Significance: Whether the dry-season length will increase is a central question in determining the fate of the rainforests over Amazonia and the future global atmospheric CO ₂ concentration. We show observationally that the dry-season length over southern Amazonia has increased significantly since 1979. We do not know what has caused this change, although it resembles the effects of anthropogenic climate change. The global climate models that were presented in the Intergovernmental Panel on Climate Change’s fifth assessment report seem to substantially underestimate the variability of the dry-season length. Such a bias implies that the future change of the dry-season length, and hence the risk of rainforest die-back, may be underestimated by the projections of these models.

Observations show that the standard precipitation index (SPI) over the southern Amazon region decreased in the period of 1970–1999 by 0.32 per decade, indicating an increase in dry conditions. Simulations of constant pre-industrial climate with recent climate models indicate a low probability ( p =0%) that the trends are due to internal climate variability. When the 23 models are forced with either anthropogenic factors or both anthropogenic and external natural factors, approximately 13% of sampled 30-year SPI trends from the models are found to be within the range of the observed SPI trend at 95% confidence level. This suggests a possibility of anthropogenic and external forcing of climate change in the southern Amazon. On average, the models project no changes in the frequency of occurrence of low SPI values in the future; however, those models which produce more realistic SPI climatology, variability and trend over the period 1970–1999 show more of a tendency towards more negative values of SPI in the future. The analysis presented here suggests a potential anthropogenic influence on Amazon drying, which warrants future, more in-depth, study.

High-yield agriculture potentially reduces pressure on forests by requiring less land to increase production. Using satellite and field data, we assessed the area deforested by industrial-scale high-yield oil palm expansion in the Peruvian Amazon from 2000 to 2010, finding that 72% of new plantations expanded into forested areas. In a focus area in the Ucayali region, we assessed deforestation for high- and smallholder low-yield oil palm plantations. Low-yield plantations accounted for most expansion overall (80%), but only 30% of their expansion involved forest conversion, contrasting with 75% for high-yield expansion. High-yield expansion minimized the total area required to achieve production but counter-intuitively at higher expense to forests than low-yield plantations. The results show that high-yield agriculture is an important but insufficient strategy to reduce pressure on forests. We suggest that high-yield agriculture can be effective in sparing forests only if coupled with incentives for agricultural expansion into already cleared lands.

Six rainfall datasets are compared over the Amazon basin, Northeast Brazil, and the Congo basin. These datasets include three gauge-only precipitation products from the Climate Prediction Center (CPC), Global Precipitation Climatology Center (GPCC), and Brazilian Weather Forecast and Climate Studies Center (CLMNLS), and three combined gauge and satellite precipitation datasets from the CPC Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and Tropical Rainfall Measuring Mission (TRMM) product. The pattern of the annual precipitation is consistently represented by these data, despite the differences in methods and periods of averaging. Quantitatively, the differences in annual precipitation among these datasets are 5% more than the Amazon domain (0°–15°S, 50°–70°W), 22% more than Northeast Brazil (5°–10°S, 35°–45°W), and 11% more than the Congo domain (5°N–10°S, 15°–30°E). Over the Amazon domain the rainfall variation is well correlated between CPC, TRMM, GPCP, and GPCC (r 2 > 0.9) except for the northwestern Amazon, whereas CMAP and CLMNLS were different from these four datasets. Over the Congo basin, the coefficient of determination between these rainfall datasets is generally below 0.7. The empirical orthogonal functions analysis suggests large discrepancies in interannual and decadal variations of rainfall among these datasets, especially for the Congo basin and for the South American region after 1998. In general, CMAP, GPCC, TRMM, and GPCP significantly agree over the tropical areas in South America.