Oceanography (Altimetry) 1

Sea level is a very sensitive index of climate change and variability. Sea level integrates the ocean warming, mountain glaciers and ice sheet melting. Understanding the sea level variability and changes implies an accurate monitoring of the sea level variable at climate scales, in addition to understanding the ocean variability and the exchanges between ocean, land, cryosphere, and atmosphere. That is why Sea Level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing long-term monitoring of the sea level ECV with regular updates, as required for climate studies.

After a first phase (2011-2013), the program has started in 2014 a second phase of 3 years. The objectives of this second phase are to involve the climate research community, to refine their needs and collect their feedbacks on product quality, to develop, test and select the best algorithms and standards to generate an updated climate time series and to produce and validate the Sea Level ECV product. This will better answer the climate user needs by improving the quality of the Sea Level products and maintain a sustain service for an up-to-date production. To this extent, the ECV time series has benefited from yearly extension and it now covers the period 1993-2014. A full reprocessing of the dataset will be available in 2016.

We will firstly present the main achievements of the ESA CCI Sea Level Project. On the one hand, the major steps required to produce the 21 years climate time series are briefly described: collect and refine the user requirements, development of adapted algorithms for climate applications and specification of the production system. On the other hand, the product characteristics are described as well as the results from product validation, performed by several groups of the ocean and climate modeling community. At last, the main improvements derived from the algorithms development dedicated to the 2016 full reprocessing of the dataset are described. Efforts have also focused on the improvement of the sea level estimation in the Arctic Ocean and in coastal areas for which preliminary results suggest that significant improvements can be achieved.

Based on the sea level budget closure approach, this study investigates the residuals between satellite altimetry based global mean sea level (GMSL) and the sum of components (steric sea level and ocean mass) for the period January 2003 to December 2013. The choice for this period results from the availability of Argo down to 2000m and GRACE space gravimetry data for estimating the steric and ocean mass components respectively. The various contributions to the GMSL are summed up to derive a ‘synthetic’ global mean sea level The synthetic global mean sea level is further compared to the global mean sea level produced in the context of the ESA Climate Change Initiative –CCI- (both in terms of time series and trends). Closure of the sea level budget equation is examined. In particular, we try to identify the impact of errors in one or several components of the sea level budget on the residual time series. This is a key issue if we want to constrain missing contributions such as the contribution to sea level rise from the deep ocean (depths not covered by observations). We generalize this approach and used a larger number of data sets involved in the sea level budget: 6 altimetry products for the GMSL (including the CCI data), 4 Argo products plus the ORAS4 ocean reanalysis for the steric sea level and 3 GRACE-based ocean mass products. The residuals of the sea level budget equation are analyzed in terms of trends and interannual variability. It is found that over the study time span, some products perform better than others.  We also investigate the impact of incomplete geographical coverage of the steric sea level data with Argo. In particular, lack of Argo data in the Indonesian region leads to an overestimate of the absolute residual trend of the sea level budget equation, by about 8% of the global mean rate of sea level rise. Accounting for this regional contribution leads to closure of the sea level budget, at least for the CCI GMSL product. 

The ocean stores more than 90% of the energy excess associated with anthropogenic climate change while the rest is stored in the atmosphere, the cryosphere and continents. The resulting ocean warming and thermal expansion and the resulting glaciers ice melt are the leading contributors to global mean sea level (GMSL) rise. Confidence in projections of GMSL rise therefore depends on the ability of climate models to reproduce global mean thermosteric sea level (GMTSL) rise and glaciers mass loss over the 20th century .
In this study, we first compare the GMSL of climate models of the Coupled Models Intercomparison Project Phase 5 (CMIP5) to observations over the 20th century and the altimetry era (1993-2014).
Although the model-ensemble mean is within the uncertainty of observations, the model ensemble exhibits a large spread. We then explain the spread in CMIP5 climate models GMSL over the 20th century. We show that climate models' GMSL is the sum of the Global Mean Thermosteric Sea Level (GMTSL) rise which  linearly depends on the time-integrated radiative forcing F (under continuously increasing radiative forcing),  and the Glaciers Mass Loss (GML) which  quadratically depends on F (under continuously increasing radiative forcing).
The constant of proportionality between GMTSL and F (nu) expresses the transient thermosteric sea level response of the climate system and the constant of proportionality between GML and F (lambda) expresses the glaciers sensitivity to climate change.  nu depends on the fraction of excess heat stored in the ocean, the expansion efficiency of heat, the climate feedback parameter and the ocean heat uptake efficiency. The across-model spread in nu and lambda explains most of the across-model spread in GMSL rise over the 20th, while the across-model spread in time-integrated F explains the rest. Over the 21st century the picture is different because of less spread in F.

The Lofoten Basin is a hot spot of mesoscale eddy activity in the Nordic Seas. For the first time a comprehensive observational based quantitative analysis of eddies in the Lofoten Basin is documented using nearly two decades of satellite altimeter data (1995-2013) together with Argo floats and surface drifter data. An automated method identified 1695 anticyclonic eddies and 1666 cyclonic eddies from more than 10000 altimeter-based eddy observations. The spatial distribution of the life, occurance, generation, radius, intensity, amplitude and drift of the eddies along with the seasonality are studied in detail. There are more long-lived (> 60 days) anticyclonic eddies in the Lofoten Basin, especially in the western part of the basin. We reveal two hotspots of eddy-occurance on either side of the Lofoten Basin. Furthermore we infer a cyclonic drift of both the cyclonic and the anticyclonic eddies, confined to the western Lofoten Basin. Barotropic energy conversion rate estimated for the basin shows the energy transfer from the slope current to the eddies. Surface drifters trapped inside eddies identified using an automated collocation method are used to validate the altimeter derived orbital motion of the eddies. The vertical structure of eddies is studied using automated colocated Argo float profiles. Anticyclonic eddies in the Lofoten Basin are found to be more non-linear compared to cyclonic eddies, which may have important implication on the heat transport from the slope current in to the basin interior.

Strong improvements have been made in our knowledge of the Ocean Mean Dynamic Topography at spatial scales down to 125 km thanks to the use of the latest GOCE geoid model. This estimate can be improved at short scales by taking advantage of in-situ measurements of the ocean surface velocities and dynamic heights by drifting buoys and Argo floats. The approach has been recently applied to compute the global ¼° CNES-CLS13 MDT (Rio et al, 2014). However, in strong currents and in coastal areas, higher resolution field is required for the optimal exploitation of the altimeter data, in particular for its assimilation into operational forecasting systems. This is all the more needed in the prospect of the upcoming SWOT satellite, which will measure the sea level at very high spatial resolution (1 km).

In this study, we first show the positive impact of GOCE mission to estimate ocean circulation down to around 125 km. Then, we deal with improving regionally the resolution of existing MDT solutions; we focus on the Agulhas Current area. On one hand, we investigate how optimally use oceanic signal from drifters. On the other hand, we investigate the feasibility of using high resolution Doppler velocities from ENVISAT SAR images over the period 2007-2012. In both cases velocity data and their errors are analyzed. Velocity data are compared to altimeter data in term of physical content and resolved spatial scales and recommendations for their potential use for high resolution regional MDT calculation are provided.