Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP).

Extreme temperature and precipitation events are the primary triggers of hazards, such as heat waves, droughts, floods, and landslides, with localized impacts. In this sense, the finer grids of Earth System models (ESMs) could play an essential role in better estimating extreme climate events. The performance of High Resolution Model Intercomparison Project (HighResMIP) models is evaluated using the Expert Team on Climate Change Detection and Indices (ETCCDI) over the 1981-2014 period and future changes (2021-2050) under Shared Socioeconomic Pathway SSP5-8.5, over ten regions in Latin America and the Caribbean. The impact of increasing the horizontal resolution in estimating extreme climate variability on a regional scale is first compared against reference gridded datasets, including reanalysis, satellite, and merging products. We used three different groups based on the resolution of the model's grid (sg): (i) low (0.8° ≤ sg ≤ 1.87°), (ii) intermediate (0.5° ≤ sg ≤ 0.7°), and (iii) high (0.23° ≥ sg ≤ 0.35°). Our analysis indicates that there was no clear evidence to support the posit that increasing horizontal resolution improves model performance. The ECMWF-IFS family of models appears to be a plausible choice to represent climate extremes, followed by the ensemble mean of HighResMIP in their intermediate resolution. For future climate, the projections indicate a consensus of temperature and precipitation climate extremes increase across most of the ten regions. Despite the uncertainties presented in this study, climate models have been and will continue to be an important tool for assessing risk in the face of extreme events. Supplementary Information: The online version contains supplementary material available at 10.1007/s41748-022-00337-7.

Techniques for monthly rainfall regionalization in southwestern Colombia.

The knowledge of rainfall regimes is a relevant requirement for many activities such as water resources planning, risk management, agriculture activities management, and other hydrologic applications. The present study has consisted of validating four techniques (one linear, one non-linear, and two hybrids) that allow identifying homogenous regions. We take the monthly rainfall in the Southwestern Colombia (Nariño), an area of 33,268 km2 characterized by complex topography and local factors that can influence the rainfall behavior, to test all techniques. The results showed overall the best performance for the approach related to non-linear principal component analysis and self-organizing map. However, in all mainly prevail two regions: the Andean Region and Pacific Region with a bimodal and unimodal regime, respectively. The bimodal one dominates over the Andes mountains range and the unimodal one the coastal zone. The application of non-linear approaches provided a better understanding of the seasonality of rainfall, and the results may be useful for water resource management.

Increase in the number of explosive low‒level cyclones around King George Island in the last three decades.

This paper documents an increase in the number of observed explosive cyclones (EC) at King George Island, South Shetland Islands, Antarctica, over the 1989‒2020 period. In ECs at 60o latitudes the surface atmospheric pressure drops ≥24 hPA in 24 hours. The annual EC frequency time series shows a significant positive trend of ~2.7 cyclones/decade, with a break in 2003 and average numbers of 7.3 and 11.8 events before and after that break, respectively. The increase follows closely earlier documented global sea surface temperature (SST) anomaly trends for the 1981‒2018 period, partially attributed to global warming and to the Pacific Decadal Oscillation (PDO). Connections between EC frequency and SST might occur through variations in SST in the southeastern Pacific and southwestern Atlantic, with anomalous cold conditions favoring an increase in ECs. We also found close relations between the number of ECs with simultaneous occurrences of PDO and Atlantic multidecadal oscillation in opposite phases, so that after 2003 they were in the cold and warm phases, respectively, and vice-versa before 2003. Both low-frequency modes seem to modulate the number of ECs. As per the authors knowledge these results have not been discussed before and may help climate modeling studies and weather forecasts.

Missing data estimation in extreme rainfall indices for the Metropolitan area of Cali - Colombia: An approach based on artificial neural networks.

Changes observed in the current climate and projected for the future significantly concern researchers, decision-makers, and the general public. Climate indices of extreme rainfall events are a trend assessment tool to detect climate variability and change signals, which have an average reliability at least in the short term and given climatic inertia. This paper shows 12 climate indices of extreme rainfall events for annual and seasonal scales for 12 climate stations between 1969 to 2019 in the Metropolitan area of Cali (southwestern Colombia). The construction of the indices starts from daily rainfall time series, which although have between 0.5% and 5.4% of missing data, can affect the estimation of the indices. Here, we propose a methodology to complete missing data of the extreme event indices that model the peaks in the time series. This methodology uses an artificial neural network approach known as Non-Linear Principal Component Analysis (NLPCA). The approach reconstructs the time series by modulating the extreme values of the indices, a fundamental feature when evaluating extreme rainfall events in a region. The accuracy in the indices estimation shows values close to 1 in the Pearson's Correlation Coefficient and in the Bi-weighting Correlation. Moreover, values close to 0 in the percent bias and RMSE-observations standard deviation ratio. The database provided here is an essential input in future evaluation studies of extreme rainfall events in the Metropolitan area of Cali, the third most crucial urban conglomerate in Colombia with more than 3.9 million inhabitants.

Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America.

The Colombian Biogeographic Choco (CBC) and the La Plata Basin (LPB) are regions with high biodiversity. However, these areas are characterized by scarce climatological information, complex orography, and rain-gauge network unevenly distributed. Interpolated data from the ground station might overcome these aspects. For this reason, is necessary to identify the best technique for the spatial interpolation of rainfall. Hence, the spatial interpolation techniques were applied to annual and seasonal rainfall in the CBC and LPB. Geostatistical results and deterministic approaches were compared by cross-validation. Cokriging with spherical (gaussian) model is the best interpolator in the CBC (LPB), as indicated by the lowest root mean square error (RMSE) and a standardized RMSE close to one. The CBC shows three rainfall cores: the northern, 9,000 mm/year; the central-southern, 10,000 mm/year; and the southern, 7,000 mm/year. The LPB shows a west-east rainfall gradient, with a minimum to the west (450 mm/year) and a maximum in the mid-west (2,000 mm/year). To the north of the LPB, rainfall reaches 1,500 mm/year, while in the south it reaches only 900 mm/year. The results in our study may be useful for scientists and decision-makers for use in environmental and hydrological models for the CBC and the LPB.