Steric sea level change in twentieth century historical climate simulation and IPCC-RCP8.5 scenario projection: A comparison of two versions of FGOALS model

2013 ◽  
Vol 30 (3) ◽  
pp. 841-854 ◽  
Author(s):  
Lu Dong ◽  
Tianjun Zhou
Keyword(s):  
Twentieth Century ◽  
Sea Level ◽  
Sea Level Change ◽  
Climate Simulation ◽  
Steric Sea Level ◽  
Level Change ◽  
Historical Climate

Steric Sea-Level Change and its Impact on the Gravity Field caused by Global Climate Change

2008 ◽  
pp. 1131-1151
Author(s):  
Sabine Roedelsperger ◽  
Michael Kuhn ◽  
Oleg Makarynskyy ◽  
Carl Gerstenecker
Keyword(s):  
Climate Change ◽  
Gravity Field ◽  
Sea Level ◽  
Global Climate Change ◽  
Global Climate ◽  
Sea Level Change ◽  
Steric Sea Level ◽  
Level Change

Closing the global and regional sea level budgets by combining multi-mission altimetry and GRACE(-FO) data

2020 ◽  
Author(s):  
Bernd Uebbing ◽  
Christina Lück ◽  
Roelof Rietbroek ◽  
Kristin Vielberg ◽  
Jürgen Kusche
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Sea Level Change ◽  
Inversion Method ◽  
Data Sets ◽  
Sea Level Changes ◽  
Steric Sea Level ◽  
Level Change ◽  
Spatial Coverage ◽  
Regional Sea Level

<p>Understanding present day sea level changes and their drivers requires the separation of the total sea level change into individual mass and steric related contributions. Total sea level rise has been observed continuously since 1993 providing a more than 25 year long time series of global and regional sea level variations. However, direct monitoring of ocean mass change has only been done since the start of the Gravity Recovery And Climate Experiment (GRACE) mission in 2002. It ended in 2017 and was succeeded by the follow-on mission (GRACE-FO) in 2018 leaving a gap of about 1 year. In the same time period of GRACE, since the early 2000s, a global array of freely drifting Argo floats samples temperature and salinity profiles of up to 2000m depth which can be converted to steric sea level change.</p><p>By combining altimetry, GRACE(-FO) and Argo data sets it is possible to derive global and regional sea level budgets. The conventional approach is to analyze at least two of the data sets and derive the residual, or compare with the third one. A more recent approach is the global joint inversion method (Rietbroek et al., 2016) which fits forward-modeled spatial fingerprints to a combination of GRACE gravity data and Jason-1/-2 satellite altimetry data. This enables us, additionally, to separate altimetric sea level change into mass contributions from terrestrial hydrology, the melting of land glaciers and the ice-sheets in Greenland and Antarctica as well as contributions from steric sea level changes due to variations in ocean temperature and salinity. It also allows to include a data weighting scheme in the analysis.</p><p>Here, we present global and regional sea level budget results from an updated inversion based on multi-mission altimetry (Jason-1/-2/-3, Envisat, Cryosat-2, Sentinel-3, …) providing better spatial coverage as well as new RL06 GRACE and GRACE-FO data which enables us to extend the time series of individual components of the sea level budget beyond the GRACE era from 2002-04 till 2019-06. The presented sea level budget is closed on global scale with a residual (unexplained) contribution of about 0.1 mm/yr, globally, originating in eddy-active regions. We provide consistent validation of our results against conventionally analyzed altimetry and GRACE data sets where we find agreement on global scales to be better than 0.1 mm/yr but a larger disagreement at regional scales as well as the implications of our results for deriving ocean heat content. We will also provide first results for filling the gap in the sea level budget estimates due to the gap between the GRACE and GRACE-FO missions by additionally incorporating time-variable gravity information from the Swarm mission as well as from Satellite Laser Ranging (SLR) to 5 satellites (Lageos-1/-2, Stella, Starlette, Ajisai).</p>

scholarly journals Twentieth century constraints on sea level change and earthquake deformation at Macquarie Island

2010 ◽  
Vol 182 (2) ◽  
pp. 781-796 ◽  
Author(s):  
Christopher Watson ◽  
Reed Burgette ◽  
Paul Tregoning ◽  
Neil White ◽  
John Hunter ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Sea Level ◽  
Sea Level Change ◽  
Macquarie Island ◽  
Level Change

scholarly journals Evaluating Model Simulations of Twentieth-Century Sea Level Rise. Part I: Global Mean Sea Level Change

2017 ◽  
Vol 30 (21) ◽  
pp. 8539-8563 ◽  
Author(s):  
Aimée B. A. Slangen ◽  
Benoit Meyssignac ◽  
Cecile Agosta ◽  
Nicolas Champollion ◽  
John A. Church ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Thermal Expansion ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Level Change ◽  
Mean Sea Level ◽  
Level Change ◽  
The World ◽  
Global Mean Sea Level ◽  
Global Mean

Sea level change is one of the major consequences of climate change and is projected to affect coastal communities around the world. Here, global mean sea level (GMSL) change estimated by 12 climate models from phase 5 of the World Climate Research Programme’s Climate Model Intercomparison Project (CMIP5) is compared to observational estimates for the period 1900–2015. Observed and simulated individual contributions to GMSL change (thermal expansion, glacier mass change, ice sheet mass change, landwater storage change) are analyzed and compared to observed GMSL change over the period 1900–2007 using tide gauge reconstructions, and over the period 1993–2015 using satellite altimetry estimates. The model-simulated contributions explain 50% ± 30% (uncertainties 1.65 σ unless indicated otherwise) of the mean observed change from 1901–20 to 1988–2007. Based on attributable biases between observations and models, a number of corrections are proposed, which result in an improved explanation of 75% ± 38% of the observed change. For the satellite era (from 1993–97 to 2011–15) an improved budget closure of 102% ± 33% is found (105% ± 35% when including the proposed bias corrections). Simulated decadal trends increase over the twentieth century, both in the thermal expansion and the combined mass contributions (glaciers, ice sheets, and landwater storage). The mass components explain the majority of sea level rise over the twentieth century, but the thermal expansion has increasingly contributed to sea level rise, starting from 1910 onward and in 2015 accounting for 46% of the total simulated sea level change.

Steric Sea-Level Change and its Impact on the Gravity Field caused by Global Climate Change

2008 ◽  
Vol 165 (6) ◽  
pp. 1131-1151 ◽  
Author(s):  
Sabine Roedelsperger ◽  
Michael Kuhn ◽  
Oleg Makarynskyy ◽  
Carl Gerstenecker
Keyword(s):  
Climate Change ◽  
Gravity Field ◽  
Sea Level ◽  
Global Climate Change ◽  
Global Climate ◽  
Sea Level Change ◽  
Steric Sea Level ◽  
Level Change

scholarly journals Exploring Sources of Uncertainty in Steric Sea‐Level Change Estimates

2020 ◽  
Vol 125 (10) ◽  
Author(s):  
Carolina M. L. Camargo ◽  
Riccardo E. M. Riva ◽  
Tim H. J. Hermans ◽  
Aimée B. A. Slangen
Keyword(s):  
Sea Level ◽  
Sea Level Change ◽  
Steric Sea Level ◽  
Sources Of Uncertainty ◽  
Level Change

Revisiting the Global and Regional Steric Sea-level Trends in the Satellite Era

2020 ◽  
Author(s):  
Carolina Camargo ◽  
Riccardo Riva ◽  
Tim Hermans ◽  
Aimée Slangen
Keyword(s):  
Sea Level ◽  
Regional Scale ◽  
Sea Level Change ◽  
Global Scale ◽  
Noise Model ◽  
Steric Sea Level ◽  
Level Change ◽  
Ocean Reanalyses ◽  
Estimation Of Uncertainties ◽  
Mean Trend

<p>The steric component of sea-level change comprises variations in the temperature (thermosteric) and salinity (halosteric) of the oceans, which alter the water’s density, leading to volumetric variations of the water column. Although its importance is unarguable, throughout the literature there is a disagreement on how much the steric component actually contributes to sea-level change.</p><p>Here, we investigate two sources of uncertainty to steric trends, both at global and regional scale. First, we look at how the use of different temperature and salinity datasets influences the estimated steric height. For that, we analyzed 15 datasets, combining different techniques (hydrographic profiles, Argo floats and ocean reanalyses). Second, since the estimation of uncertainties for linear and quadratic trends requires the adoption of a noise model, we compared the performance of several different noise models.  </p><p>We find that by varying both the dataset and noise-model, the global mean trend and uncertainty from 2005 to 2015 can vary from 0.566 to 2.334 mm/yr and 0.022 to 1.646 mm/yr, respectively. This range becomes even larger at regional scales. At a global scale, the selection of datasets has a larger influence on the trend, while at a regional scale the choice of the noise model dominates the spread in steric sea-level trends. Our results emphasize the need to use an ensemble of datasets to infer steric changes, and to carefully choose a noise model.</p>

How much of the decadal, coastal sea level variability can we describe by climate modes?

2021 ◽  
Author(s):  
Samantha Royston ◽  
Jonathan Bamber ◽  
Rory Bingham
Keyword(s):  
Pacific Ocean ◽  
Sea Level ◽  
Sea Level Change ◽  
Internal Variability ◽  
Sea Level Variability ◽  
Steric Sea Level ◽  
Climate Modes ◽  
Level Change ◽  
The Pacific ◽  
The Pacific Ocean

<p>It is well known that key climatic variability like the El Niño Southern Oscillation and Pacific Decadal Oscillation dominate steric sea-level variability in the Pacific Ocean and that this variability influences global- and regional-mean sea-level time series. Reducing the known internal variability from these time series reduces trend errors and can elucidate other factors including anthropogenic influence and sea-level acceleration, as has been demonstrated for the open ocean. Here we discuss the influence of key climate modes on coastal, decadal sea-level variability. For coastal stakeholders and managers it is important to understand the decadal-scale and local changes in the rate of sea-level rise in the context of internal variability in order to inform management decisions in the short- to medium-term. We use a 53-year run of a high-resolution NEMO ocean model run, forced by the DRAKKAR reanalysis atmospheric data set and with the global-mean sea level at each timestep removed, to investigate modes of decadal sea-level variability at the coast, in different basins and from different sea-level components. At more than 45% of Pacific Ocean coastal locations, greater than 50% of the decadal sea-level change can be explained by a regression of the leading principal component mode with key climate indices; ENSO in the Pacific Ocean. In different ocean basins, 18.5% to 61.0% of coastal locations have more than 33% of decadal sea-level variance explained by our climate index reconstructions. These areas include coastal regions lacking long-duration or good quality tide gauges for long-term observations such as the North-West Africa coastline. Because of the shallow depth of continental shelves, steric sea-level change propagates onto the shelf as a manometric (mass) sea-level signal. We use a set of tide gauge locations to demonstrate the internal, decadal sea-level change observed at many coasts has a substantial contribution from local, manometric signal that is driven by climate variability.</p>

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