RESEARCH NEWS & VIEWS Solar System, the Sun and other stars of its birth cluster would be spread across the Galaxy. There is no direct way to identify whether the Sun was ever in a stellar nursery or to find its siblings. But indirectly, the mark from those early stellar encounters, like fingerprints at a crime scene, could provide evidence for the sculpted distribution of orbits in the inner Oort cloud. Trujillo and Sheppard’s detection of 2012 VP113, which was made using the Dark Energy Camera (DECam) at the Cerro Tololo Inter-American Observatory 4-metre tele scope in Chile, brings us one step closer to reading the dynamical record of the inner Oort cloud. With its farthest distance from the Sun at 452 au and coming no closer into the Solar System than 80 au, 2012 VP113 is placed well within the expected inner Oort cloud outside the Kuiper belt, a disk-shaped region of small icy bodies that lies beyond Neptune’s orbit (Fig. 1). It is still true that, with two objects, we cannot unambiguously identify the origin of the inner Oort cloud, but Trujillo and Sheppard find that the orbits of 2012 VP113 and Sedna are consistent with models for the Sun’s birth cluster and constraints from previous ground-based, wide-field surveys11. This suggests that Sedna and 2012 VP113 are the tip of the iceberg for this population of distant inner Oort cloud objects. Most objects in the inner Oort cloud would reside at distances farther than Sedna and 2012 VP113. For only an extremely small fraction of their orbits around the Sun would these inner Oort objects be bright enough to be detected in current ground-based surveys. But there is more to the authors’ study than described so far. Trujillo and Sheppard noticed a peculiarity with the orbits of Sedna and 2012 VP113. The two objects have similar values for one of their orbital parameters: the angle between the point of perihelion and where the orbit crosses the plane of the Solar System. Interestingly, the most distant Kuiper-belt objects, with orbital semimajor axes greater than 150 au and perihelia beyond Neptune, also seem to have values for such angles comparable to those of Sedna and 2012 VP113. Such clustering of orbital angles seems to be unexplainable by the gravitational influence of Neptune alone. This result may be the first hint we have of an identifiable signature of the inner Oort cloud’s formation mechanism on the orbits of closer-in Solar System bodies. If true, any formation mechanism proposed for the origin of Sedna and 2012 VP113 will need to explain this orbital structure. More detections of objects both in the Kuiper belt and the inner Oort cloud will be needed to confirm this result. Our knowledge of the inner Oort cloud is, in many ways, in the same state as the study of the Kuiper belt was in the 1990s, when the first Kuiper-belt objects were discovered — 62 years after Pluto’s detection. 2012 VP113 is
the smoking gun for the existence of the inner Oort cloud. We now know that Sedna is not alone. The prospects of future detections using next-generation instruments and facilities, such as the Hyper Suprime-Cam on the 8.2-m Subaru telescope in Hawaii and the 8.4-m Large Synoptic Survey Telescope in Chile, are promising. With greater sky coverage and depth than that obtained by Trujillo and Sheppard, we will find more of these distant bodies lurking in the shadows and will begin to unravel the origin of the inner Oort cloud. ■ Megan E. Schwamb is at the Institute of Astronomy and Astrophysics, Academia Sinica, Taipei 10617, Taiwan.
e-mail: [email protected] 1. Brown, M. E., Trujillo, C. & Rabinowitz, D. Astrophys. J. 617, 645–649 (2004). 2. Chen, Y.-T. et al. Astrophys. J. 775, L8 (2013). 3. Millis, R. L. et al. Astron. J. 123, 2083–2109 (2002). 4. Gladman, B. et al. Icarus 157, 269–279 (2002). 5. Trujillo, C. A & Sheppard, S. S. Nature 507, 471–474 (2014). 6. Morbidelli, A. & Levison, H. F. Astron. J. 128, 2564–2576 (2004). 7. Gomes, R. S., Gallardo, T., Fernández, J. A. & Brunini, A. Celest. Mech. Dyn. Astron. 91, 109–129 (2005). 8. Brasser, R., Duncan, M. J., Levison, H. F., Schwamb, M. E. & Brown, M. Icarus 217, 1–19 (2012). 9. Brasser, R., Duncan, M. J. & Levison, H. F. Icarus 184, 59–82 (2006). 10. Kaib, N. A. & Quinn, T. Icarus 197, 221–238 (2008). 11. Schwamb, M. E., Brown, M. E., Rabinowitz, D. L. & Ragozzine, D. Astrophys. J. 720, 1691–1707 (2010). 12. Stern, S. A. Nature 424, 639–642 (2003).
BI O GEO C H EM I ST RY
Methane minimalism A meta-analysis of methane emissions at the ecosystem level reveals a simple exponential dependence on temperature, despite the complex array of factors that control this process. See Letter p.488 TORI M. HOEHLER & MARC J. ALPERIN
M
ethane is the third-largest contributor to the greenhouse effect, after water vapour and carbon dioxide. The atmospheric concentration of methane rose for much of the twentieth century, was steady between 1999 and 2006, and is now again rising, at a rate of 0.4% annually1. The cause of this resumption is not fully understood, but it is probably related to a surge in methane emissions from wetlands — nearly half of global methane emissions comes from wetlands and rice paddies that are expected to be subject to temperature-related and other feedbacks from global climate change. Although a complex set of factors influences methane emission at the ecosystem level, Yvon-Durocher et al.2 report in this issue (page 488) that the average response of methane emissions to temperature across a range of ecosystems is well described by a simple mathematical relationship. Could it be as straightforward as that? Most natural methane emissions originate with microorganisms called methanogens, the metabolic rates of which, during growth in culture with unlimited substrate, change with temperature according to the Arrhenius equation — a simple exponential dependence of rate constant on the reciprocal of absolute temperature. Such behaviour is unsurprising: although methanogenesis comprises a network of enzyme-catalysed reactions, the Arrhenius dependence reflects the kinetics of a single rate-limiting step. But Yvon-Durocher and colleagues’ report of the same temperature dependence at the ecosystem level is notable,
4 3 6 | NAT U R E | VO L 5 0 7 | 2 7 M A RC H 2 0 1 4
© 2014 Macmillan Publishers Limited. All rights reserved
because an array of physical, chemical and ecological factors controls the production of methane and its release to the atmosphere3. Methanogenesis in soils and sediments is ultimately fuelled by complex organic matter; what proportion of this organic matter is converted to methane, and at what rate, depends as much on ecosystem dynamics as on the enzyme kinetics of methanogens (Fig. 1). For example, organic carbon can be converted to carbon dioxide, rather than methane, when oxidants such as oxygen, nitrate, iron(iii) and sulphate are available to fuel methanogens’ microbial competitors. And when organic matter does get converted to methane, it must first be broken down by other microbes into the few simple substrates that methanogens can metabolize — an upstream supply that can limit the rate of methanogenesis. Transport of methane from the site of production to the atmosphere is also subject to effects that might obscure a purely biochemical temperature dependence. Methane is consumed in both aerobic and anaerobic microbial processes, and transport through zones in which such consumption occurs can markedly reduce net emissions. But consumption can largely be bypassed when physical and metabolic factors combine to promote ebullition — the formation and release of methane bubbles, a sight not uncommon in productive fresh water lakes and swamps. The vascular system of plants can also facilitate methane transport, when roots penetrate the methane-producing portions of soils and provide a direct conduit to the atmosphere; but these same conduits also transport oxygen that inhibits methanogenesis
NEWS & VIEWS RESEARCH
CH4 CO2
Oxic zone Ea
CH4 diffusion Anoxic zone CH4 bubble
O2 Ea
Oxidizers
Ebullition Complex organic matter Ea
Ea
H2, CO2, acetate Extracellular enzymes, fermenters
Methanogens
CH4
Figure 1 | Emissions network. Methane (CH4) is generated by microorganisms (methanogens) that metabolize substrates produced through the breakdown of complex organic matter by extracellular enzymes and fermentative microorganisms. The rate of methane emission to the atmosphere is influenced by these microorganisms’ individual sensitivities to temperature (indicated by Ea), and by chemical conditions, such as oxygen availability, that divert the flow of carbon to microbial competitors that oxidize organic matter to carbon dioxide. Methane emission also depends on gas transport by diffusion, by bubble ebullition and in the vasculature of plants, and on the fraction of methane that is consumed by methaneoxidizing microorganisms. Despite this complex array of factors, Yvon-Durocher et al.2 report that the response of methane emissions at the ecosystem level can be described by the simple Arrhenius relationship.
and promotes methane consumption. In their analysis of 127 studies of the eco system-level dependence of methane emission on temperature, Yvon-Durocher et al. acknowledge this complex array of factors, but conclude that the aggregate temperature response is nonetheless described by the Arrhenius equation, with an apparent activation energy (Ea) of 0.96 electronvolts, similar to the 1.10 eV observed in pure cultures of methanogens. Ea is a measure of temperature sensitivity; for example, 0.96 and 1.10 eV correspond, respectively, to a 3.5- and 4.2-fold increase in rate constant for an increase in temperature from 20 °C to 30 °C. Statistically speaking, the large number of studies considered allows for a confident statement that the calculated mean Ea (0.96 eV) accurately reflects the mean temperature sensitivity of methane-emitting ecosystems — assuming that the sites that comprise the data set represent a random sample of all such environments. But the impact of factors other than temperature seems evident in the scatter and spread of the individual data sets considered by the authors. For example, about 40% of the studies considered had Arrhenius-plot correlation coefficients (r 2) of less than 0.5, which indicates that less than half of the variance in those emission data is explained by the Arrhenius relationship, and about 10% of the studies measured methane emissions that were higher at lower temperatures (opposite to the effect predicted by the Arrhenius equation). The reported ecosystem-level Ea is higher than what has been called the “universal temperature dependence” of aerobic metabolism4 — an Ea of 0.67 ± 0.15 eV that encompasses the metabolism of a wide range of plants, protozoa, invertebrates and vertebrates — and is also higher than the average Ea (0.72 eV) observed for a diverse group of 50 aerobic and
anaerobic microorganisms5. The higher average Ea reported here for methanogenic ecosystems could reflect either that the biochemistry of methanogens (which have an average Ea of 1.10 eV) directly limits methane emissions in some ecosystems, or that the organisms that supply methanogens with substrates have similarly high temperature dependence. Nevertheless, the clear implication of these findings is that methane production will increase more steeply with temperature than would be captured by climate-change models that assume methane emission is governed by more typical (lower) values of Ea. For example, over the range of global warming projected6 for this century (1.0–3.7 °C), an Ea of 0.96 eV suggests a 14–63% increase in methane emission compared with 10–40% for an Ea of 0.67 eV.
Yvon-Durocher and colleagues’ findings constrain, and perhaps simplify, one piece of a much larger climate-change puzzle. Feedbacks from methane emissions in response to global climate change will ultimately derive from a combination of the direct temperature effects considered here and indirect effects such as thawing of permafrost (and the resultant availability of new organic matter), changes in vegetation, and large-scale inundation or drying of soils and wetlands. Moreover, methane’s proportionate contribution to global warming (about 20% over the past century) may actually diminish as carbon dioxide takes an increasingly prominent role in the future7. But in this complex problem — unlike methane’s flux to the atmosphere — every bit helps. ■ Tori M. Hoehler is in the Space Science and Astrobiology Division, NASA Ames Research Center, Moffett Field, California 94035, USA. Marc J. Alperin is in the Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3300, USA. e-mails: [email protected]; [email protected] 1. Kirschke, S. et al. Nature Geosci. 6, 813–823 (2013). 2. Yvon-Durocher, G. et al. Nature 507, 488–491 (2014). 3. Riley, W. J. et al. Biogeosciences 8, 1925–1953 (2011). 4. Gillooly, J. F. et al. Science 293, 2248–2251 (2001). 5. Tijhuis, L. et al. Biotech. Bioeng. 42, 509–519 (1993). 6. Stocker, T. F. et al. in Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Technical Summary, 33–115 (Cambridge Univ. Press, 2013). 7. Prather, M. et al. (eds) in Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Annex II, 1395–1445 (Cambridge Univ. Press, 2013). This article was published online on 19 March 2014.
C L I M ATE S CI E N CE
A high bar for decadal forecasts of El Niño Climate simulations suggest that multi-decadal periods of high and low variability in the phenomenon known as the El Niño-Southern Oscillation in the tropical Pacific Ocean may be entirely unpredictable. PEDRO DINEZIO
T
he episodic warming and cooling of the surface temperature of the tropical Pacific Ocean, known as the El Niño– Southern Oscillation (ENSO), causes year-toyear climate fluctuations, affecting weather,
ecosystems and economies around the world. The occurrence of these episodes is not regular. For example, whereas the period covering the years 1970–2000 witnessed the strongest El Niño (warming) events on record, the years since 2000 have experienced fewer and weaker such events. Writing in the Journal of
2 7 M A RC H 2 0 1 4 | VO L 5 0 7 | NAT U R E | 4 3 7
© 2014 Macmillan Publishers Limited. All rights reserved
the smoking gun for the existence of the inner Oort cloud. We now know that Sedna is not alone. The prospects of future detections using next-generation instruments and facilities, such as the Hyper Suprime-Cam on the 8.2-m Subaru telescope in Hawaii and the 8.4-m Large Synoptic Survey Telescope in Chile, are promising. With greater sky coverage and depth than that obtained by Trujillo and Sheppard, we will find more of these distant bodies lurking in the shadows and will begin to unravel the origin of the inner Oort cloud. ■ Megan E. Schwamb is at the Institute of Astronomy and Astrophysics, Academia Sinica, Taipei 10617, Taiwan.
e-mail: [email protected] 1. Brown, M. E., Trujillo, C. & Rabinowitz, D. Astrophys. J. 617, 645–649 (2004). 2. Chen, Y.-T. et al. Astrophys. J. 775, L8 (2013). 3. Millis, R. L. et al. Astron. J. 123, 2083–2109 (2002). 4. Gladman, B. et al. Icarus 157, 269–279 (2002). 5. Trujillo, C. A & Sheppard, S. S. Nature 507, 471–474 (2014). 6. Morbidelli, A. & Levison, H. F. Astron. J. 128, 2564–2576 (2004). 7. Gomes, R. S., Gallardo, T., Fernández, J. A. & Brunini, A. Celest. Mech. Dyn. Astron. 91, 109–129 (2005). 8. Brasser, R., Duncan, M. J., Levison, H. F., Schwamb, M. E. & Brown, M. Icarus 217, 1–19 (2012). 9. Brasser, R., Duncan, M. J. & Levison, H. F. Icarus 184, 59–82 (2006). 10. Kaib, N. A. & Quinn, T. Icarus 197, 221–238 (2008). 11. Schwamb, M. E., Brown, M. E., Rabinowitz, D. L. & Ragozzine, D. Astrophys. J. 720, 1691–1707 (2010). 12. Stern, S. A. Nature 424, 639–642 (2003).
BI O GEO C H EM I ST RY
Methane minimalism A meta-analysis of methane emissions at the ecosystem level reveals a simple exponential dependence on temperature, despite the complex array of factors that control this process. See Letter p.488 TORI M. HOEHLER & MARC J. ALPERIN
M
ethane is the third-largest contributor to the greenhouse effect, after water vapour and carbon dioxide. The atmospheric concentration of methane rose for much of the twentieth century, was steady between 1999 and 2006, and is now again rising, at a rate of 0.4% annually1. The cause of this resumption is not fully understood, but it is probably related to a surge in methane emissions from wetlands — nearly half of global methane emissions comes from wetlands and rice paddies that are expected to be subject to temperature-related and other feedbacks from global climate change. Although a complex set of factors influences methane emission at the ecosystem level, Yvon-Durocher et al.2 report in this issue (page 488) that the average response of methane emissions to temperature across a range of ecosystems is well described by a simple mathematical relationship. Could it be as straightforward as that? Most natural methane emissions originate with microorganisms called methanogens, the metabolic rates of which, during growth in culture with unlimited substrate, change with temperature according to the Arrhenius equation — a simple exponential dependence of rate constant on the reciprocal of absolute temperature. Such behaviour is unsurprising: although methanogenesis comprises a network of enzyme-catalysed reactions, the Arrhenius dependence reflects the kinetics of a single rate-limiting step. But Yvon-Durocher and colleagues’ report of the same temperature dependence at the ecosystem level is notable,
4 3 6 | NAT U R E | VO L 5 0 7 | 2 7 M A RC H 2 0 1 4
© 2014 Macmillan Publishers Limited. All rights reserved
because an array of physical, chemical and ecological factors controls the production of methane and its release to the atmosphere3. Methanogenesis in soils and sediments is ultimately fuelled by complex organic matter; what proportion of this organic matter is converted to methane, and at what rate, depends as much on ecosystem dynamics as on the enzyme kinetics of methanogens (Fig. 1). For example, organic carbon can be converted to carbon dioxide, rather than methane, when oxidants such as oxygen, nitrate, iron(iii) and sulphate are available to fuel methanogens’ microbial competitors. And when organic matter does get converted to methane, it must first be broken down by other microbes into the few simple substrates that methanogens can metabolize — an upstream supply that can limit the rate of methanogenesis. Transport of methane from the site of production to the atmosphere is also subject to effects that might obscure a purely biochemical temperature dependence. Methane is consumed in both aerobic and anaerobic microbial processes, and transport through zones in which such consumption occurs can markedly reduce net emissions. But consumption can largely be bypassed when physical and metabolic factors combine to promote ebullition — the formation and release of methane bubbles, a sight not uncommon in productive fresh water lakes and swamps. The vascular system of plants can also facilitate methane transport, when roots penetrate the methane-producing portions of soils and provide a direct conduit to the atmosphere; but these same conduits also transport oxygen that inhibits methanogenesis
NEWS & VIEWS RESEARCH
CH4 CO2
Oxic zone Ea
CH4 diffusion Anoxic zone CH4 bubble
O2 Ea
Oxidizers
Ebullition Complex organic matter Ea
Ea
H2, CO2, acetate Extracellular enzymes, fermenters
Methanogens
CH4
Figure 1 | Emissions network. Methane (CH4) is generated by microorganisms (methanogens) that metabolize substrates produced through the breakdown of complex organic matter by extracellular enzymes and fermentative microorganisms. The rate of methane emission to the atmosphere is influenced by these microorganisms’ individual sensitivities to temperature (indicated by Ea), and by chemical conditions, such as oxygen availability, that divert the flow of carbon to microbial competitors that oxidize organic matter to carbon dioxide. Methane emission also depends on gas transport by diffusion, by bubble ebullition and in the vasculature of plants, and on the fraction of methane that is consumed by methaneoxidizing microorganisms. Despite this complex array of factors, Yvon-Durocher et al.2 report that the response of methane emissions at the ecosystem level can be described by the simple Arrhenius relationship.
and promotes methane consumption. In their analysis of 127 studies of the eco system-level dependence of methane emission on temperature, Yvon-Durocher et al. acknowledge this complex array of factors, but conclude that the aggregate temperature response is nonetheless described by the Arrhenius equation, with an apparent activation energy (Ea) of 0.96 electronvolts, similar to the 1.10 eV observed in pure cultures of methanogens. Ea is a measure of temperature sensitivity; for example, 0.96 and 1.10 eV correspond, respectively, to a 3.5- and 4.2-fold increase in rate constant for an increase in temperature from 20 °C to 30 °C. Statistically speaking, the large number of studies considered allows for a confident statement that the calculated mean Ea (0.96 eV) accurately reflects the mean temperature sensitivity of methane-emitting ecosystems — assuming that the sites that comprise the data set represent a random sample of all such environments. But the impact of factors other than temperature seems evident in the scatter and spread of the individual data sets considered by the authors. For example, about 40% of the studies considered had Arrhenius-plot correlation coefficients (r 2) of less than 0.5, which indicates that less than half of the variance in those emission data is explained by the Arrhenius relationship, and about 10% of the studies measured methane emissions that were higher at lower temperatures (opposite to the effect predicted by the Arrhenius equation). The reported ecosystem-level Ea is higher than what has been called the “universal temperature dependence” of aerobic metabolism4 — an Ea of 0.67 ± 0.15 eV that encompasses the metabolism of a wide range of plants, protozoa, invertebrates and vertebrates — and is also higher than the average Ea (0.72 eV) observed for a diverse group of 50 aerobic and
anaerobic microorganisms5. The higher average Ea reported here for methanogenic ecosystems could reflect either that the biochemistry of methanogens (which have an average Ea of 1.10 eV) directly limits methane emissions in some ecosystems, or that the organisms that supply methanogens with substrates have similarly high temperature dependence. Nevertheless, the clear implication of these findings is that methane production will increase more steeply with temperature than would be captured by climate-change models that assume methane emission is governed by more typical (lower) values of Ea. For example, over the range of global warming projected6 for this century (1.0–3.7 °C), an Ea of 0.96 eV suggests a 14–63% increase in methane emission compared with 10–40% for an Ea of 0.67 eV.
Yvon-Durocher and colleagues’ findings constrain, and perhaps simplify, one piece of a much larger climate-change puzzle. Feedbacks from methane emissions in response to global climate change will ultimately derive from a combination of the direct temperature effects considered here and indirect effects such as thawing of permafrost (and the resultant availability of new organic matter), changes in vegetation, and large-scale inundation or drying of soils and wetlands. Moreover, methane’s proportionate contribution to global warming (about 20% over the past century) may actually diminish as carbon dioxide takes an increasingly prominent role in the future7. But in this complex problem — unlike methane’s flux to the atmosphere — every bit helps. ■ Tori M. Hoehler is in the Space Science and Astrobiology Division, NASA Ames Research Center, Moffett Field, California 94035, USA. Marc J. Alperin is in the Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-3300, USA. e-mails: [email protected]; [email protected] 1. Kirschke, S. et al. Nature Geosci. 6, 813–823 (2013). 2. Yvon-Durocher, G. et al. Nature 507, 488–491 (2014). 3. Riley, W. J. et al. Biogeosciences 8, 1925–1953 (2011). 4. Gillooly, J. F. et al. Science 293, 2248–2251 (2001). 5. Tijhuis, L. et al. Biotech. Bioeng. 42, 509–519 (1993). 6. Stocker, T. F. et al. in Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Technical Summary, 33–115 (Cambridge Univ. Press, 2013). 7. Prather, M. et al. (eds) in Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) Annex II, 1395–1445 (Cambridge Univ. Press, 2013). This article was published online on 19 March 2014.
C L I M ATE S CI E N CE
A high bar for decadal forecasts of El Niño Climate simulations suggest that multi-decadal periods of high and low variability in the phenomenon known as the El Niño-Southern Oscillation in the tropical Pacific Ocean may be entirely unpredictable. PEDRO DINEZIO
T
he episodic warming and cooling of the surface temperature of the tropical Pacific Ocean, known as the El Niño– Southern Oscillation (ENSO), causes year-toyear climate fluctuations, affecting weather,
ecosystems and economies around the world. The occurrence of these episodes is not regular. For example, whereas the period covering the years 1970–2000 witnessed the strongest El Niño (warming) events on record, the years since 2000 have experienced fewer and weaker such events. Writing in the Journal of
2 7 M A RC H 2 0 1 4 | VO L 5 0 7 | NAT U R E | 4 3 7
© 2014 Macmillan Publishers Limited. All rights reserved
