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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.CCCR-IITM.IITM-ESM.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 IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Project: High Resolution dynamically downscaled CMIP5 climate data over India - The datasets in this project are developed as a part of a project associated with the Indian Institute of Technology Delhi, India, and the National Mission for Clean Ganga, Government of India. We have dynamically downscaled a coarser resolution CMIP5 GCM (CESMv1) climate data using the Weather Research and Forecasting (WRF) model for the current (2006-2015) and future (2091-2100) RCP8.5 emission scenario to produce a 10km resolution dataset over India. This dataset is expected to be of massive value to fine-scale regional modelling based climate change adaptive and mitigative studies in fields of water resources, energy, agriculture, and forestry over India. This project is financially supported by the National Mission for Clean Ganga (NMCG), Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation, Government of India, through grant number TE-16015/02/2019/NMCG. Summary: The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

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.CMIP.CCCR-IITM.IITM-ESM.historical' 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 IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

The Danish energy system is undergoing a transition from a system based on storable fossil fuels to a system based on fluctuating renewable energy sources. At the same time, more of and more of the energy system is becoming electrified; transportation, heating and fuel usage in industry and elsewhere. This article investigates the development of the Danish energy system in a medium year 2030 situation as well as in a long-term year 2050 situation. The analyses are based on scenario development by the Danish Climate Commission. In the short term, it is investigated what the effects will be of having flexible or inflexible electric vehicles and individual heat pumps, and in the long term it is investigated what the effects of changes in the load profiles due to changing weights of demand sectors are. The analyses are based on energy systems simulations using EnergyPLAN and demand forecasting using the Helena model. The results show that even with a limited short-term electric car fleet, these will have a significant effect on the energy system; the energy system’s ability to integrated wind power and the demand for condensing power generation capacity in the system. Charging patterns and flexibility have significant effects on this. Likewise, individual heat pumps may affect the system operation if they are equipped with heat storages. The analyses also show that the long-term changes in electricity demand curve profiles have little impact on the energy system performance. The flexibility given by heat pumps and electric vehicles in the long-term future overshadows any effects of changes in hourly demand curve profiles. International Journal of Sustainable Energy Planning and Management, Vol 7 (2015)

With the rising environmental problems there are international movements towards sustainability and greening the built environments in order to mitigate the negative environmental impacts of buildings and human activities on environment and human health. This paper presents a range of K-12 Green Schools that were intentionally designed to utilize school building as a 3D-text book for Environmental Education (EE). The aim of this paper is to examine the methods and strategies of designing green school as a teaching tool through case study analysis of the selected schools. The cases provide a diversity of geographic locations, climates, green strategies and coasts. The research depends on the descriptive analytical approach for literature review; multiple-case study analysis to investigate the attributes of green schools that teach. The results revealed a set of approaches for utilizing green schools as a 3D-textbook for EE EQA - International Journal of Environmental Quality, Vol 39 (2020)

Chapter 1 : Principles of Renewable Energy & Solar Energy 01 - 50 Chapter 2 : Wind Energy 01– 29 Chapter 3 : Ocean Thermal Energy 01 - 27 Chapter 4 : Storage of Energy 01 - 52 Chapter 5 : Geothermal Energy Sources 01 - 25

Among the major applications of solar photovoltaics, Grid Interactive (GI) operation has maximum energy conversion efficiencies, easy to install & operate and offers faster paybacks. GI system’s performance is rated based on performance indicators like energy yields (daily, monthly & yearly) and Performance Ratio (PR). While the energy yields depends on intensity of sun radiation & temperature, the PR values are independent of sun radiation and PV array size, and solely dependent on the ambient temperature, Spectral Response(SR) of solar cells and sun spectrum distribution at site and time. Hence, PR values are site specific and technology dependent. For this reason it is imperative to identify site specific PV technologies those offer maximum yields based on PR values. Ministry of New and Renewable Energy (MNRE), Government of India initiated one of the world’s most ambitious Jawaharlal Nehru National Solar Mission (JNNSM) programme targeting to establish 20GWp capacity of solar power plants in the country by 2020. The proposed studies by Solar Energy Centre (SEC) are in conjunction with MNRE programme and an initial step towards preparation of PR rating maps for the country. This paper presents SEC’s studies on PR values for three PV technology modules through outdoor test beds. 26th European Photovoltaic Solar Energy Conference and Exhibition; 3566-3568

Prediction of daylight illuminance has become the prime concern for building designers. This paper presents the daylight illuminance frequency distribution, correlation coefficients amongst climate variables and linear regression models developed for moderate climate zone. The emphasis of the present study is to predict daylight illuminance intensities and to investigate its relationship amongst the important climatic variables such as temperature, percentage sky clearance and percentage relative humidity at specific timings of the day, observed during different climatic conditions and seasons. The daylight illuminance data is collected with the help of digital ‘Lux’ meter across the Pune city (India). The daylight illuminance frequency distribution, correlation coefficients and the linear regression models derived for four climate specific months are presented and explained. The minimum illuminance level of 6260 Lux is observed at 8.00 am whereas the maximum is 147300 Lux at 12.00 noon in the month of July and the highest frequency of illuminance intensities falls in the range 140000-145000 Lux. A better association (positive) of illuminance intensities has been observed with percentage sky clearance variable; the correlation coefficients in the month of July are 0.678, 0.656 and 0.453. The percentage error between predicted and measured values of daylight illuminance levels derived from the developed regression models varies from 4.47% to 12.33%.

The relevance of energy in the growth and development process necessitate giving serious attention to the planning, production and consumption of energy. This is usually referred to as energy planning and analysis or modelling carried out in recent years using sophisticated and computerized models. These models rely heavily on future assumptions regarding the expected economic conditions in consideration to the current and unfolding situations of the economies in question. However, due to uncertainty of the future economic conditions of especially developing economies, these assumptions are mostly found unable to adequately capture the evolving events. This is more evident if one looks at the alternative energy projections made by different organizations using different understandings and assumptions. This study compares the best electricity demand and supply projections of NECAL2050 as the best energy model in Nigeria and other alternatives projections by [6, 13,] and previous Energy Commission of Nigeria - ECN’s energy models by [14] to show case the discrepancies and their economic consequences. Policy implications and recommendations are discussed at the end. International Journal of Sustainable Energy Planning and Management, Vol 21 (2019)