A probabilistic approach to 21st century regional sea-level projections using RCP and High-end scenarios

Sea-level change is an integrated climate system response due to changes in radiative forcing, anthropogenic land-water use and land-motion. Projecting sea-level at a global and regional scale requires a subset of projections - one for each sea-level component given a particular climate-change scenario. We construct relative sea-level projections through the 21st century for RCP 4.5, RCP 8.5 and High-end (RCP 8.5 with increased ice-sheet contribution) scenarios by aggregating spatial projections of individual sea-level components in a probabilistic manner. Most of the global oceans adhere to the projected global average sea level change within 5 cm throughout the century for all scenarios; however coastal regions experience localised effects due to the non-uniform spatial patterns of individual components. This can result in local projections that are 10′s of centimetres different from the global average by 2100. Early in the century, RSL projections are consistent across all scenarios, however from the middle of the century the patterns of RSL for RCP scenarios deviate from the High-end where the contribution from Antarctica dominates. Similarly, the uncertainty in projected sea-level is dominated by an uncertain Antarctic fate. We also explore the effect upon projections of, treating CMIP5 model ensembles as normally distributed when they might not be, correcting CMIP5 model output for internal variability using different polynomials and using different unloading patterns of ice for the Greenland and Antarctic ice sheets

Estimating the sea-level highstand during the last interglacial: a probabilistic massive ensemble approach

Essential to understanding sea-level change and its causes during the last interglacial is the quantification of uncertainties. In order to estimate the uncertainties, we develop a statistical framework for the comparison of paleao-climatic sea-level index points and GIA model predictions. For the investigation of uncertainties, as well as to generate better model predictions, we implement a massive ensemble approach by applying a data assimilation scheme based on particle filter methods. The different runs are distinguished through varying ice sheet reconstructions based on oxygen-isotope curves and different parameter selections within the GIA model. This framework has several advantages over earlier work, such as the ability to examine either the contribution of individual observations to the results or the probability of specific input parameters. This exploration of input parameters and data leads to a larger range of estimates than previously published work. We illustrate how the assumptions that enter into the statistical analysis, such as the existence of outliers in the observational database or the initial ice volume history, can introduce large variations to the estimate of the maximum highstand. Thus, caution is required to avoid over-interpreting results. We conclude that there are reasonable doubts whether the datasets previously used in statistical analyses are able to tightly constrain the value of maximum highstand during the last interglacial (LIG)

Unraveling regional patterns of sea level acceleration over the China Seas

Accelerated sea level rise is placing coastal communities in a vulnerable position; however, the processes underlying sea level acceleration in China remain uncertain. In this study, we examine the sea level acceleration and its contributors over the China Seas. We calculate acceleration along the Chinese coast using satellite altimetry and tide gauge records. During the satellite altimetry era, sea level acceleration from tide gauge records varies across all stations, reaching up to 0.30 ± 0.20 mm/yr2, while satellite altimetry could underestimate/overestimate the sea level acceleration in most locations. Acceleration near the coast, except in the Bohai Sea, is mainly driven by changes in the mass component. In contrast, for the open ocean, changes in steric sea level are the main contributor to sea level acceleration. The evolution of spatial acceleration patterns over the China Seas reveals that the ENSO and PDO variabilities dominate the changing patterns of sea level acceleration in the open ocean, including the Philippine Sea through steric sea level, and changes in most coastal locations are due to the non-steric component

Are near-coastal sea levels accelerating faster than global during the satellite altimetry era?

Impact and risk assessments in coastal areas are informed by current and future sea level rise and acceleration, which demands a better understanding of drivers for regional sea level acceleration. In our study, we analyze the near-coastal sea level acceleration compared with global values during satellite altimetry (1993–2020) and discuss the potential drivers of regional sea level acceleration. We estimate regional sea level acceleration using high-resolution satellite altimetry sea surface height anomalies. Our study reveals a wide range of regional acceleration estimates, varying from −1.2 to 1.2 mm·yr−2, which can be up to 20 times larger or smaller than the global mean sea level acceleration of 0.07 mm·yr−2. Notably, sea level acceleration near the global coastline is calculated at 0.10 ± 0.03 mm·yr−2, exceeding the global mean sea level acceleration by 40%. Regional patterns of sea level acceleration are in good agreement with acceleration patterns calculated from the steric sea level. However, the magnitude of acceleration is only partially explained by the changes in steric sea level, with increasing contributions from the non-steric component

Development of an automatic tide gauge processing system

There are many tide gauges around the world for which research quality data is not available for sea-level studies, including gauges that are maintained primarily for tsunami monitoring. In some cases high-frequency data is available for download through the Intergovernmental Oceanographic Commission (IOC) http://www.iocsealevelmonitoring.org, but manual QC has been too labour intensive. In this report we describe Matlab software for automatic quality control (QC) for tide gauges, including generalised comparison of instrument channels, fitting and predicting tides using irregular high-frequency data. We include details of the implementation, testing, and known capability and weaknesses

Coastal sea level rise with warming above 2°C

Two degrees of global warming above the preindustrial level is widely suggested as an appropriate threshold beyond which climate change risks become unacceptably high. This “2 °C” threshold is likely to be reached between 2040 and 2050 for both Representative Concentration Pathway (RCP) 8.5 and 4.5. Resulting sea level rises will not be globally uniform, due to ocean dynamical processes and changes in gravity associated with water mass redistribution. Here we provide probabilistic sea level rise projections for the global coastline with warming above the 2 °C goal. By 2040, with a 2 °C warming under the RCP8.5 scenario, more than 90% of coastal areas will experience sea level rise exceeding the global estimate of 0.2 m, with up to 0.4 m expected along the Atlantic coast of North America and Norway. With a 5 °C rise by 2100, sea level will rise rapidly, reaching 0.9 m (median), and 80% of the coastline will exceed the global sea level rise at the 95th percentile upper limit of 1.8 m. Under RCP8.5, by 2100, New York may expect rises of 1.09 m, Guangzhou may expect rises of 0.91 m, and Lagos may expect rises of 0.90 m, with the 95th percentile upper limit of 2.24 m, 1.93 m, and 1.92 m, respectively. The coastal communities of rapidly expanding cities in the developing world, and vulnerable tropical coastal ecosystems, will have a very limited time after midcentury to adapt to sea level rises unprecedented since the dawn of the Bronze Age

A consistent sea-level reconstruction and its budget on basin and global scales over 1958–2014

Different sea level reconstructions show a spread in sea level rise over the last six decades and it is not yet certain whether the sum of contributors explains the reconstructed rise. Possible causes for this spread are, among others, vertical land motion at tide-gauge locations and the sparse sampling of the spatially variable ocean. To assess these open questions, reconstructed sea level and the role of the contributors are investigated on a local, basin, and global scale. High-latitude seas are excluded. Tide-gauge records are combined with observations of vertical land motion, independent estimates of ice-mass loss, terrestrial water storage, and barotropic atmospheric forcing in a self-consistent framework to reconstruct sea level changes on basin and global scales, which are compared to the estimated sum of contributing processes. For the first time, it is shown that for most basins the reconstructed sea level trend and acceleration can be explained by the sum of contributors, as well as a large part of the decadal variability. The sparsely sampled South Atlantic Ocean forms an exception. The global-mean sea level reconstruction shows a trend of 1.5 ± 0.2 mm yr−1 over 1958–2014 (1σ), compared to 1.3 ± 0.1 mm yr−1 for the sum of contributors. Over the same period, the reconstruction shows a positive acceleration of 0.07 ± 0.02 mm yr−2, which is also in agreement with the sum of contributors, which shows an acceleration of 0.07 ± 0.01 mm yr−2. Since 1993, both reconstructed sea level and the sum of contributors show good agreement with altimetry estimates

Drivers for seasonal variability in sea level around the China seas

Globally variable ocean and atmospheric dynamics lead to spatially complex seasonal cycles in sea level. The China Seas, that is the Bohai, Yellow, East China and the South China seas, is a region with strong seasonal amplitudes, and straddles the transition between tropical and temperature zones, monsoonal and westerlies, shelf and deep ocean zones. Here we investigate the drivers for seasonal variability in sea level from tide gauge records, satellite altimetry along with output from the NEMO (Nucleus for European Modeling of the Ocean) model including sea surface height and ocean bottom pressure along with meteorological data in the China Seas. The seasonal cycle accounts for 37% - 94% of monthly sea level variability in 81 tide gauge records, ranging from 18 to 59 cm. We divided the seasonal cycles into four types: 1) an asymmetric sinusoid; 2) a clearly defined peak on a flat background; 3) a relatively flat signal; 4) a symmetric co-sinusoid. Type 1 is found in northern China and Taiwan, Korea, Japan and The Philippines where Inverse Barometer (IB) effects dominates seasonality along with a steric contribution. The seasonal monsoon associated with barotropic response and freshwater exchange play important roles in type 2, (eastern and southern Chinese coasts), type 3 (East Malaysia) and type 4 (Vietnam and Gulf of Thailand). IB corrected seasonal cycle amplitudes are larger in continental shelf areas than the deep ocean, with a maximum in the Gulf of Thailand, and NEMO underestimates the seasonal amplitude along the coast by nearly 50%

State of the UK climate 2018

This report provides a summary of the UK weather and climate through the calendar year 2018, alongside the historical context for a number of essential climate variables. This is the fifth in a series of annual “State of the UK climate” publications and an update to the 2017 report (Kendon et al., 2018). It provides an accessible, authoritative and up‐to‐date assessment of UK climate trends, variations and extremes based on the most up to date observational datasets of climate quality. The majority of this report is based on observations of temperature, precipitation, sunshine and wind speed from the UK land weather station network as managed by the Met Office and a number of key partners and co‐operating volunteers. The observations are carefully managed such that they conform to current best practice observational standards as defined by the World Meteorological Organization (WMO). The observations also pass through a range of quality assurance procedures at the Met Office before application for climate monitoring. In addition, time series of near‐coast sea‐surface temperature (SST) and sea‐level rise are also presented. The process for generating national and regional statistics from these observations has been updated since Kendon et al., 2018. This report makes use of a new dataset, HadUK‐Grid, which provides improved quality and traceability for these national statistics along with temperature and rainfall series that extend back into the 19th Century. Differences with previous data are described in the relevant sections and appendices. The report presents summary statistics for year 2018 and the most recent decade (2009–2018) against 1961–1990 and 1981–2010 averages. Year 2009–2018 is a non‐standard reference period, but it provides a 10‐year “snapshot” of the most recent experience of the UK's climate and how that compares to historical records. This means differences between 2009 and 2018 and the baseline reference averages may reflect shorter‐term decadal variations as well as long‐term trends. These data are presented to show what has happened in recent years, not necessarily what is expected to happen in a changing climate. The majority of maps in this report show year 2018 against the 1981–2010 baseline reference averaging period—that is, they are anomaly maps which show the spatial variation in this difference from average. Maps of actual values are in most cases not displayed because these are dominated by the underlying climatology, which for this report is of a lesser interest than the year‐to‐year variability. Throughout the report's text the terms “above normal” and “above average,” etc. refer to the 1981–2010 baseline reference averaging period unless otherwise stated. Values quoted in tables throughout this report are rounded, but where the difference between two such values is quoted in the text (for example, comparing the most recent decade with 1981–2010), this difference is calculated from the original unrounded values

Monitoring sea level in the coastal zone with coastal altimetry and tide gauges

We examine the issue of sustained measurements of sea level in the coastal zone, first by summarizing the long-term observations from tide gauges, then showing how those are now complemented by improved altimetry products in the coastal ocean. We present some of the progresses in coastal altimetry, both from dedicated reprocessing of the radar waveforms and from the development of improved corrections for the atmospheric effects. This trend towards better altimetric data at the coast comes also from technological innovations such as Ka-band altimetry and SAR altimetry, and we discuss the advantages deriving from the AltiKa Ka-band altimeter and the SIRAL altimeter on CryoSat-2 that can be operated in SAR mode. A case study along the UK coast demonstrates the good agreement between coastal altimetry and tide gauge observations, with RMSD's as low as 4 cm at many stations, allowing the characterization of the annual cycle of sea level along the UK coasts. Finally we examine the evolution of the sea level trend from the open to the coastal ocean along the Western coast of Africa, comparing standard and coastally-improved products. Different products give different sea level trend profiles, so the recommendation is that additional efforts are needed to study sea level trends in the coastal zone from past and present altimeters. Further improvements are expected from more refined processing and screening of data, but in particular from the constant improvements in the geophysical corrections