Evaluation of wetland models – results

The meridional (north-south) gradient of atmospheric methane concentration is a useful diagnostic of the distribution of methane emissions with latitude. A comparison of the observed and simulated meridional concentration gradient normalized to 90°S is shown in Fig. 7.7. Some of the models appear to overestimate wetland emissions, especially at high latitudes.

In the northern mid-latitudes (20°–50°N), the models tend to exhibit a slower rise with latitude than observed, implying a low bias in anthropogenic emissions or an underestimate of wetland emissions or a combination of both at these latitudes.

Transport that is biased towards atmospheric stability may also lead to higher atmospheric concentrations at lower levels making emissions appear to be overestimated. It should also be noted that the distribution of emissions from source regions to other latitudes is an important component of the atmospheric methane budget. Fig. 7.7 illustrates the importance of having global observations since ultimately the Arctic methane budget cannot be understood without knowing the potential contributions via transport from lower latitudes.

The observed and simulated average annual methane cycle at high northern latitudes (53°–90°N) is shown in Fig. 7.8.

Comparing the amplitude and phase of the seasonal variation provides an opportunity to assess whether the models capture the timing of the onset of wetland emissions during the warm season as well as the intensity of the emissions. The observations show a July minimum in atmospheric methane concentration due to chemical loss by reaction with OH that occurs most rapidly with the annual maximum in northern hemisphere incident solar radiation. The northern hemisphere summer is also when peak production of methane from wetlands occurs, and the fact that the chemical sink decreases rapidly after the solstice, while the wetland emissions are probably still strong and increasing, often results in a late summer plateau before the winter maximum (see Sect. 6.4.2). The maximum methane concentration occurs during the boreal winter when long-range transport brings methane emitted from anthropogenic and natural sources at lower latitudes into the Arctic, and chemical loss in the Arctic is effectively zero while being at an annual minimum at lower latitudes. In combination with this, local anthropogenic emissions and a very stable polar atmosphere that traps them within the region lead to a buildup of atmospheric methane within the Arctic.

The timing of the summer minimum concentration produced by the forward modeling provides clues about whether the wetland models have emissions too soon or too late in the growing season. As described in Sect. 7.2, the minimum in methane concentration occurs during summer because photochemical loss is greatest during the boreal summer throughout the northern hemisphere (see Ch. 2 and 6). The black line in Fig. 7.8 shows that the observed summer minimum occurs in mid-summer. Note that the seasonal cycle is not symmetric about the summer solstice as would be expected from solar irradiance-driven chemistry. Instead the data show a slight plateau late in the growing season. The models, on the other hand, show a spring concentration minimum followed by a distinct peak late in the growing season. The simulated annual cycles are consistent with overestimated wetland emissions because the annual minimum occurs too early, while methane concentration late in the growing season is too high relative

90°S 60°S 30°S 0° 30°N 60°N 90°N

Fig. 7.7 Observed north-south CH₄ gradient normalized to 90°S from surface observations and simulations. Simulated concentrations were sampled at observation sites and averaged, smoothed and filtered identically to the observations. The graphic shows an average over the final year of the simulations, 2004.

Fig. 7.8 Observed average annual cycle derived from surface methane observations (sites as per Fig. 6.5 for 53°–90°N) and wetland model simulations. Simulated concentrations were sampled at observation sites and averaged, smoothed and filtered identically to the observations.

to the observations. Biases in simulated transport must also be mentioned, because a model that produces an overly-stable atmosphere can result in methane accumulation near the surface, making the apparent overestimation of emissions by the wetland models appear even worse. Overestimation of atmospheric stability is expected to be more of a problem during winter, and this would mean that methane emitted from anthropogenic sources would accumulate at lower levels of the atmosphere. This would tend to result in simulated winter methane concentrations that are higher than observed. However, Fig. 7.8 shows that simulated winter methane concentrations are lower than the annual average, unlike the observations that suggest winter concentrations that are greater than the annual average. This reinforces the idea that the differences in the observed and simulated seasonal cycles are dominated by excessive wetland emissions during the warm season.

Observed interannual variability in atmospheric methane provides an important test of the ability of wetland models to correctly simulate sensitivity to interannual variability in climate forcing parameters such as temperature and precipitation. As shown in Fig. 7.9, the observed Arctic region interannual variability in methane concentration after detrending and removing an average annual cycle suggests that variability is on the order of 20 ppb, a relatively small amount compared to a global average concentration of about 1750 ppb. This variability is thought to primarily reflect small changes in wetland and biomass burning emissions since anthropogenic emissions probably vary at longer timescales.

Figure 7.9 suggests that with some exceptions, most models are able to reproduce the observed variability, indicating reasonably good representation of sensitivity of wetlands models to precipitation and temperature variability. Note that the trend towards slower simulated methane growth is due to equilibration of atmospheric methane concentration with the input emissions from each model. This is true for models that use different strategies for locating wetlands. The DLEM, CLM and WSL models use wetland distributions derived from satellite observations, while the SDGVM model predicts wetland distribution internally. Comparisons of longer time series that capture more events would provide greater confidence in the representation of interannual variability by wetland models.

7.4

Conclusions

7.4.1

Key findings

Inverse atmospheric modeling approaches provide the ability to optimally interpolate sparse observations and to estimate emissions. The estimates may be used to evaluate bottom-up emission models. Long time-series of estimated emissions may also be used to reflect how emissions are changing over time. The two major limitations to applying inverse techniques are sparseness of observations and inadequate representation of atmospheric transport. The lack of observations in inverse models, results in larger uncertainties in estimated emissions over policy-relevant spatial scales. In sparsely observed regions, the emissions will stay close to prior estimates that may have significant errors and biases (see Sect. 7.3). Errors in atmospheric transport also introduce concomitant errors in emission estimates

that may be difficult to quantify. Inverse models can be improved if observational coverage is expanded, especially over currently sparsely observed regions. Improvements in bottom-up emission models will also help to reduce uncertainty of inverse models by providing more accurate prior emission information. Increasing the resolution of atmospheric transport models and improving the parameterizations of planetary boundary layers and convection will help to further reduce uncertainties in inverse models.

Arctic region natural and anthropogenic emission estimates from the ten atmospheric inverse model studies surveyed in the present study agree reasonably well (within ~40%) over the period 2000 to 2010 and total ~25 Tg CH₄/y. None of the inverse models show trends towards increasing emissions over this period; however, most do estimate increased emissions during the exceptionally warm and wet summer of 2007.

This increase averages 2 Tg CH₄/y above the average over the period 2000 to 2010. The inverse model-derived methane emissions vary significantly in their interannual variability.

Inverse models that put more weight on the ‘priors’ (i.e. the original emissions information used as input) which vary little from year to year will estimate emissions that consequently vary little from year to year. On the other hand, models that put more weight on observations will produce more temporal variability in flux estimates, some of which may be unrealistic.

Improving observational coverage in the Arctic will reduce uncertainties and improve the reliability of inverse models.

Improved observational coverage may also lead to earlier detection of changing emissions.

Atmospheric inverse models produce emission estimates that are significantly lower than those from bottom-up methods.

Bottom-up methods suggest that Arctic wetland emissions alone are about 25 Tg CH₄/y, according to the study of McGuire et al. (2012). This is similar to the amount simulated by inverse studies for total Arctic emissions. The inverse models considered in this study estimate only 15.5 Tg CH₄/y for wetland emissions.

Fig. 7.9 Interannual variability in methane growth rate derived from surface observations and wetland model simulations (sites as per Fig. 6.5 for 53°–90°N). Simulated concentrations were sampled at observation sites and averaged, smoothed and filtered identically to the observations.

An average seasonal cycle was removed from the time series to calculate the interannual variability.

Bern CLM4Me DLEM

Obs

Orchidee SDGVM WSL

LPJ CH4, ppb/y

1992 1994 1996 1998 2000 2002 2004

-60 -60 -40 -20 20 60 80

0 40

Indeed if all proposed Arctic sources are considered (such as emissions from lakes, the East Siberian Arctic Shelf and hydrates under permafrost), they significantly exceed the Arctic methane budget as understood from atmospheric methane concentration observations.

Given the importance of wetlands as a source of methane to the atmosphere, the ability to correctly model these natural emissions is important. This is key to integrating wetland processes within the earth system models to accommodate the carbon cycle - climate feedbacks that are required for studies of long-term responses to climate change. Bottom-up models of methane emissions from wetlands were used together with reasonable assumptions for non-wetland emissions and an atmospheric transport model to evaluate model performance against atmospheric observations. This forward modeling approach makes it possible to compare the results of process-based wetland models with atmospheric observations, providing the means to assess how small-scale, process-level information about emissions incorporated into process-based models is applied to regional and global scales.

In this review, the results of the atmospheric inverse model studies indicate that bottom-up models may overestimate emissions both globally and in the Arctic. In addition, although most models are able to reproduce the timing of observed variability, they tend to overestimate sensitivity to year-to-year variability in climate parameters (mainly temperature and precipitation).

None of the atmospheric inverse model results demonstrated an upward trend in emissions for the Arctic region over the period 2000 to 2010, as may be anticipated in response to steadily rising Arctic temperatures. Note however, that the period covered by the inverse models is only about a decade and this may be too short for detecting what may well be currently a small trend in emissions. The possibility that the atmospheric network observations are too sparse to allow detection of trends should also be considered, as well as limitations of atmospheric inverse models arising from representation of atmospheric transport processes, and/or initial estimates of the magnitude of natural and anthropogenic sources within the Arctic region.

7.4.2

Recommendations

The atmospheric inverse modeling technique is a powerful analytical tool that can increase understanding of the global and regional methane budget. Inverse techniques allow a look backwards in time to understand trends in atmospheric concentrations as a function of changing anthropogenic emissions and in response to a warming Arctic (i.e. increased release of methane from natural terrestrial and marine sources).

They also serve as a useful diagnostic tool to evaluate the ecosystem process-based models, and thereby improve earth system models for climate projections (see Ch. 8). Recognizing the challenges in estimating the magnitudes of both the natural and anthropogenic sources identified in previous chapters, the inverse technique based on atmospheric observations provides an independent approach to verifying these process- or activity-based (bottom-up) estimates. The atmospheric observations define the maximum limits and temporal variability that serve as validation of the bottom-up estimates, indicating

where sources are over- or under-estimated. They also have the potential to identify missing sources or new sources in the characterization of emissions, such as those related to a warming Arctic or human activities.

Currently, atmospheric inverse models suffer from a lack of accessible high quality, multi-decadal atmospheric methane observations, both surface- and space-based. In addition, integration and collaboration related to improving atmospheric transport processes whether in terms of air quality, or climate or numerical weather prediction will lead to significant improvements in the overall representation of atmospheric transport. Novel approaches to measuring important diagnostic quantities (such as planetary boundary layer depth) can aid in this regard. Recognizing the aggregation of uncertainties and limitations inherent in the application of inverse methodology, recommendations to improve methane emission estimates based on their applications include:

• Increasing spatial coverage of surface observations, deployment of regular aircraft campaigns to characterize specific regions and seasons, and atmospheric column observations for vertical characterization of concentrations.

• Maintaining surface observation sites over multiple decades in order to detect changes in atmospheric concentration as a result of changing anthropogenic emissions, and the response of natural sources to changing climate.

• Further development and evaluation of ecosystem process-based models for estimating wetland sources.

• Continuing improvements to atmospheric transport simulations to better represent convection and planetary boundary mixing processes at smaller spatial scales.

Acknowledgments

The authors are grateful for valuable comments and suggestions on earlier drafts of this chapter provided by J. Butler, J. Miller, P. Bergamaschi and anonymous reviewers.

Appendix: Global atmospheric inverse model studies reviewed for the comparison discussed in Section 7.2.4.

B-K B-NOAA B-ref Bg CT-CH4 Fr H-NOAA H-SCIA H-GOSAT Pison

Reference Bousquet et al.

2011; Kaplan

et al. 2013 Bruhwiler

et al. 2014a Fraser et al.

2013 Houweling

et al. 2014 Houweling et

al. 2014 Houweling

Model LMDZ LMDZ LMDZ TM5 TM5

GEOS-CHEM TM5 TM5 TM5 LMPDZ

Meteorology LMDZ online

19 levels 3.75°×2.5°, 19 levels 6°×4°, 25

Monthly Monthly Monthly Monthly Weekly 8 Days Monthly Monthly Monthly Weekly

Spatial Resolution of Emission Estimates

Grid cell Grid cell Grid cell Grid cell 120 land regions and

Grid cell Grid cell Grid cell Grid cell

Optimization

Technique Variational Variational Variational m1qn3 Ensemble Kalman

Window 1983–2010 1983–2010 1983–2010 2003–2010 2000–2010 2000–2010 2003–2010 2003–2010 2003–2010 1990–2008

Im Dokument Methane as an Arctic (Seite 96-101)