Search

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provides an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing different probability distributions for the drivers of sea-level change and their consequences for global mean, regional, and extreme sea-level change. We demonstrate its application by generating seven alternative probability distributions under multiple emissions scenarios for both future global mean sea-level change and future relative and extreme sea-level change at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheets as drivers of structural uncertainty in sea-level change projections. [ABSTRACT FROM AUTHOR]

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations. [ABSTRACT FROM AUTHOR]

Future sea-level rise projections are characterized by both quantifiable uncertainty and unquantifiable, structural uncertainty. Thorough scientific assessment of sea-level rise projections requires analysis of both dimensions of uncertainty. Probabilistic sea-level rise projections evaluate the quantifiable dimension of uncertainty; comparison of alternative probabilistic methods provide an indication of structural uncertainty. Here we describe the Framework for Assessing Changes To Sea-level (FACTS), a modular platform for characterizing alternative probability distributions of global mean, regional, and extreme sea-level rise. We demonstrate its application by generating seven alternative probability distributions under multiple alternative emissions scenarios for both future global mean sea level and future relative and extreme sea level at New York City. These distributions, closely aligned with those presented in the Intergovernmental Panel on Climate Change Sixth Assessment Report, emphasize the role of the Antarctic and Greenland ice sheet as drivers of structural uncertainty in sea-level rise projections. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ -MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1 σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ -MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of model disagreement. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on Empirical Orthogonal Functions (EOF), namely signal-to-noise maximising EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. These two methods are applied to the initial-condition large ensemble MPI-GE, so that internal variability is optimally characterized while avoiding model biases. We show that pattern filtering provides an efficient way of reducing errors compared to other conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterising their common response to external forcing reduces the random error by almost 60 %, a reduction level that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single realization modelling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern scaling leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root-mean-squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model and simplify the choice of suitable predictors. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ -MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ-MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]

Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, ocean mass change is caused by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides an analysis of regional patterns of contemporary mass redistribution associated with barystatic SLC since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself), temporal (related to the temporal variability in the time series) and spatial–structural (related to the spatial distribution of the mass change sources). Regional patterns (fingerprints) of barystatic SLC are computed from a range of estimates of the individual freshwater sources and used to analyze the different types of uncertainty. Combining all contributions, we find that regional sea-level trends range from -0.4 to 3.3 mmyr-1 for 2003–2016 and from -0.3 to 2.6 mmyr-1 for 1993–2016, considering the 5–95th percentile range across all grid points and depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.6 to 1.3 mmyr-1 for 2003–2016 and from 0.4 to 0.8 mmyr-1 for 1993–2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, responsible on average for 65% of the total uncertainty, followed by the spatial–structural and intrinsic uncertainties, which contribute on average 16% and 18% , respectively. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for 35 %–60 % of the total uncertainty, depending on the region of interest. Another important contribution comes from the spatial–structural uncertainty from Antarctica and land water storage, which shows that different locations of mass change can lead to trend deviations larger than 20%. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning. [ABSTRACT FROM AUTHOR]

The release of new and updated sea-level rise (SLR) information, such as from the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports, needs to be better anticipated in coastal risk and adaptation assessments. This requires risk and adaptation assessments to be regularly reviewed and updated as needed, reflecting the new information but retaining useful information from earlier assessments. In this paper, updated guidance on the types of SLR information available is presented, including for sea-level extremes. An intercomparison of the evolution of the headline projected ranges across all the IPCC reports show an increase from the fourth and fifth assessments to the most recent "Special Report on the Ocean and Cryosphere in a Changing Climate" assessment. IPCC reports have begun to highlight the importance of potential high-end sea-level response, mainly reflecting uncertainties in the Greenland/Antarctic ice sheet components, and how this might be considered in scenarios. The methods that are developed here are practical and consider coastal risk assessment, adaptation planning, and long-term decision-making to be an ongoing process and ensure that despite the large uncertainties, pragmatic adaptation decisions can be made. It is concluded that new sealevel information should not be seen as an automatic reason for abandoning existing assessments, but as an opportunity to review (i) the assessment's robustness in the light of new science and (ii) the utility of proactive adaptation and planning strategies, especially over the more uncertain longer term. [ABSTRACT FROM AUTHOR]

The effective climate sensitivity (EffCS) of models in the Coupled Model Intercomparison Project 6 (CMIP6) has increased relative to CMIP5. We explore the implications of this for global mean sea‐level (GMSL) change projections in 2100 for three emissions scenarios. CMIP6 projections of global surface air temperature are substantially higher than in CMIP5, but projections of global mean thermal expansion are not. Using these projections as input to construct projections of GMSL change with IPCC AR5 methods, the 95th percentile of GMSL change at 2100 only increases by 3–7 cm. Projected rates of GMSL rise around 2100 increase more strongly, though, implying more pronounced differences beyond 2100 and greater committed sea‐level rise. Intermodel differences in GMSL projections indicate that EffCS‐based model selection may substantially alter the ensemble projections. GMSL change in 2100 is accurately predicted by time‐integrated temperature change, and thus requires reducing emissions early to be mitigated. Plain Language Summary: Climate sensitivity measures how much the Earth's surface warms for a given increase in greenhouse gas concentration. In the new generation of global climate models, climate sensitivity has increased. We explore how the simulations of these models affect global mean sea‐level (GMSL) rise projections in 2100 for three different emissions scenarios. We compute GMSL projections based on simulated global surface warming (which affects land‐ice melt) and thermal expansion of the ocean, using the methods of the fifth Assessment Report of the Intergovernmental Panel on Climate Change. The latest projections of global surface warming are substantially higher than the previous projections, whereas the projections of global thermal expansion are not. Consequently, the upper limits of our GMSL projections increase by 3–7 cm depending on the emissions scenario. This difference will likely become more pronounced beyond 2100. Depending on climate sensitivity, GMSL projections for individual models can differ substantially, implying that using only a subset of models selected based on their climate sensitivity may substantially alter GMSL projections. Since GMSL in 2100 can be predicted well by the cumulative sum of surface warming up to 2100, it is important to reduce the emission of greenhouse gases early to mitigate GMSL rise. Key Points: The 95th percentile of total sea‐level change projections in 2100 is up to 7 cm higher for the Coupled Model Intercomparison Project 6 (CMIP6) ensemble than for the CMIP5 ensembleThe 95th percentile of projected sea‐level rise rates near 2100 is up to 21% larger, implying more pronounced differences beyond 2100Depending on climate sensitivity, individual models can project global mean sea‐level rise substantially outside the ensemble 5%–95% range [ABSTRACT FROM AUTHOR]

Sea level in the South Atlantic Ocean has only been measured at a small number of tide‐gauge locations, which causes considerable uncertainty in 20th‐century sea‐level trend estimates in this basin. To obtain a better‐constrained sea‐level trend in the South Atlantic Ocean, this study aims to answer two questions. The first question is: can we combine new observations, vertical land motion estimates, and information on spatial sampling biases to obtain a likely range of 20th‐century sea‐level rise in the South Atlantic? We combine existing observations with recovered observations from Dakar and a high‐resolution sea‐level reconstruction based on salt‐marsh sediments from the Falkland Islands and find that the rate of sea‐level rise in the South Atlantic has likely been between 1.1 and 2.2 mm year−1 (5%–95% confidence intervals), with a central estimate of 1.6 mm year−1. This rate is on the high side, but not statistically different compared to global‐mean trends from recent reconstructions. The second question is: are there any physical processes that could explain a large deviation from the global‐mean sea‐level trend in the South Atlantic? Sterodynamic (changes in ocean dynamics and steric effects) and gravitation, rotation, and deformation effects related to ice mass loss and land water storage have probably led to a 20th‐century sea‐level trend in the South Atlantic above the global mean. Both observations and physical processes thus suggest that 20th‐century sea‐level rise in the South Atlantic has been about 0.3 mm year−1 above the rate of global‐mean sea‐level rise, although even with the additional observations, the uncertainties are still too large to distinguish a statistically significant difference. Plain Language Summary: Before the satellite era, we depend on the tide‐gauge network to measure sea‐level changes. In the North Atlantic and Pacific Oceans, many tide gauges have been installed, but there are only a handful in the South Atlantic Ocean. Because of this, it is challenging to accurately determine 20th‐century sea‐level changes in the South Atlantic. Because the South Atlantic Ocean covers about one‐fifth of the global oceans, estimates of global sea‐level changes are also affected by the low number of observations in the South Atlantic. Here, we try to improve this situation by adding recently rescued tide‐gauge observation data from Dakar and a new paleo record that has been derived from a salt marsh in the Falklands to the existing sea‐level records. We find that since 1900, South Atlantic sea level has likely risen slightly faster than the global average. This above‐average rate makes sense, because thermal expansion in the South Atlantic has likely been faster than the global mean, and mass loss from ice sheets and glaciers results in above‐average sea‐level rise in the South Atlantic. Key Points: We estimate 20th‐century sea‐level changes in the South Atlantic Ocean from tide‐gauge data and a new paleo proxy20th‐century sea‐level rise in the South Atlantic might have been above the global mean, but uncertainties remain largeEstimates of contemporary mass redistribution and sterodynamic effects support this above‐average trend [ABSTRACT FROM AUTHOR]

Recent studies disagree about the contribution of variations in temperature and salinity of the oceans—steric change—to the observed sea‐level change. This article explores two sources of uncertainty to both global mean and regional steric sea‐level trends. First, we analyze the influence of different temperature and salinity data sets on the estimated steric sea‐level change. Next, we investigate the impact of different stochastic noise models on the estimation of trends and their uncertainties. By varying both the data sets and noise models, the global mean steric sea‐level trend and uncertainty can vary from 0.69 to 2.40 and 0.02 to 1.56 mm/year, respectively, for 1993–2017. This range is even larger on regional scales, reaching up to 30 mm/year. Our results show that a first‐order autoregressive model is the most appropriate choice to describe the residual behavior of the ensemble mean of all data sets for the global mean steric sea‐level change over the last 25 years, which consequently leads to the most representative uncertainty. Using the ensemble mean and the first‐order autoregressive noise model, we find a global mean steric sea‐level change of 1.36 ± 0.10 mm/year for 1993–2017 and 1.08 ± 0.07 mm/year for 2005–2015. Regionally, a combination of different noise models is the best descriptor of the steric sea‐level change and its uncertainty. The spatial coherence in the noise model preference indicates clusters that may be best suited to investigate the regional sea‐level budget.Plain Language Summary: Ocean temperature and salinity variations lead to changes in sea level, known as steric sea‐level change. Steric variations are important contributors to sea‐level change and reflect how the oceans have been responding to global warming. For this reason, several recent studies have quantified the contribution of steric variations to global and regional sea‐level change. However, the reported rates largely differ between studies. In this paper, we look at how the use of different temperature and salinity data sets can be one of the causes of the different estimates of steric sea‐level change published so far. We also investigate how different methods (noise models) used to obtain the rate of change can be another source of different results. We find that the rate of change can vary up to 2 mm/year for the global mean as a result of different data sets and methods used. Regionally, differences can reach up to several tens of millimeters per year. We show that the noise models should always be carefully chosen for each region, so that the rate of change is accurately estimated.Key Points: Several data sets and noise models are used to compute global and regional steric sea‐level change for 1993–2017 and 2005–2015Global mean steric sea‐level trend estimates differ up to 2 mm/year depending on the data set and noise model usedRegional sea‐level trends require more complex noise models and exhibit spatial coherency in the noise model preference [ABSTRACT FROM AUTHOR]

Sea level on the northwestern European shelf (NWES) varies substantially from year to year. Removing explained parts of interannual sea level variability from observations helps to improve estimates of long‐term sea level trends. To this end, the contributions of different drivers to interannual sea level variability need to be understood and quantified. We quantified these contributions for the entire NWES by performing sensitivity experiments with a high‐resolution configuration of the Regional Ocean Modeling System (ROMS). The lateral and atmospheric boundary conditions were derived from reanalyses. We compared our model results with satellite altimetry data and used our sensitivity experiments to show that nonlinear feedbacks cause only minor interannual sea level variability on the shelf. This indicates that our experiments can be used to separate the effects of different drivers. We find that wind dominates the variability of annual mean sea level in the southern and eastern North Sea (up to 4.7‐cm standard deviation), whereas the inverse barometer effect dominates elsewhere on the NWES (up to 1.7‐cm standard deviation). In contrast, forcing at the lateral ocean boundaries results in small and coherent variability on the shelf (0.5‐cm standard deviation). Variability driven by buoyancy fluxes ranges from 0.5‐ to 1.3‐cm standard deviation. The results of our sensitivity experiments explain the (anti)correlation between interannual sea level variability at different locations on the NWES and can be used to estimate sea level rise from observations in this region with higher accuracy.Plain Language Summary: Sea level on the continental shelf northwest of Europe is rising in the long term but is also varying strongly from year to year. This makes it difficult to determine the rate of sea level rise from observations. To improve long‐term trends computed from sea level observations, the causes of short‐term sea level variability need to be understood. Therefore, we test the influences of different components of the atmosphere and ocean on year‐to‐year sea level variability, using a numerical ocean model for northwestern Europe. We find that the varying strength and direction of winds causes large variability of sea level in the southern and eastern North Sea. In other places on the continental shelf, sea level variability is mainly influenced by the variability of atmospheric pressure. We also find that the ocean outside our model domain drives small and uniform sea level variability on the continental shelf and that there is a moderate influence of solar radiation, precipitation, and evaporation. Our results help to understand how sea level varies at different locations in northwestern Europe and to obtain better estimates of the rate of long‐term sea level rise.Key Points: We use a ROMS setup to quantify the contributions of different drivers to interannual sea level variability on the NWES for 1995–2018Atmospheric variability is the main driver, whereas ocean variability drives small and uniform sea level variability on the shelfWind drives large variability in the North Sea, north of the United Kingdom and around Norway, while the inverse barometer effect dominates elsewhere [ABSTRACT FROM AUTHOR]

Sea-level rise (SLR) not only increases the threat of coastal flooding, but may also change tidal regimes in estuaries and coastal bays. To investigate such nearshore tidal responses to SLR, a hydrodynamic model of the European Shelf is downscaled to a model of a Dutch coastal bay (the Oosterschelde, i.e., Eastern Scheldt) and forced by SLR scenarios ranging from 0 to 2 m. This way, the effect of SLR on tidal dynamics in the adjacent North Sea is taken into account as well. The model setup does not include meteorological forcing, gravitational circulation, and changes in bottom topography. Our results indicate that SLR up to 2 m induces larger increases in tidal amplitude and stronger nonlinear tidal distortion in the bay compared to the adjacent shelf sea. Under SLR up to 2 m, the bay shifts from a mixed flood- and ebb-dominant state to complete ebb dominance. We also find that tidal asymmetry affects an important component of sediment transport. Considering sand bed-load transport only, the changed tidal asymmetry may lead to enhanced export, with potential implications for shoreline management. In this case study, we find that local impacts of SLR can be highly spatially varying and nonlinear. The model coupling approach applied here is suggested as a useful tool for establishing local SLR projections in estuaries and coastal bays elsewhere. Future studies should include how SLR changes the bed morphology as well as the feedback effect on tides. [ABSTRACT FROM AUTHOR]

Sea-level rise (SLR) not only increases the threat of coastal flooding, but also may change tidal regimes in estuaries and coastal bays. To investigate such nearshore tidal responses to SLR, a hydrodynamic model of the European Shelf is downscaled to a model of a Dutch coastal bay (the Eastern Scheldt) and forced by SLR scenarios ranging from 0 to 2 m. The results indicate that SLR induces larger increases in tidal amplitude and stronger nonlinear tidal distortion in the bay compared to the adjacent shelf sea. Under SLR, the basin shifts from a mixed flood- and ebb-dominant state to complete ebb-dominance, causing enhanced sediment export and accelerated loss of tidal flats. In this case study, we find that local impacts of SLR can be highly spatially-varying and nonlinear depending on basin geometry. Our model downscaling approach is widely applicable for establishing local SLR projections in estuaries and coastal bays. [ABSTRACT FROM AUTHOR]

Global-scale 21st-century glacier mass change projections from six published global glacier models are systematically compared as part of the Glacier Model Intercomparison Project. In total 214 projections of annual glacier mass and area forced by 25 General Circulation Models (GCMs) and four Representative Concentration Pathways (RCP) emission scenarios and aggregated into 19 glacier regions are considered. Global mass loss of all glaciers (outside the Antarctic and Greenland ice sheets) by 2100 relative to 2015 averaged over all model runs varies from 18 ± 7% (RCP2.6) to 36 ± 11% (RCP8.5) corresponding to 94 ± 25 and 200 ± 44 mm sea-level equivalent (SLE), respectively. Regional relative mass changes by 2100 correlate linearly with relative area changes. For RCP8.5 three models project global rates of mass loss (multi-GCM means) of >3 mm SLE per year towards the end of the century. Projections vary considerably between regions, and also among the glacier models. Global glacier mass changes per degree global air temperature rise tend to increase with more pronounced warming indicating that mass-balance sensitivities to temperature change are not constant. Differences in glacier mass projections among the models are attributed to differences in model physics, calibration and downscaling procedures, initial ice volumes and varying ensembles of forcing GCMs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Currently a paradigm shift is made from global averaged to spatially variable sea level change (SLC) projections. Traditionally, the contribution from ice sheet mass loss to SLC is considered to be symmetrically distributed. However, several assessments suggest that the probability distribution of dynamical ice sheet mass loss is asymmetrically distributed towards higher SLC values. Here we show how asymmetric probability distributions of dynamical ice sheet mass loss impact the high-end uncertainties of regional SLC projections across the globe. For this purpose we use distributions of dynamical ice sheet mass loss presented by Church et al. (2013), De Vries and Van de Wal (2015) and Ritz et al. (2015). The global average median can be 0.18m higher compared to symmetric distributions based on IPCCAR5, but the change in the global average 95th percentile SLC is considerably larger with a shift of 0.32 m. Locally the 90th, 95th and 97.5th SLC percentiles exceed +1.4, +1.6 and C1.8 m. The high-end percentiles of SLC projections are highly sensitive to the precise shape of the probability distributions of dynamical ice sheet mass loss. The shift towards higher values is of importance for coastal safety strategies as they are based on the high-end percentiles of projections. [ABSTRACT FROM AUTHOR]

Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management. [ABSTRACT FROM AUTHOR]

The world's largest deltas are densely populated, of significant economic importance and among the most valuable coastal ecosystems. Projected twenty-first century sea-level rise (SLR) poses a threat to these low-lying coastal environments with inhabitants, resources and ecology becoming increasingly vulnerable to flooding. Large spatial differences exist in the parameters shaping the world's deltas with respect to river discharge, tides and waves, substrate and sediment cohesion, sea-level rise, and human engineering. Here, we use a numerical flow and transport model to: (1) quantify the capability of different types of deltas to dynamically respond to SLR; and (2) evaluate the resultant coastal impact by assessing delta flooding, shoreline recession and coastal habitat changes. We show three different delta forcing experiments representative of many natural deltas: (1) river flow only; (2) river flow and waves; and (3) river flow and tides. We find that delta submergence, shoreline recession and changes in habitat are not dependent on the applied combination of river flow, waves and tides but are rather controlled by SLR. This implies that regional differences in SLR determine delta coastal impacts globally, potentially mitigated by sediment composition and ecosystem buffering. This process-based approach of modelling future deltaic change provides the first set of quantitative predictions of dynamic morphologic change for inclusion in Climate and Earth System Models while also informing local management of deltaic areas across the globe. [ABSTRACT FROM AUTHOR]

Sea level is projected to rise in the coming centuries as a result of a changing climate. One of the major uncertainties is the projected contribution of the ice sheets in Greenland and Antarctica to sea-level rise (SLR). Here, we study the impact of different shapes of uncertainty distributions of the ice sheets on so-called sea-level allowances. An allowance indicates the height a coastal structure needs to be elevated to keep the same frequency and likelihood of sea-level extremes under a projected amount of mean SLR. Allowances are always larger than the projected SLR. Their magnitude depends on several factors, such as projection uncertainty and the typical variability of the extreme events at a location. Our results show that allowances increase significantly for ice sheet dynamics' uncertainty distributions that are more skewed (more than twice, compared to Gaussian uncertainty distributions), due to the increased probability of a much larger ice sheet contribution to SLR. The allowances are largest in regions where a relatively small observed variability in the extremes is paired with relatively large magnitude and/or large uncertainty in the projected SLR, typically around the equator. Under the RCP8.5 (Representative Concentration Pathway) projections of SLR, the likelihood of extremes increases more than a factor 104 at more than 50-87% of the tide gauges. [ABSTRACT FROM AUTHOR]

Determining the time when the climate change signal from increasing greenhouse gases exceeds and thus emerges from natural climate variability (referred to as the time of emergence, ToE) is an important climate change issue. Previous ToE studies were mainly focused on atmospheric variables. Here, based on three regional sea-level projection products available to 2100, which have increasing complexity in terms of included processes, we estimate the ToE for sea-level changes relative to the reference period 1986-2005. The dynamic sea level derived from ocean density and circulation changes alone leads to emergence over only limited regions. By adding the global-ocean thermal expansion effect, 50% of the ocean area will show emergence with rising sea level by the early-to-middle 2040s. Including additional contributions from land ice mass loss, land water storage change and glacial isostatic adjustment generally enhances the signal of regional sea-level rise (except in some regions with decreasing total sea levels), which leads to emergence over more than 50% of the ocean area by 2020. The ToE for total sea level is substantially earlier than that for surface air temperature and exhibits little dependence on the emission scenarios, which means that our society will face detectable sea-level change and its potential impacts earlier than surface air warming. [ABSTRACT FROM AUTHOR]