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Global land ice, hydrology and ocean mass trends

Periodic Reporting for period 2 - GlobalMass (Global land ice, hydrology and ocean mass trends)

Reporting period: 2018-02-01 to 2019-07-31

Summary of the context and overall objectives of the project
Sea level rise is likely to be one of the most serious and tangible consequences of future climate change and is, consequently, a critical research challenge. Confidence in future projections will largely be dictated by our ability to correctly account for observed sea level changes from the recent past.

Globally-averaged sea level change is quantified using the global sea level budget, a ‘balance-sheet’ approach that accounts for all the factors that contribute to sea level rise. Three main factors control changes in sea level: (i) changes in ocean mass (from ice sheets, glaciers and water storage on land); (ii) changes in ocean density (largely from thermal expansion of sea water); and (iii) changes in the elevation of the ocean floor (largely from glacial isostatic adjustment, GIA, the ongoing process by which the Earth’s crust is rebounding from the last glacial maximum ~20,000 years ago).

Since the early-1990s there has been a revolution in our ability to ‘observe’ and measure each of these components through a combination of satellite and in situ approaches. However, these new datasets have varying spatial and temporal characteristics which makes them complex to combine using conventional techniques.

This complexity, coupled with their increasing size and cross-disciplinary nature, means that the different components of sea level budget have generally been tackled separately. This has led to puzzling and at times contradictory results that do not necessarily respect fundamental principles of the coupled land-ice-ocean-solid Earth system or exploit the full capabilities of the new datasets.

The GlobalMass project will, by contrast, tackle all components simultaneously at a global scale. It will do this by adopting a powerful new approach to analysing large amounts of spatio-temporal data – a Bayesian Hierarchical Model (BHM) – which allows us to estimate the most likely combination of all the components of the sea level budget along with an indication of the uncertainty of the estimates. A key advantage of the BHM approach is that it provides a rigorous way of differentiating between observations based on their varied characteristics (or ‘smoothness’) in space and time. This allows us to separate the contribution of the different geophysical processes relevant to sea level change. Additional constraints provided by prior knowledge and fundamental physical principles (such as conservation of mass) can be added to the BHM to improve this separation further still.

Thus, the overall aim of the project is to develop and use a BHM to produce simultaneous, global, statistically-rigorous estimates of all components of the sea level budget for a common time period: (i) glacial isostatic adjustment (GIA); (ii) land ice mass trends; (iii) land hydrology trends; and (iv) steric trends and sea surface height. These estimates will be data-driven (rather than the result of numerical modelling) and will combine a wide range of satellite and in-situ data. They will, for the first time, be consistent with each other and with physical constraints on the coupled system.

There are six specific objectives:
• Objective 1: Develop the BHM methodology and software to undertake multivariate spatio-temporal modelling at a global scale.
• Objective 2: Obtain a data-driven solution for global GIA, consistent with satellite and GPS observations.
• Objective 3: Reconcile the sea level budget for 1981-2020.
• Objective 4: Produce spatially-distributed land ice mass balance trends for 1992-2020, consistent with in-situ and satellite-based observations.
• Objective 5: Re-evaluate twentieth century sea level rise from the tide gauge record using the BHM approach.
• Objective 6: Investigate catchment-scale land hydrology trends for 2003-2020.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
The GlobalMass project started in August 2016 and much of our work to date has been focussed on developing and testing the Bayesian Hierarchical Model (BHM) – the statistical framework that is fundamental to the project aim and objectives.

It is vital that we are satisfied that the framework is working correctly before we can start to add in more data and have confidence in the results it gives. Although it is based on model code developed and successfully used in a previous project, we are extending and expanding it considerably; for example, translating it to a much larger global (and therefore spherical) grid and implementing it via modifications to the functionality of a statistical computing package. We are also wanting to include many more datasets, so ensuring a computationally-efficient solution has been an important requirement.

The initial experiments have therefore seen us add data incrementally with the main aims of testing that the framework is working correctly and exploring different experimental set-ups (e.g. different methods and datasets). The key requirement has been results that look plausible given known physical processes, and do not contain unexpected or unexplained errors. In so doing, we have achieved some notable outputs:

• Extension of a BHM approach to investigate ice mass trends for Antarctica and Greenland – Extension of mass balance trends estimated for Antarctica up to 2015 and, for the first time, application of the BHM approach to the Greenland ice sheet;
• Creation of a global GPS dataset to provide a ‘clean’ signal of glacial isostatic adjustment (GIA) – An automated method for processing GPS time series to isolate the GIA signal and hence provide an observational estimate of global GIA uplift rates;
• Exploration of global steric sea level trends 2005-2015 – A global ‘prediction’ of steric sea level change (largely thermal expansion of the oceans), which despite measurement advances remains a relatively uncertain process.
• Data-driven estimate of ocean bottom deformation due to ocean bottom pressure – We have used global observations of sea surface height, ocean mass and steric changes to obtain a first data-driven estimate of ocean bottom deformation (the depression of the ocean floor caused by an increased ocean mass above it);
• New estimate of land ice contribution to sea level rise – A new synthesis of land ice mass trends during the satellite era (1992 to 2016) focusing on its contribution to sea level rise;
• New estimate of ice sheet contribution to future sea level rise – We used an approach called Structured Expert Judgement (which statistically weights and combines estimates given individually by a group of experts) to estimate future ice sheet contributions to sea level rise under different temperature scenarios; and
• Investigation of global water storage stress - We have combined gravity data with hydrological modelling to identify terrestrial water storage trends and, for the first time, produce a realistic map of global water storage stress.

We have established a dedicated project website (www.globalmass.eu) and Twitter feed (@globalmassteam) and use these to provide regular updates and news. We also upload plain language summaries of all published papers (www.globalmass.eu/plain-language-summaries).
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
The development of the Bayesian Hierarchical Model (BHM) framework has required us to adopt and develop state-of-the-art statistical methods to solve issues arising from the simultaneous analysis of large volumes of spatio-temporal data at a global scale. The statistical framework that we are producing has potentially wide applicability across any discipline that involves the study of interrelated yet uncertain spatial and temporal processes ranging, for example, from geosciences to biology, astronomy and beyond.

Once we are satisfied that the framework is robust and operating correctly [Objective 1], we will incrementally increase the complexity of the problem to address the other objectives. It is anticipated that a data-driven solution for global GIA [Objective 2] will be the next major milestone, as once this has been determined it becomes an input to the BHM. Once land ice [Objective 4] and hydrology trends [Objective 6] are accounted for, this will allow us to simultaneously ‘solve’ the global sea level budget for the first time [Objective 3].

Addition of tide gauge records into the BHM framework for the ~20-year period where they overlap with satellite observations of sea surface height is the final challenge. Doing so will allow us to produce a ‘tide-gauge only’ reconstruction of sea level rise for this period. This will highlight the impact of the inherent spatial and temporal sampling required when using long term tide gauge records that are skewed unavoidably towards measurements of sea level change along European and North American coastlines. Informed by this knowledge, we will then run the BHM to re-evaluate 20th century sea level rise from the tide gauge record [Objective 5].
Land ice contribution to sea level rise (Bamber et al 2018)
Greenland BHM summary (Chuter et al 2018)
Projected sea level rise due to ice sheets from Structured Expert Judgement (Bamber et al in review)
''Observed' GIA signal from GPS measurements (Schumacher et al 2018)
GlobalMass project logo
GlobalMass project website
GlobalMass project leaflet
GlobalMass project overview poster