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Climate change has been a significant threat to sustainable agriculture impacting trade and food security. This research investigates the impacts of climate change on agricultural trade and food security in emerging economies focusing on Southern Africa. The research employed the dynamic panel Generalised Method of Moments (GMM) using panel data for the period 2012 to 2021 obtained from a sample of 12 Southern African countries selected based on data availability. The main independent variables to the research were climate change variables (temperature and precipitation). Other control variables included were population growth, food inflation and agricultural growth. The study found significant negative effects of climate change on agriculture trade. Whilst temperature changes were found to have insignificant effects, precipitation changes were found to have significant positive effects. The research recommends concerted efforts towards climate change adaptation and mitigation for sustainable agriculture. Based on the Findings, sustainable food security and agricultural trade Southern African emerging economies may be attained through effective climate change mitigation and adaptation approaches particularly promoting climate-smart agriculture (CSA). [ABSTRACT FROM AUTHOR]

The Great Plains of North America host a stark climatic gradient, separating the humid and well-watered eastern US from the semi-arid and arid western US, and this gradient shapes the region's water availability, its ecosystems, and its economies. This climatic boundary is largely set by the influence of two competing atmospheric circulation systems that meet over the Great Plains – the wintertime westerlies bring dominantly dry air that gives way to moist, southerly air transported by the Great Plains low-level jet in the warmer months. Climate model simulations suggest that, as CO 2 rises, this low-level jet will strengthen, leading to greater precipitation in the spring but less in the summer and, thus, no change in mean annual precipitation. Combined with rising temperatures that will increase potential evapotranspiration, semi-arid conditions will shift eastward, with potentially large consequences for the ecosystems and inhabitants of the Great Plains. We examine how hydroclimate in the Great Plains varied in the past in response to warmer global climate by studying the paleoclimate record within the Ogallala Formation, which underlies nearly the entire Great Plains and provides a spatially resolved record of hydroclimate during the globally warmer late Miocene. We use the stable isotopes of oxygen (δ18 O) as preserved in authigenic carbonates hosted within the abundant paleosol and fluvial successions that comprise the Ogallala Formation as a record of past hydroclimate. Today, and coincident with the modern aridity gradient, there is a sharp meteoric water δ18 O gradient with high (-6 ‰ to 0 ‰) δ18 O in the southern Great Plains and low (-12 ‰ to -18 ‰) δ18 O in the northern plains. We find that the spatial pattern of reconstructed late Miocene precipitation δ18 O is indistinguishable from the spatial pattern of modern meteoric water δ18 O. We use a recently developed vapor transport model to demonstrate that this δ18 O spatial pattern requires air mass mixing over the Great Plains between dry westerly and moist southerly air masses in the late Miocene – consistent with today. Our results suggest that the spatial extents of these two atmospheric circulation systems have been largely unchanged since the late Miocene and any strengthening of the Great Plains low-level jet in response to warming has been isotopically masked by proportional increases in westerly moisture delivery. Our results hold implications for the sensitivity of Great Plains climate to changes in global temperature and CO 2 and also for our understanding of the processes that drove Ogallala Formation deposition in the late Miocene. [ABSTRACT FROM AUTHOR]

The continued and envisioned large-scale integration of renewable energy sources as a reaction to rising global temperatures and climate change will need a readily available DC grid to increase commissioning and operating efficiency. The effective operation of these grids is predicated on the correct control of its main control points. A plethora of DC-DC converters that find use in DC microgrids act as the main control points. DC-DC converters are non-linear and can operate in different modes with completely unique characteristics. To utilise classical control techniques, laborious equivalent linear models are derived for DC-DC converters using averaging modelling schemes. The application and limitations of these modelling techniques are well captured in the available literature. The most common limitation of the available modelling schemes is that more focus is dedicated to converter attributes like order, functionality and operating mode, even when optimal power flow and voltage regulation within the DC network are of more interest. Structure-based modelling techniques like the use of basic building blocks nullify converter attributes in the modelling process which translates to modelling efficiency. In light of the merits seen with the use of basic building blocks when modelling converters in CCM, the current study extends these merits to converters operation in DCM. Similar to modelling converters in CCM, modelling techniques that are available in the literature continue to consider converter attributes in the modelling process for DCM operation. Moreover, the two modes of operation are treated as unique entities and often modelled in a non-unified manner, which compromise modelling efficiency since the same converter can operate in a different state solely based on loading. The aim is to increase modelling efficiency but also nullify operating mode in the modelling process. The same basic building blocks are now modelled as two-port networks for DCM operation and adopted based on the exact configuration of a specified converter to compute its steady-state and dynamic models. All the advantages seen when modelling converters in CCM using basic building blocks are retained and augmented when considering DCM operation. Thus, any converter with well-defined basic building blocks can be easily modelled solely based on the connection of constituent basic building blocks. [ABSTRACT FROM AUTHOR]

The temporal relationship between global glaciations and the Great Oxidation Event (GOE) suggests that climate change played an important role in Earth's oxygenation. The potential role of temperature is captured by the stratigraphic proximity between glacial deposits and sediments containing mass‐independent fractionation of sulfur isotopes (MIF‐S). We use a time‐dependent one‐dimensional photochemical model to investigate whether temperature changes associated with global glaciations can drive oscillations in atmospheric O2 levels and MIF‐S production across the GOE. We find that extreme climate change can cause atmospheric O2 to oscillate between pre (<10−6 times the present atmospheric level, PAL) and post‐GOE (>10−5 PAL) levels. Post‐glacial hot‐moist greenhouse climates lead to post‐GOE O2 levels because the abundant H2O vapor and oxidizing radicals drive the depletion of reduced species. This pattern is generally consistent with the MIF‐S signal observed in the sedimentary record, suggesting a link between global glaciations and O2 oscillations across the GOE. Plain Language Summary: The Great Oxidation Event was the most significant environmental and chemical transformation in Earth's history, marking the first time oxygen accumulated in the atmosphere around 2.4 billion years ago. Oxygen increased from below one millionth (low) to at least one‐thousandth of a percent (intermediate) of the present oxygen concentration during this event. However, measurements of geochemical oxygen proxies suggest that oxygen levels oscillated between low and intermediate levels before stabilizing after this event. The first rise of atmospheric oxygen occurred during a period of extreme climate variability indicated by the presence of glacial rock deposits around this time. In this study, we use a time‐dependent photochemical model to show that extreme temperature changes caused by global glaciations can drive oscillations in atmospheric oxygen levels across the Great Oxidation Event (GOE). Our results can help explain why atmospheric oxygen shows drastic changes across the GOE in a way that is consistent with the geochemical record. Key Points: Across the Great Oxidation Event (GOE), extreme climate change linked to global glaciations can drive oscillations in atmospheric O2 levels and mass‐independent fractionation of sulfur isotopes (MIF‐S) productionGlacial climates and hot‐moist greenhouse climates were likely characterized by pre‐GOE and post‐GOE O2 levels respectivelyTemperature changes associated with global glaciations can help explain the MIF‐S record across the GOE [ABSTRACT FROM AUTHOR]