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Enhanced weathering (EW) is one of the most promising negative emissions technologies urgently needed to limit global warming to at least below 2 °C, a goal recently reaffirmed at the UN Global Climate Change conference (i.e., COP26). EW relies on the accelerated dissolution of crushed silicate rocks applied to soils and is considered a sustainable solution requiring limited technology. While EW has a high theoretical potential of sequestering CO 2 , research is still needed to provide accurate estimates of carbon (C) sequestration when applying different silicate materials across distinct climates and major soil types in combination with a variety of plants. Here we elaborate on fundamental advances that must be addressed before EW can be extensively adopted. These include identifying the most suitable environmental conditions, improving estimates of field dissolution rates and efficacy of CO 2 removal, and identifying alternative sources of silicate materials to meet future EW demands. We conclude with considerations on the necessity of integrated modeling− experimental approaches to better coordinate future field experiments and measurements of CO 2 removal, as well as on the importance of seamlessly coordinating EW with cropland and forest management.

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The atmospheric circulation can be recognized as one of the causes of severe rainfall events occurrence. Such events, especially when are characterized by short durations and high intensities, result in flood events in the Mediterranean area. It is very important to understand how these heavy rainfall events, which can be usually identified with convective rainfall, are related to the different types of atmospheric circulation. In order to do this, some weather circulation patterns (WPs), which have been derived for the Europe, have been first connected with the rainfall annual maxima (AMAX) recorded over the Sicily. The analyses allowed to identify those WPs that are more likely to result into the occurrence of the AMAX. Secondly, two ERA-Interim reanalysis indexes have been used to define a criterion to distinguish those AMAX mainly due to a convective component from those more related to a stratiform precipitation, also detecting a transient zone between these two types of events. Finally, the main results have been connected together with the aim to define a set of triggering factors of extreme rainfall events.

One of the most relevant and debated topics related to the effects of the climate change is whether intense rainfall events have become more frequent over the last decades. It is a crucial aspect, since an increase in the magnitude and frequency of occurrence of heavy rainfall events could result in a dramatic growth of floods and, in turn, human lives losses and economic damages.Because of its central position in the Mediterranean area, Sicily has been often screened with the aim to capture some trends in precipitation, potentially related to climate change. While Mann-Kendall test has been largely used for the rainfall trend detection, in this work a different procedure is considered. Precipitation trends are here investigated by processing the whole rainfall time-series, provided by the regional agency SIAS at a 10-min resolution, through the quantile regression method by aggregating precipitation across a wide spectrum of durations and considering different quantiles. Results show that many rain gauges are characterized by an increasing trend in sub-hourly precipitation intensity, especially at the highest quantiles, thus suggesting that, from 2002 to 2019, sub-hourly events have become more intense in most of the island. Moreover, by analysing some spatial patterns, it has been revealed that the south and the east of Sicily are more interested in significant increasing rainfall trends, especially at the 10-min duration. Finally, the comparison between the two procedures revealed a stronger reliability of the quantile regression in the trend analysis detection, mainly due to the possibility of investigating the temporal variation of the tails of precipitation distribution.