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Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NCAR.CESM2-WACCM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The CESM2-WACCM climate model, released in 2018, includes the following components: aerosol: MAM4 (same grid as atmos), atmos: WACCM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-06 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). The model was run by the National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA (NCAR) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 5 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED. 11 pages, 15 figures

Abstract This paper formulates a model that assesses the economic and policy implications of the adoption and operation of microgrids. The recent climate agreement between the US and China as well as the historic Lima accord on climate change are prompting a massive demand in climate-friendly technologies. Given the increasing demand for electricity and the mounting costs of electricity supply associated with maximizing system performance through an optimal economic scheduling of microgrids, decision makers and policymakers are already facing the problem of sketching energy management strategies for localized operation and grid penetration. We employ a modified Benders' decomposition approach to explore the optimal scheduling of supply from different technology units in the microgrid in the face of supply and demand uncertainties. We guarantee that the power generated by the units meets systemic load while satisfying the primary operational and technological constraints. We implement our model in a quantitative numerical analysis where we investigate the operational regulation in the light of a public environmental policy. We extend prior research on this subject by uncovering a greater reliance on the grid under simultaneous uncertainties in demand and supply. We unlock a strategy for a proper mitigation of the effects of emissions. We find that carbon abatement policy can be used to motivate microgrids operators to invest in fuel cells and natural gas technologies when dealing with uncertainty.

Building energy flexibility has been increasingly demonstrated as a cost-effective solution to respond to the needs of energy networks, including electric grids and district cooling and heating systems, improving the integration of intermittent renewable energy sources. Adjusting zonal temperature set-points is one of the most promising measures to unlock the energy flexibility potential of central air conditioning systems in complex commercial buildings. However, most existing studies focused on quantifying the energy flexibility on the building level since only building-level energy consumption is normally metered in commercial buildings. This study aims to investigate the impacts of temperature set-point adjustment strategies on zone-level thermal and energy performance by developing a non-intrusive data-driven load disaggregation method (i.e., a virtual zonal power meter). Three university buildings in Northern California were selected to test the proposed load disaggregation method. We found that heterogeneities of energy use and energy flexibility existed across not only buildings but also air handling units (AHUs) and zones. Moreover, a small number of zones accounted for a large amount of energy use and energy flexibility; and the most energy-intensive zones are not necessarily the most energy-flexible zones. For the three tested buildings, the top 30% most energy-intensive zones accounted for around 60% of the total energy use; and the top 30% most energy-flexible zones provided around 80% of the total energy flexibility. The proposed method enables the electric grid or district energy system operators to regard the controlled energy-flexible entities as a fleet of AHUs or zones instead of a fleet of buildings and helps unlock the possibility for targeted demand flexibility strategies that balance zone-by-zone energy reduction with zone-by-zone costs to occupants. 33 pages, 18 figures

To discuss current evidence of global climate change and its implications for allergic rhinitis and other allergic respiratory diseases.Global climate change is evidenced by increasing average earth temperature, increasing anthropogenic greenhouse gas levels, and elevated pollen levels. Pollutants of interest include carbon dioxide (CO2), ozone (O3), and nitrous oxide (NO2) because they can enhance the allergic response and lead to increased symptoms of allergic respiratory diseases. Heightened CO2 levels stimulate pollen production via photosynthesis and increased growth in multiple plant species investigated. Although worsened air quality appears to increase prevalence of allergic rhinitis, the effects of increased temperature are less certain. The findings of increased aeroallergen levels likely contribute to increases in presentation of allergic diseases, although more healthcare impact studies are necessary.Although recent literature indicates and strongly supports changes in temperature, pollution levels, and aeroallergen levels, more longitudinal epidemiologic surveillance of allergic diseases in relation to climate change as well as pathophysiologic studies on changing aeroallergen effects on allergic diseases are needed.

A promising route to transition wastewater treatment facilities (WWTFs) from energy-consuming to net energy-positive is to retrofit existing facilities with process modifications, residual biosolid upcycling, and effluent thermal energy recovery. This study assesses the economics and life cycle environmental impacts of three proposed retrofits of WWTFs that consider thermochemical conversion technologies, namely, hydrothermal liquefaction, slow pyrolysis, and fast pyrolysis, along with advanced bioreactors. The results are in turn compared to the reference design, showing the retrofitting design with hydrothermal liquefaction, and an up-flow anaerobic sludge blanket has the highest net present value (NPV) of $177.36MM over a 20-year plant lifetime despite 15% higher annual production costs than the reference design. According to the ReCiPe method, chlorination is identified as the major contributor for most impact categories in all cases. There are several uncertainties embedded in the techno-economic analysis and life cycle assessment, including the discount rate, capital investment, sewer rate, and prices of main products; among which, the price of biochar presents the widest variation from $50 to $1900/t. Sensitivity analyses reveal that the variation of discount rates causes the most significant changes in NPVs. The impact of the biochar price is more pronounced in the slow pyrolysis-based pathway compared to the fast pyrolysis since biochar is the main product of slow pyrolysis.

Due to their unique nature, construction projects are considered one of the world’s most hazardous and incident-prone industrial sectors. The present study aimed to analyze health, safety and environmental (HSE) risks relating to construction projects based on the project management body of knowledge (PMBOK) and sustainability approach. This study was conducted with the participation of 30 experts, using the semi-quantitative risk assessment technique, in nine areas of the project management’s body of knowledge, based on the fuzzy analytic hierarchy process. Risk, in this study, was estimated using a two-dimensional matrix of incident probability and severity, each of which has four sub-parameters. The HSE risks pertaining to each of the nine areas of PMBOK were identified. After that, the two dimensions of risk, including incident probability and severity, were measured. Thirty-seven risk sources associated with nine areas of the PMBOK were identified. Risk analysis revealed that 20 sources were at an unacceptable risk level, and 17 risks were at a tolerable risk level. Identifying HSE-related risk sources in accordance with the nine areas of PMBOK, and using FAHP to assess the risk of these hazards in construction projects, can lead to a more realistic estimate of risk in construction projects. The presented method in the current study can create a novel perspective in terms of the construction industry’s risk management and assessment.

Abstract The albedo of a roof determines the fraction of incoming sunlight that is reflected, which affects heat transfer into the building and exchange of energy between the built environment and the atmosphere. While the albedo of individual roofs can be easily measured, roof albedo at the city scale is unknown. In this paper we characterize the albedos of roofs in seven cities in California: Los Angeles, Long Beach, Bakersfield, San Francisco, San Jose, Sacramento, and San Diego. The fraction of urban area covered by roofs ranged by city from 10% to 25%. City-wide average roof albedo ranged from 0.17 ± 0.08 to 0.20 ± 0.11 (mean ± standard deviation) for five of the cities; values were higher in Sacramento (0.24 ± 0.11) and San Diego (0.29 ± 0.15). Buildings with small roofs were found to constitute a large fraction of city roof area and to have low mean albedos. This suggests that efforts to increase urban albedo through the use of reflective roofs should include small roofs, which are presumably mostly residential. Roof albedos derived for Bakersfield were used in a regional climate model (Weather Research and Forecasting Model) to estimate temperature changes attainable by converting the current stock of roofs to “cool” high albedo roofs. It was found that seasonal mean afternoon (15:00 LST) temperatures could be reduced by up to 0.2 °C during both the summer and winter. Changes in precipitation were not significant at the 95% confidence level.