Developing the next-generation climate system models: challenges and achievements

By J. Slingo, K. Bates, Nikos Nikiforakis, Matthew D. Piggott, M. Roberts, L. Shaffrey, I. Stevens, P. Vidale, Hilary Weller. 2009

75 citations recorded by the source corpus.

Although climate models have been improving in accuracy and efficiency over the past few decades, it now seems that these incremental improvements may be slowing. As tera/petascale computing becomes massively parallel, our legacy codes are less suitable, and even with the increased resolution that we are now beginning to use, these models cannot represent the multiscale nature of the climate system. This paper argues that it may be time to reconsider the use of adaptive mesh refinement for weather and climate forecasting in order to achieve good scaling and representation of the wide range of spatial scales in the atmosphere and ocean. Furthermore, the challenge of introducing living organisms and human responses into climate system models is only just beginning to be tackled. We do not yet have a clear framework in which to approach the problem, but it is likely to cover such a huge number of different scales and processes that radically different methods may have to be considered. The challenges of multiscale modelling and petascale computing provide an opportunity to consider a fresh approach to numerical modelling of the climate (or Earth) system, which takes advantage of the computational fluid dynamics developments in other fields and brings new perspectives on how to incorporate Earth system processes. This paper reviews some of the current issues in climate (and, by implication, Earth) system modelling, and asks the question whether a new generation of models is needed to tackle these problems.

Subfields: Atmospheric Science, Global and Planetary Change

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Citations in the corpus

Control functionals for Monte Carlo integration (2014, methodology): “Statistical methods are increasingly being employed to analyse complex models of physical phenomena (e.g. in climate forecasting or simulations of molecular dynamics; Slingo et al., 2009; Angelikopoulos et al., 2012).”
Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate (2015, background): “From this development it has been argued that radically new approaches are required to significantly advance the field of climate modeling and prediction (Shukla et al. 2009; Slingo et al. 2009).”
Horizontal grids for global weather and climate prediction models: a review (2012, background): “Although very attractive in principle, in practice there are problems with this approach, principally due to spurious dispersion issues; Slingo et al. (2009) provide a recent review of this area of research.”
The effect of climate change and adaptation policy on agricultural production in Eastern Africa (2016, background): “Rain-fed agriculture accounts for more than 95% of the cultivated area (FAOSTAT 2005) making agriculture in the region highly dependent on climatic conditions (Slingo et al. 2009).”
A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, methodology): “Multiscale models using adaptive mesh refinement (i.e., dynamic local refinement of the grid) might also have considerable potential to improve the representation of deep convection at a reasonable cost [Slingo et al., 2009].”
Physics-informed machine learning: case studies for weather and climate modelling (2021, background): “…accurate weather and climate models is the ability to characterize accurately the multi-scale nature of these systems, in which small-scale, high-frequency (hours to days) variations play a key role in determining the large-scale, low-frequency (months to years) evolution of the system [48].”