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[1] The multidecadal observational CO2 record from the Mauna Loa Observatory (MLO) in Hawaii exhibits variations at subannual frequencies associated with variations in both carbon sources and sinks and atmospheric transport. However, the atmospheric transport influence on MLO's CO2 variability in general and its seasonal characteristics in particular have received relatively little attention. In this study, we explore the impact of seasonal atmospheric circulation on MLO CO2 variability. Because of MLO's central tropical North Pacific location between zones of intense midlatitude westerly flow to the north and tropical easterly flow to the south, the air mass transport to the site broadly consists of either long-range transport, typically originating from the west over or near the Eurasian continent, and short-range transport, typically originating from the east over the Pacific Ocean. Climatologically, the source regions of air masses arriving at MLO reflect seasonal modulation; most notably, while the period October-June is characterized by both the short- and long-range transport pathways, with the latter more prominent, especially during Northern Hemisphere (NH) spring, the period July-September is dominated by the short-range pathway with little long-range transport. At interannual timescales, the relationship between atmospheric transport and MLO CO2 also displays distinct seasonality: The correlation of observed MLO CO2 variability with simulated variability associated with cyclostationary sources interacting with an interannually varying reanalysis circulation field, i.e., an “atmospheric circulation only” estimate of MLO CO2 variability, is relatively strong during the NH cold season (November-May) but is weak during the warm season (June-October). Use of both Eulerian- and Lagrangian-based techniques, including composite analysis and clustering of Lagrangian back trajectories, highlights distinct atmospheric circulation flow patterns associated with anomalous positive and negative excursions of MLO CO2 concentrations, with the El Nino/Southern Oscillation (ENSO) playing a significant, though seasonally variable, role. Consistent with simple correlation analysis, the strongest concurrence between circulation fields composited with respect to observed and simulated MLO CO2 is evident during the NH autumn, winter, and spring seasons. Back trajectory cluster analysis supports the physicality of the composite analysis in terms of the origins of air masses arriving at MLO. The seasonal role of atmospheric transport variability may have important implications for the interpretation of the recent downward trends in the observed CO2 seasonal cycle amplitude and maxima, features that also appear in the simulated CO2 record. Indeed, both the circulation analyses as well as available radon-222 data for MLO point toward a decadal shift in the origin of air masses arriving at MLO during the April-June period that favors lower CO2 concentrations.

[1] The analysis of a global data set of monthly leaf area index (LAI), derived from satellite observations of normalized difference vegetation index (NDVI) for the period July 1981 to September 1994, is discussed in this paper. Validation of this retroactive, coarse resolution (8 km) global multiyear data set is a challenging task because repetitive ground measurements from all representative vegetation types are not available. Therefore the magnitudes and interannual variations in the derived LAI fields were assessed as follows. First, the use of a NDVI-based algorithm, as opposed to a more physically based approach, is estimated to result in relative errors in LAI of about 10–20%, which is comparable to the mean uncertainty of AVHRR NDVI data. Second, the satellite LAI values compared reasonably well to ground measurements from three field campaigns. Third, comparison with an existing multiyear LAI data set showed qualitative agreement with regards to interannual variability, although the LAI values of the earlier data were consistently larger than those derived here. Fourth, interannual variations in LAI were evaluated through correlations with climate data sets, e.g., sea surface temperatures and precipitation in tropical semiarid regions known for ENSO impacts, temperature dependence of vegetation growth, and therefore LAI, in the northern latitudes. The general consistency between these independent data sets imbues confidence in the LAI data set, at least for use in large-scale modeling studies. Finally, improvements in near-surface climate simulation are documented in a companion article when satellite LAI values were used in a global climate model. The data set is available to the community via our Web server (http://cybele.bu.edu).