ENSO impact on simulated South American hydro-climatology

• Published in: Advances in Geosciences
• Main Authors: Stuck, J., Güntner, A., Merz, B.
• Format: Text
• Language: English
• Published: 2018
• Subjects: Pacific; Sea ice
• Online Access: https://doi.org/10.5194/adgeo-6-227-2006; https://adgeo.copernicus.org/articles/6/227/2006/

The variability of the simulated hydro-climatology of the WaterGAP Global Hydrology Model (WGHM) is analysed. Main object of this study is the ENSO-driven variability of the water storage of South America. The horizontal model resolution amounts to 0.5 degree and it is forced with monthly climate variables for 1961-1995 of the Tyndall Centre Climate Research Unit dataset (CRU TS 2.0) as a representation of the observed climate state. Secondly, the model is also forced by the model output of a global circulation model, the ECHAM4-T42 GCM. This model itself is driven by observed monthly means of the global Sea Surface Temperatures (SST) and the sea ice coverage for the period of 1903 to 1994 (GISST). Thus, the climate model and the hydrological model represent a realistic simulated realisation of the hydro-climatologic state of the last century. Since four simulations of the ECHAM4 model with the same forcing, but with different initial conditions are carried out, an analysis of variance (ANOVA) gives an impression of the impact of the varying SST on the hydro-climatology, because the variance can be separated into a SST-explained and a model internal variability (noise). Also regional multivariate analyses, like Empirical Orthogonal Functions (EOF) and Canonical Correlation Analysis (CCA) provide information of the complex time-space variability. In particular the Amazon region and the South of Brazil are significantly influenced by the ENSO-variability, but also the Pacific coastal areas of Ecuador and Peru are affected. Additionally, different ENSO-indices, based on SST anomalies (e.g. NINO3.4, NINO1+2), and its influence on the South American hydro-climatology are analysed. Especially, the Pacific coast regions of Ecuador, Peru and Chile show a very different behaviour dependant on those indices.