Low - Lying Islands and Coasts

Ocean and cryosphere changes already impact Low-Lying Islands and Coasts (LLIC), including Small Island Developing States (SIDS), with cascading and compounding risks. Disproportionately higher risks are expected in the course of the 21st century. Reinforcing the findings of the IPCC Special Report on Global Warming of 1.5°C, vulnerable human communities, especially those in coral reef environments and polar regions, may exceed adaptation limits well before the end of this century and even in a low greenhouse gas emission pathway (high confidence1). Depending on the effectiveness of 21st century mitigation and adaptation pathways under all emission scenarios, most of the low-lying regions around the world may face adaptation limits beyond 2100, due to the long-term commitment of sea level rise (medium confidence). LLIC host around 11% of the global population, generate about 14% of the global Gross Domestic Product and comprise many world cultural heritage sites. LLIC already experience climate-related ocean and cryosphere changes (high confidence), and they share both commonalities in their exposure and vulnerability to climate change (e.g., low elevation, human disturbances to terrestrial and marine ecosystems), and context-specificities (e.g., variable ecosystem climate sensitivities and risk perceptions by populations). Options to adapt to rising seas, e.g., range from hard engineering to ecosystem-based measures, and from securing current settings to relocating people, built assets and activities. Effective combinations of measures vary across geographies (cities and megacities, small islands, deltas and Arctic coasts), and reflect the scale of observed and projected impacts, ecosystems’ and societies’ adaptive capacity, and the existence of transformational governance (high confidence). In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence

Projecting Antarctica's contribution to future sea level rise from basal ice shelf melt using linear response functions of 16 ice sheet models (LARMIP-2)

International audienceThe sea level contribution of the Antarctic ice sheet constitutes a large uncertainty in future sea level projections. Here we apply a linear response theory approach to 16 state-of-the-art ice sheet models to estimate the Antarctic ice sheet contribution from basal ice shelf melting within the 21st century. The purpose of this computation is to estimate the uncertainty of Antarctica's future contribution to global sea level rise that arises from large uncertainty in the oceanic forcing and the associated ice shelf melting. Ice shelf melting is considered to be a major if not the largest perturbation of the ice sheet's flow into the ocean. However, by computing only the sea level contribution in response to ice shelf melting, our study is neglecting a number of processes such as surface-mass-balance-related contributions. In assuming linear response theory, we are able to capture complex temporal responses of the ice sheets, but we neglect any self-dampening or self-amplifying processes. This is particularly relevant in situations in which an instability is dominating the ice loss. The results obtained here are thus relevant, in particular wherever the ice loss is dominated by the forcing as opposed to an internal instability, for example in strong ocean warming scenarios. In order to allow for comparison the methodology was chosen to be exactly the same as in an earlier study (Levermann et al., 2014) but with 16 instead of 5 ice sheet models. We include uncertainty in the atmospheric warming response to carbon emissions (full range of CMIP5 climate model sensitivities), uncertainty in the oceanic transport to the Southern Ocean (obtained from the time-delayed and scaled oceanic subsurface warming in CMIP5 models in relation to the global mean surface warming), and the observed range of responses of basal ice shelf melting to oceanic warming outside the ice shelf cavity. This uncertainty in basal ice shelf melting is then convoluted with the linear response functions of each of the 16 ice sheet models to obtain the ice flow response to the individual global warming path. The model median for the observational period from 1992 to 2017 of the ice loss due to basal ice shelf melting is 10.2 mm, with a likely range between 5.2 and 21.3 mm. For the same period the Antarctic ice sheet lost mass equivalent to 7.4mm of global sea level rise, with a standard deviation of 3.7mm (Shepherd et al., 2018) including all processes, especially surface-mass-balance changes. For the unabated warming path, Representative Concentration Pathway 8.5 (RCP8.5), we obtain a median contribution of the Antarctic ice sheet to global mean sea level rise from basal ice shelf melting within the 21st century of 17 cm, with a likely range (66th percentile around the mean) between 9 and 36 cm and a very likely range (90th percentile around the mean) between 6 and 58 cm. For the RCP2.6 warming path, which will keep the global mean temperature below 2 °C of global warming and is thus consistent with the Paris Climate Agreement, the procedure yields a median of 13 cm of global mean sea level contribution. The likely range for the RCP2.6 scenario is between 7 and 24 cm, and the very likely range is between 4 and 37 cm. The structural uncertainties in the method do not allow for an interpretation of any higher uncertainty percentiles.We provide projections for the five Antarctic regions and for each model and each scenario separately. The rate of sea level contribution is highest under the RCP8.5 scenario. The maximum within the 21st century of the median value is 4 cm per decade, with a likely range between 2 and 9 cm per decade and a very likely range between 1 and 14 cm per decade

Calcium signalling: dynamics, homeostasis and remodelling

Ca2+ is a highly versatile intracellular signal that operates over a wide temporal range to regulate many different cellular processes. An extensive Ca2+-signalling toolkit is used to assemble signalling systems with very different spatial and temporal dynamics. Rapid highly localized Ca2+ spikes regulate fast responses, whereas slower responses are controlled by repetitive global Ca2+ transients or intracellular Ca2+ waves. Ca2+ has a direct role in controlling the expression patterns of its signalling systems that are constantly being remodelled in both health and disease

Plant traits and wood fates across the globe: rotted, burned, or consumed?

Wood represents the defining feature of forest systems, and often the carbon in woody debris has a long residence time. Globally, coarse dead wood contains 36-72 Pg C, and understanding what controls the fate of this C is important for predicting C cycle responses to global change. The fate of a piece of wood may include one or more of the following: microbial decomposition, combustion, consumption by insects, and physical degradation. The probability of each fate is a function of both the abiotic environment and the wood traits of the species. The wood produced by different species varies substantially in chemical, micro- and macro-morphological traits; many of these characteristics of living species have 'afterlife' effects on the fate and turnover rate of dead wood. The colonization of dead wood by microbes and their activity depends on a large suite of wood chemical and anatomical traits, as well as whole-plant traits such as stem-diameter distributions. Fire consumption is driven by a slightly narrower range of traits with little dependence on wood anatomy. Wood turnover due to insects mainly depends on wood density and secondary chemistry. Physical degradation is a relatively minor loss pathway for most systems, which depends on wood chemistry and environmental conditions. We conclude that information about the traits of woody plants could be extremely useful for modeling and predicting rates of wood turnover across ecosystems. We demonstrate how this trait-based approach is currently limited by oversimplified treatment of dead wood pools in several leading global C models and by a lack of quantitative empirical data linking woody plant traits with the probability and rate of each turnover pathway. Explicitly including plant traits and woody debris pools in global vegetation climate models would improve predictions of wood turnover and its feedback to climate