Increased concentrations of atmospheric greenhouse gases have led to a global mean surface temperature 1.0°C higher than during the pre-industrial period. We expand on the recent IPCC Special Report on global warming of 1.5°C and review the additional risks associated with higher levels of warming, each having major implications for multiple geographies, climates, and ecosystems. Limiting warming to 1.5°C rather than 2.0°C would be required to maintain substantial proportions of ecosystems and would have clear benefits for human health and economies. These conclusions are relevant for people everywhere, particularly in low- and middle-income countries, where the escalation of climate-related risks may prevent the achievement of the United Nations Sustainable Development Goals.

In most of Argentina, the warming since 1901 was a little lower than the global average, although with strong trends in temperature extremes and in heat waves during the most recent decades. There was a remarkable increase in precipitation over most of subtropical Argentina, especially since 1960. This has favored agriculture yields and the extension of crop lands into semiarid regions, but this increase also came with more frequent heavy rainfalls and consequent flooding of rural and urban areas. Since the early 1970s, the main rivers of the Plata Basin have increased their mean flows, but this was attributable not only to increased precipitation, but also to land use changes. In contrast, over the Andes Mountains, reduced rainfall and increased temperature has led to glaciers receding and reduced river flows. Climate projections for the first half of this century maintain observed trends and raise additional concerns that in most cases can be dealt with timely adaptation policies. However, by the end of this century, under an extreme emissions scenario, the projected warming reaches 3.5°C in the north of the country with respect to present‐day conditions. There is insufficient knowledge to assume that this warming would not create severe damages to the people and the economy of Argentina. Because of the damages and casualties that heat waves and extreme precipitation events are already producing, the first and most urgent adaptation required is to reinforce early warning systems and contingency planning to cope with climatic extremes and their consequences on health. WIREs Clim Change 2015, 6:151–169. doi: 10.1002/wcc.316 This article is categorized under: Trans‐Disciplinary Perspectives > National Reviews

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Seasonal linear trends of precipitation from South American station data, which have been averaged onto grids, are examined, with emphasis on the central continent. In the period 1976-99, the largest trend south of 20ЊS occurs during the January-March season, is positive, and is centered over southern Brazil. From 1948 to 1975 the trend is also positive, but with less than half the slope. The trend is not due to a systematic change in the timing of the rainy season, which almost always starts before January and usually ends after March, but rather results from an increase in the percent of rainy days, and an increase in the rainy day average. The dynamic causes of the trend are not obvious. It does not appear to be accounted for by an increase in synoptic wave activity in the region. The precipitation trend is related to a positive sea surface temperature trend in the nearby Atlantic Ocean, but apparently not causally. The trend in the Atlantic seems to result from a decrease in mechanical stirring and coastal upwelling associated with a decrease in the strength of the western edge of the circulation associated with the South Atlantic high.

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The purpose of this study is to evaluate the ability of two sets of global climate models (GCMs) derived from the Coupled Model Intercomparison Projects Phase 3 (CMIP3) and Phase 5 (CMIP5) to represent the summer, winter, and annual precipitation mean patterns in South America south of the equator and in three particular sub‐regions, between years 1960 and 1999. Different metrics (relative bias, spatial correlation, RMSE, and relative errors) were calculated and compared between both projects to determine if there has been improvement from CMIP3 to CMIP5 models in the representation of regional rainfall. Results from this analysis indicate that for the analysed seasons, precipitation simulated by both CMIP3 and CMIP5 models' ensembles exhibited some differences. In DJF, the relative bias over Amazonia, central South America, eastern Argentina, and Uruguay is reduced in CMIP5 compared with CMIP3. In JJA, the same occurs in some areas of Amazonia. Annual precipitation is also better represented by the CMIP5 than CMIP3 GCMs as they underestimate precipitation to a lesser extent, although in NE Brazil the overestimation values are much larger in CMIP5 than in CMIP3 analysis. In line with previous studies, the multi‐model ensembles show the best representation of the observed patterns in most seasons and regions. Only in some cases, single GCMs [MIROC3.2(hires) – CMIP3– and MIROC4h – CMIP5] presented better results than the ensemble. The high horizontal resolution of these models suggests that this could be a relevant issue for a more adequate estimation of rainfall at least in the analysed regions.

Box 11.1 (continued) Box 11.1, Figure 2 | A schematic illustrating the progression from an initial-value based prediction at short time scales to the forced boundary-value problem of climate projection at long time scales. Decadal prediction occupies the middle ground between the two. (Based on Meehl et al., 2009b.)