Insights from simulations with high-resolution ...

Click Here

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, D12301, doi:10.1029/2005JD006278, 2006

for

Full Article

Insights from simulations with high-resolution transport and process models on sampling of the atmosphere for constraining midlatitude land carbon sinks U. Karstens,1 M. Gloor,1,2 M. Heimann,1 and C. Ro¨denbeck1 Received 27 May 2005; revised 16 December 2005; accepted 2 March 2006; published 17 June 2006.

[1] We analyze the requirements for detecting changes in midlatitude land carbon sources

or sinks from sampling the atmospheric CO2 concentration. Programs to sample the continental lower troposphere have only recently started, and it is not yet clear which atmospheric sampling strategy is most adequate. To shed some light on this question, we use simulations of atmospheric CO2 over Eurasia with two regional-scale atmospheric transport models. Our analysis focuses on the detection of the monthly mean CO2 signal caused by a perturbation of Eurasian summer biospheric fluxes by 20% (0.06 PgC/month). The main results are (1) that several measurements per day, preferably during the afternoon, are necessary to permit the detection of the additional land sink and (2) that the ratio between signal and background variation, corrected for autocorrelation in time, suggests no preferred level in the vertical for sampling. However, (3) the signals in the free troposphere are very small (0.2 ppm per 0.06 PgC/month) given the precision of atmospheric measurements. In contrast, signals in the planetary boundary layer (PBL) are on the order of 1 ppm per 0.06 PgC/month. This suggests that optimal sampling on continents should concentrate on the mixed portion of the PBL during afternoon. (4) Finally, the spatial correlation structure of the atmospheric CO2 concentrations suggests that a horizontal sampling density on the order of a few 100 km is needed. Citation: Karstens, U., M. Gloor, M. Heimann, and C. Ro¨denbeck (2006), Insights from simulations with high-resolution transport and process models on sampling of the atmosphere for constraining midlatitude land carbon sinks, J. Geophys. Res., 111, D12301, doi:10.1029/2005JD006278.

1. Introduction [2] Most of our information about the dynamics and magnitude of large-scale carbon sources and sinks on land is derived from a limited spatial sampling of atmospheric CO2 concentration. Available data indicate that approximately one quarter of carbon emitted to the atmosphere as a result of fossil fuel burning, cement manufacture, and landuse change is absorbed by terrestrial ecosystems [e.g., Prentice et al., 2001]. One of these uptake regions is likely located in the Northern Hemisphere midlatitudes [Keeling et al., 1989; Tans et al., 1990]. The same data indicate also that there are large interannual variations in the growth rate of atmospheric carbon [e.g., Prentice et al., 2001]. While results from several studies based on atmospheric CO2 data [e.g., Randerson et al., 1997; Bousquet et al., 2000; Ro¨denbeck et al., 2003] indicate a pattern of response of the land biosphere to the anthropogenic perturbation of the atmospheric composition, it has been difficult to determine the underlying mechanisms on a large scale. Also, recent model-based predictions of the future behavior of the land 1 2

Max Planck Institute for Biogeochemistry, Jena, Germany. Now at School of Geography, University of Leeds, Leeds, UK.

Copyright 2006 by the American Geophysical Union. 0148-0227/06/2005JD006278$09.00

biosphere under a warming climate exhibit very different temporal and spatial patterns [cf. Friedlingstein et al., 2001; Cox et al., 2000]. [3] One important reason for the limited capability to relate source/sink processes on land to external forcing is the sparse sampling of atmospheric CO2 on continents; another is that atmospheric source/sink signatures are degraded rapidly by strong mixing in the lower troposphere. Indeed, atmospheric observations have traditionally been made mainly at the Earth’s surface at remote oceanic stations with weekly to biweekly flask air sampling, and the number of stations is small (currently on the order of 100 stations) (e.g., Conway et al. [1994], GLOBALVIEW). The reason why sampling of the atmosphere has focused on remote oceanic stations is that the variability of CO2 signals over vegetated areas are very large compared to mean gradient signals. As an example, a Northern Hemisphere midlatitude carbon sink on the order of 1 – 3 PgC yr
1 has been inferred from an observed interhemispheric annual mean CO2 gradient on the order of 1 – 2 ppm [Keeling et al., 1989; Tans et al., 1990]. In comparison, Bakwin et al. [1995] measured daynight differences of up to 100 ppm during summer at the Wisconsin tower site as a consequence of the diurnal cycle of photosynthesis and respiration. [4] That current atmospheric observation capabilities are insufficient to understand the underlying processes has been

D12301

1 of 19

D12301

KARSTENS ET AL.: DETECTABILITY OF SIGNALS IN CO2

recognized for some time [e.g., Bakwin et al., 1995; Gloor et al., 2001]. Accordingly, increasing measurement efforts have started or are soon starting that have the goal to close the observational gaps on continents (e.g., AEROCARB [Ciais et al., 1999], TCOS-Siberia [Heimann et al., 2001], North American Carbon Plan [Wofsy and Harris, 2002]). [5] However, as attempts to sample the continental atmosphere are relatively recent and the best sampling strategy is not clear yet, it is of interest to inquire what simulations with spatially highly resolving transport models that use fluxes from land biosphere models with a realistic diurnal cycle do suggest. The reason for following such an approach is threefold. First, dissipative processes responsible for tracer transport within the PBL (planetary boundary layer) and exchange between the PBL and the free troposphere as well as the dynamics of the PBL itself can be represented more realistically with high-resolution models compared to global transport models used so far. Traditionally used models have a spatial resolution on the order of 5
 5
latitude by longitude. Second, the spatial variability of transport processes and fluxes should be as close to observed variability as possible, which calls for a flux resolution in time on the order of an hour. Finally, a major factor for properly interpreting lower troposphere CO2 data is that the interplay between the diurnal cycle of surface fluxes and vertical transport processes is represented adequately in the models. Thus the diurnal cycle of photosynthesis and respiration of the land biosphere should be represented in a realistic way. [6] To fulfill to some extent the request for high spatial resolution of the simulations, we use here two regional high-resolution transport models (REMO and MM5/ HANK) for the atmospheric transport simulations. The use of more than one model permits us to obtain a handle on the dependence of our conclusions on the representation of transport. For simulating quite realistically the land biosphere variability, we use the TURC (Terrestrial Uptake and Release of Carbon) biosphere model [Lafont et al., 2002] that is based on incoming solar radiation (including the intermittent nature of cloud cover) and temperature from meteorological analysis together with the NDVI (normalized difference vegetation index) from satellite observations. To simulate the remaining two carbon flux components that determine the atmospheric CO2 distribution, atmosphereocean CO2 exchange and CO2 release as a result of fossil fuel burning and cement manufacture, we use monthly varying fluxes estimated by Takahashi et al. [1999] and data from the Emission Database EDGAR V2.0 [Olivier et al., 1996], respectively. In our study we focus on Europe and western Siberia but as the main determinants of the variability of atmospheric CO2 are similar, the conclusions of this study should be transferable to North America as well. The simulations cover a summer period (July 1998). This is the period when the terrestrial biosphere is most active and consequently the CO2 variability is particularly large. [7] Our approach to answer the question posed follows roughly a study by Kjellstro¨m et al. [2002]. They investigated the response of the atmospheric CO2 concentrations over Eurasia to an alteration of the surface fluxes and concluded that small differences ( s or STN = S/s > 1. If there are n measurements and if the measurements were independent, the variance of the sample mean X would decrease with the number of measurements n according to Var(X ) = s2/n with would be detectable at s2 population variancepand ffiffiffi a signal p ffiffiffi the 1-s level if S > s/ n or STN  n > 1. However, the measurements are temporally correlated which results in the sample being effectively smaller than n. Following von Storch and Zwiers [1999] in such a case, an equivalent sample size n0 can be estimated according to n0 ¼

n ;  n
1  X k 1þ2 1
rðk Þ n k¼1

ð1Þ

with r(k) autocorrelation function at lag k. [40] The variance of the sample mean must then be computed using the equivalent sample size n0. It should be noted that the approach to increase the signal-to-noise ratio by simply increasing the number of samples at a fixed location is thus effectively limited due to an increasing temporal autocorrelation, and therefore there is a saturation in the return of high-frequency sampling. The higher-order terms of the sum in equation (1) are poorly estimable because of the increasing uncertainty in the autocorrelation function at longer time lags [Thie´baux and Zwiers, 1984]. An alternative approach is to approximate the time series by an autoregressive process and use the corresponding autocorrelation function in the estimation of n0 [von Storch and Zwiers, 1999]. We use here a first-order autoregressive process to represent the afternoon CO2 time series. For a first-order autoregressive process the following approximation 1þ2

n
1  X k¼1

 k 1 þ rð1Þ 1
rðk Þ

n 1
rð1Þ

ð2Þ

holds [cf. von Storch and Zwiers, 1999]. This expression for the calculation of the ratio n/n0 effectively truncates higherorder terms in the left-hand-side sum and thus avoids noisy features (confirmed by calculating n0 in both ways). [41] Besides the variability in CO2 signals, instrument precision also puts a limit on the signal detectability. Assuming 0.5 ppm as an upper limit of the precision of atmospheric CO2 measurements (inferred from comparisons of simultaneous measurements [e.g., Levin et al., 2002]), we can estimate the limit of a detectable monthly mean pffiffiffiffi difference in the CO2 concentration to be 0.5 ppm/ n0 . From Figure 10a it appears that in the free troposphere, where the signals are strongly diluted, instrument precision limits the detectability if only few measurements are available. The instrument precision is therefore included in the signalto-noise ratio by defining the noise as the quadratic sum of variability and instrument precision. [42] Accordingly, the signal-to-noise ratio for a 20% difference in biospheric activity in Europe and western Siberia as presented in the previous section (cf. Figure 9c)

D12301

is modified by including instrument precision and also multiplied by the square root of the number of effectively independent pffiffiffiffi observations. The scaled signal-to-noise ratio STN  n0 is shown in Figure 11 for a layer within the PBL (300 m) and along the cross section from 45
N/20
W to 64
N/120
E, again assuming hourly afternoon sampling every day of the month. Both models predict a similar pattern at the 300 m level but they deviate in the vertical distribution. In HANK, taking into account the temporal autocorrelation further reduces the detectability in the boundary layer east of 60
E. pffiffiffiffi [43] The scaled signal-to-noise ratio STN  n0 in Figure 11 can be translated either into a minimum sampling frequency needed to detect a given signal or into a minimum signal, which would still be detectable at a 1-s significance level with a given sampling frequency. For the detection of the 20% difference in biospheric fluxes from observations during daytime a signal-to-noise value of 1 defines the limit when all (i.e., hourly) measurements are needed to detect the signal at a 1-s significance level. At higher values the sampling frequency can be reduced to reach the same detection limit. For example a signal-to-noise value of 3 would still allow the detection with 1/9 of the measurements, i.e., approximately one measurement during the preselected daytime period (1100 – 1700 local time). Accordingly, signal-to-noise values of 5 or 7 would imply a minimum sampling frequency of approximately twice or once per week, respectively. Both models agree in the general pattern that in central Europe hourly observations at ground stations and in the PBL are needed to allow the detection of possible changes, daily values do not suffice. In western Siberia the sampling can be less frequent but at least daily observations are needed. The simulations suggest that above the PBL (at 2000 – 3000 m) a lower sampling frequency could be accommodated but still weekly observations, like they are available from aircraft profiles, might not always suffice. The other way round, the detectability of a signal depends on the strength of the source changes. From Figure 11 we can infer that in some regions (like parts of Siberia) observations with high temporal sampling frequency would still allow the detection of much smaller signals, e.g., from hourly observations during daytime even a signal of 10 or 5% difference in the biospheric sink in July would still be detectable in areas with a signal-tonoise value of 2 or 4, respectively. [44] Our model results do not clearly indicate a preferred height level for the detection of large-scale surface flux differences. However, as a consequence of the existing model deficiencies in resolving the structure of vertical gradients in CO2 profiles (cf. section 3), the predictions of our study on where to distribute sensors in the air column have to be interpreted with some caution. [45] Next, we investigate if the described signals could be detected with the current network of CO2 measurement stations in Europe and western Siberia. This network consists of a combination of stations belonging to several global networks (NOAA/CMDL, CSIRO), national agencies and stations installed in the framework of special projects (AEROCARB, CARBOEUROFLUX, TCOS-Siberia, and EUROSIBERIAN CARBONFLUX). The number of stations is still increasing and at the moment the network includes approximately 40 ground stations, of which approximately 20 provide continuous (hourly) and approx-

15 of 19

D12301

KARSTENS ET AL.: DETECTABILITY OF SIGNALS IN CO2

D12301

Figure 11. Calculated signal-to-noise ratio STN scaled with the effectively independent number of observations per month n0 from (left) HANK and (right) REMO simulations, with daytime sampling (1100– 1700 local was included in the calculation of pffiffiffiffitime). A potential instrument precision of 0.5 ppm pffiffiffiffi STN. (a) STN  n0 in the PBL at approximately 300 m. (b) STN  n0 along cross section from 45
N/ 20
W to 64
N/120
E (cf. red line in maps) in the lower troposphere (0 – 3000 m). imately 10 weekly high precision measurements, another 10 stations provide continuous, but less well calibrated, measurements. At least three of the stations are towers with continuous measurements at several heights up to 200 m. Monthly aircraft profiles are currently carried out at seven pffiffiffiffi sites. For each station the signal-to-noise ratio STN  n0 computed using the REMO simulations is evaluated at four height levels, corresponding to 30 m, 300 m, 1000 m, and 3000 m, and is represented by a bar in Figure 12. Here we split up the signal into signals emanating from Europe (west of 60
E) and from western Siberia (east of 60
E) in order to separately investigate their detectability. Those stations and levels where a 20% difference in the biospheric activity in Europe or western Siberia could be detected at the 1-s level are shown in green and dark blue, respectively. The light blue color indicates that both signals would be detectable at the same place. Where the difference in the monthly mean is not significant, the bar remains black. In this evaluation we do not specify the type of each station (surface, tower, or aircraft) and we also assume an hourly sampling at all levels. Assuming a 20% difference in surface fluxes that extends all over Europe it will be possible to detect this signal at most of the surface stations and towers in the central part of the network (Figure 12, upper panel). In measurements above the PBL the signal will be detectable downwind of sources and even at stations east of 60
E. Reducing the sampling frequency to only one measurement per day at 1000 m and 3000 m would significantly reduce the number of sites, which permit signal detection. The present network includes

only five stations east of 60
E. A 20% difference in the biospheric fluxes in this area will be detectable at most of these stations. A comparison with the detectability of a 20% increase of fossil fuel emissions (Figure 12, lower panel) shows that measurements will be affected by both signals at most European stations except for the stations in southwestern Europe. Figure 12 presents the artificial situation where at each site measurements are available within and above the PBL. At present most of the stations measure the CO2 close to the surface (corresponding to the lowest level in Figure 12). With these stations combined in a network it would be possible to detect large-scale changes in the surface fluxes like the ones we prescribe in our study. However, it is obvious from Figure 12 that in some areas differences confined to small regions could easily go undetected. The picture clearly shows the need for an extension of the network to the east. In reality surface stations are much more influenced by local conditions than in the model simulations where they are represented by a grid cell with a size of 55 km  55 km and 90 km  90 km in REMO and HANK, respectively. The underestimation of the heterogeneity of surface fluxes probably results in an overestimation of the signal-to-noise ratios and therefore restricts our conclusions concerning individual surface stations.

5. Summary and Conclusions [46] From our model study we infer some general guidelines for the sampling of the continental troposphere for the

16 of 19

D12301

KARSTENS ET AL.: DETECTABILITY OF SIGNALS IN CO2

D12301

Figure 12. Detectability of the signal in the CO2 concentration caused by a 20% difference in surface fluxes, evaluated at the sites of the current CO2 observing network. The evaluation is based on the signalto-noise ratio computed from REMO simulations. Colored bars represent the detectability at each station location in four different height levels, corresponding to 30 m, 300 m, 1000 m, and 3000 m. Colors indicate whether a specific type of signal is detectable. Green: signal of a 20% difference in biospheric fluxes in Europe. Blue: signal of a 20% difference in biospheric fluxes in western Siberia. Cyan: both biospheric signals. Red: signal of a 20% difference in fossil fuel emissions in Europe and western Siberia. Black: differences not detectable. purpose of constraining carbon sinks in general and land sinks in particular. Comparisons of model simulations with near ground observations of summertime CO2 concentration in Europe suggest that nighttime concentrations are predicted with very large uncertainty only and thus render them useless for a quantification of regional-scale carbon sources and sinks that employs atmospheric transport models for the interpretation of the signals. The influence of nighttime trapping of respired CO2 decreases with altitude and becomes negligible in the free troposphere. The remaining summary restricts itself to results based on selective daytime sampling (1100 to 1700 local time). [47] Our simulation results do not indicate a preferred location in the vertical for sampling of the continental CO2 field when taking into account autocorrelation of the signals. Near-surface, mixed layer, and free-troposphere measurements lead approximately to the same uncertainty range of inferences on the magnitude of sources and sinks. However, because signals in the free troposphere are quite small, precision and accuracy of the CO2 measurements will also influence the detectability of sources and sinks. As

furthermore local conditions, which are not properly represented in our modeling framework, affect near-ground measurements, sampling the mixed layer somewhat above ground (a few 100 m) may be the most promising strategy. [48] With regards to sampling in time, the simulations indicate that carbon sources and sinks can be detected and quantified with a frequent sampling of the continental troposphere. However, to achieve this goal generally at least daily sampling and, for most locations, more frequent (effectively continuous) measurements are needed to detect the assumed additional sink of 0.06 PgC/month in Eurasia. [49] Our analysis is limited in its capability to make predictions on the necessary spatial density of sampling stations. Nevertheless, we can consult the correlation length of the simulated atmospheric CO2 field. The results indicate that a spatial density of stations with mesh size on the order of a few 100 km is necessary for detecting continental carbon sources and sinks at the one-sigma uncertainty level. [50] In summary, we conclude that in order to detect regional scale changes in the surface fluxes on continents from atmospheric CO2 concentration measurements, a net-

17 of 19

D12301

KARSTENS ET AL.: DETECTABILITY OF SIGNALS IN CO2

work of stations is needed that permits to sample the PBL several times per day and with a spatial distance between sampling stations not exceeding a few hundreds of kilometers. A possible realization is an array of continuously measuring tower sites located approximately every 300 – 500 km across the continent, ideally complemented by frequent vertical aircraft profiles. [51] Acknowledgments. We would like to thank all participants of the projects AEROCARB and TCOS-Siberia for useful discussions and for providing their measurements and valuable advice. Similarly, we would like to thank CARBODATA for providing CO2 records that have been measured as part of the EUROFLUX program. MG would like to thank P. Hess and A. Klonecki for providing the HANK model and for help with the model. We also thank P. Ciais for constructive comments and S. Lafont who provided the biospheric fluxes from the TURC model. ECMWF and NCEP are acknowledged for providing meteorological analysis and reanalysis data. The work was partially funded by the European Commission under contracts EVK2-CT-1999-00013 (AEROCARB) and EVK2-CT-200100131 (TCOS-Siberia).

References Bakwin, P. S., P. P. Tans, C. Zhao, W. Ussler III, and E. Quesnell (1995), Measurements of carbon dioxide on a very tall tower, Tellus, Ser. B, 47, 535 – 549. Bernhofer, C. (2003), Tharandt, The Euroflux dataset 2000, in: Fluxes of Carbon, Water and Energy of European Forests, edited by R. Valentini, 270 pp, Springer, New York. Bousquet, P., P. Peylin, P. Ciais, C. Le Que´re´, P. Friedlingstein, and P. P. Tans (2000), Regional changes in carbon dioxide fluxes of land and oceans since 1980, Science, 290, 1342 – 1346. Chevillard, A., et al. (2002a), Transport of 222Rn using the regional model REMO: A detailed comparison with measurements over Europe, Tellus, Ser. B, 54, 850 – 871. Chevillard, A., U. Karstens, P. Ciais, S. Lafont, and M. Heimann (2002b), Simulation of atmospheric CO2 over Europe and western Siberia using the regional scale model REMO, Tellus, Ser. B, 54, 872 – 894. Ciais, et al. (1999), AEROCARB: Airborne European Regional Observations of the Carbon Balance, Cent. Natl. de la Recherche Sci., Paris. (Available at http://www.aerocarb.cnrs-gif.fr/) Conway, T. J., P. P. Tans, L. W. Waterman, K. W. Thoning, D. R. Kitzis, K. A. Masarie, and N. Zhang (1994), Evidence for interannual variability of the carbon cycle from the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory Global Air Sampling Network, J. Geophys. Res., 99(D11), 22,831 – 22,855. Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell (2000), Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model, Nature, 408, 184 – 187. Friedlingstein, P., L. Bopp, P. Ciais, J.-L. Dufresne, L. Fairhead, H. LeTreut, P. Monfray, and J. Orr (2001), Positive feedback between future climate change and the carbon cycle, Geophys. Res. Lett., 28, 1543 – 1546. Geels, C., et al. (2006), Comparing atmospheric transport models for future regional inversions over Europe. Part 1: Mapping the CO2 atmospheric signals, Atmos. Chem. Phys. Discuss., 6, 3709 – 3756. Gerbig, C., J. C. Lin, S. C. Wofsy, B. C. Daube, A. E. Andrews, B. B. Stephens, P. S. Bakwin, and C. A. Grainger (2003), Towards constraining regional scale fluxes of CO2 with atmospheric observations over a continent: 1. Observed spatial variability from airborne platforms, J. Geophys. Res., 108(D24), 4756, doi:10.1029/2002JD003018. Gloor, M., P. Bakwin, D. Hurst, L. Lock, R. Draxler, and P. Tans (2001), What is the footprint of a tall tower?, J. Geophys. Res., 106(D16), 17,831 – 17,840. Grell, G. (1993), A prognostic evaluation of assumptions used by cumulus parameterizations, Mon. Weather Rev., 121, 764 – 787. Grell, G., J. Dudhia, and D. Stauffer (1993), A description of the fifthgeneration Penn State/NCAR Mesoscale Model (MM5), NCAR/TN398+STR, Natl. Cent. for Atmos. Res., Boulder, Colo. Haszpra, L., Z. Barcza, P. S. Bakwin, B. W. Berger, K. J. Davis, and T. Weidinger (2001), Measuring system for the long-term monitoring of biosphere/atmosphere exchange of carbon dioxide, J. Geophys. Res., 106(D3), 3057 – 3070. Heimann, M., and S. Ko¨rner (2003), The global atmospheric tracer model TM3, Tech. Rep. 5, 131 pp., Max-Planck-Inst. fu¨r Biogeochem., Jena, Germany. Heimann, M., et al. (2001), TCOS-Siberia, Terrestrial Carbon Observation System Siberia, EU-Proj. EVK2-CT-2001-00131, Max-Planck-Inst. fu¨r

D12301

Biogeochem., Jena, Germany. (Available at http://www.bgc-jena. mpg.de/public/carboeur/web_TCOS/) Hess, P. G., S. Flocke, J.-F. Lamarque, M. C. Barth, and S. Madronich (2000), Episodic modeling of the chemical structure of the troposphere as revealed during the spring MLOPEX 2 intensive, J. Geophys. Res., 105(D22), 26,809 – 26,839. Holtslag, A. A. M., and B. A. Boville (1993), Local versus nonlocal boundary-layer diffusion in a global climate model, J. Clim., 6, 1825 – 1842. Jacob, D., and R. Podzun (1997), Sensitivity Studies with the Regional Climate Model REMO, Meteorol. Atmos. Phys., 63, 119 – 129. Jacob, D., et al. (2001), A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period, Meteorol. Atmos. Phys., 77, 19 – 43. Karstens, U., R. Nolte-Holube, and B. Rockel (1996), Calculation of the water budget over the Baltic Sea Catchment Area using the Regional Forecast Model REMO for June 1993, Tellus, Ser. A, 48, 684 – 692. Keeling, C. D., S. C. Piper, and M. Heimann (1989), A three dimensional model of atmospheric CO2 transport based on observed winds: 4. Mean annual gradients and interannual variations, in Aspects of Climate Variability in the Pacific and the Western Americas, Geophys. Monogr. Ser., vol. 55, edited by D. H. Peterson, pp. 305 – 363, AGU, Washington, D. C. Kjellstro¨m, E., K. Holme´n, K. Eneroth, and M. Enghardt (2002), Summertime Siberian CO2 simulations with the regional transport model MATCH: A feasibility study of carbon uptake calculations from EUROSIB data, Tellus, Ser. B, 54, 834 – 849. Lafont, S., L. Kergoat, G. Dedieu, A. Chevillard, E. Kjellstro¨m, U. Karstens, and O. Kolle (2002), Spatial and temporal variability of land CO2 fluxes estimated with remote sensing and analysis data over western Eurasia, Tellus, Ser. B, 54, 820 – 833. Langmann, B. (2000), Numerical modelling of regional scale transport and photochemistry directly together with meteorological processes, Atmos. Environ., 34, 3585 – 3598. Levin, I., et al. (2002), Three years of trace gas observations over the EuroSiberian domain derived from aircraft sampling - a concerted action, Tellus, Ser. B, 54, 696 – 712. Lloyd, J., et al. (2002a), A trace-gas climatology above Zotino, central Siberia, Tellus, Ser. B, 54, 749 – 767. Lloyd, J., O. Shibistova, D. Zolotoukhine, O. Kolle, A. Arneth, C. Wirth, J. M. Styles, N. M. Tchebakova, and E. D. Schulze (2002b), Seasonal and annual variations in the photosynthetic productivity and carbon balance of a central Siberian pine forest, Tellus, Ser. B, 54, 590 – 610. Louis, J.-F. (1979), A parametric model of vertical eddy fluxes in the atmosphere, Boundary Layer Meteorol., 17, 187 – 202. Majewski, D. (1991), The Europamodell of the Deutscher Wetterdienst, Numer. Methods in Atmos. Models, vol. 2., pp. 147 – 191, Eur. Cent. for Medium Range Weather Forecast., Reading, UK. Mellor, B., and T. Yamada (1974), A hierarchy of turbulence closure models for planetary boundary layers, J. Atmos. Sci., 31, 1791 – 1806. Moncrieff, J. (2003), Aberfeldy, The Euroflux dataset 2000, in: Fluxes of Carbon, Water and Energy of European Forests, edited by R. Valentini, 270 pp, Springer, New York. Olivier, J. G. J., A. F. Bouwman, C. W. M. Van der Maas, J. J. M. Berdowski, C. Veldt, J. P. J. Bloos, A. J. H. Visschedijk, P. Y. J. Zandyelt, and J. L. Haverlag (1996), Description of EDGAR version 2.0: A set of global emission inventories of greenhouse gases and ozone-depleting substances for all anthropogenic and most natural sources on a per country basis and on 1  1 grid, RIVM/TNO Rep. 771060 002, Natl. Inst. for Public Health and the Environ., Bilthoven, Netherlands. Prentice, I. C., G. D. Farquhar, M. J. R. Fasham, M. L. Goulden, M. Heimann, V. J. Jaramillo, H. S. Khehgi, C. Le Que´re´, R. J. Scholes, and D. W. R. Wallace (2001), The carbon cycle and atmospheric carbon dioxide, in Climate Change 2001: The Scientific Basis, edited by J. T. Houghton et al., pp. 183 – 237, Cambridge Univ. Press, New York. Randerson, J. T., M. V. Thompson, T. J. Conway, I. Y. Fung, and C. B. Field (1997), The contribution of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide, Global Biogeochem. Cycles, 11, 535 – 560. Rebmann, C. (2003), Bayreuth, The Euroflux dataset 2000, in: Fluxes of Carbon, Water and Energy of European Forests, edited by R. Valentini, 270 pp, Springer, New York. Rockel, B., and U. Karstens (2001), Development of the water budget for three extra-tropical cyclones with intense rainfall over Europe, Meteorol. Atmos. Phys., 77, 75 – 83. Ro¨denbeck, C., S. Houweling, M. Gloor, and M. Heimann (2003), CO2 Flux history 1982 – 2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys., 3, 1919 – 1964. Ruimy, A., G. Dedieu, and B. Saugier (1996), TURC: A diagnostic model of continental gross primary productivity as net primary productivity, Global Biogeochem. Cycles, 10, 269 – 285.

18 of 19

D12301

KARSTENS ET AL.: DETECTABILITY OF SIGNALS IN CO2

Smolarkiewicz, P. K. (1983), A simple positive definite advection scheme with small implicit diffusion, Mon. Weather Rev., 111, 479 – 486. Smolarkiewicz, P. K. (1984), A fully multidimensional positive definite advection algorithm with small implicit diffusion, J. Comput. Phys., 54, 325 – 362. Takahashi, T., R. H. Wanninkhof, R. A. Feely, R. F. Weiss, D. W. Chipman, N. Bates, J. Olafsson, C. Sabine, and S. C. Sutherland (1999), Net sea-air CO2 flux over the global oceans: An improved estimate based on the seaair pCO2 difference, paper presented at 2nd CO2 in Oceans Symposium, Cent. for Global Environ. Res., Natl. Inst. for Environ. Stud., Tsukuba, Japan, 18 – 23 January. Tans, P. P., I. Y. Fung, and T. Takahashi (1990), Observational constraints on the global atmospheric CO2 budget, Science, 247, 1431. Thie´baux, H. J., and F. W. Zwiers (1984), The interpretation and estimation of effective sample size, J. Clim. Appl., 23,800 – 23,811.

D12301

Tiedtke, M. (1989), A comprehensive mass flux scheme for cumulus parameterization in large scale models, Mon. Weather Rev., 117, 1779 – 1800. Von Storch, H., and F. W. Zwiers (1999), Statistical Analysis in Climate Research, Cambridge Univ. Press, New York. Wanninkhof, R. (1992), Relationship between wind speed and gas exchange, J. Geophys. Res., 97, 7373 – 7382. Wofsy, S. C., and R. C. Harris (2002), The North American Carbon Program (NACP), report, NACP Comm. of the U.S. Interagency Carbon Cycle Sci. Progr., US Global Change Res. Progr., Washington, D. C.























M. Gloor, School of Geography, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK. M. Heimann, U. Karstens, and C. Ro¨denbeck, Max Planck Institute for Biogeochemistry, D-20146 Jena, Germany. ([email protected])

19 of 19