– Junhong Lee, and Cathy Hohenegger

To date, numerous studies have explored the connection between water stored in the soil, also called soil moisture, and precipitation. Using coarse-resolution global climate models, these studies have consistently found a positive feedback between soil moisture and precipitation. In other words, wet soils favor rain. And as rain itself wets the soil, a positive feedback between rain and soil moisture is maintained: soil moisture matters!

In a pioneer study, Hohenegger et al. (2009), looked for the first time at this same soil moisture-precipitation feedback in km-scale simulations conducted over the Alpine region and found the opposite result: dry soils favor rain. Hohenegger et al. delved into the reasons behind these conflicting findings and related them to how different models represent convection. Coarse-resolution models rely on simplified statistical representations, known as parametrization, to describe convective processes. In contrast, kilometer-scale models represent convection explicitly by solving the underlying fluid dynamical equation (a mathematical description that explain how liquids and gases move and behave). Yet, due to computational constraints, the study of Hohenegger et al. (2009) could only simulate the climate over a small region. It could not be excluded that the lateral boundary conditions (LBCs) required at the border of that region, which are taken from a coarse-resolution global model, may spuriously affect the sign of the soil moisture-precipitation feedback (What are LBCs? Read: Davies, 2013).

Correlation coefficient between SMI and subsequent 9-d mean precipitation for (A) the 60-y mean of the coarse-resolution model (LR) and (B) the SRM. Gray areas as in Fig. 1.

Building on these insights, Lee and Hohenegger, in their 2024 study, sought to overcome the limitations of Hohenegger et al. (2009) and following studies. They employed a global, coupled climate model with explicit convection and a 5km resolution. Using the storm-resolving version of the ICON model allowed Lee and Hohenegger to represent the feedback more accurately than coarse-resolution models by representing convection explicitly, while allowing the large-scale circulation to freely evolve and interact with convection by using a global domain (a simulation over the full Earth). Their remarkable findings suggest that precipitation is less influenced by soil moisture and evapotranspiration (the combined effect of evaporation, the transport of moisture from the earth surface directly to the air, and transpiration, the transport of moisture from the soil to the air via plants) than coarse-resolution climate models have led us to believe.

The study revealed several key points:

  1. The feedback between soil moisture and precipitation is weaker and more negative than coarse-resolution models suggest. The presence of a negative feedback indicates that drier soils are favorable for precipitation and that soil moisture is not so important for precipitation.
  2. The feedback between evapotranspiration and precipitation is also weaker. In contrast to the coarse-resolution model, precipitation does not consistently increase with evapotranspiration in the storm-resolving model.

When compared with observational data, the global, coupled storm-resolving model provided more accurate representations of the strength of the correlation between soil moisture and precipitation for over 80% of the locations, suggesting that this type of model may be better suited for global precipitation modeling.

These findings indicate that coarse-resolution climate models may overestimate the role of land cover change and of the land surface in general for precipitation. They challenge our understanding of climate over land and may indicate that precipitation patterns may be more robust than previously thought.

For more on this topic, find the entire publication here.


Hohenegger, C., Brockhaus, P., Bretherton, C. S., & Schär, C. (2009). The Soil Moisture–Precipitation Feedback in Simulations with Explicit and Parameterized Convection. Journal of Climate, 22(19), 5003-5020. DOI: 10.1175/2009JCLI2604.1

Lee, J. & Hohenegger, C. (2024). Weaker land–atmosphere coupling in global storm-resolving simulation. Proceedings of the National Academy of Sciences (PNAS), 21(12). DOI: 10.1073/pnas.2314265121

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.


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