Combination of geodetic observations and models for glacial isostatic adjustment fields in Fennoscandia

We demonstrate a new technique for using geodetic data to update a priori predictions for Glacial Isostatic Adjustment (GIA) in the Fennoscandia region. Global Positioning System (GPS), tide gauge, and Gravity Recovery and Climate Experiment (GRACE) gravity rates are assimilated into our model. The technique allows us to investigate the individual contributions from these data sets to the output GIA model in a self-consistent manner. Another benefit of the technique is that we are able to estimate uncertainties for the output model. These are reduced with each data set assimilated. Any uncertainties in the GPS reference frame are absorbed by reference frame adjustments that are estimated as part of the assimilation. Our updated model shows a spatial pattern and magnitude of peak uplift that is consistent with previous models, but our location of peak uplift is slightly to the east of many of these. We also simultaneously estimate a spatially averaged rate of local sea level rise. This regional rate (similar to 1.5 mm/yr) is consistent for all solutions, regardless of which data sets are assimilated or the magnitude of a priori GPS reference frame constraints. However, this is only the case if a uniform regional gravity rate, probably representing errors in, or unmodeled contributions to, the low-degree harmonic terms from GRACE, is also estimated for the assimilated GRACE data. Our estimated sea level rate is consistent with estimates obtained using a more traditional approach of direct "correction" using collocated GPS and tide gauge site

Impact of self-attraction and loading on Earth rotation

The impact of self-attraction and loading (SAL) on Earth rotation has not been previously considered except at annual timescales. We estimate Earth rotation excitations using models of atmospheric, oceanic, and land hydrology surface mass variations and investigate the importance of including SAL over monthly to interannual timescales. We assess SAL effects in comparison with simple mass balance effects where net mass exchanged with the atmosphere and land is distributed uniformly over the global ocean. For oceanic polar motion excitations, SAL impacts are important even though mass balance impact is minor except at the annual period. This is true of global (atmosphere + land + ocean) polar motion excitations as well, although the SAL impacts are smaller. When estimating length-of-day excitations, mass balance effects have a dominant impact, particularly for oceanic excitation. Although SAL can have a significant impact on estimated Earth rotation excitations, its consideration generally did not improve comparisons with geodetic observations. This result may change in the future as surface mass models and Earth rotation observations improve

Stochastic filtering for determining gravity variations for decade-long time series of GRACE gravity

We present a new stochastic filter technique for statistically rigorous separation of gravity signals and correlated “stripe” noises in a series of monthly gravitational spherical harmonic coefficients (SHCs) produced by the Gravity Recovery and Climate Experiment (GRACE) satellite mission. Unlike the standard destriping process that removes the stripe contamination empirically, the stochastic approach simultaneously estimates gravity signals and correlated noises relying on covariance information that reflects both the spatial spectral features and temporal correlations among them. A major benefit of the technique is that by estimating the stripe noise in a Bayesian framework, we are able to propagate statistically rigorous covariances for the destriped GRACE SHCs, i.e. incorporating the impact of the destriping on the SHC uncertainties. The Bayesian approach yields a natural resolution for the gravity signal that reflects the correlated stripe noise, and thus achieve a kind of spatial smoothing in and of itself. No spatial Gaussian smoothing is formally required although it might be useful for some circumstances. Using the stochastic filter, we process a decade-length series of GRACE monthly gravity solutions, and compare the results with GRACE Tellus data products that are processed using the “standard” destriping procedure. The results show that the stochastic filter is able to remove the correlated stripe noise to a remarkable degree even without an explicit smoothing step. The estimates from the stochastic filter for each destriped GRACE field are suitable for Bayesian integration of GRACE with other geodetic measurements and models, and the statistically rigorous estimation of the time-varying rates and seasonal cycles in GRACE time series

Dynamic adjustment of the ocean circulation to self-attraction and loading effects

The oceanic response to surface loading, such as that related to atmospheric pressure, freshwater exchange, and changes in the gravity field, is essential to our understanding of sea level variability. In particular, so-called self-attraction and loading (SAL) effects caused by the redistribution of mass within the land–atmosphere–ocean system can have a measurable impact on sea level. In this study, the nature of SAL-induced variability in sea level is examined in terms of its equilibrium (static) and nonequilibrium (dynamic) components, using a general circulation model that implicitly includes the physics of SAL. The additional SAL forcing is derived by decomposing ocean mass anomalies into spherical harmonics and then applying Love numbers to infer associated crustal displacements and gravitational shifts. This implementation of SAL physics incurs only a relatively small computational cost. Effects of SAL on sea level amount to about 10% of the applied surface loading on average but depend strongly on location. The dynamic component exhibits large-scale basinwide patterns, with considerable contributions from subweekly time scales. Departures from equilibrium decrease toward longer time scales but are not totally negligible in many places. Ocean modeling studies should benefit from using a dynamical implementation of SAL as used here

Concepts and terminology for sea level: mean, variability and change, both local and global

Changes in sea level lead to some of the most severe impacts of anthropogenic climate change. Consequently, they are a subject of great interest in both scientific research and public policy. This paper defines concepts and terminology associated with sea level and sea-level changes in order to facilitate progress in sea-level science, in which communication is sometimes hindered by inconsistent and unclear language. We identify key terms and clarify their physical and mathematical meanings, make links between concepts and across disciplines, draw distinctions where there is ambiguity, and propose new terminology where it is lacking or where existing terminology is confusing. We include formulae and diagrams to support the definitions

Variations in the Difference between Mean Sea Level measured either side of Cape Hatteras and Their Relation to the North Atlantic Oscillation

We consider the extent to which the difference in mean sea level (MSL) measured on the North American Atlantic coast either side of Cape Hatteras varies as a consequence of dynamical changes in the ocean caused by fluctuations in the North Atlantic Oscillation (NAO). From analysis of tide gauge data, we know that changes in MSL-difference and NAO index are correlated on decadal to century timescales enabling a scale factor of MSL-difference change per unit change in NAO index to be estimated. Changes in trend in the NAO index have been small during the past few centuries (when measured using windows of order 60–120 years). Therefore, if the same scale factor applies through this period of time, the corresponding changes in trend in MSL-difference for the past few centuries should also have been small. It is suggested thereby that the sea level records for recent centuries obtained from salt marshes (adjusted for long-term vertical land movements) should have essentially the same NAO-driven trends south and north of Cape Hatteras, only differing due to contributions from other processes such as changes in the Meridional Overturning Circulation or ‘geophysical fingerprints’. The salt marsh data evidently support this interpretation within their uncertainties for the past few centuries, and perhaps even for the past millennium. Recommendations are made on how greater insight might be obtained by acquiring more measurements and by improved modelling of the sea level response to wind along the shelf

Causes of the regional variability in observed sea level, sea surface temperature and ocean colour over the period 1993-2011

We analyse the regional variability in observed sea surface height (SSH), sea surface temperature (SST) and ocean colour (OC) from the ESA Climate Change Initiative (CCI) datasets over the period 1993-2011. The analysis focuses on the signature of the ocean large-scale climate fluctuations driven by the atmospheric forcing and do not address the mesoscale variability. We use the ECCO version 4 ocean reanalysis to unravel the role of ocean transport and surface buoyancy fluxes in the observed SSH, SST and OC variability. We show that the SSH regional variability is dominated by the steric effect (except at high latitude) and is mainly shaped by ocean heat transport divergences with some contributions from the surface heat fluxes forcing that can be significant regionally (confirming earlier results). This is in contrast with the SST regional variability, which is the result of the compensation of surface heat fluxes by ocean heat transport in the mixed layer and arises from small departures around this background balance. Bringing together the results of SSH and SST analyses, we show that SSH and SST bear some common variability. This is because both SSH and SST variability show significant contributions from the surface heat fluxes forcing. It is evidenced by the high correlation between SST and buoyancy forced SSH almost everywhere in the ocean except at high latitude. OC, which is determined by phytoplankton biomass, is governed by the availability of light and nutrients that essentially depend on climate fluctuations. For this reason OC show significant correlation with SST and SSH. We show that the correlation with SST display the same pattern as the correlation with SSH with a negative correlation in the tropics and subtropics and a positive correlation at high latitude. We discuss the reasons for this pattern