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Large tropical volcanic eruptions can affect the climate of many regions on Earth, yet it is uncertain how the largest eruptions over the past millennium may have altered Earth’s hydroclimate. Here, we analyze the global hydroclimatic response to all the tropical volcanic eruptions over the past millennium that were larger than the Mount Pinatubo eruption of 1991. Using the Paleo Hydrodynamics Data Assimilation product (PHYDA), we find that these large volcanic eruptions tended to produce dry conditions over tropical Africa, Central Asia and the Middle East and wet conditions over much of Oceania and the South American monsoon region. These anomalies are statistically significant, and they persisted for more than a decade in some regions. The persistence of the anomalies is associated with southward shifts in the Intertropical Convergence Zone and sea surface temperature changes in the Pacific and Atlantic oceans. We compare the PHYDA results with the stand-alone model response of the Community Earth System Model (CESM)-Last Millennium Ensemble. We find that the proxy-constrained PHYDA estimates are larger and more persistent than the responses simulated by CESM. Understanding which of these estimates is more realistic is critical for accurately characterizing the hydroclimate risks of future volcanic eruptions. 

Abstract Monthly and daily gridded precipitation datasets are one of the most demanded products in climatology and hydrology. These datasets describe the high spatial and temporal variability of precipitation as a continuous surface and for defined periods. However, due to the complex characteristics of precipitation, it is difficult to obtain accurate estimations. Thus, the creation of a gridded dataset from observations requires the comprehensive and precise application of quality control, reconstruction, and gridding procedures. Yet, despite multiple advances, most of the gridded datasets created and published since the mid‐1990s to the present use a wide variety of techniques, methods, and outputs, which can completely change the final representativity of the data. It is, therefore, critical to provide general guidelines for the development of future and more robust gridded datasets based on the data characteristics, geographical factors, and advanced statistical techniques. We identified gaps and challenges for near‐future perspectives and provide guidelines for implementing improved approaches based on the performance of 48 products. Finally, we concluded that, despite better spatial and temporal resolutions, data access, and data processing capabilities, observational coverage remains a challenge. Moreover, scientists should adopt tailored strategies to improve the representativity and uncertainty of the estimates. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water ExtremesScience of Water > Methods 

Abstract The long‐term hydroclimatic variability in Santiago (Chile) was analysed by means of a new 481‐year (1536–2016 CE) tree‐ring reconstruction of the Standardized Precipitation Evapotranspiration Index (SPEI) of August, integrating the hydroclimatic conditions during the preceding 14 months. Results show a high frequency of extreme drought events in the late 20th and early 21st centuries, while the frequency of extreme wet events was higher in the 17th–18th centuries. The mid‐20th century represents a breaking point for the hydroclimatic history in the region, including some significant changes: (a) the interannual variability increased; (b) the wet events became less intense; (c) the extreme dry events became more frequent; and (d) the most intense dry event of the entire period was identified, coinciding with the so‐called Megadrought (2006–2016). A correlation analysis between the reconstructed SPEI and three climate indices (PDO, SOI and Niño3.4) was performed at monthly scale, considering different multi‐annual aggregations. The analysis shows diverse impacts on the hydroclimatic variability, with positive correlations between SPEI and PDO as well as Niño3.4, and negative correlations between SPEI and SOI. The most significant correlations were, overall, found at multi‐annual time scales (>7 years). Results help to better understand the current hydroclimatic changes (Megadrought) in a long‐term context. 

Abstract AimPrevious work demonstrated the global variability of synchrony in tree growth within populations, that is, the covariance of the year‐to‐year variability in growth of individual neighbouring trees. However, there is a lack of knowledge about the causes of this variability and its trajectories through time. Here, we examine whether climate can explain variation in within‐population synchrony (WPS) across space but also through time and we develop models capable of explaining this variation. These models can be applied to the global tree cover under current and future climate change scenarios. LocationGlobal. Time period1901–2012. Major taxa studiedTrees. MethodsWe estimated WPS values from a global tree‐ring width database consisting of annual growth increment measurements from multiple trees at 3,579 sites. We used generalized linear mixed effects models to infer the drivers of WPS variability and temporal trends of global WPS. We then predicted WPS values across the global extent of tree cover. Finally, we applied our model to predict future WPS based on the RCP 8.5 (2045–2065 period) emission scenario. ResultsAreas with the highest WPS are characterized by a combination of environments with both high mean annual temperature (>10°C) and low precipitation (<300 mm). Average WPS across all temperate forests has decreased historically and will continue to decrease. Potential implications of these patterns include changes in forest dynamics, such as higher tree growth and productivity and an increase in carbon sequestration. In contrast, the WPS of tropical forests of Central and South America will increase in the near future owing to reduced annual precipitation. Main conclusionsClimate explains WPS variability in space and time. We suggest that WPS might have value as an integrative ecological measure of the level of environmental stress to which forests are subjected and therefore holds potential for diagnosing effects of global climate change on tree growth.