Abstract. The Atlantic and Arctic Oceans are critical components of the global carbon cycle. Here we quantify the net sea–air CO2 flux, for the first time, across different methodologies for consistent time and space scales for the Atlantic and Arctic basins. We present the long-term mean, seasonal cycle, interannual variability and trends in sea–air CO2 flux for the period 1990 to 2009, and assign an uncertainty to each. We use regional cuts from global observations and modeling products, specifically a pCO2-based CO2 flux climatology, flux estimates from the inversion of oceanic and atmospheric data, and results from six ocean biogeochemical models. Additionally, we use basin-wide flux estimates from surface ocean pCO2 observations based on two distinct methodologies. Our estimate of the contemporary sea–air flux of CO2 (sum of anthropogenic and natural components) by the Atlantic between 40° S and 79° N is −0.49 ± 0.05 Pg C yr−1, and by the Arctic it is −0.12 ± 0.06 Pg C yr−1, leading to a combined sea–air flux of −0.61 ± 0.06 Pg C yr−1 for the two decades (negative reflects ocean uptake). We do find broad agreement amongst methodologies with respect to the seasonal cycle in the subtropics of both hemispheres, but not elsewhere. Agreement with respect to detailed signals of interannual variability is poor, and correlations to the North Atlantic Oscillation are weaker in the North Atlantic and Arctic than in the equatorial region and southern subtropics. Linear trends for 1995 to 2009 indicate increased uptake and generally correspond between methodologies in the North Atlantic, but there is disagreement amongst methodologies in the equatorial region and southern subtropics.
Abstract.Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time-mean sea level were evaluated using the process-based climate model data and methods presented in the United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Regional surge and wave solutions extending from 1980 to 2100 were generated using ∼ 12 km resolution surge (Nucleus for European Modelling of the Ocean -NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled (∼ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980 to 2010, enabling a quantitative assessment of model skill. Simulated historical sea-surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data, respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. Trends in surge and significant wave height 2-year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ∼ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region.
The Atlantic and Arctic oceans are critical components of the global carbon cycle. Here we quantify the net sea-air CO2 flux, for the first time, across different methodologies for consistent time and space scales, for the Atlantic and Arctic basins. We present the long-term mean, seasonal cycle, interannual variability and trends in sea-air CO2 flux for the period 1990 to 2009, and assign an uncertainty to each. We use regional cuts from global observations and modelling products, specifically a pCO2-based CO2 flux climatology, flux estimates from the inversion of oceanic and atmospheric data, and results from six ocean biogeochemical models. Additionally, we use basin-wide flux estimates from surface ocean pCO2 observations based on two distinct methodologies. Our best estimate of the contemporary sea-to-air flux of CO2 (sum of anthropogenic and natural components) by the Atlantic between 40° S and 79° N is −0.49 ± 0.11 Pg C yr−1 and by the Arctic is −0.12 ± 0.06 Pg C yr−1, leading to a combined sea-to-air flux of −0.61 ± 0.12 Pg C yr−1 for the two decades (negative reflects ocean uptake). We do find broad agreement amongst methodologies with respect to the seasonal cycle in the subtropics of both hemispheres, but not elsewhere. Agreement with respect to detailed signals of interannual variability is poor; and correlations to the North Atlantic Oscillation are weaker in the North Atlantic and Arctic than in the equatorial region and South Subtropics. Linear trends for 1995 to 2009 indicate increased uptake and generally correspond between methodologies in the North Atlantic, but there is disagreement amongst methodologies in the equatorial region and South Subtropics
Abstract. Sustainable management and utilisation of the North-west European Shelf (NWS) seas could benefit from reliable forecasts of the marine environment on monthly to seasonal timescales. Recent advances in global seasonal forecast systems and regional marine reanalyses for the NWS allow us to investigate the potential for seasonal forecasts of the state of the NWS. We identify three possible approaches to address this issue: (A) basing NWS seasonal forecasts directly on output from the Met Office's GloSea5 global seasonal forecast system; (B) developing empirical downscaling relationships between large-scale climate drivers predicted by GloSea5 and the state of the NWS; and (C) dynamically downscaling GloSea5 using a regional model. We show that the GloSea5 system can be inadequate for simulating the NWS directly (approach A). We explore empirical relationships between the winter North Atlantic Oscillation (NAO) and NWS variables estimated using a regional reanalysis (approach B). We find some statistically significant relationships and present a skillful prototype seasonal forecast for English Channel sea surface temperature. We find large-scale relationships between inter-annual variability in the boundary conditions and inter-annual variability modelled on the shelf, suggesting that dynamic downscaling may be possible (approach C). We also show that for some variables there are opposing mechanisms correlated with the NAO, for which dynamic downscaling may improve on the skill possible with empirical forecasts. We conclude that there is potential for the development of reliable seasonal forecasts for the NWS and consider the research priorities for their development.
The complex nature of the energy industry across extraction, transportation, processing, delivery and decommissioning creates significant challenges to how the sector responds, adapts and mitigates against risks posed by the changing future climate. Any disruption in this interconnected system will affect both industry and society. For example, in the summer of 2005 Hurricane Katrina and a month later Hurricane Rita had wide reaching impacts on the US offshore Oil and Gas industry which resulted in an increase in global oil prices due to loss of production and refinery shutdowns in the Gulf of Mexico. Preparing, mitigating and adapting to these climate changes is dependent upon identifying appropriate climate indicators as well as the associated critical operational thresholds and design criteria of the identified vulnerable assets. The characterization and understanding of the likely changes in these climate indicators will form the basis for adaptation plans and mitigating actions. The Met Office in collaboration with energy industry partners, under the Copernicus Clim4energy European project, has developed a Climate Change Risk Assessment tool, which allows the visualization and extraction of the most recent sea level and wave climate information to evaluate their future changes. This study illustrates the application of this tool for evaluation of the potential vulnerability of an offshore infrastructure in the North Sea. The analysis shows that for this asset there is a small increase in sea level of 0.20–0.30 m at the location of interest by 2050. However, there is a small decrease or no consistent changes projected in the future wave climate. This wave signal is small compared to the uncertainty of the wave projections and the associated inter-annual variability. Therefore, for the 2050s time horizon, at the location of interest, there is no strong impact of climate change at the annual scale on the significant wave height, the sea level and thus the associated climate change driven extreme water level. However, further analysis are required at the seasonal and monthly scales.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.