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We summarize some of the past year's most important findings within climate change-related research. New research has improved our understanding about the remaining options to achieve the Paris Agreement goals, through overcoming political barriers to carbon pricing, taking into account non-CO2 factors, a well-designed implementation of demand-side and nature-based solutions, resilience building of ecosystems and the recognition that climate change mitigation costs can be justified by benefits to the health of humans and nature alone. We consider new insights about what to expect if we fail to include a new dimension of fire extremes and the prospect of cascading climate tipping elements.
A synthesis is made of 10 topics within climate research, where there have been significant advances since January 2020. The insights are based on input from an international open call with broad disciplinary scope. Findings include: (1) the options to still keep global warming below 1.5 °C; (2) the impact of non-CO2 factors in global warming; (3) a new dimension of fire extremes forced by climate change; (4) the increasing pressure on interconnected climate tipping elements; (5) the dimensions of climate justice; (6) political challenges impeding the effectiveness of carbon pricing; (7) demand-side solutions as vehicles of climate mitigation; (8) the potentials and caveats of nature-based solutions; (9) how building resilience of marine ecosystems is possible; and (10) that the costs of climate change mitigation policies can be more than justified by the benefits to the health of humans and nature.
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How do we limit global warming to 1.5 °C and why is it crucial? See highlights of latest climate science.
Individual organisms on land and in the ocean sequester massive amounts of the carbon emitted into the atmosphere by humans. Yet the role of ecosystems as a whole in modulating this uptake of carbon is less clear. Here, we study several different mechanisms by which climate change and ecosystems could interact. We show that climate change could cause changes in ecosystems that reduce their capacity to take up carbon, further accelerating climate change. More research on – and better governance of – interactions between climate change and ecosystems is urgently required.
Complex network theory provides a powerful toolbox for studying the structure of statistical interrelationships between multiple time series in various scientific disciplines. Complementing frequently used methods of eigenanalysis such as empirical orthogonal functions, climate networks allow to flexibly combine advanced nonlinear and informationtheoretical measures for quantifying interactions between climatological time series with manifold concepts and methods from complex network theory. This chapter summarizes the corresponding theoretical foundations as well as recent applications in the field of climate network analysis for different climatic observables, including the treatment of coupled climatological fields and heterogeneous spatial distributions of climate observations.
Gaining information on climate variability using sophisticated methods of data analysis is one of the foremost tasks of statistical climatology. Inspired by classical methods from multivariate statistics, a large body of approaches has been utilized in past studies, including empirical orthogonal function (EOF) analysis, maximum covariance analysis (MCA) or canonical correlation analysis (CCA), to mention only some of the most prominent examples (von Storch and Zwiers, 2003). These purely statistical approaches have been successfully applied for studying a broad variety of climatological problems.
However, during recent decades concerns have been raised regarding the methodological limitations of the aforementioned approaches as well as the appropriate interpretation of the resulting findings. A first possible point of criticism is the implicit assumption of linearity of statistical interdependencies underlying methods like EOF analysis and MCA. In order to account for more general statistical relationships, nonlinear generalizations of these methods have been developed, relieving the requirement of linear independence between patterns to be addressed. Corresponding approaches include isometric feature mapping (Isomap, Tenenbaum et al. (2000); Gámez et al. (2004)), nonlinear (neural network-based) principal component analysis (Hsieh, 2004, 2009), and a variety of other techniques based on machine learning principal component analysis (Hsieh, 2004, 2009).
In addition to the linearity assumption, Monahan et al. (2009) identified several concerns regarding the interpretation of modes revealed by EOF analysis (which apply in a similar spirit also to other established techniques of statistical climatology).