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Land ice discharge from the Antarctic continent into the ocean is restrained by ice shelves, floating extensions of grounded ice that buttress the glacier outflow. The ongoing thinning of these ice shelves – largely due to enhanced melting at their base in response to global warming – is known to accelerate the release of glacier meltwater into the world oceans, augmenting global sea level. Mechanisms of ocean heat intrusion under the ice base are therefore crucial to project the future of Antarctic ice shelves. Furthermore, ice shelves are weakened by the presence of kilometer-wide full-thickness ice rifts, which are observed all around Antarctica. However, their impact on ocean circulation around and below ice shelves has been largely unexplored as ocean models are commonly characterized by resolutions that are too coarse to resolve their presence. Here, we apply the Massachusetts Institute of Technology general circulation model at high resolution to investigate the sensitivity of sub-shelf ocean dynamics and ice-shelf melting to the presence of a kilometer-wide rift in proximity of the ice front. We find that (a) the rift curtails water and heat intrusion beneath the ice-shelf base and (b) the basal melting of a rifted ice shelf is on average 20 % lower than for an intact ice shelf under identical forcing. Notably, we calculate a significant reduction in melting rates of up to 30 % near the grounding line of a rifted ice shelf. We therefore posit that rifts and their impact on the sub-shelf dynamics are important to consider in order to accurately reproduce and project pathways of heat intrusion into the ice-shelf cavity. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ -MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1 σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ -MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ-MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]

Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ -MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. [ABSTRACT FROM AUTHOR]

Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, ocean mass change is caused by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides an analysis of regional patterns of contemporary mass redistribution associated with barystatic SLC since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself), temporal (related to the temporal variability in the time series) and spatial–structural (related to the spatial distribution of the mass change sources). Regional patterns (fingerprints) of barystatic SLC are computed from a range of estimates of the individual freshwater sources and used to analyze the different types of uncertainty. Combining all contributions, we find that regional sea-level trends range from -0.4 to 3.3 mmyr-1 for 2003–2016 and from -0.3 to 2.6 mmyr-1 for 1993–2016, considering the 5–95th percentile range across all grid points and depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.6 to 1.3 mmyr-1 for 2003–2016 and from 0.4 to 0.8 mmyr-1 for 1993–2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, responsible on average for 65% of the total uncertainty, followed by the spatial–structural and intrinsic uncertainties, which contribute on average 16% and 18% , respectively. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for 35 %–60 % of the total uncertainty, depending on the region of interest. Another important contribution comes from the spatial–structural uncertainty from Antarctica and land water storage, which shows that different locations of mass change can lead to trend deviations larger than 20%. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning. [ABSTRACT FROM AUTHOR]

Ocean mass change is one of the main drivers of present-day sea-level change (SLC). Also known as barystatic SLC, it is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. While many studies have quantified the present-day barystatic contribution to global mean SLC, fewer works have looked into regional changes. This study provides a comprehensive analysis of regional barystatic SLC trends since 1993 (the satellite altimetry era), with a focus on the uncertainty budget. We consider three types of uncertainties: intrinsic (the uncertainty from the data/model itself); temporal (related to the temporal variability in the time series); and spatial-structural (related to the location/distribution of the mass change sources). We collect a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic SLC and analyse the different types of uncertainty. When all the contributions are combined, we find that the barystatic sea-level trends regionally ranges from -0.43 to 2.55 mm year-1 for 2003-2016, and from -0.39 to 2.00 mm year-1 for 1993-2016, depending on the choice of dataset. When all types of uncertainties from all contributions are combined, the total barystatic uncertainties regionally range from 0.62 to 1.29 mm year-1 for 2003-2016, and from 0.35 to 0.90 mm year-1 for 1993-2016, also depending on the dataset choice. We find that the temporal uncertainty dominates the budget, although the spatial-structural also has a significant contribution. On average, the intrinsic uncertainty is almost negligible. The main source of uncertainty is the temporal uncertainty from the land water storage contribution, which is responsible for at least 50 % of the total uncertainty, depending on the region of interest. The second main contributions come from the spatial-structural uncertainty from Antarctica and land water storage, which show that different locations of mass change can lead to trend deviations larger than 20 %. As the barystatic SLC contribution and its uncertainty vary significantly from region to region, better insights into regional SLC are important for local management and adaptation planning. [ABSTRACT FROM AUTHOR]

Joint effects of the dynamic sea‐level rise projected changes in the large‐scale atmosphere/ocean circulation, and wave climate on hurricane‐induced extreme water levels in the Caribbean region are assessed. We use the 2D‐depth integrated ADCIRC + SWAN wave‐ocean model, baroclinically coupled to an ocean‐eddying version of the Community Earth System Model, to compare impacts of the September 2017 hurricanes with projected impacts of similar hypothetical tropical storms occurring in the future. The model predicts only minor changes in the hurricane‐induced extreme water levels for those Caribbean islands which were severely devastated by the 2017 tropical storms (Irma and Maria). That is, provided that the hurricane intensity remains at the present‐day level, the global mean sea‐level rise is the main future coastal flood risk factor. Plain Language Summary: The unique geophysical setting makes the Caribbean region extremely vulnerable to climatic changes experienced over the past few decades. The majority of the population and important infrastructures such as ports and airports are concentrated in the coastal zones which are at risk of permanent flooding as sea level continues to rise. The economy of the small Caribbean islands is heavily dependent on tourism and associated industries which are under pressure due to coral bleaching and coastal erosion. Additionally, the Caribbean region experiences intense and frequent extreme weather events which can potentially become more destructive in the future. As it is uncertain whether the hurricanes themselves will change, we investigate how the present‐day tropical cyclones will impact the Caribbean in the future. Our results indicate that islands in the Northeast Caribbean will not be at higher risk of hurricane‐induced flooding in the future if coastal infrastructure and ecosystems (coral reefs and seagrass meadows) can withstand the sea‐level rise and continue to provide important coastal protection services. In contrast, the projected increase in wave height in the southern Caribbean can lead to more coastal flooding and beach erosion in these areas. Key Points: Only minor changes in the hurricane‐induced extreme water levels in the Northeast Caribbean are expected under future climate conditionsGlobal sea‐level rise remains the main coastal flood risk factor provided that hurricane intensity is unchangedSouthern Caribbean can become more vulnerable to coastal erosion and flooding by increased wave setup [ABSTRACT FROM AUTHOR]

Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, two challenges on illumination estimation were conducted. The main advantage of testing a method on a challenge over testing it on some of the known datasets is the fact that the ground‐truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The First illumination estimation challenge (IEC#1) had only a single task, global illumination estimation. The second illumination estimation challenge (IEC#2) was enriched with two additional tracks that encompassed indoor and two‐illuminant illumination estimation. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest‐like markup for the images from the Cube++ dataset. This article focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the first and second challenge that can be useful for similar future developments. [ABSTRACT FROM AUTHOR]

Recent studies disagree about the contribution of variations in temperature and salinity of the oceans—steric change—to the observed sea‐level change. This article explores two sources of uncertainty to both global mean and regional steric sea‐level trends. First, we analyze the influence of different temperature and salinity data sets on the estimated steric sea‐level change. Next, we investigate the impact of different stochastic noise models on the estimation of trends and their uncertainties. By varying both the data sets and noise models, the global mean steric sea‐level trend and uncertainty can vary from 0.69 to 2.40 and 0.02 to 1.56 mm/year, respectively, for 1993–2017. This range is even larger on regional scales, reaching up to 30 mm/year. Our results show that a first‐order autoregressive model is the most appropriate choice to describe the residual behavior of the ensemble mean of all data sets for the global mean steric sea‐level change over the last 25 years, which consequently leads to the most representative uncertainty. Using the ensemble mean and the first‐order autoregressive noise model, we find a global mean steric sea‐level change of 1.36 ± 0.10 mm/year for 1993–2017 and 1.08 ± 0.07 mm/year for 2005–2015. Regionally, a combination of different noise models is the best descriptor of the steric sea‐level change and its uncertainty. The spatial coherence in the noise model preference indicates clusters that may be best suited to investigate the regional sea‐level budget.Plain Language Summary: Ocean temperature and salinity variations lead to changes in sea level, known as steric sea‐level change. Steric variations are important contributors to sea‐level change and reflect how the oceans have been responding to global warming. For this reason, several recent studies have quantified the contribution of steric variations to global and regional sea‐level change. However, the reported rates largely differ between studies. In this paper, we look at how the use of different temperature and salinity data sets can be one of the causes of the different estimates of steric sea‐level change published so far. We also investigate how different methods (noise models) used to obtain the rate of change can be another source of different results. We find that the rate of change can vary up to 2 mm/year for the global mean as a result of different data sets and methods used. Regionally, differences can reach up to several tens of millimeters per year. We show that the noise models should always be carefully chosen for each region, so that the rate of change is accurately estimated.Key Points: Several data sets and noise models are used to compute global and regional steric sea‐level change for 1993–2017 and 2005–2015Global mean steric sea‐level trend estimates differ up to 2 mm/year depending on the data set and noise model usedRegional sea‐level trends require more complex noise models and exhibit spatial coherency in the noise model preference [ABSTRACT FROM AUTHOR]

The effect of glacial isostatic adjustment (GIA) on the shape and gravity of the Earth is usually described by numerical models that solve for both glacial evolution and Earth's rheology, being mainly constrained by the geological evidence of local ice extent and globally distributed sea level data, as well as by geodetic observations of Earth's rotation. In recent years, GPS and GRACE observations have often been used to improve those models, especially in the context of regional studies. However, consistency issues between different regional models limit their ability to answer questions from global-scale geodesy. Examples are the closure of the sea level budget, the explanation of observed changes in Earth's rotation, and the determination of the origin of the Earth's reference frame. Here, we present a global empirical model of present-day GIA, solely based on GRACE data and on geoid fingerprints of mass redistribution. We will show how the use of observations from a single space-borne platform, together with GIA fingerprints based on different viscosity profiles, allows us to tackle the questions from global-scale geodesy mentioned above. We find that, in the GRACE era (2003–2016), freshwater exchange between land and oceans has caused global mean sea level to rise by 1.2±0.2 mm yr -1 , the geocentre to move by 0.4±0.1 mm yr -1 , and the Earth's dynamic oblateness (J2) to increase by 6.0±0.4×10-11 yr -1. [ABSTRACT FROM AUTHOR]

The mesoscale variability in the Caribbean Sea is dominated by anticyclonic eddies that are formed in the eastern part of the basin. These anticyclones intensify on their path westward while they pass the coastal upwelling region along the Venezuelan and Colombian coast. In this study, we used a regional model to show that this westward intensification of Caribbean anticyclones is steered by the advection of cold upwelling filaments. Following the thermal wind balance, the increased horizontal density gradients result in an increase in the vertical shear of the anticyclones and in their westward intensification. To assess the impact of variations in upwelling on the anticyclones, several simulations were performed in which the northward Ekman transport (and thus the upwelling strength) is altered. As expected, stronger (weaker) upwelling is associated with stronger (weaker) offshore cooling and a stronger (weaker) westward intensification of the anticyclones. Moreover, the simulations with weaker upwelling show farther advection of the Amazon and Orinoco River plumes into the basin. As a result, in these simulations the horizontal density gradients were predominantly set by horizontal salinity gradients. The importance of the horizontal density gradients driven by temperature, which are associated with the upwelling, increased with increasing upwelling strength. The results of this study highlight that both upwelling and the advection of the river plumes affect the life cycle of mesoscale eddies in the Caribbean Sea. [ABSTRACT FROM AUTHOR]

The effect of Glacial Isostatic Adjustment (GIA) on the shape and gravity of the Earth is usually described by numerical models that simultaneously solve for glacial evolution and Earth's rheology, being mainly constrained by the geological evidence of local ice extent and global sea level, as well as by geodetic observations of Earth's rotation. In recent years, GPS and GRACE observations have often been used to improve those models, especially in the context of regional studies. However, consistency issues between different regional models limit their ability to answer questions from global scale geodesy. Examples are the closure of the sea level budget, the explanation of observed changes in Earth's rotation, and the determination of the origin of the Earth's reference frame. Here, we present a global empirical model of present-day GIA, solely based on GRACE data and on geoid fingerprints of mass redistribution. We will show how the use of observations from a single space-borne platform, together with GIA fingerprints based on different viscosity profiles, allows us to tackle the questions from global scale geodesy mentioned above. We find that, in the GRACE era (2003-2016), freshwater exchange between land and oceans has caused global mean sea level to rise by 1.5 ± 0.3 mm/yr, the geocentre to move by 0.5 mm/yr, and the Earth's dynamic oblateness (J2) to increase by 6.7 × 10-11/yr. [ABSTRACT FROM AUTHOR]

We present a new approach to estimate time variations in J2. Those variations are represented as the sum of contributions from individual sources. This approach uses solely Gravity Recovery And Climate Experiment (GRACE) data and the geoid fingerprints of mass redistributions that take place both at the surface and in the interior of the solid Earth. The results agree remarkably well with those based on satellite laser ranging, while estimates of the sources explain the observed variations in J2. Seasonal variations are dominated by terrestrial water storage and by mass redistribution in the atmosphere and ocean. Trends, however, are primarily controlled by the Greenland and Antarctic ice sheets and by glacial isostatic adjustment. The positive trend from surface mass variations is larger than the negative trend due to glacial isostatic adjustment and leads to an overall rising trend during the GRACE period (2002–2017). Plain Language Summary: J2 variations indicate changes in the flattening of the Earth, which are mainly due to the Earth's response to large‐scale mass redistribution at its surface and related to ongoing climate change. Though monitored over four decades by Earth observations satellites, the contributing sources to J2 variations have not yet been accurately constrained, mostly due to deficiencies in geophysical models. In this study, we propose an approach to simultaneously estimate and interpret J2 temporal variations based on gravity observations from the Gravity Recovery And Climate Experiment satellite mission. We reconstruct the observed gravity changes by a superimposition of spatial patterns characteristic of individual sources. We find that the seasonal and interannual variations are well explained by mass changes in atmosphere, oceans, and land water storage. The secular trend, on the other hand, is mainly caused by ice sheet melt, which has a positive effect (causing the Earth to be flatter), and by the ongoing solid Earth response to past glaciations, which has a negative trend (causing the Earth to be rounder). The trend due to ice sheet melting during 2002–2017 has a larger absolute value, so that the overall trend is rising, and the Earth is currently becoming flatter. Key Points: The proposed approach allows to both estimate and interpret the J2 time seriesThe obtained data‐driven time series of J2 variations is consistent with SLR solutionsInterannual J2 variations are primarily explained by mass variations in atmosphere, oceans, and land waters [ABSTRACT FROM AUTHOR]

The glacial isostatic adjustment (GIA) signal at present day is constrained via the joint inversion of geodetic observations and GIA models for a region encompassing northern Europe, the British Isles, and the Barents Sea. The constraining data are Global Positioning System (GPS) vertical crustal velocities and GRACE (Gravity Recovery and Climate Experiment) gravity data. When the data are inverted with a set of GIA models, the best-fit model for the vertical motion signal has a χ2 value of approximately 1 and a maximum a posteriori uncertainty of 0.3-0.4mmyr-1. An elastic correction is applied to the vertical land motion rates that accounts for present-day changes to terrestrial hydrology as well as recent mass changes of ice sheets and glaciered regions. Throughout the study area, mass losses from Greenland dominate the elastic vertical signal and combine to give an elastic correction of up to +0.5mmyr-1 in central Scandinavia. Neglecting to use an elastic correction may thus introduce a small but persistent bias in model predictions of GIA vertical motion even in central Scandinavia where vertical motion is dominated by GIA due to past glaciations. The predicted gravity signal is generally less well-constrained than the vertical signal, in part due to uncertainties associated with the correction for contemporary ice mass loss in Svalbard and the Russian Arctic. The GRACE-derived gravity trend is corrected for present-day ice mass loss using estimates derived from the ICESat and CryoSat missions, although a difference in magnitude between GRACE-inferred and altimetryinferred regional mass loss rates suggests the possibility of a non-negligible GIA response here either from millennialscale or Little Ice Age GIA. [ABSTRACT FROM AUTHOR]

Tide gauge (TG) records are affected by vertical land motion (VLM), causing them to observe relative instead of geocentric sea level. VLM can be estimated from global navigation satellite system (GNSS) time series, but only a few TGs are equipped with a GNSS receiver. Hence, (multiple) neighboring GNSS stations can be used to estimate VLM at the TG. This study compares eight approaches to estimate VLM trends at 570 TG stations using GNSS by taking into account all GNSS trends with an uncertainty smaller than 1mmyr-1 within 50 km. The range between the methods is comparable with the formal uncertainties of the GNSS trends. Taking the median of the surrounding GNSS trends shows the best agreement with differenced altimetry-tide gauge (ALT-TG) trends. An attempt is also made to improve VLM trends from ALT-TG time series. Only using highly correlated along-track altimetry and TG time series reduces the SD of ALT-TG time series by up to 10 %. As a result, there are spatially coherent changes in the trends, but the reduction in the root mean square (RMS) of differences between ALT-TG and GNSS trends is insignificant. However, setting correlation thresholds also acts like a filter to remove problematic TG time series. This results in sets of ALT-TG VLM trends at 344-663 TG locations, depending on the correlation threshold. Compared to other studies, we decrease the RMS of differences between GNSS and ALT-TG trends (from 1.47 to 1.22mmyr-1), while we increase the number of locations (from 109 to 155), Depending on the methods the mean of differences between ALT-TG and GNSS trends vary between 0.1 and 0.2mmyr-1. We reduce the mean of the differences by taking into account the effect of elastic deformation due to present-day mass redistribution. At varying ALT-TG correlation thresholds, we provide new sets of trends for 759 to 939 different TG stations. If both GNSS and ALT-TG trend estimates are available, we recommend using the GNSS trend estimates because residual ocean signals might correlate over long distances. However, if large discrepancies (> 3mmyr-1) between the two methods are present, local VLM differences between the TG and the GNSS station are likely the culprit and therefore it is better to take the ALT-TG trend estimate. GNSS estimates for which only a single GNSS station and no ALT-TG estimate are available might still require some inspection before they are used in sea level studies. [ABSTRACT FROM AUTHOR]

Abstract: The Yucatan Channel connects the Caribbean Sea with the Gulf of Mexico and is the main outflow region of the Caribbean Sea. Moorings in the Yucatan Channel show high‐frequent variability in kinetic energy (50–100 days) and transport (20–40 days), but the physical mechanisms controlling this variability are poorly understood. In this study, we show that the short‐term variability in the Yucatan Channel transport has an upstream origin and arises from processes in the North Brazil Current. To establish this connection, we use data from altimetry and model output from several high resolution global models. A significant 40–70 day variability is found in the sea surface height in the North Brazil Current retroflection region with a propagation toward the Lesser Antilles. The frequency of variability is generated by intrinsic processes associated with the shedding of eddies, rather than by atmospheric forcing. This sea surface height variability is able to pass the Lesser Antilles, it propagates westward with the background ocean flow in the Caribbean Sea and finally affects the variability in the Yucatan Channel volume transport. [ABSTRACT FROM AUTHOR]

The long-term glacial isostatic adjustment (GIA) signal at present-day is constrained via joint inversion of GPS vertical land motion rates and GRACE gravity data for a region encompassing Scandinavia, northern Europe and the British Isles, and the Barents Sea. The best-fit model for the vertical motion signal has a χ² value of approximately 1 and a maximum posterior uncertainty of 0.3-0.4 mm/yr. An elastic correction is applied to the vertical land motion rates that accounts for present-day changes to terrestrial hydrology as well as recent mass changes of ice sheets and glaciered regions. Throughout the study area, mass losses from Greenland dominate the elastic vertical signal and combine to give an elastic correction of up to +0.5 mm/yr in central Scandinavia. Neglecting to use an elastic correction may thus introduce a small but persistent bias in model predictions of GIA vertical motion even in central Scandinavia where vertical motion is dominated by long-term GIA. The predicted gravity signal is generally less well-constrained than the vertical signal, in part due to uncertainties associated with the correction for contemporary ice mass loss in Svalbard and the Russian Arctic. The GRACE-derived gravity trend is corrected for present-day ice mass loss using estimates derived from the ICESat and CryoSat missions, although a difference in magnitude between GRACE-inferred and altimetry-inferred regional mass loss rates suggests the possibility of a non-negligible GIA response here either from millennial-scale or Little Ice Age GIA. [ABSTRACT FROM AUTHOR]

Abstract: Present‐day mass redistribution increases the total ocean mass and, on average, causes the ocean bottom to subside elastically. Therefore, barystatic sea level rise is larger than the resulting global mean geocentric sea level rise, observed by satellite altimetry and GPS‐corrected tide gauges. We use realistic estimates of mass redistribution from ice mass loss and land water storage to quantify the resulting ocean bottom deformation and its effect on global and regional ocean volume change estimates. Over 1993–2014, the resulting globally averaged geocentric sea level change is 8% smaller than the barystatic contribution. Over the altimetry domain, the difference is about 5%, and due to this effect, barystatic sea level rise will be underestimated by more than 0.1 mm/yr over 1993–2014. Regional differences are often larger: up to 1 mm/yr over the Arctic Ocean and 0.4 mm/yr in the South Pacific. Ocean bottom deformation should be considered when regional sea level changes are observed in a geocentric reference frame. [ABSTRACT FROM AUTHOR]

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C, including a potential overshoot and longterm impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the lowemissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0.With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity). [ABSTRACT FROM AUTHOR]

This study compares eight weighting techniques for Global Navigation Satellite System (GNSS)-derived Vertical Land Motion (VLM) trends at 570 tide gauge (TG) stations. The spread between the methods has a comparable size as the formal uncertainties of the GNSS trends. Taking the median of the surrounding GNSS trends shows the best agreement with differenced altimetry – tide gauge (ALT-TG) trends. An attempt is also made to improve VLM trends from ALT-TG time series. Only using highly correlated along-track altimetry and TG time series, reduces the standard deviation of ALT-TG time series up to 10 %. As a result, there are spatially coherent changes in the trends, but the reduction in the RMS of differences between ALT-TG and GNSS trends is insignificant. However, setting correlation thresholds also acts like a filter to remove problematic TG stations. This results in sets of ALT-TG VLM trends at 344-663 TG locations, depending on the correlation threshold. Compared to other studies, we decrease the RMS of differences between GNSS and ALT-TG trends (from 1.47 to 1.22 mm/yr), while we increase the number of locations (from 109 to 155), Depending on the weighting methods the mean of differences between ALT-TG and GNSS trends varies between 0.1-0.2 mm/yr. We reduce the mean of differences by taking into account the effect of elastic deformation due to present-day mass redistribution into account. [ABSTRACT FROM AUTHOR]

In this study, we develop a methodology to estimate monthly variations in degree-1 and C20 coefficients by combing Gravity Recovery and Climate Experiment (GRACE) data with oceanic mass anomalies (combination approach).With respect to the method by Swenson et al., the proposed approach exploits noise covariance information of both input data sets and thus produces stochastically optimal solutions supplied with realistic error information. Numerical simulations show that the quality of degree-1 and -2 coefficients may be increased in this way by about 30 per cent in terms of RMS error. We also proved that the proposed approach can be reduced to the approach of Sun et al. provided that the GRACE data are noise-free and noise in oceanic data is white. Subsequently, we evaluate the quality of the resulting degree-1 and C20 coefficients by estimating mass anomaly time-series within carefully selected validation areas, where mass transport is small. Our validation shows that, compared to selected Satellite Laser Ranging (SLR) and joint inversion degree-1 solutions, the proposed combination approach better complements GRACE solutions. The annual amplitude of the SLR-based C10 is probably overestimated by about 1 mm. The performance of the C20 coefficients, on the other hand, is similar to that of traditionally used solution from the SLR technique. [ABSTRACT FROM AUTHOR]

The mass and steric components of sea level changes have been separated in the Tropical Asian Seas (TAS) using a statistically optimal combination of Jason satellite altimetry, GRACE satellite gravimetry, and ocean reanalyses. Using observational uncertainties, statistically optimally weighted time series for both components have been obtained in four regions within the TAS over the period January 2005 to December 2012. The mass and steric sea level variability is regressed with the first two principal components (PC1&2) of Pacific equatorial wind stress and the Dipole Mode Index (DMI). Sea level in the South China Sea is not affected by any of the indices. Steric variability in the TAS is largest in the deep Banda and Celebes seas and is affected by both PCs and the DMI. Mass variability is largest on the continental shelves, which is primarily controlled by PC1. We argue that a water flux from the Western Tropical Pacific Ocean is the cause for mass variability in the TAS. The steric trends are about 2 mm yr−1 larger than the mass trends in the TAS. A significant part of the mass trend can be explained by the aforementioned indices and the nodal cycle. Trends obtained from fingerprints of mass redistribution are statistically equal to mass trends after subtracting the nodal cycle and the indices. Ultimately, the effect of omitting the TAS in global sea level budgets is estimated to be 0.3 mm yr−1. [ABSTRACT FROM AUTHOR]

Sea-level rise and decadal variability along the northwestern coast of the North Atlantic Ocean are studied in a self-consistent framework that takes into account the effects of solid-earth deformation and geoid changes due to large-scale mass redistribution processes. Observations of sea and land level changes from tide gauges and GPS are compared to the cumulative effect of GIA, present-day mass redistribution, and ocean dynamics over a 50 year period (1965-2014). GIA explains the majority of the observed sea-level and land motion trends, as well as almost all interstation variability. Present-day mass redistribution resulting from ice melt and land hydrology causes both land uplift and sea-level rise in the region. We find a strong correlation between decadal steric variability in the Subpolar Gyre and coastal sea level, which is likely caused by variability in the Labrador Sea that is propagated southward. The steric signal explains the majority of the observed decadal sea-level variability and shows an upward trend and a significant acceleration, which are also found along the coast. The sum of all contributors explains the observed trends in both sea-level rise and vertical land motion in the region, as well as the decadal variability. The sum of contributors also explains the observed acceleration within confidence intervals. The sea-level acceleration coincides with an accelerating density decrease at high latitudes. [ABSTRACT FROM AUTHOR]