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By using a liquid desiccant ventilation system for dehumidification and an air-handling unit for cooling, the liquid desiccant cooling system (LDCS) system became a promising alternative for traditional technology. Solar thermal energy is suitable to deal with the heat requirement of LDCS in buildings, especially in the areas with abundant solar radiation. The energy saving of solar-assisted liquid desiccant air-conditioning system is significantly affected by various operation conditions, and multi-parameter optimization was necessary to improve the system applicability. In this paper, we investigated the impact of five main parameters on the system performance via self-developed system modelling, including the solution mass flow rate, concentration, cooling tower flow rate, and solar water flow rate and installation area of solar collector. A typical commercial building in Hong Kong was selected as a case study, which air-conditioning load was obtained by Energy-plus. The results indicated that the installation area of solar collector showed the greatest impact, and the effect of heating water flow rate was also important. The effect of desiccant flow rate was significant, but the influence of solution concentration was slight. Then, the multi-parameter optimization was conducted for obtaining a maximum annual electricity saving rate based on the Multi-Population Genetic Algorithm. The optimized installation area of solar collector was 72 m2, and the heating water flow rate was 0.66 kg/s. The optimized solution flow rate was 0.17 kg/s. The required cooling water flow rate was around 0.8 kg/s.

This file contains the complete dataset collected and underpinning the estimated carbon emissions data in the companion paper, in Microsoft Excel (XLSX) format. The workbook contains an index sheet with full details of each included worksheet. The file has been verified to open in Microsoft Excel (https://products.office.com/excel) and LibreOffice (https://www.libreoffice.org)

This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"

Replication data and materials for "Changing Prices in a Changing Climate: Electoral Competition and Fossil Fuel Taxation".

The data resource consists of half hourly time series of heat (latent and sensible) and trace gas (carbon dioxide and methane) fluxes obtained by eddy-covariance, gas concentrations and ancillary meteorological data (e.g. air temperature, relative humidity, pressure, photosynthetically active radiation, total incoming radiation, wind speed and direction). The data were collected at Guma Lagoon (18°57'53.01"S; 22°22'16.20"E), in the perennially flooded area of the Okavango Delta, Botswana, for the purpose of quantifying greenhouse gas fluxes over a Cyperus papyrus stand. The measurement period was 01/01/2018 to 31/12/2020. The instrumentation was installed the UK Centre for Ecology and Hydrology; monthly maintenance and data collection visits were effected by the Okavango Research Institute, University of Botswana. The research was funded through NERC grant reference NE/N015746/2 - The Global Methane Budget. Raw data from the eddy-covariance instrumentation were processed into half-hourly fluxes using the EddyPro software package version 7.0.6 and quality controlled by Dr Helfter. An automatic weather station recorded recorded air temperature, pressure, relative humidity, wind speed and wind direction, total solar radiation and photosynthetically active radiation (PAR). Data from both instruments were downloaded, processed and deposited into the EIDC as a comma separted value (.csv) file.

This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

The dataset comprises full list of species of macrozoobenthos collected from the Pechora Sea (SE Barents Sea). Grab samples were collected from 10 stations in the Pechora Bay from aboard RV Kartesh in 2020-2021. Macrobenthic invertebrates were identified with the maximum level of certainty through optical microscopy using regional taxonomic keys. All taxonomic names were standardised using the World Register of Marine Species (WoRMS). All specimens have been counted and weighted (wet biomass) on Ohaus Adventurer scales with reported accuracy to 0.01 g. Bivalve molluscs and gastropods were weighed in shells. Biomass (g. m-2) and abundance (ind m-2) are used to characterise macrozoobenthos. The sampling and identification work was carried out in collaboration with specialists from Lomonosov Moscow State University Marine Research Center and P.P. Shirshov Institute of Oceanology.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

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.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' 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.