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The database presents the scenario results of an exploratory research, carried out at the International Institute for Applied Systems Analysis (IIASA): the Low Energy Demand (LED) study (Grubler et al. 2018). The LED scenario explored how far transformative changes that combine technological changes, end-use efficiency, and new business models for energy service provision can lead for lowering energy demand, and how these changes could drive deep decarbonisation in the long-term. The scenario development methodology included a bottom-up analysis of how currently existing, though often embryonic, social, institutional, and technological trends could become mainstream with resulting step-changes in efficiency and resulting lowered energy demand. The bottom-up demand estimations were then further explored for their supply side and emissions and climate implications with a top-down modeling framework drawing on the Shared Socioeconomic Pathways (SSP) framework (Riahi et al. 2017). The results show that global final energy demands can be drastically reduced in 2050, to around 245 EJ/yr, or 40% lower than today, whilst significantly expanding human welfare and reducing global development inequalities. According to the knowledge of the authors, LED is the lowest long-term global energy demand scenario ever published. The LED scenario meets the 1.5°C climate target in 2100 without overshoot and keeps the global mean temperature increase below 1.5°C with a probability of more than 60%, without requiring controversial negative emission technologies, such as bioenergy with carbon capture and storage (BECCS), that figure prominently in the emission scenario literature (Rogelj et al. 2015, Anderson and Peters 2016, Creutzig et al. 2016, Smith et al. 2016). Furthermore, the beneficial impacts of the LED scenario on a range of other sustainable development goals are also shown, demonstrating that efficiency of energy services provision plays a critical role in reaching low-energy futures without compromising increased living standards in the Global South, while at the same time reducing adverse social and environmental impacts of climate mitigation strategies that focus predominantly on large-scale supply-side transformations. The research is published in a peer-reviewed article in Nature Energy (Grubler et al. 2018) with ample supplementary information. Water consumption and withdrawal data are published in Parkinson et al. (2018). The data is available for download from the LED Database. The content of the LED database and any derived analysis may only be used for non-commercial scientific publications, articles, educational purposes, figures and data tables provided that the source reference pursuant to section 'Required citation' is included and all relevant publications are correctly cited. Partial reproductions of the database content may be stored in online repositories, if this is necessary to comply with a journal's data archiving and access requirements. Such reproductions must be limited to the scope of the manuscript in question, and must include a hyperlink to the source database hosted at https://db1.ene.iiasa.ac.at/LEDDB and the download date from the source database. However, any wholesale duplication, translation, reworking, processing, arrangement, transformation, or reproduction through the internet or any other channels, of the https://db1.ene.iiasa.ac.at/LEDDEB for commercial or non-commercial purposes is not permitted without the explicit written approval of IIASA.

Analyzing historical maps and Landsat imagery indicates that coastal glaciers in the western Prince William Sound (PWS) have retreated since the end of the Little Ice Age, exhibiting accelerated retreat after the mid-2000s. A multi-temporal inventory of 43 glaciers was developed using historical field observations, topographic maps, and Landsat imagery. Area and length measurements are derived from digitized outlines, and center lines are derived from a semi-automatic, GIS-based algorithm. Land-based glaciers retreated at a peak rate of 48 m a-1 from the mid-2000s to 2018, more than doubling the average rate of retreat (22 m a-1) for the preceding 50-year period. From ~1950 to 2018, the total area of land-based glaciers decreased by 228 km2, with 36% of the glacier loss occurring after the mid-2000s. Simple upscaling of area and volume changes to unmeasured glaciers across the entire PWS resulted in an estimated aggregate glacier mass loss of 379 Gt, equivalent to a 1.047 mm rise in sea level from the 1950s to 2018. Tidewater glaciers respond asynchronously with differing periods of peak area and length loss and lower average rate of retreat compared to land-based glaciers. Glacier retreat correlates with increased summer and winter temperatures and decreased winter precipitation. I manually digitize outlines from historical maps, topographic maps, and Landsat images for glaciers 10 km2 or larger. Each study glacier is identified by a project identification number; Global Land Ice Measurements (GLIMS) and Randolph Glacier Inventory (RGI) identification numbers; and glacier name, if available. I manually digitize and adjust glacier boundaries based on the interpretation of 1950/57 topographic maps and Landsat images acquired in 1973/75, 1986, 1994, 2004/06, and 2018. Glacier length changes are measured from the intersection of the centerline with each glacier terminus. I repeat measurements for 1950/57 topographic maps and the Landsat images acquired in 1973/75, 1986, 1994, 2004/06, and 2018, resulting in a glacier length change chronology for each glacier. Glacier outlines are available from the GLIMS database (www.glims.org). See disclaimer in the "Data" section.

Detailed experimental conditions and measured elastic wave velocities and ETC values at different methane hydrate saturations.

# Heatwave grazing kelp microbes sequences [https://doi.org/10.5061/dryad.vhhmgqns7](https://doi.org/10.5061/dryad.vhhmgqns7) We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp *Ecklonia radiata*’s microbiota in sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid *Sargassum linearifolium*’s microbiota was unaffected by temperature\*.\* Consumption by the tropical sea-urchin *Tripneustes gratilla* was greater on *Ecklonia* where the microbiota had been altered by higher temperatures, while *Sargassum*’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. ## Description of the data and file structure Juvenile *Ecklonia radiata* (length \~15cm; N=140) and *Sargassum linearifolium* (length \~10cm; N=140) were collected haphazardly (>2m apart) at Cronulla rocky reef, Sydney, Australia. We exposed seaweeds to one of four temperature profiles over seven weeks: Ambient, Warming, marine heatwave MHW, MHW variable. After seven weeks of exposure to temperature treatments, a subset of individuals from each species/temperature treatment (*Ecklonia*: n=4-6; *Sargassum*: n=3) were randomly selected. Sterile cotton swabs were used to sample microbiota on algal surfaces, with the same area (20cm2) and swabbing time (30s) sampled for all individuals. Swabs were immediately stored in liquid nitrogen and transported to the University of New South Wales (UNSW, Sydney) and kept at -80°C until DNA extraction. DNA was extracted from swabs using the DNeasy PowerSoil Kit (Qiagen) and amplified using Polymerase Chain Reaction (PCR) primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 785R (5’-GACTACHVGGGTATCTAATCC-3’), targeting the 16S rRNA gene V3-V4 regions (bacteria and archaea), and were sequenced with a 2x250bp MiSeq reagent kit v2 on the Illumina MiSeq2000 Platform. The range-expansion of tropical herbivores due to ocean warming can profoundly alter temperate reef communities by overgrazing the seaweed forests that underpin them. Such ecological interactions may be mediated by changes to seaweed-associated microbiota in response to warming, but empirical evidence demonstrating this is rare. We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp Ecklonia radiata’s microbiotain sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid Sargassum linearifolium’s microbiota was unaffected by temperature. Consumption by the tropical sea-urchin Tripneustes gratilla was greater on Ecklonia where the microbiota had been altered by higher temperatures, while Sargassum’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. Effects of warming and MHWs on seaweed holobionts (host plus its microbiota) are likely species-specific. The effect of increased temperature on Ecklonia’s microbiota and subsequent increased consumption suggest that changes to kelp microbiota may underpin kelp-herbivore interactions, providing novel insights into potential mechanisms driving change in species’ interactions in warming oceans.

Workers employed for a hydropower project in Fujian province had their dormitories close to the river. On 7th May 2016, a landslide was triggered through heavy rainfall. More than 40 construction workers died in this event. The pre-event acquisition is from 7th February 2016 (Sentinel-2) and the post-event acquisition is from 26th July 2016 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image. Contains modified Copernicus Sentinel data (2016) {"references": ["http://blogs.agu.org/landslideblog/2016/05/10/chitan-hydropower-landslide/"]}

Public acceptance (PA) is nowadays essential for the sustainable development of nuclear energy and becomes animportant issue for research community. Although some studies had investigated the factors influencing PA ofnuclear energy, few researches were founded to verify the impact of cultural values. This research proposed atheoretical model to explore how individualism and collectivism, as an important dimension of culture, moderated the relevance between perceived risk/benefit and PA. A questionnaire survey was conducted nationwidein China whose number of under-construction nuclear power plants ranks first in the world, and received 887valid responses. The analysis of moderating effect showed individualism weakened the relevance betweenperceived benefit and PA, whereas collectivism had no significant moderating role on the relevance betweenperceived benefit and PA. Collectivism strengthened the relevance between perceived risk and PA, whereasindividualism had no significant moderating role on the relevance between perceived risk and PA. Moreover,perceived benefit was confirmed to be a more important predictor for PA than perceived risk. The abovementioned findings could not only provide new insights that help to understand the difference in energy policiesbetween China and the developed countries, but also provide new reference and guidance for the future policymaking. Public acceptance (PA) is nowadays essential for the sustainable development of nuclear energy and becomes animportant issue for research community. Although some studies had investigated the factors influencing PA ofnuclear energy, few researches were founded to verify the impact of cultural values. This research proposed atheoretical model to explore how individualism and collectivism, as an important dimension of culture, moderated the relevance between perceived risk/benefit and PA. A questionnaire survey was conducted nationwidein China whose number of under-construction nuclear power plants ranks first in the world, and received 887valid responses. The analysis of moderating effect showed individualism weakened the relevance betweenperceived benefit and PA, whereas collectivism had no significant moderating role on the relevance betweenperceived benefit and PA. Collectivism strengthened the relevance between perceived risk and PA, whereasindividualism had no significant moderating role on the relevance between perceived risk and PA. Moreover,perceived benefit was confirmed to be a more important predictor for PA than perceived risk. The abovementioned findings could not only provide new insights that help to understand the difference in energy policiesbetween China and the developed countries, but also provide new reference and guidance for the future policymaking.

Description: The eddy covariance technique was used to record continuous, non-invasive measurements of CO2, H2O and energy exchange between the ecosystem and the atmosphere. The measuring system consists of a semi-open path infrared gas analyser LI-7200 (LI-COR, USA), and a CSAT3 Sonic Anemometer (Campbell Scientific, USA) at a measuring height of 52 m over a canopy height of ~25 m. Data were recorded at a frequency of 20 Hz that was treated using the post-processing software EddyPro® (v.7.0.6; www.licor.com/eddypro) to compute fluxes for each 30-minute averaging period. To treat the raw fluxes, primary data processing steps were applied, including spike removal (Vickers, 1997 J Atmos Ocean Technol), coordinate rotation, block averaging detrending of CO2, H2O and sonic temperature, time lag compensation using covariance maximisation detection method, random uncertainty estimation (Finkelstein et al. 2001 Journal of Geophysical Research Atmospheres), computation of turbulent fluxes and mean fluxes, spectral corrections (Moncrieff et al. 1997 J Hydrol Amst) using correction of low-pass filtering effects, planar fit rotation (Wilczak et al. 2001 Boundary Layer Meteorol) and quality flagging policy (Göckede et al. 2006 Boundary Layer Meteorol). Eddy covariance meteorological data from above and below canopy is available at DOI 10.5281/zenodo.3888374. Cells with -9999 represent not enough data collected, which can be regarded as NA. This data has been collected over a heavily logged landscape between 2012 - 2018, please note 2016 was removed from this dataset. Before 2015, the landscape was ~10 years recovering from it's previous round of logging (four times logged). During 2015 the landscape was salvaged logged, removing 75% of tree stand basal area. The first data sheet, named "Raw_data" contains all raw fluxes that have been treated by EddyPro, which have not been filtered or quality controlled. The second sheet, named "Daily_fluxes" contains daily mean fluxes of net ecosystem CO2 exchange (NEE), ecosystem respirationn (Reco) and gross primary productivity and their associated standard errors. Net ecosystem CO2 exchange (NEE) was calculated by adding the estimated CO2 storage flux to the observed CO2 flux. Data was subjecto quality control including the removal of quality flags 4 and 5 (Göckede et al. 2006 Boundary Layer Meteorol) and the application of a mean u* threshold of >0.29 m s-1 to the dataset, as established using the package "REddyProc" (v.1.2; (Wultzer et al. 2019 Biogeosciences)) in based on the Moving Point Method (Reichstein et al. 2005, GCB). Data was subsequently gap filled and partitioned, as descripted within the variable methods of this sheet. This data was part of an analysis of carbon fluxes within three periods of data collection: in 2012 – 2013, which captured the four-times logged ecosystem ~10 years after its previous round of logging, in 2015 during a new round of active salvage logging, and in 2017 – 2018 when the ecosystem was recovery 2-3 years after the salvage logging. Days with large standard errors for Reco (> ± 5 µmol m−2 s−1) were deemed as bad quality and removed from the dataset and we used only days that had four or more observed half-hourly values of NEE. Of the final dataset , 29.5% of the half-hourly values are original observed fluxes, and 70.5% gap-filled. Of the 455 days remaining after all filtering processes were applied, 65 days were during the 10-years recovery phase (2012-2013), 100 during the active salvage logging (2015) and 290 during the 2-3 years recovery from active salvage logging phase (2017-2018). Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_EC_byYear.xlsx SAFE_EC_byYear.xlsx This file contains dataset metadata and 6 data tables: Raw_data_2012_2013 (described in worksheet Raw_data_2012_2013) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. Number of fields: 105 Number of data rows: 24213 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_h2o_flux: Random error of CO2 flux (Field type: numeric) H_strg: Estimate of storage sensible heat flux (Field type: numeric) LE_strg: Estimate of storage latent heat flux (Field type: numeric) co2_strg: Estimate of storage CO2 flux (Field type: numeric) h2o_strg: Estimate of storage H20 flux (Field type: numeric) co2_v.adv: Estimate of vertical advection flux of CO2 (Field type: numeric) h2o_v.adv: Estimate of vertical advection flux of H20 (Field type: numeric) co2_molar_density: Measured or estimated molar density of gas (Field type: numeric) co2_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) co2_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) co2_time_lag: Time lag used to synchronize gas time series (Field type: numeric) co2_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) h2o_molar_density: Measured or estimated molar density of gas (Field type: numeric) h2o_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) h2o_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) h2o_time_lag: Time lag used to synchronize gas time series (Field type: numeric) h2o_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) sonic_temperature: Mean temperature of ambient air as measured by the anemometer (Field type: numeric) air_temperature: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric) air_pressure: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric) air_density: Density of ambient air (Field type: numeric) air_heat_capacity: Specific heat at constant pressure of ambient air (Field type: numeric) air_molar_volume: Molar volume of ambient air (Field type: numeric) ET: Evapotranspiration flux (Field type: numeric) water_vapor_density: Ambient mass density of water vapor (Field type: numeric) e: Ambient water vapor partial pressure (Field type: numeric) es: Ambient water vapor partial pressure at saturation (Field type: numeric) specific_humidity: Ambient specific humidity on a mass basis (Field type: numeric) RH: Ambient relative humidity (Field type: numeric) VPD: Ambient water vapor pressure deficit (Field type: numeric) Tdew: Ambient dew point temperature (Field type: numeric) u_unrot: Wind component along the u anemometer axis (Field type: numeric) v_unrot: Wind component along the v anemometer axis (Field type: numeric) w_unrot: Wind component along the w anemometer axis (Field type: numeric) u_rot: Rotated u wind component (mean wind speed) (Field type: numeric) v_rot: Rotated v wind component (should be zero) (Field type: numeric) w_rot: Rotated w wind component (should be zero) (Field type: numeric) wind_speed: Mean wind speed (Field type: numeric) max_wind_speed: Maximum instantaneous wind speed (Field type: numeric) wind_dir: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric) yaw: First rotation angle (Field type: numeric) pitch: Second rotation angle (Field type: numeric) u.: Friction velocity (Field type: numeric) TKE: Turbulent kinetic energy (Field type: numeric) L: Monin-Obukhov length (Field type: numeric) X.z.d..L: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric) bowen_ratio: Sensible heat flux to latent heat flux ratio (Field type: numeric) T.: Scaling temperature (Field type: numeric) model: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric) x_peak: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric) x_offset: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric) x_10.: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_30.: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_50.: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_70.: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_90.: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric) un_Tau: Uncorrected momentum flux (Field type: numeric) Tau_scf: Spectral correction factor for momentum flux (Field type: numeric) un_H: Uncorrected sensible heat flux (Field type: numeric) H_scf: Spectral correction factor for sensible heat flux (Field type: numeric) un_LE: Uncorrected latent heat flux (Field type: numeric) LE_scf: Spectral correction factor for latent heat flux (Field type: numeric) un_co2_flux: Uncorrected gas flux (Field type: numeric) co2_scf: Spectral correction factor for gas flux (Field type: numeric) un_h2o_flux: Uncorrected gas flux (Field type: numeric) h2o_scf: Spectral correction factor for gas flux (Field type: numeric) spikes_hf: Hard flags for individual variables for spike test (Field type: numeric) amplitude_resolution_hf: Hard flags for individual variables for amplitude resolution (Field type: numeric) drop_out_hf: Hard flags for individual variables for drop-out test (Field type: numeric) absolute_limits_hf: Hard flags for individual variables for absolute limits (Field type: numeric) skewness_kurtosis_hf: Hard flags for individual variables for skewness and kurtosis (Field type: numeric) skewness_kurtosis_sf: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric) discontinuities_hf: Hard flags for individual variables for discontinuities test (Field type: numeric) discontinuities_sf: Soft flags for individual variables for discontinuities test (Field type: numeric) timelag_hf: Hard flags for gas concentration for time lag test (Field type: numeric) timelag_sf: Soft flags for gas concentration for time lag test (Field type: numeric) attack_angle_hf: Hard flags for gas concentration for time lag test (Field type: numeric) non_steady_wind_hf: Soft flags for gas concentration for time lag test (Field type: numeric) u_spikes: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric) v_spikes: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric) w_spikes: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric) ts_spikes: Number of spikes detected and eliminated for ts variable (Field type: numeric) co2_spikes: Number of spikes detected and eliminated for co2 variable (Field type: numeric) h2o_spikes: Number of spikes detected and eliminated for h2o variable (Field type: numeric) Raw_data_2014 (described in worksheet Raw_data_2014) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. There is a significant data gap, with some intermittent records available during the daytime, between 17/2/2014-17/06/2014 due to the problems in the power supply. Number of fields: 105 Number of data rows: 17520 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best,

Solar radiation over and under sea ice was measured by radiation station 2020R22, an autonomous platform, installed on drifting sea ice in the Arctic Ocean during MOSAiC (Leg 5) 2019/20. The resulting time series describes radiation measurements as a function of place and time between 21 August 2020 and 12 September 2020 in sample intervals of 10 minutes. The radiation measurements have been performed with spectral radiometers. All data are given in full spectral resolution interpolated to 1.0 nm, and integrated over the entire wavelength range (broadband, total: 320 to 950 nm). Two sensors, solar irradiance and upward reflected solar irradiance, were mounted on a on a platform about 1 m above the sea ice surface. The third sensor was mounted 0.5 m underneath the sea ice measuring the downward transmitted irradiance. The data set has been processed and contains quality flags for different kinds for erroneous data. Flag values are the sum of individual error codes. The value of 0 refers to no error. Quality flag, sun: If the suns position is close to the horizon, the radiometers measure a very noisy signal. Radiometer measurements and variables which are computed from them are flagged +1 if the sun elevation is below 10 degrees; +2 if the broad band albedo exceeds the threshold 1.05 .