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.