Carbon Mitigation Initiative
CMI

CMI Science

CMI Science

CMI Science focuses on how terrestrial vegetation and the oceans soak up carbon and thereby determine the fraction of the carbon dioxide (CO2) emitted into the atmosphere that actually stays there (the fraction is about one-half). CMI science increasingly features close collaboration with Princeton’s neighbor, the Geophysical Fluid Dynamics Laboratory (GFDL) of the US Department of Commerce. A recent and growing component of CMI addresses climate variability and departures from the historical frequency of extreme events, such as heat waves, droughts, and hurricanes.

Research Highlights – At a Glance

Stephen Pacala: Tropical forests represent a key terrestrial carbon sink, yet their levels of carbon storage have been challenging to estimate due to the multi-layered structure of tropical forest tree communities. The Pacala group has used data from Panama’s Barrow Colorado Island Rainforest to develop a model for investigating carbon storage in tropical forests with improved accuracy. Another challenge is understanding methane emissions from oil and gas infrastructure, due to the ephemeral nature of high-emitting sources. The Pacala group has created a new method for estimating methane emissions that combines systematic and biased sampling data with meteorological factors.

Jorge Sarmiento: Biological and geological processes occurring in the Southern Ocean around Antarctica have important impacts on global carbon and climate cycles. Recent modeling results show that the Southern Ocean acts as a key sink for atmospheric CO2, thus mitigating global temperature increases caused by rising levels of CO2. To examine the dynamics of these processes across space and time, Jorge Sarmiento is directing the world’s first large-scale deployment of robotic floats equipped with biogeochemical measurement instruments. The project will enable unprecedented observations of pH, biological productivity, carbon cycling, and phytoplankton dynamics in the Southern Ocean.

François Morel: Increasing concentrations of atmospheric CO2 lead to higher concentrations of dissolved CO2 in surface seawater. This results in ocean acidification, which may affect the growth of the photosynthetic phytoplankton that form the basis of marine food webs. The Morel group has conducted both field and laboratory experiments to examine the effects of acidification on phytoplankton productivity. The results will enable future assessments and predictions of how CO2 concentration changes impact marine ecosystems.

Michael Bender: Studies of ice cores from Greenland show that the Greenland ice sheet has persisted for at least 1 million years. This result puts limits on the sensitivity of the Greenland ice sheet to climate change, and provides a test for models of the ice sheet.

Stephen Pacala and Elena Shevliakova: Beyond assessing effects of greenhouse gas emissions on trends in global temperature increases, research efforts led by Pacala and Shevliakova have advanced analysis of extreme precipitation from observations and climate model simulations, as well as improved representation of processes that affect climate extremes on regional scales, such as urbanization and dust emissions.


Modeling Tropical Forest Carbon Storage and Estimating Methane Emissions
Principal Investigator: Stephen Pacala

At a Glance

Tropical forests represent a key terrestrial carbon sink, yet their levels of carbon storage have been challenging to estimate due to the multi-layered structure of tropical forest tree communities. The Pacala group has used data from Panama’s Barrow Colorado Island Rainforest to develop a model for investigating carbon storage in tropical forests with improved accuracy. Another challenge is understanding methane emissions from oil and gas infrastructure, due to the ephemeral nature of high-emitting sources. The Pacala group has created a new method for estimating methane emissions that combines systematic and biased sampling data with meteorological factors.

Research Highlight

Modeling Tropical Forest Carbon Storage

The terrestrial carbon sink is thought to be dominated by tropical forests. Tropical forests store roughly twice as much carbon in living trees as temperate forests per unit area. About half of this disparity stems from the rapid year-round growth of tropical trees, which creates a few extremely large individuals.

The other half is caused by a spectacular difference that temperate zone dwellers notice immediately when first walking into a tropical forest: if one drops a weighted line from a helicopter through the canopy of a temperate forest, the weight will, on average, pass through the crown of one canopy tree, and sometimes (i.e. a quarter of the time), the crown of one sapling before hitting the ground. Oldgrowth temperate forests are often compared to cathedrals because of this property.

If one drops the same line through a tropical forest, it will pass through three or four tree crowns on average before hitting the ground. In other words, tropical forests have a canopy, a sub-canopy, a subsub- canopy, and finally, a layer of saplings with some space between their crowns. This material in the middle (the sub-canopy and sub-sub-canopy) is the main reason why tropical forests appear so full, chaotic, and claustrophobic to temperate dwellers. Again, the material in the middle is important to the global carbon cycle because it accounts for half the difference between the living carbon in tropical and temperate forests. Although our Earth System models can predict the existence of very large trees in the tropics, they have not been able to predict the material in the middle.

The most amazing thing about the plant growth in the middle is that it is highly organized in every moist tropical forest, despite its tangled appearance. If one plots the logarithm of tree diameter on the horizontal axis against the logarithm of tree abundance on the vertical axis, one sees that the material in the middle—all but the smallest and largest size classes—follows a tight inverse-square power law: a tree with twice the stem diameter has one-fourth the abundance. (Figure 1.1.1 shows the plot for the Barrow Colorado Island Rainforest in Panama). In contrast, size distributions for the temperate zone and for the driest tropical forests do not follow a power law.

The Pacala group has shown that the power law is a consequence of competition for light following large disturbances, many of which are created by thunderstorm microbursts. They developed a simple model that accurately predicts the data (see Figure 1.1.1) and showed with an extensive data analysis that the mechanism in the model is also operating in an extensively studied Panamanian rainforest. The mechanism in the model has already been added to the Geophysical Fluid Dynamics Laboratory/Princeton Earth System Model. Other modeling centers will also add to this mechanism to predict the fate of rainforest carbon. The publication reporting this model won the CMI Best Paper Award this year.1

Figure 1.1.1 Figure 1.1.1. Size distribution of trees in a tropical forest. P(d) is the fraction of total stems with diameter d, blue bars indicate confidence limits, black line is an inverse-square (y = x-2) power law, red line is from a simple analytically tractable model, and gray bars are predictions of a slightly more complex simulation model. Parameters in both models were estimated from Barrow Colorado Island (BCI) Rainforest data.

Estimating Methane Emissions

Other recent work from the Pacala group focuses on methane emissions from the Barnett Shale region in Texas. Attempts to measure fugitive emissions are bedeviled by the fact that most of the emitted methane at any one time is due to a few sources (2-5% of the sources). High-emitting sources are also ephemeral, and typically last for hours to days, so that the high-emissions sources jump around in space.

Previous studies have systematically sampled methane emissions to achieve an unbiased sample, but this procedure cannot provide a large enough sample of the rare high-emitters to characterize them with any accuracy. Moreover, a systematic sample has a counter-intuitively high probability of underestimating total emissions when total emissions are dominated by ephemeral and rare high emitters. A recent study claims that all previous emissions estimates, except one by the Pacala group, underestimate fugitive emissions by a factor of two for this reason.2

In its study of the Barnett region, the Pacala group supplemented a systematic sample of emissions from oil and gas infrastructure with a sample deliberately biased toward high emitters (to achieve a sufficient sample size of the large sources). When a source emits methane, the gas spreads horizontally and vertically as it drifts downwind, until its concentration falls beneath the detection limit of an instrument. This means that a source has a detectable plume length that grows with the size of the source and depends on meteorological conditions. By driving a grid of roads with a methane detector, one can detect and identify upwind sources. By inverting a meteorological model, one can also estimate the size of the emission of methane from a given source. But this method is biased toward high emitters because the long plumes of high-emitting sources are more likely to be detected than the short plumes of low emitters. This bias is large; it artificially elevated the mean emissions level in the Barnett campaign by a factor of 80.

Figure 1.1.2 Figure 1.1.2. Former CMI postdoctoral fellow Caroline Farrior was presented with the 2016 CMI Best Paper Award by Gardiner Hill, Director of Carbon Solutions, during a ceremony this past February. Farrior, who worked in Stephen Pacala’s lab, was selected for her paper “Dominance of the suppressed: Power-law size structure in tropical forests” in recognition of the important finding that commonalities among tropical forests in their structure are due to simple and biologically intuitive mechanisms. The paper was published in the journal Science in early 2016.

To overcome this bias, the Pacala group developed a statistical method that correctly knits together systematic samples and biased samples obtained by searching a grid of roads. For the first time, this method successfully matched bottom-up estimates from on-the-ground samples with topdown estimates from aircraft, which greatly increased confidence in both types of estimates. This new method solved a long-standing problem, and has now greatly streamlined methane emissions estimates for other regions.

Other Work

In 2015, the Pacala group also produced a prototype of the terrestrial sink for a new Geophysical Fluid Dynamics Laboratory (GFDL)/Princeton carbon cycle model3 and addressed the interaction of the hydrological and carbon cycles, focusing on drought kill.4

Reference

  1. Farrior, C.E., S.A. Bohlman, S. Hubbell, and S.W. Pacala, 2016. Dominance of the suppressed: Power-law size structure in tropical forests. Science, 351(6269): 155-157. doi:10.1126/science.aad0592.
  2. Zavala-Araiza, D., D.R. Lyon, R.A. Alvarez, K.J. Davis, R. Harriss, S.C. Herndon, A. Karion, E.A. Kort, B.K. Lamb, X. Lan, A.J. Marchesei, S.W. Pacala, A.L. Robinson, P.B. Shepson, C. Sweeney, R. Talbot, A. Townsend-Small, T.I. Yacovitch, D. Zimmerle, and S.P. Hamburg, 2015. Reconciling divergent estimates of oil and gas methane emissions. Proc. Natl. Acad. Sci., 112(51): 15597-15602. doi:10.1073/pnas.1522126112.
  3. Weng, E.S., S. Malyshev, J.W. Lichstein, C.E. Farrior, R. Dybzinski, T. Zhang, E. Shevliakova, and S.W. Pacala, 2015. Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition. Biogeosciences, 12(9): 2655-2694. doi:10.5194/bg-12-2655-2015.
  4. Anderegg, W.R.L., C. Schwalm, F. Biondi, J.J. Camarero, G. Koch, M. Litvak, K. Ogle, J.D. Shaw, E. Shevliakova, A.P. Williams, A. Wolf, E. Ziaco, and S. Pacala, 2015. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science, 349(624): 528-532. doi:10.1126/science.aab1833.

Update on the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project
Principal Investigator: Jorge Sarmiento

At a Glance

Biological and geological processes occurring in the Southern Ocean around Antarctica have important impacts on global carbon and climate cycles. Recent modeling results show that the Southern Ocean acts as a key sink for atmospheric carbon dioxide (CO2), thus mitigating global temperature increases caused by rising levels of CO2. To examine the dynamics of these processes across space and time, Jorge Sarmiento is directing the world’s first large-scale deployment of robotic floats equipped with biogeochemical measurement instruments. The project will enable unprecedented observations of pH, biological productivity, carbon cycling, and phytoplankton dynamics in the Southern Ocean.

Research Highlight

Launched in September 2014, the National Science Foundation-funded Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project, directed by CMI member Jorge Sarmiento, is the world’s first large-scale deployment of biogeochemical (BGC) Argo floats. Aiming to dramatically increase biogeochemical observations in the harsh and remote ocean around Antarctica, SOCCOM scientists have augmented conventional robotic Argo floats, which measure ocean temperature and salinity, with newly developed biogeochemical sensors to measure pH, nitrate, and oxygen. The project will allow unprecedented year-round monitoring of ocean pH, biological productivity, carbon cycling, and phytoplankton bloom dynamics in the Southern Ocean (for example, see Figure 1.2).

Currently more than 30 SOCCOM BGC floats are operating and by June, the end of the second float deployment season, 52 floats will be reporting from the Southern Ocean—more than a quarter of the way to the goal of 200 floats within six years. Data from the floats are made available to the public in real time on the SOCCOM website (http://soccom.princeton.edu), and will soon also be incorporated into the global Argo data system to provide easy access to researchers around the world.

Analysis of two years of data shows that the ice-capable floats successfully survive Antarctic winters beneath the ice and re-emerge to transmit data via satellite back to SOCCOM scientists on shore. The new biogeochemical sensors are also performing well—shipboard observations made when each SOCCOM float is deployed are used to calibrate the biogeochemical sensors, and have shown that the sensors’ measurements are consistent with shipboard data. Newly developed methods for evaluating float measurements after deployment (including air calibration for oxygen, and an alkalinity algorithm for assessing nitrate and pH sensor performance over time), suggest that the sensors will likely be stable in the long term and that the floats may eventually be suitable for deployment without calibration by other research vessels and cargo ships, as is the case with conventional Argo floats.

In addition to actively participating in the SOCCOM project, the Sarmiento group continues to carry out model simulations of Southern Ocean biogeochemistry with support from CMI. This research provided the primary motivation for the SOCCOM project by illustrating the importance of the Southern Ocean to the planet’s carbon and climate cycles, and modeling results continue to inform observational efforts.

New research initiated this year has focused on using high-resolution climate models to separate carbon cycle trends due to climate change from those due to natural climate variability, and to determine “times of emergence” of these signals. This work highlights the role of natural variability in enhancing or suppressing carbon cycle trends that observational programs like SOCCOM aim to quantify, and is helping to inform strategies for detecting changes in the ocean carbon sink.

Figure 1.2 Figure 1.2. Annual net CO2 flux estimated from data collected by SOCCOM floats deployed in the Southern Ocean in April 2014. Negative fluxes indicate CO2 uptake by the ocean. Inset map shows trajectories of floats after two years; gray land area at upper left is New Zealand. Fluxes were calculated using float-measured pH, estimated alkalinity, ERA-Interim 6-hourly winds, and Wanninkhof gas exchange coefficient.1

References

  1. Wanninkhof, R., 2014. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr. Methods, 12(6): 351-362. doi:10.4319/lom.2014.12.351.

The Greenland Ice Sheet, a Million-Year Record of Climate Change and Sea Level Rise
Principal Investigator: Michael Bender

At a Glance

Studies of ice cores from Greenland show that the Greenland ice sheet has persisted for at least 1 million years. This result puts limits on the sensitivity of the Greenland ice sheet to climate change, and provides a test for models of the ice sheet.

Research Highlight

In 2015, the Bender group completed a study of properties of the Greenland ice sheet during the last interglacial period (about 120,000–130,000 years ago) and earlier times. The ultimate motivation is to understand how much Greenland ice will melt, and thus cause sea level to rise, due to global warming.

The study included ice from the last interglacial period preserved in the GISP2 ice core drilled through the center of the Greenland ice sheet around 1995. This core contains a continuous climate record extending back to about 105,000 years ago. Below this continuous archive, there are about 200 meters of clean glacial ice with an intact climate record, but out of stratigraphic order due to the flow of the glacier. Some of this ice formed at temperatures (inferred from the isotopic composition of the ice) as least as warm as today, and is understood to have originated during the last interglacial period.

The Bender group dated all such samples by measuring the concentration of methane and the isotopic composition of oxygen in fossil air trapped in the ice. Antarctic ice core records extend back 800,000 years, and reveal how these two properties have changed with time, allowing researchers to date each ice sample. Plotting climate properties against time allowed reconstruction of the climate record at the center of Greenland during the last interglacial period.

This work shows that temperature at the summit of Greenland, in the center of the landmass, rose to its present value (-31˚C mean annual temperature) about 127,000 years ago, and continued rising until the temperature was about 5˚C warmer than during the current interglacial period. Temperatures began to fall around 120,000 years ago.

According to a related modeling study, this warming would have melted about half of the Greenland ice sheet, and meltwater flowing into the oceans would have raised sea level by about 4 meters. This rise would have occurred toward the end of the warmest period in our record, around 120,000 years ago, and such a sea level maximum has been observed in ancient beaches at several locations around the world.

At the very bottom of two other Greenland ice cores, there is about 5 meters of ice that was deposited under warm temperatures at some unknown time in the past. We have developed a method to date this ice based on the isotopic composition of argon in the fossil air. This property has continuously changed with time at a known constant rate. Argon dating of ice from the Dome C core, from Southern Greenland, shows that ice was present in this region during the last interglacial period. In other words, the melting of Greenland did not progress to the point where the vulnerable southern region was deglaciated.

In an ice core taken from the summit, in central Greenland, ice at the base of the core dates to at least 1 million years of age. This great antiquity is further evidence that Greenland was not completely deglaciated during the last interglacial period, during a previous, longer interglacial period around 400,000 years ago, or during the shifts in climate patterns (for example, to somewhat higher carbon dioxide (CO2) concentrations and less cold glacial periods) observed between 800,000 and 1 million years ago.

This work shows that, with the level of warming possible in the coming centuries, melting of the Greenland ice sheet may contribute several meters to sea level rise over several thousand years. The results also present a target for ice sheet and climate models that can be tested against these observations. The evidence is encouraging, in that it demonstrates that no climate change over the past million years was sufficient to melt the ice sheet entirely.

Figure 1.3 Figure 1.3. On July 12, 1992, Sigfus Johnsen of the University of Copenhagen triumphantly holds up the deepest section of the GRIP core, at 3,029 meters depth, drilled through the ice at the summit of the Greenland ice sheet. The brown color is due to dirt in the ice. The dirt originates from soil, lake water, bogs, and mud, and dates roughly to the time when the ice sheet formed. For 20 years, the age of this ice remained unknown for lack of an accurate dating method. Michael Bender recently invented a method based on the isotopic composition of argon in trapped air, applied it to this 24-year-old ice archived in freezers in Copenhagen, and found that this ice is at least 1 million years old.

Effects of Ocean Acidification on Marine Phytoplankton
Principal Investigator: François Morel

At a Glance

Increasing concentrations of atmospheric carbon dioxide (CO2) lead to higher concentrations of dissolved CO2 in surface seawater. This results in ocean acidification, which may affect the growth of the photosynthetic phytoplankton that form the basis of marine food webs. The Morel group has conducted both field and laboratory experiments to examine the effects of acidification on phytoplankton productivity. The results will enable future assessments and predictions of how CO2 concentration changes impact marine ecosystems.

Research Highlight

About one-third of human-generated CO2 emissions into the Earth’s atmosphere dissolve in surface seawater, increasing the CO2 concentration and decreasing the pH of the seawater. The resulting ocean acidification leads to a host of interrelated chemical and biological consequences. Among the documented biological effects of seawater acidification are changes in the growth of some species of phytoplankton, the photosynthetic organisms that form the primary production base of marine food webs.

In several instances, increased phytoplankton growth rates have been observed at high CO2 concentration, but other experiments have shown no effect or a negative effect. These variable results make it difficult to generalize and to predict how oceanic primary production may respond to ocean acidification. Over the past several years, with support from the Carbon Mitigation Initiative and the National Science Foundation, the Morel group has been carrying out laboratory and field experiments aimed at elucidating the physiological responses of marine phytoplankton to increasing CO2/decreasing pH. The results will enable assessment, and eventually prediction, of future changes in phytoplankton ecology and ocean productivity.

The inconsistent results reported for the effect of ocean acidification on phytoplankton may result from opposite, and partly compensating, effects of increasing CO2 and decreasing pH on the rates of photosynthesis and respiration by the organisms. The Morel group explored these possibilities by independently varying the CO2 concentration and the pH in cultures of a model diatom species (see Figure 1.4), and simultaneously quantifying the organisms’ rates of photosynthesis and respiration using oxygen isotopes.

The results have demonstrated that many organisms maintain constant photosynthesis and respiration rates over a range of pH and CO2 conditions that encompass predicted CO2 changes over the next century and beyond. This remarkable outcome results from the ability of the organisms to maintain efficiently, and with very low energy expenditures, a constant intracellular CO2 concentration for photosynthesis (via their “carbon concentrating mechanism”) and a constant internal pH, despite variations in the external seawater. Species for which maintaining their internal chemistry is either less efficient or more costly will be comparatively more affected by ocean acidification, leading to a shift in species assemblages. These changes have been observed in the few field experiments conducted to date.

High Latitudes

Because high latitudes are inordinately subject to global change (rapid warming and large decreases in pH) the Morel group extended these studies to psychrophilic (i.e., cold-adapted) phytoplankton species. These organisms support some of the most productive regions of the oceans and, because of a short food chain, some of the most efficient and spectacular ecosystems on Earth: from phytoplankton to krill to seals and whales. This work included both field experiments at Palmer Station in the West Antarctic Peninsula and complementary laboratory experiments with model phytoplankton species.

The experiments revealed some fascinating biochemical adaptations that allow certain cold-adapted phytoplankton to grow rapidly at very low ambient temperatures. These include very high cellular concentrations of key proteins (such as Rubisco, the enzyme responsible for the fixation of CO2 during photosynthesis) that cannot be adapted to turn over rapidly when temperatures are near freezing. However, increasing CO2 did not have a significant effect on growth rates, despite a clear downregulation of the organisms’ carbon concentrating mechanism. This result reflects the very low amount of energy required to maintain a high intracellular CO2 concentration, even under very low temperature conditions.

Figure 1.4. Figure 1.4. Scanning electron micrograph of Thalassiosira weissflogii, a coastal marine diatom. The diameter is approximately 10 micrometers—about one-tenth the width of a typical human hair or the thickness of a typical sheet of paper. This diatom, which is typical of species that are responsible for a large fraction of photosynthetic production in large regions of the temperate oceans, was used to study the physiological responses of marine phytoplankton to increasing CO2/decreasing pH in the laboratory.

Trace Metals

A poorly studied but potentially important consequence of the CO2-induced acidification of the surface ocean is a change in the bioavailability of trace metals, which play a critical role in the productivity and population dynamics of marine ecosystems. The Morel group conducted laboratory and field experiments designed to quantify the effects of acidification on the bioavailability of iron and zinc, two metals that are essential to the growth of phytoplankton.

In all laboratory and field experiments, acidification decreased the bioavailability of iron, a metal known to limit primary production in large regions of the oceans. This result is consistent with the predicted decrease in the labile concentration of iron under acidic conditions. In the case of zinc, both positive and negative effects on bioavailability were observed at low pH in the laboratory, depending on the mix of organic compounds present in the medium. This variability in response to acidification was also observed in the field and was consistent with changes in the chemical lability of zinc measured by electrochemistry.

References

  1. Goldman, J.A.L., S.A. Kranz, J.N. Young, P.D. Tortell, R.H.R. Stanley, M.L. Bender, and F.M.M. Morel, 2015. Gross and net production during the spring bloom along the Western Antarctic Peninsula. New Phytologist, 205(1): 182-191. doi:10.1111/nph.13125.
  2. Hopkinson, B.M., C.L. Dupont, A.E. Allen, and F.M.M. Morel, 2011. Efficiency of the CO2 concentrating mechanism of diatoms. Proc. Natl. Acad. Sci., 108(10): 3830-3837. doi:10.1073/ pnas.1018062108.
  3. Hopkinson, B.M., Y. Xu, D. Shi, P.J. McGinn, and F.M.M. Morel, 2010. The effect of CO2 on the photosynthetic physiology of phytoplankton in the Gulf of Alaska. Limnol. Oceanogr., 55(4): 2011- 2024. doi:10.4319/lo.2010.55.5.2011.
  4. Kranz, S. A., J.N. Young, B.M. Hopkinson, J.A.L. Goldman, P.D. Tortell, and F.M.M. Morel, 2015. Low temperature reduces the energetic requirement for the CO2 concentrating mechanism in diatoms. New Phytologist, 205(1): 192-201. doi:10.1111/nph.12976.
  5. Lomas, M.W., B.M. Hopkinson, J.L. Losh, D.E. Ryan, D.L. Shi, Y. Xu, and F.M.M. Morel, 2012. Effect of ocean acidification on cyanobacteria in the subtropical North Atlantic. Aquat. Microb. Ecol., 66: 211-222. doi:10.3354/ame01576.
  6. Losh, J.L., F.M.M. Morel, and B.M. Hopkinson, 2012. Modest Increase in the C:N Ratio of N-limited Phytoplankton in the California Current in Response to High CO2. Mar. Ecol. Prog. Ser., 468: 31-42. doi:10.3354/meps09981.
  7. Losh, J.L., J.N. Young, and F.M.M. Morel, 2013. Rubisco is a small fraction of total protein in marine phytoplankton. New Phytologist, 198(1): 52-58. doi:10.1111/nph.12143.
  8. Mackey, K., J.J. Morris, F.M.M. Morel, and S.A. Kranz, 2015. Response of Photosynthesis to Ocean Acidification. Oceanography, 25(2): 74-91. http://dx.doi.org/10.5670/oceanog.2015.33.
  9. Shi, D., S.A. Kranz, J.-M. Kim, and F.M.M. Morel, 2012. Ocean acidification slows nitrogen fixation and growth in the dominant diazotroph Trichodesmium under low-iron conditions. Proc. Natl. Acad. Sci., 109(45): 18255-18256. doi:10.1073/pnas.1216012109.
  10. Shi, D., Y. Xu, B.M. Hopkinson, and F.M.M. Morel, 2010. Effect of ocean acidification on iron availability to marine phytoplankton. Science, 327 (5966): 676-679. doi:10.1126/science.1183517.
  11. Tortell, P.D., E.C. Asher, H.W. Ducklow, J.A.L. Goldman, J.H. Dacey, J.J. Grzymski, J.N. Young, S.A. Kranz, K.S. Bernard, and F.M.M. Morel, 2014. Metabolic balance of coastal Antarctic waters revealed by autonomous pCO2 and ΔO2 /Ar measurements. Geophysical Research Letters, 41(19): 6803-6810. doi:10.1002/2014GL061266.
  12. Xu, Y., D. Shi, L. Aristilde, and F.M.M. Morel, 2012. The effect of pH on the uptake of zinc and cadmium in marine phytoplankton: Possible role of weak complexes. Limnol. Oceanogr., 57(1): 293- 304. doi:10.4319/lo.2012.57.1.0000.
  13. Young J.N., J.A.L. Goldman, S.A. Kranz, P.D. Tortell, and F.M.M. Morel, 2015. Slow carboxylation of Rubisco constrains the rate of carbon fixation during Antarctic phytoplankton blooms. New Phytologist, 205(1): 172-181. doi:10.1111/nph.13021.
  14. Young, J.N., S.A. Kranz, J.A.L. Goldman, P.D. Tortell, and F.M.M. Morel, 2015. Antarctic phytoplankton down-regulate their carbon-concentrating mechanisms under high CO2 with no change in growth rates. Mar. Ecol. Prog. Ser., 532: 13-28. doi:10.3354/meps11336.
  15. Young, J.N., and F.M.M. Morel, 2015. Biological oceanography: The CO2 switch in diatoms. Nat. Clim. Chang., 5(8): 722-723. doi:10.1038/nclimate2691.

Climate Variability and Changes in Future Extremes
Principal Investigators: Stephen Pacala and Elena Shevliakova

At a Glance

Beyond assessing effects of greenhouse gas emissions on trends in global temperature increases, research efforts led by Pacala and Shevliakova have advanced analysis of extreme precipitation from observations and climate model simulations, as well as improved representation of processes that affect climate extremes on regional scales, such as urbanization and dust emissions.

Research Highlight

During the last few decades, the main focus of numerous climate studies has been on changes in the Earth’s mean climate and how anthropogenic emissions of greenhouse gases will affect future climate projections. However, a growing body of research has acknowledged a need to understand past and future changes in climate variability and climate extremes.

In 2015, CMI postdoctoral fellow Monika Barcikowska, in collaboration with Geophysical Fluid Dynamics Laboratory (GFDL) scientists, refined statistical tools to analyze changes in extreme precipitation.1 The new analysis identified two dominant patterns of multi-decadal scale internal climate variability over the North Atlantic. The study assessed the impact of these patterns on Mediterranean winter precipitation using long (1,000 to 4,000 years) GFDL CM2.1 and CM2.5 preindustrial simulations. The first pattern resembles the North Atlantic Oscillation, which could explain over 30% of decadal winter precipitation variability observed in the regions of Spain, Morocco, Italy and the Balkans. The second pattern has a longer period, which varies from approximately 55 to 62 years.

The joint Atmospheric and Ocean Sciences Program (AOS)-CMI postdoctoral fellow Dan Li has completed implementation and evaluation of the urban component in the GFDL Earth System Model (ESM) framework. This analysis involved historical (1850 to 2005) simulations with the GFDL urban tile, forced by the high-frequency ESM output, for historical and future climates. This enabled characterization of how, over given historical periods, the magnitude of urban heat island effect has interacted with climate variability and change in the continental United States.2,3 A new set of additional simulations has explored implications of urbanization for current regional climate and climate variability using a novel stretch-grid (~10 km over North America, ~25 km over the rest of the world) implemented in the GFDL atmospheric general circulation model.

In a third project, CMI postdoctoral fellow Stuart Evans developed a capability to interactively simulate land dust emissions in the GFDL climate models.4 Previously, land dust source emissions were prescribed based on 1990s observations and did not change throughout historical or future simulations. This study involved performing a set of 500-year simulations for preindustrial climate conditions with the new land dust emission configuration. Analysis of the new simulations with interactive dust emissions has shown that land surface is indeed important to accurately model dust variability and its implications for climate variability. By accounting for soil moisture and vegetation, the new runs produce inter-annual variability that closely matches satellite observations and recreate the relationships seen in observations between specific dust sources and major climate indices such as El Niño. The new capability is enabling a novel exploration of the interplay between enhanced incidences of dust and droughts in different regions of the world, particularly Australia (Figure 1.5) and Africa.

Figure 1.5 Figure 1.5. (Left) Schematic diagram of dust-ecosystem-climate interactions, showing how the “Dusty” CM3 model connects Australian dust emissions with the El Niño–Southern Oscillation (ENSO). (Right) DJF composite anomalies of Australia in La Niña and El Niño phases of ENSO, showing relationships between precipitation and dust optical depth. ENSO leads to precipitation anomalies, which in turn lead to dust anomalies. Contours of optical depth are superimposed on a color scale for deviation from mean precipitation.

References

  1. Barcikowska, M., and S. Kapnick, 2016. Impact of Large-Scale Circulation in the North Atlantic Sector on Mediterranean Winter Hydroclimate. Manuscript in preparation, March, 2016.
  2. Li, D., S. Malyshev, and E. Shevliakova, 2016. Exploring Historical and Future Urban Climate in the Earth System Modeling Framework. Part I: Model Development and Evaluation. J. Adv. Model Earth Sy., in review.
  3. Li, D, S. Malyshev, and E. Shevliakova, 2016. Exploring Historical and Future Urban Climate in the Earth System Modeling Framework. Part II: Interactions Between Urban Heat Islands and Climate Change Over the Continental United States. J. Adv. Model Earth Sy., in review.
  4. Evans, S.M., P.A. Ginoux, S. Malyshev, and E. Shevliakova, 2016. The Importance of the Land Surface to Australian Dust Variability in Models and Observations. Manuscript in preparation, March, 2016.

Science Publications

Anderegg, W.R., A.P. Ballantyne, W.K. Smith, J.D. Majkut, S. Rabin, C. Beaulieu, R.A. Birdsey, J.P. Dunne, R.A. Houghton, R.B. Myneni, Y. Pan, J.L. Sarmiento, N. Serota, E. Shevliakova, P. Tans, and S.W. Pacala, 2015. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl. Acad. Sci., 112(51): 15591-15596. doi:10.1073/ pnas.1521479112.

Anderegg, W.R.L., C. Schwalm, F. Biondi, J.J. Camarero, G. Koch, M. Litvak, K. Ogle, J.D. Shaw, E. Shevliakova, A.P. Williams, A. Wolf, E. Ziaco, and S. Pacala, 2015. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science, 349(624): 528-532. doi:10.1126/ science.aab1833.

Buermann, W., C. Beaulieu, B. Parida, D. Medvigy, G.J. Collatz, J. Sheffield, and J.L. Sarmiento, 2015. Climate-driven shifts in continental net primary production implicated as a driver of a recent abrupt increase in the land carbon sink. Biogeosciences Discuss., 12: 13767-13791. doi:10.5194/bgd-12-13767-2015.

Dufour, C.O., S.M. Griffies, G.F. de Souza, I. Frenger, A.K. Morrison, J.B. Palter, J.L. Sarmiento, E.D. Galbraith, J.P. Dunne, W.G. Anderson, and R.D. Slater, 2015. Role of mesoscale eddies in cross-frontal transport of heat and biogeochemical tracers in the Southern Ocean. J. Phys. Oceanogr., 45(12): 3057-3081. doi:10.1175/JPO-D-14-0240.1.

Dybzinski, R., C.E. Farrior, and S.W. Pacala, 2015. Increased forest carbon storage with increased atmospheric CO2 despite nitrogen limitation: a game‐theoretic allocation model for trees in competition for nitrogen and light. Glob. Change Biol., 21(3): 1182-1196. doi:10.1111/gcb.12783.

Farrior, C.E., S.A. Bohlman, S. Hubbell, and S.W. Pacala, 2016. Dominance of the suppressed: Power-law size structure in tropical forests. Science, 351(6269): 155-157. doi:10.1126/science.aad0592.

Farrior, C.E., I. Rodriguez-Iturbe, R. Dybzinski, S.A. Levin, and S.W. Pacala, 2015. Decreased water limitation under elevated CO2 amplifies potential for forest carbon sinks. Proc. Natl. Acad. Sci., 112(23): 7213-7218. doi:10.1073/pnas.1506262112.

Frölicher, T.L., J.L. Sarmiento, D.J. Paynter, J.P. Dunne, J.P. Krasting, and M. Winton, 2015. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J. Climate, 28(2): 862-886. doi:10.1175/JCLI-D-14-00117.1.

Galbraith, E.D., J.P. Dunne, A. Gnanadesikan, R.D. Slater, J.L. Sarmiento, C.O. Dufour, G.F. de Souza, D. Bianchi, M. Claret, K.B. Rodgers, and S.S. Marvasti, 2015. Complex functionality with minimal computation: Promise and pitfalls of reduced-tracer ocean biogeochemistry models. J. Adv. Model Earth Sy., 7(4): 2012- 2028. doi:10.1002/2015MS000463.

Galbraith, E.D., E.Y. Kwon, D. Bianchi, M.P. Hain, and J.L. Sarmiento, 2015. The impact of atmospheric pCO2 on carbon isotope ratios of the atmosphere and ocean. Global Biogeochem. Cycles, 29(3): 307-324. doi:10.1002/2014GB004929.

Higgins, J.A., A.V. Kurbatov, N.E. Spaulding, E. Brook, D.S. Introne, L.M. Chimiak, Y. Yan, P.A. Mayewski, and M.L. Bender, 2015. Atmospheric composition 1 million years ago from blue ice in the Allan Hills, Antarctica. Proc. Natl. Acad. Sci., 112(22): 6887-6891. doi:10.1073/pnas.1420232112.

Mackey, K., J.J. Morris, F.M.M. Morel, and S.A. Kranz, 2015. Response of Photosynthesis to Ocean Acidification. Oceanography, 25(2): 74-91. http://dx.doi. org/10.5670/oceanog.2015.33.

Mislan, K.A., J.P. Dunne, and J.L. Sarmiento, 2015. The fundamental niche of blood-oxygen binding in the pelagic ocean. Oikos. doi:10.1111/oik.02650.

Morrison, A.K., S.M. Griffies, M. Winton, W.G. Anderson, and J.L. Sarmiento, 2016. Mechanisms of Southern Ocean heat uptake and transport in a global eddying climate model. J. Climate, 29(6). doi:10.1175/ JCLI-D-15-0579.1.

Rabin, S.S., B.I. Magi, E. Shevliakova, and S.W. Pacala, 2015. Quantifying regional, time-varying effects of cropland and pasture on vegetation fire. Biogeosciences, 12(13): 6591-6604. doi:10.5194/bg-12-6591- 2015.

Smith, N.G., S. Malyshev, E. Shevliakova, J. Kattge, and J.S. Dukes, 2016. Foliar temperature acclimation reduces simulated carbon sensitivity to climate. Nat. Clim. Chang., 6: 407-411. doi:10.1038/ nclimate2878.

Wang, S., A. Chen, S.W. Pacala, and J. Fang, 2015. Density-dependent speciation alters the structure and dynamics of neutral communities. J. Theor. Biol., 372: 128-134. doi:10.1016/j.jtbi.2015.02.007.

Watson, J.R., C.A. Stock, and J.L. Sarmiento, 2015. Exploring the role of movement in determining the global distribution of marine biomass using a coupled hydrodynamic – Size-based ecosystem model. Prog. Oceanogr., 138, Part B: 521- 532. doi:10.1016/j.pocean.2014.09.001.

Westberry, T.K., P. Schultz, M.J. Behrenfeld, J.P. Dunne, M.R. Hiscock, S. Maritorena, J.L. Sarmiento, and D.A. Siegel, 2016. Annual cycles of phytoplankton biomass in the subarctic Atlantic and Pacific Ocean. Global Biogeochem. Cycles, 30(2): 175-190. doi:10.1002/2015GB005276.

Weng, E.S., S. Malyshev, J.W. Lichstein, C.E. Farrior, R. Dybzinski, T. Zhang, E. Shevliakova, and S.W. Pacala, 2015. Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of heightstructured competition. Biogeosciences, 12(9): 2655-2694. doi:10.5194/bg-12-2655- 2015.

Yau, A.M., M.L. Bender, T. Blunier, and J. Jouzel, 2016. Setting a chronology for the basal ice at Dye-3 and GRIP: Implications for the long-term stability of the Greenland Ice Sheet. Earth Planet. Sci. Lett., in review.

Yau, A.M., M.L. Bender, A. Robinson, and E.J. Brook, 2016. Reconstructing the Last Interglacial at Summit, Greenland: Insights from GISP2. Proc. Natl. Acad. Sci., in review.

Young, J.N., S.A. Kranz, J.A.L. Goldman, P.D. Tortell, and F.M.M. Morel, 2015. Antarctic phytoplankton down-regulate their carbon-concentrating mechanisms under high CO2 with no change in growth rates. Mar. Ecol. Prog. Ser., 532: 13-28. doi:10.3354/meps11336.

Young, J.N., and F.M.M. Morel, 2015. Biological oceanography: The CO2 switch in diatoms. Nat. Clim. Chang., 5(8): 722- 723. doi:10.1038/nclimate2691.

Zanowski, H., R. Hallberg, and J.L. Sarmiento, 2015. Abyssal Ocean Warming and Salinification after Weddell Polynyas in the GFDL CM2G Coupled Climate Model. J. Phys. Oceanogr., 45(11): 2755- 2772. doi:10.1175/JPO-D-15-0109.1.

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