions…

  • Where does the balance between sufficient complexity and computational cost lie?
  • Does adding extra model complexity actually make it harder to understand what the model is doing and therefore to interpret the results?
  • Do climate models need any further improvements to sea ice in order to better simulate global climate? There is still large uncertainty surrounding other climate model components, such as clouds and ocean eddies, which are believed to explain a lot of the discrepancy between models and observations, particularly in the Southern Ocean.

A lot of these questions depend on the scientific question that is being asked. And the question is not necessarily always ‘how is global climate going to change in the future’. Sea ice is fascinating because of its complexity, and there are still many interesting questions to investigate, hopefully before it all melts!

 Images clockwise from top left: grease ice 4, pancake ice 5, surface melt ponds 6, ice floes 7

The Future Developments in Climate Sea Ice Modelling Workshop

This blog stems from a one day workshop I attended on ‘Future developments in climate sea ice modelling’ at the Isaac Newton Centre as part of a four month programme on the ‘Mathematics of Sea Ice Phenomena’. The format of the day was that three different strands of sea ice researchers gave 40 min talks giving their strand’s point of view of current sea ice developments and what the focus should be for sea ice modelers, each followed by 40 mins of open discussion with the audience.

The three (very good!) talks were:

  1. Dirk Notz: What do climate models need sea ice for? A top-down, system level view of what sea ice models should produce from the perspective of a climate modeller.
  2. Cecilia Bitz: What sea ice physics is missing from models? A bottom-up view of what is missing from current sea ice models from the perspective of a sea ice scientist.
  3. Elizabeth Hunke: What modelling approaches can be used to address the complexity of sea ice and the needs of climate models?

 

  1. https://nsidc.org/cryosphere/seaice/characteristics/formation.html.
  2. https://www.eduplace.com/science/hmxs/ls/mode/cricket/sect7cc.shtml
  3. https://en.wikipedia.org/wiki/Fast_ice
  4. https://www.travelblog.org/Photos/2101807
  5. http://www.antarctica.gov.au/about-antarctica/environment/icebergs-and-ice/sea-ice
  6. https://en.wikipedia.org/wiki/Sea_ice#/
  7. https://www.shutterstock.com/video/clip-15391768-stock-footage-flying-over-arctic-ice-floes.html

Adventures in Modelling – NCAS Climate Modelling Summer School

At the beginning of September 3 PhD students from Reading, including myself, went to Cambridge to attend the NCAS Climate Modelling Summer School. This is an annual event aimed at PhD students and early career scientists who want to develop their understanding of climate models, with topics covering parameterisations to supercomputers.

IMG_3847
Staff and students of the course pose outside the Chemistry department, which played host to morning lectures

The course ran over two weeks with lectures on the components of climate models in the morning, covering fundamental dynamics and thermodynamics, numerical methods and different parameterisations. This was followed by an afternoon of computer practicals and then more topical lectures in the evening, such as “User engagement in climate science” and “The Sun and Earth’s climate system”. The lectures were very fast paced but this was a great opportunity to cover so many topics in a short space of time and get a grounding in lots of different topics that I will definitely be looking over in future. A poster session on the second evening gave us the chance to learn about other people’s work and make connections with other people starting out their careers in climate science, including a few readers of the blog, that will hopefully last throughout our careers.

One of the highlights of the course was the chance to run some (rather interesting) experiments with an earth system model. This involved breaking into groups with each being given a different project. It was exciting to go  through the whole process of having an idea, developing a hypothesis, thinking of specific experiments to answer the hypothesis and then analysing the results in just a week – something that takes much longer when you’re doing a PhD! My group worked on the Flat Earth experiment, which looked at the effect of removing all of the earth’s orography not, to our dismay, turning the earth into a flat disk. I learned a lot about how to run models, something which I have never done even though I use the output. It also developed my understanding of different climate processes that I don’t work with such as the monsoons, and even dynamical vegetation.

monsoons
Flat earth experiment looking at the change in the monsoon winds

Throughout the course we stayed at St Catharine’s College. Right in the centre of Cambridge it quickly felt like a home from home, keeping us well fed to get through the intense science. Although the weekend was rainy, apparently breaking a run of excellent weather for the school, we still had plenty of time to explore beautiful Cambridge. A few people were even brave enough to go punting!

An interesting, hectic and inspiring two weeks later we may have been glad to head back to Reading for a good sleep but having thoroughly enjoyed the summer school.

Buildings2000-®TimRawle-resized-w680px
The beautiful St Catharine’s College, image from http://www.caths.cam.ac.uk/

 

Synchronisation: how can this help weather forecasts in the future?

Current numerical modelling and data assimilation methods still face problems in strongly nonlinear cases, like in convective scales. A different, but interesting tool to help overcome these issues can be found in the synchronisation theory.

It all started in 1665, when Christiaan Huygens, a Dutch scientist, discovered that his two pendulum clocks were suddenly oscillating in opposite directions, but in a synchronised way. He tried to desynchronise them, by perturbing randomly one of the clocks, but surprisingly, after some time, both devices were synchronised again. He has attributed the phenomenon to the frame both clocks were sharing and after that, synchronisation field was opened to the world.

figure1

Figure 1: A drawing by Christiaan Huygens of his experiment in 1665.

Nowadays, researchers use these synchronisation concepts to reach a main goal: synchronise a model (any) with the true evolution of a system, using measurements. And even when only a reduced part of this system is observed, synchronisation between models and the true state can still be achieved. This is quite similar to what data assimilation looks for, as it aims to synchronise a model evolution with the truth by using observations, finding the best estimate of the state evolution and its uncertainty.

So why not investigate the benefits of recent synchronisation findings and combine these concepts with a data assimilation methodology?

At the start of this project, the first noticeable step that should be taken was to open up the synchronisation field to higher-dimension systems, as the experiments performed in the area were all focused on low-dimension, non-realistic systems. To this end, a first new idea was proposed:  an ensemble version of a synchronisation scheme, what we are calling EnSynch (Ensemble Synchronisation). Tests with a partly observed 1000-dimension chaotic model show a very efficient correspondence between the model and the true trajectories, both for estimation and prediction periods. Figures 2 and 3 show how our estimates and the truth are on top of each other, i.e. synchronised. Note that we do not have observations for all of the variables in our system. So, it is amazing to obtain the same successful results for the observed and also for the unobserved variables in this system!

figure2

Figure 2: Trajectories of 2 variables (top:observed and bottom: unobserved). Blue lines: truth. Green lines: estimates/predictions. (Predictions start after the red lines, i.e. no data assimilation is used.)

figure3

Figure 3: Zoom in the trajectory of a variable, showing how the model matches with the truth. Blue line: truth. Red line: our model. Yellow dots: observations.

The second and main idea is to test a combination of this successful EnSynch scheme with a data assimilation method called Particle Filter. As a proper data assimilation methodology, a particle filter provides us the best estimation of the state evolution and its uncertainty. Just to illustrate the importance of data assimilation in following the truth, figure 4 compares the case of only counting on an ensemble of models running freely in a chaotic nonlinear system, with the case of a data assimilation method applied to it.

figure4

Figure 4: Trajectories of ensemble members. Blue: with data assimilation. Red: without data assimilation. Truth is in black.

Efficient results are found with the combination between the new EnSynch and the particle filters. An example is shown in figure 5, where particles (ensemble members) of an unobserved variable nicely follow the truth during the assimilation period and also during the forecast stage (after t=100).

figure5

Figure 5: Trajectory for an unobserved variable in a 1000-dimension system. Observations occur at every 10 time steps until t=100. Predictions start after t=100.

These results are motivating and the next and big step is to implement this combined system in a bigger atmospheric model.  This methodology has been shown to be a promising solution for strongly nonlinear problems and potential benefits are expected for numerical weather prediction in the near future.

References:

Rey, D., M. Eldridge, M. Kostuk, H. Abarbanel, J. Schumann-Bischoff, and U. Parlitz, 2014a: Accurate state and parameter estimation in nonlinear systems with sparse observations. Physics Letters A, 378, 869-873, doi:10.1016/j.physleta.2014.01.027.

Zhu, M., P. J. van Leeuwen, and J. Amezcua, 2016: Implicit equal-weights particle filter. Quart. J. Roy. Meteorol. Soc., 142, 1904-1919, doi:10.1002/qj.2784.

 

Should we be ‘Leaf’-ing out vegetation when parameterising the aerodynamic properties of urban areas?

Email: C.W.Kent@pgr.reading.ac.uk

When modelling urban areas, vegetation is often ignored in attempt to simplify an already complex problem. However, vegetation is present in all urban environments and it is not going anywhere… For reasons ranging from sustainability to improvements in human well-being, green spaces are increasingly becoming part of urban planning agendas. Incorporating vegetation is therefore a key part of modelling urban climates. Vegetation provides numerous (dis)services in the urban environment, each of which requires individual attention (Salmond et al. 2016). However, one of my research interests is how vegetation influences the aerodynamic properties of urban areas.

Two aerodynamic parameters can be used to represent the aerodynamic properties of a surface: the zero-plane displacement (zd) and aerodynamic roughness length (z0). The zero-plane displacement is the vertical displacement of the wind-speed profile due to the presence of surface roughness elements. The aerodynamic roughness length is a length scale which describes the magnitude of surface roughness. Together they help define the shape and form of the wind-speed profile which is expected above a surface (Fig. 1).

blogpostpic

Figure 1: Representation of the wind-speed profile above a group of roughness elements. The black dots represent an idealised logarithmic wind-speed profile which is determined using the zero-plane displacement (zd) and aerodynamic roughness length (z0) (lines) of the surface.

For an urban site, zd and z0 may be determined using three categories of methods: reference-based, morphometric and anemometric. Reference-based methods require a comparison of the site to previously published pictures or look up tables (e.g. Grimmond and Oke 1999); morphometric methods describe zd and z0 as a function of roughness-element geometry; and, anemometric methods use in-situ observations. The aerodynamic parameters of a site may vary considerably depending upon which of these methods are used, but efforts are being made to understand which parameters are most appropriate to use for accurate wind-speed estimations (Kent et al. 2017a).

Within the morphometric category (i.e. using roughness-element geometry) sophisticated methods have been developed for buildings or vegetation only. However, until recently no method existed to describe the effects of both buildings and vegetation in combination. A recent development overcomes this, whereby the heights of all roughness elements are considered alongside a porosity correction for vegetation (Kent et al. 2017b). Specifically, the porosity correction is applied to the space occupied and drag exerted by vegetation.

The development is assessed across several areas typical of a European city, ranging from a densely-built city centre to an urban park. The results demonstrate that where buildings are the dominant roughness elements (i.e. taller and occupying more space), vegetation does not obviously influence the calculated geometry of the surface, nor the aerodynamic parameters and the estimated wind speed. However, as vegetation begins to occupy a greater amount of space and becomes as tall as (or larger) than buildings, the influence of vegetation is obvious. Expectedly, the implications are greatest in an urban park, where overlooking vegetation means that wind speeds may be slowed by up to a factor of three.

Up to now, experiments such as those in the wind tunnel focus upon buildings or trees in isolation. Certainly, future experiments which consider both buildings and vegetation will be valuable to continue to understand the interaction within and between these roughness elements, in addition to assessing the parameterisation.

References

Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol and Clim 38:1262-1292.

Kent CW, Grimmond CSB, Barlow J, Gatey D, Kotthaus S, Lindberg F, Halios CH (2017a) Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas. Boundary-Layer Meteorology 164: 183-213.

Kent CW, Grimmond CSB, Gatey D (2017b) Aerodynamic roughness parameters in cities: Inclusion of vegetation. Journal of Wind Engineering and Industrial Aerodynamics 169: 168-176.

Salmond JA, Tadaki M, Vardoulakis S, Arbuthnott K, Coutts A, Demuzere M, Dirks KN, Heaviside C, Lim S, Macintyre H (2016) Health and climate related ecosystem services provided by street trees in the urban environment. Environ Health 15:95.

Future of Cumulus Parametrization conference, Delft, July 10-14, 2017

Email: m.muetzelfeldt@pgr.reading.ac.uk

For a small city, Delft punches above its weight. It is famous for many things, including its celebrated Delftware (Figure 1). It was also the birthplace of one of the Dutch masters, Johannes Vermeer, who coincidentally painted some fine cityscapes with cumulus clouds in them (Figure 2). There is a university of technology with some impressive architecture (Figure 3). It holds the dubious honour of being the location of the first assassination using a pistol (or so we were told by our tour guide), when William of Orange was shot in 1584. To this list, it can now add hosting a one-week conference on the future of cumulus parametrization, and hopefully bringing about more of these conferences in the future.

Delftware_display

Figure 1: Delftware.

Vermeer-view-of-delft

Figure 2: Delft with canopy of cumulus clouds. By Johannes Vermeer, 1661.

Delft_AULA

Figure 3: AULA conference centre at Delft University of Technology – where we were based for the duration of the conference.

So what is a cumulus parametrization scheme? The key idea is as follows. Numerical weather and climate models work by splitting the atmosphere into a grid, with a corresponding grid length representing the length of each of the grid cells. By solving equations that govern how the wind, pressure and heating interact, models can then be used to predict what the weather will be like days in advance in the case of weather modelling. Or a model can predict how the climate will react to any forcings over longer timescales. However, any phenomena that are substantially smaller than this grid scale will not be “seen” by the models. For example, a large cumulonimbus cloud may have a horizontal extent of around 2km, whereas individual grid cells could be 50km in the case of a climate model. A cumulonimbus cloud will therefore not be explicitly modelled, but it will still have an effect on the grid cell in which it is located – in terms of how much heating and moistening it produces at different levels. To capture this effect, the clouds are parametrized, that is, the vertical profile of the heating and moistening due to the clouds are calculated based on the conditions in the grid cell, and this then affects the grid-scale values of these variables. A similar idea applies for shallow cumulus clouds, such as the cumulus humilis in Vermeer’s painting (Figure 2), or present-day Delft (Figure 3).

These cumulus parametrization schemes are a large source of uncertainty in current weather and climate models. The conference was aimed at bringing together the community of modellers working on these schemes, and working out which might be the best directions to go in to improve these schemes, and consequently weather and climate models.

Each day was a mixture of listening to presentations, looking at posters and breakout discussion groups in the afternoon, as well as plenty of time for coffee and meeting new people. The presentations covered a lot of ground: from presenting work on state-of-the-art parametrization schemes, to looking at how the schemes perform in operational models, to focusing on one small aspect of a scheme and modelling how that behaves in a high resolution model (50m resolution) that can explicitly model individual clouds. The posters were a great chance to see the in-depth work that had been done, and to talk to and exchange ideas with other scientists.

Certain ideas for improving the parametrization schemes resurfaced repeatedly. The need for scale-awareness, where the response of the parametrization scheme takes into account the model resolution, was discussed. One idea for doing this was the use of stochastic schemes to represent the uncertainty of the number of clouds in a given grid cell. The concept of memory also cropped up – where the scheme remembers if it had been active at a given grid cell in a previous point in time. This also ties into the idea of transitions between cloud regimes, e.g. when a stratocumulus layer splits up into individual cumulus clouds. Many other, sometimes esoteric, concepts were discussed, such as the role of cold pools, how much tuning of climate models is desirable and acceptable, how we should test our schemes, and what the process of developing the schemes should look like.

In the breakout groups, everyone was encouraged to contribute, which made for an inclusive atmosphere in which all points of view were taken on board. Some of the key points of agreement from these were that it was a good idea to have these conferences, and we should do it more often! Hopefully, in two years’ time, another PhD student will write a post on how the next meeting has gone. We also agreed that it would be beneficial to be able to share data from our different high resolution runs, as well as to be able to compare code for the different schemes.

The conference provided a picture of what the current thinking on cumulus parametrization is, as well as which directions people think are promising for the future. It also provided a means for the community to come together and discuss ideas for how to improve these schemes, and how to collaborate more closely with future projects such as ParaCon and HD(CP)2.

4th ICOS Summer School

Email: R.Braghiere@pgr.reading.ac.uk

The 4th ICOS Summer School on challenges in greenhouse gases measurements and modelling was held at Hyytiälä field station in Finland from 24th May to 2nd June, 2017. It was an amazing week of ecosystem fluxes and measurements, atmospheric composition with in situ and remote sensing measurements, global climate modelling and carbon cycle, atmospheric transport and chemistry, and data management and cloud (‘big data’) methods. We also spent some time in the extremely hot Finnish sauna followed by jumps into a very cold lake, and many highly enjoyable evenings by the fire with sunsets that seemed to never come.

sunset_Martijn Pallandt
Figure 1. Sunset in Hyytiälä, Finland at 22:49 local time. Credits: Martijn Pallandt

Our journey started in Helsinki, where a group of about 35 PhD students, with a number of postdocs and master students took a 3 hours coach trip to Hyytiälä.  The group was very diverse and international with people from different backgrounds; from plant physiologists to meteorologists. The school started with Prof. Dr. Martin Heimann  introducing us to the climate system and the global carbon cycle, and Dr. Alex Vermeulen highlighted the importance of good metadata practices and showed us more about ICOS research infrastructure. Dr. Christoph Gerbig joined us via Skype from Germany and talked about how atmospheric measurements methods with aircrafts (including how private air companies) can help scientists.

Hyytiala_main_tower_truls_Andersen_2
Figure 2. Hyytiälä flux tower site, Finland. Credits: Truls Andersen

On Saturday we visited the Hyytiälä flux tower site, as well as a peatland field station nearby, where we learned more about all the flux data they collect and the importance of peatlands globally. Peatlands store significant amounts of carbon that have been accumulating for millennia and they might have a strong response to climate change in the future. On Sunday, we were divided in two groups to collect data on temperature gradients from the lake to the Hyytiälä main flux tower, as well as on carbon fluxes with dark (respiration only) and transparent (photosynthesis + respiration) CO2 chambers.

chamber_measurements_renato
Figure 3: Dark chamber for CO2 measurements being used by a group of students in the Boreal forest. Credits: Renato Braghiere

On the following day it was time to play with some atmospheric modelling with Dr. Maarten Krol and Dr. Wouter Peters. We prepared presentations with our observation and modelling results and shared our findings and experiences with the new data sets.

The last two days have focused on learning how to measure ecosystem fluxes with Prof. Dr. Timo Vesala, and insights on COS measurements and applications with Dr. Kadmiel Maseyk. Timo also shared with us his passion for cinema with a brilliant talk entitled “From Vertigo to Blue Velvet: Connotations between Movies and Climate change” and we watched a really nice Finnish movie “The Happiest Day in the Life of Olli Mäki“.

4th_icos_summer_school_group_photo
Figure 4: 4th ICOS Summer School on Challenges in greenhouse gases measurements and modelling group photo. Credits: Wouter Peters

Lastly, it was a fantastic week where we were introduced to several topics and methods related to the global carbon budget and how it might impact the future climate. No doubt all information gained in this Summer School will be highly valuable for our careers and how we do science. A massive ‘cheers’ to Olli Peltola, Alex Vermeulen, Martin Heimann, Christoph Gerbig, Greet Maenhout, Wouter Peters, Maarten Krol, Anders Lindroth , Kadmiel Maseyk, Timo Vesala, and all the staff at the Hyytiälä field station.

This post only scratches the surface of all of the incredible material we were able to cover in the 4th ICOS Summer School, not to mention the amazing group of scientists that we met in Finland, who I really look forward to keeping in touch over the course of the years!