Tim Lucas, 919-613-8084, firstname.lastname@example.org
DURHAM, N.C. – A new study suggests the southern portion of the Amazon rainforest is at a much higher risk of dieback due to climate change than projections made in the latest report by the Intergovernmental Panel on Climate Change (IPCC).
If severe enough, the loss of rainforest could cause the release of large volumes of the greenhouse gas carbon dioxide into the atmosphere. It could also disrupt plant and animal communities in one of the world’s most biodiverse regions.
Researchers at the University of Texas at Austin led the new study. Wenhong Li, assistant professor of climatology at Duke University’s Nicholas School of the Environment, co-authored it.
The study appears this week in the Proceedings of the National Academy of Science.
Using ground-based rainfall measurements from the last three decades, a research team led by Rong Fu, professor at UT-Austin’s Jackson School of Geosciences, found that since 1979, the dry season in southern Amazonia has lasted about a week longer per decade. At the same time, the annual fire season has become longer. The researchers say the most likely explanation for the lengthening dry season is global warming.
“The dry season over the southern Amazon is already marginal for maintaining rainforest,” said Fu. “At some point, if it becomes too long, the rainforest will reach a tipping point.”
The new results are in stark contrast to forecasts made by climate models used by the IPCC. Even under future scenarios in which atmospheric greenhouse gases rise dramatically, the models project the dry season in the southern Amazon to be only a few to ten days longer by the end of the century and therefore the risk of climate change-induced rainforest dieback should be relatively low.
“The length of the dry season in the southern Amazon is the most important climate condition controlling the rainforest,” said Fu. “If the dry season is too long, the rainforest will not survive.”
“Our early work using drought index shows an increase in dry condition in the southern Amazon. The increase of dry-season length found in this study is consistent with those previous results,” said Li.
The researchers say the most likely explanation for the lengthening dry season in the southern Amazon in recent decades is human-caused greenhouse warming which inhibits rainfall in two ways: First, it makes it harder for warm, dry air near the surface to rise up and freely mix with cool, moist air above. And second, it blocks cold front incursions from outside the tropics that could trigger rainfall. The climate models used by the IPCC do a poor job representing these processes, which might explain why they project only a slightly longer Amazonian dry season, said Fu.
The Amazon rainforest normally removes the greenhouse gas carbon dioxide from the atmosphere, but during a severe drought in 2005, it released 1 petagram of carbon (about one tenth of annual human emissions) to the atmosphere. Fu and her colleagues estimate that if dry seasons continue to lengthen at just half the rate of recent decades, the Amazon drought of 2005 could become the norm, rather than the exception, by the end of this century.
“Because of the potential impact on the global carbon cycle, we need to better understand the changes of the dry season over southern Amazonia,” Fu said.
Some scientists have speculated that the combination of longer dry seasons, higher surface temperatures and more fragmented forests due to ongoing human-caused deforestation could eventually convert much of southern Amazonia from rainforest to savanna.
Fu and Li’s co-authors are Lei Yin, Robert Dickinson, Lei Huang and Sudip Chakraborty, all of UT-Austin; Paola A. Arias of Universidad de Antioquia in Colombia; Katia Fernandes of Columbia University’s Lamont-Doherty Earth Observatory; Brant Liebmann of NOAA; Rosie Fisher of the National Center for Atmospheric Research; and Ranga Myneni of Boston University.
This work is supported by the National Science Foundation (AGS 0937400) and NOAA Climate Program Office Modeling, Analysis, Prediction and Projection Program (NA10OAAR4310157).