Another thing not in climate models 'Synchronized leaf aging' in the tropics

From the DOE/BROOKHAVEN NATIONAL LABORATORY

Synchronized leaf aging in the Amazon responsible for seasonal increases in photosynthesis

High-tech photography in the Amazon reveals that young leaves grow in at the same times as older ones perish

Pictures like this one, taken from special cameras installed on towers above the rainforest canopy, recorded the changes in hundreds of individual tree crowns over the seasons in three different forests across the central Amazon. CREDIT Aline Lopes, INPA (National Institute of Amazonian Research).
Pictures like this one, taken from special cameras installed on towers above the rainforest canopy, recorded the changes in hundreds of individual tree crowns over the seasons in three different forests across the central Amazon. CREDIT Aline Lopes, INPA (National Institute of Amazonian Research).

UPTON, NY-One hundred and fifty feet above the ground in the Amazonian rainforest, a vast ocean of green spreads out in every direction. The rainforest canopy is made up of mostly tropical evergreen trees, which take in enormous amounts of carbon from Earth’s atmosphere. Understanding the carbon cycle in these forests – how carbon is stored in plants and soil and then returned to the atmosphere – is crucial to creating accurate models that predict how global climate will change in the future. Key to that puzzle is understanding photosynthesis in tropical forests.

“We want to understand whether photosynthesis in tropical evergreen forests is driven primarily by seasonal climate or by the internal dynamics of the rainforest,” said Jin Wu, a post-doctoral research associate at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory. Wu is the lead author on a study completed while he was a Ph.D. student with senior author Scott Saleska, Associate Professor of Ecology and Evolutionary Biology at the University of Arizona, published online in the February 26 issue of Science.

Wu, together with other members of Scott Saleska’s lab and international collaborators from Brazil, Australia, and Japan found that new leaf growth is synchronized with old leaf loss in the dry-season of the Amazon rainforest. This shifts the makeup of the tree canopy towards younger leaves, which display higher photosynthetic capacity, and explains the large observed seasonal increases in photosynthesis throughout the ecosystem.

Climate models have long represented the tropics in an overly simplistic way, often due to the lack of data from these hard to reach regions. That view assumed that tropical forests have consistent canopy greenness throughout the year–unlike the dramatic seasonal changes in temperate forests, heralded by vibrant reds and yellows.

“At the landscape level, it always looks evergreen,” Wu said. But broad-scale images–for example, those taken by satellites–often can’t discern the ground level subtleties that have a large impact on the level of photosynthesis. “Evergreen doesn’t mean there are no internal dynamics,” Wu said.

Seeing the trees through the forest

To better examine the impact of these internal dynamics on photosynthesis, Wu and his colleagues used all available data from four sites in the Amazon with a wide range of tree species, rainfall gradients, and soil types: three spots near the equator along the Amazon River, and one water-limited site on the southern side of the Amazon.

At these sites, the researchers measured variables that allowed them to calculate the aggregated photosynthesis rate across the whole forest. They found that the derived photosynthetic capacity from these measurements is seasonal. That is, though the forest is evergreen, the internal photosynthetic machinery changes throughout the year.

To determine what caused these changes, they used tower-mounted cameras perched over the treetops to survey a plot about a third of a mile square, observing the changing quantities and qualities of leaves in the canopy crowns. They found that leaf area increased significantly during the dry season, but these increases precede photosynthetic capacity by at least 1 month, which is increased twice as much as would have been expected from the increase in leaf area alone.

“It’s not just the quantity of leaves that makes a difference. In tropical evergreen forests, the overall quantity of leaves doesn’t change that much, so the quality of leaves is an important driver in photosynthesis,” Wu said.

Leaf age matters

To investigate the quality of the leaves, expert tree climbers accompanied the researchers as they trekked into the jungle, scaling the trees to tag individual leaves from the time they emerge and take photographs weekly and then monthly. This work revealed important changes in leaf biophysical and physiological properties through their life cycles.

“Photosynthesis is like a metabolism,” Wu said. “As human beings, our metabolic rates are strongly age-dependent. Leaves are similar. During their first two months, leaves expand and acquire more chlorophyll, becoming greener.” But Wu and his team found that leaves don’t reach their photosynthetic peak until they are fully expanded at two to five months old. At that point, they are more efficient in absorbing light and more efficient in converting light to food — that is, stored carbon. After six months, their photosynthetic rates decline as they enter ‘old’ age.

The effect of leaf age on physiology explained the surprisingly high seasonal changes in photosynthetic capacity.

Wu said that incorporating these details about tropical evergreen leaves into earth system models will allow for more accurate predictors of carbon exchange and, ultimately, their feedbacks to climate.

Taking the Tropics Into Account

“Tropical rainforests are biologically really important, but our understanding is so limited because the available data is very limited,” Wu said.

He is continuing his PhD research as a post-doc in a program designed to remedy the paucity of data from this region, the Next Generation Ecosystem Experiments – Tropics (NGEE-Tropics). This project is supported by the DOE Office of Science and led by Lawrence Berkeley National Laboratory’s Earth Sciences Division with partner institutes including Brookhaven.

NGEE-Tropics is an ambitious 10-year project to dramatically reduce the uncertainty in climate models and increase scientific understanding of how tropical forest ecosystems will respond to climate and atmospheric change.

Wu was recently in Panama, getting up close and personal with the rainforest. Together with Brookhaven scientists Kim Ely, Shawn Serbin, and Alistair Rogers, he is studying the impact of the El Niño-Southern Oscillation (ENSO) on the response of photosynthesis to drought, and building relationships between important physiological properties that drive model uncertainty and other observations.

“If we want to advance our understanding about the terrestrial carbon cycle in tropical forests, we need to know what types of leaves are present at what times of year, and their physiological properties,” he said. “We can improve our models with this data, and better understand what to look for in the future with remote sensing from tower-mounted cameras, aircraft, and satellites.”

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February 28, 2016 8:22 am

Except that any modest change in co2 (never mind local seasonal variation) has an insignificant impact on climate. Even doubling total average atmospheric co2 has only a modest effect. So the claim that this bit of knowledge about previously unknown local seasonal variation in co2 will be important for climate research is just one of the many thousands of examples of researchers making tenuous claims to climate implications because that is where the money is. If there is no better reason to be doing this research then it shouldn’t be funded.

February 28, 2016 3:22 pm

Dang! I didn’t include that. 😎

Gunga Din says:
May 14, 2012 at 1:21 pm
joeldshore says:
May 13, 2012 at 6:10 pm
Gunga Din: The point is that there is a very specific reason involving the type of mathematical problem it is as to why weather forecasts diverge from reality. And, the same does not apply to predicting the future climate in response to changes in forcings. It does not mean such predictions are easy or not without significant uncertainties, but the uncertainties are of a different and less severe type than you face in the weather case.
As for me, I would rather hedge my bets on the idea that most of the scientists are right than make a bet that most of the scientists are wrong and a very few scientists plus lots of the ideologues at Heartland and other think-tanks are right…But, then, that is because I trust the scientific process more than I trust right-wing ideological extremism to provide the best scientific information.
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What will the price of tea in China be each year for the next 100 years? If Chinese farmers plant less tea, will the replacement crop use more or less CO2? What values would represent those variables? Does salt water sequester or release more or less CO2 than freshwater? If the icecaps melt and increase the volume of saltwater, what effect will that have year by year on CO2? If nations build more dams for drinking water and hydropower, how will that impact CO2? What about the loss of dry land? What values do you give to those variables? If a tree falls in the woods allowing more growth on the forest floor, do the ground plants have a greater or lesser impact on CO2? How many trees will fall in the next 100 years? Values, please. Will the UK continue to pour milk down the drain? How much milk do other countries pour down the drain? What if they pour it on the ground instead? Does it make a difference if we’re talking cow milk or goat milk? Does putting scraps of cheese down the garbage disposal have a greater or lesser impact than putting in the trash or composting it? Will Iran try to nuke Israel? Pakistan India? India Pakistan? North Korea South Korea? In the next 100 years what other nations might obtain nukes and launch? Your formula will need values. How many volcanoes will erupt? How large will those eruptions be? How many new ones will develop and erupt? Undersea vents? What effect will they all have year by year? We need numbers for all these things. Will the predicted “extreme weather” events kill many people? What impact will the erasure of those carbon footprints have year by year? Of course there’s this little thing called the Sun and its variability. Year by year numbers, please. If a butterfly flaps its wings in China, will forcings cause a tornado in Kansas? Of course, the formula all these numbers are plugged into will have to accurately reflect each ones impact on all of the other values and numbers mentioned so far plus lots, lots more. That amounts to lots and lots and lots of circular references. (And of course the single most important question, will Gilligan get off the island before the next Super Moon? Sorry. 😎
There have been many short range and long range climate predictions made over the years. Some of them are 10, 20 and 30 years down range now from when the trigger was pulled. How many have been on target? How many are way off target?
Bet your own money on them if want, not mine or my kids or their kids or their kids etc.

I’m sure the advances in computer programming that have produced the current batch of computer climate models have advanced enough to account for all of the unknowables.
(Or maybe the advances in computing have only accentuated the error of a programming variable that has never been corrected?)