A2 Scenario Research Articles

Risk assessment of Citripestis sagittiferella for the EU.

Following a request from the European Commission, the EFSA Panel on Plant Health performed a risk assessment of Citripestis sagittiferella (Lepidoptera: Pyralidae), the citrus pulp borer, an oligophagous pest reported from South-East Asia and restricted to Citrus spp. The entry risk assessment focused on the citrus fruit pathway. Two scenarios were considered: scenario A0 (current practice) and A2 (additional post-harvest cold treatment). Based on the outputs of the entry model obtained in scenario A0, the median number of founder populations in the EU citrus-growing area is estimated to be slightly less than 10 per year (90%-uncertainty interval between about one entry per 180 years and 1,300 entries per year). The risk of entry and the simulated numbers of founder populations are orders of magnitude lower for scenario A2 compared to scenario A0. The key uncertainties in the entry model include transfer, the cold treatment effectiveness, the disaggregation factor and sorting. The simulated numbers of established populations are only slightly lower than the numbers of founder populations. As the probability of establishment has little impact on the number of established populations, it is not a major source of uncertainty, despite the lack of data on the thermal biology of the pest. The median lag period between establishment and spread is estimated to be slightly more than 1 year (90%-uncertainty interval between about 2months and 33months). After the lag period, the median spread rate by natural means (flying) and due to transport of harvested citrus fruit from orchards to packinghouses is estimated at about 100km/year (90%-uncertainty interval between about 40 and 500km/year). The main sources of uncertainties affecting the spread rate include the extent to which environmental factors could hamper the build-up of the populations and the lack of data on the spread rate at the origin. The median impact of C. sagittiferella in the EU citrus-growing area is estimated at about 10% of infested fruits among the harvested citrus fruits (90%-uncertainty interval between about 2% and 25%). Uncertainties affecting the impact assessment include the susceptibility of different Citrus species and cultivars.

Tamarixia radiata global distribution to current and future climate using the climate change experiment (CLIMEX) model

The phloem-limited bacteria, “Candidatus Liberibacter asiaticus” and “Ca. L. americanus”, are the causal pathogens responsible for Huanglongbing (HLB). The Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) is the principal vector of these “Ca. Liberibacter” species. Though Tamarixia radiata Waterston (Hymenoptera: Eulophidae) has been useful in biological control programmes against D. citri, information on its global distribution remains vague. Using the Climate Change Experiment (CLIMEX) model, the potential global distribution of T. radiata under the 2050s, 2070s, and 2090s for Special Report on Emissions Scenarios A1B and A2 was defined globally. The results showed that habitat suitability for T. radiata covered Africa, Asia, Europe, Oceania, and the Americas. The model predicted climate suitable areas for T. radiata beyond its presently known native and non-native areas. The new locations predicted to have habitat suitability for T. radiata included parts of Europe and Oceania. Under the different climate change scenarios, the model predicted contraction of high habitat suitability (EI > 30) for T. radiata from the 2050s to the 2090s. Nevertheless, the distribution maps created using the CLIMEX model may be helpful in the search for and release of T. radiata in new regions.

Pest Risk Assessment of Aeolesthes sarta (Coleoptera: Cerambycidae) in Pakistan under Climate Change Scenario

Aeolesthes sarta (Solsky 1871) (Coleoptera: Cerambycidae) is a polyphagous longhorned beetle species that primarily damages broadleaved tree species. This pest is distributed in the western and northern regions of Pakistan, where it caused serious damage to Populus spp. plantations. However, the growth and dispersal patterns of insects and pests are changing due to climate change. Modeling the range expansion or contraction of A. sarta development regions in Pakistan was the goal of the current study, assuming climate change might influence the geographical distribution of A. sarta in Pakistan. Under historical and future climatic conditions, A. sarta distribution areas were estimated using the CLIMEX model. Three time periods, 2030 (early century), 2070 (late century), and 2100 (end century), were forecasted for habitat suitability using the two climate change scenarios (CCSs) A1B and A2. Under the historic climatic condition (HCC), A. sarta was distributed in most areas of Pakistan, and its optimum habitat accounted for 71.67% of its total potential distribution. In the early-century period, optimum habitat dropped to 50.60% and 52.22% under A1B and A2 scenarios in the suitable condition. In the late-century period, optimum habitat further reduced to 31.76% and 30.60% under A1B and A2 scenarios. Moreover, at the end-century period, severe range shrinkage was predicted in the optimum habitat (19.99% under both CSSs). The model predicted a shift in the suitable habitat areas for A. sarta to the west and north. Furthermore, most climatically suitable areas under historic conditions became unsuitable during the end-century period. These projected results will assist in identifying the impacts of global warming on the possible distribution of A. sarta, thereby offering vital information for developing early forecasting and pest-prevention techniques to prevent further loss of forest and woodland trees.

Global-scale parameters for ecological models

This paper presents a collection of environmental, geophysical, and other marine-related data for marine ecological models and ecological-niche models. It consists of 2132 raster data for 58 distinct parameters at regional and global scales in the ESRI-GRID ASCII format. Most data originally belonged to open data owned by the authors of this article but residing on heterogeneous repositories with different formats and resolutions. Other data were specifically created for the present publication. The collection includes 565 data with global scale range; 154 at 0.5° resolution and 411 at 0.1° resolution; 196 data with annual temporal aggregation over ~10 key years between 1950 and 2100; 369 data with monthly aggregation at 0.1° resolution from January 2017 to ~May 2021 continuously. Data were also cut out on 8 European marine regions. The collection also includes forecasts for different future scenarios such as the Representative Concentration Pathways 2.6 (63 data), 4.5 (162 data), and 8.5 (162 data), and the A2 scenario of the Intergovernmental Panel on Climate Change (180 data).

Adaptive thermal comfort assessment of a naturally ventilated log house during summer under climate change impacts

The study deals with a three-storey log house located in the suburbs of Ljubljana, Slovenia (temperate climate). Firstly, the calibrated thermal model of the log house was defined. The calibrated model had an hourly NMBE between −2.12 % and 1.84 % and a CV(RMSE) between 3.16 % and 3.57 %. Then, the adaptive thermal comfort during the warmer part of the year was assessed according to EN 16798-1 and future climate (SRES A2 scenario). Additionally, various building-related and organisational measures for overheating prevention were evaluated. It was found that the most effective measures to prevent overheating are the organisational measures of shading activation and night ventilation. It was demonstrated that the efficiency of night ventilation would even improve over time. Thus, at the end of the 21st century, discomfort hours could be reduced by 67 % compared to the baseline. In contrast, building-related measures have a significant effect only when combined with organisational measures. Overall, in 2071–2100 adaptive thermal comfort was improved most when the measure of increased thermal insulation was coupled with shading and night ventilation, resulting in 1053 discomfort hours less than the baseline case.

Study on Chinese Farmland Ecosystem Service Value Transfer Based on Meta Analysis.

An analytic database was built based on meta-regression analysis (MRA) method, mainly including ecosystem service type, farmland division, cultivated land type, value assessment method, and farmland characteristics. The feasible weighted least square (FWLS) method was adopted to comprehensively investigate the seventy observations from empirical studies. The results indicate that: (1) except the negative impact of farmland area on farmland value, such factors as paddy field, good soil conservation function, mainly providing agricultural products, and using market value method for assessment all produce positive effect on the promotion of farmland value. (2) In meta-regression analysis, the average transfer error is 36.74%, and the median transfer error is 14.59%. (3) Under the A1B, A2, B1, and B2 scenarios of IPCC SRES, it is discovered from calculation that the value changes under different scenarios have some differences, in which, the total value rises significantly under A2 scenario and will reach to 15,220 billion yuan until the year of 2100; while the total value loss is the greatest under B1 scenario and will fall to 6320 billion yuan until the year of 2100. Finally, this paper gives some suggestions for scholars to deeply study the service value of farmland ecosystem as well as for the government to formulate differentiation policies.

Effects of climate and land-cover change on the conservation status of gibbons.

Species shift their distribution in response to climate and land-cover change, which may result in a spatial mismatch between currently protected areas (PAs) and priority conservation areas (PCAs). We examined the effects of climate and land-cover change on potential range of gibbons and sought to identify PCAs that would conserve them effectively. We collected global gibbon occurrence points and modeled (ecological niche model) their current and potential 2050s ranges under climate-change and different land-cover-change scenarios. We examined change in range and PA coverage between the current and future ranges of each gibbon species. We applied spatial conservation prioritization to identify the top 30% PCAs for each species. We then determined how much of the PCAs are conserved in each country within the global range of gibbons. On average, 31% (SD 22) of each species' current range was covered in PAs. PA coverage of the current range of 9 species was <30%. Nine species lost on average 46% (SD 29) of their potential range due to climate change. Under climate-change with an optimistic land-cover-change scenario (B1), 12 species lost 39% (SD 28) of their range. In a pessimistic land-cover-change scenario (A2), 15 species lost 36% (SD 28) of their range. Five species lost significantly more range under the A2 scenario than the B1 scenario (p=0.01, SD 0.01), suggesting that gibbons will benefit from effective management of land cover. PA coverage of future range was <30% for 11 species. On average, 32% (SD 25) of PCAs were covered by PAs. Indonesia contained more species and PCAs and thus has the greatest responsibility for gibbon conservation. Indonesia, India, and Myanmar need to expand their PAs to fulfill their responsibility to gibbon conservation. Our results provide a baseline for global gibbon conservation, particularly for countries lacking gibbon research capacity.

Risk assessment of Xanthomonas citri pv. viticola for the EU.

Following a request from the European Commission, the EFSA Panelon Plant Health performed a risk assessment of Xanthomonas citri pv. viticola (Xcv). This pest causes bacterial canker of grapevine and is reported from Brazil and India. Two scenarios were considered: scenario A0 (current practice) and A2 (additional control measures). For the fresh grape import pathway, scenario A0 results in an order of magnitude of about one entry per 10 years (median; 90% uncertainty interval between ca. one entry per 18,000 years and ca. five entries per year). For the Vitis spp. plants for planting for research/breeding purposes import pathway, the risk of entry is several orders of magnitude smaller than the risk due to fresh grape import. This outcome is also obtained under scenario A2. The key entry uncertainties include import volume and transfer (for plants for planting), transfer and the disaggregation factor (for fresh grapes) and the limited availability of epidemiological data. The extent of the area favourable for Xcv establishment in the EU is uncertain, illustrating the limitations of climate suitability assessments when based on few data points and little epidemiological information. Nevertheless, the risk of Xcv establishment is only slightly lower than the risk of Xcv entry, i.e. no major establishment constraints are expected for most entries. Similarly, the risk of Xcv establishment is assessed as only slightly lower under current climate compared to the climate of 2041-2060. For grapevine growing areas in the EU with average yearly temperature above 17°C, the lag phase between establishment and spread is expected to be about 3 years (median; 90% range between ca. 6months and ca. 6 years). Under the same scenario, the rate of spread by natural means is assessed to be ca. 300m/year (median; 90% range between ca. 35 and ca. 800m/year). The spread rate would be considerably higher considering movements of plants and cutting tools or machinery. The percentage of grapevine plants infected by Xcv in production sites as yearly average over a 30-year production cycle is estimated to be ca. 17% (median; 90% range between ca. 1.5% and ca. 46%) in table grapes and ca. 12% (median; 90% range between ca. 0.7% and ca. 37%) in wine grapes. Impacts have been reported to be severe in Brazil and India, but the estimates provided here show that there is considerable uncertainty about expected impacts in the EU.

Predicting the potential global distribution of an invasive alien pest Trioza erytreae (Del Guercio) (Hemiptera: Triozidae)

The impact of invasive alien pests on agriculture, food security, and biodiversity conservation has been worsened by climate change caused by the rising earth’s atmospheric greenhouse gases. The African citrus triozid, Trioza erytreae (Del Guercio; Hemiptera: Triozidae), is an invasive pest of all citrus species. It vectors the phloem-limited bacterium “Candidatus Liberibacter africanus”, a causal agent of citrus greening disease or African Huanglongbing (HLB). Understanding the global distribution of T. erytreae is critical for surveillance, monitoring, and eradication programs. Therefore, we combined geospatial and physiological data of T. erytreae to predict its global distribution using the CLIMEX model. The model’s prediction matches T. erytreae present-day distribution and shows that parts of the Mediterranean region have moderate (0 < EI < 30) to high (EI > 30) suitability for the pest. The model predicts habitat suitability in the major citrus-producing countries, such as Mexico, Brazil, China, India, and the USA. In the Special Report on Emissions Scenarios (SRES) A1B and A2 scenarios, the model predicts a reduction in habitat suitability from the current time to 2070. The findings show that global citrus production will continue to be threatened by T. erytreae. However, our study provides relevant information for biosecurity and risk assessment.

Variety-specific sugarcane yield simulations and climate change impacts on sugarcane yield using DSSAT-CSM-CANEGRO model

Crop simulation models are still little used for sugarcane crops due to the lack of understanding of their capabilities and experience in calibration as compared to other crops. Realistic assessment of future environmental change effects on crop production is also necessary for successful agricultural management and planning. The objective of this study is to provide DSSAT-CANEGRO simulated variety-wise sugarcane yield models for twelve different locations of Muzaffarnagar District of India. The CANEGRO model calibration is performed under three different dates of planting (early, mid, late) during the spring season 2013–2014. The results of the models are validated for consecutive two years datasets (2014–15 and 2015–16). The model shows the best calibration and validation results under the mid planting date scenario (simulated yield +0.85 %, +2.80 % and +5.20 %). The impact of climate change (sensitivity analysis) on yield of sugarcane has also been made putting different values of Tmax ( ± 1 to ± 3 ºC), Tmin ( ± 1 to ± 3 ºC), solar radiation ( ± 1 to ± 3MJ/m2/day), and atmospheric CO2 concentration of 380 ppm (720 ppm scenario A2, 500 ppm scenario B2). The study highlighted that Sugarcane yield simulation mid-planting model presenting the highest R (0.81, 0.83), and D (0.88, 0.91), and the lowest errors (RMSE = 8.37 q ha-1, 10.70 q ha-1 and MAPE = 1.10 %, 1.30 %) for the years 2014–15 and 2015–16 as compared to the other two models. The CANEGRO model simulated yield under incremental values of Tmax and Tmin in the range of (+1 to +3 ºC) shows gradual decrement in the yield ranges while, gradual increment of solar radiation from 1 to 3 MJ/m2/day showed a yield increment.

Drivers of climate variability and increasing water salinity impacts on the farmer’s income risk with future outlook mitigation

PurposeThe main aim of this study is to investigate the impact of climate change and water salinity on farmer’s income risk with future outlook mitigation. Salinity and climate change are a threat to agricultural productivity worldwide. However, the combined effects of climate change and salinity impacts on farmers' income are not well understood, particularly in developing countries.Design/methodology/approachThe response-yield function and general maximum entropy methods were used to predict the impact of temperature, precipitation and salinity on crop yield. The target minimization of total absolute deviations (MOTAD)-positive mathematical programming model was used to simulate the impact of climate change and salinity on socioeconomic and environmental indicators. In the end, a multicriteria decision-making model was used, aiming at the selection of suitable climate scenarios.FindingsThe results revealed that precipitation shows a significantly decreasing trend, while temperature and groundwater salinity (EC) illustrate a significantly increasing trend. Climate change and EC negatively impact the farmer's income and water shadow prices. Maximum reduction in income and water shadow prices was observed for A2 scenario (−12.4% and 19.4%) during 2050. The environmental index was the most important, with priority of 43.4% compared to socioeconomic indicators. Subindex amount of water used was also significant in study area, with 28.1% priority. The technique for order preference by similarity to ideal solution ranking system found that B1 was the best climatic scenario for adopting climate change adaptation in the research region.Originality/valueIn this study, farmers' income threats were assessed with the aspects of different climate scenario (A1, A1B and B1) over the horizons of 2030, 2040 and 2050 and three different indicators (economic, social and environmental) in Northwestern region of Pakistan. Only in arid and semiarid regions has climate change raised temperature and reduced rainfall, which are preliminary symptoms of growing salinity.

Effects of climate change in the thermal and energy performance of low-income housing in Brazil—assessing design variable sensitivity over the 21st century

To increase the accuracy of energy efficiency interventions over a building's lifetime, it is essential to predict the impact of climate change. Studies have analyzed building performance under future environmental conditions in Brazil, but the influence of design variables in the whole thermal and energy performance is a topic that still needs to be addressed. The objective of this study was to analyze the effects of climate change on the thermo-energetic performance of Brazilian low-income housing in the climates of two cities: Belém and São Paulo. For this, the research was based on computer simulations and Morris global sensitivity analysis, observing the influence of design variables in different periods. Weather files were generated to represent future environmental conditions according to the Intergovernmental Panel on Climate Change A2 scenario. The results prove that building design must necessarily consider climate change for a resilient built environment and highlight the importance of design variables, especially in the São Paulo climate. In view of future higher temperatures, the sensitivity indices indicate that natural ventilation is crucial for building performance improvements, followed by solar absorptance of the envelope and thermal transmittance of the walls. Finally, this study contributes to design decision-making for low-income housing in tropical climates and underscores the need for investigation of how to design resilient buildings, focusing on strategies to mitigate climate change and reduce pollutant emissions.

Scenarios of future land use/land cover changes: impacts on cropland use in Šiauliai region (Lithuania)

Agricultural systems supply a wide range of ecosystem services. Projecting future agricultural land-use changes is key to anticipating the potential impacts of human activities. In this work, we assessed future agricultural land-use changes, using the Dinamica-EGO platform, under three scenarios: A0-(business-as-usual), A1-(sustainable agriculture), and A2-(agricultural intensification) for 2040 in the Šiauliai region (Lithuania). Spatial autocorrelation was evaluated by using a Moran’s I index and the spatial patterns with the Getis analysis and landscape metrics. The results showed that croplands will increase 29.6% in the A0, 14.95% in the A1, and 29.63% in the A3 scenario. According to the Getis results, cold spots are in the surrounding of Šiauliai city, and hot spots in the northeast of the Šiauliai region. It was verified a high cropland fragmentation in A1 and low fragmentation in A0 and A2 scenarios. These results are critical for land management to understand cropland impacts under different scenarios.

Prospecting brown rust of sugarcane in São Paulo-Brazil under climate change scenarios

Climate change can cause significant shifts in the occurrence and severity of agricultural diseases, altering the distribution of phytosanitary problems with severe economic, social, and environmental consequences. Despite their importance, these new geographic and temporal distributions of plant diseases still demand scrutiny. In the present study, we prospect the geographic distribution of sugarcane brown rust (Puccinia melanocephala) considering average temperature and relative humidity in the main producing region of the state of São Paulo. The geographic database was structured using GIS with projections of future climate change provided by IPCC. Mathematical logic equations were defined and applied to data of average temperature and relative humidity, resulting in monthly maps of climate favorability for the occurrence of the disease. Prospective maps for three future periods (2011-2040, 2041-2070, and 2071-2100) and two greenhouse gas emission scenarios were compared to the reference period (1961-1990). The analysis considered months from December to May. Our study shows a tendency of decrease in the favorability for the disease in a longer term (2071-2100), more noticeable in the scenario A2 than in B1. These results suggest; however, that this disease demands attention in the management of the producing region for future climate scenarios.

China's carbon budget inventory from 1997 to 2017 and its challenges to achieving carbon neutral strategies

The global climate change situation partly depends on the climate change policies of countries around the world, including China. Therefore, it is necessary to reasonably reduce carbon emissions (CEs) and increase carbon sink in order to progressively achieve the carbon neutral (CN) goal. However, the capacity and the potential to achieve carbon neutral with the support of its ecosystem, that is to say the carbon neutral capacity (CNC) and potential, are still unclear. To this end, based on China's energy emissions data, meteorological and hydrological data, lithology data, and vegetation data, we used the GEM-CO2 model, soil respiration model, spatial autocorrelation analysis method and other methods to establish the spatial information map of China city-scale CNC from 1997 to 2017. Furthermore, based on the future climate, vegetation data, CEs data and their influencing factors in 2025–2060, the Back Propagation neural network model was used to predict the CN potential of China's provinces. This study found during the study period, annual CEs of 5.63 Pg CO2 were not absorbed, which is about 90% of the average annual CEs. And the carbon surplus regions were mainly concentrated in the less developed northeastern and southwestern border regions. Moreover, the change in the CNC in China from 1997 to 2017 was −13.37 Tg/yr, indicating that the CNC of China's terrestrial ecosystems overall reduced. It should be noted that most provinces are not highly polarized, that is, there is no significant differences in CNC between cities in the provinces. Moreover, the scenario simulation's method of IPCC provides a reference for this manuscript, so we set up two future scenarios (A2 and B1 scenarios). In the future, China is expected to achieve a carbon emission peak before 2030 under the B1 scenario while it will continue to grow under the A2 scenario. From 2017 to 2060, the CNC under the two scenarios (A2/B1) will decrease by 44.58% and increase by 15.54%. It follows then that the road to CN in China will be difficult without corresponding policy intervention. In short, this study has clarified China's CNC from the past to the future, as well as CNC's spatial distribution and changing trends. This provided theoretical and data support for China to introduce corresponding zero-carbon solutions based on its understanding of CNC.

Assessment of impacts of climate change on rice and wheat in the Indo-Gangetic plains

In this paper, the climate change scenarios of A2 and B2 for 2070-2100 time scale (denoted as 2080) for several key locations of India and its impact on rice and wheat crops based on regional climate model (PRECIS) were described. The PRECIS projects an increase in temperature over most parts of India especially in the IGP (Indo-Gangetic Plains), the region that presently experiences relatively low temperatures. Extreme high temperature episodes and rainfall intensity days are projected to become more frequent and the monsoon rainfall is also projected to increase. Rabi (mid Nov-March) season is likely to experience higher increase in temperature which could impact and hence become threat to the crops which really require low temperature for their growth. Climatic variability is also projected to increase in both A2 and B2 scenarios. All these projected changes are likely to reduce the wheat and rice yields in Indo-Gangetic plains of India. It is likely that there will be more number of years with low yields occurs towards the end of the century. Such yield reductions in rice and wheat crops due to climate change are mediated through reduction in crop duration, grain number and grain filling duration. The yield loss will be more in A2 scenario compared to B2. These quantitative estimates still have uncertainties associated with them, largely due to uncertainties in climate change projections, future technology growth, availability of inputs such as water for irrigation, changes in crop management and genotype. These projections nevertheless provide a direction of likely change in crop productivity in future climate change scenarios.

Estimation, analysis and comparison of carbon emissions and construction cost of the two tallest buildings located in United States and China

Nowadays, it is noticed that more than a third of the carbon dioxide (CO2) emitted in the atmosphere comes from the construction sector. This CO2 concentration has a significant effect on climate change. In the new cities, tall buildings multiply as mushrooms. Some specialists believe that they can be one of the solutions to reduce the carbon content in the atmosphere. The main aim of this study is to simulate, analyze and compare the embodied carbon and operational carbon of the two tallest buildings located in the United States and China, by using Design Builder and Pleiades software: the One World Trade Center in USA and the Shanghai Tower in China. Even if the embodied carbon of these super tall buildings is very high, the operational carbon remains the most important source of carbon emissions on their whole life cycle. Future carbon emissions of these two buildings were estimated in three periods (2030, 2050 and 2080) following the A2 scenario from the Intergovernmental Panel on Climate Change (IPCC). The results show that the operational carbon will increase by 10.6% at One World Trade Center (1WTC, USA) and 7.8% at Shanghai Tower (ST, China) in 2050. In addition, this study analyzed the impacts of the electricity mix and photovoltaic panels on their carbon dioxide emissions. Replacing energy production based on coal by renewable energy sources in the electricity mix of these countries could induce a reduction of 47.5% and 65.6% of total operational carbon emitted by the 1WTC and ST, respectively, by 2050. Finally, 46% of the building construction cost of these skyscrapers is related to their structure and superstructure.

A survey on the Koppen climate classification of Iran in 1975 and Comparing it with the CSIR Model Outputs for the years 2030, 2050, 2080 and 2100 under the A1B and A2 Scenarios

A survey on the Koppen climate classification of Iran in 1975 and Comparing it with the CSIR Model Outputs for the years 2030, 2050, 2080 and 2100 under the A1B and A2 Scenarios

Asthma exacerbation due to climate change-induced wildfire smoke in the Western US

Climate change and human activities have drastically altered the natural wildfire balance in the Western US and increased population health risks due to exposure to pollutants from fire smoke. Using dynamically downscaled climate model projections, we estimated additional asthma emergency room visits and hospitalizations due to exposure to smoke fine particulate matter (PM2.5) in the Western US in the 2050s. Isolating the amount of PM2.5 from wildfire smoke is both difficult to estimate and, thus, utilized by relatively few studies. In this study, we use a sophisticated modeling approach to estimate future increase in wildfire smoke exposure over the reference period (2003–2010) and subsequent health care burden due to asthma exacerbation. Average increases in smoke PM2.5 during future fire season ranged from 0.05 to 9.5 µg m−3 with the highest increases seen in Idaho, Montana, and Oregon. Using the Integrated Climate and Land-Use Scenarios (ICLUS) A2 scenario, we estimated the smoke-related asthma events could increase at a rate of 15.1 visits per 10 000 persons in the Western US, with the highest rates of increased asthma (25.7–41.9 per 10 000) in Idaho, Montana, Oregon, and Washington. Finally, we estimated healthcare costs of smoke-induced asthma exacerbation to be over $1.5 billion during a single future fire season. Here we show the potential future health impact of climate-induced wildfire activity, which may serve as a key tool in future climate change mitigation and adaptation planning.

Simulated impacts of climate change on Lake Simcoe water quality

ABSTRACT Lake Simcoe has undergone eutrophication and hypoxia since the 1960s. Climate change, leading to enhanced summer thermal stratification, has been identified as a key stressor. In this study, we modeled the impacts of climate change on hydrodynamics and biogeochemistry in Lake Simcoe by applying a 1-dimensional (vertical) model forced with A2 and B1 scenario outputs from a global climate model over 2000–2100. The model was calibrated in 2008 and validated in 2009, with maximum root mean square error (RMSE) of modelled temperature between 1.5 and 3.0 °C and dissolved oxygen RMSE between 0.5 and 2.5 mg L−1. Phytoplankton chlorophyll a was simulated with RMSE between 1.25 µg L−1 (large diatoms) and ∼0.5 µg L−1 (other groups). Interannual variability in spring water temperature and length of stratification were related to changes in the North Atlantic and Artic Oscillation indices, respectively. Under A2 and B1 forcing, the duration of stratification will increase by 45 and 38 days in summer between spring and fall turnover, respectively. The extended stratified period leads to a reduction in hypolimnetic dissolved oxygen from 3–7 to <3 mg L−1, thereby reducing the quality of cold-water fish habitat and increasing internal phosphorus loading from the benthos. These internal loads, combined with increased water temperatures, lead to increased cyanobacteria concentrations, beginning around 2070.