Oceans
Once GIA has been ‘solved’ (in work package 2), it can become an input to the Bayesian Hierarchical Model, meaning we will have observations for each term in the sea level budget equation:
Change in mean sea level | = | Change in water density | + | Change in water mass | + | Change in ocean floor |
Or alternatively:
dh/dtMSL | = | dh/dtsteric | + | dh/dtbarystatic | + | GIA |
Altimetry / Tide gauges | Argo | GRACE | Solution for 2005-2015 |
For the forty year epoch we intend to consider (1981-2020), there are a range of observations with different lengths of record, spatial coverage and different information content related to the latent processes. The most comprehensive epoch is from 2005 onward, where separation of ocean density (steric) and ocean mass (barystatic) contributions will be most robust as we have direct observations of barystatic and steric terms from Argo and GRACE, respectively, as well as global mean sea level estimates from altimetry and tide gauges.
Prior information related to the amplitude and pattern of the steric term come from global ocean GCMs, while the spatio-temporal characteristics of the barystatic term could, for example, come from simulated ocean bottom pressure [1]. Additional spatio-temporal behaviour for both terms come from the observational record (e.g. radar altimetry), providing knowledge about their spatio-temporal signatures, which will improve their separability and hence their respective contributions to sea level budget [2].
Next page: Progress
References
[1] Brunnabend, S. E., R. Rietbroek, R. Timmermann, J. Schröter and J. Kusche (2011). “Improving mass redistribution estimates by modeling ocean bottom pressure uncertainties.” J. Geophys. Res.: Oceans 116(C8): C08037.
[2] Rietbroek, R., S. E. Brunnabend, J. Kusche and J. Schroter (2012). “Resolving sea level contributions by identifying fingerprints in time-variable gravity and altimetry.” J. Geodynamics 59-60: 72-81.