cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1011517
title: Summary of wind and load for ERCOT and MISO during the time periods of the data we used in this study

description: <b>Table 1.</b> Summary of wind and load for ERCOT and MISO during the time periods of the data we used in this study. <strong>Abstract</strong> Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16–0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07–0.13 MW of dispatchable capacity for each MW of additional wind capacity.
DOI: 10.6084/m9.figshare.1011501
title: Measured EC concentrations from each sampling event

description: <b>Table 1.</b> Measured EC concentrations from each sampling event. (Note: coefficient of variation is expressed in percentage.) <strong>Abstract</strong> Surface snow investigated for its elemental carbon (EC) concentration, based on a thermal–optical method, at two different sites during winter and spring of 2010 demonstrates metre scale horizontal variability in concentration. Based on the two sites sampled, a clean and a polluted site, the clean site (Arctic Finland) presents the greatest variability. In side-by-side ratios between neighbouring samples, 5 m apart, a ratio of around two was observed for the clean site. The median for the polluted site had a ratio of 1.2 between neighbouring samples. The results suggest that regions exposed to snowdrift may be more sensitive to horizontal variability in EC concentration. Furthermore, these results highlight the importance of carefully choosing sampling sites and timing, as each parameter will have some effect on EC variability. They also emphasize the importance of gathering multiple samples from a site to obtain a representative value for the area.
DOI: 10.6084/m9.figshare.1011502
title: Summary of the side-by-side ratios within each sampling event

description: <b>Table 2.</b> Summary of the side-by-side ratios within each sampling event. <strong>Abstract</strong> Surface snow investigated for its elemental carbon (EC) concentration, based on a thermal–optical method, at two different sites during winter and spring of 2010 demonstrates metre scale horizontal variability in concentration. Based on the two sites sampled, a clean and a polluted site, the clean site (Arctic Finland) presents the greatest variability. In side-by-side ratios between neighbouring samples, 5 m apart, a ratio of around two was observed for the clean site. The median for the polluted site had a ratio of 1.2 between neighbouring samples. The results suggest that regions exposed to snowdrift may be more sensitive to horizontal variability in EC concentration. Furthermore, these results highlight the importance of carefully choosing sampling sites and timing, as each parameter will have some effect on EC variability. They also emphasize the importance of gathering multiple samples from a site to obtain a representative value for the area.
DOI: 10.6084/m9.figshare.1011501.v1
title: Measured EC concentrations from each sampling event

description: <b>Table 1.</b> Measured EC concentrations from each sampling event. (Note: coefficient of variation is expressed in percentage.) <strong>Abstract</strong> Surface snow investigated for its elemental carbon (EC) concentration, based on a thermal–optical method, at two different sites during winter and spring of 2010 demonstrates metre scale horizontal variability in concentration. Based on the two sites sampled, a clean and a polluted site, the clean site (Arctic Finland) presents the greatest variability. In side-by-side ratios between neighbouring samples, 5 m apart, a ratio of around two was observed for the clean site. The median for the polluted site had a ratio of 1.2 between neighbouring samples. The results suggest that regions exposed to snowdrift may be more sensitive to horizontal variability in EC concentration. Furthermore, these results highlight the importance of carefully choosing sampling sites and timing, as each parameter will have some effect on EC variability. They also emphasize the importance of gathering multiple samples from a site to obtain a representative value for the area.
DOI: 10.6084/m9.figshare.1011502.v1
title: Summary of the side-by-side ratios within each sampling event

description: <b>Table 2.</b> Summary of the side-by-side ratios within each sampling event. <strong>Abstract</strong> Surface snow investigated for its elemental carbon (EC) concentration, based on a thermal–optical method, at two different sites during winter and spring of 2010 demonstrates metre scale horizontal variability in concentration. Based on the two sites sampled, a clean and a polluted site, the clean site (Arctic Finland) presents the greatest variability. In side-by-side ratios between neighbouring samples, 5 m apart, a ratio of around two was observed for the clean site. The median for the polluted site had a ratio of 1.2 between neighbouring samples. The results suggest that regions exposed to snowdrift may be more sensitive to horizontal variability in EC concentration. Furthermore, these results highlight the importance of carefully choosing sampling sites and timing, as each parameter will have some effect on EC variability. They also emphasize the importance of gathering multiple samples from a site to obtain a representative value for the area.
DOI: 10.6084/m9.figshare.1011491
title: Sources of uncertainty and values used in the uncertainty analysis

description: <b>Table 2.</b> Sources of uncertainty and values used in the uncertainty analysis. <strong>Abstract</strong> The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries' emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki <em>et al</em> (2012 <em>Environ. Res. Lett.</em> <strong>7</strong> 024005) to perform sensitivity and uncertainty analysis distinguishing between 'modelling sources' of uncertainty, which refers to model-specific parameters and assumptions, and 'recurring sources' of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology used to evaluate reference level and emission reductions would strengthen the credibility of the system by promoting accountability and transparency. To secure conservativeness and deal with uncertainty, we consider the need for further research using real data available to developing countries to test the applicability of conservative discounts including the trend uncertainty and other possible options that would allow real incentives and stimulate improvements over time. Finally, we argue that REDD+ result-based actions assessed on the basis of a dashboard of performance indicators, not only in 'tonnes CO<sub>2</sub> equ. per year' might provide a more holistic approach, at least until better accuracy and certainty of forest carbon stocks emission and removal estimates to support a REDD+ policy can be reached.
DOI: 10.6084/m9.figshare.1011491.v1
title: Sources of uncertainty and values used in the uncertainty analysis

description: <b>Table 2.</b> Sources of uncertainty and values used in the uncertainty analysis. <strong>Abstract</strong> The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries' emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki <em>et al</em> (2012 <em>Environ. Res. Lett.</em> <strong>7</strong> 024005) to perform sensitivity and uncertainty analysis distinguishing between 'modelling sources' of uncertainty, which refers to model-specific parameters and assumptions, and 'recurring sources' of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology used to evaluate reference level and emission reductions would strengthen the credibility of the system by promoting accountability and transparency. To secure conservativeness and deal with uncertainty, we consider the need for further research using real data available to developing countries to test the applicability of conservative discounts including the trend uncertainty and other possible options that would allow real incentives and stimulate improvements over time. Finally, we argue that REDD+ result-based actions assessed on the basis of a dashboard of performance indicators, not only in 'tonnes CO<sub>2</sub> equ. per year' might provide a more holistic approach, at least until better accuracy and certainty of forest carbon stocks emission and removal estimates to support a REDD+ policy can be reached.
DOI: 10.6084/m9.figshare.1011473
title: Median values of daytime (11:00–17:00 LST) and nighttime (00:00–06:00 LST) turbulence intensity (TI) (%), shear exponent (α) (unitless), and vertical potential temperature gradient (Δθ/Δ<em>z</em>) (°C m<sup>−1</sup>) for each wind sector (see figure 1)

description: <b>Table 1.</b> Median values of daytime (11:00–17:00 LST) and nighttime (00:00–06:00 LST) turbulence intensity (TI) (%), shear exponent (α) (unitless), and vertical potential temperature gradient (Δθ/Δ<em>z</em>) (°C m<sup>−1</sup>) for each wind sector (see figure 1). <strong>Abstract</strong> Observations of wakes from individual wind turbines and a multi-megawatt wind energy installation in the Midwestern US indicate that directly downstream of a turbine (at a distance of 190 m, or 2.4 rotor diameters (<em>D</em>)), there is a clear impact on wind speed and turbulence intensity (TI) throughout the rotor swept area. However, at a downwind distance of 2.1 km (26 <em>D</em> downstream of the closest wind turbine) the wake of the whole wind farm is not evident. There is no significant reduction of hub-height wind speed or increase in TI especially during daytime. Thus, in high turbulence regimes even very large wind installations may have only a modest impact on downstream flow fields. No impact is observable in daytime vertical potential temperature gradients at downwind distances of &gt;2 km, but at night the presence of the wind farm does significantly decrease the vertical gradients of potential temperature (though the profile remains stably stratified), largely by increasing the temperature at 2 m.
DOI: 10.6084/m9.figshare.1011466
title: Regional premature annual deaths attributable to past climate change (2000–Em2000Cl1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 9 models for ozone and 5 models for PM<sub>2.5</sub> (3 significant figures shown)

description: <b>Table 2.</b> Regional premature annual deaths attributable to past climate change (2000–Em2000Cl1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 9 models for ozone and 5 models for PM<sub>2.5</sub> (3 significant figures shown). Also shown are deaths per million people (present-day exposed population aged 30 and over) in each region, in parenthesis. <strong>Abstract</strong> Increased concentrations of ozone and fine particulate matter (PM<sub>2.5</sub>) since preindustrial times reflect increased emissions, but also contributions of past climate change. Here we use modeled concentrations from an ensemble of chemistry–climate models to estimate the global burden of anthropogenic outdoor air pollution on present-day premature human mortality, and the component of that burden attributable to past climate change. Using simulated concentrations for 2000 and 1850 and concentration–response functions (CRFs), we estimate that, at present, 470 000 (95% confidence interval, 140 000 to 900 000) premature respiratory deaths are associated globally and annually with anthropogenic ozone, and 2.1 (1.3 to 3.0) million deaths with anthropogenic PM<sub>2.5</sub>-related cardiopulmonary diseases (93%) and lung cancer (7%). These estimates are smaller than ones from previous studies because we use modeled 1850 air pollution rather than a counterfactual low concentration, and because of different emissions. Uncertainty in CRFs contributes more to overall uncertainty than the spread of model results. Mortality attributed to the effects of past climate change on air quality is considerably smaller than the global burden: 1500 (−20 000 to 27 000) deaths yr<sup>−1</sup> due to ozone and 2200 (−350 000 to 140 000) due to PM<sub>2.5</sub>. The small multi-model means are coincidental, as there are larger ranges of results for individual models, reflected in the large uncertainties, with some models suggesting that past climate change has reduced air pollution mortality.
DOI: 10.6084/m9.figshare.1011465
title: Regional premature annual deaths from anthropogenic outdoor air pollution (2000–1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 14 models for ozone and 6 models for PM<sub>2.5</sub> (3 significant figures shown)

description: <b>Table 1.</b> Regional premature annual deaths from anthropogenic outdoor air pollution (2000–1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 14 models for ozone and 6 models for PM<sub>2.5</sub> (3 significant figures shown). Also shown are deaths per million people (present-day exposed population aged 30 and over) in each region, in parenthesis. <strong>Abstract</strong> Increased concentrations of ozone and fine particulate matter (PM<sub>2.5</sub>) since preindustrial times reflect increased emissions, but also contributions of past climate change. Here we use modeled concentrations from an ensemble of chemistry–climate models to estimate the global burden of anthropogenic outdoor air pollution on present-day premature human mortality, and the component of that burden attributable to past climate change. Using simulated concentrations for 2000 and 1850 and concentration–response functions (CRFs), we estimate that, at present, 470 000 (95% confidence interval, 140 000 to 900 000) premature respiratory deaths are associated globally and annually with anthropogenic ozone, and 2.1 (1.3 to 3.0) million deaths with anthropogenic PM<sub>2.5</sub>-related cardiopulmonary diseases (93%) and lung cancer (7%). These estimates are smaller than ones from previous studies because we use modeled 1850 air pollution rather than a counterfactual low concentration, and because of different emissions. Uncertainty in CRFs contributes more to overall uncertainty than the spread of model results. Mortality attributed to the effects of past climate change on air quality is considerably smaller than the global burden: 1500 (−20 000 to 27 000) deaths yr<sup>−1</sup> due to ozone and 2200 (−350 000 to 140 000) due to PM<sub>2.5</sub>. The small multi-model means are coincidental, as there are larger ranges of results for individual models, reflected in the large uncertainties, with some models suggesting that past climate change has reduced air pollution mortality.
DOI: 10.6084/m9.figshare.1011466.v1
title: Regional premature annual deaths attributable to past climate change (2000–Em2000Cl1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 9 models for ozone and 5 models for PM<sub>2.5</sub> (3 significant figures shown)

description: <b>Table 2.</b> Regional premature annual deaths attributable to past climate change (2000–Em2000Cl1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 9 models for ozone and 5 models for PM<sub>2.5</sub> (3 significant figures shown). Also shown are deaths per million people (present-day exposed population aged 30 and over) in each region, in parenthesis. <strong>Abstract</strong> Increased concentrations of ozone and fine particulate matter (PM<sub>2.5</sub>) since preindustrial times reflect increased emissions, but also contributions of past climate change. Here we use modeled concentrations from an ensemble of chemistry–climate models to estimate the global burden of anthropogenic outdoor air pollution on present-day premature human mortality, and the component of that burden attributable to past climate change. Using simulated concentrations for 2000 and 1850 and concentration–response functions (CRFs), we estimate that, at present, 470 000 (95% confidence interval, 140 000 to 900 000) premature respiratory deaths are associated globally and annually with anthropogenic ozone, and 2.1 (1.3 to 3.0) million deaths with anthropogenic PM<sub>2.5</sub>-related cardiopulmonary diseases (93%) and lung cancer (7%). These estimates are smaller than ones from previous studies because we use modeled 1850 air pollution rather than a counterfactual low concentration, and because of different emissions. Uncertainty in CRFs contributes more to overall uncertainty than the spread of model results. Mortality attributed to the effects of past climate change on air quality is considerably smaller than the global burden: 1500 (−20 000 to 27 000) deaths yr<sup>−1</sup> due to ozone and 2200 (−350 000 to 140 000) due to PM<sub>2.5</sub>. The small multi-model means are coincidental, as there are larger ranges of results for individual models, reflected in the large uncertainties, with some models suggesting that past climate change has reduced air pollution mortality.
DOI: 10.6084/m9.figshare.1011465.v1
title: Regional premature annual deaths from anthropogenic outdoor air pollution (2000–1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 14 models for ozone and 6 models for PM<sub>2.5</sub> (3 significant figures shown)

description: <b>Table 1.</b> Regional premature annual deaths from anthropogenic outdoor air pollution (2000–1850), for ozone (respiratory) and PM<sub>2.5</sub> calculated as a sum of species (CPD + LC), shown for the mean and full range across 14 models for ozone and 6 models for PM<sub>2.5</sub> (3 significant figures shown). Also shown are deaths per million people (present-day exposed population aged 30 and over) in each region, in parenthesis. <strong>Abstract</strong> Increased concentrations of ozone and fine particulate matter (PM<sub>2.5</sub>) since preindustrial times reflect increased emissions, but also contributions of past climate change. Here we use modeled concentrations from an ensemble of chemistry–climate models to estimate the global burden of anthropogenic outdoor air pollution on present-day premature human mortality, and the component of that burden attributable to past climate change. Using simulated concentrations for 2000 and 1850 and concentration–response functions (CRFs), we estimate that, at present, 470 000 (95% confidence interval, 140 000 to 900 000) premature respiratory deaths are associated globally and annually with anthropogenic ozone, and 2.1 (1.3 to 3.0) million deaths with anthropogenic PM<sub>2.5</sub>-related cardiopulmonary diseases (93%) and lung cancer (7%). These estimates are smaller than ones from previous studies because we use modeled 1850 air pollution rather than a counterfactual low concentration, and because of different emissions. Uncertainty in CRFs contributes more to overall uncertainty than the spread of model results. Mortality attributed to the effects of past climate change on air quality is considerably smaller than the global burden: 1500 (−20 000 to 27 000) deaths yr<sup>−1</sup> due to ozone and 2200 (−350 000 to 140 000) due to PM<sub>2.5</sub>. The small multi-model means are coincidental, as there are larger ranges of results for individual models, reflected in the large uncertainties, with some models suggesting that past climate change has reduced air pollution mortality.