Adaptation to multi-meter sea-level rise should start now

Sea-level rise will fundamentally change coastal zones worldwide (Cooley et al 2022). A global two meters rise of sea level will be exceeded sooner or later within a time window ranging from one century to as long as two millennia, depending on future greenhouse gas emissions and polar ice-sheet melting (Fox-Kemper et al 2021). Here, we show that in addition to climate mitigation to slow this rise, adaptation to two meters of sea-level rise should start now. This involves changing our mindset to define a strategic vision for these threatened coastal areas and identify realistic pathways to achieve this vision. This can reduce damages, losses, and lock-ins in the future, identify problems before they become critical and exploit opportunities if they emerge. To meet this challenge, it is essential that coastal adaptation becomes core to coastal development, especially for long-lived critical infrastructure. Coastal adaptation will be an ongoing process for many decades and centuries, requiring the support of climate services, which make the links between science, policy and adaptation practice

Sea-level trend variability in the Mediterranean during the 1993–2019 period

Sea-level change is one of the most concerning climate change and global warming consequences, especially impacting coastal societies and environments. The spatial and temporal variability of sea level is neither linear nor globally uniform, especially in semi-enclosed basins such as the Mediterranean Sea, which is considered a hot spot regarding expected impacts related to climate change. This study investigates sea-level trends and their variability over the Mediterranean Sea from 1993 to 2019. We use gridded sea-level anomaly products from satellite altimetry for the total observed sea level, whereas ocean temperature and salinity profiles from reanalysis were used to compute the thermosteric and halosteric effects, respectively, and the steric component of the sea level. We perform a statistical change point detection to assess the spatial and temporal significance of each trend change. The linear trend provides a clear indication of the non-steric effects as the dominant drivers over the entire period at the Mediterranean Sea scale, except for the Levantine and Aegean sub-basins, where the steric component explains the majority of the sea-level trend. The main changes in sea-level trends are detected around 1997, 2006, 2010, and 2016, associated with Northern Ionian Gyre reversal episodes, which changed the thermohaline properties and water mass redistribution over the sub-basins

Integrating new sea‐level scenarios into coastal risk and adaptation assessments: An ongoing process

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 sea‐level 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

GlacierMIP – A model intercomparison of global-scale glacier mass-balance models and projections

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

Sea-level rise: from global perspectives to local services

Coastal areas are highly diverse, ecologically rich, regions of key socio-economic activity, and are particularly sensitive to sea-level change. Over most of the 20th century, global mean sea level has risen mainly due to warming and subsequent expansion of the upper ocean layers as well as the melting of glaciers and ice caps. Over the last three decades, increased mass loss of the Greenland and Antarctic ice sheets has also started to contribute significantly to contemporary sea-level rise. The future mass loss of the two ice sheets, which combined represent a sea-level rise potential of ∼65 m, constitutes the main source of uncertainty in long-term (centennial to millennial) sea-level rise projections. Improved knowledge of the magnitude and rate of future sea-level change is therefore of utmost importance. Moreover, sea level does not change uniformly across the globe and can differ greatly at both regional and local scales. The most appropriate and feasible sea level mitigation and adaptation measures in coastal regions strongly depend on local land use and associated risk aversion. Here, we advocate that addressing the problem of future sea-level rise and its impacts requires (i) bringing together a transdisciplinary scientific community, from climate and cryospheric scientists to coastal impact specialists, and (ii) interacting closely and iteratively with users and local stakeholders to co-design and co-build coastal climate services, including addressing the high-end risks

The sea level response to ice sheet freshwater forcing in the Community Earth System Model

We study the effect of a realistic ice sheet freshwater forcing on sea-level change in the fully coupled Community Earth System Model (CESM) showing not only the effect on the ocean density and dynamics, but also the gravitational response to mass redistribution between ice sheets and the ocean. We compare the ‘standard’ model simulation (NO-FW) to a simulation with a more realistic ice sheet freshwater forcing (FW) for two different forcing scenario’s (RCP2.6 and RCP8.5) for 1850–2100. The effect on the global mean thermosteric sea-level change is small compared to the total thermosteric change, but on a regional scale the ocean steric/dynamic change shows larger differences in the Southern Ocean, the North Atlantic and the Arctic Ocean (locally over 0.1 m). The gravitational fingerprints of the net sea-level contributions of the ice sheets are computed separately, showing a regional pattern with a magnitude that is similar to the difference between the NO-FW and FW simulations of the ocean steric/dynamic pattern. Our results demonstrate the importance of ice sheet mass loss for regional sea-level projections in light of the projected increasing contribution of ice sheets to future sea-level rise

Adaptation to multi-meter sea-level rise should start now

Sea-level rise will fundamentally change coastal zones worldwide (Cooley et al 2022). A global two meters rise of sea level will be exceeded sooner or later within a time window ranging from one century to as long as two millennia, depending on future greenhouse gas emissions and polar ice-sheet melting (Fox-Kemper et al 2021). Here, we show that in addition to climate mitigation to slow this rise, adaptation to two meters of sea-level rise should start now. This involves changing our mindset to define a strategic vision for these threatened coastal areas and identify realistic pathways to achieve this vision. This can reduce damages, losses, and lock-ins in the future, identify problems before they become critical and exploit opportunities if they emerge. To meet this challenge, it is essential that coastal adaptation becomes core to coastal development, especially for long-lived critical infrastructure. Coastal adaptation will be an ongoing process for many decades and centuries, requiring the support of climate services, which make the links between science, policy and adaptation practice

Improving statistical projections of ocean dynamic sea-level change using pattern recognition techniques

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 60g€¯%, 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 20g€¯% to about 70g€¯% 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.This publication was supported by PROTECT. This project has received funding from the European Union's Horizon 2020 research and innovation program (grant no. 869304, PROTECT contribution number 61).Peer reviewe