– 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).

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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.

References:

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

Nowadays, Machine Learning (ML) can assist in identifying climate models based on daily output. How? Firstly, it is important to understand ML as the process of developing algorithms that enable computers to learn and make predictions or decisions based on data, without being explicitly programmed for each scenario. In that context, ML techniques, such as Convolutional Neural Networks (or CNNs), are increasingly utilized in Climate Science to evaluate climate models; identify model characteristics; and assess model performance in comparison to observational data.

The study “Identifying climate models based on their daily output using machine learning”, by researchers Lukas Brunnel and Sebastian Sippel, shed light on the use of ML classifiers – such as the CNNs mentioned before. Specifically, on how they can be trained to robustly identify climate models using daily temperature output. 

By analyzing individual daily temperature maps, ML methods can separate models from observations and from each other, even in the presence of considerable noise from internal variability on specific weather timescales (Brunnel & Sippel, 2023). Internal variability refers to the fluctuations in the climate system that arise from various processes within the Earth’s atmosphere, oceans, and land surfaces that we might refer to as weather. Hence, the ML approach allows for the identification of models and observations based on short timescales, providing new ways to evaluate and interpret model differences.

Separating models from observations, and from each other

The study used daily temperature maps from 43 Coupled Model Intercomparison Projects (CMIP6) models and four different observational datasets. Additionally, ICON-Sapphire, one of the Earth system models developed by nextGEMS, was utilized as an experimental km-scale model. With that basis, two different statistical and ML methods were used to separate models from observations, and from each other.

Firstly, through logistic regression, the researchers were able to distinguish between models and observations because it allows the appreciation of the learned coefficients (Brunnel & Sippel, 2023). The coefficients learned by the logistic regression classifier reveal that many well-known climatological model biases are already emerging as important for identifying daily maps. Nonetheless, other regions like the Arctic are not relevant for daily classification at all. 

It is important to mention that logistic regression is a linear method and, after bias correction with the mean seasonal cycle, it is no longer skilful. To complement logistic regression, a second methodnamely CNN, specially due to the possibility of obtaining more trainable parameters that can also lean more complex, non-linear relationships within the data. (Brunnel & Sippel, 2023)

Main findings and future directions

Some of the main results of this research work are related to the high accuracy achieved by CNN classifiers in identifying models and observational datasets, even when faced with complex classification tasks. Overall accuracy of 83% was achieved in identifying 43 models and four observational datasets (Brunnel & Sippel, 2023). Moreover, CNNs could pick up unique patterns specific to each dataset, enabling successful separation from other datasets. Generally, it is important to take away that dependencies between models – and observations – emerge even on daily time scales.

On another note, Brunnel and Sippel (2023) clarified that misclassifications often occurred within model families or were related to common “ancestors”, indicating shared features among related datasets. However, the study revealed the ability of the CNN to correctly identify a significant portion of test samples, even those from distant time periods and under different climate scenarios.

The authors anticipate a planned follow-up study that aims to analyze the origin of classification skill in more detail, using explainable ML techniques and domain-specific approaches from Climate Sciences. In other words, the follow-up study will investigate the coupling of atmosphere and ocean; surface energy balance in models; and targeted masking of regions to understand model performance dependencies. 

If you are interested in working with this method, feel free to do so! The researchers have made the code used in the paper freely available on Github.

References:

Brunner, L., & Sippel, S. (2023). Identifying climate models based on their daily output using machine learning. Environmental Data Science, 2. https://doi.org/10.1017/eds.2023.23

Aerosols, defined as tiny particles suspended in the atmosphere, play a pivotal role in the Earth’s climate system. Despite their minuscule size, often smaller than a human hair, these particles exert a significant influence on the planet’s climate. They originate from natural sources, including sea spray, dust storms, and wildfires, as well as human activities, such as industrial emissions and transportation.

How do aerosols impact our climate?

Aerosols affect the climate through both direct and indirect mechanisms. They can absorb or scatter solar radiation, and act as nuclei for cloud droplet formation. These dynamics result in:

– Cooling the Earth’s surface by reflecting solar radiation.

– Warming the atmosphere by absorbing heat.

– Influencing cloud formation, brightness, and lifetime.

While aerosols generally contribute to cooling the Earth, quantifying this effect is a complex scientific challenge due to uncertainties, particularly related to indirect effects.

Challenges in Aerosol Modeling

Accurately incorporating aerosol interactions within climate simulations has been a persistent difficulty. There are coarse-scale climate simulations with interactive aerosol models, but these models are very expensive. Moreover, they are not feasible to be used in km-scale climate simulations. Therefore, nowadays aerosols in km-scale climate simulations are not interactive but prescribed based on historical data. This approach fails to account for real-time atmospheric processes, such as the deposition of aerosols by winds and precipitation. In other words, this appeal overlooks critical dynamics.

Interactive aerosols in High-Resolution Climate Models

New high-resolution climate models, like those developed in the nextGEMS project, are addressing the limitations mentioned above. These models resolve essential processes in the Earth’s system down to a few kilometers, enabling detailed simulations of phenomena such as thunderstorms and tropical cyclones.

nextGEMS aims to integrate aerosols interactively within these advanced climate models. The process begins with a complex aerosol module, which operates at coarse resolutions. Scientists then simplify the micro-physical aerosol processes, before coupling the simplified version with the new climate model.

This research has produced a streamlined and efficient model, making the aerosol simulations more accurate and usable. The model’s design facilitates understanding and adaptation for other researchers, enabling detailed simulations of long-term processes on a global scale. It now includes intricate processes, such as the transport of aerosols by winds, cloud formation, precipitation, and the scattering or absorption of solar radiation.

Practical Implications and Future Research

The nextGEMS model provides a groundbreaking tool for examining specific events or broader phenomena related to aerosol movement through the Earth system. It improves our understanding of aerosol-cloud-radiation interactions and helps quantify aerosols‘ cooling effects more precisely. This advancement is crucial for better predicting both short-term weather events and long-term climate trends.

Practical applications of this model include investigating how future wildfires across regions like the Congo and the Amazon could affect local cloud formation and precipitation, or assessing the potential damage tropical cyclones may cause to coastal areas of Japan and Florida. Additionally, simulation with interactive aerosols could help us to better understand the radiative forcing and therefore better estimate by how much aerosols actually cool the Earth.

Future research will focus on tracking phenomena over time with high-resolution data and conducting regional studies in areas with unique characteristics or significant events. Furthermore, it will allow the performing of long-term simulations to explore different emissions scenarios. Collaboration with other projects and scientists will continuously refine the model, fostering interdisciplinary research within and beyond the nextGEMS initiative.

Understanding aerosols and their interactions with our climate, we magnify our ability to predict and mitigate the impacts of Climate Change, contributing to a sustainable future for our planet.

Rainfall patterns around the tropics, the regions encircling the Earth’s equator, are crucial for understanding global water and energy cycles. These tropical rain belts migrate seasonally, following the sun’s path north and south. Scientists have long struggled to accurately model these complex dynamics using traditional climate models.

The study, „Learning by Doing: Seasonal and Diurnal Features of Tropical Precipitation in a Global-Coupled Storm-Resolving Model“ by Hans Segura and colleagues, offers a breakthrough. Their research utilizes the new generation of high-resolution simulations that nextGEMS is developing, which incorporate both atmospheric and oceanic interactions. This level of detail allows for the explicit representation of convection (the process by which warm, moist air rises and cools, forming clouds and precipitation) and mesoscale ocean eddies (large, swirling currents).

In this research video, produced by Latest Thinking, researcher Hans Segura highlights the promising results. On one hand, the simulations accurately capture the seasonal migration of the rainbelt over land, including its movement north and south, east and west, and expansion during summer. This is particularly true for the eastern Pacific and Atlantic regions. However, Segura clarifies that the model struggles to replicate these patterns over the Eastern Hemisphere’s oceans. The researchers suggest this discrepancy might be due to limitations in representing sea surface temperature patterns in these areas. In that sense, Segura points out that the model needs to be further developed, in addition to theoretical work and observations to understand the mechanisms influencing sea surface temperature.

Hans Segura is currently pursuing post doctoral research in the Climate Physics department of the Max Planck Institute for Meteorology. Previously, he completed his doctorate at Université Grenoble Alpes  and conducted research at the Geophysics Institute of Peru. His research interests include precipitation-convection, clouds, and tropical climatology. 

– A keynote by Sarah Kang

In the spirit of furthering collaboration and exchange, the program of the 4th km-scale hackathon hosted at the Max-Planck-Institute for Meteorology (MPI-M) earlier this year centered on intensifying interactions within and between different working groups. To expand upon these critical periods of mutual exchange, several keynote speeches were presented on overarching topics that string together the diverse themes convoluted in nextGEMS, WarmWorld, and EERIE. On Wednesday – the halfway point of the hackathon – the MPI-M’s newest member on the board of directors, Sarah Kang, gave an insightful talk, providing an outlook on her prospective work and its links to ongoing research at the institute.

From the outset, Sarah Kang noted that the research underpinning her talk, “Possible shifting mechanism for tropical Pacific surface warming pattern”, was motivated by a “passion for understanding why things do what they do and how they work”. With this indicative motto in mind, Kang’s presentation indexed the central problem tackled by her research: the discrepancy between model and observed sea surface temperatures in the Southern Pacific Ocean (SPO). This divergence was highlighted by contrasting simulations based on observational records with different model projections from 1979-2014. However, since the large observed trends cannot be explained by natural variability alone, there may be an external factor influencing the temperature of the SPO that is presently not captured by the applied models – What are the mechanisms underlying this forced response?

The real difficulty in identifying such mechanisms lies in isolating them from the many convoluted processes that induce warming and cooling patterns over the greater Pacific area. One factor impacting the cooling in the South Pacific is the relative warming trend in the Indian and Atlantic Oceans. For the former, this teleconnection can be explained by a strengthening of the so-called trade winds, the prevailing easterly winds at the equator, caused by a warming of the Indian Ocean. Another process tthought to influence the South Pacific cooling is a decrease in sea surface temperature (SST) in the Southern Ocean (SO). Here, increasing amounts of Antarctic meltwater impact the internal variability associated with deep ocean convection, thereby amplify a milder cooling effect associated natural variability. This latter point may offer an inroad to study the model-observation discrepancy as commonly used GCMs do not represent the SO cooling.

The experimental setup to test these links between the SO and the Southern Pacific is based on this insight. By using historical simulations from CMIP5 and CMIP6 runs, and comparing them to simulation scenarios adapted by Sarah Kang and her collaborators, the team could discern a substantial difference in outcome. In the latter simulations runs, termed Southern Ocean Pacemaker (SOPACE) simulations, historical radiative forcing was included and the sea surface temperature anomalies poleward of 40°S were restored to observations from 1970-2014. While global warming trends again dominated historical simulations, the SOPACE runs clearly linked the SO SST declines to the Southern Pacific cooling. 

Although the experiments have detected SO SST as an external forcing of Southern Pacific cooling, trends in multi-decadal cooling are expected to be transient features that eventually subside into the dominant global warming trend. However, open questions remain attached to this line of research, particularly about the role of mesoscale processes in modulating the mechanisms at fast time scales. Therefore, one of the next steps in bringing this research to the MPI-M will be to adopt the globally coupled version of ICON (5km ocean and 10km atmosphere) in experimental setups.