A new approach to modelling ecosystems
Earth system models have become increasingly complicated over recent years, yet they aren’t improving in terms of their ability to predict changes to ecosystems and their wider effects, so a re-think is required. Researchers in the REALM project are looking again at modelling practice as they work to develop a new kind of vegetation model, as Professor Colin Prentice explains.
Early land biosphere models were developed essentially as a proof-of-concept, to demonstrate that it was possible to make dynamic models of interactions between vegetation and the climate which would help build a deeper understanding of how climate and ecosystems were likely to evolve. While Earth System models have become increasingly complicated over recent years, they aren’t improving in terms of their ability to predict changes to ecosystems and their carbon exchanges. “The models are not actually getting any better,” says Professor Colin Prentice, Chair in Biosphere and Climate Impacts at Imperial College London. This is a major motivating factor behind the work of the REALM project, an initiative in which Professor Prentice and his colleagues are looking again at some fundamental aspects of modelling practice as they work to develop a new vegetation model. “It’s necessary to go back to the drawing board,” he acknowledges.
Earth system models
Current vegetation models all model the processes of photosynthesis, respiration, and transpiration, but there are marked differences in the level of detail that the models include. “The level of hydrological detail varies a lot between models for example, while the extent to which they deal with nutrient interactions also varies. More worryingly, some processes are represented in models in ways that don’t correspond to what is going on in the real world,” outlines Professor Prentice. The wider aim in the project is to address this issue and lay the foundations for improved ecosystem models, with researchers synthesising and analysing data gathered from many different sources.
“There’s plenty of work to be done in terms of analysis,” continues Professor Prentice. “Most of the literature about plant traits begins and ends with purely statistical analysis, with no underlying theoretical basis.”
The project team is taking a different approach, developing ideas about what may be occurring, on the basis of eco-evolutionary optimality (EEO) hypotheses that make explicit, quantitative predictions that can then be tested against observed data. Large volumes of observational data are now available, which are the focus of a lot of attention in the project. “We spend a lot of time compiling site data, associating climate with each site, and analysing how various traits vary with climate and soil properties,” explains Professor Prentice. EEO hypotheses are essentially about trade-offs, and the process by which a particular property of a plant is optimised through natural selection. “For example we’ve done a lot of work on the control of photosynthetic capacity,” says Professor Prentice. “There is a common misconception that the photosynthetic capacity of leaves is controlled by nutrient availability. It isn’t, rather it’s optimised to the environment.”
A plant species living in a given habitat whose leaves had suboptimal photosynthetic capacity would soon be displaced. On the other hand if its leaves had superoptimal capacity then the plant would be wasting resources on maintenance, which has a cost in terms of respiration. This capacity is optimised on different timescales, both for the immediate circumstances and also the longer term, while there are also many other traits to consider. “Leaf thickness for example has a strong phylogenetic component. One family will typically have thick leaves and another will have thin leaves. Optimisation is then mainly done by selection among species, rather than adjustment within a species,” says Professor Prentice. “Some traits can change relatively fast, others are pretty much hardwired for the species. Nevertheless, you see an optimal outcome, because there is environmental selection and competition.”
well as – or even better than – far more complex models. Our approach is far simpler and more transparent,” he continues.
The core model developed in the project takes in satellite data on green vegetation cover and uses that as one of the main drivers, alongside climate data. This basic structure has been around for a long time; what the project has provided is a strong theoretical basis. “That also means we have one equation for gross primary production that is applicable across all vegetation types,” explains Professor Prentice. The results of the model can then be compared with data from other sources, such as the halfhourly measurements of CO2 exchange from eddy covariance flux towers, which provide a strong test. “The flux data, satellite data and meteorological data are all independent. So if we can reproduce the patterns that we see from the flux towers, then we are doing the right thing” he outlines.
REALM
Reinventing Ecosystem And Landsurface Models
Project Objectives
The REALM (Reinventing Earth And Landsurface Models) applies eco-evolutionary optimality concepts to develop and test new quantitative theory for plant and ecosystem function and land-atmosphere exchanges of energy, water and carbon dioxide, with the goal of more robust and reliable numerical modelling of land processes in the Earth System.
Project Funding
REALM was awarded 5-year European Research Council (ERC) grant under the European Union’s Horizon 2020 research and innovation programme (grant agreement No: 787203 REALM).
Laboratory Members https://prenticeclimategroup.wordpress. com/lab-members/
Contact Details
Project Coordinator, Iain Colin Prentice FRS Chair of Biosphere and Climate Impacts and Director, Leverhulme Centre for Wildfires, Environment and Society Imperial College London E: c.prentice@imperial.ac.uk : @LabPrentice W: https://prenticeclimategroup.wordpress.com/
Vegetation cover
Researchers are tapping into different sources of data to test and refine predictions on multiple traits and processes in plants. One important source of data is satellite imagery going back several decades, which provides abundant data on green vegetation cover and its trends. For example satellite data from the Tibetan plateau show that some regions have responded differently to environmental change.
“A greening of vegetation has been observed over much of the plateau, but we’ve also seen a browning in other regions,” outlines Professor Prentice. Two separate equations to predict green vegetation cover have been developed in the project; which one applies depends on whether or not water supply is limiting. On this basis Professor Prentice, collaborating with colleagues in China, has been able to reproduce regional and global patterns of greening and browning as seen from space. “We can do it as
This research holds wider implications, with Professor Prentice looking to integrate these insights into land-surface models that form part of Earth System models. Formal collaborations have been established with the European Centre for Medium Range Weather Forecasts (ECMWF) and the Met Office. “We are looking to try out these ideas in the context of weather and climate models. Ultimately we want to improve the accuracy of predictions, at least about the climate near the land surface,” he says. While existing models may not be able to forecast the exact nature of the weather on a particular day, they are pretty accurate on the seasonal level, which has enormous economic value. “The ECMWF model is fairly effective in predicting what the weather will be like in May this year for example, which is important for farmers,” points out Professor Prentice.
Dong, N., Wright, I.J., Chen, J.M., Luo, X., Wang, H., Keenan, T.F., Smith, N.G. and Prentice, I.C. 2022. Rising CO2 and warming reduce global canopy demand for nitrogen. New Phytologist, https://doi.org/10.1111/nph.18076
Mengoli, G., Agustí-Panareda, A., Boussetta, S., Harrison, S.P., Trotta, C, Prentice, I.C. (2021). Ecosystem photosynthesis in land-surface models: a first-principles approach. Journal of Advances in Modelling Earth Systems, https://doi.org/10.1029/2021MS002767
Professor Iain Colin Prentice, FRS is a renowned Earth System scientist in the Department of Life Science at Imperial College London. He has made significant contributions to the field, including the first global biome model and the widely used LPJ model. He has held prestigious positions at research institutions worldwide.
Most of the literature about plant traits begins and ends with purely statistical analysis, with no underlying theoretical basis, but we’re taking a different approach.Photo taken at the Guyaflux Tower in French Guyana, Brazil. © Dr Keith Bloomfield Professor Iain Colin Prentice, FRS Photo taken at the Guyaflux Tower in French Guyana, Brazil. © Dr Keith Bloomfield Professor Prentice’s team of researchers and postgraduate students.