Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

A framework for physically consistent storylines of UK future mean sea level rise - Matthew D. Palmer; Benjamin J. Harrison; Jonathan M. Gregory; Helene T. Hewitt; Jason A. Lowe; Jennifer H. Weeks (Climatic Change, 2024)
We present a framework for developing storylines of UK sea level rise to aid risk communication and coastal adaptation planning. Our approach builds on the UK national climate projections (UKCP18) and maintains the same physically consistent methods that preserve component correlations and traceability between global mean sea level (GMSL) and local relative sea level (RSL). Five example storylines are presented that represent singular trajectories of future sea level rise drawn from the underlying large Monte Carlo simulations. The first three storylines span the total range of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) likely range GMSL projections across the SSP1-2.6 and SSP5-8.5 scenarios. The final two storylines are based upon recent high-end storylines of GMSL presented in AR6 and the recent literature. Our results suggest that even the most optimistic sea level rise outcomes for the UK will require adaptation of up to 1 m of sea level rise for large sections of coastline by 2300. For the storyline most consistent with current international greenhouse gas emissions pledges and a moderate sea level rise response, UK capital cities will experience between about 1 and 2 m of sea level rise by 2300, with continued rise beyond 2300. The storyline based on the upper end of the AR6 likely range sea level projections yields much larger values for UK capital cities that range between about 3 and 4 m at 2300. The two high-end scenarios, which are based on a recent study that showed accelerated sea level rise associated with ice sheet instability feedbacks, lead to sea level rise for UK capital cities at 2300 that range between about 8 m and 17 m. These magnitudes of rise would pose enormous challenges for UK coastal communities and are likely to be beyond the limits of adaptation at some locations.

Beyond the local climate change uplift – The importance of changes in spatial structure on future fluvial flood risk in Great Britain - Paul Sayers; Adam Griffin; Jason Lowe; Dan Bernie; Sam Carr; Alison Kay; Lisa Stewart (Natural Hazards, 2024)
Widespread spatially coherent flood events can cause severe damage and disruption. Climate change has the potential to change the severity and frequency of such events. Despite this, assessment of future fluvial flood risk typically gives little to no consideration to potential changes in the spatial structure of future events. To understand the significance of this gap, climate model simulations are coupled with a national hydrological model to identify event spatially coherent present and future flood events. A statistical Empirical Copula is used to generate a large number of unseen events and linked to a national flood risk simulation model. The research finds that including changes in the spatial structure of flood events materially increases projected changes in risk when compared to conventional approaches based on local uplifts alone; increasing the projected change in Expected Annual Damage across Great Britain by a factor of ~ 1.5. The event-based approach is also shown to provide new insights into the extreme distribution fluvial risk including single event damage, damage seasons, and damage years. The results suggest the 1-in-100-year winter flood may increase from £1.3b to £2.1b, and the 1-in-100 year single event damage may rise from £1.1b today to £1.7b by the 2080s given a 4 °C rise in Global Mean Surface Temperature (assuming current adaptation policies continue and no population growth). Consequently, the findings suggest a much greater emphasis is needed on spatial ‘flood events’ if future risk is to be understood and adaptation responses appropriately framed. Graphical abstract

The influence of future weather pattern changes and projected sea-level rise on coastal flood impacts around the UK - Rachel J. Perks; Dan Bernie; Jason Lowe; Robert Neal (Climatic Change, 2023)
When local extreme water levels surpass defences, the consequences can be devastating. We assess the importance of sea-level rise and future weather pattern changes on UK coastal flood impacts. Historical weather pattern classifications are matched with the observed skew surges and significant wave heights. Coastal-risk weather patterns are then defined as ≥ 1% of events in the distribution exceeding the local warning threshold. We combine this methodology with projections of sea-level rise and weather pattern frequency occurrences, to determine the relative importance of each on future coastal risk. A deep low-pressure system situated to the west of Ireland (WP29) has the highest probability (6.3%) of exceeding Newlyn’s present-day warning threshold; this is projected to increase under climate change to 46.2% by 2050 under RCP2.6. This work found that weather patterns associated with storm surges are increasing and decreasing in frequency; a synoptic situation causing windy conditions in the north of the UK (WP23) will increase by > 40% under RCP8.5 by the end of the century (2079–2090). When combining the impact of sea-level rise and changing frequency of weather patterns, this study found that sea-level rise dominates future coastal risk and is highly linked to the future emission scenarios. The need for successful adaptation, such as coastal defence improvements and early warning systems, will become even more important under the higher emission pathway. The most significant increases in coastal risk are found along the east coast, through the English Channel to the north Devon coastline.

Testing umbrella species and food-web properties of large carnivores in the Rocky Mountains - Robin Steenweg; Mark Hebblewhite; Cole Burton; Jesse Whittington; Nikki Heim; Jason T. Fisher; Andrew Ladle; Winsor Lowe; Tyler Muhly; John Paczkowski; Marco Musiani (Biological Conservation, 2023)

Echofilter: A Deep Learning Segmention Model Improves the Automation, Standardization, and Timeliness for Post-Processing Echosounder Data in Tidal Energy Streams - Scott C. Lowe; Louise P. McGarry; Jessica Douglas; Jason Newport; Sageev Oore; Christopher Whidden; Daniel J. Hasselman (Frontiers in Marine Science, 2022)
Understanding the abundance and distribution of fish in tidal energy streams is important for assessing the risks presented by the introduction of tidal energy devices into the habitat. However, tidal current flows suitable for tidal energy development are often highly turbulent and entrain air into the water, complicating the interpretation of echosounder data. The portion of the water column contaminated by returns from entrained air must be excluded from data used for biological analyses. Application of a single algorithm to identify the depth-of-penetration of entrained air is insufficient for a boundary that is discontinuous, depth-dynamic, porous, and varies with tidal flow speed. Using a case study at a tidal energy demonstration site in the Bay of Fundy, we describe the development and application of deep machine learning models with a U-Net based architecture that produce a pronounced and substantial improvement in the automated detection of the extent to which entrained air has penetrated the water column. Our model, Echofilter, was found to be highly responsive to the dynamic range of turbulence conditions and sensitive to the fine-scale nuances in the boundary position, producing an entrained-air boundary line with an average error of 0.33 m on mobile downfacing and 0.5–1.0 m on stationary upfacing data, less than half that of existing algorithmic solutions. The model’s overall annotations had a high level of agreement with the human segmentation, with an intersection-over-union score of 99% for mobile downfacing recordings and 92–95% for stationary upfacing recordings. This resulted in a 50% reduction in the time required for manual edits when compared to the time required to manually edit the line placement produced by the currently available algorithms. Because of the improved initial automated placement, the implementation of the models permits an increase in the standardization and repeatability of line placement.

Prediction of disability-free survival in healthy older people - Johannes Tobias Neumann; Le T. P. Thao; Anne M. Murray; Emily Callander; Prudence R. Carr; Mark R. Nelson; Rory Wolfe; Robyn L. Woods; Christopher M. Reid; Raj C. Shah; Anne B. Newman; Jeff D. Williamson; et al. (GeroScience, 2022)
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people. Trial registration Clinicaltrials.gov (NCT01038583)

Population genetic structure of a broadcast‐spawning coral across a tropical–temperate transition zone reveals regional differentiation and high‐latitude reef isolation - Richard D. Evans; Luke Thomas; W. Jason Kennington; Nicole M. Ryan; Nerida G. Wilson; Zoe Richards; Ryan J. Lowe; Chenae Tuckett (Journal of Biogeography, 2021)
Aim Genetic connectivity is a key component of species resilience to climate change in terms of recovery capacity following disturbance and capacity to disperse to novel locations as the climate warms and isotherms shift poleward. We aimed to strengthen our understanding of resilience in this context by characterizing patterns of connectivity and genetic diversity in a broadcast spawning coral across a tropical–temperate transition zone. We hypothesize genetic differentiation between tropical and temperate populations and decreasing genetic diversity with higher latitudes. Location Western Australia (WA). Taxon Turbinaria species complex. Turbinaria ‘reniformis’ Oken, 1815 (Scleractinia: Dendrophylliidae). Methods A total of 930 target corals were sampled from 10 locations between 13 and 32° latitude spanning a 9°C mean temperature range. In situ species identification of T . reniformis is hindered by morphological plasticity and homoplasy with sister species. We combined Sanger sequencing of two mitochondrial DNA markers and high‐throughput genotyping by sequencing (GBS) to isolate a single genetic Turbinaria lineage from our dataset through which patterns of genetic flow and diversity along the WA coastline could be explored using population‐ and individual‐based clustering analyses. Results Mitochondrial DNA sequence variation was low among Turbinaria samples and could not resolve individual species. Using GBS, we identified three genetically distinct lineages. Subsequent analyses within one of these lineages revealed strong spatial subdivision with 2–3 genetic clusters. While temperate populations were genetically diverged from more tropical sites, we did not observe declines in genetic diversity with latitude. Main conclusions Tropical populations of T . ‘reniformis’ in Western Australia exhibit strong genetic connectivity, which extends to a southern limit at sub‐tropical Shark Bay. Temperate populations are genetically isolated from their tropical counterparts but have relatively high genetic diversity. While the maintenance of genetic variation in temperate populations may provide some resilience to future climate scenarios, their isolation may increase their vulnerability.

Future changes to high impact weather in the UK - Helen M. Hanlon; Dan Bernie; Giulia Carigi; Jason A. Lowe (Climatic Change, 2021)
High impact weather events such as extreme temperatures or rainfall can cause significant disruption across the UK affecting sectors such as health, transport, agriculture and energy. In this study we draw on the latest set of UK climate projections, UKCP, to examine metrics relating to high-impact weather over the UK and how these change with different levels of future global warming from 1.5 °C to 4 °C above pre-industrial. The changes to these hazards show increases in the frequency of extremely hot days and nights, with a UK average increase in hot days of between 5 and 39 days per year between 1.5 °C and 4 °C of global warming. Projections indicate an increase in cooling degree days of 134–627% and an increase in growing degree days of 19–60% between 1.5 °C and 4 °C of global warming. Extremely hot nights, which are currently rare, are emerging as more common occurrences. The frequency of high daily temperatures and rainfall increase systematically, while the frequency of very cold conditions (based on days where temperatures fall below 0 °C) is shown to decrease by 10 to 49 days per year. A reduction in heating degree days, of 11–32% between 1.5 °C and 4 °C of warming, is projected. Levels of daily rainfall, which currently relate to increased risk of river flooding, are shown to increase across the country, with increases of days with high impact levels of rainfall occurring by 1 to 8 days per year between 1.5 °C and 4 °C of warming. Average drought severity is projected to increase for 3-, 6-, 12- and 36-month-long droughts. The largest changes in the severity of the 12-month drought are between −3 and +19% between 1.5 °C and 4 °C of warming and for 36-month drought between −2 and +54% between 1.5 °C and 4 °C of warming. The projected future changes in high impact weather from this study will enable the characterization of climate risks and ultimately be able to better inform adaptation planning in different sectors to support the increase in resilience of the UK to future climate variability and change.

Eye tracking and attentional bias for depressive internet memes in depression - Umair Akram; Jason G. Ellis; Glhenda Cau; Frayer Hershaw; Ashlieen Rajenthran; Mollie Lowe; Carissa Trommelen; Jennifer Drabble (Experimental Brain Research, 2021)
Previous research highlights the potential benefits of engaging with depressive internet memes for those experiencing symptoms of depression. This study aimed to determine whether: compared to non-depressed controls, individuals experiencing depressive symptoms were quicker to orient and maintain overall attention for internet memes depicting depressive content relative to neutral memes. N = 21 individuals were grouped based on the severity of reported depression symptoms using the PhQ-9. Specifically, a score of: ≤ 4 denoted the control group; and ≥ 15 the depressive symptoms group. Participants viewed a series of meme pairs depicting depressive and neutral memes for periods of 4000 ms. Data for the first fixation onset and duration, total fixation count and total fixation and gaze duration of eye-movements were recorded. A significant group x meme-type interaction indicated that participants with depressive symptoms displayed significantly more fixations on depressive rather than neutral memes. These outcomes provide suggestive evidence for the notion that depressive symptoms are associated with an attentional bias towards socio-emotionally salient stimuli.

Dynamical downscaling of unforced interannual sea-level variability in the North-West European shelf seas - Jonathan Tinker; Matthew D. Palmer; Dan Copsey; Tom Howard; Jason A. Lowe; Tim H. J. Hermans (Climate Dynamics, 2020)
Variability of Sea-Surface Height (SSH) from ocean dynamic processes is an important component of sea-level change. In this study we dynamically downscale a present-day control simulation of a climate model to replicate sea-level variability in the Northwest European shelf seas. The simulation can reproduce many characteristics of sea-level variability exhibited in tide gauge and satellite altimeter observations. We examine the roles of lateral ocean boundary conditions and surface atmospheric forcings in determining the sea-level variability in the model interior using sensitivity experiments. Variability in the oceanic boundary conditions leads to uniform sea-level variations across the shelf. Atmospheric variability leads to spatial SSH variability with a greater mean amplitude. We separate the SSH variability into a uniform loading term (change in shelf volume with no change in distribution), and a spatial redistribution term (with no volume change). The shelf loading variance accounted for 80% of the shelf mean total variance, but this drops to ~ 60% around Scotland and in the southeast North Sea. We analyse our modelled variability to provide a useful context to coastal planners and managers. Our 200-year simulation allows the distribution of the unforced trends (over 4–21 year) of sea-level changes to be quantified. We found that the 95th percentile change over a 4-year period can lead to coastal sea-level changes of ~ 58 mm, which must be considered when using smooth sea level projections. We also found that simulated coastal SSH variations have long correlation length-scales, suggesting that observations of interannual sea-level variability from tide gauges are typically representative of > 200 km of the adjacent coast. This helps guide the use of tide gauge variability estimates.

Correction to: Concepts and Terminology for Sea Level: Mean, Variability and Change, Both Local and Global - Jonathan M. Gregory; Stephen M. Griffies; Chris W. Hughes; Jason A. Lowe; John A. Church; Ichiro Fukumori; Natalya Gomez; Robert E. Kopp; Felix Landerer; Gonéri Le Cozannet; Rui M. Ponte; Detlef Stammer; et al. (Surveys in Geophysics, 2019)

Concepts and Terminology for Sea Level: Mean, Variability and Change, Both Local and Global - Jonathan M. Gregory; Stephen M. Griffies; Chris W. Hughes; Jason A. Lowe; John A. Church; Ichiro Fukimori; Natalya Gomez; Robert E. Kopp; Felix Landerer; Gonéri Le Cozannet; Rui M. Ponte; Detlef Stammer; et al. (Surveys in Geophysics, 2019)

Clinical heterogeneity of patients with stool samples testing PCR+/Tox− from a two-step Clostridium difficile diagnostic algorithm - Jason Zou; Victor Leung; Sylvie Champagne; Michelle Hinch; Anna Wong; Elisa Lloyd-Smith; Trong Tien Nguyen; Marc G. Romney; Azra Sharma; Michael Payne; Christopher F. Lowe (European Journal of Clinical Microbiology & Infectious Diseases, 2018)

Southern Ocean carbon-wind stress feedback - Ben Bronselaer; Laure Zanna; David R. Munday; Jason Lowe (Climate Dynamics, 2018)

The impacts avoided with a 1.5 °C climate target: a global and regional assessment - Nigel W. Arnell; Jason A. Lowe; Ben Lloyd-Hughes; Timothy J. Osborn (Climatic Change, 2018)

Sea Level Change and Coastal Climate Services: The Way Forward - Gonéri Le Cozannet; Robert Nicholls; Jochen Hinkel; William Sweet; Kathleen McInnes; Roderik Van de Wal; Aimée Slangen; Jason Lowe; Kathleen White (Journal of Marine Science and Engineering, 2017)
For many climate change impacts such as drought and heat waves, global and national frameworks for climate services are providing ever more critical support to adaptation activities. Coastal zones are especially in need of climate services for adaptation, as they are increasingly threatened by sea level rise and its impacts, such as submergence, flooding, shoreline erosion, salinization and wetland change. In this paper, we examine how annual to multi-decadal sea level projections can be used within coastal climate services (CCS). To this end, we review the current state-of-the art of coastal climate services in the US, Australia and France, and identify lessons learned. More broadly, we also review current barriers in the development of CCS, and identify research and development efforts for overcoming barriers and facilitating their continued growth. The latter includes: (1) research in the field of sea level, coastal and adaptation science and (2) cross-cutting research in the area of user interactions, decision making, propagation of uncertainties and overall service architecture design. We suggest that standard approaches are required to translate relative sea level information into the forms required to inform the wide range of relevant decisions across coastal management, including coastal adaptation.

Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore - Heather Cannaby; Matthew D. Palmer; Tom Howard; Lucy Bricheno; Daley Calvert; Justin Krijnen; Richard Wood; Jonathan Tinker; Chris Bunney; James Harle; Andrew Saulter; Clare O'Neill; et al. (Ocean Science, 2016)
Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time-mean sea level were evaluated using the process-based climate model data and methods presented in the United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Regional surge and wave solutions extending from 1980 to 2100 were generated using ∼ 12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled ( ∼ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980 to 2010, enabling a quantitative assessment of model skill. Simulated historical sea-surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data, respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. Trends in surge and significant wave height 2-year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ∼ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.

Spatial variations of sea-level rise and impacts: An application of DIVA - Sally Brown; Robert J Nicholls; Jason A Lowe; Jochen Hinkel (Climatic Change, 2016)

The reversibility of CO2 induced climate change - Peili Wu; Jeff Ridley; Anne Pardaens; Richard Levine; Jason Lowe (Climate Dynamics, 2015)

Model structure in observational constraints on transient climate response - Richard J. Millar; Alexander Otto; Piers M. Forster; Jason A. Lowe; William J. Ingram; Myles R. Allen (Climatic Change, 2015)

Transient climate changes in a perturbed parameter ensemble of emissions-driven earth system model simulations - James M. Murphy; Ben B. B. Booth; Chris A. Boulton; Robin T. Clark; Glen R. Harris; Jason A. Lowe; David M. H. Sexton (Climate Dynamics, 2014)

Erratum to: Spatial variations of sea-level rise and impacts: An application of DIVA - Sally Brown; Robert J. Nicholls; Jason A. Lowe; Jochen Hinkel (Climatic Change, 2014)

Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections - Peter Good; Jonathan M. Gregory; Jason A. Lowe; Timothy Andrews (Climate Dynamics, 2013)

A step-response approach for predicting and understanding non-linear precipitation changes - Peter Good; William Ingram; F. Hugo Lambert; Jason A. Lowe; Jonathan M. Gregory; Mark J. Webb; Mark A. Ringer; Peili Wu (Climate Dynamics, 2012)

The benefits of climate change mitigation in integrated assessment models: the role of the carbon cycle and climate component - Andries F. Hof; Chris W. Hope; Jason Lowe; Michael D. Mastrandrea; Malte Meinshausen; Detlef P. van Vuuren (Climatic Change, 2012)

The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates - Simon N. Gosling; Glenn R. McGregor; Jason A. Lowe (Climatic Change, 2012)

The Tyndall coastal simulator - Mustafa Mokrech; Susan Hanson; Robert J. Nicholls; Judith Wolf; Mike Walkden; Corentin M. Fontaine; Sophie Nicholson-Cole; Simon R. Jude; James Leake; Peter Stansby; Andrew R. Watkinson; Mark D. A. Rounsevell; et al. (Journal of Coastal Conservation, 2011)

Contributions of individual countries’ emissions to climate change and their uncertainty - Niklas Höhne; Helcio Blum; Jan Fuglestvedt; Ragnhild Bieltvedt Skeie; Atsushi Kurosawa; Guoquan Hu; Jason Lowe; Laila Gohar; Ben Matthews; Ana Claudia Nioac de Salles; Christian Ellermann (Climatic Change, 2011)

How well do integrated assessment models simulate climate change? - Detlef P. van Vuuren; Jason Lowe; Elke Stehfest; Laila Gohar; Andries F. Hof; Chris Hope; Rachel Warren; Malte Meinshausen; Gian-Kasper Plattner (Climatic Change, 2011)

Thresholds for irreversible decline of the Greenland ice sheet - Jeff Ridley; Jonathan M. Gregory; Philippe Huybrechts; Jason Lowe (Climate Dynamics, 2010)

Associations between elevated atmospheric temperature and human mortality: a critical review of the literature - Simon N. Gosling; Jason A. Lowe; Glenn R. McGregor; Mark Pelling; Bruce D. Malamud (Climatic Change, 2009)

Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate - Peter Good; Jason A. Lowe; David P. Rowell (Climate Dynamics, 2009)

Climate change and heat-related mortality in six cities Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change - Simon N. Gosling; Glenn R. McGregor; Jason A. Lowe (International Journal of Biometeorology, 2009)

The demise of Arctic sea ice during stabilisation at high greenhouse gas concentrations - Jeff Ridley; Jason Lowe; David Simonin (Climate Dynamics, 2008)