Environ. Sci. Technol. 2007, 41, 2131-2137
Atmospheric Methane: Trends and Cycles of Sources and Sinks M . A S L A M K H A N K H A L I L , * ,† CHRISTOPHER L. BUTENHOFF,† AND REINHOLD A. RASMUSSEN‡ Department of Physics, Portland State University, P.O. Box 751, Portland, Oregon 97207, and Department of Environmental Science, Oregon Graduate Institute, Beaverton, Oregon 97006
For more than 20 years the global emissions and the lifetime of methane have probably been constant, so the buildup of methane in the atmosphere has been slowing down for as long. During this time, there have been periodic events occurring every seven to eight years, when global methane concentrations increased by some 10 ppb and later fell back, in some cases due to temporary increases of emissions from the northern tropics that spread to the global scale. These conclusions are derived from the accumulated global observations that now span 23 years and define the role of human activities in the recent cycle of atmospheric methane.
1. Introduction Methane concentrations in the atmosphere have more than doubled over the last century, raising concerns that it is contributing to global warming and will continue to do so in the future. Although these past increases were alarmingly rapid, subsequent measurements showed a persistent slowdown in the trends to nearly zero at present (1-4). Here we discuss the nature and consequences of these observations and present the long composite atmospheric time series for further research. We will use a unique deconstruction of the time series that provides estimates for the annual emissions and lifetime of methane and isolates the effect of the trends and interannual variations of the sources and sinks. The data consist of weekly flask sampling measurements taken at six strategically located sites, one in each of the polar, middle, and tropical latitudes of both hemispheres (Barrow, Alaska 71.16 N, 156.5 W; Cape Meares, Oregon 45.5 N, 124 W; Mauna Loa 21.08 N, 157.2 W and Cape Kumukahi 19.3 N, 154.5 W, Hawaii; Samoa 14.1 S, 170.6 W; Cape Grim, Tasmania 42 S, 145 E; and Antarctica including Palmer Station 64.46 S, 64 W and the South Pole 90 S). Data from most sites are available between 1981 and 1998 from the Oregon Graduate Institute (OGI) network and between 1983 and 2004 from the NOAA/CMDL network (now NOAA/ESRL). The earliest measurements are from Cape Kumukahi and Samoa and begin in late 1979, while data from the South Pole are available only from the middle of 1983. The two networks overlap for nearly 15 years. The data sets can be combined into a composite time series that extends over 23 years; longer than either data set alone. We begin the composite time series on January 1981 when measurements are available from four of the seven main sites. Although samples were collected weekly, monthly average data are the most useful. There are some gaps in the monthly * Corresponding author phone: (503) 725-8396; fax: (503) 7258550; e-mail:
[email protected]. † Portland State University. ‡ Oregon Graduate Institute. 10.1021/es061791t CCC: $37.00 Published on Web 02/14/2007
2007 American Chemical Society
OGI data. Short gaps were linearly interpolated between existing data. Longer gaps were filled in using data from neighboring sites. An average ratio was calculated between sites when data were available from both. This ratio was taken to be the typical gradient between the sites and was used to interpolate values between neighboring sites. For both data sets, measurements from Cape Kumukahi and Mauna Loa were combined into a single Hawaiian site and measurements from Palmer Station and the South Pole were combined into a single Antarctic site. Additionally for the CMDL data set we combined the measurements from Cape Meares and Mace Head into a single northern midlatitude station as neither site had complete coverage over the entire time. After these adjustments, the OGI data set runs from 1/1981 to 12/1997. Measurements from the CMDL network were begun in 1983 in collaboration with the OGI program and continue to the present day (4, 5). To determine the calibration ratio between the two data sets we used measurements from the common sites, namely Barrow, Cape Meares, Cape Kumukahi, Mauna Loa, Samoa, and Cape Grim. The ratios were then averaged over all months and sites. Overall the OGI data were higher by a factor of 1.019, though this ratio is not constant. There is an abrupt transition from a ratio of 1.008 to 1.024 near 2/1988. Such differences arise when one of the networks revises its absolute calibration standard. Thus, we used these two different ratios over their respective time periods to put the OGI data on the CMDL scale. The composite time series was constructed by taking a simple average of the rescaled OGI data and the CMDL data and is therefore on the CMDL calibration scale (Supporting Information). We express the concentration in molar mixing ratios as ppb. In Figures 1 and 2 we show the globally averaged concentrations and the trends of methane. The trends are calculated as moving slopes using a standard linear regression model. Based on the results shown here, there are two key facts that we want to emphasize. First, there is agreement on the trends between the data from the two networks during the period of overlap. This lends confidence that the time series, even when only one network was operating, captures the main features of changes in the methane budget. The trends for the composite data are also shown. Second, it is apparent that there are ups and downs, but these are superimposed on a systematically declining rate of accumulation. The slowdown of methane trends has been known and discussed for a long time; however, it is particularly noteworthy that the decrease of the methane trend is not a new phenomenon, but rather it has occurred from the time that systematic measurements were first taken. Most probably it started even before then. Since fluctuations are superimposed on a generally decreasing trend, there have been years in recent times when methane has not increased at all or has even fallen slightly relative to the previous year. This has attracted more interest than in the past when there were similar short-term down turns but on the whole methane still increased over previous years. The recent incursions of the trend into negative territory are therefore part of a much longer term process.
2. Trends, Cycles and Budgets To delineate the major conceptual results we will use a globally averaged model to describe the expected atmospheric concentrations:
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FIGURE 1. Global monthly methane concentration (ppb). The data (red circles) are calculated as an area weighted average of measurements taken at six strategically chosen background air sites situated in the polar, middle, and tropical latitudes of both hemispheres. The calculated series (solid line) consists of four terms: the long-term trend (C1) determined by theoretical constant source-constant lifetime equation discussed in the text with emissions of 490 Tg/yr and a lifetime of 10 years; an average seasonal cycle (∆C); an average long-term cycle (rC); and residual fluctuations (EC). The sum of the first three terms, C1 + rC + ∆C, is shown as the solid line.
FIGURE 2. Trend of global methane over the last two decades (ppb/y). The trend is determined by a standard linear regression model applied to each successive 2-year period. Results from two data sets (OGI and NOAA/CMDL) which overlap in time are shown along with the same calculation for the composite time series. The solid line is the trend according to the model for C1 discussed in the text and shown in Figure 1 resulting in dC1/dt ) B0e-t/τ0. where C is the global burden (Tg), S is the emissions (Tg/yr), and τ is the lifetime (yrs) (1 ppb is about 2.75 Tg in the entire atmosphere). We note that the trends (dC/dt) are a direct indicator of the quantitative imbalance between the global sources and sinks. For methane the sinks can be written approximately as 1/τ ) k[OH] + 1/τother where k is the reaction rate constant between methane and the atmospheric hydroxyl radical (OH) and k[OH] . 1/τother. k, [OH], and τother are 2132
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averaged over the entire atmosphere by suitable area and density weighting. Changes in the sources and sinks can be separated into four time scales: The long-term trends that consist of increasing or decreasing levels over decades, the interannual variations that span several years, seasonal variations confined to a year, and aperiodic or even random shortterm variations occurring over a few months at most. The
FIGURE 3. Interannual variability of the global methane concentrations (rC). This component of the global time series contains interannual variations that are visible in Figure 1 and more so in Figure 2. The starting point of this figure is a 1-yr moving average of the data in Figure 1. These are isolated in this figure and suggest a cyclical variation at least over the time span of these data. changes of sources and sinks are reflected in the concentrations over the same four time scales because of the connecting mass balance eq 1. The main benefit of the decomposition into these time scales is that it provides unique insights into the global budget because the mechanisms of changes are generally quite different from one of these scales to another. We can further refine eq 1 by separating changes in sources and sinks over these time scales as follows:
S ) S0 + RS +∆S + S
(2a)
1/τ ) 1/τ0 + Rτ +∆τ + τ
(2b)
C ) C1 + RC +∆C + C
(2c)
where S0 and τ0 are constant base emissions and lifetime, R’s are the interannual variability, ∆’s are seasonal variations, ’s are short-term variations and C1 is the long-term trend to be discussed in more detail later; the later four variables are functions of time. The connection between these variables and the corresponding features of the observed concentrations, C(t), can be quite complicated, but will not concern us here. Our focus is the long-term behavior of the methane budget and so we will not deal with seasonal variations and short-term fluctuations either. To isolate the long-term features, the equation is filtered over a time period T (of integer years). With the substitution of eqs 2a and 2b into eq 1 and filtering, the resulting equation is approximately
dC/dt = [S0 - C(t)/τ0] + [RS(t) - Rτ(t)C(t)]
(3)
where the filtering of the variables C, RS, and Rτ may be implemented by using a moving average of a suitable integer number of years (usually 1 yr of monthly data). In practice, we replace the dC/dt with a regression estimate over the filtering time (T) instead of the point estimate: [C(t + T) C(t)]/T. It is noteworthy that the Rs representing the interannual variability can be zero, but not any other constant, nor do they contain constant terms, since these are already represented by S0 and τ0. Normally we do not expect the Rs to contain cyclic terms, but in the present case we think there might be one, probably from the sources as will be discussed later. Also, if we assume that the only sink capable
of causing significant long-term changes of methane concentration is the possible trend of OH, Rτ(t) then describes the change of global hydroxyl concentrations as [OH] ) [OH]0 + Rτ(t)/k. We return now to applying eq 3 to the data. We can split the solution of eq 3 into two parts: C ) C1 + C2 where C1 is the solution of dC1/dt ) [S0 - C1/τ0] and C2 is the solution of dC2/dt ) C2/τ0 + δ(t), where δ(t) ) [RS(t) - Rτ (t)C(t)]. The solution for C1 is
C1 ) C0e-t/τ0 + S0τ0[1 - e-t/τ0]
(4)
and dC1/dt ) B0e-t/τ0, where B0 ) [S0 - C0/τ0] (shown in Figure 2 as the solid line). A plot of dC/dt as a function of C using the 1-yr smoothed values of the measured concentrations results in a straight line which suggests that the terms in δ(t) do not contain long-term trends. The slope and intercept obtained from regression analysis give us estimates of τ0 and S0, respectively. Iterative methods are used to refine this estimate resulting in a slope of 0.1006 ( 0.003/y and an intercept of 179 ( 3 ppb which, according to eq 3, yields a lifetime of 9.9 ( 0.4 years and a global source of about 490 ( 8 Tg/yr (the ( values are 90% confidence limits). C0 is found to be 1553.5 ppb, which is close to the measured concentration at time zero and makes the interannual variations positive. It is remarkable that both the global source and lifetime can be so calculated based entirely on observed concentrations. We calculate C2 ) C(measured; 1-yr smoothed) - C1 (eq 4), which represents the effects of the changes of sources and sinks on the measured concentrations without seasonal variations or high frequency noise. The C2 so obtained shows an unexpected cycle of about 7.7 yrs (Supporting Information). The C2 time series is shown in Figure 3 along with the average cycle that is calculated by averaging the like points of the cycle as it repeats 3 times (Supporting Information). The last two events seen in Figure 3 were reported earlier (6-8). It should be noted that we are not assuming that the sources and sinks are constant in deriving these results. That is a deduction from the application of the mass balance theory as discussed above. The theory, which can apply to any VOL. 41, NO. 7, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Latitudinal variation of the interannual variability. The cycle shown in Figure 3 is calculated for each of four equal area regions of the earth. The starting point is a 1-yr moving average of the original data. The results suggest that the disturbance starts in the northern tropics and spreads to other latitudes later. atmospheric trace gas, shows that the observed concentrations can be separated into the two pieces C1 and C2. The C1 represents the part of the mass balance that is driven by the constant components of the sources and sinks, which is theoretically fixed to be a straight line in the variables dC/dt vs C as seen in the defining equation for C1 (dC1/dt ) [S0C1/τ0]). If we plot the observed data in the same variables, namely dC/dt vs C, it would contain the combined effects of C1 and C2. By subtracting a linear function, as obtained with a regression estimate for instance, from the observed dC/dt, we would be left with C2. This C2 could again be an increasing or decreasing function of time, contain various cyclical terms or any other part of the mass balance that does not arise from the constant sources and sinks. If, for instance, the sources were increasing over the time of the analysis, then this residual term C2 would be an increasing function of time. If the C2 has no long-term trend, it necessarily means that neither the total source nor the sink has any trends over the period of the analysis, or that both the sources and sinks have trends in the same direction so that their effects on the atmospheric concentration cancel entirely. For the methane data of concern here, we see that the long-term behavior of the plot of dC/dt vs C is a straight line, which immediately implies that sources and sinks must have been constant during the period of these observations (or have changed together). The residual C2 obtained from subtracting C1 from the observed data is constant over the whole period spanned by the observations, but changes over intermediate time scales in a cyclic manner. We will discuss this further and return to the implications of this analysis for the C1 component. Still more information can be obtained regarding the longterm cycle by examining the data from individual sites. We combined the Pt. Barrow data with C. Meares and Tasmania data with that from Antarctica to create four time series representing the equal area regions from 0-30 degrees and 30-90 degrees latitude in each hemisphere. As before these time series were then smoothed with a 12-month running 2134
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average filter to remove seasonal variations and the high frequency noise. The resulting data contain the long-term trends and the 7.7 yr cycle. The cycle was isolated by subtracting third-order polynomials in time from the 1-yr filtered data. The resulting time series for each region contains only the long-term cycle. The average cycle in each region, over the three events, is shown in Figure 4. Visual inspection suggests that the cycle starts with the northern tropics and is followed by increases in the other three regions. This is represented more quantitatively by calculating the lagged correlations between the concentrations in the 30-90 degree band and from each of the other three locations (bands). The results are shown in Figure 5. The correlations for the composite cycle peak with lags of about 4 months earlier in the northern tropics and 2-5 months later in the southern latitudes. While the composite is a good representation of the cycle, a closer examination of the individual events shows important differences. Applying the methods described above to each case shows that in the second event there is almost no lag between the northern and southern tropics, while in the other two cases the northern tropics clearly lead, as is the case in the composite. It is probable therefore, that more than one phenomenon contributes to these interannual events and both tropics may be involved, but for the first and third events, these results might be caused by higher than normal emissions that originate in the northern tropics and spread to the other latitudes with the lags caused by transport times (Supporting Information). In previous publications, Dlugokencky et al. have suggested that the second of these events in the early 1990s may have been caused by possible reductions of tropospheric OH caused by the Mt. Pinatubo eruption (6, 7). The most recent episode of this interannual variability may have been caused by changes in wetland emissions driven by climate variability or possibly global warming (8). It is apparent that there are no long-term trends in δ(t). The residuals (t) obtained by from eq 2c calculated as C ) C(measured; unfiltered) - (C1 + RC +∆C), where ∆C and RC
FIGURE 5. Lagged correlation coefficients that determine the timing of the interannual variability. Lagged correlations show that differences in the timing of the interannual events shown in Figure 4. All regions are compared with the northern middle and polar sites. The interannual events occur about 3 months earlier in the northern tropics and 3-5 months later in the southern hemisphere. Latitude ranges: Cn ) 30o90o N, Cnt ) 0o - 30o N, Cst ) 0o - 30o S, Cs ) 30o- 90o S. are the average seasonal and long-term cycles discussed earlier, are almost normally distributed with a standard deviation of 2.4 ppb and a slight bias of +0.5 ppb (mean). The sum (C1 + RC +∆C) is shown as a solid line in Figure 1, further supporting the fact that these three components fully represent the observed time series. Here the average ∆C is repeated over the length of the time series (as in Figure 3). We can conclude that over the time of observations there are no long-term changes of sources and sinks or δ(t)LT ) [RSLT(t) - RJLT(t)C(t)] ) 0. However, the sources and sinks could have changed simultaneously, but not one without the other, that is: RSLT must equal RJLTC. If the sources increase, the lifetime must decrease by a related amount and vice versa by amounts that makes δ(t)LT ) 0. Alternatively, if OH is taken as the agent for sink changes, increasing sources of methane would have to be balanced by increasing OH to be compatible with the observations (that δ(t)LT ) 0). As a special case, for instance, it can be shown that the percentage change of the source would have to about the same as the percentage change of OH over this period to account of the observational data. One may say that the global observations have an invariance under simultaneous changes of sources and sinks over any time scale, as is intuitively well-known. Whether in actual circumstances such simultaneous changes would happen is unlikely since the sources and sinks are either completely independent of each other, or have weak connections. For OH particularly, increasing methane sources would be expected to lower OH, not increase it. With these thoughts in mind, we show further consequences of this data set for the trends of global emissions. We used two models to calculate the time series of emissions that are consistent with the observational data here. The first method was to use eq 1 to calculate the emissions as S ) dC/dt + C/τ where τ can be constant or not. If constant, we take it to be τ0 ) 10 years. The second method was to de-convolute the sources using our low-resolution 2-dimensional chemistry-transport model (9). This model divides the atmosphere horizontally into 6 regions consistent with the locations of the sampling sites. In the vertical there are 2 layers in the troposphere, 2 in the oceans representing
the mixed layer and the deep, and 6 in the stratosphere; however, the stratospheric layers are not subdivided horizontally. The model is designed to be as highly constrained by available data as is possible and still include various cross media and atmospheric processes. For OH we have used our photochemistry model from which we can derive monthly concentrations at any latitude and altitude (10). This model does not include long-term changes of OH, but is used here to provide a seasonality and latitudinal change of the OH concentration needed in the 2-d model. We did two sets of calculations with each of these methods: one with constant lifetime and one with a lifetime that reflects changing OH concentrations. The change of OH has been difficult to detect or to validate. Nonetheless, possible changes in OH from 1979 to 2000 have been calculated using methyl chloroform as an indicator of OH trends (11). From this work we can calculate the year by year changes of the lifetime of methane by putting in the calculated values of global OH levels. For the model of eq 1 we obtain the source as S ) dC/dt + Cf/τ0 where f ) [OH](t)/[OH]0 and [OH]0 is the OH concentration in the base year, here 1981 when the global methane time series starts. The same OH changes were used in the 2-d model but other constant sinks were explicitly included. The results are shown in Figure 6. As would be expected from the previous discussion, the case of constant lifetime leads to emissions that are generally constant except for the times during which the long-term cycle of concentrations is at its peak; then some 20-30 Tg/y of added emissions are required as shown in Figure 6, or that much less destruction if OH is reduced as has been suggested for the second event. For changing lifetime the temporal pattern of emissions reflects a like pattern in the lifetime (or OH) as would be expected to make δ(t)LT ) 0. Although it would seem that the constant emissions is the most probable and credible explanation for the patterns of observed trends, it is possible that both OH and the emissions have changed simultaneously, as discussed earlier. In all four of these calculations the monthly observed data were used and a monthly source was calculated. It was then averaged for each year. In addition, in the 2-d model, VOL. 41, NO. 7, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Estimated global emissions of methane (Tg/yr). The emissions are calculated based on the observed concentrations shown in Figure 1 and using a global average model and a low-resolution 2-d model and under the assumption that the lifetime of methane is constant during the course of the experiments reported here, and the alternative assumption that the lifetime changes according to changes in OH levels since reactions with OH radicals control the lifetime. The changes of OH are taken from estimates based on the global mass balance of methyl chloroform (11). Constant lifetime leads to nearly constant emissions. A changing lifetime is reflected in a changing source that shows a pattern proportional to the change of OH. since the lifetime is explicitly calculated using a photochemical model, it comes out with a somewhat different value than obtained here by using observations only. The results are therefore scaled to an overall lifetime of 10 years to make them comparable to the global model results from eq 1. A scale uncertainty remains in the absolute lifetime and hence the emissions, but it is not expected to be large. The results are quite comparable between the two disparate models, one with detailed processes and one without. Similar results were reported by Cunnold et al. (12) based on an independent data set from the ALE/GAGE program and their chemistry-transport model.
3. Perspectives We have seen that by combining data from two networks, we can construct a long time series that contains new information about the temporal changes of methane sources and sinks. The OGI network was among the first of its kind and was set up to systematically document the impact of human activities on the global atmosphere. By using high quality flasks, dozens of trace constituents could be measured and were. The network stopped in late 1998, but continues to provide early data for greenhouse gases and ozonedepleting compounds. At some sites there are data going back to 1979, but global measurements are not available until 1981. There are data from yet other networks that may be used to refine the time series or validate the results discussed here but none can extend it backward further in time. The present data set spans more than two decades. It shows that both methane emissions and its lifetime may have been constant over this period. To quantitatively explain the decreasing trends of methane it is not necessary to look for decreasing sources or increasing OH. During the time of the measurements if either has changed the other must also 2136
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have changed by a proportionate amount to account for the observed trends. Moreover, while such circumstances may have occurred, if the sources have increased in the past they would have no environmental consequences such as increased global warming, because the extra amounts put into the atmosphere would be the same amounts that would have to be taken out by the increased OH to be consistent with the observed concentrations of methane during the last two decades. The concentrations behave exactly as if the sources and sinks had been constant. A confirming aspect of this apparent constancy of sources and sinks is that the trend has been decreasing for the last two decades until the present when it has reached near zero, thus attracting renewed attention. The major agricultural sources such as rice agriculture and cattle have little or no potential for large increases in the future and have already shown reduction in emissions from some regions. Seeing that the total source has remained constant for at least the last two decades, it is questionable whether human activities can cause methane concentrations to increase greatly in the future (13). This prediction is within the lower range of the IPCC SRES scenarios (14). During the span of these experiments there have been three periods when the concentrations have risen and fallen creating an apparent cycle of about 7.7 years. Since it is not known with certainty what may have caused these peaks, it is not clear whether it is a cycle or just coincidence of disparate causes that give an appearance of periodicity. In general long-term cycles of atmospheric trace gases are not known partly because precise data have not been taken for long enough times, but for methane we see that such cycles or events exist and influence the observed concentrations and trends much more than any systematic increase or decreases of sources and sinks over the last two decades.
Acknowledgments We thank Martha Shearer for her contributions to this work. NOAA/CMDL’s program provided major logistical support for collecting the samples over the years of these experiments. This research was supported by the Office of Science (BER), U.S. Department of Energy, Grant No. DE-FG02-04ER63913 and DE-FG03-01ERG63262 and the resources of the Andarz Co.
Supporting Information Available The monthly average methane time series from the Oregon Graduate Institute sampling network from 1981 to 1997 and the composite OGI/CMDL methane record from 1981 to 2003. Figure illustrating the inter-comparison between the OGI and CMDL data sets. Details of the formulas and calculations used to analyze the methane time series. This material is available free of charge via the Internet at http://pubs.acs.org.
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(6) Dlugokencky, E. J.; Masarie, K. A.; Lang, P. M.; Tans, P. P.; Steele, L. P.; Nisbet, E. G. A dramatic decrease in the growth rate of atmospheric methane in the northern hemisphere during. Geophys. Res. Lett. 1994, 1992, 21, 45-48. (7) Dlugokencky, E. J.; Dutton, E. G.; Novelli, P. C.; Tans, P. P.; Masarie, K. A.; Lantz, K. O.; Madronich, S. Changes in CH4 and CO growth rates after the eruption of Mt. Pinatubo and their link with changes in the tropospheric UV flux. Geophys. Res. Lett. 1996, 23, 2761-2764. (8) Dlugokencky, E. J.; Walter, B. P.; Masarie, K. A.; Lang, P. M.; Kasischke, E. S. Measurements of an anomalous global methane increase during 1998. Geophys. Res. Lett. 2001, 28, 449-502. (9) Butenhoff, C. L. M.S. thesis; Portland State University, Portland, OR, 2002. (10) Bahm, K.; Khalil, M. A. K. A new model of tropospheric hydroxyl radical concentrations. Chemosphere 2004, 54, 143-166. (11) Prinn, R. G.; Huang, J.; Weiss, R. F.; Cunnold, D. M.; Fraser, P. J.; Simmonds, P. G.; McCulloch, A.; Harth, C.; Salameh, T.; O’Doherty, S.; Wang, R. H. J.; Porter, L.; Miller, B.R. Evidence for substantial variations of atmospheric hydroxyl radicals in the past two decades. Science 2001, 292, 1882-1888. (12) Cunnold, D. M.; Steele, L. P.; Fraser, P. J.; Simmonds, P. G.; Prinn, R. G.; Weiss, R. F.; Porter, L. W.; O’Doherty, S.; Langenfelds, R.; Krummel, P. B.; Wang, H. J.; Emmons, L.; Tie, X. X.; Dlugokencky, E. J. In situ measurements of atmospheric methane at GAGE/AGAGE sites during 1985-2000 and resulting source inferences. J. Geophys. Res. 2002, 107 (D14), doi:10.1029/ 2001JD001226. (13) Khalil, M. A. K.; Shearer, M. J. In Greenhouse Gases and Animal Agriculture: An Update; Soliva, C. R., Takahashi, J., Kreuzer, M., Eds.; International Congress Series 1293; Elsevier: Amsterdam, The Netherlands, 2006; pp 33-41. (14) IPCC Emission Scenarios (SRES); Cambridge University Press: New York, 2000.
Received for review July 27, 2006. Revised manuscript received January 10, 2007. Accepted January 13, 2007. ES061791T
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