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A characterization of the diurnal cycle of precipitation (DCP) over the whole South America is still lacking in the literature and the scarcity of sub‐daily rain ground measurements limits the data available for analysis and forbids a correct validation of alternative datasets. In this paper we analyse the climatological mean DCP during the monsoon active season using five gridded datasets: two satellite precipitation estimates, two regional climate models and one reanalysis. Amazonia, the Brazilian Highlands, the northeastern South American coast, the Andes and the western Colombian coast are identified as the areas with most prominent DCPs. The afternoon convection triggered by solar heating over land and the coastal and topographic effects are the main modes of sub‐daily variability, based on an EOF decomposition of the 3 hourly mean precipitation fields. We explore the contribution of mean frequency and intensity to amount of precipitation and nighttime–daytime differences. In general, both models precipitate earlier and more frequently than the satellite products and do not reproduce correctly areas of observed predominant nighttime precipitation where mesoscale convective systems are active, like La Plata Basin or Amazonia. Over the analysed areas, the high frequency of precipitation is the driving mechanism of total amount in the models, whereas in the satellite products there is also considerable contribution from intensity. Overall, the reanalysis shows features in between the models and the satellite estimates, sharing characteristics with both types of data. The results presented here point at the diversity of sub‐daily precipitation characteristics in South America, the issues with conventional climate models and the uncertainty in satellite products and reanalyses. The growing interest in the DCP by the scientific community and the development of new techniques like convection permitting modelling will hopefully continue to improve our knowledge of the precipitation dynamics and its influence on climate. [ABSTRACT FROM AUTHOR]

The South American low‐level jet (SALLJ) is a narrow northerly wind speed maximum present just above the boundary layer. It is an important component of the tropical–extratropical heat and moisture exchange in South America and can favour deep moist convection in southeastern South America. The main objective of this study is to analyse the SALLJ characteristics at 21 upper‐air stations deployed between the Tropics and the subtropics from just east of the Andes to the eastern plains during the SALLJ Experiment (SALLJEX). The greatest wind speed occurs between 300 and 2,000 m AGL between 0300 and 1200 UTC, mainly in connection with greater northerly winds during an anticlockwise rotation of the wind from sunset to sunrise, thus suggesting the important role of the inertial oscillation in the wind's diurnal cycle. The spatial variation of the LLJ throughout the SALLJEX network shows a weakening of the maximum wind speed from stations near the Andes towards the Plains suggesting the presence of the LLJ core just east of the Andes around Santa Cruz de la Sierra's latitude (17°48′S). Weak, moderate, and strong SALLJ categories defined from a local maximum northerly wind speed threshold at each station are defined to analyse the relationship between the SALLJ intensity and the thermodynamic properties of the lower layers of the atmosphere. Strong SALLJs are frequently observed at night‐time, while weak SALLJs are likely to occur at any time of the day. Strong cases have deeper and less stable nocturnal boundary layers, which could be due to the SALLJ warm advection near the time of wind speed maximum (0600 UTC). Deeper convective boundary layers and higher low‐level temperatures observed at 1800 UTC prior to strong nocturnal SALLJs can potentially lead to larger amplitudes of inertial oscillation and contribute to generating stronger SALLJs. [ABSTRACT FROM AUTHOR]

Temperature extreme indices were analyzed for five continental regions of southern South America defined according to their climatic characteristics. Gridded observations, reanalysis, and global‐coupled climate models from CMIP5 were used with the approach of temperature extreme trend attribution analysis on fixed‐threshold and percentile‐based temperature extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The largest positive trends are exhibited in the tropical nights index, and a clear anthropogenic signal is evident in the subtropical region. In the subtropical central Andes, there is a decrease in the frost days index and increases in the tropical nights and summer days indices, and an anthropogenic signal is evident. In the Patagonian region, all trends from the historical runs were significant, while the ones from the natural experiment were nonsignificant, showing the marked effect of anthropogenic forcing in this region in the extreme temperature events. Projected changes in extreme indices for the 21st century are consistent with a warming climate, and larger changes are expected in the warm nights index. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi‐Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) and database for Policy Decision making for Future climate change in reproducing the variability and change of the austral summer precipitation observed in South America along the second part of the 20th century and beginning of the 21st. LE show similar biases in the mean austral summer rainfall and interannual variability than those detected in previous model sets, such as the Coupled Model Intercomparison Project Phase 5 (CMIP5). The positive trends in south‐eastern South America (SESA) and negative ones in the southern Andes (SAn), that are the most significant observed regional features, are identified to some extent in LE simulations. The negative trend in SAn is a feature consistently shown among different models. For all models, mean trend is negative in that particular region and larger than the inter‐member dispersion for each large ensemble. Trend magnitudes in the SESA region show a larger dispersion between models than the SAn region. While the mean trend among models is consistent with the observations, models underestimate the observed trend. The multi‐model ensemble of selected models that best reproduce both mean and interannual variability features of rainfall in South America shows an 18% larger positive mean trend in SESA rainfall than those that have the worst performance. However, internal variability uncertainty is still higher than the mean trend for both sets of multi‐model ensembles [5% (18%) for selected (non‐selected) models]. Improvements in the representation of the main features of mean rainfall and variability are needed in order to better describe and reduce the uncertainties associated with the main climate change related signals in the continent. [ABSTRACT FROM AUTHOR]

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