Ever wondered how human-induced climate change can influence km-scale whirls in the ocean, so-called ocean eddies? The study by Beech et al. (2022) about long-term evolution of ocean eddy activity in a warming world, delves into this question by investigating the response of ocean eddy activity to anthropogenic climate change through climate modeling. Here is a summary of the key messages.

Specifically, the research employs climate change projections to analyze the variability and long-term trends of ocean currents and eddy kinetic energy (EKE) in different regions. By examining the representation of EKE in a climate model and comparing it with observed EKE from satellite altimetry data, the study aims to improve our understanding of ocean eddies – the weather of the ocean – and how they change in a warming planet.

This research field is crucial in the context of climate change because eddies are known to subsequently impact ocean systems through ventilation, volume transport, carbon sequestration and heat, and nutrient transport.

About the model

The study uses the AWI-CM-1-1-MR climate model. This model is used as part of the CMIP6 (Coupled Model Intercomparison Project Phase 6) ensemble, and it has been developed to simulate the Earth’s climate system. This model is also used in nextGEMS and one of its unique features is its variable-resolution ocean grid, which allows for a more accurate representation of eddy activity in the world’s oceans by employing enhanced resolution in dynamically active regions. The ocean component also utilizes a highly scalable and dynamical core, in addition to an unstructured mesh. This enables it to overcome the computational challenges associated with simulating long time series at sufficient resolutions needed to represent eddies.

How does anthropogenic climate change affect ocean eddy activity?

The study projects several shifts on ocean eddy activity due to anthropogenic climate change impacts, resulting in implications for various oceanic processes and circulation patterns. For instance, EKE is expected to shift poleward in most eddy-rich regions, whereas it is expected to intensify in the Kuroshio Current, Brazil and Malvinas currents, and Antarctic Circumpolar Current. Conversely, the Gulf Stream is projected to experience a decrease in EKE, which is due to a decline of the Atlantic Meridional Overturning Circulation (AMOC). Overall, these projections of EKE in the world’s oceans show pronounced transitions on a global scale. Furthermore, these changes are linked to broader climate elements such as the decline of the AMOC; the intensification of Agulhas leakage, and the shifting Southern Hemisphere westerlies.

The study shows that it is difficult to conclude something so robust from relatively short satellite time series. Additionally, the authors highlight it will be important to revisit the results of the study with truly eddy-rich models, like the ones employed in nextGEMS.

References:

Beech, N., Rackow, T., Semmler, T., Danilov, S., Wang, Q., & Jung, T. (2022). Long-term evolution of ocean eddy activity in a warming world. Nature Climate Change, 12, 910-917. https://doi.org/10.1038/s41558-022-01478-3

nextGEMS is working on high-resolution simulations (see post) and part of the projects’ goals is to make this novel science adequate and accessible for society, with the aim to reduce the climate action gap. To achieve this, nextGEMS is working around two main societal challenges: the uptake of renewable energy in national decarbonisation processes and the sustainability of fisheries and marine ecosystems. In this article we discuss our work on energy, and how we are creating links with the energy community using nextGEMS data outputs as our basis.

Diverse Perspectives: Envisioning Energy Transitions

The world’s shift towards decarbonization is intensifying, driven by ambitious goals from supranational initiatives such as in the context of the European Union. Due to its complexity, there is a rich societal debate about the best pathways towards decarbonization, and especially the role of renewable energies in it. One of the sources of uncertainty on how these different pathways might unfold is given by the evolution of our climate, which interplays with policy decisions by amplifying or reducing the impact they are expected to have.

There are several research methods available to scholars which allow for taking a plurality of perspectives into account, as well as the evolution of our climate conjointly, and one of these is scenario modelling. In NextGEMS, we have implemented this methodology to study the interlinkage between climate, energy and socio-political dimensions using as case study the national integrated energy and climate plans, which is a policy present across EU Member States.

Engaging with a diversity of stakeholders

Given the diversity of the actors who directly or indirectly intervene in the policy process and the plurality of perspectives and interests they might bring in, scholars are incredibly using participatory approaches to avoid limiting the range of futures we envisage. Renewables‘ pivotal role in the energy transition equally demands for a co-production approach, as the best form to ensure that all voices are heard. This method starts by constructing together ‘storylines’, qualitative descriptions of our collective future, and thus considering multifaceted narratives that might include societal concerns, biodiversity considerations, land use competition, and democratic decision-making in the construction of decarbonization pathways.

Spain’s Case Study: A Closer Look at Energy Transition Challenges

In the context of the cycle 3 hackathon held at Universidad Complutense de Madrid (see news archive), we organised a stakeholder workshop focused on the energy sector in Spain. Employing a co-production framework, the workshop aimed at generating storylines that, later on, would be used for scenario modelling. Preparatory steps involved engagements with energy experts, brainstorming sessions on renewable energy optimization, and systematic literature review to identify key discourses. Stakeholder mapping and interviews with 50 individuals shaped the workshop’s direction, culminating in 22 participants contributing to the co-production of storylines.

The workshop revolved around three broad storylines—actual, integrative, and distributive: playing with the current system, integrating biodiversity concerns and adding in the role of energy communities. Participants engaged in discussions, challenging and enhancing these storylines, evaluating their pros and cons within the context of their own positions -being these the public, private, third and academic sectors. For 2.5h, stakeholders deliberated around these aspects and proposed forms to evaluate the storylines as well as for their operationalization (see more information about the workshop here).

NextGEMS is working now in this operationalisation, so stay tuned for the results of this study!

Post by Eulàlia Baulenas (BSC)

by Thorsten Mauritsen, MISU

Model performance in the renewable energy sector

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.

Expert interacting with Hackathon participant.

Findings during the Cycle 2 Hackathon

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.

Figure 1. Dependency of power output to wind speed.
Figure 2. Power output for four different stations.
Figure 3. Bias of model performance compared to observations. Y-axis shows the deviation from the observation.

Model Performance

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).

Solar Power generation

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.)

Figure 4. Model performance compared to observations throughout the year for Cabauw, NL.

Figure 5. Left panel shows a map from ICON of the solar energy reaching the surface in kW hours per year. To the right the maximum which can be extracted with such panels. We see that although there is a lot of radiation available in the sub-tropics, e.g. the Sahara, much of this advantage is counteracted by the warm temperatures.

Challenges for the renewable energy industry

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.

For specified locations on Earth, it is now possible to output variables, for instance temperature or radiation or wind, at a very high frequency, a frequency which can be as high as the model time-step (in the order of seconds to a few minutes). As a comparison, over the full Earth, two­ dimensional variables are only outputted every 30 minutes due to the high storage requirement. This high-frequency data logging is needed for model evaluation activities and for the nextGEMS pilot project on solar power. Variables, location and sampling frequency can be chosen according to the user needs, as communicated to the modeling centers. After discussion with scientists and stakeholders, 30 locations were chosen for the high frequency data logging. These locations correspond to sites with high quality observations, with a renewable energy park or locations of previous field campaigns.

The table below shows a list of high-frequency logging site. More information can be found in the Deliverable 8.3.

Latitude [°]Longitude [°]Description
13.2-59.4EUREC4A 01
13.613-56.7529EUREC4A 02
14.1196-57.121EUREC4A 03
14.3131-57.7165EUREC4A 04
14.1196-58.312EUREC4A 05
13.613-58.6801EUREC4A 06
12.9869-58.6801EUREC4A 07
12.4803-58.312EUREC4A 08
12.2868-57.7165EUREC4A 09
12.4803-57.121EUREC4A 10
12.9869-56.7529EUREC4A 11
52.1714.12Lindenberg
51.974.93Cabauw
67.3726.63Sodankyla Finland
36.61-97.49ARMS OKL
71.17-156.48Barrow Alaska
72.58-38.48Summit Greenland
-74.99122.96Dome C Antarctica
46.8136.942Payerne Switzerland
-2.145933-59.005583ATTO
-15.775435-43.466896IBER Minas Gerais/rad
-10.309269-41.31803IBER Bahia/wind
22.466893-100.777205IBER San Luis Potosi Mex/wind
22.087895-101.602993IBER Zacatecas Mex/rad
55.305482-4.088157IBER Scotland/wind
42.304491-4.014777IBER Burgos/wind
37.571131-7.208683IBER Huelva/rad
84.591414.7372MOSAIC Polarstern in spring (warm spells)
88.885996.0429MOSAIC Polarstern in autumn (rain event)
85.585213.248MOSAIC Maximal ice production event in March

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.

Figure 1: Ocean resolution used in the IFS-FESOM simulations for Cycle 2. The grid points of the NG5 configuration are concentrated in higher latitudes in order to resolve ocean eddies over larger areas of the globe compared to a more homogeneous distribution of grid points.

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.

Figure 2: 40-day forecast of sea ice concentration in the coupled IFS-FESOM Cycle 2 simulation with 2.5km IFS and FESOM2-NG5 (left), compared to data from OSI SAF (right). (figure kindly provided by Lorenzo Zampieri, NCAR, https://ncar.ucar.edu)

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* 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.

Figure 1: Daily mean water non-conservation in the IFS, computed as the difference between evaporation and precipitation, subtracted from the time derivative of total column water, as a fraction of precipitation. Results are shown as a function of lead time for NextGEMS Cycle 1 and Cycle 1.2 simulations, with and without deep convection parameterisation, at 9 km and 4 km, started on January 20, 2020.

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.

Figure 2: Scaled differences between forecasts with and without global water conservation with respect to the mean absolute error in precipitation against rain gauge measurements over the Northern Hemisphere, as a function of lead time. Forecasts are run at 9 km resolution for summer 2020 and winter 2021.

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* 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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