Evaluation of the AR4 CMIP3 and the AR5 CMIP5 Model and Projections for Precipitation in Northeast Brazil

Abstract

This article compares the sensitivity of IPCC CMIP3-AR4 and CMIP5-AR5 models used on the latest reports from the Intergovernmental Panel on Climate Change (IPCC) in representing the annual average variations (austral summer and autumn) on three regions in Northeastern Brazil (NNEB) for the periods 1979-2000 using the CMAP (Climatology Merged Analysis of Precipitation) data as reference. The three areas of NNEB chosen for this analysis were the semiarid, eastern, and southern regions. The EOF analysis was performed to investigate how the coupled models resolve the temporal variability of the spatial modes in the Tropical Atlantic Sea Surface Temperature (SST), which drives the interannual variations of the rainfall in the Northeastern Brazil. CMIP3-AR4 and CMIP5-AR5 models presented a good representation of the annual cycle of precipitation.Results from correlation and mean absolute error analysis indicate that both CMIP3 and CMIP5 models produce large errors and barely capture the interannual rainfall variance during austral summer and autumn in Northeast Brazil, this features is closely related to the poor representation of the modes of SST variability in the Tropical Atlantic Ocean. For the summer and autumn rainfall projections in the semiarid region, there was no convergence between the CMIP3 and CMIP5 models. During the summer and autumn in the eastern sector, both the CMIP3 and CMIP5 models projected rainfall above the mean for the 2040-2070 period.

Austral summer precipitation biases over tropical South America in five CMIP5 earth system models

“…CCSM4 model shows higher maximum values than GPCP (Figure 1b), and the CAN model has the lowest values over Brazil among models results (Figure 1e). These results are consistent with previous works using several coupled models (e.g., Li et al ., 2006; Vera et al ., 2006; Bombardi and Carvalho, 2009; Jones and Carvalho, 2013; Gulizia and Camilloni, 2015; Sierra et al ., 2015; Alves et al ., 2016; Díaz and Vera, 2017). Jones and Carvalho (2013) results suggest problems with CMIP5 models to simulate the maximum precipitation over the core of the monsoon.…”

“…In contrast, Alves et al . (2016) compared the CMIP3 and CMIP5 models for Northeastern Brazil and found similar errors in both phase models. Sierra et al .…”

“…However, in Northeastern Brazil, they found the overestimated values are higher in CMIP5 than in CMIP3 analysis. In contrast, Alves et al (2016) compared the CMIP3 and CMIP5 models for Northeastern Brazil and found similar errors in both phase models. Sierra et al (2015) and Díaz and Vera (2017) also found negative (positive) bias at Amazon basin (Northeastern Brazil) at CMIP5 models during austral summer.…”

“…Most models in CMIP Phase 3 (CMIP3) misrepresent tropical SA precipitation, with biases varying greatly in Amazonia rainfall representation (e.g., Li et al ., 2006; Vera et al ., 2006; Bombardi and Carvalho, 2009; Alves et al ., 2016). Significant improvements have been made from CMIP3 to CMIP5 to simulate precipitation over the Amazon basin.…”

Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

“…Although interest in climate change studies has increased, there have been fewer studies investigating climate extreme events. Most studies have examined the variation of monthly, annual, and seasonal mean values of climate parameters (Almazroui, Nazrul Islam, Saeed, Alkhalaf, & Dambul, 2017; Alves et al, 2016; Gagnon, Singh, Rousselle, & Roy, 2005; Ludwing et al, 2019; Marengo et al, 2009; Palatella, Miglietta, Paradisi, & Lionello, 2010; Pandey, Das, Jhajharia, & Pandey, 2018; Pervez & Henebry, 2014; Silva & Mendes, 2015). To facilitate research on climate extremes and their trends, the Expert Team on Climate Change Detection and Indices (ETCCDI) of WMO and the World Climate Research Program (WCRP) have identified a set of 27 indicators describing the extreme character of climate events.…”