by Chiel van Heerwaarden, Wageningen University
In preparation for the upcoming Madrid hackathon, the Storms and Land theme met in Wageningen from 6 to 8 April 2023. With multiple positions that have recently started, our goals were to get to know each other better, and to shape up a document that will be a first overview of the ability of the storm-resolving models to capture the climate over land.
Our first focus will be on variability over land in the broadest sense. With the unprecedented global resolution of storm-resolving models we see for the first time mesoscale phenomena, such as cloud systems, mountain winds, sea breezes, or heterogeneity-driven circulations appearing at such a detail level that we can directly compare their statistics directly to field observations. Our hypothesis is that this will largely influence (and hopefully ultimately improve) resolved variability in thermodynamic variables, wind, precipitation, and derived variables such as river runoff or solar power production.
With the expertise and research focus of the different groups, we have an interesting collection of modes of variability that the Storms and Land theme will study in detail in the coming months. The ETH Zurich group is analysing temperature and drought, Wageningen University is studying solar and thermal radiation variability and river runoff, MPI-M and Wageningen together are investigating whether this generation of models is able to pick up soil-moisture precipitation coupling well. Also, the University of Lissabon is evaluating the simulated surface temperatures, while the University of Bern is analysing precipitation in the Alps and the representation of atmospheric blockings in the storm-resolving simulations.
What makes nextGEMS challenging and also exciting for that reason, is that the step from research ideas to actual output in the form of graphs is for most of us far more complex than ever before. This is due to the very large amount of data produced by the storm-resolving models and the different ways of storing those. Postprocessing model data requires advanced engineering skills, for which we are crucially dependent on the (fortunately excellent) support teams from MPI-M and ECMWF. Nonetheless, after three days of hacking, we were able to bring our first plots to the screen and are warmed up for the Madrid hackathon, where we will hopefully find the first answers to our research questions.
by Thorsten Mauritsen, MISU
By directly and more physically simulating specific events (e.g., tropical cyclones, rainfall extremes, blockings) most associated with hazards, nextGEMS provides an improved basis for assessing risk globally. The importance of simulating fine scales for assessing hazards, but also for other applications, is well understood and motivates the patchwork of downscaling approaches known as the value chain. A Challenge Problem, co-defined with stakeholder groups, hereby, will help guide the development of the SR-ESMs, and their associated workflows, in ways that better expose their information content to application communities. This will allow us to “short-circuit” the value chain and develop a new model of Integrated Assessment. Activities are planned in the form of pilot projects on near surface (wind/solar) renewable energy, marine productivity, and changing weather or climate related hazards.
For the challenge problem in the renewable energy sector, we addressed specific challenges:
Challenge 1: What is the minimal amount of information needed to optimize the design of a regional renewable energy system and how can we extract this information from global storm-resolving models.
Challenge 2: How does the potential renewable energy output landscape change with a changing climate?
During the Cycle 2 Hackathon our stipends were provided with meteogram station data from two different models. Along with temporally sparse snapshots of full three dimensional model output. The main goal was to either find a condensation of the high-frequency output or suggest other output variables, to support the design of renewable energy systems.
Support was given by technical consultants (members of the modelling groups) to help them understand the model output and ways of accessing it since the focus shall lay on the science rather than solving technical problems.
Additionally, the teams were supported by energy consultants, to for instance help them understand design parameters (hub height, diffuse versus direct efficiency, etc.). In particular we had two sessions with Iberdrola and with Vestas Wind Systems. These sessions were particularly useful for the participants to understand the problems that the energy industry is facing, and provided an opportunity to discuss their results with experts. There was also a discussion of how the participants potentially develop a carreer in the renewable energy sector.
The approach taken in the Hackathon is to use the ability of the nextGEMS models to resolve the mesoscales and represent relevant motion and fields for renewable energy production. We used primarily dedicated high frequency output for a series of stations, but also the complete mapped output.
For example the wind at rotor height of a typical wind power plant around 100 m above ground is used directly to calculate the power output from a typical turbine. Figure 1 shows how the output depends on the wind speed. The output starts at a minimum wind speed and increases with the wind speed to the third power up to a maximum value which is limited by the generator size. At high wind speeds the turbines automatically turn off to limit wear and for safety reasons.
Because the power output curve is highly non-linear in the wind speed, we were interested in seeing how much estimated power is biased if using lower temporal resolution. Figure 2 shows output for four different stations. First we note that estimated output is monotonically decreasing with lower resolution suggesting that all stations, except perhaps the EURECA ocean site, are mostly seeing winds on the qubic wind speed range. For this a different combination of turbine, generator and tower height can be used to extract more energy at these sites. We also see that the bias is very low when degrading from 3 minute to 1 hour means, and even monthly mean wind speeds provide a reasonable estimate. Here it is important to note that this is the average of the instaneous wind speed. If the wind components were average the degradation would be much larger.
We checked how the models perform in terms of representing the observed wind speed at the flat surface Cabauw site in the Netherlands. It turns out that both the IFS and ICON grossly underestimates the occurrence of high wind speeds.
This bias in both the IFS and the standard ICON-Sapphire setup is related to the parameterisation of turbulent drag. This can be seen from the 10 km resolution test simulation conducted using the TTE scheme, which exhibits a more realistic distribution at Cabauw (see figure 3).
Figure 4 shows a comparison of the monthly mean modelled downwelling shortwave radiation with observations at the Cabauw site. Here both IFS and ICON do a good job in predicting the observations. Also, the IFS model was analysed in three different resolutions but there is no obvious difference.
To convert the downwelling shortwave radiation to power output one must take into account various losses, here assumed to amount to 12 percent, as well as the temperature degradation, here assumed to be -0.5 %/K (see Figure 5.)
What became clear through the Hackathon was that the wind industry is already working with quite advanced modelling tools for site planning and short term forecasting of wind power, along with on site observations. The situation is slightly less advanced for solar power, partly because the modelling tools are not nearly of the same quality due to problems with modelling clouds. New demands on the industry to also assess the impact of the changing climate on production, safety and durability/maintenance needs is a challenge that the industry is not well suited to meet, and where more research is urgently needed. Also, industry is looking forward to leveraging DestinationEarth digital twin simulations in their workflow.
by Marta Mrozowska, Niels Bohr Institute, UCPH
Under the cloudy skies of the Swedish summer, the members of the WP6 group gathered on a beautiful remote island on the Gullmarsfjorden – Stora Bornö. The long hours of intensive work and discussion during the day were rewarded by scenic starry nights, both above and below, thanks to the local bioluminescent algae. This environment motivated an inspired and thorough evaluation of the two ocean mixing schemes, a turbulent kinetic energy (TKE) and an empirical K-profile (KPP) based scheme. Using sensitivity experiments of the uncoupled ICON and FESOM runs, we have discovered that biases in the model output cannot be attributed to a single mixing scheme. While TKE was slightly better on large scales, KPP was better at reproducing some local processes such as near-inertial wave induced mixing.
We also uncovered biases in the ERA5 forcing, and an interesting trend of the mixed layer becoming shallower with increasing model resolution. Aside from the analysis, we shared expert knowledge through lectures given by the professors in the group, and project presentations by the early career scientists. On the final days of the workshop, we also received two guest lectures: a master’s student from AWI, Jan Gärtner, explained how he coupled a sea ice component to the python-based ocean model Veros; and dr. James Avery gave a talk about the future of programming languages tailored for constructing comprehensive and transparent climate model code.
For more insights of the summer slackathon have a look here.
by Thomas Rackow*, Daniel Klocke**, and the MPI-M and ECMWF-AWI modelling teams***
The nextGEMS model development is structured into simulation cycles. Each simulation cycle is followed by a hackathon, where simulation results are evaluated extensively by the nextGEMS community. The first nextGEMS hackathon in Berlin in October 2021 analysed the very first simulations. Based on the results, the two models participating in the project, IFS and ICON, were updated significantly for the cycle 2 simulations. These simulations where just completed and the unique datasets are waiting for the community to analyse them at the upcoming hackathon at the end of June.
In case of IFS, besides updates to the atmospheric model component (read more here) and a more realistic treatment of snow, another update has been the use of a higher-resolution ocean that resolves eddies over large parts of the globe. Eddies in the ocean impact exchanges of energy and matter across the ocean-atmosphere interface, they transport heat both horizontally and vertically, and they were shown to alter projections of global climate in a warming world.
The operational high-resolution 9km forecasts at ECMWF include an ocean that applies a ¼ degree resolution (about 25km at the equator). While many coupled effects such as the atmospheric and oceanographic interaction during tropical cyclone conditions (Mogensen et al. 2017) can be realistically simulated at this resolution, ocean eddies are still only ‘permitted’ in mid-latitudes compared to the even coarser 1 degree standard-resolution climate models. This is far from the goal to explicitly resolve mesoscale ocean eddies all around the globe and is a potential source of many long-standing biases in climate models. Importantly, mesoscale features can also affect the predictability of European weather downstream of the Gulf Stream area.
A recent development for the IFS Cycle 2 simulations is a nextGEMS grid (NG5) for FESOM2, which was designed to be of equivalent size to the ICON-5km ocean grid with about 7.5 million surface nodes (Figure 1). Making use of the multi-resolution capability of FESOM2, relatively more surface nodes were concentrated in higher latitudes in order to extend the area where eddies are resolved – from the mid-latitudes into higher latitudes. Similar to the initialization strategy in ICON, the ocean grid has been spun up for several years with ERA5 forcing until 20 January 2020, the common starting point of the nextGEMS simulations, before being coupled to IFS.
Early preliminary analyses of Cycle 2 compare rather well to the ERA5 reanalysis, OSI-SAF, and observational data, such as a long 40-day forecast of sea ice concentration evolution (Figure 2). We are looking very much forward to the next Hackathon in Vienna later this month where in-depth analyses from all project partners might reveal new surprises – both in terms of weaknesses but also in terms of novel strengths that only this new generation of climate models can provide.
* Scientist at ECMWF
** Model development at MPI-M
*** Special thanks to all participants of the 1st nextGEMS hackathon
by Tobias Becker* and ECMWF modelling teams**
One of the advantages of having a community of people looking at the behaviour of our models is that it makes us look at old problems in a new light. A good example is the first nextGEMS hackathon in October 2021, at which we found that both of our nextGEMS models, ICON and the Integrated Forecasting System (IFS), do not conserve energy well. The atmospheric energy leakage amounts to 6.6 W m-2 in ICON and 6.3 W m-2 in the IFS, at 4 to 5 km resolution with no parametrisation of deep convection. Analysis quickly showed that most of the energy imbalance in the IFS is related to water non-conservation, and that this issue gets worse when spatial resolution is increased and when the parametrisation of deep convection is switched off. Figure 1 shows that the nextGEMS Cycle 1 simulations with the IFS have an artificial source of water in the atmosphere, which is responsible for 4.6% and 10.7% of the total precipitation, in the simulation with deep convection parameterisation at 9 km (the configuration used for ECMWF’s operational high resolution ten-day forecasts) and without deep convection parameterisation at 4 km, respectively.
The water non-conservation of the IFS had been known for a long time, given that the departure point interpolation of the Semi-Lagrangian advection scheme used in the IFS is non-conserving. However, while this issue was acknowledged to be detrimental for the accuracy of climate integrations (Roberts et al., 2018), so far it was thought that it does not affect the quality of numerical weather forecasts which span timescales ranging from a few hours to seasons ahead. Further analysis after the hackathon by the modelling teams at ECMWF has shown that about 50% of this artificial atmospheric water source is created as water vapour. The additional water vapour not only affects the radiation energy budget of the atmosphere, but can also cause energy non-conservation when heat is released through condensation. The other 50% of water is created as cloud liquid, cloud ice, rain or snow. The artificial source of water is related to higher-order interpolation in the semi-Lagrangian advection scheme, causing spurious extrema. For the moist species, spurious minima can result in negative values, which are clipped, leaving the spurious maxima to increase condensate mass.
To address the problem of water non-conservation in the IFS, three adjustments were required: a small bug fix in the IFS code, a switch from cubic to linear horizontal interpolation in the advection scheme for cloud liquid, cloud ice, rain and snow, and most importantly, the activation of a global tracer mass fixer for all moist species, including water vapour. Activating tracer mass fixers increases the computational cost of running a simulation, but we succeeded to find an accurate yet cost-effective setup that uses a Finite Differences approach rather than a Finite Elements approach to calculate the vertical integrals for the tracer mass fixers. Note that tracer mass fixers assure global mass conservation, so locally tracer non-conservation is still possible but is expected to have been significantly reduced. Figure 1 shows that with these three model changes, global water non-conservation is essentially eliminated (less than 0.1%) in our new nextGEMS simulations (labelled Cycle1.2), while the global energy budget imbalance has reduced to less than 1 W m-2 (not shown).
Importantly, global water conservation turns out to be beneficial not only for long integrations, but also for the quality of ECMWF’s medium-range weather forecasts. Preliminary results suggest that the model changes performed to fix the water and energy imbalances improve the skill scores of the medium-range weather forecasts for many variables, but most robustly for precipitation. Figure 2 shows that the mean absolute error against rain gauge measurements is about 2-3% smaller in 9 km forecasts that ensure global water conservation compared to the default 9 km forecasts.
* Alexander von Humboldt Fellow at ECMWF
** Special thanks to Thomas Rackow, Xabier Pedruzo, Irina Sandu, Richard Forbes, Michail Diamantakis, Peter Bechtold, Inna Polichtchouk and to the participants of the 1st nextGEMS hackathon
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