Global Gridded Datasets Research Articles

Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ( S r ) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified S r and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale S r with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of S r globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with S r globally, the relation between precipitation and S r varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and S r for precipitation of up to 3 mm d − 1 , above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and S r . Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of S r , which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate S r on a global scale has the potential to be readily and easily integrated into the parameterization of S r in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.

A high temporal resolution global gridded dataset of human thermal stress metrics

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

Constructing a long-term global dataset of direct and diffuse radiation (10 km, 3 h, 1983–2018) separating from the satellite-based estimates of global radiation

In addition to global radiation (Rg), direct radiation (Rdir) and diffuse radiation (Rdif) are important fundamental data urgently needed in scientific and industrial fields. However, compared with Rg, Rdir and Rdif have received little attention in the past, either in observations or in satellite retrievals, mainly due to the high cost of their observations and the difficulty of retrieving them effectively from satellites. In this study, a long-term global gridded dataset of Rdir and Rdif was constructed by separating from a high-precision satellite-based product of Rg using the Light Gradient Boosting Machine (LightGBM) model, trained with in-situ observations measured at the Baseline Surface Radiation Network (BSRN). The inputs to construct the dataset are the four variables of Rg, the cloud transmittance for Rg, the ratio of Rdif to Rg under clear sky condition (call the clear diffuse ratio), and the cosine of the solar zenith angle. The developed dataset was validated against in-situ observations and compared with other satellite-based products. Evaluations against the BSRN observations indicated that our proposed method has good generality and outperforms the machine learning-based direct estimation method of Hao et al. (2020). Independent validations were further performed against the observations measured at 17 China Meteorological Administration (CMA) radiation stations and the estimation based on sunshine duration observations at >2400 CMA routine meteorological stations, respectively. It was found that the accuracies of our estimates for both Rdir and Rdif were improved when upscaled to ≥ 30 km. Comparisons with three other satellite-based products indicate that our developed dataset of both Rdir and Rdif was generally more accurate than the global products of the Earth's Radiant Energy System (CERES) and Hao et al. (2020) based on the Deep Space Climate Observatory/Earth Polychromatic Imaging Camera (EPIC) (DSCOVER/EPIC) satellite, and the regional gridded product (JIEA) of Jiang et al. (2020a). The dataset developed in this study will contribute to ecological research and solar engineering applications.

Fast, accurate and accessible calculations of leaf temperature and its physiological consequences with NicheMapR

Abstract Mechanistic predictions of ecological niches are fundamentally based on energy and mass exchange processes that, to be realistic, must be linked to microclimate and incorporate behaviour and physiology. Estimates of leaf temperature and associated transpiration and net photosynthesis rates are fundamental in ecology, agriculture and global change biology. Equations for calculating leaf energy budgets have been available for over 60 years and complex models of stomatal behaviour have been developed. However, user‐friendly ways of estimating leaf temperature under realistic microclimatic regimes, including soil moisture effects, remain limited. Here we show that the integrated microclimate and ectotherm functions of the NicheMapR package for the R programming environment can be used to make fast and effective estimates of leaf temperature and associated rates of water loss and photosynthesis for a wide range of leaf functional types, root depths and microhabitats. Stomatal conductance may be constant or simulated to change dynamically (a) as a function of vapour pressure deficit and CO2 concentration, (b) as a function of soil water potential and (c) as a thermoregulatory mechanism to avoid heat stress. We tested the approach against hourly leaf temperature observations in summer from an arid shrubland in Australia, a temperate woodland in Montana USA, along an elevational gradient in Colorado USA, and at a botanical garden in Florida, USA. National or global gridded data sets can provide the required forcing data but local observational data or some combination of local and gridded data can be used. The global ERA5 and NCEP datasets allow historical leaf energy budget calculations to be made anywhere in the world and a Shiny app has been developed to facilitate use of the model with these datasets. Situated leaf energy budget calculations can be used to design informative field programs to collect much needed detailed temporal data on leaf temperature under natural settings, enabling better predictions and interpretations of how plants respond to environmental change.

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model

AbstractThe global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund‐Potsdam‐Jena General Ecosystem Simulator (LPJ‐GUESS), using a broad range of the latest available global observation‐based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ‐GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ‐GUESS accurately captures both inter‐ and intra‐annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ‐GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML‐V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ‐GUESS aligns with ESA‐CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use.

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.

A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning

Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validation results also reveal that the GlobPOP dataset performs consistently well across eight representative countries and cities despite their unique population dynamics. With the availability of GlobPOP datasets in both population count and population density formats, researchers and policymakers can leverage the new dataset to conduct time-series analysis of the population and explore the spatial patterns of population development at global, national, and city levels.

Evaluating the Performance of Satellite Derived Temperature and Precipitation Datasets in Ecuador

Temperature and precipitation data are crucial for hydrology and meteorology. In 2014, Ecuador started an automatic gauge network which monitors these variables. The measurements are not publicly available. Global gridded datasets from numerical models and remote sensors were previously the only way to obtain measurements for temperature and precipitation. Now that in situ measurements are beginning to be available in significant quantities, we assessed the performance of IMERG, CHIRPS, GLDAS and ERA5 for both temperature and precipitation using the in situ data. We used the Pearson R correlation coefficient, ME (Mean Error), MAE (Mean Absolute Error), and RMSE (Root Mean Square Error). We found that global gridded data were more suited for determining averages over time rather than for giving exact values at specific times for in situ gauges. The Pearson R values increased for all datasets when we used monthly aggregations in place of daily aggregations, suggesting that the monthly values are more correlated than the daily. The Pearson R value for temperature increased from 0.158 to 0.719 for the ERA5 dataset. Additionally, we show the statistical values for each of the three regions in Ecuador. We found that the IMERG and CHIRPS datasets, which contain station data, performed significantly better for both RMSE and MAE. Both IMERG and CHIRPS have a RMSE value a little over 260, whereas ERA5 and GLDAS had values over 300. We discuss the short comings of these datasets as being related to their relatively coarse resolution, lack of in situ data in Ecuador to calibrate against, and the rapidly varying terrain of Ecuador. We recommend using higher temporal and spatial resolution datasets for immediate applications. We recommend repeating this analysis in the future when more automatic gauges and longer time periods are available to facilitate a more detailed analysis which is presently not possible.

Comparing the suitability of global gridded population datasets for local landslide risk assessments

AbstractLandslide risk assessments are increasingly crucial for meeting global disaster risk reduction strategies and mobilising knowledge for local governments to protect communities and infrastructure. These assessments are data intensive, requiring large amounts of spatial demographic and natural hazard information. There are a growing number of high-resolution gridded population datasets that have global coverage with significant potential to transform disaster risk modelling, however landslide research has not yet compared the suitability of these gridded datasets for local risk assessments. Combining social vulnerability indicators with high-resolution settlement layer (HRSL) and WorldPop gridded population datasets, as well as a local household survey, we layer landslide susceptibility maps to compare three landslide risk estimates, examining the case of the Municipality of Carigara located in the central Philippines. Using statistical t tests, we compare aggregated community landslide risk for 49 communities. Findings revealed that HRSL data resulted in similar landslide risk at community scales when compared to local surveys, however WorldPop data greatly overestimated risk. Our findings point to a high level of accuracy of HRSL when used as an exposure dataset for local landslide risk studies and recommend avoiding WorldPop for such purposes. This research advances understanding of the suitability of open population datasets for use in landslide risk assessments in resource-constrained communities.

Future shrinking cities on the globe: A projection map for 2020–2100 based on global gridded population dataset

Shrinking cities have become increasingly prevalent worldwide due to various factors, which pose serious challenges to affected areas in terms of population decline, economic decline, and spatial deterioration. While existing research studies have focused on identifying shrinking cities, there is a need for global projections to mitigate uncertainties in their growth trajectories. Spatially explicit population grids offer a new approach to identifying potentially shrinking cities with sufficient spatial resolution. By utilizing a global gridded population dataset from 2020 to 2100 under the SSP2 (Middle of the Road) scenario, we produce a global projection map for future shrinking cities. Among the total 19,024 natural cities, 9682 cities (50.9%) will face population decline and 1751 cities (9.2%) may lose more than half population by 2100. Cities in East Asia and East Europe may face serious population decline.

Global and regional soil organic carbon estimates: Magnitudes and uncertainties

Globally, soil is the largest terrestrial carbon (C) reservoir. Robust quantification of soil organic C (SOC) stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends. We investigated the magnitudes and distributions of global and regional SOC estimates (i.e., density and stocks) based on five widely used global gridded SOC datasets, a regional permafrost dataset developed in 2021 (UM2021), and a global-scale soil profile database (World Soil Information Service) reporting measurements of a series of physical and chemical edaphic attributes. The five global gridded SOC datasets were the Harmonized World Soil Database (HWSD), World Inventory of Soil Emission Potentials at 30 arc-second resolution (WISE30sec), Global Soil Dataset for Earth System Models (GSDE), Global Gridded Soil Information at 250-m resolution (SoilGrids250m), and Global Soil Organic Carbon Map (GSOCmap). Our analyses showed that the magnitude and distribution of SOC varied widely among datasets, with certain datasets showing region-specific robustness. At the global scale, SOC stocks at the top 30 and 100 cm were estimated to be 828 (range: 577–1 171) and 1 873 (range: 1 086–2 678) Pg C, respectively. The estimates from GSDE, GSOCmap, and WISE30sec were comparable, and those of SoilGrids250m and HWSD were at the upper and lower ends. The spatial SOC distribution varied greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. Regionally, UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions, and GSDE performed well in China. The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes. The discrepancies were generally smaller for the 0–30 cm soil than the 0–100 cm soil. The datasets demonstrated relatively higher agreement in grasslands, croplands, and shrublands/savannas than in other biomes (e.g., wetlands). The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses. Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density, different sources, and various mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates.

A general unit hydrograph distribution and its application on the marginal distribution of global wind speed

We here introduce a simple, general, and analytical method based on the general unit hydrograph (GUH) theory proposed by Guo (2022a, 2022b, 2022c) to describe wind speed marginal distribution. Wind speed probability is generally described by the Weibull distribution; however, its accuracy depends on the contribution made by null wind speed (owing to its zero initial condition). To overcome this disadvantage, we propose a three-parameter GUH distribution with significant flexibility in modeling symmetric heavy-tailed distributions as well as skewed distributions. We show that our proposed GUH distribution corresponds to a generalized logistic distribution that contains both type I and type II generalized logistic distributions. The shape properties of the GUH distribution are discussed along with a simple method for estimating parameters by means of least squares methods. The flexibility of the proposed GUH distribution was assessed by applying it to a well reconstructed global gridded wind speed dataset. Subsequently, we quantify the changes caused by a reversal in global surface wind speed stilling using our GUH distribution. Finally, we confirm that our proposed GUH model agrees with reconstructed wind speed data better than conventional Weibull distribution models, which suggests that the new distribution can be a useful tool for sustainable development of wind energy.

Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa

Study regionData-scarce basins located in West Africa and northern Central Africa. Study focusMultiple studies have shown that global gridded precipitation datasets could provide an alternative to the lack of observed data in Sub-Saharan Africa. This work evaluated 15 precipitation datasets based on satellite rainfall (ARC v.2, CHIRP v.2, CHIRPS v.2, PERSIANN-CDR, MSWEP v2.2 and TAMSAT v3), reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU and WFDEI-GPCC) and ground measurements (CPC v.1, CRU TS v.4.00 and GPCC v.7), as well as a regional estimation method, based on spatial proximity, for the parameters of a simple monthly water balance model, GR2M. The regional simulations of the GR2M model were evaluated based on a Kling-Gupta Efficiency score in a split-sample spatiotemporal validation scheme. New hydrological insights for the regionThe results show that among all the precipitation products, CHIRPS is the most effective for hydrological modeling in West and Central Africa at a monthly timestep. Also, among the top five products are WFDEI-CRU, CRU, WFDEI-GPCC and GPCC. Overall, regional hydrological modeling is more effective for basins smaller than 80,000 km2. The method of regionalization by spatial proximity causes an overall drop in the ability of the various precipitation products to reproduce discharge, most notably with WFDEI-GPCC and GPCC. CHIRPS remains the best product in terms of KGE2 values in regionalization.

Increasing likelihood of global compound hot-dry extremes from temperature and runoff during the past 120 years

Compound hot-dry extremes pose great threats to human health, ecosystems, and food production. A lot of attention has been paid to compound meteorological hot-dry extremes (based on high near-surface air temperature and low precipitation); however, there is a gap on the compound hydrological hot-dry extremes (based on high near-surface air temperature and low surface runoff) which is more directly related to damages on social and natural systems. Here, using the ensemble empirical mode decomposition method and Copula function based on multiple global gridded datasets during 1902–2019, we find that the globe is dominated by accumulated increases in zonal average of warm-season (i.e., May-September in the Northern Hemisphere and November-March in the Southern Hemisphere) mean precipitation (WMP), while zonal average of warm-season mean runoff (WMR) mainly shows accumulated decreases. This discrepancy is possibly due to the modulation effect of zonally accelerated warming trends in warm-season mean temperature (WMT). Despite of the discrepancy in changes of individual WMP and WMR, compound meteorological hot-dry extremes (from WMT and WMP) show increased occurrence probability, expanded affected area, and enlarged spatial homogeneity (i.e., connectedness), and these features are worse for compound hydrological extremes (from WMT and WMR). Both high WMT and low WMP can lead to negative anomalies of WMR; however, the negative anomalies of WMR can be largely amplified by additional low WMP under high WMT conditions. The increased occurrence probability of compound hydrological hot-dry extremes is directly related to WMT, WMR, and their dependence. Although a strong dependence is found between WMT and WMR especially at mid-latitudes in the Northern Hemisphere, attribution results show that increased WMT plays a dominant role in reducing return periods of compound hydrological hot-dry extremes at the global scale. Our results provide a deeper understanding of changes in compound hydrological hot-dry extremes at the global scale from a longer-term period.

Representativeness of Two Global Gridded Precipitation Data Sets in the Intensity of Surface Short-Term Precipitation over China

This study evaluates the representativeness of two widely used next-generation global satellite precipitation estimates data for short-term precipitation over China, namely the satellite data from the Climate Prediction Center morphing (CMORPH) and the satellite data from the Global Precipitation Measurement (GPM) mission. These two satellite precipitation data sets were compared with the hourly liquid in-situ precipitation from China national surface stations from 2016 to 2020. The results showed that the GPM precipitation data has better representativeness of surface short-term precipitation than that of the CMORPH data, and these two quantitative precipitation estimate (QPE) data sets underestimated extreme precipitation. Moreover, we analyzed the influence of the error between two QPE data sets and the in-situ precipitation on the classification of short-term precipitation intensity. China uses 8.1–16 mm/h as the definition of heavy precipitation, but the accuracy of the satellite QPE product was different due to the different lowest threshold of heavy rain (more than 8.1 mm/h or more than 16 mm/h). Increasing the threshold value of the QPE data for short-term strong precipitation resulted in lower accuracy for detecting such events, but higher accuracy for detecting moderate intensity rainfall. When studying short-term strong precipitation over China using precipitation grade, selecting an appropriate threshold was important to ensure accurate judgments. Additionally, it is important to account for errors caused by QPE data, which can significantly affect the accuracy of precipitation grading.

The monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea

This paper analyzes a monthly global gridded sea-surface wind dataset from Jan 1991 to Dec 2020, sourced from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The goal is to construct schematic maps depicting the monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea. These maps provide a visual representation of the monsoon patterns at different points and areas in the region. The statistical and analytical findings reveal that the East Vietnam Sea is primarily influenced by two types of monsoons: Northeast and Southwest. When one is strong, the other is weak, and vice versa. The Northeast monsoon dominates from September to April of the following year, while the Southwest monsoon is more dominant from May to August. The intensity of the Northeast monsoon is usually greater than that of the Southwest monsoon. These results provide an overview of the monsoon distributions in the East Vietnam Sea, which can assist scientists and managers in proposing policies and measures suitable to the climate environment and wind seasons. These findings also help in the effective exploitation of abundant wind energy resources in coastal Vietnam and the East Vietnam Sea. Furthermore, the results are consistent with previous studies of monsoons in the East Vietnam Sea, indicating that the data source used in this study is reliable and can be utilized as input data for tidal, currents, or oil spread models.

Estimating local agricultural gross domestic product (AgGDP) across the world

Abstract. Economic statistics are frequently produced at an administrative level such as the subnational division. However, these measures may lack sufficient local variation for effective analysis of local economic development patterns and exposure to natural hazards. Agricultural gross domestic product (GDP) is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend for their livelihoods, and it provides a key source of income for the entire household (FAO, 2021). Through a data-fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of agricultural GDP into a global gridded dataset at approximately 10×10 km for the year 2010 using satellite-derived indicators of the components that make up agricultural GDP, i.e., crop, livestock, fishery, hunting and forestry production. To illustrate the use of the new dataset, the paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, which amounts to around USD 432 billion of agricultural GDP circa 2010, with nearly 1.2 billion people living in those areas. The data are available on the World Bank Development Data Hub (https://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022).

A global gridded ocean salinity dataset with 0.5° horizontal resolution since 1960 for the upper 2000 m

A gridded salinity dataset with high resolution is essential for investigating global ocean salinity variability and understanding its role in climate and the ocean ecosystem. In this study, a new version of the Institute of Atmospheric Physics gridded salinity dataset with a higher resolution (0.5° by 0.5°) is provided by using a revised ensemble optimal interpolation scheme with a dynamic ensemble. The performance of this dataset is evaluated using “subsample test” and the high-resolution satellite-based data. Compared with the previous 1° by 1° resolution IAP product, the new dataset is more capable of representing regional salinity changes with the meso-scale and small-scale signals (i.e., in the coastal and boundary currents regions), meanwhile, maintains the large-scale structure and variability. Therefore, the new dataset complements the previous data product. Besides, the new dataset is compared with in situ observations and several international salinity products for the salinity multiscale variabilities and patterns. The comparison shows the smaller magnitude of mean difference and Root-mean-square deviation (RMSD) in basin scale for the new dataset, some differences in strength and fine structure of the “fresh gets fresher, salty gets saltier” surface and subsurface salinity pattern amplification trends from 1980 to 2017, a broad similarity for the salinity changes associated with El Niño-Southern Oscillation (ENSO) and a consistent salinity dipole mode in the tropical Indian Ocean (S-IOD). These results support the future use of gridded salinity data.

CO2 emissions from C40 cities: citywide emission inventories and comparisons with global gridded emission datasets

Under the leadership of the C40 Cities Climate Leadership Group (C40), approximately 1100 global cities have signed to reach net-zero emissions by 2050. Accurate greenhouse gas emission calculations at the city-scale have become critical. This study forms a bridge between the two emission calculation methods: (a) the city-scale accounting used by C40 cities—the Global Protocol for Community-Scale Greenhouse Gas Emission Inventories (GPC) and (b) the global-scale gridded datasets used by the research community—the Emission Database for Global Atmospheric Research (EDGAR) and Open‐Source Data Inventory for Anthropogenic CO2 (ODIAC). For the emission magnitudes of 78 C40 cities, we find good correlations between the GPC and EDGAR (R 2 = 0.80) and the GPC and ODIAC (R 2 = 0.72). Regionally, African cities show the largest variability in the three emission estimates. For the emission trends, the standard deviation of the differences is ±4.7% yr−1 for EDGAR vs. GPC and is ±3.9% yr−1 for ODIAC vs. GPC: a factor of ∼2 larger than the trends that many C40 cities pledged (net-zero by 2050 from 2010, or −2.5% yr−1). To examine the source of discrepancies in the emission datasets, we assess the impact of spatial resolutions of EDGAR (0.1°) and ODIAC (1 km) on estimating varying-sized cities’ emissions. Our analysis shows that the coarser resolution of EDGAR can artificially decrease emissions by 13% for cities smaller than 1000 km2. We find that data quality of emission factors (EFs) used in GPC inventories vary regionally: the highest quality for European and North American and the lowest for African and Latin American cities. Our study indicates that the following items should be prioritized to reduce the discrepancies between the two emission calculation methods: (a) implementing local-specific/up-to-date EFs in GPC inventories, (b) keeping the global power plant database current, and (c) incorporating satellite-derived CO2 datasets (i.e. NASA OCO-3).