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Extreme precipitation occurring on consecutive days may substantially increase the risk of related impacts, but changes in such events have not been studied at a global scale. Here we use a unique global dataset based on in situ observations and multimodel historical and future simulations to analyze the changes in the frequency of extreme precipitation on consecutive days (EPCD). We further disentangle the relative contributions of variations in precipitation intensity and temporal correlation of extreme precipitation to understand the processes that drive the changes in EPCD. Observations and climate model simulations show that the frequency of EPCD is increasing in most land regions, in particular, in North America, Europe, and the Northern Hemisphere high latitudes. These increases are primarily a consequence of increasing precipitation intensity, but changes in the temporal correlation of extreme precipitation regionally amplify or reduce the effects of intensity changes. Changes are larger in simulations with a stronger warming signal, suggesting that further increases in EPCD are expected for the future under continued climate warming.

The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.

In this review, seven pieces of research on climate variability and its impact on human health in Buenos Aires City between 1995 and 2015 were evaluated. The review highlighted continuities and ruptures in the methodology, variables, and statistics data of the research, considering their similarities and differences in the period of study and the methodology applied. Contributions, pending issues, and public policies on climate change challenges in the city aimed at improving living conditions were considered. Six studies contributed evidence on the relationship between climate and health and its impacts on the population; two studies suggested the development of early warning systems and one study is a preliminary approach.

The ability of four regional climate models to reproduce the present-day South American climate is examined with emphasis on La Plata Basin. Models were integrated for the period 1991–2000 with initial and lateral boundary conditions from ERA-40 Reanalysis. The ensemble sea level pressure, maximum and minimum temperatures and precipitation are evaluated in terms of seasonal means and extreme indices based on a percentile approach. Dispersion among the individual models and uncertainties when comparing the ensemble mean with different climatologies are also discussed. The ensemble mean is warmer than the observations in South Eastern South America (SESA), especially for minimum winter temperatures with errors increasing in magnitude towards the tails of the distributions. The ensemble mean reproduces the broad spatial pattern of precipitation, but overestimates the convective precipitation in the tropics and the orographic precipitation along the Andes and over the Brazilian Highlands, and underestimates the precipitation near the monsoon core region. The models overestimate the number of wet days and underestimate the daily intensity of rainfall for both seasons suggesting a premature triggering of convection. The skill of models to simulate the intensity of convective precipitation in summer in SESA and the variability associated with heavy precipitation events (the upper quartile daily precipitation) is far from satisfactory. Owing to the sparseness of the observing network, ensemble and observations uncertainties in seasonal means are comparable for some regions and seasons.

The consequences of climate change are particularly noticeable through extreme events, which have already changed in intensity and frequency worldwide. This study aims to evaluate the ability of 33 CMIP6 models to simulate the observed trends of four extreme temperature indices in South America during the period 1979–2014. We use daily minimum and maximum temperatures from an observational database, ERA5 reanalysis, and CMIP6 models to estimate the international indices: cold nights, warm nights, cold days, and warm days. Trends are calculated using Sen’s slope for different seasons and spatial scales (continental, sub-regional, and at each grid point) and tested with the Mann–Kendall test. All databases agree on an increase (decrease) in the frequency of warm (cold) extremes in South America, with the most intense changes in the austral spring. In particular, the warm nights index and the northern sub-regions of South America show the most pronounced trends. In contrast, in the southern sub-regions of South America, the observations do not indicate significant trends of the minimum temperature indices, which differ from the trends estimated by the CMIP6 ensemble median and most of the individual models. In general, the ensemble median simulates significant long-term changes at almost all grid points, unlike the observations and reanalysis. Finally, the simulated trends related to minimum temperature are slightly better represented than those related to maximum temperatures. Nevertheless, neither model stands out as the best, and all of them have difficulty simulating trends, especially for cold days.

Compound hot and dry events can cause greater impacts than those generated by individual extreme events. Understanding the physical mechanisms that lead to their development is particularly important for an early warning. The aim of this study is to assess the ability of global climate models (GCMs) to simulate hot/dry compound events in South America (SA) during the historical period 1979–2014, in comparison with observational and reanalysis datasets. Additionally, this work seeks to investigate the potential changes in these events under two future climate scenarios for the period 2065–2100. Furthermore, we analyze the spatial patterns of sea surface temperature anomalies (SSTA) in the Pacific Ocean associated with these events in tropical and extratropical SA. In the historical period, reanalysis tends to overestimate the number of hot/dry events, while the ensemble median of GCMs performs better than the individual ones. The future projections under the high emissions scenario show longer heat waves, but a low model agreement about the number of compound events in tropical SA. For southern SA, an increase in the annual frequency of compound events is projected, and more than two hot/dry events per year are expected to occur relative to the 1979–2014 baseline. Finally, we find that compound events in tropical SA are favored during the El Niño phase, even though two other SSTA patterns have gained prominence in recent years. In southern SA, hot/dry events are associated with the negative phase of the Pacific Decadal Oscillation and the La Niña phase.

Predicting extreme temperature events can be very useful for different sectors that are strongly affected by their variability. The goal of this study is to analyze the influence of the main atmospheric, oceanic, and soil moisture forcing on the occurrence of summer warm days and to predict extreme temperatures in Argentina northern of 40°S by fitting a statistical model. In a preliminary analysis, we studied trends and periodicities. Significant positive trends, fundamentally in western Argentina, and two main periodicities of summer warm days were detected: 2–4 years and approximately 8 years. Lagged correlations allowed us to identify the key predictors: El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Standardized Precipitation Indices (SPI). We also noticed that the frequency of warm days in spring acts as a good predictor of summer warm days. Due to the collinearity among many predictors, principal component regression was used to simulate summer warm days. We obtained negative biases (i.e., the model tends to underestimate the frequency of summer warm days), but the observed and simulated values of summer warm days were significantly correlated, except in northwest Argentina. Finally, we analyzed the predictability of the summer warm days under ENSO neutral conditions, and we found new predictors: the geopotential height gradient in 850 hPa (between the Atlantic Anticyclone and the Chaco Low) and the Atlantic Multidecadal Oscillation (AMO), while the PDO and SPI lost some relevance.

This study assesses the performance of 15 high resolution global climate models (GCMs) over the complex orographic region of the subtropical central Andes from available simulations of the Fifth Coupled Model Intercomparison Project (CMIP5). The simulated past climate (1980–2005) was compared against the Climate Research Unit (CRU) dataset and the ERA-Interim reanalysis, considered as reference datasets, to evaluate regional and seasonal surface temperature and precipitation, as well as sea level pressure and circulation. A good agreement was found between the simulations and the reference datasets for winter precipitation and for temperature over both seasons. Whilst all models correctly reproduce the annual cycle of precipitation, some of them overestimate winter totals. ERA-Interim does not adequately represent summer precipitation over the region, and some of the models analyzed also show the same deficiency. All models correctly reproduce the northward migration of the South Pacific subtropical high during winter, although some of them underestimate the maximum central pressure. During summer, most models fail to show the low level north–south flow parallel to the eastern foothills of the Andes, a feature known as the Low Level Jet. Further analysis of the results of the simulations led to the selection of a sub-set of five CMIP5 GCMs to construct a reduced ensemble. This reduced ensemble is a better representation than the multi-model mean of the 15 GCMs of the past climate at this region and would be recommended for future studies.

The goal of the CLARIS project was to build an integrated European–South American network dedicated to promote common research strategies to observe and predict climate changes and their consequent socio-economic impacts taking into account the climate and societal peculiarities of South America. Reaching that goal placed the present network as a privileged advisor to contribute to the design of adaptation strategies in a region strongly affected by and dependent on climate variability (e.g. agriculture, health, hydro-electricity). Building the CLARIS network required fulfilling the following three objectives: (1) The first objective of CLARIS was to set up and favour the technical transfer and expertise in earth system and regional climate modelling between Europe and South America together with the providing of a list of climate data (observed and simulated) required for model validations; (2) The second objective of CLARIS was to facilitate the exchange of observed and simulated climate data between the climate research groups and to create a South American high-quality climate database for studies in extreme events and long-term climate trends; (3) Finally, the third objective of CLARIS was to strengthen the communication between climate researchers and stakeholders, and to demonstrate the feasibility of using climate information in the decision-making process.