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Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.

This paper presents a new method, based on the Smart Energy Systems concept. The aim is to increase the share of renewable energy penetration on islands. The method is applied to the island of Gran Canaria (Spain), considering the entire energy system of the island. Several smart renewable energy strategies are proposed following a cross-sectoral approach between the electricity, heating/cooling, desalination, transport and gas sectors. The different smart renewable energy strategies were applied in a series of steps, while looking for a transition from the current energy system to a nearly 100% renewable energy system. Based on the results, the study concludes that the suggested method is applicable for increasing renewable integration on islands and can potentially be used in helping energy planners to take decisions about priorities in development of the sector to improve such integration. The results indicate that, for the case of Gran Canaria, a 75.9% renewable energy system could be attained with technologies that can be implemented at present. Furthermore, it is shown that a nearly 100% renewable energy system in Gran Canaria is technically feasible and could be achieved if certain technologies acquire greater maturity. © 2018 Elsevier Ltd 443 421 2,048 5,537 Q1 Q1 SCIE

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.

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.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.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 MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 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 ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Household Surveys performed in four villages selected from Oromia, Amhara and Southern Nations, Nationalities, and Peoples’ Region (SNNPR) following from the ‘Ethiopian Rural Household Survey’ (ERHS) conducted in 2004.It contains detailed data on household consumption and expenditures, assets, income, agricultural activities, land allocation, demographic characteristics, and other variables. From September 2011 to January 2012 another survey of 221 households was conducted in three major regions of central and southern Ethiopia. At the time of this latest survey effort the most recent ERHS survey data available was from 2004. The selection of respondents, determination of sample size, and apportionment of the sample were based on a proportional sampling technique.In addition to addressing important questions from the ERHS survey data, the field survey was designed to generate detailed information on household biomass energy production and consumption practices; as well as farming activities; labour and land allocation; economic and demographic characteristics; and expenditures on food, non-food items, and energy. The 2011 survey effort collected detailed household biomass energy use data. The measurement of household biomass energy use was obtained in traditional units and later converted into kilograms. The conversion factors for each of the biomass were collected from the closest urban centre of each of the study areas. Information obtained on household biomass energy use was collected for a time period of one week before the survey was conducted. It was then aggregated into annual figures, although household biomass energy use may vary seasonally. Quality/Lineage: The data was collected by qualified enumerators who had participated in previous ERHS survey. In addition to myself I recruited assistant supervisor to check the accuracy and quality of data on daily basis and followup interview process closely. Before the survey commenced a pilot survey was conducted in each of the study areas to identify the different types of energy households are using and other critical variables of interest for the research. This information was used to revise and improve questionnaire. Moreover, a one day in-depth training was given to enumerators and assistant supervisor to enrich their deeper understanding of each the question in the survey and to further improve questionnaire from their earlier experiences in those villages. Purpose: Over 90% of Ethiopian rural population rely on biomass energy. However, biomass energy utilization is linked to household livelihood as in rural households produce and consume biomass energy simultaneously with other (on and off-farm)activities. With the rampant rate of deforestation that Ethiopia is facing it is important to investigate the effect of deforestation or fuelwood scarcity which is assumed affect household welfare through influence on wage and price. In light of this, the survey effort collected information on household use of biomass energy sources, expenditure and labour allocation choices and amount of labour time used for each activities.This helped me to investigate the effect of fuelwood scarcity on household welfare from three aspects: labour allocation decision, energy expenditure and fuel choice and biomass energy consumption behavior to better understand the related linkage of household production and utilization of biomass with livelihoods or food security. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c08e08aa-3055-4651-801b-0383610c1987}.

Format: raw binary. The raw binary archive is composed of 576 zipped files, corresponding to the SPEI index at time scales between 1 and 48 months for the whole World and divided by decades (except the last file, containing only data for the period 2001-2006). Each zipped file contains three files, one with the data itselt (.img), and two headers (.doc and .hdr). The information contained in the header files is equivalent, and allows direct access to the data using some widely used commercial programs. Naming convention: spei[tempscale]_[decade].zip, where [tempscale] is a number between 1 and 48 indicating the temporal scale of the index (months), and [decade] indicates the years of data contained in the file. Example: spei12_1910-1919.zip. All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI, scPDSI). The new dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained from the CRU TS3.0 data, covering time scales from 1 to 48 months for the period 1901-2006, and has a spatial resolution of 0.5°. The advantages of the new dataset are that: i) it improves the spatial resolution of the unique global drought dataset at a global scale; ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI, and, in particular; iii) it enables identification of various drought types, given the multiscalar character of the SPEI. More details at: http://www.eead.csic.es/spei/spei.html All currently available gridded drought datasets at continental and global scales are based on either the PDSI or the sc-PDSI. A new global drought dataset based on the Standardised Precipitation-Evapotranspiration Index (SPEI) has been developed, which covers time scales from 1-48 months at a spatial resolution of 0.5°, and provides temporal coverage for the period 1901-2006. This dataset represents an improvement in spatial resolution and operative capability of previous gridded drought datasets based on the PDSI, and enables identification of various drought types. The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. Use of the newest version is recommended. Older versions are still available to allow replicability. The dataset is freely available on the web repository of the Spanish National Research Council (CSIC) in three different formats (NetCDF, binary raster, and plain text).

The increasing depletion of natural resources, combined with a wider set of pressures on the environment, has, in recent years, highlighted the need for a more efficient use of energy and a development process that involves alternative energy sources. Energy efficiency has received much attention as a solution, implying both monetary and emissions savings. However, the latter may be partially offset by the income and demand effects of the former, both in more efficient sectors and in spreading to the wider economy. This is the problem of rebound effects. Taking Spain as a case study, and introducing an energy-related CGE model that develops the inclusion of renewables, this paper evaluates a combination of efficiency initiatives to deliver both reduced energy use by households and a more sustainable supply of energy. Our findings suggest that a package aimed at improving efficiency in household electricity and petroleum use, combined with a more competitive supply of energy from renewable sources, may be the only way to get reductions in all energy use, and thus benefit the economy. Specifically, we consider how this package may lead to positive economic impacts and associated rebound effects, where the latter are focused on a greener energy supply.