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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hessd-12-671-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
15 Jan 2015
Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The revised manuscript was not accepted.
Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation
G. Martins1, C. von Randow1, G. Sampaio1, and A. J. Dolman2 1Earth System Science Center/National Institute for Space Research, Sao Paulo, Brazil
2Department of Earth Sciences/Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Abstract. Studies on numerical modeling in Amazonia show that the models fail to capture important aspects of climate variability in this region and it is important to understand the reasons that cause this drawback. Here, we study how the general circulation models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate the inter-relations between regional precipitation, moisture convergence and Sea Surface Temperature (SST) in the adjacent oceans, to assess how flaws in the representation of these processes can translate into biases in simulated rainfall in Amazonia. Using observational data (GPCP, CMAP, ERSST.v3, ERAI and evapotranspiration) and 21 numerical simulations from CMIP5 during the present climate (1979–2005) in June, July and August (JJA) and December, January and February (DJF), respectively, to represent dry and wet season characteristics, we evaluate how the models simulate precipitation, moisture transport and convergence, and pressure velocity (omega) in different regions of Amazonia. Thus, it is possible to identify areas of Amazonia that are more or less influenced by adjacent ocean SSTs. Our results showed that most of the CMIP5 models have poor skill in adequately representing the observed data. The regional analysis of the variables used showed that the underestimation in the dry season (JJA) was twice in relation to rainy season as quantified by the Standard Error of the Mean (SEM). It was found that Atlantic and Pacific SSTs modulate the northern sector of Amazonia during JJA, while in DJF Pacific SST only influences the eastern sector of the region. The analysis of moisture transport in JJA showed that moisture preferentially enters Amazonia via its eastern edge. In DJF this occurs both via its northern and eastern edge. The moisture balance is always positive, which indicates that Amazonia is a source of moisture to the atmosphere. Additionally, our results showed that during DJF the simulations in northeast sector of Amazonia have a strong bias in precipitation and an underestimation of moisture convergence due to the higher influence of biases in the Pacific SST. During JJA, a strong precipitation bias was observed in the southwest sector associated, also with a negative bias of moisture convergence, but with weaker influence of SSTs of adjacent oceans. The poor representation of precipitation-producing systems in Amazonia by the models and the difficulty of adequately representing the variability of SSTs in the Pacific and Atlantic oceans may be responsible for these underestimates in Amazonia.

Citation: Martins, G., von Randow, C., Sampaio, G., and Dolman, A. J.: Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation, Hydrol. Earth Syst. Sci. Discuss., 12, 671-704, https://doi.org/10.5194/hessd-12-671-2015, 2015.
G. Martins et al.

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Short summary
Studies on numerical modeling in Amazonia show that the models fail to capture important aspects of climate variability in this region and it is important to understand the reasons that cause this drawback. We study how the general circulation models of the CMIP5 simulate the inter-relations between regional precipitation, moisture convergence and SST in the adjacent oceans, to assess how flaws in the representation of these processes can translate into biases in simulated rainfall in Amazonia.
Studies on numerical modeling in Amazonia show that the models fail to capture important aspects...
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