Abstract: Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Abstract: Abstract. This paper presents a state-of-the-art anthropogenic emission inventory developed for the European domain for an 18-year time series (2000–2017) at a 0.05∘ × 0.1∘ grid resolution, specifically designed to support air quality modelling. The main air pollutants are included: NOx, SO2, non-methane volatile organic compounds (NMVOCs), NH3, CO, PM10 and PM2.5, and also CH4. To stay as close as possible to the emissions as officially reported and used in policy assessment, the inventory uses the officially reported emission data by European countries to the UN Framework Convention on Climate Change, the Convention on Long-Range Transboundary Air Pollution and the EU National Emission Ceilings Directive as the basis where possible. Where deemed necessary because of errors, incompleteness or inconsistencies, these are replaced with or complemented by other emission data, most notably the estimates included in the Greenhouse gas Air pollution Interaction and Synergies (GAINS) model. Emissions are collected at the high sectoral level, distinguishing around 250 different sector–fuel combinations, whereafter a consistent spatial distribution is applied for Europe. A specific proxy is selected for each of the sector–fuel combinations, pollutants and years. Point source emissions are largely based on reported facility-level emissions, complemented by other sources of point source data for power plants. For specific sources, the resulting emission data were replaced with other datasets. Emissions from shipping (both inland and at sea) are based on the results from a separate shipping emission model where emissions are based on actual ship movement data, and agricultural waste burning emissions are based on satellite observations. The resulting spatially distributed emissions are evaluated against earlier versions of the dataset as well as against alternative emission estimates, which reveals specific discrepancies in some cases. Along with the resulting annual emission maps, profiles for splitting particulate matter (PM) and NMVOCs into individual components are provided, as well as information on the height profile by sector and temporal disaggregation down to the hourly level to support modelling activities. Annual grid maps are available in csv and NetCDF format (https://doi.org/10.24380/0vzb-a387, Kuenen et al., 2021).

Abstract: Abstract. There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale. These data can support a range of environmental services, since cities are places of intense resource consumption and waste generation and of concentrated infrastructure and human settlement exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data are also required to explore future urbanization pathways and urban design strategies in order to lock in long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability and mitigation role. To serve this purpose, we present a 100 m-resolution global map of local climate zones (LCZs), a universal urban typology that can distinguish urban areas on a holistic basis, accounting for the typical combination of micro-scale land covers and associated physical properties. The global LCZ map, composed of 10 built and 7 natural land cover types, is generated by feeding an unprecedented number of labelled training areas and earth observation images into lightweight random forest models. Its quality is assessed using a bootstrap cross-validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open-source data on surface cover, surface imperviousness, building height, and anthropogenic heat. As each LCZ type is associated with generic numerical descriptions of key urban canopy parameters that regulate atmospheric responses to urbanization, the availability of this globally consistent and climate-relevant urban description is an important prerequisite for supporting model development and creating evidence-based climate-sensitive urban planning policies. This dataset can be downloaded from https://doi.org/10.5281/zenodo.6364594 (Demuzere et al., 2022a).

Abstract: Abstract. Monitoring the thermal state of permafrost (TSP) is important in many environmental science and engineering applications. However, such data are generally unavailable, mainly due to the lack of ground observations and the uncertainty of traditional physical models. This study produces novel permafrost datasets for the Northern Hemisphere (NH), including predictions of the mean annual ground temperature (MAGT) at the depth of zero annual amplitude (DZAA) (approximately 3 to 25 m) and active layer thickness (ALT) with 1 km resolution for the period of 2000–2016, as well as estimates of the probability of permafrost occurrence and permafrost zonation based on hydrothermal conditions. These datasets integrate unprecedentedly large amounts of field data (1002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modeling with an ensemble strategy. Thus, the resulting data are more accurate than those of previous circumpolar maps (bias = 0.02±0.16 ∘C and RMSE = 1.32±0.13 ∘C for MAGT; bias = 2.71±16.46 cm and RMSE = 86.93±19.61 cm for ALT). The datasets suggest that the areal extent of permafrost (MAGT ≤0 ∘C) in the NH, excluding glaciers and lakes, is approximately 14.77 (13.60–18.97) × 106 km2 and that the areal extent of permafrost regions (permafrost probability >0) is approximately 19.82×106 km2. The areal fractions of humid, semiarid/subhumid, and arid permafrost regions are 51.56 %, 45.07 %, and 3.37 %, respectively. The areal fractions of cold (≤-3.0 ∘C), cool (−3.0 ∘C to −1.5 ∘C), and warm (>-1.5 ∘C) permafrost regions are 37.80 %, 14.30 %, and 47.90 %, respectively. These new datasets based on the most comprehensive field data to date contribute to an updated understanding of the thermal state and zonation of permafrost in the NH. The datasets are potentially useful for various fields, such as climatology, hydrology, ecology, agriculture, public health, and engineering planning. All of the datasets are published through the National Tibetan Plateau Data Center (TPDC), and the link is https://doi.org/10.11888/Geocry.tpdc.271190 (Ran et al., 2021a).

Abstract: Abstract. Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60 000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.5281/zenodo.6163413 (Wang et al., 2022). GeoDAR v1.0 holds 22 560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24 783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21 515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved ∼ 40 % of the records in the WRD, which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.

Abstract: Abstract. In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N2O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).

Abstract: Abstract. The main objective of wastewater treatment plants (WWTPs) is to remove pathogens, nutrients, organics, and other pollutants from wastewater. After these contaminants are partially or fully removed through physical, biological, and/or chemical processes, the treated effluents are discharged into receiving waterbodies. However, since WWTPs cannot remove all contaminants, especially those of emerging concern, they inevitably represent concentrated point sources of residual contaminant loads into surface waters. To understand the severity and extent of the impact of treated-wastewater discharges from such facilities into rivers and lakes, as well as to identify opportunities of improved management, detailed information about WWTPs is required, including (1) their explicit geospatial locations to identify the waterbodies affected and (2) individual plant characteristics such as the population served, flow rate of effluents, and level of treatment of processed wastewater. These characteristics are especially important for contaminant fate models that are designed to assess the distribution of substances that are not typically included in environmental monitoring programs. Although there are several regional datasets that provide information on WWTP locations and characteristics, data are still lacking at a global scale, especially in developing countries. Here we introduce a spatially explicit global database, termed HydroWASTE, containing 58 502 WWTPs and their characteristics. This database was developed by combining national and regional datasets with auxiliary information to derive or complete missing WWTP characteristics, including the number of people served. A high-resolution river network with streamflow estimates was used to georeference WWTP outfall locations and calculate each plant's dilution factor (i.e., the ratio of the natural discharge of the receiving waterbody to the WWTP effluent discharge). The utility of this information was demonstrated in an assessment of the distribution of treated wastewater at a global scale. Results show that 1 200 000 km of the global river network receives wastewater input from upstream WWTPs, of which more than 90 000 km is downstream of WWTPs that offer only primary treatment. Wastewater ratios originating from WWTPs exceed 10 % in over 72 000 km of rivers, mostly in areas of high population densities in Europe, the USA, China, India, and South Africa. In addition, 2533 plants show a dilution factor of less than 10, which represents a common threshold for environmental concern. HydroWASTE can be accessed at https://doi.org/10.6084/m9.figshare.14847786.v1 (Ehalt Macedo et al., 2021).

Abstract: Abstract. Accurately mapping impervious-surface dynamics has great scientific significance and application value for research on urban sustainable development, the assessment of anthropogenic carbon emissions and global ecological-environment modeling. In this study, a novel and automatic method of combining the advantages of spectral-generalization and automatic-sample-extraction strategies was proposed, and then an accurate global 30 m impervious-surface dynamic dataset (GISD30) for 1985 to 2020 was produced using time-series Landsat imagery on the Google Earth Engine cloud computing platform. Firstly, the global training samples and corresponding reflectance spectra were automatically derived from prior global 30 m land-cover products after employing the multitemporal compositing method and relative radiometric normalization. Then, spatiotemporal adaptive classification models, trained with the migrated reflectance spectra of impervious surfaces from 2020 and transferred pervious-surface samples in each epoch for every 5∘×5∘ geographical tile, were applied to map the impervious surface in each period. Furthermore, a spatiotemporal-consistency correction method was presented to minimize the effects of independent classification errors and improve the spatiotemporal consistency of impervious-surface dynamics. Our global 30 m impervious-surface dynamic model achieved an overall accuracy of 90.1 % and a kappa coefficient of 0.865 using 23 322 global time-series validation samples. Cross-comparisons with five existing global 30 m impervious-surface products further indicated that our GISD30 dynamic product achieved the best performance in capturing the spatial distributions and spatiotemporal dynamics of impervious surfaces in various impervious landscapes. The statistical results indicated that the global impervious surface has doubled in the past 35 years, from 5.116×105 km2 in 1985 to 10.871×105 km2 in 2020, and Asia saw the largest increase in impervious surface area compared to other continents, with a total increase of 2.946×105 km2. Therefore, it was concluded that our global 30 m impervious-surface dynamic dataset is an accurate and promising product and could provide vital support in monitoring regional or global urbanization as well as in related applications. The global 30 m impervious-surface dynamic dataset from 1985 to 2020 generated in this paper is free to access at https://doi.org/10.5281/zenodo.5220816 (Liu et al., 2021b).

Abstract: Abstract. We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990–2019. We find that in 2019, global agri-food system emissions were 16.5 (95 %; CI range: 11–22) billion metric tonnes (Gt CO2 eq. yr−1), corresponding to 31 % (range: 19 %–43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate – from crop and livestock production processes including on-farm energy use – were 7.2 Gt CO2 eq. yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2 eq. yr−1; and emissions from pre- and post-production processes – manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal – were 5.8 Gt CO2 eq. yr−1. Over the study period 1990–2019, agri-food system emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAOSTAT data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased 9 %. In 2019, in terms of individual greenhouse gases (GHGs), pre- and post-production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre- and post-production processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open dumps. One key trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along the agri-food system, at global, regional and national scales. In fact, our data show that by 2019, pre- and post-production processes had overtaken farm gate processes to become the largest GHG component of agri-food system emissions in Annex I parties (2.2 Gt CO2 eq. yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2 eq. yr−1), becoming larger than emissions from land use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2 eq. yr−1), the USA (700 Mt CO2 eq. yr−1) and the EU-27 (600 Mt CO2 eq. yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (https://www.fao.org/faostat/en/#data/EM; FAO, 2021a), with annual updates.

Abstract: Abstract. This paper presents the new global Mesoscale Eddy Trajectory Atlases (META3.1exp DT all-satellites, https://doi.org/10.24400/527896/a01-2021.001, Pegliasco et al., 2021a; and META3.1exp DT two-satellites, https://doi.org/10.24400/527896/a01-2021.002, Pegliasco et al., 2021b), composed of eddy identifications and trajectories produced with altimetric maps. The detection method used is inherited from the py-eddy-tracker (PET) algorithm developed by Mason et al. (2014), and is optimized to efficiently manage large datasets, and thus long time series. These products are an improvement on the earlier META2.0 product, which was produced by SSALTO/DUACS and distributed by AVISO+ (https://aviso.altimetry.fr, last access: 8 March 2022) with support from CNES, in collaboration with Oregon State University and support from NASA, and based on the Chelton et al. (2011) code. META3.1exp provides supplementary eddy information, such as eddy shapes, eddy edges, maximum speed contours, and mean eddy speed profiles from the center to the periphery. The tracking algorithm is based on overlapping contours, includes virtual observations, and acts as a filter with respect to the shortest trajectories. The absolute dynamic topography (ADT) field is now used for eddy detection, instead of the previous sea level anomaly (SLA) maps, in order to better represent the dynamics in the more energetic oceanic regions and in the vicinity of coasts and islands. To evaluate the impact of the changes from META2.0 to META3.1exp, a comparison methodology has been applied. The similarity coefficient (SC) is based on the ratio of the eddy overlaps to their cumulative area, and allows for extensive comparison of the different datasets in terms of geographic distribution, statistics on the main physical characteristics, changes in the lifetimes of the trajectories, etc. After evaluating the impact of each change separately, we conclude that the major differences between META3.1exp and META2.0 are due to the change in the detection algorithm. META3.1exp contains smaller eddies and trajectories lasting at least 10 d; these were not available in the META2.0 product. Nevertheless, 55 % of the structures in META2.0 are similar to META3.1exp, thereby ensuring continuity between the two products and their physical characteristics. Geographically, the eddy distributions differ mainly in the strong current regions, where the mean dynamic topography (MDT) gradients are sharp. The additional information on the eddy contours allows for more accurate collocation of mesoscale structures with data from other sources, and so META3.1exp is recommended for multi-disciplinary application.

Abstract: Abstract. A multitude of physical and biological processes on which ecosystems and human societies depend are governed by the climate, and understanding how these processes are altered by climate change is central to mitigation efforts. We developed a set of climate-related variables at as yet unprecedented spatiotemporal detail as a basis for environmental and ecological analyses. We downscaled time series of near-surface relative humidity (hurs) and cloud area fraction (clt) under the consideration of orography and wind as well as near-surface wind speed (sfcWind) using the delta-change method. Combining these grids with mechanistically downscaled information on temperature, precipitation, and solar radiation, we then calculated vapor pressure deficit (vpd), surface downwelling shortwave radiation (rsds), potential evapotranspiration (pet), the climate moisture index (cmi), and site water balance (swb) at a monthly temporal and 30 arcsec spatial resolution globally from 1980 until 2018 (time-series variables). At the same spatial resolution, we further estimated climatological normals of frost change frequency (fcf), snow cover days (scd), potential net primary productivity (npp), growing degree days (gdd), and growing season characteristics for the periods 1981–2010, 2011–2040, 2041–2070, and 2071–2100, considering three shared socioeconomic pathways (SSP126, SSP370, SSP585) and five Earth system models (projected variables). Time-series variables showed high accuracy when validated against observations from meteorological stations and when compared to alternative products. Projected variables were also highly correlated with observations, although some variables showed notable biases, e.g., snow cover days. Together, the CHELSA-BIOCLIM+ dataset presented here (https://doi.org/10.16904/envidat.332, Brun et al., 2022) allows improvement to our understanding of patterns and processes that are governed by climate, including the impact of recent and future climate changes on the world's ecosystems and the associated services on societies.

Abstract: Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution with global coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination, indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in each pixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhang et al., 2021).

Abstract: Abstract. The injection of sulfur into the stratosphere by volcanic eruptions is the dominant driver of natural climate variability on interannual to multidecadal timescales. Based on a set of continuous sulfate and sulfur records from a suite of ice cores from Greenland and Antarctica, the HolVol v.1.0 database includes estimates of the magnitudes and approximate source latitudes of major volcanic stratospheric sulfur injection (VSSI) events for the Holocene (from 9500 BCE or 11 500 years BP to 1900 CE), constituting an extension of the previous record by 7000 years. The database incorporates new-generation ice-core aerosol records with a sub-annual temporal resolution and a demonstrated sub-decadal dating accuracy and precision. By tightly aligning and stacking the ice-core records on the WD2014 chronology from Antarctica, we resolve long-standing inconsistencies in the dating of ancient volcanic eruptions that arise from biased (i.e., dated too old) ice-core chronologies over the Holocene for Greenland. We reconstruct a total of 850 volcanic eruptions with injections in excess of 1 teragram of sulfur (Tg S); of these eruptions, 329 (39 %) are located in the low latitudes with bipolar sulfate deposition, 426 (50 %) are located in the Northern Hemisphere extratropics (NHET) and 88 (10 %) are located in the Southern Hemisphere extratropics (SHET). The spatial distribution of the reconstructed eruption locations is in agreement with prior reconstructions for the past 2500 years. In total, these eruptions injected 7410 Tg S into the stratosphere: 70 % from tropical eruptions and 25 % from NH extratropical eruptions. A long-term latitudinally and monthly resolved stratospheric aerosol optical depth (SAOD) time series is reconstructed from the HolVol VSSI estimates, representing the first Holocene-scale reconstruction constrained by Greenland and Antarctica ice cores. These new long-term reconstructions of past VSSI and SAOD variability confirm evidence from regional volcanic eruption chronologies (e.g., from Iceland) in showing that the Early Holocene (9500–7000 BCE) experienced a higher number of volcanic eruptions (+16 %) and cumulative VSSI (+86 %) compared with the past 2500 years. This increase coincides with the rapid retreat of ice sheets during deglaciation, providing context for potential future increases in volcanic activity in regions under projected glacier melting in the 21st century. The reconstructed VSSI and SAOD data are available at https://doi.org/10.1594/PANGAEA.928646 (Sigl et al., 2021).

Abstract: Abstract. Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage of the Earth's land surface rose from 0.22 % to 0.69 % between 1992 and 2020. Urban dynamics over the past 3 decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021).

Abstract: Abstract. Developing a big data analytics framework for generating the Long-term Gap-free High-resolution Air Pollutant concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and Earth system science analysis. By synergistically integrating multimodal aerosol data acquired from diverse sources via a tensor-flow-based data fusion method, a gap-free aerosol optical depth (AOD) dataset with a daily 1 km resolution covering the period of 2000–2020 in China was generated. Specifically, data gaps in daily AOD imageries from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on a set of AOD data tensors acquired from diverse satellites, numerical analysis, and in situ air quality measurements via integrative efforts of spatial pattern recognition for high-dimensional gridded image analysis and knowledge transfer in statistical data mining. To our knowledge, this is the first long-term gap-free high-resolution AOD dataset in China, from which spatially contiguous PM2.5 and PM10 concentrations were then estimated using an ensemble learning approach. Ground validation results indicate that the LGHAP AOD data are in good agreement with in situ AOD observations from the Aerosol Robotic Network (AERONET), with an R of 0.91 and RMSE equaling 0.21. Meanwhile, PM2.5 and PM10 estimations also agreed well with ground measurements, with R values of 0.95 and 0.94 and RMSEs of 12.03 and 19.56 µg m−3, respectively. The LGHAP provides a suite of long-term gap-free gridded maps with a high resolution to better examine aerosol changes in China over the past 2 decades, from which three major variation periods of haze pollution in China were revealed. Additionally, the proportion of the population exposed to unhealthy PM2.5 increased from 50.60 % in 2000 to 63.81 % in 2014 across China, which was then reduced drastically to 34.03 % in 2020. Overall, the generated LGHAP dataset has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environmental management. The daily resolution AOD, PM2.5, and PM10 datasets are publicly available at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 (Bai et al., 2021b), and https://doi.org/10.5281/zenodo.5652263 (Bai et al., 2021c), respectively. Monthly and annual datasets can be acquired from https://doi.org/10.5281/zenodo.5655797 (Bai et al., 2021d) and https://doi.org/10.5281/zenodo.5655807 (Bai et al., 2021e), respectively. Python, MATLAB, R, and IDL codes are also provided to help users read and visualize these data.

Abstract: Abstract. This work introduces the S2M (SAFRAN–SURFEX/ISBA–Crocus–MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2021. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2021) and the best possible set of available in situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset, available at https://doi.org/10.25326/37#v2020.2. The S2M data are provided by Météo-France – CNRS, CNRM, Centre d'Études de la Neige, through AERIS (Vernay et al., 2022).

Abstract: Abstract. The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2022 is an update of the previous version, GLODAPv2.2021 (Lauvset et al., 2021). The major changes are as follows: data from 96 new cruises were added, data coverage was extended until 2021, and for the first time we performed secondary quality control on all sulfur hexafluoride (SF6) data. In addition, a number of changes were made to data included in GLODAPv2.2021. These changes affect specifically the SF6 data, which are now subjected to secondary quality control, and carbon data measured on board the RV Knorr in the Indian Ocean in 1994–1995 which are now adjusted using certified reference material (CRM) measurements made at the time. GLODAPv2.2022 includes measurements from almost 1.4 million water samples from the global oceans collected on 1085 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, chlorofluorocarbon-11 (CFC-11), CFC-12, CFC-113, CCl4, and SF6) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 96 new cruises were derived by comparing those data with the data from the 989 quality-controlled cruises in the GLODAPv2.2021 data product using crossover analysis. SF6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO2) chemistry comparisons to estimates based on empirical algorithms provided additional context for adjustment decisions. The adjustments that we applied are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon and Acidification Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2_2022/, last access: 15 August 2022). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/1f4w-0t92 (Lauvset et al., 2022). These bias-adjusted product files also include significant ancillary and approximated data, which were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2022 methods and provides a broad overview of the secondary quality control procedures and results.

Abstract: Abstract. High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, land cover type, topography and soil properties as predictors. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimation accuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4, and SoMo.ml. However, the high errors of soil moisture are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological, meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

Abstract: Abstract. With rapid population growth and socioeconomic development over the last century, a great number of dams/reservoirs have been constructed globally to meet various needs. China has strong economical and societal demands for constructing dams and reservoirs. The official statistics reported more than 98 000 dams/reservoirs in China, including nearly 40 % of the world's largest dams. Despite the availability of several global-scale dam/reservoir databases (e.g., the Global Reservoir and Dam database (GRanD), the GlObal geOreferenced Database of Dams (GOODD), and the Georeferenced global Dams And Reservoirs (GeoDAR)), these databases have insufficient coverage of the reservoirs in China, especially for small or newly constructed ones. The lack of reservoir information impedes the estimation of water budgets and the evaluation of dam impacts on hydrologic and nutrient fluxes for China and its downstream countries. Therefore, we presented the China Reservoir Dataset (CRD), which contains 97 435 reservoir polygons and fundamental attribute information (e.g., name and storage capacity) based on existing dam/reservoir products, national basic geographic datasets, multi-source open map data, and multi-level governmental yearbooks and databases. The reservoirs compiled in the CRD have a total maximum water inundation area of 50 085.21 km2 and a total storage capacity of about 979.62 km3 (924.96–1060.59 km3). The quantity of reservoirs decreases from the southeast to the northwest, and the density hotspots mainly occur in hilly regions and large plains, with the Yangtze River basin dominating in reservoir count, area, and storage capacity. We found that these spatial accumulations of reservoirs are closely related to China's socioeconomic development and the implementation of major policies. Finally, we presented the comparison of the CRD with GOODD, GeoDAR, and GRanD databases. The CRD has significantly increased the reservoir count, area, and storage capacity in China, especially for reservoirs smaller than 1 km2. The CRD database provides more comprehensive reservoir spatial and attribute information and is expected to benefit water resources managements and the understanding of ecological and environmental impacts of dams across China and its affected transboundary basins. The CRD database is publicly available at https://doi.org/10.5281/zenodo.6984619 (Song et al., 2022).

Abstract: Abstract. Biogenic volatile organic compounds (BVOCs) emitted from the terrestrial vegetation into the Earth's atmosphere play an important role in atmospheric chemical processes. Gridded information of their temporal and spatial distribution is therefore needed for proper representation of the atmospheric composition by the air quality models. Here we present three newly developed high-resolution global emission inventories of the main BVOC species including isoprene, monoterpenes, sesquiterpenes, methanol, acetone and ethene. Monthly mean and monthly averaged daily profile emissions were calculated by the Model of Emission of Gases and Aerosols from Nature (MEGANv2.1) driven by meteorological reanalyses of the European Centre for Medium-Range Weather Forecasts for the period of 2000–2019. The dataset CAMS-GLOB-BIOv1.2 is based on ERA-Interim meteorology (0.5∘ × 0.5∘ horizontal spatial resolution); the datasets CAMS-GLOB-BIOv3.0 and v3.1 were calculated with ERA5 (both 0.25∘ × 0.25∘ horizontal spatial resolution). Furthermore, European isoprene emission potential data were updated using high-resolution land cover maps and detailed information of tree species composition and emission factors from the EMEP MSC-W model system. Updated isoprene emissions are included in the CAMS-GLOB-BIOv3.1 dataset. The effect of annually changing land cover on BVOC emissions is captured by the CAMS-GLOB-BIOv3.0 as it was calculated with land cover data provided by the Climate Change Initiative of the European Space Agency (ESA-CCI). The global total annual BVOC emissions averaged over the simulated period vary between the datasets from 424 to 591 Tg (C) yr−1, with isoprene emissions from 299.1 to 440.5 Tg (isoprene) yr−1. Differences between the datasets and variation in their emission estimates provide the emission uncertainty range and the main sources of uncertainty, i.e. meteorological inputs, emission potential data and land cover description. The CAMS-GLOB-BIO time series of isoprene and monoterpenes were compared to other available data. There is a general agreement in an interannual variability in the emission estimates, and the values fall within the uncertainty range. The CAMS-GLOB-BIO datasets (CAMS-GLOB-BIOv1.2, https://doi.org/10.24380/t53a-qw03, Sindelarova et al., 2021a; CAMS-GLOB-BIOv3.0, https://doi.org/10.24380/xs64-gj42, Sindelarova et al., 2021b; CAMS-GLOB-BIOv3.1, https://doi.org/10.24380/cv4p-5f79, Sindelarova et al., 2021c) are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: June 2021).

Abstract: Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).

Abstract: Abstract. Active faults are those faults on which movement is possible in the future. This draws particular attention to active faults in geodynamic studies and seismic hazard assessment. Here, we present a high-detail continental-scale geodatabase: The Active Faults of Eurasia Database (AFEAD). It comprises 48 205 objects stored in shapefile format with spatial detail sufficient for a 1 : 1 000 000 map scale. The fault sense, a rank of confidence in activity, a rank of slip rate, and a reference to source publications are provided for each database entry. Where possible, this information is supplemented by a fault name, fault zone name, abbreviated fault parameters (e.g., slip rate, age of the last motion, and total offset), and text information from the sources. The database was collected from 612 published sources, including regional maps, databases, and research papers. AFEAD facilitates a spatial search for local studies. It provides sufficient detail for planning a study of a particular fault system and guides deeper bibliographical investigations. This scenario is particularly significant for vast central and northern Asian areas, where most studies are available only in Russian and hard copy. Moreover, the database model provides the basis for regional- and continental-scale integrative studies based on geographic information systems (GISs). The database is available at https://doi.org/10.13140/RG.2.2.25509.58084 (Bachmanov et al., 2022) and via web map at http://neotec.ginras.ru/index/mapbox/database_map.html (last access: 5 May 2022). Database representations and supplementary data are hosted at http://neotec.ginras.ru/index/english/database_eng.html (last access: 5 May 2022).

Abstract: Abstract. Ecosystems in arid areas are fragile and are easily disturbed by various natural and human factors. As natural tracers widely exist in nature, stable isotopes can be valuable for studying environmental change and the water cycle. From 2015 to 2020, we took the Shiyang River basin, which has the highest utilization rate of water resources and the most prominent contradiction of water use, as a typical demonstration basin to establish and improve the isotope hydrology observation system. The data in the observation system are classified by water type (precipitation, river water, lake water, groundwater, soil water, and plant water). Six observation systems with stable isotopes as the main observation elements have been built. These include river source region, oasis region, reservoir channel system region, oasis farmland region, ecological engineering construction region, and salinization process region; meteorological and hydrological data have also been collected. We will gradually improve the various observation systems, increase the data of observation sites, and update the data set yearly. We can use these data to research the continental river basin ecological hydrology, such as surface water evaporation loss, landscape river water cycle impact of the dam, dam water retention time, oasis farmland irrigation methods, and the atmosphere, such as the contribution of inland water circulation to inland river precipitation, climate transformation, below-cloud evaporation effect, and extreme climate events, which provides a scientific basis for water resources utilization and ecological environment restoration in the arid area. The data sets are available at https://doi.org/10.17632/vhm44t74sy.1 (Zhu, 2022).

Abstract: Abstract. Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022​​​​​​​), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al., 2021).

Abstract: Abstract. Quantitative reconstructions of past land cover are necessary to determine the processes involved in climate–human–land-cover interactions. We present the first temporally continuous and most spatially extensive pollen-based land-cover reconstruction for Europe over the Holocene (last 11 700 cal yr BP). We describe how vegetation cover has been quantified from pollen records at a 1∘ × 1∘ spatial scale using the “Regional Estimates of VEgetation Abundance from Large Sites” (REVEALS) model. REVEALS calculates estimates of past regional vegetation cover in proportions or percentages. REVEALS has been applied to 1128 pollen records across Europe and part of the eastern Mediterranean–Black Sea–Caspian corridor (30–75∘ N, 25∘ W–50∘ E) to reconstruct the percentage cover of 31 plant taxa assigned to 12 plant functional types (PFTs) and 3 land-cover types (LCTs). A new synthesis of relative pollen productivities (RPPs) for European plant taxa was performed for this reconstruction. It includes multiple RPP values (≥2 values) for 39 taxa and single values for 15 taxa (total of 54 taxa). To illustrate this, we present distribution maps for five taxa (Calluna vulgaris, Cerealia type (t)., Picea abies, deciduous Quercus t. and evergreen Quercus t.) and three land-cover types (open land, OL; evergreen trees, ETs; and summer-green trees, STs) for eight selected time windows. The reliability of the REVEALS reconstructions and issues related to the interpretation of the results in terms of landscape openness and human-induced vegetation change are discussed. This is followed by a review of the current use of this reconstruction and its future potential utility and development. REVEALS data quality are primarily determined by pollen count data (pollen count and sample, pollen identification, and chronology) and site type and number (lake or bog, large or small, one site vs. multiple sites) used for REVEALS analysis (for each grid cell). A large number of sites with high-quality pollen count data will produce more reliable land-cover estimates with lower standard errors compared to a low number of sites with lower-quality pollen count data. The REVEALS data presented here can be downloaded from https://doi.org/10.1594/PANGAEA.937075 (Fyfe et al., 2022).

Abstract: Abstract. Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributors. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling and available at https://doi.org/10.5285/17c2ce31784048de93996275ee976fff (Horwath et al., 2021), include global mean sea-level (GMSL) anomalies from satellite altimetry, the global mean steric component from Argo drifter data with incorporation of sea surface temperature data, the ocean-mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry, the contribution from global glacier mass changes assessed by a global glacier model, the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes assessed by satellite radar altimetry and by GRACE, and the contribution from land water storage anomalies assessed by the global hydrological model WaterGAP (Water Global Assessment and Prognosis). Over the period January 1993–December 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend), and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period January 2003–August 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions, now about 2.40 ± 0.13 mm yr−1 (66 % of the GMSL trend), with the largest increase contributed from Greenland, while the steric contribution remained similar at 1.19 ± 0.17 mm yr−1 (now 33 %). The SLB of linear trends is closed for P1 and P2; that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB, which can be evaluated only for P2, shows that our preferred GRACE-based estimate of the ocean-mass trend agrees with the sum of mass contributions within 1.5 times or 0.8 times the combined 1σ uncertainties, depending on the way of assessing the mass contributions. Combined uncertainties (1σ) of the elements involved in the budgets are between 0.29 and 0.42 mm yr−1, on the order of 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.

Abstract: Abstract. Near-surface air temperature (Ta) is an important physical parameter that reflects climate change. Many methods are used to obtain the daily maximum (Tmax⁡), minimum (Tmin⁡), and average (Tavg) temperature, but are affected by multiple factors. To obtain daily Ta data (Tmax⁡, Tmin⁡, and Tavg) with high spatio-temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data. Different Ta reconstruction models were constructed for different weather conditions, and the data accuracy was improved by building correction equations for different regions. Finally, a dataset of daily temperature (Tmax⁡, Tmin⁡, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1∘. For Tmax⁡, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 to 1.78∘, the mean absolute error (MAE) varies from 0.63 to 1.40∘, and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin⁡, the RMSE ranges from 0.78 to 2.09∘, the MAE varies from 0.58 to 1.61∘, and the R2 ranges from 0.95 to 0.99. For Tavg, the RMSE ranges from 0.35 to 1.00∘, the MAE varies from 0.27 to 0.68 ∘, and the R2 ranges from 0.99 to 1.00. Furthermore, various evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.03 ∘C yr−1, which is consistent with the general global warming trend. In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (Tmax⁡, Tmin⁡, and Tavg) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).

Abstract: Abstract. Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP) information is essential for understanding the large-scale water and carbon dynamics. However, substantial uncertainties exist in the current ET and GPP datasets in China because of insufficient local ground measurements used for model constraint. This study utilizes a water–carbon coupled model, Penman–Monteith–Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2(China) were well calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China, indicated by a Nash–Sutcliffe efficiency (NSE) of 0.75 and a root mean square error (RMSE) of 0.69 mm d−1 for daily ET, respectively, and a NSE of 0.82 and a RMSE of 1.71 g C m−2 d−1 for daily GPP. The model estimates get a small Bias of 6.28 % and a high NSE of 0.82 against water-balance annual ET estimates across 10 major river basins in China. Further evaluations suggest that the newly developed product is better than other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2(China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. The new data showed that, during 2001–2018, the annual GPP and water use efficiency experienced a significant (p<0.001) increase (8.99 g C m−2 yr−2 and 0.02 g C mm−1 H2O yr−1, respectively), but annual ET showed a non-significant (p>0.05) increase (0.43 mm yr−2). This indicates that vegetation in China exhibits a huge potential for carbon sequestration with little cost in water resources. The PML-V2(China) product provides a great opportunity for academic communities and various agencies for scientific studies and applications, freely available at https://doi.org/10.11888/Terre.tpdc.272389 (Zhang and He, 2022).

Abstract: Abstract. Laser scanning from different acquisition platforms enables the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve detailed information on the individual tree and forest structure. We conducted airborne laser scanning (ALS), uncrewed aerial vehicle (UAV)-borne laser scanning (ULS) and terrestrial laser scanning (TLS) in two German mixed forests with species typical of central Europe. We provide the spatially overlapping, georeferenced point clouds for 12 forest plots. As a result of individual tree extraction, we furthermore present a comprehensive database of tree point clouds and corresponding tree metrics. Tree metrics were derived from the point clouds and, for half of the plots, also measured in the field. Our dataset may be used for the creation of 3D tree models for radiative transfer modeling or lidar simulation studies or to fit allometric equations between point cloud metrics and forest inventory variables. It can further serve as a benchmark dataset for different algorithms and machine learning tasks, in particular automated individual tree segmentation, tree species classification or forest inventory metric prediction. The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher at https://doi.org/10.1594/PANGAEA.942856 (Weiser et al., 2022a).