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This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the high-resolution reconstruction.Time Range:1993.01-2018.12Region:GlobalLongitude:180°W~180°ELatitude:70°S~70°NParameters:SalinityHorizontal Resolution:0.25° × 0.25°Vertical Resolution:41 levels (1-2000 m)Temporal Resolution:monthlyStorage Format:netcdf This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the high-resolution reconstruction.Time Range:1993.01-2018.12Region:GlobalLongitude:180°W~180°ELatitude:70°S~70°NParameters:SalinityHorizontal Resolution:0.25° × 0.25°Vertical Resolution:41 levels (1-2000 m)Temporal Resolution:monthlyStorage Format:netcdf

This repository contains the dataset linked to the following publication: Article title: Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication Journal: Environmental Science & Technology DOI: 10.1021/acs.est.2c07181 Abstract: Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes. SYNOPSIS: Warming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation. Data description by Xiangzhen Kong (xzkong@niglas.ac.cn), 2023-02-20 ---Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8. File name: BAB_BA1_TN_mgL.obs (total nitrogen concentration) BAB_BA1_NH4_mgL.obs (ammonium nitrogen concentration) BAB_BA1_NO3_mgL.obs (nitrate nitrogen concentration) BAB_BA1_TP_mgL.obs (total phosphorus concentration) BAB_BA1_SRP_mgL.obs (Soluble reactive phosphorus concentration) BAB_BA1_DP_mgL.obs (dissolved P concentration) BAB_BA1_DOC_mgL.obs (Dissolved organic carbon concentration) BAB_BA1_Si_mgL.obs (dissolved silicon concentration) BAB_BA1_Chla_HPLC_DIN_mgL.obs (Chl-a concentration) ---CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: t_prof_file_barleber_ctm644.obs (water temperature) oxy_prof_file_barleber_ctm644 (Dissolved oxygen) turb_prof_file_barleber_ctm644.obs (Turbidity) chla_prof_file_barleber_ctm644.obs (Chl-a concentration) ---BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution File name: totalChla_prof_file_barleber_FP2101.obs (Chl-a concentration) bluegreen_prof_file_barleber_FP2101.obs (Blue-green algae Chl-a concentration) green_prof_file_barleber_FP2101.obs (Green algae Chl-a concentration) diatom_prof_file_barleber_FP2101.obs (Diatom Chl-a concentration)

This dataset provides grided species phenology (SP) maps of 24 woody plants and ground phenology (GP) maps of forests over China (18°N-54°N，72′°E-136°E) from 1951 to 2020, with a spatial resolution of 0.1° and a temporal resolution of 1 day. Three phenophases, namely the first leaf date (FLD), first flower date (FFD), and 100% leaf coloring date (LCD), were included for each species. Data Quality: The SP maps of 24 species are largely consistent with the in-situ observations in China, with an average error of 6.4, 7.5 and 10.8 days for FLD, FFD and LCD, respectively. The GP maps of forests have good consistency with the existing LSP products in China, particularly in DF areas, where the correlation coefficients between GP and LSP in FLD and LCD were 0.91 and 0.84, respectively, and the differences were 8.8 days and 15.1 days, respectively. Method: Based on the in-situ phenology observations from the Chinese Phenology Observation Network (CPON) in the past 70 years, this dataset employed three spring phenology models (Unichill, Unified and Temporal-Spatial Coupling) and two autumn phenology models (Multiple Regression, Temperature-Photoperiod) to simulate and upscale the phenology data on the national scale, and generate the SP maps of woody plants in China. Four aggregation methods (weighted average (mean), weighted percentile (pct50, pct20\80, pct10\90)) were used to generate the GP maps of forests in China based on the SP maps. The weight of each species was determined by the species distribution probability. Dataset composition: The dataset contains the yearly SP maps of 24 woody plants (China_SP.zip) and GP maps of forests(China_GP.zip) over China from 1951 to 2020, including spring FLD, FFD and autumn LCD. Each map is stored in a GeoTIFF formatted 16-bit signed integer file containing a raster with two dimensions (641 row × 361 column). Data files are named according to "China + phenophase (XXD) + species/method + year (YYYY)". For example, "China_FLD_Acer_pictum_2020.tif" is the SP map of Acer pictum’s FLD in 2020, and “China_FLD_mean_2020.tif” is the GP map of weighted averaged FLD in 2020. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is -1. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset provides grided species phenology (SP) maps of 24 woody plants and ground phenology (GP) maps of forests over China (18°N-54°N，72′°E-136°E) from 1951 to 2020, with a spatial resolution of 0.1° and a temporal resolution of 1 day. Three phenophases, namely the first leaf date (FLD), first flower date (FFD), and 100% leaf coloring date (LCD), were included for each species. Data Quality: The SP maps of 24 species are largely consistent with the in-situ observations in China, with an average error of 6.4, 7.5 and 10.8 days for FLD, FFD and LCD, respectively. The GP maps of forests have good consistency with the existing LSP products in China, particularly in DF areas, where the correlation coefficients between GP and LSP in FLD and LCD were 0.91 and 0.84, respectively, and the differences were 8.8 days and 15.1 days, respectively. Method: Based on the in-situ phenology observations from the Chinese Phenology Observation Network (CPON) in the past 70 years, this dataset employed three spring phenology models (Unichill, Unified and Temporal-Spatial Coupling) and two autumn phenology models (Multiple Regression, Temperature-Photoperiod) to simulate and upscale the phenology data on the national scale, and generate the SP maps of woody plants in China. Four aggregation methods (weighted average (mean), weighted percentile (pct50, pct20\80, pct10\90)) were used to generate the GP maps of forests in China based on the SP maps. The weight of each species was determined by the species distribution probability. Dataset composition: The dataset contains the yearly SP maps of 24 woody plants (China_SP.zip) and GP maps of forests(China_GP.zip) over China from 1951 to 2020, including spring FLD, FFD and autumn LCD. Each map is stored in a GeoTIFF formatted 16-bit signed integer file containing a raster with two dimensions (641 row × 361 column). Data files are named according to "China + phenophase (XXD) + species/method + year (YYYY)". For example, "China_FLD_Acer_pictum_2020.tif" is the SP map of Acer pictum’s FLD in 2020, and “China_FLD_mean_2020.tif” is the GP map of weighted averaged FLD in 2020. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is -1. The spatial reference system of the data is EPSG:4326 (WGS84).

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.CAS.FGOALS-g3.ssp245' 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 FGOALS-g3 climate model, released in 2017, includes the following components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 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.CAS.FGOALS-f3-L.ssp370' 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 FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84).

Climate trends during maize growing period and their impacts on maize yield in Southern hills was investigated. This dataset contains: 1) information of stations in cultivation region for maize in Southern hills; 2) Trend in temperature and its effect on yield in cultivation region for maize in Southern hills; 3) Trend in radiation and its effect on yield in cultivation region for maize in Southern hills; 4) Trend in precipitation and its effect on yield in cultivation region for maize in Southern hills. Climate trends during maize growing period and their impacts on maize yield in Southern hills was investigated. This dataset contains: 1) information of stations in cultivation region for maize in Southern hills; 2) Trend in temperature and its effect on yield in cultivation region for maize in Southern hills; 3) Trend in radiation and its effect on yield in cultivation region for maize in Southern hills; 4) Trend in precipitation and its effect on yield in cultivation region for maize in Southern hills.

Triplet-triplet annihilation (TTA) upconversion has shown promising potentials in the augmentation of solar energy conversion. However, challenging issues exist in improving TTA upconversion efficiencies in solid-states, one of which is the back energy transfer from upconverted singlet annihilators to sensitizers resulting in decreasing upconversion emission. Here we present a light-harvesting molecular wire consisting of dendrons with 9,10-diphenylanthracene derivatives (DPAEH) at the periphery and para-phenylene ethynylene oligomers (PPE) as the wire core. The peripheral DPAEH antenna funnels singlet excitonic energy to the wire on a 12 ps timescale. Incorporating the molecular wire into the TTA upconversion solid consisting of the DPAEH annihilator and the porphyrin sensitizer evidently improves the upconversion quantum yield from 1.5% to 2.7% upon 532 nm excitation by suppressing the back energy transfer from the singlet annihilator to the sensitizer. This finding offers a potential route to use singlet energy light-harvesting architecture for enhancing TTA upconversion. Triplet-triplet annihilation (TTA) upconversion has shown promising potentials in the augmentation of solar energy conversion. However, challenging issues exist in improving TTA upconversion efficiencies in solid-states, one of which is the back energy transfer from upconverted singlet annihilators to sensitizers resulting in decreasing upconversion emission. Here we present a light-harvesting molecular wire consisting of dendrons with 9,10-diphenylanthracene derivatives (DPAEH) at the periphery and para-phenylene ethynylene oligomers (PPE) as the wire core. The peripheral DPAEH antenna funnels singlet excitonic energy to the wire on a 12 ps timescale. Incorporating the molecular wire into the TTA upconversion solid consisting of the DPAEH annihilator and the porphyrin sensitizer evidently improves the upconversion quantum yield from 1.5% to 2.7% upon 532 nm excitation by suppressing the back energy transfer from the singlet annihilator to the sensitizer. This finding offers a potential route to use singlet energy light-harvesting architecture for enhancing TTA upconversion.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).