STUDY ON SEAWATER THERMAL EXPANSION AND SEA LEVEL RISE USING LONG-TERM REANALYSIS DATA FORA-WNP30

Author(s):  
Zhiyuan LUO ◽  
Satoshi TAKEWAKA
2020 ◽  
Vol 191 ◽  
pp. 103212 ◽  
Author(s):  
Bing Yuan ◽  
Jian Sun ◽  
Binliang Lin ◽  
Fanyi Zhang

2021 ◽  
pp. 228-248
Author(s):  
Eelco J. Rohling

This chapter considers processes we cannot reverse, at least in the short term: it is already too late. These are processes related to slow responses or feedbacks in the climate system, including ocean warming and sea-level rise, and they will continue to drive change whatever we do. As explained in the chapter, ocean warming operates on timescales of centuries and resulting changes in Earth’s major ice sheets take many centuries to millennia. Sea-level rise is caused by thermal expansion due to ocean warming and by reduction in the volume of land-based ice, due to global warming. Because of the timescales involved, the oceans will keep warming for centuries, dragging global mean temperature along with them, and sea level will also rise for many centuries to come. The chapter reviews the impacts of these processes, whose inevitability means that humanity has no choice but to adapt to them.


2021 ◽  
Author(s):  
Bing Yuan ◽  
Jian Sun ◽  
Binliang Lin ◽  
Fanyi Zhang

<p>Globally the riverine sediment supply to estuaries is decreasing and the mean sea level is rising, while the effects of these changes on the long-term estuarine morphodynamics have not been fully investigated. An idealized numerical model was used to explore the long-term morphodynamics of a large estuary subject to these changes. In the model, a funnel-shaped channel with fixed banks, constant riverine water and sediment fluxes, a single grain size and a semi-diurnal tide were used. A range of values of changes in the sediment supply (50-90% reduction) and sea level (1-5~mm/yr increase) were considered. Starting from an equilibrium state for an initial sediment supply, the estuary shifts to a new equilibrium for the considered changes on a timescale of millennia. Half of the bed level change occurs within several hundreds of years. A larger decrease in the sediment supply leads to a stronger bed erosion, while the corresponding adjustment time has minor changes in its range for the considered settings. When combined with sea level rise, the erosion is weakened and the adjustment time is shortened. The equilibrium state under sea level rise is characterized by a bed level keeping pace with the sea level and a significant amount of sediment being trapped in the estuary. Additional numerical experiments that use more realistic geometry and forcing of the Yangtze Estuary show that overall erosion of the estuary is expected for centuries.</p>


2017 ◽  
Vol 10 (6) ◽  
pp. 2495-2524 ◽  
Author(s):  
Alexander Nauels ◽  
Malte Meinshausen ◽  
Matthias Mengel ◽  
Katja Lorbacher ◽  
Tom M. L. Wigley

Abstract. Sea level rise (SLR) is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from SLR and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-the-art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact science and climate policy analysis, which require a sea level projection methodology that is flexible with regard to the climate scenario yet comprehensive and bound by the physical constraints provided by process-based models. To fill this gap, we present a sea level model that emulates global-mean long-term process-based model projections for all major sea level components. Thermal expansion estimates are calculated with the hemispheric upwelling-diffusion ocean component of the simple carbon-cycle climate model MAGICC, which has been updated and calibrated against CMIP5 ocean temperature profiles and thermal expansion data. Global glacier contributions are estimated based on a parameterization constrained by transient and equilibrium process-based projections. Sea level contribution estimates for Greenland and Antarctic ice sheets are derived from surface mass balance and solid ice discharge parameterizations reproducing current output from ice-sheet models. The land water storage component replicates recent hydrological modeling results. For 2100, we project 0.35 to 0.56 m (66 % range) total SLR based on the RCP2.6 scenario, 0.45 to 0.67 m for RCP4.5, 0.46 to 0.71 m for RCP6.0, and 0.65 to 0.97 m for RCP8.5. These projections lie within the range of the latest IPCC SLR estimates. SLR projections for 2300 yield median responses of 1.02 m for RCP2.6, 1.76 m for RCP4.5, 2.38 m for RCP6.0, and 4.73 m for RCP8.5. The MAGICC sea level model provides a flexible and efficient platform for the analysis of major scenario, model, and climate uncertainties underlying long-term SLR projections. It can be used as a tool to directly investigate the SLR implications of different mitigation pathways and may also serve as input for regional SLR assessments via component-wise sea level pattern scaling.


2019 ◽  
Vol 124 (12) ◽  
pp. 9235-9257 ◽  
Author(s):  
Ryan P. Mulligan ◽  
David J. Mallinson ◽  
Gregory J. Clunies ◽  
Alexander Rey ◽  
Stephen J. Culver ◽  
...  

2016 ◽  
Author(s):  
Alexander Nauels ◽  
Malte Meinshausen ◽  
Matthias Mengel ◽  
Katja Lorbacher ◽  
Tom M. L. Wigley

Abstract. Sea level rise is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from Sea Level Rise (SLR) and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-the-art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact science and climate policy advice, which require a sea level projection methodology that is flexible with regard to the climate scenario yet comprehensive and bound to the physical constraints provided by process-based models. To fill this gap, we present a sea level model that emulates global mean long-term process-based model projections for all major sea level components. Thermal expansion estimates are calculated with the hemispheric upwelling-diffusion ocean component of the simple carbon cycle-climate model MAGICC, which has been updated and calibrated against CMIP5 ocean temperature profiles and thermal expansion data. Global glacier contributions are estimated based on a parameterization constrained by transient and equilibrium process-based projections. Sea level contribution estimates for Greenland and Antarctic ice sheets are derived from surface mass balance and solid ice discharge parameterizations reproducing current output from ice-sheet models. The land water storage component replicates the latest hydrological modeling results. For 2100, we project 0.38 m to 0.59 m (66 % range) total SLR based on the RCP2.6 scenario, 0.48 m to 0.68 m for RCP4.5, 0.48 m to 0.72 m for RCP6.0, and 0.67 m to 0.97 m for RCP8.5. These projections lie within the range of the latest IPCC SLR estimates. SLR projections for 2300 yield median responses of 0.97 m for RCP2.6, 1.66 m for RCP4.5, 2.32 m for RCP6.0, and 5.12 m for RCP8.5. The MAGICC sea level model provides a powerful and efficient platform for probabilistic uncertainty analyses of long-term SLR projections. It can be used as a tool to directly investigate the SLR implications of different mitigation pathways and may also serve as input for regional SLR assessments via component-wise sea level pattern scaling.


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