Neo-Atlantis: Dutch Responses to Five Meter Sea Level Rise

What would happen to the Netherlands if, in 2030, the sea level starts to rise and eventually, after 100 years, a sea level of five meters above current level would be reached? Two socio-economic scenarios are developed from a literature review and by interviews with researchers and practicionersin the domains of social sciences, economics, civil engineering, and land use planning. One scenario describes what would happen in a future characterised by a trend towards further globalisation, marketisation and high economic growth, while the other scenario happens in a future under opposite trends. Under both scenarios, the Southwest and Northwest of the Netherlands – already now below seal level - would be abandoned because of sea level rise. Although most experts believe that geomorphology and current engineering skills allow to largely maintain the territorial integrity of the Netherlands, there are some reasons to assume that this is not likely to happen. Social processes that precede important political decisions – such as the growth of the belief in the reality of SLR and the framing of such decision in a proper political context (policy window) – evolve slowly. Although a flood disaster would speed up decision-making, the general expectation is that decisions would come too late in view of the rate of SLR and the possible pace of construction of works.Extreme sea level rise, The Netherlands, flood defences

Weather extremes over Europe under 1.5 °C and 2.0 °C global warming from HAPPI regional climate ensemble simulations

This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C

The existential risk space of climate change

Climate change is widely recognized as a major risk to societies and natural ecosystems but the high end of the risk, i.e., where risks become existential, is poorly framed, defined, and analyzed in the scientific literature. This gap is at odds with the fundamental relevance of existential risks for humanity, and it also limits the ability of scientific communities to engage with emerging debates and narratives about the existential dimension of climate change that have recently gained considerable traction. This paper intends to address this gap by scoping and defining existential risks related to climate change. We first review the context of existential risks and climate change, drawing on research in fields on global catastrophic risks, and on key risks and the so-called Reasons for Concern in the reports of the Intergovernmental Panel on Climate Change. We also consider how existential risks are framed in the civil society climate movement as well as what can be learned in this respect from the COVID-19 crisis. To better frame existential risks in the context of climate change, we propose to define them as those risks that threaten the existence of a subject, where this subject can be an individual person, a community, or nation state or humanity. The threat to their existence is defined by two levels of severity: conditions that threaten (1) survival and (2) basic human needs. A third level, well-being, is commonly not part of the space of existential risks. Our definition covers a range of different scales, which leads us into further defining six analytical dimensions: physical and social processes involved, systems affected, magnitude, spatial scale, timing, and probability of occurrence. In conclusion, we suggest that a clearer and more precise definition and framing of existential risks of climate change such as we offer here facilitates scientific analysis as well societal and political discourse and action

Risk Management and Adaptation for Extremes and Abrupt Changes in Climate and Oceans: Current Knowledge Gaps

Perspectives for risk management and adaptation have received ample attention in the recent IPCC Special Report on Changes in the Oceans and Cryosphere (SROCC). However, several knowledge gaps on the impacts of abrupt changes, cascading effects and compound extreme climatic events have been identified, and need further research. We focus on specific climate change risks identified in the SROCC report, namely: changes in tropical and extratropical cyclones; marine heatwaves; extreme ENSO events; and abrupt changes in the Atlantic Meridional Overturning Circulation. Several of the socioeconomic impacts from these events are not yet well-understood, and the literature is also sparse on specific recommendations for integrated risk management and adaptation options to reduce such risks. Also, past research has mostly focussed on concepts that have seen little application to real-world cases. We discuss relevant research needs and priorities for improved social-ecological impact assessment related to these major physical changes in the climate and oceans. For example, harmonised approaches are needed to better understand impacts from compound events, and cascading impacts across systems. Such information is essential to inform options for adaptation, governance and decision-making. Finally, we highlight research needs for developing transformative adaptation options and their governance

Approaches to analyse and model changes in impacts:reply to discussions of “How to improve attribution of changes in drought and flood impacts”^*

We thank the authors, Brunella Bonaccorso and Karsten Arnbjerg-Nielsen for their constructive contributions to the discussion about the attribution of changes in drought and flood impacts. We appreciate that they support our opinion, but in particular their additional new ideas on how to better understand changes in impacts. It is great that they challenge us to think a step further on how to foster the collection of long time series of data and how to use these to model and project changes. Here, we elaborate on the possibility to collect time series of data on hazard, exposure, vulnerability and impacts and how these could be used to improve e.g. socio-hydrological models for the development of future risk scenarios.</p

Machine learning models to predict myocardial infarctions from past climatic and environmental conditions

Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, sociodemographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015.Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2 = 0.62–0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approachprovides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes