PIK

The increase in migration into EU member countries in 2015, and the socio-political challenges associated with it, have led the EU to set out a strategy to tackle root causes of irregular migration in the countries of origin. Crop failure caused by climate change, in particular, is forecast to be a major driver of international migration in the coming decades. In this project, we will integrate state-of-the-art climate, crop, economic and migration models in order to predict potential migration flows, driven by climate-change-related crop failure, from African and Middle Eastern countries into EU member states until the year 2050. We go on identify optimal adaptation strategies in countries at risk, which can stabilise agricultural production and help minimise future migration pressures. By using multi-model projections, and performing in-depth model uncertainty and sensitivity analyses, we will generate robust projections driven by the best data available, and ensure a transparent assessment of uncertainties. Our results will inform evidence-based action by EU policy makers aimed at minimising future large-scale forced migration driven by climate change. This highly interdisciplinary project will give the Experienced Researcher the opportunity to complement his background in agro-environmental modelling, by developing expertise in the socio-economic dimensions of global food production under climate change. Home to world-leading experts of all disciplines relevant to the project, and exceptionally well connected with national and international governmental and non-governmental bodies, both the Host Institution (Potsdam Institute for Climate Impact Research) and the Secondment Host Institution (International Organization for Migration) will provide a unique environment for ensuring the highest-quality research and training during the fellowship, as well as the effective communication of results to EU policy makers and the wider public.

Minimising the welfare cost of climate change requires effective adaptation policies. Consequently, the EU strategy on climate change adaptation prioritises the creation of ‘frameworks, models and tools to support decision-making and to assess [the effectiveness of] various adaptation measures’. Accurate measurement of welfare cost is a prerequisite for effective policy prioritisation. The current state of the art quantifies welfare costs using aggregated methodologies. This has been shown to bias cost estimation and does not capture the socioeconomic distribution of welfare impacts. This bias may therefore affect the effectiveness of adaptation measures. Integrating climate impact models (with which Prof Lotze-Campen and the Potsdam Institute for Climate Impact Research [PIK] are experts) with Spatial Microsimulation (SM) modelling (with which the experienced researcher, Dr. Farrell, is an expert) provides a framework to estimate micro-level welfare impacts and thus overcome these deficiencies. Furthermore, this project integrates these estimates into decision-making models to aid policy. This project provides: (1) a timely contribution; (2) a strong multidisciplinary focus; (3) a transfer of SM knowledge to PIK, improving their ability to quantify the welfare impacts of climate change; (4) strong training in climate science and decision-making tools for Dr. Farrell; (5) strong communication/dissemination strategy drawing on resources of Dr. Farrell and PIK. The value of this project is in the collaborative opportunities provided by the research fellowship. PIK provides the best possible opportunities for mentorship and training/professional development for Dr. Farrell. This establishes Dr. Farrell as the leading expert in an emerging field of micro-based welfare estimation of climate change impacts and, coupled with the integration of the research networks of Dr. Farrell and PIK, provides a platform through which many further career developments are possible.

The proposed research aims to establish groundbreaking new methods for the numerical analysis of dynamical systems by using tools from the field of machine learning. The intersection of the fields of machine learning and computational dynamics is largely unexplored, and this proposal aims at the first systematic development of a unified theory, with a view to applying the ideas to problems in the commercial and energy sectors. Recent results by the applicant in set approximation for control systems demonstrate the power of this approach, the results of which significantly improve on the current state-of-the-art methods for set approximation. This approach is based on a functional analytic framework frequently exploited in modern machine learning methods: the reproducing kernel Hilbert space (RKHS). Algorithms are designed to seek functions in the RKHS that characterise important dynamical properties of the system. This highly interdisciplinary research programme will develop a powerful and unified approach to create new algorithms that can either use input data generated from the evolution equations (if they are available) or measured data obtained directly from applications. The host institution PIK is a transdisciplinary host institution focused on climate modeling and sustainability. The tools developed during the course of the fellowship will be applied to the problem of basin stability and synchronisation of power grid networks. This proposal also includes two secondment phases to be spent at the non-academic partner organisation Ambrosys GmbH (AMB). There, the applicant will apply the research results to problems in image rendering in movies and turbulent flow across aerofoils, which are commercial applications already studied at AMB. The applicant will benefit from training in climate modeling and complex systems at PIK, and industrial training during the secondment phases.

In 2015, the UN Sustainable Development Goals (SDGs) and the Paris Agreement on climate recognised the deteriorating resilience of the Earth system in the Anthropocene. Maintaining Earth in the interglacial state that enabled the world’s societies to evolve over the past 12,000 years will require industrialised societies to embark on global-scale social transformations. Otherwise, there is a real risk of crossing tipping points in the Earth system triggering abrupt and irreversible changes. A critical gap is that although nonlinear social and biophysical dynamics are recognized, we remain trapped in linear thinking. Global modelling and analyses – despite much progress – do not adequately represent nonlinear processes and abrupt changes, and social responses to sustainable development are incremental. The goal of this project is to fill this gap, by exploring the biophysical and social determinants of the Earth’s long-term stability, building up a novel community modelling platform for analysis of nonlinearity and abrupt shifts, and informing global sustainability policy processes. The project will investigate two hypotheses: 1) Interactions, feedbacks and tipping points in the biosphere could, even in the absence of continued high emissions from fossil-fuel burning, tip Earth into a new state, committing to global warming over 2C and possibly beyond 4C; and 2) Only nonlinear societal transformations that aggregate to the global scale can assure long-term stability of the Earth and keep it in a manageable interglacial state. The five research tasks are Task 1: analysis of nonlinear biosphere dynamics governing Earth resilience. Task 2: integrating nonlinear dynamics in World-Earth models. Task 3: exploring tipping points in social systems for large-scale transformation. Task 4: backcasting pathways for achieving the SDGs. Task 5: integrating World-Earth dynamics into online learning and virtual-reality games, e.g. Planet3 and Minecraft.

Speleothems (cave deposits, e.g. stalagmites) represent unique terrestrial archives that allow for accurately dated, high-resolution (often annual), continuous and long (many millennia) climate reconstructions. Such records are vital for understanding how climate varies and how our environments respond on seasonal to millennial timescales. However, current speleothem studies can only make qualitative inferences about climate parameters – i.e. they can tell us the direction of change (warmer, drier, etc.) but not the amount of change (how warm? how dry?). Quantitative information is crucial to make speleothem-based data more useful to climate modellers and policy makers. QUEST (QUantitative palaeoEnvironments from SpeleoThems) will develop new techniques for extracting quantitative information from speleothems and link field and laboratory experiments on water/mineral chemistry with innovative physical and numerical analyses on speleothems. The combination of these techniques, based on physical and chemical properties and statistical methods, will allow us to deliver quantitative reconstructions of two key parameters: hydrology and temperature. We will test our methods using speleothems from Australasia, a region vulnerable to El Niño-Southern Oscillation (ENSO) variability. At present, there is a relative dearth of millennial-scale palaeoclimate data from this region. Our team members come from a variety of backgrounds including environmental chemistry, environmental mineral magnetism, and numerical data analysis. Each group within the team has already begun developing innovative methods for palaeoclimate reconstruction within their own subfield, but this project will be the first time these methods are combined and applied collectively to speleothems. Our combination of interdisciplinary expertise, state-of-the-art instrumentation, and novel techniques means that we are ideally placed to develop quantitative climate records from speleothems.