Predicted Sea Level Rise Research Articles

Optimal transportation and shoreline infrastructure investment planning under a stochastic climate future

This paper studies the problem of optimal long-term transportation investment planning to protect from and mitigate impacts of climate change on roadway performance. The problem of choosing the extent, specific system components, and timing of these investments over a long time horizon (e.g., 40–60 years) is modeled as a multi-stage, stochastic, bi-level, mixed-integer program wherein cost-effective investment decisions are taken in the upper level. The effects of possible episodic precipitation events on experienced travel delays are estimated from solution of a lower-level, traffic equilibrium problem. The episodic events and longer-term sea level changes exist on different time scales, making their integration a crucial element in model development. The optimal investment strategy is obtained at a Stackelberg equilibrium that is reached upon solution to the bilevel program. A recursive noisy genetic algorithm (rNGA), designed to address large-scale applications, is proposed for this purpose. The rNGA seeks the optimal combination of investment decisions to take now given only probabilistic information on the predicted sea level rise trend for a long planning horizon and associated likely extreme climatic events (in terms of their frequencies and intensities) that might arise over that planning period. The proposed solution method enables the evaluation of decisions concerning where, when and to what level to make infrastructure investments. The proposed rNGA has broad applicability to more general multi-stage, stochastic, bilevel, nonconvex, mixed integer programs that arise in many applications. The proposed solution methodology is demonstrated on an example representing a portion of the Washington, D.C. Greater Metropolitan area adjacent to the Potomac River.

Global Sea Surface Temperature and Sea Level Rise Estimation with Optimal Historical Time Lag Data

Prediction of global temperatures and sea level rise (SLR) is important for sustainable development planning of coastal regions of the world and the health and safety of communities living in these regions. In this study, climate change effects on sea level rise is investigated using a dynamic system model (DSM) with time lag on historical input data. A time-invariant (TI-DSM) and time-variant dynamic system model (TV-DSM) with time lag is developed to predict global temperatures and SLR in the 21st century. The proposed model is an extension of the DSM developed by the authors. The proposed model includes the effect of temperature and sea level states of several previous years on the current temperature and sea level over stationary and also moving scale time periods. The optimal time lag period used in the model is determined by minimizing a synthetic performance index comprised of the root mean square error and coefficient of determination which is a measure for the reliability of the predictions. Historical records of global temperature and sea level from 1880 to 2001 are used to calibrate the model. The optimal time lag is determined to be eight years, based on the performance measures. The calibrated model was then used to predict the global temperature and sea levels in the 21st century using a fixed time lag period and moving scale time lag periods. To evaluate the adverse effect of greenhouse gas emissions on SLR, the proposed model was also uncoupled to project the SLR based on global temperatures that are obtained from the Intergovernmental Panel on Climate Change (IPCC) emission scenarios. The projected SLR estimates for the 21st century are presented comparatively with the predictions made in previous studies.

Annual Greenland accumulation rates (2009–2012) from airborne snow radar

Abstract. Contemporary climate warming over the Arctic is accelerating mass loss from the Greenland Ice Sheet through increasing surface melt, emphasizing the need to closely monitor its surface mass balance in order to improve sea-level rise predictions. Snow accumulation is the largest component of the ice sheet's surface mass balance, but in situ observations thereof are inherently sparse and models are difficult to evaluate at large scales. Here, we quantify recent Greenland accumulation rates using ultra-wideband (2–6.5 GHz) airborne snow radar data collected as part of NASA's Operation IceBridge between 2009 and 2012. We use a semiautomated method to trace the observed radiostratigraphy and then derive annual net accumulation rates for 2009–2012. The uncertainty in these radar-derived accumulation rates is on average 14 %. A comparison of the radar-derived accumulation rates and contemporaneous ice cores shows that snow radar captures both the annual and long-term mean accumulation rate accurately. A comparison with outputs from a regional climate model (MAR) shows that this model matches radar-derived accumulation rates in the ice sheet interior but produces higher values over southeastern Greenland. Our results demonstrate that snow radar can efficiently and accurately map patterns of snow accumulation across an ice sheet and that it is valuable for evaluating the accuracy of surface mass balance models.

Greenland Ice Sheet seasonal and spatial mass variability from model simulations and GRACE (2003–2012)

Abstract. Improving the ability of regional climate models (RCMs) and ice sheet models (ISMs) to simulate spatiotemporal variations in the mass of the Greenland Ice Sheet (GrIS) is crucial for prediction of future sea level rise. While several studies have examined recent trends in GrIS mass loss, studies focusing on mass variations at sub-annual and sub-basin-wide scales are still lacking. At these scales, processes responsible for mass change are less well understood and modeled, and could potentially play an important role in future GrIS mass change. Here, we examine spatiotemporal variations in mass over the GrIS derived from the Gravity Recovery and Climate Experiment (GRACE) satellites for the January 2003–December 2012 period using a "mascon" approach, with a nominal spatial resolution of 100 km, and a temporal resolution of 10 days. We compare GRACE-estimated mass variations against those simulated by the Modèle Atmosphérique Régionale (MAR) RCM and the Ice Sheet System Model (ISSM). In order to properly compare spatial and temporal variations in GrIS mass from GRACE with model outputs, we find it necessary to spatially and temporally filter model results to reproduce leakage of mass inherent in the GRACE solution. Both modeled and satellite-derived results point to a decline (of −178.9 ± 4.4 and −239.4 ± 7.7 Gt yr−1 respectively) in GrIS mass over the period examined, but the models appear to underestimate the rate of mass loss, especially in areas below 2000 m in elevation, where the majority of recent GrIS mass loss is occurring. On an ice-sheet-wide scale, the timing of the modeled seasonal cycle of cumulative mass (driven by summer mass loss) agrees with the GRACE-derived seasonal cycle, within limits of uncertainty from the GRACE solution. However, on sub-ice-sheet-wide scales, some areas exhibit significant differences in the timing of peaks in the annual cycle of mass change. At these scales, model biases, or processes not accounted for by models related to ice dynamics or hydrology, may lead to the observed differences. This highlights the need for further evaluation of modeled processes at regional and seasonal scales, and further study of ice sheet processes not accounted for, such as the role of subglacial hydrology in variations in glacial flow.

Predicting the Extent of Inundation due to Sea-Level Rise: Al Hamra Development, Ras Al Khaimah, UAE. A Pilot Project

Abstract As new information is received, predictions of sea-level rise resulting from global warming continue to be revised upwards. Measurements indicate that the rise in sea-level is continuing at, or close to, the worst case forecasts (Kellet et al. 2014). Coastal areas are coming under increasing risk of inundation and flooding as storms are predicted to increase in frequency and severity, adding to the risk of inundation due to higher sea levels. Stakeholders, government agencies, developers and land owners require accurate, up to date information to be able to protect coastal areas. Geographic Information Systems (GIS) along with accurate remote sensing technologies such as LiDAR provides the best means for delivering this information. Using these technologies, this paper predicts the risk posed to a large multi-use development in the emirate of Ras Al Khaimah, UAE. This development, Al Hamra Village, is situated on the coast of the Arabian Gulf. Al Hamra’s physical relationship to the Gulf is in common with other developments in Ras Al Khaimah in its and for this reason has been used as a pilot project. The resulting GIS model shows that Al Hamra is indeed at risk from predicted flood events. How this information can be used as a planning tool for numerous strategies is discussed in this paper.

Modeling the WorldView-derived seasonal velocity evolution of Kennicott Glacier, Alaska

ABSTRACTGlacier basal motion generates diurnal to multi-annual fluctuations in glacier velocity and mass flux. Understanding these fluctuations is important for prediction of future sea-level rise and for gaining insight into glacier physics and erosion. Here, we derive glacier velocity through cross-correlation of WorldView satellite imagery to document the evolution of ice surface velocity on Kennicott Glacier, Alaska, over the 2013 melt season. The summer speedup is spatially uniform over a ~12 km2area, over which the spring velocity varies significantly. Velocity increases by 1.4-fold to tenfold across the study domain, with larger values where spring velocities are low. To investigate the cross-glacier distribution of basal motion required to explain the observed surface speedup, we employ a two-dimensional cross-sectional glacier flow model. We find the model is insensitive to the spatial distribution of basal slip because stress gradient ice coupling diffuses the surface expression of the basal velocity field. While the temporal evolution of the subglacial hydrologic system is critical for predicting a glacier's response to meltwater inputs, our work suggests that glacier and ice-sheet models do not require a detailed representation of subglacial hydrology to accurately capture the spatial pattern of glacier speedup.

Geochronology and historical deposition of trace metals in three tropical estuaries in the Gulf of Guinea

The depositional histories of trace metals (Pb, Cu, and Zn) in sediment cores from three Ghanaian estuaries were reconstructed using radioisotope-derived (210Pb and 137Cs) geochronologies. A core collected from each of the Amisa, Sakumo II and Volta estuaries was analyzed for trace metals and radionuclides. Lead-210 and 137Cs dating via gamma spectroscopy, and trace metal analysis via inductively coupled plasma mass spectrometry (ICP-MS) were used in deriving sedimentation rates, geochronologies and accumulation trends of trace metals. The sedimentation rates in all three estuaries (in the range of 0.54–0.83 cm yr−1) were greater than the predicted sea level rise (∼0.33 cm yr−1) for the Accra Coast of Ghana. The 210Pb depositional rates of 6.83 dpm cm−2 y−1, 2.74 dpm cm−2 y−1 and 1.75 dpm cm−2 y−1 estimated for the Amisa, Sakumo II and Volta estuaries, respectively, are higher than those recorded in other latitudes. Trace metal analysis revealed differences in the concentrations of Cu, Pb and Zn between deeper and surficial layers of each core to be in the range of 10–20%, which is well within the natural variations attributed to geochemical factors. Relative to the Amisa and Volta estuaries, the temporal profiles of Al-normalized metal concentrations and estimated fluxes suggest anthropogenic processes augmented the natural fluxes of trace metals, particularly Zn into the Sakumo II estuary during the last 7 years.

Impacts of predicted sea level rise on land use/land cover categories of the adjacent coastal areas of Mumbai megacity, India

Physical and ecological responses of the coastal areas in the vicinity of Mumbai, India, due to relative sea level rise are examined by different inundation scenarios. Evaluation of potential habitat loss under sea level rise was made by incorporating the land use/land cover (LULC) adopted from the digital elevation model with the satellite imagery. LULC categories overlaid on 1.0, 2.0, 3.0 and 4.0 m coastal elevation showed that the coastal areas of Mumbai were mostly covered by vegetation followed by barren land, agricultural land, urban areas and water bodies. For the relative sea level rise scenarios of 1.0, 2.0, 3.0 and 4.0 m, the tidal inundation areas were estimated to be 257.85, 385.58, 487.56 and 570.63 km2, respectively, using GIS techniques. The losses of urban areas were also estimated at 25.32, 41.64, 54.61 and 78.86 km2 for the 1.0, 2.0, 3.0 and 4.0 m relative sea level rise, respectively, which is most alarming information for the most populated city on the eastern coast of India. The results conclude that relative sea level rise scenario will lead profound impacts on LULC categories as well as on coastal features and landforms in the adjoining part of Mumbai. The sea level rise would also reduce the drainage gradients that promote flooding condition to rainstorms and subsequently increase saltwater intrusion into coastal regions. Alterations in the coastal features and landforms correlated with inundation characteristics that make the coastal region more vulnerable in the coming decades due to huge development activities and population pressures in Mumbai.

Responses of estuarine salinity and transport processes to sea level rise in the Zhujiang (Pearl River) Estuary

Understanding the changes of hydrodynamics in estuaries with respect to magnitude of sea level rise is important to understand the changes of transport process. Based on prediction of sea level rise over the 21st century, the Zhujiang (Pearl River) Estuary was chosen as a prototype to study the responses of the estuary to potential sea level rise. The numerical model results show that the average salt content, saltwater intrusion distance, and stratification will increase as the sea level rises. The changes of these parameters have obvious seasonal variations. The salt content in the Lingdingyang shows more increase in April and October (the transition periods). The saltwater intrusion distance has larger increase during the low-flow periods than during the high-flow periods in the Lingdingyang. The result is just the opposite in Modaomen. The stratification and its increase are larger during the low-flow periods than during the high-flow periods in Lingdingyang. The response results of transport processes to sea level rise demonstrate that: (1) The time of vertical transport has pronounced increase. The increased tidal range and currents would reinforce the vertical mixing, but the increased stratification would weaken the vertical exchange. The impact of stratification changes overwhelms the impact of tidal changes. It would be more difficult for the surface water to reach the bottom. (2) The lengthways estuarine circulation would be strengthened. Both the offshore surface residual current and inshore bottom residual current will be enhanced. The whole meridional resident flow along the transect of the Lingdingyang would be weakened. These phenomena are caused by the decrease of water surface slope (WWS) and the change of static pressure with the increase of water depth under sea level rise.

New Insights from 60 Years of Crevasse Research

Deep cracks in the ice may provide insight into glacier decay and help predict sea level rise.

Pleistocene Rocks Tell Tale of Past Ice Sheet Melting

Researchers examine evidence from a past interglacial period to predict sea level rise in the future.

Absolute Surface Elevations Accuracies Assessment of Different DEMs Using Ground Truth Data Over Kingdom of Bahrain

For small islands, accurate digital elevation model (DEM) can help to understand the sea level rise prediction and scenarios impact on coastal zones, flooding risks assessment, flood inundation modelling, erosion and landslide, and environmental disaster process management. Currently, DEMs are available from several different sources using space borne systems, photogrammetry, surveying, topographic contour lines, etc. The aim of this study focuses on a comparison of absolute surface heights accuracies of four independent DEMs datasets over small island as Kingdom of Bahrain. The first two DEMs were acquired with space borne, Shuttle Radar Topographic Mission (SRTM-V4.1) and Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER-V2.1) with 30 m pixel size. The second two DEMs with 2.5 m (DEM-2.5) and 5 m (DEM-5) spatial resolutions were derived from two different topographic contour lines maps at scales, respectively, 1:5000 and 1:25000 using inverse distance weighted (IDW) interpolation method. For validation purposes, a datasets of 400 ground control points uniformly distributed over the study site were used. They were measured using a Differential Global Position System (DGPS) assuring ± 1 and ± 2 cm accuracies, respectively, for planimetry and altimetry. The obtained results show that the derived DEM-2.5 exhibit the best accuracy ± 0.55 m which is excellent by reference to the tolerance or maximum error ± 0.78 m calculated based on errors sources propagation. As well, the DEM-5 shows very good accuracy ± 1.37 m by reference to the calculated tolerance ± 1.54 m. Then, SRTM shows a satisfactory performance with ± 3.00 m accuracy which is less than the absolute vertical height accuracy (± 5.6 m) advocated by NASA for African continent and Middle-East regions. Finally, the achieved ASTER accuracy ± 8.40 m is better than the estimated error (± 17.01 m) by USGS and JAXA.

Prospects for predicting changes to coastal wetland bird populations due to accelerated sea level rise

Accelerating sea level rise (SLR) is likely to cause considerable changes to estuarine and other coastal wetlands. Efforts to forecast the effects of SLR on coastal wetland vegetation communities should be useful in making predictions for individual species that depend upon those communities. However, considerable uncertainty exists when predicting a chain of events that passes from the global climate to local effects to implications for a single species. One component of this uncertainty is the classification resolution used by SLR landscape change models such as the Sea Level Affects Marshes Model (SLAMM). To isolate and assess the effects of this kind of uncertainty on species‐level SLR prediction, we analyzed surveys of birds and plants in the lower Altamaha River and its estuary in Georgia, USA. For 19 marsh and forest bird species, we tested the predictive value of three classes of covariates of site occupancy: (1) field‐measured habitat variables and spatial information, (2) information available from a SLAMM map, including the spatial configuration of the SLAMM habitat classes, and (3) SLAMM habitat class alone. We found that the predictive ability of occupancy models built from these three kinds of information varies widely among species. We therefore suggest criteria for classifying species according to the amount of detail necessary to describe their habitat niche, and thus to maximize the accuracy of predictive models. We point out that for species with habitat requirements that can be represented well by SLAMM classes, such as the Clapper Rail, forecasts of SLR‐induced population change are probably feasible. For species with more narrow habitat needs, however, such as the Seaside Sparrow, reasonable predictions of SLR effects may not be possible without further refinement of SLR landscape change models. We suggest that improved thematic resolution of such models should be a priority, if the implications of SLR models for individual species are to be ascertained fully.

Mental health issues from rising sea level in a remote coastal region of the Solomon Islands: current and future

There is little published research about mental health and climate change in the Pacific, including Solomon Islands. Solomon Islands has one of the highest rates of sea-level rise globally. The aim of this research was to document mental health issues related to sea-level rise for people in East Malaita, Solomon Islands. A cross-sectional study was carried out in six low-lying villages in East Malaita, Solomon Islands. The researcher travelled to villages by dugout canoe. In addition to quantitative, closed-ended questions, open-ended questions with villagers explored individual and community responses to rising sea level. Of 60 people asked, 57 completed the questionnaire. Of these, 90% reported having seen a change in the weather patterns. Nearly all participants reported that sea-level rise is affecting them and their family and is causing fear and worry on a personal and community level. Four themes emerged from the qualitative analysis: experience of physical impacts of climate change; worry about the future; adaptation to climate change; government response needed. Given predictions of ongoing sea-level rise in the Pacific it is essential that more research is conducted to further understand the human impact of climate change for small island states which will inform local, provincial and national-level mental health responses.

Towards a global land subsidence map

Abstract. Land subsidence is a global problem, but a global land subsidence map is not available yet. Such map is crucial to raise global awareness of land subsidence, as land subsidence causes extensive damage (probably in the order of billions of dollars annually). With the global land subsidence map relative sea level rise predictions may be improved, contributing to global flood risk calculations. In this paper, we discuss the approach and progress we have made so far in making a global land subsidence map. Initial results will be presented and discussed, and we give an outlook on the work needed to derive a global land subsidence map.

Coastal vulnerability assessment of the predicted sea level rise in the coastal zone of Krishna–Godavari delta region, Andhra Pradesh, east coast of India

The Krishna–Godavari coastal region in east coast of India has a 525.15-km-long coastline with low-lying tidal mudflats, beaches, mangrove swamp, creek and tidal channels. Recently, the increasing frequency of tropical cyclones in the Bay of Bengal, i.e., Phylin and Hudhud in Andhra Pradesh coast, and the devastating impact of the 2004 tsunami in India increased the significance in assessing the vulnerability of the coastal lands to inundation and flooding, notably in the context of climate change-induced sea level rise. This study aims to estimate a coastal vulnerability index (CVI) for the coastal subregion of Krishna–Godavari delta and to use the calculated index to evaluate the vulnerability of 14 coastal talukas of the Krishna–Godavari delta region. This CVI is calculated by using four geological and three physical parameters characterizing the vulnerability of the study coastal region, including regional slope, coastal elevation, geomorphology, significant wave height, mean tidal range and relative sea level using different conventional and remotely sensed data. Using a composite coastal vulnerability index based on the relative risk rating of those parameters, each of the 14 coastal talukas was classified according to their vulnerability. The CVI results depict that coasts are least and most vulnerable to inundation, flooding and erosion of coastal lands where geological parameters are more efficient to CVI. The paper alerts to decision makers and planners to mitigate the natural disaster and manage the coastal zone and is a primary step toward prioritizing coastal lands for climate change adaptation strategies in the view of increased storminess and projected sea level rise.

HOMOGENEOUS GEOVISUALIZATION OF COASTAL AREAS FROM HETEROGENEOUS SPATIO-TEMPORAL DATA

Abstract. On coastal areas, recent increase in production of open-access high-quality data over large areas reflects high interests in modeling and geovisualization, especially for applications of sea level rise prediction, ship traffic security and ecological protection. Research interests are due to tricky challenges from the intrinsic nature of the coastal area, which is composed of complex geographical objects of which spatial extents vary in time, especially in the intertidal zone (tides, sands, etc.). Another interest is the complex modeling of this area based on imprecise cartographic objects (coastline, highest/lowest water level, etc.). The challenge of visualizing such specific area comes thus from 3D+t information, i.e. spatio-temporal data, and their visual integration. In this paper, we present a methodology for geovisualization issues over coastal areas. The first challenge consists in integrating multi-source heterogeneous data, i.e. raster and vector, terrestrial and hydrographic data often coming from various ‘paradigms’, while providing a homogeneous geovisualization of the coastal area and in particular the phenomenon of the water depth. The second challenge consists in finding various possibilities to geovisualize this dynamic geographical phenomenon in controlling the level of photorealism in hybrid visualizations. Our approach is based on the use of a high-resolution Digital Terrain Model (DTM) coming from high resolution LiDAR data point cloud, tidal and topographic data. We present and discuss homogeneous hybrid visualizations, based on LiDAR and map, and on, LiDAR and orthoimagery, in order to enhance the realism while considering the water depth.

Effective LiDAR-based modelling and visualisation of managed retreat scenarios for coastal planning: An example from the southern UK

This study presents a visualisation of proposed managed retreat options for a scientifically important estuarine area within the South Downs National Park, United Kingdom. A range of GIS and remote sensing techniques are used to facilitate the manipulation of LiDAR data for landscape visualisation. Landscape and flood modelling methods are incorporated and developed. The extent of inundation and tidal prism are assessed following proposed coastal planning and landscape changes.Ground-truthed LiDAR elevation data are used to model a series of proposed managed retreat scenarios (scenarios A, B, C and D) for the river Cuckmere estuary. Following detailed landscape visualisation and manipulation, a 'pour-point' flood modelling method is used to simulate tidal inundation and calculate the tidal prism for proposed managed retreat scenarios using current tidal data. Further analysis was undertaken to assess the potential impact of sea level rise (for the years 2025, 2050, 2075 and 2100) in this estuarine area if the managed retreat proposals are actioned.The use of GIS-based techniques to alter and visualise landscape features under proposed managed retreat alternatives proved successful. Clear differences in tidal inundation and the tidal prism were observed following the modelling of large scale engineering features. These differences in inundation were apparent both using current tidal data and predicted sea level rise data. The tidal prism was strongly affected by the choice of the managed retreat scenario. Subtle changes in landscape features were shown to strongly affect the water accommodation potential of the estuary. The study demonstrates that high resolution data facilitated the detailed visualisation and modelling of fine-scale landscape change. Furthermore, the alteration of landscape features suggested that the area of inundation could potentially vary by over 20% (>100 ha); and that these landscape changes would noticeably alter tidal impact in this estuary.

Tidal dynamics in a changing lagoon: Flooding or not flooding the marginal regions

Coastal lagoons are low-lying systems under permanent changes motivated by natural and anthropogenic factors. Ria de Aveiro is such an example with its margins currently threatened by the advance of the lagoonal waters recorded during the last decades. This work aims to study the tidal modifications found between 1987 and 2012 in this lagoon, motivated by the main channels deepening which induce higher inland tidal levels. Additionally it aims to study the impact that protective walls designed to protect the margins against flooding may have in those modifications under sea level rise predictions. The hydrodynamic model ELCIRC previously calibrated for Ria de Aveiro was used and tidal asymmetry, tidal ellipses and residual currents were analyzed for different scenarios, considering the mean sea level rise predicted for 2100 and the implementation of probable flood protection walls. Results evidenced that lagoon dominance remained unchanged between 1987 and 2012, but distortion decreased/increased in the deeper/shallower channels. The same trend was found under mean sea level rise conditions. Tidal currents increased over this period inducing an amplification of the water properties exchange within the lagoon, which will be stronger under mean sea level rise conditions. The deviations between scenarios with or without flood protection walls can achieve 60% for the tidal distortion and residual currents and 20% for the tidal currents, highlighting that tidal properties are extremely sensitive to the lagoon geometry. In summary, the development of numerical modelling applications dedicated to study the influence of mean sea level rise on coastal low-lying systems subjected to human influence should include structural measures designed for flood defence in order to accurately predict changes in the local tidal properties.

Changes in Global Ocean Bottom Properties and Volume Transports in CMIP5 Models under Climate Change Scenarios

AbstractChanges in bottom temperature, salinity, and density in the global ocean by 2100 for CMIP5 climate models are investigated for the climate change scenarios RCP4.5 and RCP8.5. The mean of 24 models shows a decrease in density in all deep basins, except the North Atlantic, which becomes denser. The individual model responses to climate change forcing are more complex: regarding temperature, the 24 models predict a warming of the bottom layer of the global ocean; in salinity, there is less agreement regarding the sign of the change, especially in the Southern Ocean. The magnitude and equatorward extent of these changes also vary strongly among models. The changes in properties can be linked with changes in the mean transport of key water masses. The Atlantic meridional overturning circulation weakens in most models and is directly linked to changes in bottom density in the North Atlantic. These changes are the result of the intrusion of modified Antarctic Bottom Water, made possible by the decrease in North Atlantic Deep Water formation. In the Indian, Pacific, and South Atlantic Oceans, changes in bottom density are congruent with the weakening in Antarctic Bottom Water transport through these basins. The authors argue that the greater the 1986–2005 meridional transports, the more changes have propagated equatorward by 2100. However, strong decreases in density over 100 yr of climate change cause a weakening of the transports. The speed at which these property changes reach the deep basins is critical for a correct assessment of the heat storage capacity of the oceans as well as for predictions of future sea level rise.