In spite of the high importance of forests, global forest loss has remained alarmingly high during the last decades. Forest loss at a global scale has been unveiled with increasingly finer spatial resolution, but the forest extent and loss in protected areas (PAs) and in large intact forest landscapes (IFLs) have not so far been systematically assessed. Moreover, the impact of protection on preserving the IFLs is not well understood. In this study we conducted a consistent assessment of the global forest loss in PAs and IFLs over the period 2000–2012. We used recently published global remote sensing based spatial forest cover change data, being a uniform and consistent dataset over space and time, together with global datasets on PAs’ and IFLs’ locations. Our analyses revealed that on a global scale 3% of the protected forest, 2.5% of the intact forest, and 1.5% of the protected intact forest were lost during the study period. These forest loss rates are relatively high compared to global total forest loss of 5% for the same time period. The variation in forest losses and in protection effect was large among geographical regions and countries. In some regions the loss in protected forests exceeded 5% (e.g. in Australia and Oceania, and North America) and the relative forest loss was higher inside protected areas than outside those areas (e.g. in Mongolia and parts of Africa, Central Asia, and Europe). At the same time, protection was found to prevent forest loss in several countries (e.g. in South America and Southeast Asia). Globally, high area-weighted forest loss rates of protected and intact forests were associated with high gross domestic product and in the case of protected forests also with high proportions of agricultural land. Our findings reinforce the need for improved understanding of the reasons for the high forest losses in PAs and IFLs and strategies to prevent further losses.

The El Niño Southern Oscillation (ENSO) peaked strongly during the boreal winter 2015–2016, leading to food insecurity in many parts of Africa, Asia and Latin America. Besides ENSO, the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) are known to impact crop yields worldwide. Here we assess for the first time in a unified framework the relationships between ENSO, IOD and NAO and simulated crop productivity at the sub-country scale. Our findings reveal that during 1961–2010, crop productivity is significantly influenced by at least one large-scale climate oscillation in two-thirds of global cropland area. Besides observing new possible links, especially for NAO in Africa and the Middle East, our analyses confirm several known relationships between crop productivity and these oscillations. Our results improve the understanding of climatological crop productivity drivers, which is essential for enhancing food security in many of the most vulnerable places on the planet.

Highlights d Safe climatic space method to assess climatic niche for global food production d Climate change risks pushing 1/3 of food production outside safe climatic space d Following the Paris Agreement would reduce this risk considerably d We call for interlinked climate change mitigation and adaptation actions

Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño-Southern Oscillation (ENSO), which has been found to impact crop yields on all continents that produce crops, while two other climate oscillations -the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) -have been shown to especially impact crop production in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD, and NAO on the growing conditions of maize, rice, soybean, and wheat at the global scale by utilising crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that, while accounting for their potential co-variation, climate oscillations are correlated with simulated crop yield variability to a wide extent (half of all maize and wheat harvested areas for ENSO) and in several important crop-producing areas, e.g. in North America (ENSO, wheat), Australia (IOD and ENSO, wheat), and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed and fully fertilised scenarios, while the sensitivity tends to be lower if crops were to be fully irrigated. Since the development of ENSO, IOD, and NAO can potentially be forecasted well in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate-related shocks.

Despite being a top concern on global agenda, global-scale, high resolution quantification of net-migration and its major drivers, is still missing for recent decades. We created a global dataset of annual net-migration between 2000–2019 (~ 10km grid), based on reported and here-downscaled sub-national birth and death ratios. We show that globally, internal migration has increased rapidly, dominating over international migration. Around 50% of world’s urban population lived in urban areas where migration accelerated urban population growth, while a third of global population lived in provinces where rural areas experienced positive net-migration. Finally, we show that socio-economic factors play a more important role than climatic ones to explain the migration patterns globally. By capturing migration patterns not only between but also within countries, socio-economic and geophysical zonings, our study highlights the importance of sub-national analysis of migration – a necessity for policy design, international cooperation and shared responsibility for managing internal and international migration.

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The importance of green water (moisture from rain stored in soils) for global food and water security is widely recognized, with process-based simulation models and field-level studies demonstrating its role in supporting rainfed agriculture. Despite this evidence, the relationship between green water anomalies and rainfed agriculture has not yet been investigated using statistical models that identify a causal relationship between the variables. Here, we address this gap and use disaggregated statistical regression (panel data analysis) at the 30 arc-min grid level to study the response of observed yields of four main crops (maize, rice, soybean and wheat) to green water anomalies globally over rainfed areas. Dry green water anomalies (1 or 2 standard deviations below long-term average) decrease rainfed crop yields worldwide. This effect is more pronounced for wheat and maize, whose yields decline by 12%-18% and 7%-12% respectively. Globally, agricultural production benefits from wet green water anomalies. This effect is intensified in arid climates and weakened in humid climates where, for wheat, soybean and rice, periods of green water availability 2 standard deviations above long-term averages lead to declines in crop yield. This confirms existing evidence that excess soil moisture is detrimental to crop yield. These findings (1) advance our understanding of the impact of green water on rainfed food production and (2) provide empirical evidence supporting arguments for better management of local green water resources to reduce the impact of agricultural drought and waterlogging on rainfed crop production and capture the yield increasing effects of positive green water anomalies.

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño Southern Oscillation (ENSO), which has been found to impact crop yields in all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to impact crop production especially in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD and NAO on the growing conditions of maize, rice, soybean and wheat at the global scale, by utilizing crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that simulated crop yield variability is correlated to climate oscillations to a wide extent (up to almost half of all maize and wheat harvested areas for ENSO) and in several important crop producing areas, e.g. in North America (ENSO, wheat), Australia (IOD & ENSO, wheat) and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed, and fully fertilized scenarios, while the sensitivity tends to be lower if crops are fully irrigated. Since, the development of ENSO, IOD and NAO can be reliably forecasted in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate related shocks.