The evolution of 21st century sea-level projections from IPCC AR5 to AR6 and beyond

Sea-level science has seen many recent developments in observations and modelling of the different contributions and the total mean sea-level change. In this overview, we discuss (1) the evolution of the Intergovernmental Panel on Climate Change (IPCC) projections, (2) how the projections compare to observations and (3) the outlook for further improving projections. We start by discussing how the model projections of 21st century sea-level change have changed from the IPCC AR5 report (2013) to SROCC (2019) and AR6 (2021), highlighting similarities and differences in the methodologies and comparing the global mean and regional projections. This shows that there is good agreement in the median values, but also highlights some differences. In addition, we discuss how the different reports included high-end projections. We then show how the AR5 projections (from 2007 onwards) compare against the observations and find that they are highly consistent with each other. Finally, we discuss how to further improve sea-level projections using high-resolution ocean modelling and recent vertical land motion estimates

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Combining Multivariate Statistical and Numerical Modeling for Coastal Hazards Analysis

Increased population density along the world\u27s coastlines is driven by the wide range of services provided by costal systems, such as transportation, fisheries, trade, and tourism. Despite offering fundamental services, coastal systems are exposed to a range of natural hazards such as riverine and/or coastal flooding, and erosion, thus robust risk analyses are needed to protect coastal ecosystems, infrastructure, and people dwelling in proximity to the shoreline. Numerical and multivariate statistical models have become crucial tools in coastal risk management to support robust risk analyses, and their combined use provides many benefits in the analysis of coastal hazards. This dissertation addresses two of the most pressing issues in coastal management, which are coastal dune erosion and compound flooding, by exploiting the advantages of combining multivariate statistical and numerical models. One of the shortcomings of numerical models is that they are often computationally expensive, thus their application is hindered when fast and accurate predictions are needed. Surrogate modelling, a process that simplifies complex numerical simulations through statistical modelling, can expedite predictions without significantly compromising accuracy. Here, surrogate modelling is successfully applied to predict dune erosion under stormy conditions, and water level variability along rivers caused by the interaction of oceanographic and fluvial variables, which is known as compound flooding. The latter is further investigated through the application of several multivariate statistical frameworks, assessing discrepancies between subjective model setups when data are limited. A sensitivity test is provided for two commonly used multivariate statistical models, highlighting the need for long overlapping records to attain robust estimates of the compound flooding hazard. The effect of data shortness is further explored by using extended data of flooding drivers via climate simulations. By shorting the data set, the uncertainty introduced by natural climate variability is demonstrated for each component of the multivariate modelling framework

Spatial and temporal clustering analysis of extreme wave events around the UK coastline

Densely populated coastal regions are vulnerable to extreme wave events, which can cause loss of life and considerable damage to coastal infrastructure and ecological assets. Here, an event-based analysis approach, across multiple sites, has been used to assess the spatial footprint and temporal clustering of extreme storm-wave events around the coast of the United Kingdom (UK). The correlated spatial and temporal characteristics of wave events are often ignored even though they amplify flood consequences. Waves that exceeded the 1 in 1-year return level were analysed from 18 different buoy records and declustered into distinct storm events. In total, 92 extreme wave events are identified for the period from 2002 (when buoys began to record) to mid-2016. The tracks of the storms of these events were also captured. Six main spatial footprints were identified in terms of extreme wave events occurrence along stretches of coastline. The majority of events were observed between November and March, with large inter-annual differences in the number of events per season associated with the West Europe Pressure Anomaly (WEPA). The 2013/14 storm season was an outlier regarding the number of wave events, their temporal clustering and return levels. The presented spatial and temporal analysis framework for extreme wave events can be applied to any coastal region with sufficient observational data and highlights the importance of developing statistical tools to accurately predict such processes

Spatial and Temporal Clustering Analysis of Extreme Wave Events around the UK Coastline

Densely populated coastal regions are vulnerable to extreme wave events, which can cause loss of life and considerable damage to coastal infrastructure and ecological assets. Here, an event-based analysis approach, across multiple sites, has been used to assess the spatial footprint and temporal clustering of extreme storm-wave events around the coast of the United Kingdom (UK). The correlated spatial and temporal characteristics of wave events are often ignored even though they amplify flood consequences. Waves that exceeded the 1 in 1-year return level were analysed from 18 different buoy records and declustered into distinct storm events. In total, 92 extreme wave events are identified for the period from 2002 (when buoys began to record) to mid-2016. The tracks of the storms of these events were also captured. Six main spatial footprints were identified in terms of extreme wave events occurrence along stretches of coastline. The majority of events were observed between November and March, with large inter-annual differences in the number of events per season associated with the West Europe Pressure Anomaly (WEPA). The 2013/14 storm season was an outlier regarding the number of wave events, their temporal clustering and return levels. The presented spatial and temporal analysis framework for extreme wave events can be applied to any coastal region with sufficient observational data and highlights the importance of developing statistical tools to accurately predict such processes

Spatial And Temporal Clustering Analysis Of Extreme Wave Events Around The Uk Coastline

Densely populated coastal regions are vulnerable to extreme wave events, which can cause loss of life and considerable damage to coastal infrastructure and ecological assets. Here, an event-based analysis approach, across multiple sites, has been used to assess the spatial footprint and temporal clustering of extreme storm-wave events around the coast of the United Kingdom (UK). The correlated spatial and temporal characteristics of wave events are often ignored even though they amplify flood consequences. Waves that exceeded the 1 in 1-year return level were analysed from 18 different buoy records and declustered into distinct storm events. In total, 92 extreme wave events are identified for the period from 2002 (when buoys began to record) to mid-2016. The tracks of the storms of these events were also captured. Six main spatial footprints were identified in terms of extreme wave events occurrence along stretches of coastline. The majority of events were observed between November and March, with large inter-annual differences in the number of events per season associated with theWest Europe Pressure Anomaly (WEPA). The 2013/14 storm season was an outlier regarding the number of wave events, their temporal clustering and return levels. The presented spatial and temporal analysis framework for extreme wave events can be applied to any coastal region with sufficient observational data and highlights the importance of developing statistical tools to accurately predict such processes

Predicting Dune Erosion With Combined Process-Based And Multivariate Statistical Models

The new modelling framework presented here uses advanced statistical models to simulate plausible sea-storm events covering data outside the observational range and machine-learning algorithms to develop meta-models capable of mimicking a state-of-the-art numerical model to simulate dune response during storm events in a computationally highly efficient way. Our results show great potential of the framework be used for both probabilistic long-term coastal change hazard simulations and short-term (ensemble) forecasting (using wave and water level forecast information). The framework itself is transferrable to other coastline stretches where water level and wave observations as well as bathymetric information are available

Illustrative Multi‐Centennial Projections of Global Mean Sea‐Level Rise and Their Application

We produce projections of global mean sea-level rise to 2500 for low and medium emissions scenarios (Shared Socioeconomic Pathways SSP1-2.6 and SSP2-4.5) relative to 2020, based on extending and combining model ensemble data from current literature. We find that emissions have a large effect on sea-level rise on these long timescales, with [5, 95]% intervals of [0.3, 4.3]m and [1.0, 7.6]m under SSP1-2.6 and SSP2-4.5 respectively, and a difference in the 95% quantile of 1.6 m at 2300 and 3.3 m at 2500 for the two scenarios. The largest and most uncertain component is the Antarctic ice sheet, projected to contribute 5%–95% intervals of [−0.1, 2.3]m by 2500 under SSP1-2.6 and [0.0, 3.8]m under SSP2-4.5. We discuss how the simple statistical extensions used here could be replaced with more physically based methods for more robust predictions. We show that, despite their uncertainties, current multi-centennial projections combined into multi-study projections as presented here can be used to avoid future “lock-ins” in terms of risk and adaptation needs to sea-level rise