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AbstractGlobal coastal flood exposure (population and assets) has been growing since the beginning of the industrial age and is likely to continue to grow through 21st century. Three main drivers are responsible: (a) climate‐related mean sea‐level change, (b) vertical land movement contributing to relative sea‐level rise, and (c) socio‐economic development. This paper attributes growing coastal exposure and flood risk from 1860 to 2100 to these three drivers. For historic flood exposure (1860–2005) we find that the roughly six‐fold increase in population exposure and 53‐fold increase in asset exposure are almost completely explained by socio‐economic development (>97% for population and >99% for assets). For future exposure (2005–2100), assuming a middle‐of‐the‐road regionalized socio‐economic scenario (SSP2) without coastal migration and sea‐level rise according to RCP2.6 and RCP6.0, climate‐change induced sea‐level rise will become the most important driver for the growth in population exposure, while growth in asset exposure will still be mainly determined by socio‐economic development.

The UN Sustainable Development Goals (SDGs) and the Paris Agreement have ushered in a new era of policymaking to deliver on the formulated goals. Energy policies are key to ensuring universal access to affordable, reliable, sustainable, and modern energy (SDG7). Yet they can also have considerable impact on other goals. To successfully achieve multiple goals concurrently, policies need to balance different objectives and manage their interactions. Refining previously contemplated design principles, we identify three key principles - complementary, transparency and adaptability - as highly pertinent for multiple-objective energy policies based on a synthesis of seventeen coordinated policy case studies. First, policies should entail complementary measures and design provisions that specifically target non-energy objectives (complementarity). Second, policy impacts should be tracked comprehensively in both energy and non-energy domains to uncover diminishing returns and facilitate policy learning (transparency). Third, policies should be capable of adapting to changing objectives over time (adaptability). These principles are rarely considered in current policies, implying the need to mainstream them into the next generation of policymaking by pointing to best practices and new tools.

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question 75 pages, 8 figures, 3 tables, 6 appendixes, supplement https://doi.org/10.5194/esd-12-1295-2021-supplement.-- Code and data availability: The python code that was used for the analysis and to create the plots is available at https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021; Volpi, 2021) and as a Renku project https://renkulab.io/projects/ACE-ASAID/spca-decomposition (last access: 29 March 2021; Volpi and Landwehr, 2021). For the availability of the data used in this study please refer to Tables B1 to B8 in Appendix B Rafel Simó, Marina Zamanillo, Pau Cortés-Greus, and Pablo Rodríguez-Ros were supported by the Spanish Ministry of Science through the BIOGAPS project (CTM2016-81008-R) With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) Peer reviewed

AbstractCryptosporidiosis is a major global health problem and a primary cause of diarrhoea, particularly in young children in low- and middle-income countries (LMICs). The zoonoticCryptosporidium parvumand anthroponoticC. hominiscause most human infections. Here, we present a comprehensive whole-genome study ofC. hominis, comprising 114 isolates from 16 countries within five continents. We detect two lineages with distinct biology and demography, which diverged circa 500 years ago. We consider these lineages two subspecies and propose the namesC. hominis hominisandC. hominis aquapotentis(gp60subtype IbA10G2). In our study,C. h. hominisis almost exclusively represented by isolates from LMICs in Africa and Asia and appears to have undergone recent population contraction. In contrast,C. h. aquapotentiswas found in high-income countries, mainly in Europe, North America and Oceania, and appears to be expanding. Notably,C. h. aquapotentisis associated with high rates of direct human-to-human transmission, which may explain its success in countries with well-developed environmental sanitation infrastructure. Intriguingly, we detected genomic regions of introgression following secondary contact between the subspecies. This resulted in high diversity and divergence in genomic islands of putative virulence genes (GIPVs), includingmuc5(CHUDEA2_430) and a hypothetical protein (CHUDEA6_5270). This diversity is maintained by balancing selection, suggesting a coevolutionary arms race with the host. Lastly, we find that recent gene flow fromC. h. aquapotentistoC. h. hominis, likely associated with increased human migration, may be driving evolution of more virulentC. hominisvariants.

Abstract This study provides a literature‐based comparative assessment of uncertainties and biases in global to world‐regional scale assessments of current and future coastal flood risks, considering mean and extreme sea‐level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea‐level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio‐economic development (factors 2.3–5.8), digital elevation data (factors 1.2–3.8), ice sheet models (factor 1.6–3.8) and greenhouse gas emissions (factors 1.6–2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth‐damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea‐levels, storm surges, tides and waves. Advancing the state‐of‐the‐art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth‐damage functions would be best reduced through open community‐based efforts, in which many scholars work together in collecting and validating these data. Key Points We present the first comparison of uncertainties in global to world‐regional scale assessments of current and future coastal flood riskThe largest uncertainty relates to future coastal adaptation, which can influence future coastal flood risk by factors of 20–27Uncertainties in socioeconomic development, elevation data, defense levels, emissions and ice sheets can affect risks by factors of 2–6

AbstractProcess-based integrated assessment models (IAMs) project long-term transformation pathways in energy and land-use systems under what-if assumptions. IAM evaluation is necessary to improve the models’ usefulness as scientific tools applicable in the complex and contested domain of climate change mitigation. We contribute the first comprehensive synthesis of process-based IAM evaluation research, drawing on a wide range of examples across six different evaluation methods including historical simulations, stylised facts, and model diagnostics. For each evaluation method, we identify progress and milestones to date, and draw out lessons learnt as well as challenges remaining. We find that each evaluation method has distinctive strengths, as well as constraints on its application. We use these insights to propose a systematic evaluation framework combining multiple methods to establish the appropriateness, interpretability, credibility, and relevance of process-based IAMs as useful scientific tools for informing climate policy. We also set out a programme of evaluation research to be mainstreamed both within and outside the IAM community.