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Climate science provides strong evidence of the necessity of limiting global warming to 1.5 °C, in line with the Paris Climate Agreement. The IPCC 1.5 °C special report (SR1.5) presents 414 emissions scenarios modelled for the report, of which around 50 are classified as '1.5 °C scenarios', with no or low temperature overshoot. These emission scenarios differ in their reliance on individual mitigation levers, including reduction of global energy demand, decarbonisation of energy production, development of land-management systems, and the pace and scale of deploying carbon dioxide removal (CDR) technologies. The reliance of 1.5 °C scenarios on these levers needs to be critically assessed in light of the potentials of the relevant technologies and roll-out plans. We use a set of five parameters to bundle and characterise the mitigation levers employed in the SR1.5 1.5 °C scenarios. For each of these levers, we draw on the literature to define 'medium' and 'high' upper bounds that delineate between their 'reasonable', 'challenging' and 'speculative' use by mid century. We do not find any 1.5 °C scenarios that stay within all medium upper bounds on the five mitigation levers. Scenarios most frequently 'over use' CDR with geological storage as a mitigation lever, whilst reductions of energy demand and carbon intensity of energy production are 'over used' less frequently. If we allow mitigation levers to be employed up to our high upper bounds, we are left with 22 of the SR1.5 1.5 °C scenarios with no or low overshoot. The scenarios that fulfil these criteria are characterised by greater coverage of the available mitigation levers than those scenarios that exceed at least one of the high upper bounds. When excluding the two scenarios that exceed the SR1.5 carbon budget for limiting global warming to 1.5 °C, this subset of 1.5 °C scenarios shows a range of 15–22 Gt CO2 (16–22 Gt CO2 interquartile range) for emissions in 2030. For the year of reaching net zero CO2 emissions the range is 2039–2061 (2049–2057 interquartile range).
ArcticBeach v1.0
(2022)
In the Arctic, air temperatures are increasing and sea ice is declining, resulting in larger waves and a longer open water season, all of which intensify the thaw and erosion of ice-rich coasts. Climate change has been shown to increase the rate of Arctic coastal erosion, causing problems for Arctic cultural heritage, existing industrial, military, and civil infrastructure, as well as changes in nearshore biogeochemistry. Numerical models that reproduce historical and project future Arctic erosion rates are necessary to understand how further climate change will affect these problems, and no such model yet exists to simulate the physics of erosion on a pan-Arctic scale. We have coupled a bathystrophic storm surge model to a simplified physical erosion model of a permafrost coastline. This Arctic erosion model, called ArcticBeach v1.0, is a first step toward a physical parameterization of Arctic shoreline erosion for larger-scale models. It is forced by wind speed and direction, wave period and height, sea surface temperature, all of which are masked during times of sea ice cover near the coastline. Model tuning requires observed historical retreat rates (at least one value), as well as rough nearshore bathymetry. These parameters are already available on a pan-Arctic scale. The model is validated at three study sites at 1) Drew Point (DP), Alaska, 2) Mamontovy Khayata (MK), Siberia, and 3) Veslebogen Cliffs, Svalbard. Simulated cumulative retreat rates for DP and MK respectively (169 and 170 m) over the time periods studied at each site (2007-2016, and 1995-2018) are found to the same order of magnitude as observed cumulative retreat (172 and 120 m). The rocky Veslebogen cliffs have small observed cumulative retreat rates (0.05 m over 2014-2016), and our model was also able to reproduce this same order of magnitude of retreat (0.08 m). Given the large differences in geomorphology between the study sites, this study provides a proof-of-concept that ArcticBeach v1.0 can be applied on very different permafrost coastlines. ArcticBeach v1.0 provides a promising starting point to project retreat of Arctic shorelines, or to evaluate historical retreat in places that have had few observations.
Nature-based solutions (NBS) are seen as a promising adaptation measure that sustainably deals with diverse societal challenges, while simultaneously delivering multiple benefits. Nature-based solutions have been highlighted as a resilient and sustainable means of mitigating floods and other hazards globally. This study examined diverging conceptualizations of NBS, as well as the attitudinal (for example, emotions and beliefs) and contextual (for example, legal and political aspects) barriers and drivers of NBS for flood risks in South Korea. Semistructured interviews were conducted with 11 experts and focused on the topic of flood risk measures and NBS case studies. The analysis found 11 barriers and five drivers in the attitudinal domain, and 13 barriers and two drivers in the contextual domain. Most experts see direct monetary benefits as an important attitudinal factor for the public. Meanwhile, the cost-effectiveness of NBS and their capacity to cope with flood risks were deemed influential factors that could lead decision makers to opt for NBS. Among the contextual factors, insufficient systems to integrate NBS in practice and the ideologicalization of NBS policy were found to be peculiar barriers, which hinder consistent realization of initiatives and a long-term national plan for NBS. Understanding the barriers and drivers related to the mainstreaming of NBS is critical if we are to make the most of such solutions for society and nature. It is also essential that we have a shared definition, expectation, and vision of NBS.
During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007-2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash-Sutcliffe efficiency of 0.72 and a Kling-Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century.
Pathways toward limiting global warming to well below 2 ∘C, as used by the IPCC in the Fifth Assessment Report, do not consider the climate impacts already occurring below 2 ∘C. Here we show that accounting for such damages significantly increases the near-term ambition of transformation pathways. We use econometric estimates of climate damages on GDP growth and explicitly model the uncertainty in the persistence time of damages. The Integrated Assessment Model we use includes the climate system and mitigation technology detail required to derive near-term policies. We find an optimal carbon price of $115 per tonne of CO2 in 2030. The long-term persistence of damages, while highly uncertain, is a main driver of the near-term carbon price. Accounting for damages on economic growth increases the gap between the currently pledged nationally determined contributions and the welfare-optimal 2030 emissions by two thirds, compared to pathways considering the 2 ∘C limit only.
We present a new set of global and local sea‐level projections at example tide gauge locations under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. Compared to the CMIP5‐based sea‐level projections presented in IPCC AR5, we introduce a number of methodological innovations, including (i) more comprehensive treatment of uncertainties, (ii) direct traceability between global and local projections, and (iii) exploratory extended projections to 2300 based on emulation of individual CMIP5 models. Combining the projections with observed tide gauge records, we explore the contribution to total variance that arises from sea‐level variability, different emissions scenarios, and model uncertainty. For the period out to 2300 we further breakdown the model uncertainty by sea‐level component and consider the dependence on geographic location, time horizon, and emissions scenario. Our analysis highlights the importance of local variability for sea‐level change in the coming decades and the potential value of annual‐to‐decadal predictions of local sea‐level change. Projections to 2300 show a substantial degree of committed sea‐level rise under all emissions scenarios considered and highlight the reduced future risk associated with RCP2.6 and RCP4.5 compared to RCP8.5. Tide gauge locations can show large ( > 50%) departures from the global average, in some cases even reversing the sign of the change. While uncertainty in projections of the future Antarctic ice dynamic response tends to dominate post‐2100, we see substantial differences in the breakdown of model variance as a function of location, time scale, and emissions scenario.
Terrestrial reptiles are particularly vulnerable to climate change. Their highest density and diversity can be found in hot drylands, ecosystems which demonstrate extreme climatic conditions. However, reptiles are not isolated systems but part of a large species assemblage with many trophic dependencies. While direct relations among climatic conditions, invertebrates, vegetation, or reptiles have already been explored, to our knowledge, species’ responses to direct and indirect pathways of multiple climatic and biotic factors and their interactions have rarely been examined comprehensively. We investigated direct and indirect effects of climatic and biotic parameters on the individual (body condition) and population level (occupancy) of eight abundant lizard species with different functional traits in an arid Australian lizard community using a 30‐yr multi‐trophic monitoring study. We used structural equation modeling to disentangle single and interactive effects. We then assessed whether species could be grouped into functional groups according to their functional traits and their responses to different parameters. We found that lizard species differed strongly in how they responded to climatic and biotic factors. However, the factors to which they responded seemed to be determined by their functional traits. While responses on body condition were determined by habitat, activity time, and prey, responses on occupancy were determined by habitat specialization, body size, and longevity. Our findings highlight the importance of indirect pathways through climatic and biotic interactions, which should be included into predictive models to increase accuracy when predicting species’ responses to climate change. Since one might never obtain all mechanistic pathways at the species level, we propose an approach of identifying relevant species traits that help grouping species into functional groups at different ecological levels, which could then be used for predictive modeling.
In many species, dispersal is decisive for survival in a changing climate. Simulation models for population dynamics under climate change thus need to account for this factor. Moreover, large numbers of species inhabiting agricultural landscapes are subject to disturbances induced by human land use. We included dispersal in the HiLEG model that we previously developed to study the interaction between climate change and agricultural land use in single populations. Here, the model was parameterized for the large marsh grasshopper (LMG) in cultivated grasslands of North Germany to analyze (1) the species development and dispersal success depending on the severity of climate change in subregions, (2) the additional effect of grassland cover on dispersal success, and (3) the role of dispersal in compensating for detrimental grassland mowing. Our model simulated population dynamics in 60-year periods (2020-2079) on a fine temporal (daily) and high spatial (250 x 250 m(2)) scale in 107 subregions, altogether encompassing a range of different grassland cover, climate change projections, and mowing schedules. We show that climate change alone would allow the LMG to thrive and expand, while grassland cover played a minor role. Some mowing schedules that were harmful to the LMG nevertheless allowed the species to moderately expand its range. Especially under minor climate change, in many subregions dispersal allowed for mowing early in the year, which is economically beneficial for farmers. More severe climate change could facilitate LMG expansion to uninhabited regions but would require suitable mowing schedules along the path. These insights can be transferred to other species, given that the LMG is considered a representative of grassland communities. For more specific predictions on the dynamics of other species affected by climate change and land use, the publicly available HiLEG model can be easily adapted to the characteristics of their life cycle.
The current awareness of the high importance of urban green leads to a stronger need for tools to comprehensively represent urban green and its benefits. A common scientific approach is the development of urban ecosystem services (UES) based on remote sensing methods at the city or district level. Urban planning, however, requires fine-grained data that match local management practices. Hence, this study linked local biotope and tree mapping methods to the concept of ecosystem services. The methodology was tested in an inner-city district in SW Germany, comparing publicly accessible areas and non-accessible courtyards. The results provide area-specific [m(2)] information on the green inventory at the microscale, whereas derived stock and UES indicators form the basis for comparative analyses regarding climate adaptation and biodiversity. In the case study, there are ten times more micro-scale green spaces in private courtyards than in the public space, as well as twice as many trees. The approach transfers a scientific concept into municipal planning practice, enables the quantitative assessment of urban green at the microscale and illustrates the importance for green stock data in private areas to enhance decision support in urban development. Different aspects concerning data collection and data availability are critically discussed.
Multi-year index-based insurance for adapting Water Utility Companies to hydrological drought
(2020)
The sustainability of water utility companies is threatened by non-stationary drivers, such as climate and anthropogenic changes. To cope with potential economic losses, instruments such as insurance are useful for planning scenarios and mitigating impacts, but data limitations and risk uncertainties affect premium estimation and, consequently, business sustainability. This research estimated the possible economic impacts of business interruption to the Sao Paulo Water Utility Company derived from hydrological drought and how this could be mitigated with an insurance scheme. Multi-year insurance (MYI) was proposed through a set of "change" drivers: the climate driver, through forcing the water evaluation and planning system (WEAP) hydrological tool; the anthropogenic driver, through water demand projections; and the economic driver, associated with recent water price policies adopted by the utility company during water scarcity periods. In our study case, the evaluated indices showed that MYI contracts that cover only longer droughts, regardless of the magnitude, offer better financial performance than contracts that cover all events (in terms of drought duration). Moreover, through MYI contracts, we demonstrate solvency for the insurance fund in the long term and an annual average actuarially fair premium close to the total expected revenue reduction.