Climate plays a major role in the spatiotemporal distribution of most agricultural systems, and the economic losses related to climate and weather extremes have escalated significantly in the last decades. South America is one of the most productive agricultural areas of the globe. In recent years, remote sensing data and geographic information systems have been used to improve geo-environmental hazard assessment. However, food security is still highly dependent on small farmer practices that are frequently the most vulnerable to climate extremes. This work reviews climate and weather extremes’ impacts on crop production for South American countries, focusing on the projected ones considering different climate scenarios and countries. A positive trend in the productivity of maize, mainly related to agricultural improvements, was recently observed in Colombia, Ecuador, and Uruguay by up to 200%, as well as in the case of soybean in Bolivia and Uruguay by about 125%. Despite the generalized adverse impacts of climate extremes, results from agrometeorological models generally indicate an increase in crop production in southern regions of Chile (and highlands) and Brazil mainly related to increased temperature. Positive impacts in response to CO₂ fertilization are also foreseen in Peru and Brazil (southeast, south, and Minas Gerais); in particular, in Brazil, increases in productivity can be raised by about 40%. The use of double-cropping systems, although with very good results in recent years, may also be at risk in a few decades, mainly due to forecasted precipitation decrease, delay in rainy season onset, and temperature increase. The development of timely early warning systems is imperative to produce technically accurate alerts and the interpretation of the risk assessment based on the link between producers and consumers. Promoting climate index insurance is crucial to build resilient food production, but its implementation should rely on regional or international support systems. Moreover, the implementation of adaptation and mitigation also requires climate-resilient technologies that involve an interdisciplinary approach.

Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of the ERA5 and CHIRPS datasets, and the spatio-temporal evolution of extreme weather indices and their potential relationship/response to climate variability modes in the Pacific, Indian, and Atlantic oceans, namely, the El Niño−Southern Oscillation, Indian Ocean Dipole, and Tropical Atlantic Variability (ENSO, IOD, and TAV). The CHIRPS dataset showed strong positive correlations with CPC in spatial patterns and similarity in simulating interannual variability and in almost all seasons. Based on daily CHIRPS and CPC data, nine extreme indices were evaluated focusing on regional trends and change detection, and the maximum lag correlation method was applied to investigate fluctuations caused by climate variability modes. The results revealed a significant decrease in total precipitation (PRCPTOT) in north−central SSA, accompanied by a reduction in Consecutive Wet Days (CWDs) and maximum 5-day precipitation indices (RX5DAYS). At the same time, there was an increase in Consecutive Dry Days (CDDs) and maximum rainfall in 1 day (RX1DAY). With regard to temperatures, absolute minimums and maximums (TNn and TXn) showed a tendency to increase in the center−north and decrease in the south of the SSA, while daily maximums and minimums (TXx and TNx) showed the opposite pattern. The IOD, TAV, and ENSO modes of climate variability influence temperature and precipitation variations in the SSA, with distinct regional responses and lags between the basins.

The Antarctic sea ice concentration (SIC) plays a crucial role in global climate dynamics by influencing atmospheric and oceanic circulation. This study examines SIC variability and its relationship with major climate modes, including the El Niño-Southern Oscillation (ENSO), Pacific-South American (PSA) pattern, Southern Annular Mode (SAM), and Antarctic Dipole (ADP). Using NSIDC satellite-derived sea ice data and ERA5 reanalysis from 1980 to 2022, we analyzed SIC anomalies in the Weddell, Ross, and Bellingshausen and Amundsen (B&A) Seas, assessing their response to climatic forcings across different timescales. Our findings reveal strong linkages between SIC variability and large-scale atmospheric circulation. ENSO-related teleconnections drive a dipolar SIC response, with warming in the Pacific sector and cooling in the Atlantic during El Niño, and the opposite pattern during La Niña. PSA and ADP further modulate this response by altering Rossby wave propagation and heat fluxes, leading to significant SIC fluctuations. The ADP emerges as a dominant driver of interannual SIC anomalies, showing an out-of-phase relationship between the Atlantic and Pacific sectors of the Southern Ocean. Regional SIC trends exhibit contrasting patterns: the Ross Sea shows a significant positive SIC trend, while the B&A and Weddell Seas experience persistent negative anomalies due to enhanced meridional heat transport and stronger westerly winds. SAM strongly influences SIC, particularly in the Atlantic sector, with delayed responses of up to six months, likely due to ice-albedo feedbacks and ocean memory effects. These results enhance our understanding of Antarctic sea ice variability and its sensitivity to large-scale climate oscillations. Given the observed trends and ongoing climate change, further research is needed to assess how these processes will evolve under future warming scenarios. This study highlights the importance of continuous satellite observations and high-resolution climate modeling for improving projections of Antarctic sea ice behavior and its implications for the global climate system.

This study examines the complex interactions among soil moisture, evaporation, extreme weather events, and lightning, and their influence on fire activity across the extratropical and Pan-Arctic regions. Leveraging reanalysis and remote-sensing datasets from 2000 to 2020, we applied cross-correlation analysis, a modified Mann–Kendall trend test, and assessments of interannual variability to key variables including soil moisture, fire frequency and risk, evaporation, and lightning. Results indicate a significant increase in dry days (up to 40%) and heatwave events across Central Eurasia and Siberia (up to 50%) and Alaska (25%), when compared to the 1980–2000 baseline. Upward trends have been detected in evaporation across most of North America, consistent with soil moisture trends, while much of Eurasia exhibits declining soil moisture. Fire danger shows a strong positive correlation with evaporation north of 60° N (r ≈ 0.7, p ≤ 0.005), but a negative correlation in regions south of this latitude. These findings suggest that in mid-latitude ecosystems, fire activity is not solely driven by water stress or atmospheric dryness, highlighting the importance of region-specific surface–atmosphere interactions in shaping fire regimes. In North America, most fires occur in temperate grasslands, savannas, and shrublands (47%), whereas in Eurasia, approximately 55% of fires are concentrated in forests/taiga and temperate open biomes. The analysis also highlights that lightning-related fires are more prevalent in Eastern Europe and Southeastern Asia. In contrast, Western North America exhibits high fire incidence in temperate conifer forests despite relatively low lightning activity, indicating a dominant role of anthropogenic ignition. These findings underscore the importance of understanding land–atmosphere interactions in assessing fire risk. Integrating surface conditions, climate extremes, and ignition sources into fire prediction models is crucial for developing more effective wildfire prevention and management strategies.

Climatic feedbacks associated with orbitally driven Sea Surface Temperature (SST) lead to a profound impact on the Southern Hemisphere (SH) vegetation cover during the Mid-Holocene, MIS5e, MIS11c, and MIS31 interglacials. Results are based on a suite of coupled climate simulations conducted with the ICTP-CGCM (Speed-Nemo), which provides the boundary conditions for the CCM3/IBIS vegetation model. The CCM3/IBIS model was run from the Speed-Nemo output of the global ocean and individualized Atlantic, Pacific and Indian ocean basins forcing, in addition to orbital parameters and greenhouse gases. For interglacials, MH, MIS 5e, and 11c, areas have been found to be significantly reduced in tropical evergreen forests, but more extensive savanna and grasslands, over parts of the southern and central African region and into northern South America (Amazon region). In another important period, the results showed that there have been changes in vegetation cover due to insolation forcing during MIS31 compared to other interglacials, but the impact was greater in Australia and central South America. However, the southern tropical climate became drier due to negative SST anomalies, induced by the Atlantic and Tropical Pacific basins, and thus reduced continental precipitation. This study demonstrates that vegetation responses across tropical regions of the Southern Hemisphere are not solely driven by orbital variations but are also significantly modulated by internal changes in Atlantic and Pacific SST patterns. In the study regions, in particular, during the MH, MIS5e and MIS11c, the seasonality of insolation was reduced in the SH, leading to the cooling of the southern tropical ocean basins, resulting in the migration of the summer precipitation zone to boreal latitudes. Therefore, combined with increased low-latitude summer temperatures and a prolonged dryer period, the forcing led to a slow retraction, but steady in the rainforests of Congo and Amazonia, causing an effect of extreme aridity.