Why do weather and climate models get the Indian Ocean wrong?

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Scientific backgroundThe Indian Ocean is a key component of global climate, surrounded by monsoon systems on which billions of people depend, and warming faster than any other ocean basin. However, state-of-the-art climate models fail to accurately capture the dynamical and thermodynamical processes that govern climatic variability around the Indian Ocean. The UK Met Office has identified model errors and biases in this region to be a significant problem for making seasonal climate forecasts, yet little is known about the source of these errors or how they could be reduced. Research methodologyYou will identify the key processes that generate errors in the Met Office weather and climate models to identify potential model improvements. Initially you will compute the ocean surface mixed layer heat budget, which controls variability in sea-surface temperature and atmosphere-ocean interaction, and compare this budget against observations to identify errors. You will then extend this work to evaluate model experiments where the atmosphere and ocean are "nudged" towards observed values, to identify the role of different regions and components of the climate system in generating model errors and biases. Finally you will run short sensitivity studies to identify optimal model setups and pathways for future development.Aims and objectives1. Analysis of a mixed layer heat budget in seasonal forecasts of the Met Office Unified Model, following the methodology of Graham & Vellinga (2013), to separate the role of surface heat fluxes, horizontal and vertical advection, vertical mixing, vertical diffusion and mixed layer dynamics. This step enables the drivers of SST variability and biases to be identified and referenced against a range of in-situ observations, including atmosphere and ocean observations from moorings and publicly available data from several recent intensive field campaigns (e.g., Vijith et al., 2020). The student will quantify the extent to which erroneous representation of contributors to SST variability may contribute to the overall SST bias. 2. Analysis of nudged runs, where the atmosphere or ocean is nudged towards observed values in certain region (e.g., Rodriguez et al., 2017). This comparison will reveal the role of biases in the ocean dynamics in generating SST biases. Comparison of the dynamics with the freely coupled model will be used to reveal regions where there may be high sensitivity to ocean processes. Dynamical links between regions can be further investigated using regional ocean nudging over a set of locations to evaluate the remote impacts of local ocean biases. 3. Comparison of various modelling configurations to identify pathways for future model development. It is expected that the new GC5 coupled model configuration will be available for comparison with the earlier GC3 using existing simulations. The new OSMOSIS mixed-layer scheme (described in Damerell et al., 2020) is expected to be available during the project, which will provide an opportunity to evaluate its performance in the Indian Ocean. The student may also run short sensitivity studies in which parameters in either the existing (TKE) mixing scheme or OSMOSIS can be varied to identify potential avenues for further model development.