70 research outputs found

    Effectiveness of Foliar Fungicides by Timing on Northern Leaf Blight on Hybrid Corn in Southwest Iowa

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    Fungicide use on hybrid corn has increased considerably, primarily due to reports of increased yields, even in the absence of disease and higher corn prices. A number of fungicides are registered for use on corn. The objectives of this project were to 1) assess the effect of timing of application of fungicides on disease, 2) evaluate the yield response of hybrid corn to foliar fungicide application, and 3) discern differences, if any, between fungicide products

    Beyond the local climate change uplift – the importance of changes in spatial structure on future fluvial flood risk in Great Britain

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    Widespread spatially coherent flood events can cause severe damage and disruption. Climate change has the potential to change the severity and frequency of such events. Despite this, assessment of future fluvial flood risk typically gives little to no consideration to potential changes in the spatial structure of future events. To understand the significance of this gap, climate model simulations are coupled with a national hydrological model to identify event spatially coherent present and future flood events. A statistical Empirical Copula is used to generate a large number of unseen events and linked to a national flood risk simulation model. The research finds that including changes in the spatial structure of flood events materially increases projected changes in risk when compared to conventional approaches based on local uplifts alone; increasing the projected change in Expected Annual Damage across Great Britain by a factor of ~ 1.5. The event-based approach is also shown to provide new insights into the extreme distribution fluvial risk including single event damage, damage seasons, and damage years. The results suggest the 1-in-100-year winter flood may increase from £1.3b to £2.1b, and the 1-in-100 year single event damage may rise from £1.1b today to £1.7b by the 2080s given a 4 °C rise in Global Mean Surface Temperature (assuming current adaptation policies continue and no population growth). Consequently, the findings suggest a much greater emphasis is needed on spatial ‘flood events’ if future risk is to be understood and adaptation responses appropriately framed

    Performance of Pattern-Scaled Climate Projections under High-End Warming. Part I: Surface Air Temperature over Land

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    Pattern scaling is widely used to create climate change projections to investigate future impacts. We consider the performance of pattern scaling for emulating the HadGEM2-ES general circulation model (GCM) paying particular attention to “high end” warming scenarios and to different choices of GCM simulations used to diagnose the climate change patterns. We demonstrate that evaluating pattern-scaling projections by comparing them with GCM simulations containing unforced variability gives a significantly less favorable view of the actual performance of pattern scaling. Using a four-member initial-condition ensemble of HadGEM2-ES simulations, we infer that the root-mean-square errors of pattern-scaled monthly temperature changes over land are less than 0.25°C for global warming up to approximately 3.5°C. Some regional errors are larger than this and, for this GCM, there is a tendency for pattern scaling to underestimate warming over land. For warming above 3.5°C, the pattern-scaled projection errors grow but remain small relative to the climate change signal. We investigate whether patterns diagnosed by pooling GCM experiments from several scenarios are suitable for emulating the GCM under a high-end warming scenario. For global warming up to 3.5°C, pattern scaling using this pooled pattern closely emulates GCM simulations. For warming beyond 3.5°C, pattern-scaling performance is notably improved by using patterns diagnosed only from the high-forcing representative concentration pathway 8.5 (RCP8.5) scenario. Assessments of climate change impacts under high-end warming using pattern-scaling projections could be improved by using change patterns diagnosed from pooled scenarios for projections up to 3.5°C above preindustrial levels and patterns diagnosed from only strong forcing simulations for projecting beyond that. Similar findings are obtained for five other GCMs

    Quantifying overheating risk in English schools: A spatially coherent climate risk assessment

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    Climate adaptation decision making can be informed by a quantification of current and future climate risk. This is important for understanding which populations and/or infrastructures are most at risk in order to prioritise adaptation action. When assessing the risk of overheating in buildings, many studies use advanced building models to comprehensively represent the vulnerability of the building to overheating, but often use a limited representation of the meteorological (hazard) information which does not vary realistically in space. An alternative approach for quantifying risk is to use a spatial risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way, allowing for risk to be compared across different locations. Here we present a novel application of an open-source CLIMADA-based spatial risk assessment framework to an ensemble of climate projections to assess overheating risk in ∌20,000 schools in England. In doing so, we demonstrate an approach for bringing together the advantages of open-source spatial risk assessment frameworks, data science techniques, and physics-based building models to assess climate risk in a spatially consistent way, allowing for the prioritisation of adaptation action in this vulnerable young population. Specifically, we assess the expected number of days each school overheats (internal operative temperature exceeds a high threshold) in a school-year based on three global warming levels (recent past, 2 °C and 4 °C warmer than pre-industrial). Our results indicate an increase in this risk in future warmer climates, with the relative frequency of overheating at internal temperatures in excess of 35 °C increasing more than at 26 °C. Indeed, this novel demonstration of the approach indicates that the most at-risk schools could experience up to 15 school days of internal temperature in excess of 35 °C in an average year if the climate warms to 2 °C above pre-industrial. Finally, we demonstrate how the spatial consistency in the output risk could enable the prioritisation of high risk schools for adaptation action

    Exploring the Feasibility of Low-Carbon Scenarios Using Historical Energy Transitions Analysis

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    The scenarios generated by energy systems models provide a picture of the range of possible pathways to a low-carbon future. However, in order to be truly useful, these scenarios should not only be possible but also plausible. In this paper, we have used lessons from historical energy transitions to create a set of diagnostic tests to assess the feasibility of an example 2 °C scenario (generated using the least cost optimization model, TIAM-Grantham). The key assessment criteria included the rate of deployment of low carbon technologies and the rate of transition between primary energy resources. The rates of deployment of key low-carbon technologies were found to exceed the maximum historically observed rate of deployment of 20% per annum. When constraints were added to limit the scenario to within historically observed rates of change, the model no longer solved for 2 °C. Under these constraints, the lowest median 2100 temperature change for which a solution was found was about 2.1 °C and at more than double the cumulative cost of the unconstrained scenario. The analysis in this paper highlights the considerable challenge of meeting 2 °C, requiring rates of energy supply technology deployment and rates of declines in fossil fuels which are unprecedented

    The Contribution of Non-CO2 Greenhouse Gas Mitigation to Achieving Long-Term Temperature Goals

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    This paper analyses the emissions and cost impacts of mitigation of non-CO2 greenhouse gases (GHGs) at a global level, in scenarios aimed at meeting a range of long-term temperature goals (LTTGs). The study combines an integrated assessment model (TIAM-Grantham) representing CO2 emissions (and their mitigation) from the fossil fuel combustion and industrial sectors, coupled with a model covering non-CO2 emissions (GAINS), using the latest global warming potentials from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report. We illustrate that in general non-CO2 mitigation measures are less costly than CO2 mitigation measures, with the majority of their abatement potential achievable at US2005100/tCO2eorlessthroughoutthe21stcentury(comparedtoamarginalCO2mitigationcostwhichisalreadygreaterthanthisby2030inthemoststringentmitigationscenario).Asaresult,thetotalcumulativediscountedcostovertheperiod2010–2100(ata5100/tCO2e or less throughout the 21st century (compared to a marginal CO2 mitigation cost which is already greater than this by 2030 in the most stringent mitigation scenario). As a result, the total cumulative discounted cost over the period 2010–2100 (at a 5% discount rate) of limiting global average temperature change to 2.5 °C by 2100 is 48 trillion (about 1.6% of cumulative discounted GDP over the period 2010–2100) if only CO2 from the fossil fuel and industrial sectors is targeted, whereas the cost falls to $17 trillion (0.6% of GDP) by including non-CO2 GHG mitigation in the portfolio of options—a cost reduction of about 65%. The criticality of non-CO2 mitigation recommends further research, given its relatively less well-explored nature when compared to CO2 mitigation

    Assessing the Feasibility of Global Long-Term Mitigation Scenarios

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    This study explores the critical notion of how feasible it is to achieve long-term mitigation goals to limit global temperature change. It uses a model inter-comparison of three integrated assessment models (TIAM-Grantham, MESSAGE-GLOBIOM and WITCH) harmonized for socio-economic growth drivers using one of the new shared socio-economic pathways (SSP2), to analyse multiple mitigation scenarios aimed at different temperature changes in 2100, in order to assess the model outputs against a range of indicators developed so as to systematically compare the feasibility across scenarios. These indicators include mitigation costs and carbon prices, rates of emissions reductions and energy efficiency improvements, rates of deployment of key low-carbon technologies, reliance on negative emissions, and stranding of power generation assets. The results highlight how much more challenging the 2 °C goal is, when compared to the 2.5–4 °C goals, across virtually all measures of feasibility. Any delay in mitigation or limitation in technology options also renders the 2 °C goal much less feasible across the economic and technical dimensions explored. Finally, a sensitivity analysis indicates that aiming for less than 2 °C is even less plausible, with significantly higher mitigation costs and faster carbon price increases, significantly faster decarbonization and zero-carbon technology deployment rates, earlier occurrence of very significant carbon capture and earlier onset of global net negative emissions. Such a systematic analysis allows a more in-depth consideration of what realistic level of long-term temperature changes can be achieved and what adaptation strategies are therefore required
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