Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding

Getting a deep insight into the role of coastal flooding drivers is of great interest for the planning of adaptation strategies for future climate conditions. Using global sensitivity analysis, we aim to measure the contributions of the offshore forcing conditions (wave–wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of a flooding event at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations and (2) the statistical dependence between the forcing conditions. By applying a Monte Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties by calculating sensitivity measures dedicated to dependent input variables (named Shapley effects) using Gaussian process (GP) metamodels. On this basis, our results show the increasing influence of SLR over time and a small-to-moderate contribution of wave–wind characteristics or even negligible importance in the very long term (beyond 2100). These results were discussed in relation to our modelling choices, in particular the climate change scenario, as well as the uncertainties of the estimation procedure (Monte Carlo sampling and GP error).</p

Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community by combining the game-theoretic approach known as &lsquo;SHAP&rsquo; (SHapley Additive exPlanation) with machine-learning regression models. We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea-level, taking into account different modelling choices related to (1) the numerical implementation, (2) the initial conditions, and (3) the modelling of ice-sheet processes. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation.</p

Solar forcing synchronizes decadal North Atlantic climate variability

Quasi-decadal variability in solar irradiance has been suggested to exert a substantial effect on Earth’s regional climate. In the North Atlantic sector, the 11-year solar signal has been proposed to project onto a pattern resembling the North Atlantic Oscillation (NAO), with a lag of a few years due to ocean-atmosphere interactions. The solar/NAO relationship is, however, highly misrepresented in climate model simulations with realistic observed forcings. In addition, its detection is particularly complicated since NAO quasi-decadal fluctuations can be intrinsically generated by the coupled ocean-atmosphere system. Here we compare two multi-decadal ocean-atmosphere chemistry-climate simulations with and without solar forcing variability. While the experiment including solar variability simulates a 1–2-year lagged solar/NAO relationship, comparison of both experiments suggests that the 11-year solar cycle synchronizes quasi-decadal NAO variability intrinsic to the model. The synchronization is consistent with the downward propagation of the solar signal from the stratosphere to the surface

Predictability of variable solar-terrestrial coupling

In October 2017, the Scientific Committee on Solar-Terrestrial Physics (SCOSTEP) Bureau established a committee for the design of SCOSTEP's Next Scientific Programme (NSP). The NSP committee members and authors of this paper decided from the very beginning of their deliberations that the predictability of the Sun-Earth System from a few hours to centuries is a timely scientific topic, combining the interests of different topical communities in a relevant way. Accordingly, the NSP was christened PRESTO - PREdictability of the variable Solar-Terrestrial cOupling. This paper presents a detailed account of PRESTO; we show the key milestones of the PRESTO roadmap for the next 5 years, review the current state of the art and discuss future studies required for the most effective development of solar-terrestrial physics.Peer reviewe

Nd-isotope evidence for the distal provenance of the historical (c. <3000BP) lateritic surface cover underlying the Equatorial forest in Gabon (Western Africa)

Highlights: • The first Nd isotopic data on the lateritic surface cover (Cover Horizon) of western Equatorial Africa. • Clearly different Nd isotopic signatures between the Cover Horizon and underlying basement. • A consistent model attributing the Cover Horizon to the settling of aeolian particles derived from the Namib desert. Abstract: Surficial formations in Gabon, as well as in other places of western Central Africa include a ubiquitous, homogeneous and 1–3 m-thick clayey to sandy lateritic surface cover known as the ‘Cover Horizon’. From 14C radiometric dating it has been concluded that the emplacement of this unit was correlative with a major environmental crisis which affected Central Africa c. 3000–2000 years ago. 10Be and Nd-isotopic analyses have been performed to provide new constraints on the age and origin of this layer. Six samples from two depth profiles investigated for 10Be exhibit an almost constant concentration consistent with a very recent deposition age. Nd-isotopic analyses performed on the silt to clay fraction of eleven samples from widely spaced locations over Gabon attest for mildly radiogenic signatures (εNd = −23 to −17) in ten of them, and a slightly radiogenic signature (εNd = −9) in one sample. TDM model ages range from 1.6 to 2.6 Ga, and a perfect discrimination is observed between the Nd-isotopic signature of the Cover Horizon and that of the underlying Congo Craton. This makes an aeolian origin as the most probable for the Cover Horizon. The average εNd (c. −20) is however rather unusual for aeolian sediments or aerosols. A possible source of particles is therefore tested by considering the present-day atmospheric flux over Gabon and adjacent regions. Combined atmospheric modeling and Nd-isotopes leads to the conclusion that the fine fraction of the Cover Horizon could have originated from the northern part of the Namib desert

Partitioning the uncertainty contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding at a macrotidal site

Getting a deep insight into the role of coastal flooding drivers is of high interest for the planning of adaptation strategies for future climate conditions. Using global sensitivity analysis, we aim to measure the contributions of the offshore forcing conditions (wave/wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of the flooding event (defined when the inland water volume exceeds a given threshold YC) at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations; (2) the statistical dependence between the forcing conditions. By applying a Monte-Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties through the computation of sensitivity measures dedicated to dependent input variables (named Shapley effects) with the help of Gaussian process (GP) metamodels. On this basis, our results outline the key influence of SLR over time. Its contribution rapidly increases over time until 2100 where it almost exceeds the contributions of all other uncertainties (with Shapley effect > 40 % considering the representative concentration pathway RCP4.5 scenario). After 2100, it continues to linearly increase up to > 50 %. The SLR influence depends however on our modelling assumptions. Before 2100, it is strongly influenced by the digital elevation Model (DEM); with a DEM with lower topographic elevation (before the raise of dykes in some sectors), the SLR effect is smaller by ~40 %. This influence reduction goes in parallel with an increase in the importance of wave/wind characteristics, hence indicating how the relative effect of the flooding drivers strongly change when protective measures are adopted. By 2100, the joint role of RCP and of YC impacts the SLR influence, which is reduced by 20–30 % when the mode of the SLR probability distribution is high (for RCP8.5 in particular) and when YC is low (of 50 m3). Finally, by showing that these results are robust to the main uncertainties in the estimation procedure (Monte-Carlo sampling and GP error), the combined GP-Shapley effect approach proves to be a valuable tool to explore and characterize uncertainties related to compound coastal flooding under SLR

Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

International audienceProcess-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community known as "SHAP" (SHapley Additive exPlanations). We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea level, taking into account different modelling choices related to (1) numerical implementation, (2) initial conditions, (3) modelling of ice-sheet processes, and (4) environmental forcing. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea-level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation