Determination of climatic conditions related to precipitation anomalies in the Tropical Andes by means of the random forest algorithm and novel climate indices

Abstract

Understanding precipitation and its relation with atmospheric and oceanic conditions is vital in the face of climate change. This is crucial in the Tropical Andes (TA) because millions of people depend on water originated in the cordillera. Unfortunately, the paucity of meteorological monitoring that exists in mountainous regions is accentuated in the tropics. In this context, climate indices, remotely sensed, and gridded datasets, are useful tools to study climate and precipitation in the TA, and additional climate indices can be calculated from reanalysis datasets. The combination of this information with traditional indices has the potential to improve our understanding of precipitation. Our objective was to use the k‐means algorithm to regionalize precipitation in the TA (different regions have different climate), and then use the random forest algorithm to study the variables related to precipitation in each of these regions in seasonal timescales. Here, we show the suitability of the random forest algorithm to reveal climate processes and the high potential of the novel climate indices to improve the regressions. We found that convective available potential energy was the most important variable for precipitation in the northern TA, except for the Chocó, where v at 850 hPa was the most important one. Meanwhile, vertical integral of divergence of moisture flux was the most important one in the southern TA. Interestingly, in the DJF season when the South American low‐level jet (SALLJ) is more active, u and v at 850 hPa showed their lowest relative importance and the total column of water vapour showed its maximum, this could indicate that precipitation anomalies are controlled by atmospheric moisture availability rather than by the speed of the SALLJ during DJF. These results deepen our understanding of precipitation anomalies in the TA and the related oceanic and atmospheric conditions. The proposed methodology was proven to be suitable and it could be used in the future to test and formulate new hypotheses, and to forecast seasonal precipitation.

Moisture Origin and Transport for Extreme Precipitation over Indonesia's New Capital City, Nusantara in August 2021

“…This method also has the ability to produce more stable cluster boundaries [37]. In addition, to determine the best number of clusters, we applied the within-cluster sum of square errors (WCSS) metric based on the elbow method [37,38]. The optimum number is selected when the WCSS reaches the minimum value, which is 3 cluster centres in our study.…”

“…5). To categorize these trajectories objectively, the pathways are grouped by k-mean clustering [35,36], resulting in three main clusters based on the elbow method [37,38]. The results show that moisture responsible for the extreme rainfall event was transported from 3 directions: from the southeast as cluster 1, from the northwest as cluster 2, and from the east as cluster 3 (Fig.…”

Regionalization of rainfall in the upper Madeira basin based on interannual and decadal variability: A multi‐seasonal approach

“…This approach is useful to identify regions with similar seasonal regimes, but it does not allow studying interannual and decadal variability. Along the same path, (Córdova et al, 2022) used 15 variables (12‐monthly and one annual rainfall averages, latitude and longitude) to regionalize rainfall in the Tropical Andes and then, the random forest algorithm to study the atmospheric variables related to precipitation in each of those regions at seasonal timescales.…”