Gregory Munday: Publications

Multi-century sea-level projections and human impacts under the indicative ScenarioMIP-CMIP7 forcings

(2026)

Authors:

Jennifer Weeks, Gregory Munday, Norman Julius Steinert, Hemant Khatri, Matthew Palmer, Laila Gohar, Rachel Perks

Abstract:

Projections of future sea-level rise are critical for informing adaptation planning and risk assessments. However, physical modelling frameworks, due to significant computational requirements, lack the flexibility required for rapid analysis and exploration of the latest global emission scenarios. Data-driven and statistical sea-level emulators can fill this requirement and continue to calibrate themselves to the latest large physical modelling experiments, such as ISMIP, and literature evidence which is published in slower-time - while providing new insights derived from observational constraints and combinations of multiple lines of evidence. Here, we present multi-century sea-level projections using an enhanced version of the ProFSea sea-level emulator tool, and quantify human exposure under high and low-likelihood ice-sheet processes. Due to the flexibility, performance and probabilistic structure of the model, we can explore a large suite of scenarios as well as their observationally constrained counterparts, identify dominant sources of model and process uncertainty, and go further in our analysis to determine human-relevant impacts for vulnerable regions around the world. In addition, we push the emulator out-of-sample to explore its behaviour under idealised very-high emission and overshoot scenarios - a potentially critical limitation of non-physically based emulators.

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A learned surface roughness scheme for climate prediction

(2026)

Authors:

Gregory Munday, Milan Klöwer, Laura Mansfield, Maximilian Gelbrecht

Abstract:

In weather and climate models, momentum, heat, humidity and tracer fluxes between the Earth’s surface and atmosphere strongly depend on surface roughness. The roughness length depends on space and time-dependent surface properties over ocean, sea-ice and land. For example, surface winds impact wave height over sea-ice free oceans; vegetation and orography determine roughness length over land, where its effect on near-surface turbulence strongly impacts the surface fluxes. Here, we present a set of machine learning models trained on reanalysis data to predict surface roughness over both land and ocean grid cells in SpeedyWeather, a Julia-based climate model. More accurately representing the surface roughness has been shown to significantly improve model bias against observations over a range of variables such as surface air temperatures and near-surface wind speed. We explore the downstream impacts of using this parameterisation in the climate model, and test the generalisability of an offline-learned surface roughness scheme in future climates with reduced sea ice and land-use change. Spatial generalisation is achieved through surface roughness being a function of local variables only. We discuss efficient inference on CPU and GPU for every grid cell on each integration time-step. So-called model distillation via symbolic regression minimises the trade-off between speed versus accuracy, enabling another route to rapid inference on a grid-cell basis. Further, we investigate online learning through differentiable physics parameterisations to calibrate the learned parameterisation to surface variables from ERA5 reanalysis. We generally propose machine-learned schemes of individual climate processes towards interpretable, data-driven climate modelling.

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Assessing emulated multi-century global mean sea level projections - the Sea Level Emulator Intercomparison Project (SLEIP)

(2026)

Authors:

Alexander Nauels, Tessa Möller, Victor Couplet, Robert E Kopp, Praveen Kumar, Matthias Mengel, Gregory Munday, Zebedee RJ Nicholls, Matthew D Palmer, Lennart Ramme, Aimée BA Slangen, Chris Smith, Jennifer H Weeks, Tony E Wong

Abstract:

Simplified sea level modelling approaches are developed to efficiently explore future sea level rise and associated uncertainties. Sea level emulators (SLEs) are mostly calibrated against the responses of process-based complex models, they can be run on multi-century timescales and feed into regionalisation efforts, integrated assessment and coastal risk modelling. Here, we introduce the Sea Level Emulator Intercomparison Project (SLEIP) to systematically assess available sea level emulators and identify future research needs to maximise the utility of this modelling approach. SLEIP covers 13 datasets from the participating models BRICK (with DOECLIM and SNEASY climate forcing), FACTS (7 individual emulator workflows), FRISIA, MAGICC, ProFSea and SURFER. All of the participating SLEs produce projections out to the year 2300 for the main sea level drivers thermal expansion, glacier mass loss, Greenland and Antarctic ice sheet mass loss, and land water storage. Participating SLEs differ in whether and how they account for low-confidence, high-impact processes of poorly known likelihood, such as marine ice-cliff instability (MICI). The SLE components with the largest response range are the Greenland and Antarctic ice sheet, with the Antarctic ice sheet becoming the most uncertain sea level driver in 2300. With identical MAGICC climate forcing input, 2300 median global mean sea level rise estimates range from 0.46 m to 1.71 m (outer 17th-83rd percentile range: 0.32-3.20 m) under very low emissions (SSP1-1.9), 0.67 m to 2.01 m (0.47-3.56 m) under low emissions (SSP1-2.6), 1.64 m to 4.07 m (1.15-10.53 m) under moderate emissions (SSP2-4.5), 2.35 m to 9.33 m (1.68-14.39 m) under high emissions (SSP3-7.0), and 2.44 m to 11.16 m (1.74-15.79 m) under very high emissions (SSP5-8.5), all relative to 1995-2014. SLEIP also allows investigating the sea level response under overshoot. Under the overshoot scenario SSP5-3.4-OS (peak GMT: 2.3 °C, 2100 GMT: 1.9 °C), median projections range from 0.45 m to 0.86 m (0.36-1.31 m) in 2100 and 0.80 m to 2.30 m (0.56-9.82 m) in 2300.

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AMOC tipping slows permafrost carbon release during overshoot

(2025)

Authors:

Norman Julius Steinert, Jörg Schwinger, Eleanor Burke, Biqing Zhu, Thomas Gasser, Gregory Munday, Camilla Mathison, So-Won Park, Hanna Lee

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Risks of unavoidable impacts on forests at 1.5 °C with and without overshoot

Nature Climate Change Springer Science and Business Media LLC 15:6 (2025) 650-655

Authors:

Gregory Munday, Chris D Jones, Norman J Steinert, Camilla Mathison, Eleanor J Burke, Chris Smith, Chris Huntingford, Rebecca M Varney, Andy J Wiltshire

Abstract:

Abstract With global warming heading for 1.5 °C, understanding the risks of exceeding this threshold is increasingly urgent. Impacts on human and natural systems are expected to increase with further warming and some may be irreversible. Yet impacts under policy-relevant stabilization or overshoot pathways have not been well quantified. Here we report the risks of irreversible impacts on forest ecosystems, such as Amazon forest loss and high-latitude woody encroachment, under three scenarios that explore low levels of exceedance and overshoot beyond 1.5 °C. Long-term forest loss is mitigated by reducing global temperatures below 1.5 °C. The proximity of dieback risk thresholds to the bounds of the Paris Agreement global warming levels underscores the need for urgent action to mitigate climate change—and the risks of irreversible loss of an important ecosystem.

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Gregory Munday

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Multi-century sea-level projections and human impacts under the indicative ScenarioMIP-CMIP7 forcings

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Abstract:

A learned surface roughness scheme for climate prediction

Authors:

Abstract:

Assessing emulated multi-century global mean sea level projections - the Sea Level Emulator Intercomparison Project (SLEIP)

Authors:

Abstract:

AMOC tipping slows permafrost carbon release during overshoot

Authors:

Risks of unavoidable impacts on forests at 1.5 °C with and without overshoot

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