Search

The action required to stem the environmental and social implications of climate change depends crucially on how humankind shapes technology, economy, lifestyle and policy. With transport CO2 emissions accounting for about a quarter of the total, we examine the contribution of CO2 output by scientific travel. Thankfully for the reputation of the scientific community, CO2 emissions associated with the trips required to present a paper at a scientific conference account for just 0.003% of the yearly total. However, with CO2 emissions for a single conference trip amounting to 7% of an average individual’s total CO2 emissions, scientists should lead by example by demonstrating leadership in addressing the issue. [ABSTRACT FROM AUTHOR]

The climate change policy debate and ensuing discussions about industrial energy use and carbon emissions have highlighted the need to: (a) aggregate engineering information to a level relevant for economic policy analysis while maintaining sufficient detail so that results are meaningful for industry decision makers, (b) properly represent an industry’s capital vintage structure to better understand inertia associated with changes in aggregate industrial emissions profiles, and (c) identify policy instruments that leverage an industry’s potential for technological change such that carbon emissions can be noticeably reduced. This paper presents an econometric analysis of energy use and emissions profiles of the US Pulp and Paper Industry and uses the resulting set of equations to specify a dynamic model for the analysis of select climate change policies. Scenarios of cost of carbon, energy tax, and investment-led policies indicate that a combination of cost of carbon and investment-led policies can achieve the desired result of rapidly improving overall efficiency of the industry and promoting changes in fuel mix, which together can result in drastic reductions of carbon emissions. [Copyright &y& Elsevier]

Application for Research Ethics Committee (REC) approval and the conduct of medical research is paper intensive. This retrospective study examined all applications to a single REC in the south of England over one year. It estimated the mass of paper used, comparing the proportional paper consumption of different trial types and during different stages of the research process, quantifying the consumption in terms of carbon dioxide emissions. In 2009, 68 trials were submitted to the REC. Total paper consumption for the REC process and study conduct was 176,150 sheets of A4 paper (879 kg), equivalent to an estimated 11.5 million sheets (88 tonnes, 2,100 trees) a year for the UK; the REC process accounted for 26.4%. REC applications and the conduct of approved trials generate considerable environmental impact through paper consumption contributing to the NHS's carbon footprint. Paper use might be reduced through the implementation of digital technologies and revised research methods, namely changing attitudes in both researchers and ethics committees. [ABSTRACT FROM AUTHOR]

This bibliometric study of a large publication set dealing with research on climate change aims at mapping the relevant literature from a bibliometric perspective and presents a multitude of quantitative data: (1) The growth of the overall publication output as well as (2) of some major subfields, (3) the contributing journals and countries as well as their citation impact, and (4) a title word analysis aiming to illustrate the time evolution and relative importance of specific research topics. The study is based on 222,060 papers (articles and reviews only) published between 1980 and 2014. The total number of papers shows a strong increase with a doubling every 5–6 years. Continental biomass related research is the major subfield, closely followed by climate modeling. Research dealing with adaptation, mitigation, risks, and vulnerability of global warming is comparatively small, but their share of papers increased exponentially since 2005. Research on vulnerability and on adaptation published the largest proportion of very important papers (in terms of citation impact). Climate change research has become an issue also for disciplines beyond the natural sciences. The categories Engineering and Social Sciences show the strongest field-specific relative increase. The Journal of Geophysical Research, the Journal of Climate, the Geophysical Research Letters, and Climatic Change appear at the top positions in terms of the total number of papers published. Research on climate change is quantitatively dominated by the USA, followed by the UK, Germany, and Canada. The citation-based indicators exhibit consistently that the UK has produced the largest proportion of high impact papers compared to the other countries (having published more than 10,000 papers). Also, Switzerland, Denmark and also The Netherlands (with a publication output between around 3,000 and 6,000 papers) perform top—the impact of their contributions is on a high level. The title word analysis shows that the term climate change comes forward with time. Furthermore, the term impact arises and points to research dealing with the various effects of climate change. The discussion of the question of human induced climate change towards a clear fact (for the majority of the scientific community) stimulated research on future pathways for adaptation and mitigation. Finally, the term model and related terms prominently appear independent of time, indicating the high relevance of climate modeling. [ABSTRACT FROM AUTHOR]

Broad-scale spatial patterns of species richness are very strongly correlated with climatic variables. If there is a causal link, i.e. if climate directly or indirectly determines patterns of richness, then when the climatic variables change, richness should change in the manner that spatial correlations between richness and climate would predict. The present study tests this prediction using seasonal changes in climatic variables and bird richness. We used a grid of equal area quadrats (37 000 km2) covering North and Central America as far south as Nicaragua. Summer and winter bird distribution data were drawn from monographs and field guides. Climatic data came from published sources. We also used remotely sensed NDVI (normalized difference vegetation index — a measure of greenness). Bird species richness changes temporally (between summer and winter) in a manner that is close to, but statistically distinguishable from, the change one would predict from models relating the spatial variation in richness at a single time to climatic variables. If one further takes into account the seasonal changes in NDVI and within-season variability of temperature and precipitation, then winter and summer richness follow congruent, statistically indistinguishable patterns. Our results are consistent with the hypothesis that climatic variables (temperature and precipitation) and vegetation cover directly or indirectly influence patterns of bird species richness. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) could revolutionize our ability to understand and address climate change. Studies to date have focused on specific AI applications to climate science, technologies, and policy. Yet despite the vast demonstrated potential for AI to change the way in which climate research is conducted, no study has presented a systematic and comprehensive understanding of the way in which AI is intersecting with climate research around the world. Using a novel merged corpus of scholarly literature which contains millions of unique scholarly documents in multiple languages, we review the community of knowledge at the intersection of climate change and AI to understand how AI methods are being applied to climate-related research and which countries are leading in this area. We find that Chinese research institutions lead the world in publishing and funding research at the intersection of climate and AI, followed by the United States. In mapping the specific AI tasks or methods being applied to specific climate research fields, we highlight gaps and identify opportunities to expand the use of AI in climate research. This paper can therefore greatly improve our understanding of both the current use and the potential use of AI for climate research. [ABSTRACT FROM AUTHOR]

As part of the terrestrial branch of the Japan-funded Arctic Climate Change Research Project (GRENE-TEA), which aims to clarify the role and function of the Arctic terrestrial system in the climate system, and assess the influence of its changes on a global scale, this model intercomparison project (GTMIP) is planned and being conducted to (1) enhance communication and understanding between the "minds and hands" (i.e., between the modelling and field scientists) and (2) assess the uncertainty and variations stemming from variability in model implementation/design and in model outputs due to climatic and historical conditions in the Arctic terrestrial regions. This paper provides an overview and the experiment protocol of Stage 1 of the project, site simulations driven by statistically fitted data created using the GRENE-TEA site observations for the last three decades. The target metrics for the model evaluation cover key processes in both physics and biogeochemistry, including energy budgets, snow, permafrost, phenology, and carbon budgets. The preliminary results on four metrics (annual mean latent heat flux, annual maximum snow depth, gross primary production, and net ecosystem production) already demonstrate the range of variations in reproducibility among existing models and sites. Full analysis on annual as well as seasonal time scales, to be conducted upon completion of model outputs submission, will delineate inter-dependence among the key processes, and provide the clue for improving the model performance. [ABSTRACT FROM AUTHOR]

This bibliometric analysis dealswith research on the collapse of the Maya civilization--a research topic with a long-lasting history, which has been boosted significantly by recent paleoclimatic research. The study is based on a publication set of 433 papers published between 1923 and 2016. The publications covered by theWeb of Science (WoS) show a significant increase since 1990, reaching about 30 papers per year at present. The results show that the current discourse on the collapse of the Maya civilization is focused on the role of climate as a major factor for the demise of this ancient civilization. The bibliometric analyses also reveal that (1) paleoclimatic records become numerous and are increasingly better dated; (2) the explanatory power of the records has been significantly increased by analyzing samples from regions closer to the relevant Maya sites; and (3) interdisciplinary cooperation of the humanities (archeology, anthropology, history) with natural sciences disciplines (geoscience, ecology, paleoclimatology, meteorology) seems to be highly promising. The collapse of the Maya civilization is a good example of how natural sciences entered research in the humanities and social sciences (anthropology, archeology, history) and boosted research (and solutions) around long-discussed, but unsolved questions. [ABSTRACT FROM AUTHOR]

The chaos caused by the global climate crisis is in the news in many forms and has also entered the consulting room: clients are increasingly naming their fear, despair, rage, and experience of impotence in the face of the unknown. This paper builds on the work of G. Albrecht and J. Bernstein, to investigate how we can face our feelings about climate crisis and live through this time without resorting to unhelpful defenses that block our ability to be present, engaged and effective. It examines the unconscious beliefs, habitual patterns, and defenses of the Western ego, which it presents as the mindset of Economism and the Capitalocene, and investigates its identification with the hero archetype. It pays homage to indigenous analyses of the issue in the work of J. Forbes and I Merculieff, and draws on the work of eco-ethical thinkers such as K. D. Moore, J. Butler, and A. L. Tsing, to suggest that the archetype of the initiate may be a better guide as we move into the uncertain, contingent future. [ABSTRACT FROM AUTHOR]

Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

The ammonia cycle and nitrate particle formation have been introduced in the LMDz- INCA global model. Both fine nitrate particles formation in the accumulation mode and coarse nitrate forming on existing dust and sea-salt particles are considered. The model simulates distributions of nitrates and related species in agreement with previous studies and observations. The calculated present-day total nitrate direct radiative forcing since the pre-industrial is -0.056Wm-2. This forcing has the same magnitude than the forcing associated with organic carbon particles and represents 18% of the sulfate forcing. Fine particles largely dominate the nitrate forcing representing close to 90% of this value. The model has been used to investigate the future changes in nitrates and direct radiative forcing of climate based on snapshot simulations for the four Representative Concentration Pathway (RCP) scenarios and for the 2030, 2050 and 2100 time horizons. Due to a decrease in fossil fuel emissions in the future, the concentrations of most of the species involved in the nitrate-ammonium-sulfate system drop by 2100 except for ammonia which originates from agricultural practices and for which emissions significantly increase in the future. Despite the decrease of nitrate surface levels in Europe and Northern America, the global burden of accumulation mode nitrates increases by up to a factor of 2.6 in 2100. This increase in nitrate in the future arises despite decreasing NOx emissions due to increased availability of ammonia to form ammonium nitrate. The total aerosol direct forcing decreases from its present-day value of -0.234Wm-2 to a range of -0.070 to -0.130Wm-2 in 2100 based on the considered scenario. The direct forcing decreases for all aerosols except for nitrates for which the direct negative forcing increases to a range of -0.060 to -0.115Wm-2 in 2100. Including nitrates in the radiative forcing calculations increases the total direct forcing of aerosols by a factor of 1.3 in 2000, by a factor of 1.7-2.6 in 2030, by 1.9-4.8 in 2050 and by 6.4-8.6 in 2100. These results show that agricultural emissions of ammonia will play a key role in the future mitigation of climate change with nitrates becoming the dominant contributor to the anthropogenic aerosol optical depth during the second half of the XXIst century and significantly increasing the calculated aerosol direct forcing. This significant increase in the influence that nitrate exert on climate in the future will at the same time affect regional air quality and nitrogen deposition to the ecosystems. [ABSTRACT FROM AUTHOR]

Climate change impacts on the marine biogeochemistry and lower trophic level dynamics in the North Sea and Baltic Sea have been assessed using regional downscaling in a number of recent studies. However, most of these where only forced by physical conditions from Global Climate Models (GCMs) and regional downscaling considering the climate change impact on oceanic nutrient conditions from Global Earth System Models (ESMs) are rare and so far solely based on CMIP3-generation climate models. The few studies published show a large range in projected future primary production and hydrodynamic condition. With the addition of CMIP5 models and scenarios, the demand to explore the uncertainty in regional climate change projections increased. Moreover, the question arises how projections based on CMIP5-generation models compare to earlier projections and multi-model ensembles comprising both AR4 and AR5 generation forcing models. Here, we investigated the potential future climate change impacts to the North Sea and the Baltic Sea ecosystem using a coherent regional downscaling strategy based on the regional coupled bio-physical model ECOSMO. ECOSMO was forced by output from different ESMs from both CMIP3 and CMIP5 models. Multi-model ensembles using CMIP3/A1B and CMIP5/RCP4.5 scenarios are examined, where the selected CMIP5 models are the successors of the chosen CMIP3 models. Comparing projected changes with the present day reference condition, all these simulations predicted an increase in Sea Surface Temperature (SST) in both North Sea and Baltic Sea, reduction in sea ice in the Baltic, decrease in primary production in the North Sea and an increase in primary production in the Baltic Sea. Despite these largely consistent results on the direction of the projected changes, our results revealed a broad range in the amplitude of projected climate change impacts. Our study strengthens the claim that the choice of the ESM is a major factor for regional climate projections. The change in oceanic nutrient input appeared to be the major driver for the projected changes in North Sea primary production. Assessing the spread in ensemble groups, we found that there is for the North Sea a significant reduction in the spread of projected changes among CMIP5 forced model simulations compared to those forced by CMIP3 ESMs, except for salinity. The latter was due to an unexpected salinification observed in one of the CMIP5 model while all other models exhibit freshening in the future. However, for the Baltic Sea substantial differences in inter-model variability in projected climate change impact to primary production is lacking. [ABSTRACT FROM AUTHOR]

The article presents a study that demonstrates the applications of the Biogeochemical Transport and Reactions (CLM4-BeTR) in regional and global biogeochemistry modeling and climate predictability. It outlines the functions of the model in which its transport code are evaluated through several analytical test cases. The study shows that the method provides detailed comparison between ecosystem observation and predictions.

The article presents a study that investigates the effectiveness of Earth System Model (ESM) in determining carbon-cycle climate feedback and the future climate. It describes the method of the study that analyzes the paleoclimate experiments proposed by the Coupled Model Intercomparison Project. It notes the result of the study, which shows that the complexity of the model requires various steps to correctly configure the experiments.

Copyright of European Journal of Psychotraumatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community‐engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project‐specific knowledge co‐production protocols. This paper provides a novel model of design and implementation to co‐produce knowledge with communities across a large study domain. Plain Language Summary: The Arctic and rivers located in the Arctic and subarctic are warming due to climate change. To understand the impacts this warming will have on people, partnering with impacted Indigenous communities in the region is important. It is also important that these partnerships are ethical and equitable and produce science that is actionable. This paper discusses efforts undertaken by a specific project, the Arctic Rivers Project, to conduct ethical and equitable research with Indigenous communities and generate science that is useful to those communities. Through this research our goal is to better understand potential future impacts of climate change on rivers, fish, and Indigenous communities in central northern Alaska and the Yukon Territory in Canada. To achieve this goal, the project formed an Indigenous Advisory Council (IAC) and together developed guidelines for how we can work collaboratively with Indigenous communities. Our specific process of forming an IAC and guidelines is, to our knowledge, a new way to approach collaborative research when working across a large geographic area. We present our process here so that it may provide an example for other research efforts. Key Points: Arctic climate information can be made useful, useable, and used by equitably accounting for diverse community adaptation needs through knowledge co‐productionInstitutional and community capacity, including means and ability, is necessary for equitable knowledge co‐production to occurWe present an approach for co‐producing knowledge with Indigenous communities that can serve as an example for other scientific efforts [ABSTRACT FROM AUTHOR]

An important component of the risks from climate change arises from outcomes that are very unlikely, but whose impacts if they were to occur would be extremely severe. Examples include levels of surface warming, or changes in the water cycle, that are at the extreme of plausible ranges, or crossing of a climate system "tipping point" such as ice sheet or ocean circulation instability. If such changes were to occur their impacts on infrastructure or ecosystems may exceed existing plans for adaptation. The traditional approach of ensemble climate change projections is not well suited to managing these High Impact‐Low Likelihood (HILL) risks, where the objective is to "prepare for the worst" rather than to "plan for what's likely." In this paper we draw together a number of ideas from recent literature, to classify four types of HILL climate outcome and to propose the development of a practical "toolkit" of physical climate information that can be used in future to inform HILL risk management. The toolkit consists of several elements that would need to be developed for each plausible HILL climate outcome, then deployed individually to develop targeted HILL risk management approaches for individual sectors. We argue that development of the HILL toolkit should be an important focus for physical climate research over the coming decade, and that the time is right for a focused assessment of HILL risks by the Intergovernmental Panel on Climate Change in its 7th Assessment Cycle. Plain Language Summary: To prepare for the risks that arise from climate change (and avoid them where possible), it is important to understand how climate is likely to change in future, and what the impacts are likely to be. Over many years, climate science has developed sophisticated climate projections to estimate these likely impacts, and these are widely used to plan how people and societies will need to adapt to climate change. However it is also important to understand possibilities that are unlikely, but would have even more severe impacts if they did occur—for example, global warming levels at the high end of plausible estimates, or crossing a "tipping point" for major changes in ice sheets or ocean currents. A different type of information is needed to plan for these risks. In this paper we propose a new set of climate information "tools" to respond to these high‐impact risks. The tools include plausible scenarios of extreme outcomes, and early warning systems to detect if they are on the horizon. Combining these new tools with existing climate projections will allow society to understand more fully the risks of climate change, and to plan for the likely effects while preparing for the worst. Key Points: Climate outcomes or events that have a high impact are a key component of climate risk, even if their likelihood is lowTraditional climate projections are of limited use to inform management of high impact‐low likelihood risksPhysical climate science needs an increased focus on storylines, early warning and monitoring to inform management of high impact risks [ABSTRACT FROM AUTHOR]

Lake Ohrid (Macedonia/Albania) is a rare example of a deep, ancient Mediterranean lake and is a key site for palaeoclimate research in the northeastern Mediterranean region. This study conducts the first high-resolution diatom analysis during the Lateglacial and Holocene in Lake Ohrid. It demonstrates a complex diatom response to temperature change, with a direct response to temperature-induced productivity and an indirect response to temperature-related stratification/mixing regime and epilimnetic nutrient availability. During the Lateglacial (ca. 12 300-11 800 cal yr BP), the low-diversity dominance of hypolimnetic Cyclotella fottii indicates low temperature-dependent lake productivity. During the earliest Holocene (ca. 11 800-10 600 cal yr BP), although the slight increase in small, epilimnetic C. minuscula suggests climate warming and enhanced thermal stratification, diatom concentration remains very low as during the Lateglacial, indicating that temperature increase was muted. The early Holocene (ca. 10 600- 8200 cal yr BP) marked a sustained increase in epilimnetic taxa, with mesotrophic C. ocellata indicating high temperature-induced lake productivity between ca. 10 600- 10 200 cal yr BP and between ca. 9500-8200 cal yr BP, and with C. minuscula in response to low nutrient availability in the epilimnion between ca. 10 200-9500 cal yr BP. During the mid Holocene (ca. 8200-2600 cal yr BP), when sedimentological and geochemical proxies provide evidence for high temperature, anomalously low C. ocellata abundance is probably a response to epilimnetic nutrient limitation, almost mimicking the Lateglacial flora apart from mesotrophic Stephanodiscus transylvanicus indicative of high temperature-induced productivity in the hypolimnion. During the late Holocene (ca. 2600-0 cal yr BP), high abundance and fluctuating composition of epilimnetic taxa is largely a response to enhanced anthropogenic nutrient input. In this deep, oligotrophic lake, this study demonstrates the strong influence of lake physical and chemical processes in mediating the complex response of diatoms to climate change with particular respect to temperature. [ABSTRACT FROM AUTHOR]

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales. [ABSTRACT FROM AUTHOR]

Coastal hypoxia, the depletion of oxygen concentration in coastal waters, is becoming more prominent on a global scale. Changes in climate and nutrient loadings are two aspects of global change that are expected to profoundly impact coastal hypoxia. We investigated the role of these drivers on the evolution of hypoxia in a stratified, temperate coastal system using a one-dimensional model. The model couples three submodels, describing the physical characteristics, the pelagic ecosystem and benthic diagenesis. The model is calibrated for the Central North Sea but the model approach is generic, and can be applied in stratified coastal ecosystems. Our results indicate that the projected changes in climatological conditions for the North Sea over the next 100 yr will increase the risk of hypoxia. On average the oxygen concentration is predicted to decrease by 17 µM, mostly due to a reduced solubility at higher water temperature (responsible for 65% of the decrease). Increased stratification (22 %) and enhanced biological rates due to higher water temperature (13 %) also affect the future oxygen concentration. Nutrient loadings also have a strong effect on the occurrence of hypoxia. Decreasing nutrient concentrations strongly decreases the probability of hypoxic events, stressing the importance of continued extensive eutrophication management to mitigate the effect of increased temperature. [ABSTRACT FROM AUTHOR]

The article presents a study that demonstrates the usefulness of the Norwegian Climate Center's Earth System Model (NorESM1-M) in the coupled model intercomparison project phase 5 (CMIP5). It outlines the role of the model in providing complementary results to the evaluation of possible man made climate change. It also demonstrates the effectiveness of the model in accurately depicting the changes in the atmospheric water cycle in precipitation events.

The article presents a research which investigates the proposed framework for downscaling coarse climate change projections for oceans in the Australian region. It explores significance of the downscaling model in projecting marine impacts of climate change. The restoration of the ocean feedback on the air-sea fluxes to surface temperature and salinity is explored. It notes that the framework offers an attractive way to explore the impact of climate change on mesoscale ocean environment.

This article charts the successes and failures of urban regeneration policies in the UK. Aspects of both continuity and change in the direction and implementation of urban policy are explored. It is argued that while New Labour's approach since 1997 has been distinctive and, in some respects, innovative, especially in relation to community engagement, it has continued to adopt a flawed conceptualisation of the urban problem which has led to a limited policy response. That legacy is likely to have a continuing influence on policy. Looking ahead, new challenges need to be faced, notably economic recession and climate change. [ABSTRACT FROM AUTHOR]

The article presents a research study of the Galápagos Island and its effect on the El Nião-Southern Oscillation (ENSO). ENSO is considered as one of the climate oscillations on Earth. The researchers used an ocean general circulating model and a hybrid coupled model of the tropical Pacific Ocean to examine the effects of the Galápagos Island on ENSO. Results such as the shift lead by the island in the ENSO period from biennial to quasi-quadrinneal are presented. Also, further studies in relation to the subject are encouraged.

This paper studies early arguments in Sweden for combating climate change. We show how scientific results in relation to climate change entered the political sphere as part of the debate on energy in the 1970s, a process we propose to name energysation. We argue that the use of climate science by pro-nuclear political actors served as a way of maintaining a course set by a high-energy society while simultaneously trying to outmanoeuvre the growing environmental anti-nuclear and low-energy movement. When the pro-nuclear power side met with resistance, this led to a displacement of climate change knowledge away from the realm of the national political sphere and specific energy forms, a process we conceptualise as de-energysation. By highlighting conflicts and the political framings of climate change in the early years 1974-1983, we suggest that the history of these frames influences current delay in climate change mitigation and limits the range of actions and ways of addressing the ongoing climate emergency. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The North Atlantic is the most intense region of ocean CO2 uptake. Here, we investigate multidecadal timescale variability of the partial pressure CO2 (pCO2) that is due to the natural carbon cycle using a regional model forced with realistic climate and pre-industrial atmospheric pCO2 for 1948-2009. Large-scale patterns of natural pCO2 variability are primarily associated with basin-averaged sea surface temperature (SST) that, in turn, is composed of two parts: the Atlantic Multidecadal Oscillation (AMO) and a long-term positive SST trend. The North Atlantic Oscillation (NAO) drives a secondary mode of variability. For the primary mode, positive AMO and the SST trend modify pCO2 with different mechanisms and spatial patterns. Warming with the positive AMO increases subpolar gyre pCO2, but there is also a significant reduction of dissolved inorganic carbon (DIC) due primarily to reduced vertical mixing. The net impact of positive AMO is to reduce pCO2 in the subpolar gyre. Through direct impacts on SST, the net impacts of positive AMO is to increase pCO2 in the subtropical gyre. From 1980 to present, long-term SST warming has amplified AMO impacts on pCO2. [ABSTRACT FROM AUTHOR]

To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 µm AOD and 0.55/0.86 µm-based Ångstrom Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievibility through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS ? VIIRS aerosol CDR. [ABSTRACT FROM AUTHOR]

de Freitas et al. (2015) (henceforth dFDB) report a trend of 0.28 °C per century over the period 1909-2009 for New Zealand land surface temperatures, from their reanalysis of a composite of seven long-term records. This is much lower than the warming trend of about 0.9 °C per century reported previously by other researchers and much smaller than trends estimated from independent sea surface temperature data from the surrounding region. We show these differences result primarily from the way inhomogeneities in temperature time series at individual stations due to site or instrument changes are identified and adjusted for in the dFDB paper. The adjustments reported in that paper are based on a method designed by one of us (Salinger), but use only a short (1-2-year) overlap period with comparison stations and consider only inhomogeneities in monthly mean (rather than monthly maximum and minimum) temperatures. This leads to underestimates of the statistical significance of individual temperature discontinuities and hence rejection of many valid adjustments. Since there was a systematic tendency for the seven-station sites to be relocated to colder locations as the early half of the twentieth century progressed, this rejection of valid adjustments produces an artificially low rate of warming. We therefore disagree with the trend calculations in the dFDB paper and consider there is no reason to reject the previous estimates of around 0.9 °C warming per century. [ABSTRACT FROM AUTHOR]

The article discusses the Hadley Centre Global Environment Model version 2 (HadGEM2) family of climate configurations that comprises a range of specific model configurations. It offers a detailed descriptions and evaluations of HadGEM2 family members and its overall performance using a set of model metrics. It notes that HadGEM2 family of configurations includes atmosphere and ocean components with well-resolved stratosphere and earth-system.

Global response to climate‐sensitive infectious diseases has been uncertain and slow. The understanding of the underlying vulnerabilities which forms part of changes created by forces within the Earth system has never before been critical until the coronavirus disease 2019, "COVID‐19" pandemic with the initial developmental phase linked to weather elements and climate change. Hence, the heightened interest in climate‐sensitive infectious diseases and GeoHealth, evident in the renewed calls for "One Health" approach to disease management. "One Health" explains the commonality of human and animal medicine, and links to the bio‐geophysical environment, yet are at crossroads with how forces within the Earth system shape etiologies, incidences, and transmission dynamics of infectious diseases. Hence, the paper explores how these forces, which are multistage and driven by climate change impacts on ecosystems affect emerging infectious diseases, leading to the question "what drive the drivers of diseases?" Three questions that challenge broad theories of Earth system science on boundaries and connectivity emerged to guide study designs to further interrogating disease surveillance and health early warning systems. This is because, climate change (a) drives prevailing biological health hazards as part of forces within the Earth system, (b) shifts disease control services of ecosystems and functioning to effectively regulate disease incidence, and (c) modifies pathogen—species hosts relationships. Hence, the need to rethink pluralistic concepts of climate‐sensitive diseases in their infection and management from a GeoHealth perspective, which "One Health" potentially conveys, and to also maintain ecosystem health. Plain Language Summary: Climate change, an Earth system process (forces deep inside the earth that bring about adjustments) is considered the greatest threat to human health in the twenty‐first century. Climate change has been linked to shifts in ecosystem processes, services and interactions that affect how many organisms thrive including those causing diseases. Yet, how climate change influences specific processes between air, land, life, and water on earth to ultimately affect diseases is still emerging. Collectively partly defined as GeoHealth, it has become topical amidst the coronavirus disease 2019 (COVID‐19) pandemic. Thus, its origin partly links to changes in land‐atmosphere interactions. Hence, the paper poses questions to better appreciate how climate change influence forces within the Earth system to subsequently impact infectious diseases and management in a holistic approach called "One Health," which GeoHealth is positioned to contribute to. Thus, "One Health" provides pathways to environmentally safeguard and sustain ecosystem health and human well‐being. Key Points: Earth system science of ecosystems and climate change hardly tells mechanisms of disease‐causing organisms, detection, and managementRevisiting "One Health" amidst "coronavirus disease 2019" is opportune pathway for GeoHealth's influence of climate‐sensitive infectious diseasesModern Earth system science of climate context of "One Health" is key to maintaining human well‐being and ecosystem integrity [ABSTRACT FROM AUTHOR]

Wetland loss and climate change are known to alter regional and global methane (CH4) budgets. Over the last six decades, an extensive area of marshland has been converted to cropland on the Sanjiang Plain in Northeast China, and a significant increase in air temperature has also been observed there, while the impacts on regional CH4 budgets remain uncertain. Through model simulation, we estimated the changes in CH4 emissions associated with the conversion of marshland to cropland and climate change in this area. Model simulations indicated a significant reduction of 1.1 Tgyr-1 from the 1950s to the 2000s in regional CH4 emissions. The cumulative reduction of CH4 from 1960 to 2009 was estimated to be ~ 36Tg relative to the 1950s, and marshland conversion and the climate contributed 86% and 14% of this change, respectively. Interannual variation in precipitation (linear trend with P > 0.2) contributed to yearly fluctuations in CH4 emissions, but the relatively lower amount of precipitation over the period 1960-2009 (47 mmyr-1 lower on average than in the 1950s) contributed ~91% of the reduction in the area-weighted CH4 flux. Global warming at a rate of 0.3 °C per decade (P < 0.001) has increased CH4 emissions significantly since the 1990s. Relative to the mean of the 1950s, the warming-induced increase in the CH4 flux has averaged 19 kg ha-1 yr-1 over the last two decades. For the RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 scenarios of the fifth IPCC assessment report (AR5), the CH4 flux is predicted to increase by 36 %, 52 %, 78% and 95 %, respectively, by the 2080s compared to 1961-1990 in response to climate warming and wetting. [ABSTRACT FROM AUTHOR]

Land surface albedo is a relevant variable in many climatic, environmental, and hydrological studies; its monitoring allows researchers to identify changes on the Earth’s surface. The open satellite data that is provided by the USGS/NASA Landsat mission is quite suitable for estimating this parameter through the remote sensing technique. The purpose of this paper is to evaluate the potentialities of the new Landsat 9 data for retrieving Earth’s albedo by applying da Silva et al.’s algorithm (developed in 2016 for the Landsat 8 data) using the Google Earth Engine cloud platform and R software. Two urban areas in Southern Italy with similar geomorphologic and climatic characteristics were chosen as study sites. After obtaining thematic maps of the albedos here, a statistical analysis and comparison among the Landsat 8 and Landsat 9 results was performed considering the entire study areas and each land use/land cover class that is provided by the Copernicus Urban Atlas 2018. This approach was also applied to the data after being filtered through Tukey’s test (used to detect and remove outliers). The analysis showed a very good correlation between the Landsat 8 and Landsat 9 estimations (ρ > 0.94 for both sites), with some exceptions that were related to some mis-corresponding values. Furthermore, the Landsat 8 and Landsat 9 outliers were generally overlapping. In conclusion, da Silva et al.’s approach appears to also be reasonably applicable to the Landsat 9 data despite some radiometric differences. [ABSTRACT FROM AUTHOR]