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Abstract

Numerous initiatives towards sustainable development rely on global gridded population data. Such data have been calibrated primarily for urban environments, but their accuracy in the rural domain remains largely unexplored. This study systematically validates global gridded population datasets in rural areas, based on reported human resettlement from 307 large dam construction projects in 35 countries. We find large discrepancies between the examined datasets, and, without exception, significant negative biases of −53%, −65%, −67%, −68%, and −84% for WorldPop, GWP, GRUMP, LandScan, and GHS-POP, respectively. This implies that rural population is, even in the most accurate dataset, underestimated by half compared to reported figures. To ensure equitable access to services and resources for rural communities, past and future applications of the datasets must undergo a critical discussion in light of the identified biases. Improvements in the datasets’ accuracies in rural areas can be attained through strengthened population censuses, alternative population counts, and a more balanced calibration of population models.

Significance

Marine phytoplankton, which contribute ~45% of global net primary production, are projected to be affected by ongoing ocean acidification (OA). However, the response of phytoplankton to acidification is not well constrained in ultraoligotrophic tropical and subtropical oceans where small (<20 µm) phytoplankton dominate. By conducting onboard microcosm experiments, we found community-level primary production decreased consistently following CO2 enrichment in the North Pacific Subtropical Gyre and northern South China Sea, while no significant changes were observed at the northernmost boundary of the subtropical gyre. Eukaryotic phytoplankton but not cyanobacteria were key drivers of these responses which occur primarily under nitrogen limitation. These findings enhance our understanding of OA impacts on phytoplankton and marine productivity in a changing climate.

Abstract

Ocean acidification caused by increasing anthropogenic CO2 is expected to impact marine phytoplankton productivity, yet the extent and even direction of these changes are not well constrained. Here, we investigate the responses of phytoplankton community composition and productivity to acidification across the western North Pacific. Consistent reductions in primary production were observed under acidified conditions in the North Pacific Subtropical Gyre and the northern South China Sea, whereas no significant changes were found at the northern boundary of the subtropical gyre. While prokaryotic phytoplankton showed little or positive responses to high CO2, small (<20 µm) eukaryotic phytoplankton which are primarily limited by low ambient nitrogen drove the observed decrease in community primary production. Extrapolating these results to global tropical and subtropical oceans predicts a potential decrease of about 5 Pg C y−1 in primary production in low Chl-a oligotrophic regions, which are anticipated to experience both acidification and stratification in the future.

Abstract

The rate of natural sequestration of CO2 from the atmosphere by the terrestrial biosphere peaked in 2008. Atmospheric concentrations will rise more rapidly than previously, in proportion to annual CO2 emissions, as natural sequestration is now declining by 0.25% per year. The current atmospheric increment of +2.5ppm CO2 per year would have been +1.9ppm CO2, if the biosphere had maintained its 1960s growth rate. This effect will accelerate climate change and emphasises the close connection between the climate and nature emergencies. Effort is urgently required to rebuild global biodiversity and to recover its ecosystem services, including natural sequestration.

Abstract

Rising greenhouse gas concentrations and declining global aerosol emissions are causing energy to accumulate in Earth's climate system at an increasing rate. Incomplete understanding of increases in Earth's energy imbalance and ocean warming reduces the capability to accurately prepare for near term climate change and associated impacts. Here, satellite-based observations of Earth's energy budget and ocean surface temperature are combined with the ERA5 atmospheric reanalysis over 1985–2024 to improve physical understanding of changes in Earth's net energy imbalance and resulting ocean surface warming. A doubling of Earth's energy imbalance from 0.6±0.2 Wm−2 in 2001–2014 to 1.2±0.2 Wm−2 in 2015–2023 is primarily explained by increases in absorbed sunlight related to cloud-radiative effects over the oceans. Observed increases in absorbed sunlight are not fully captured by ERA5 and determined by widespread decreases in reflected sunlight by cloud over the global ocean. Strongly contributing to reduced reflection of sunlight are the Californian and Namibian stratocumulus cloud regimes, but also recent Antarctic sea ice decline in the Weddell Sea and Ross Sea. An observed increase in near-global ocean annual warming by 0.1 for each 1 Wm−2 increase in Earth's energy imbalance is identified over an interannual time-scale (2000–2023). This is understood in terms of a simple ocean mixed layer energy budget only when assuming no concurrent response in heat flux below the mixed layer. Based on this simple energy balance approach and observational evidence, the large observed near-global ocean surface warming of 0.27  from 2022 to 2023 is found to be physically consistent with the large energy imbalance of 1.85±0.2 Wm−2 from August 2022 to July 2023 but only if (1) a reduced depth of the mixed layer is experiencing the heating or (2) there is a reversal in the direction of heat flux beneath the mixed layer associated with the transition from La Niña to El Niño conditions. This new interpretation of the drivers of Earth's energy budget changes and their links to ocean warming can improve confidence in near term warming and climate projections.

Abstract

The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000–2022) from 107 countries using unsupervised machine learning (ML) techniques. Our analysis reveals strong positive and negative correlations between certain SDGs (Sustainable Development Goals). Our findings show that progress toward the SDGs is heavily influenced by geographical, cultural and socioeconomic factors, with no country on track to achieve all the goals by 2030. This highlights the need for a region-specific, systemic approach to sustainable development that acknowledges the complex interdependencies between the goals and the variable capacities of countries to reach them. For this our machine learning based approach provides a robust framework for developing efficient and data-informed strategies to promote cooperative and targeted initiatives for sustainable progress.