Widespread retreat of coastal habitat is likely at warming levels above 1.5 °C.

Several coastal ecosystems-most notably mangroves and tidal marshes-exhibit biogenic feedbacks that are facilitating adjustment to relative sea-level rise (RSLR), including the sequestration of carbon and the trapping of mineral sediment1. The stability of reef-top habitats under RSLR is similarly linked to reef-derived sediment accumulation and the vertical accretion of protective coral reefs2. The persistence of these ecosystems under high rates of RSLR is contested3. Here we show that the probability of vertical adjustment to RSLR inferred from palaeo-stratigraphic observations aligns with contemporary in situ survey measurements. A deficit between tidal marsh and mangrove adjustment and RSLR is likely at 4 mm yr-1 and highly likely at 7 mm yr-1 of RSLR. As rates of RSLR exceed 7 mm yr-1, the probability that reef islands destabilize through increased shoreline erosion and wave over-topping increases. Increased global warming from 1.5 °C to 2.0 °C would double the area of mapped tidal marsh exposed to 4 mm yr-1 of RSLR by between 2080 and 2100. With 3 °C of warming, nearly all the world's mangrove forests and coral reef islands and almost 40% of mapped tidal marshes are estimated to be exposed to RSLR of at least 7 mm yr-1. Meeting the Paris agreement targets would minimize disruption to coastal ecosystems.

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management.

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.

Evaluation of NAQFC model performance in forecasting surface ozone during the 2011 DISCOVER-AQ campaign.

The National Air Quality Forecast Capability (NAQFC) and an experimental version of the NAQFC (NAQFC-ß) provided flight decision support during the July 2011 NASA DISCOVER-AQ field campaign around Baltimore, Maryland. Ozone forecasts from the NAQFC and NAQFC-ß were compared to surface observations at six air quality monitoring stations in the DISCOVER-AQ domain. A bootstrap algorithm was used to test for significant bias and error in the forecasts from each model. Both models produce significant positively biased forecasts in the morning while generally becoming insignificantly biased in the afternoon during peak ozone hours. The NAQFC-ß produces higher forecast bias, higher forecast error, and lower correlations than the NAQFC. Forecasts from the two models were also compared to each other to determine the spatial and temporal extent of significant differences in forecasted ozone using a bootstrap algorithm. The NAQFC-ß tends to produce an average background ozone mixing ratio of at least 3.51 ppbv greater than the NAQFC throughout the domain at 95 % significance. The difference between the two models is significant during the overnight and early morning hours likely due to the way the Carbon Bond 5 mechanism in the NAQFC-ß handles reactive nitrogen recycling and organic peroxide species. The value of information each model provides was tested using a static cost-loss ratio model. By standard measures of forecast skill, the NAQFC generally outperforms the NAQFC-ß; however, the NAQFC-ß provides greater value of information. This is because standard measures of forecast skill often hide the sensitivity of end users' needs to forecast error.