Evaluating extreme climate indices from CMIP3&5 global climate models and reanalysis data sets: a case study for present climate in the Andes of Ecuador

Abstract

The reliability of climate models depends ultimately on their adequacy in relevant real situations. However, climate in mountains, a very sensitive system, is scarcely monitored, making the assessment of global climate models (GCMs) projections problematic. This is even more critical for tropical mountain regions, where complex atmospheric processes acting across scales are specially challenging for GCMs. To help bridge this gap, we evaluated the representation of extreme climate indices by GCMs and reanalysis data in the Andes of Ecuador. This work presents an intercomparison of 11 climate precipitation indices (Climate Change Detection and Indices, ETCCDIs) reconstructed for the period 1 January 1981–31 December 2000 using the data of six climate stations situated in a medium‐sized Andean catchment in southern Ecuador, reanalysis data sets (RAD) ERA40, ERA‐Interim, NCEP/NCAR Reanalysis 1 (NCEP/NCAR‐R1) and NCEP/DOE Reanalysis 2 (NCEP/DOE‐R2), and the data sets of 19 and 29 models of the Coupled Model Intercomparison Project, Phases 3 and 5 (CMIP3&5). Temporal and spatial analysis highlights that the values and the variability of ETCCDIs based on reanalysis and CMIP3&5 data overestimate observations, especially in ENSO years. However, frequency‐type indices are in general better captured than amount‐related indices in RAD. In general, reanalysis data displayed a similar uncertainty as the CMIP model data sets and some indices present lower uncertainty. The uncertainty of ETCCDIs based on CMIP5 remains similar to CMIP3 GCMs, with small variations. The indices using NCEP/NCAR‐R1, NCEP/DOE‐R2, and ERA‐Interim data performed better than those obtained with the ERA40 data sets, with no discernible improvement between both NCEP products. It can be concluded that for the given study region CMIP3&5 models and reanalysis products with respectively good and poor performance, exist, however data should be carefully screened before use. Furthermore, these results confirm that the specificity of the studied region is a key to identify limiting aspects on the GCMs and reanalysis extreme climate representation.

Projected Heat Waves in Ecuador under Climate Change: Insights from HadGEM-RegCM4 Coupled Model

“…The reliability of climate projections, particularly in regions with complex topographies such as the Ecuadorian Andes, is contingent upon the precision in which Global Climate Models (GCMs) can simulate relevant climate phenomena. Evaluation of extreme climate indices generated by GCMs and reanalysis data for the Andes underscores the challenges in accurately capturing the nuances of tropical mountain climates [24]. Our study's projections for the Ecuadorian regions suggest both a significant warming trend and increasing variability, which must be interpreted in light of the findings of Campozano et al [24] (2017) that both reanalysis and CMIP datasets tend to overestimate observed values, particularly in ENSO years.…”

“…Evaluation of extreme climate indices generated by GCMs and reanalysis data for the Andes underscores the challenges in accurately capturing the nuances of tropical mountain climates [24]. Our study's projections for the Ecuadorian regions suggest both a significant warming trend and increasing variability, which must be interpreted in light of the findings of Campozano et al [24] (2017) that both reanalysis and CMIP datasets tend to overestimate observed values, particularly in ENSO years. Their work suggested that frequency-type indices are generally better represented in reanalysis data than amount-related indices, a finding that aligns with our observed trends around increasing temperatures.…”

“…Temperature variations across Ecuador are notable, with average temperatures ranging from 12 • C in the Highlands to 22 • C in the Coastal region. Additionally, the Pacific Ocean's surface temperature, particularly in the Niño 1 + 2 region, plays a significant role in the interannual variability of the country's temperature and precipitation, as it is closely linked with the El Niño-Southern Oscillation (ENSO) phenomenon [24][25][26].…”

10-year Characteristics of Moisture Source Regions and Their Potential Effect on Seasonal Isotopic Signatures of δ18O in Tropical Trees of Southern Ecuador

“…In South America, ERA-Interim, the forerunner of ERA5 performed appropriately for different variables (Campozano et al, 2017;Montini et al, 2019;Sanabria et al, 2019) and also for trajectory analysis with and without moisture diagnostics (Knippertz et al, 2013;Drumond et al, 2014;Trachte, 2018). These studies have in common that their calculations were accomplished within the complex topography of the Andes.…”

Assessment of seven CMIP5 model precipitation extremes over Iran based on a satellite‐based climate data set

“…The results provided by Jiang et al () indicated that the model performances in simulating four precipitation indices are more reliable in eastern China than in western China. Campozano et al () showed that the CMIP5 simulations overestimate the extreme precipitation indices over the Andes Mountains in Ecuador, especially in ENSO years.…”