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- ItemReply to Burgess et al: Catastrophic climate risks are neglected, plausible, and safe to study(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.
- ItemInterfacial photochemistry at the ocean surface is a global source of organic vapors and aerosols([London] : Nature Publishing Group UK, 2018) Brüggemann, Martin; Hayeck, Nathalie; George, ChristianThe surface of the oceans acts as a global sink and source for trace gases and aerosol particles. Recent studies suggest that photochemical reactions at this air/water interface produce organic vapors, enhancing particle formation in the atmosphere. However, current model calculations neglect this abiotic source of reactive compounds and account only for biological emissions. Here we show that interfacial photochemistry serves as a major abiotic source of volatile organic compounds (VOCs) on a global scale, capable to compete with emissions from marine biology. Our results indicate global emissions of 46.4-184 Tg C yr-1 of organic vapors from the oceans into the marine atmosphere and a potential contribution to organic aerosol mass of more than 60% over the remote ocean. Moreover, we provide global distributions of VOC formation potentials, which can be used as simple tools for field studies to estimate photochemical VOC emissions depending on location and season.
- ItemAssessing inter-sectoral climate change risks: The role of ISIMIP(Bristol : IOP Publishing, 2017) Rosenzweig, Cynthia; Arnell, Nigel W.; Ebi, Kristie L.; Lotze-Campen, Hermann; Raes, Frank; Rapley, Chris; Smith, Mark Stafford; Cramer, Wolfgang; Frieler, Katja; Reyer, Christopher P.O.; Schewe, Jacob; van Vuuren, Detlef; Warszawski, LilaThe aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socio-economic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.
- ItemClimate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios(Bristol : IOP Publishing, 2015) Wiebe, Keith; Lotze-Campen, Hermann; Sands, Ronald; Tabeau, Andrzej; van der Mensbrugghe, Dominique; Biewald, Anne; Bodirsky, Benjamin; Islam, Shahnila; Kavallari, Aikaterini; Mason-D'Croz, Daniel; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; van Meijl, Hans; Willenbockel, DirkPrevious studies have combined climate, crop and economic models to examine the impact of climate change on agricultural production and food security, but results have varied widely due to differences in models, scenarios and input data. Recent work has examined (and narrowed) these differences through systematic model intercomparison using a high-emissions pathway to highlight the differences. This paper extends that analysis to explore a range of plausible socioeconomic scenarios and emission pathways. Results from multiple climate and economic models are combined to examine the global and regional impacts of climate change on agricultural yields, area, production, consumption, prices and trade for coarse grains, rice, wheat, oilseeds and sugar crops to 2050. We find that climate impacts on global average yields, area, production and consumption are similar across shared socioeconomic pathways (SSP 1, 2 and 3, as we implement them based on population, income and productivity drivers), except when changes in trade policies are included. Impacts on trade and prices are higher for SSP 3 than SSP 2, and higher for SSP 2 than for SSP 1. Climate impacts for all variables are similar across low to moderate emissions pathways (RCP 4.5 and RCP 6.0), but increase for a higher emissions pathway (RCP 8.5). It is important to note that these global averages may hide regional variations. Projected reductions in agricultural yields due to climate change by 2050 are larger for some crops than those estimated for the past half century, but smaller than projected increases to 2050 due to rising demand and intrinsic productivity growth. Results illustrate the sensitivity of climate change impacts to differences in socioeconomic and emissions pathways. Yield impacts increase at high emissions levels and vary with changes in population, income and technology, but are reduced in all cases by endogenous changes in prices and other variables.
- ItemSimulating the Earth system response to negative emissions(Bristol : IOP Publishing, 2016) Jones, C.D.; Ciais, P.; Davis, S.J.; Friedlingstein, P.; Gasser, T.; Peters, G.P.; Rogelj, J.; van Vuuren, D.P.; Canadell, J.G.; Cowie, A.; Jackson, R.B.; Jonas, M.; Kriegler, E.; Littleton, E.; Lowe, J.A.; Milne, J.; Shrestha, G.; Smith, P.; Torvanger, A.; Wiltshire, A.Natural carbon sinks currently absorb approximately half of the anthropogenic CO2 emitted by fossil fuel burning, cement production and land-use change. However, this airborne fraction may change in the future depending on the emissions scenario. An important issue in developing carbon budgets to achieve climate stabilisation targets is the behaviour of natural carbon sinks, particularly under low emissions mitigation scenarios as required to meet the goals of the Paris Agreement. A key requirement for low carbon pathways is to quantify the effectiveness of negative emissions technologies which will be strongly affected by carbon cycle feedbacks. Here we find that Earth system models suggest significant weakening, even potential reversal, of the ocean and land sinks under future low emission scenarios. For the RCP2.6 concentration pathway, models project land and ocean sinks to weaken to 0.8 ± 0.9 and 1.1 ± 0.3 GtC yr−1 respectively for the second half of the 21st century and to −0.4 ± 0.4 and 0.1 ± 0.2 GtC yr−1 respectively for the second half of the 23rd century. Weakening of natural carbon sinks will hinder the effectiveness of negative emissions technologies and therefore increase their required deployment to achieve a given climate stabilisation target. We introduce a new metric, the perturbation airborne fraction, to measure and assess the effectiveness of negative emissions.
- ItemA systematic review of non-productivity-related animal-based indicators of heat stress resilience in dairy cattle(San Francisco, California, US : PLOS, 2018-11-1) Galán, Elena; Llonch, Pol; Villagrá, Arantxa; Levit, Harel; Pinto, Severino; del Prado, AgustínIntroduction Projected temperature rise in the upcoming years due to climate change has increased interest in studying the effects of heat stress in dairy cows. Environmental indices are commonly used for detecting heat stress, but have been used mainly in studies focused on the productivity-related effects of heat stress. The welfare approach involves identifying physiological and behavioural measurements so as to start heat stress mitigation protocols before the appearance of impending severe health or production issues. Therefore, there is growing interest in studying the effects of heat stress on welfare. This systematic review seeks to summarise the animal-based responses to heat stress (physiological and behavioural, excluding productivity) that have been used in scientific literature. Methods Using systematic review guidelines set by PRISMA, research articles were identified, screened and summarised based on inclusion criteria for physiology and behaviour, excluding productivity, for animal-based resilience indicators. 129 published articles were reviewed to determine which animal-based indicators for heat stress were most frequently used in dairy cows. Results The articles considered report at least 212 different animal-based indicators that can be aggregated into body temperature, feeding, physiological response, resting, drinking, grazing and pasture-related behaviour, reactions to heat management and others. The most common physiological animal-based indicators are rectal temperature, respiration rate and dry matter intake, while the most common behavioural indicators are time spent lying, standing and feeding. Conclusion Although body temperature and respiration rate are the animal-based indicators most frequently used to assess heat stress in dairy cattle, when choosing an animal-based indicator for detecting heat stress using scientific literature to establish thresholds, characteristics that influence the scale of the response and the definition of heat stress must be taken into account, e.g. breed, lactation stage, milk yield, system type, climate region, bedding type, diet and cooling management strategies. © 2018 Galan∗E.∗Elena et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- ItemBayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition([London] : Macmillan Publishers Limited, part of Springer Nature, 2019) Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Kurths, Juergen; Feigin, AlexanderCurrently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.
- ItemReply to Bhowmik et al.: Democratic climate action and studying extreme climate risks are not in tension(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.[no abstract available]
- ItemReply to Kelman: The foundations for studying catastrophic climate risks(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.
- ItemDecay radius of climate decision for solar panels in the city of Fresno, USA([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Barton-Henry, Kelsey; Wenz, Leonie; Levermann, AndersTo design incentives towards achieving climate mitigation targets, it is important to understand the mechanisms that affect individual climate decisions such as solar panel installation. It has been shown that peer effects are important in determining the uptake and spread of household photovoltaic installations. Due to coarse geographical data, it remains unclear whether this effect is generated through geographical proximity or within groups exhibiting similar characteristics. Here we show that geographical proximity is the most important predictor of solar panel implementation, and that peer effects diminish with distance. Using satellite imagery, we build a unique geo-located dataset for the city of Fresno to specify the importance of small distances. Employing machine learning techniques, we find the density of solar panels within the shortest measured radius of an address is the most important factor in determining the likelihood of that address having a solar panel. The importance of geographical proximity decreases with distance following an exponential curve with a decay radius of 210 meters. The dependence is slightly more pronounced in low-income groups. These findings support the model of distance-related social diffusion, and suggest priority should be given to seeding panels in areas where few exist.