In February 2017 I was fortunate enough to spend a month in Bristol on a Travel award by the University of Wollongong Global Challenges Program, aimed at furthering an already existing
collaboration between the University of Wollongong and the University of Bristol. The collaboration is based on work stemming from my previous postdoctoral position at Bristol, and it is in the field of climate science and the statistics that enable it. In this post I discuss two projects which were at the focus of this visit.
The first project is based on carbon dioxide flux inversion which, in a nutshell, is any technique that allows us to determine where the carbon dioxide is coming from, and where it is going, just from readings of carbon dioxide at the surface or from satellites.
The process by which sources and sinks, that is, the flux, generate carbon dioxide (the generative process) is well-understood. Recovering the flux from measurements of carbon dioxide (inversion) is a much harder problem.
Together with Bristol, the Centre for Environmental Informatics at the University of Wollongong is looking at ways in which sophisticated statistical techniques can be used to take into account uncertainty at each of the numerous stages in flux inversion. This means taking into account uncertainty in the satellite retrieval right down to the uncertainty in how sinks and sources of carbon dioxide evolve in time. This work hopes to reconcile several of the existing more conventional approaches that frequently neglect uncertainties at every stage of the flux inversion process, and that may easily result in over-confident estimates of flux. The approach should also allow us to objectively assess to what extent we can reliably estimate flux from, say, satellite data, when taking all uncertainties into account.
With Bristol we are specifically focusing on the OCO-2 satellite, launched by NASA in July 2014. The satellite takes readings of column-averaged carbon dioxide. Given a set of flux fields, we can reproduce the column-averaged carbon dioxide by passing the flux through a transport model (a model which simulates the movement of carbon dioxide in the atmosphere) driven by meteorology fields. These ‘model-outputs’ were provided to us by the team at Bristol. From a statistical perspective, the problem of inversion is complex, as it involves the consideration of transport, as well as all the biases and uncertainties in OCO-2 retrievals, which can be significant. There is also a lot of data: OCO-2 generates on the order of millions usable retrievals per month; how to reliably assemble all these data within an inversion framework is still an open problem. My visit to Bristol proved invaluable in sorting out some of the teething issues that emerge when attempting to do the inversion from satellite data in this way. Our joint work will be presented at the European Geophysical Union in Vienna in April 2017. This project is carried out in collaboration with Noel Cressie (Wollongong), Ann Stavert (Bristol), Matt Rigby (Bristol), Anita Ganesan (Bristol), and Peter Rayner (Melbourne).
Artist’s rendering of NASA’s Orbiting Carbon Observatory (OCO)-2 (Source: NASA)
The second project is based on global sea-level rise. Sea-level rise is both a symptom of, and a contributor to, climate change, and one of the most concerning. For example, 85% of the Australian population lives within 50 km of the coast, and it’s not so much the sea-level rise in itself which is the problem, but the increased risk and impact of storm surges which accompany a higher sea level. Considerable infrastructure re-planning will be needed in the next few decades to counter the effects of what seems an unstoppable rise in sea level.
Bruce C. Douglas (1997). “Global Sea Rise: A Redetermination”. Surveys in Geophysics 18: 279–292. DOI:10.1023/A:1006544227856. Can be redistributed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
The GlobalMass team based in Bristol is trying to answer a simple, yet unanswered, question — what are the contributors to global sea-level rise? Global sea-level rise can be due to a number of factors: (i) thermal expansion (as the ocean heats, it expands), (ii) change in salinity (salt increases the water density), (iii) hydrology (dams, rivers, etc.) and (iv) ice sheets and ice caps (as ice sheets melt, the sea level rises). Further, sea-level rise is highly spatially and temporally dependent, making it very difficult to assess what is contributing to it without a deep understanding of the physical processes driving it, and what their regional and temporal impacts are. In order to “source separate” the contributors, we incorporate a large variety of data sources, including Argo buoy data, gravimetry, altimetry and tide gauges, all at once. The uncertainties, as well as the footprints of the observations, are all taken into account when solving for the separate sea-level rise contributions. This work, funded by the European Union, is a follow-on of the UK-funded RATES project which I worked on for two years, and which resulted in a statistical framework for identifying the contributors to sea-level rise just from Antarctica. This visit to Bristol helped to flesh out some of the statistical problems related to the GlobalMass project objective, and devise methods and a way forward to solve them.
The GlobalMass team consists of 8 researchers from the University of Bristol and project partners from the University of Wollongong, University of Tasmania, NASA, LEGOS, and the University of Colorado, Boulder. More information on the project can be found at globalmass.eu.
GlobalMass meeting, February 2017.
Andrew Zammit Mangion 