Seasonal Arctic sea ice forecasting with probabilistic deep learning

Nature Communications Springer Nature 12:1 (2021) 5124

Authors:

Tom R Andersson, J Scott Hosking, María Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C Jones, Jeremy Wilkinson, Tony Phillips, James Byrne, Steffen Tietsche, Beena Balan Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov, Rod Downie, Emily Shuckburgh

On the Treatment of Soil Water Stress in GCM Simulations of Vegetation Physiology

Frontiers in Environmental Science Frontiers 9 (2021) 689301

Authors:

PL Vidale, G Egea, PC McGuire, M Todt, W Peters, O Müller, B Balan-Sarojini, A Verhoef

Bell's Theorem, Non-Computability and Conformal Cyclic Cosmology: A Top-Down Approach to Quantum Gravity

ArXiv 2108.10902 (2021)

Machine learning emulation of gravity wave drag in numerical weather forecasting

Journal of Advances in Modeling Earth Systems American Geophysical Union 13:7 (2021) e2021MS002477

Authors:

Matthew Chantry, Sam Hatfield, Peter Dueben, Inna Polichtchouk, Tim Palmer

Abstract:

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.

Dynamical mechanisms linking Indian monsoon precipitation and the circumglobal teleconnection

Climate Dynamics Springer 57:9-10 (2021) 2615-2636

Authors:

Jonathan Beverley, Steven Woolnough, Laura Baker, Stephanie Johnson, Antje Weisheimer, Christopher O'Reilly

Abstract:

The circumglobal teleconnection (CGT) is an important mode of circulation variability, with an influence across many parts of the northern hemisphere. Here, we examine the excitation mechanisms of the CGT in the ECMWF seasonal forecast model, and the relationship between the Indian summer monsoon (ISM), the CGT and the extratropical northern hemisphere circulation. Results from relaxation experiments, in which the model is corrected to reanalysis in specific regions, suggest that errors over northwest Europe are more important in inhibiting the model skill at representing the CGT, in addition to northern hemisphere skill more widely, than west-central Asia and the ISM region, although the link between ISM precipitation and the extratropical circulation is weak in all experiments. Thermal forcing experiments in the ECMWF model, in which a heating is applied over India, suggest that the ISM does force an extratropical Rossby wave train, with upper tropospheric anticyclonic anomalies over east Asia, the North Pacific and North America associated with increased ISM heating. However, this eastward-propagating branch of the wave train does not project into Europe, and the response there occurs largely through westward-propagating Rossby waves. Results from barotropic model experiments show a response that is highly consistent with the seasonal forecast model, with similar eastward- and westward-propagating Rossby waves. This westward-propagating response is shown to be important in the downstream reinforcement of the wave train between Asia and North America.