Where do fossil fuel carbon dioxide emissions from California go? An analysis based on radiocarbon observations and an atmospheric transport model



[1] Characterizing flow patterns and mixing of fossil fuel-derived CO2 is important for effectively using atmospheric measurements to constrain emissions inventories. Here we used measurements and a model of atmospheric radiocarbon (14C) to investigate the distribution and fluxes of atmospheric fossil fuel CO2 across the state of California. We sampled 14C in annual C3 grasses at 128 sites and used these measurements to test a regional model that simulated anthropogenic and ecosystem CO2 fluxes, transport in the atmosphere, and the resulting Δ14C of annual grasses (Δg). Average measured Δg levels in Los Angeles, San Francisco, the Central Valley, and the North Coast were 27.7 ± 20.0, 44.0 ± 10.9, 48.7 ± 1.9, and 59.9 ± 2.5‰, respectively, during the 2004–2005 growing season. Model predictions reproduced regional patterns reasonably well, with estimates of 27.6 ± 2.4, 39.4 ± 3.9, 46.8 ± 3.0, and 59.3 ± 0.2‰ for these same regions and corresponding to fossil fuel CO2 mixing ratios (Cf) of 13.7, 6.1, 4.8, and 0.3 ppm. Δg spatial heterogeneity in Los Angeles and San Francisco was higher in the measurements than in the predictions, probably from insufficient spatial resolution in the fossil fuel inventories (e.g., freeways are not explicitly included) and transport (e.g., within valleys). We used the model to predict monthly and annual transport patterns of fossil fuel-derived CO2 within and out of California. Fossil fuel CO2 emitted in Los Angeles and San Francisco was predicted to move into the Central Valley, raising Cf above that expected from local emissions alone. Annually, about 21, 39, 35, and 5% of fossil fuel emissions leave the California airspace to the north, east, south, and west, respectively, with large seasonal variations in the proportions. Positive correlations between westward fluxes and Santa Ana wind conditions were observed. The southward fluxes over the Pacific Ocean were maintained in a relatively coherent flow within the marine boundary layer, while the eastward fluxes were more vertically dispersed. Our results indicate that state and continental scale atmospheric inversions need to consider areas where mixing ratio measurements are sparse (e.g., over the ocean to the south and west of California), transport within and across the marine boundary layer, and terrestrial boundary layer dynamics. Radiocarbon measurements can be very useful in constraining these estimates.

1. Introduction

[2] Fossil fuel combustion is the largest anthropogenic CO2 source, accounting for approximately 7.0 Pg C a−1 in 2000, and increasing rapidly to over 7.9 Pg C a−1 in 2004 [Marland et al., 2006; Raupach et al., 2007]. This combustion is associated with a range of societal and economic benefits, including transportation, electricity generation, heating, air-conditioning, and others. There are, however, many costs associated with the climate consequences of this greenhouse gas that will occur across a wide range of timescales, including impacts to agricultural productivity, sea level, water resources, terrestrial and oceanic ecosystem health, disease propagation, and fire regimes [Parry et al., 2007].

[3] Accurate quantification of fossil fuel CO2 emissions is needed to properly account for these costs [Stern, 2006], aid in policy development [Parry et al., 2007], improve climate prediction and climate change attribution, and facilitate atmospheric inversion approaches used to quantify contemporary anthropogenic and ecosystem C fluxes [Fan et al., 1998; Gurney et al., 2002; Stephens et al., 2007]. Further, other primary atmospheric pollutants of interest (e.g., carbon monoxide and black carbon) are often produced concurrently with CO2 and surface emissions estimates for these gases and aerosols can be improved using accurate fossil fuel CO2 emissions estimates [e.g., Turnbull et al., 2006]. This paper describes an approach using the 14C content of annual grasses and an atmospheric transport model to characterize the impacts of spatially and temporally heterogeneous surface and atmospheric processes on fossil fuel CO2 transport within and out of California.

[4] The first attempts to quantify fossil fuel CO2 emissions used inventories based on proxy measurements such as fuel sales and population density [Andres et al., 1996; Franco, 2002; Olivier et al., 1999]. The mix of fossil fuels used varies substantially around the world. In California, fossil fuel is used for transportation (∼60%), electric power generation (∼16%), industry (∼13%), and residences (∼10%) [Bemis, 2006; Franco, 2002]. Although important in characterizing regional fossil fuel CO2 emissions, the accuracy of fuel use-based emissions inventories still requires improvement [Marr et al., 2002], particularly at fine spatial scales. These inventories are also potentially vulnerable to political pressure, creating the need for independent verification approaches.

[5] Another approach to estimating fossil fuel CO2 emissions has been to use atmospheric measurements of radiocarbon (14C) in CO2. Because 14C has a relatively short half live (∼5730 years) compared to the ancient plant material from which fossil fuels are derived, carbon in fossil fuels is effectively free of 14C (i.e., Δ14C = −1000‰). With atmospheric nuclear weapon testing, the 14C content of tropospheric CO2 rapidly increased and by 1963 was over 900‰ in the northern hemisphere. Following the 1963 Test Ban treaty, atmospheric Δ14C levels declined, primarily as a consequence of air-sea gas exchange, uptake by land plants, dilution from fossil fuel combustion, and radioactive decay. By 2000, atmospheric levels had dropped to about 60‰, with a rate of change of about 6‰ a−1 [Levin et al., 2003]. While there are important latitudinal and seasonal variations in the background atmospheric (i.e., remote marine boundary layer) Δ14C, almost all of the spatial variation over North America is due to fossil fuel CO2 emissions [Hsueh et al., 2007; Randerson et al., 2002]. For current atmospheric CO2 levels, about a 2.8‰ change in 14C content is equivalent to 1 ppm fossil fuel CO2 (equation image). Since current 14C accelerator mass spectrometry measurement techniques have a precision of 2.5 to 3.0‰, measurements of the Δ14C of atmospheric CO2 can be used to infer fossil fuel CO2 levels to a precision of about 1 ppm.

[6] Turnbull et al. [2006] compared 14CO2, CO, and SF6 as tracers of fossil fuel CO2 at two sites. They concluded that CO is limited as a tracer due to uncertainty in its CO2 emission ratio, and that, as a tracer, SF6 showed large biases as compared to 14CO2, possibly because of differences in the spatial pattern of surface sources. Levin et al. [1995] studied two sites in Germany where atmospheric 14CO2 and radon measurements had been made. They derived fossil fuel CO2 emissions and concluded that emissions estimates derived from fuel sales substantially underestimated the seasonal amplitude, likely leading to errors in the inferred seasonal cycle of terrestrial biosphere exchange. Using a longer data record, Levin et al. [2003] applied a similar method at two sites to estimate fossil fuel CO2 emissions. They concluded that their method compared well with bottom-up statistical emissions inventories and the seasonality of fossil fuel CO2 emissions was substantially larger than previously assumed. Measurements of 14C in plant biomass can be used as an integrator of spatial and temporal variability in fossil fuel CO2. Hsueh et al. [2007], for example, mapped patterns of 14C content in an annual plant (Zea mays) across North America. They found that relative to the intermountain West, fossil fuel CO2 mixing ratios were substantially higher in California and in the Ohio Valley.

[7] In addition to constraining surface emission estimates, accurately characterizing CO2 transport out of a particular region is critical for testing larger-scale atmospheric inversions [Gurney et al., 2002]. Such independent measures of fossil fuel CO2 production and transport will become increasingly important as society develops regulations of regional and national GHG emissions [e.g., Schwarzenegger, 2005]. Transport of CO2 within and out of California is dominated by three transport mechanisms: the large-scale Pacific High, the Great Basin High (which, in combination with the Pacific High establishes wintertime offshore Santa Ana (SA) wind conditions [Conil and Hall, 2006; Raphael, 2003]), and the high elevation jet stream. The strong westerly jet streamflow has led some investigators to hypothesize that measuring CO2 mixing ratios on the west and east coasts of the contiguous U.S. will facilitate continental CO2 exchange estimates [Fan et al., 1998]. One goal of the present work is to test this hypothesis by studying the impact of smaller scale, more variable mechanisms (e.g., Santa Ana winds) on CO2 fluxes leaving the state.

[8] As a first step toward characterizing transport of fossil fuel CO2 within and out of California, we used 14C measurements in annual grasses to test a model that integrates fossil fuel CO2 emissions, ecosystem CO2 exchanges, and atmospheric transport. We then used the model over a full year to predict the pathways by which fossil fuel CO2 leaves California and their relationships with atmospheric transport processes. Our results can be used to inform atmospheric inversion measurement strategies and inventory approaches to quantifying fossil fuel CO2 emissions.

2. Methods

2.1. Δ14C Measurements of California C3 Grasses

[9] Samples of winter annual grasses were collected at 128 sites across California at the end of the 2004–2005 growing season. Packets were sent to colleagues with a letter describing our sampling protocol. To avoid point CO2 sources, samples in relatively rural areas were collected more than 3.2 km away from highways, more than 45 m from paved roads, and more than 20 m from houses or buildings. In cities, where remote sample locations were difficult or impractical to find, samples were collected in residential streets, neighborhood parks, or abandoned parking lots. We collected samples throughout California, with relatively higher collection density in the San Francisco Bay Area, Los Angeles Basin, and Central Valley Region to explore urban to rural gradients. At each site, three separate stalks of grass were collected. All of the samples consisted of annual plants that germinated in the fall of 2004 and senesced in the spring of 2005, primarily from the genera Bromus and Avena, which are highly invasive and currently widespread throughout California.

[10] Upon arrival at UCI, samples were dried at 60–70°C for at least 48 h. Plants were then ground to pass a size 40 sieve and stored in individual vials. Samples were converted to graphite and analyzed at UC Irvine's W.M. Keck Carbon Cycle Accelerator Mass Spectrometer (KCCAMS) facility [Santos et al., 2004]. To ensure that we quantified the overall accuracy and to minimize differences due to running samples in different batches (or sample wheels) on the AMS, we (1) included 6–7 primary and 6–7 secondary standards with each batch of plant samples (24–27 plant samples comprised a single batch); (2) repeatedly analyzed samples collected at five sites across different batches; and (3) used three secondary standards: barley (FIRI G; SD = 2.3‰ based on 20 replicates distributed across multiple batches), oxalic acid (SD = 3.2‰ with 5 replicates), and an Australian National University standard (SD = 2.6‰ with 5 replicates).

2.2. Coupled MM5, LSM1, and Atmospheric Tracer Model

[11] MM5 [Grell et al., 1995] is a nonhydrostatic, terrain-following sigma-coordinate mesoscale meteorological model used in weather forecasting and in studies of atmospheric dynamics, surface and atmosphere coupling, and pollutant dispersion. The model has been applied in many studies in a variety of terrains, including areas of complex topography and heterogeneous land-cover (for a partial list: http://www.mmm.ucar.edu/mm5/Publications/mm5-papers.html). The following physics packages were used for the simulations shown here: Grell convection scheme, simple ice microphysics, MRF planetary boundary layer (PBL) scheme, and the CCM2 radiation package. The MRF PBL scheme [Hong and Pan, 1996] is a high-resolution PBL transport model that includes both local and non-local vertical transport. The inert tracer model follows the current MM5 transport calculations for water vapor. We tested the numerical solution of the tracer transport predictions and successfully compared predicted and measured CO2 mixing ratios at the Trinidad Head station (located on the northern California coast) [Riley et al., 2005].

[12] LSM1 [Bonan, 1996] is a “big-leaf” [e.g., Dickinson et al., 1986; Sellers et al., 1996] land-surface model that simulates CO2, H2O, and energy fluxes between ecosystems and the atmosphere. Modules are included that simulate fluxes of radiation, momentum, sensible heat, and latent heat; belowground energy and water fluxes, and coupled CO2 and H2O exchange between soil, plants, and the atmosphere. Twenty-eight land surface types, comprising varying fractional covers of thirteen plant types, are simulated in the model. Soil hydraulic characteristics are determined from soil texture. LSM1 has been tested in a range of ecosystems at the site level [e.g., Bonan et al., 1997, 1995; Riley et al., 2003]. Cooley et al. [2005] described the integration of LSM1 with MM5 and demonstrated that the model accurately predicted surface latent, sensible, and ground heat fluxes; near-surface air temperatures; and soil moisture and temperature by comparing model simulations with data collected during the FIFE campaign [Betts and Ball, 1998].

[13] We imposed constant atmospheric CO2 mixing ratio (380 ppm) and Δ14C (Δb = 60‰) boundary conditions at the edges of the domain. In reality, there are vertical, horizontal, and temporal variations in these boundary conditions. These variations should be relatively small and we did not expect them to substantially influence model estimates of Δg. Our use of constant boundary conditions had no effect on our model predictions of fossil fuel CO2 transport within and out of California.

2.3. Fossil Fuel CO2 Emissions

[14] We estimated spatially and temporally resolved fossil fuel CO2 emissions by scaling fossil fuel NOx emission estimates reported in the 2002 U.S. Environmental Protection Agency's National Emissions Inventory (NEI) in a manner similar to that used for CO by Gerbig et al. [2003]. This approach provides finer spatial and temporal resolution than is present in available inventories of fossil fuel consumption. For our work, the NEI emission estimates were distributed at 36 km resolution by the Lake Michigan Air Directors Consortium with hourly resolution on weekdays, Saturdays, and Sundays of each month in 2002 (http://www.ladco.org/). The overall scaling of CO2 from NOx emissions was estimated using the ratio of California's annual NOx (1600 Mg NOx a−1) to CO2 (370 ± 3 Tg CO2 a−1) emissions [Blasing et al., 2004; EIA, 2003] for 2002. Fossil CO2 emissions are concentrated in the urban centers of the Los Angeles Basin and the San Francisco Bay area, with significant emissions present in the Central Valley (Figure 1).

Figure 1.

Cumulative annual fossil fuel CO2 emissions (kg C m−2 a−1) with the spatial pattern derived from a high resolution NOx inventory and scaled to match state-wide CO2 emissions inventory. Background color interpolation was generated using the Inverse Distance Weighted (IDW) method based on 15 nearest neighbors within the Geostatistics Analyst tools in ESRI's ArcMap software.

[15] One of the largest errors in our CO2 emissions estimates was likely a result of spatial variations in the CO2:NOx emission ratio. However, the model's relatively large spatial resolution (36 km) and the expected variety of sources within this resolution (particularly in urban areas where the preponderance of CO2 emissions occur) will reduce uncertainty resulting from spatial variability in emission ratios associated with different point sources. Further, although seasonal cycles of the NEI emissions inventory are specific to a given state or region, the diurnal and day-to-day temporal variations in fossil CO2 emissions are characterized by national averages. These small timing errors probably have a relatively small effect on our model estimates of Cf.

2.4. Simulation Approach

[16] We used the standard initialization procedure for MM5v3.5, which applies first-guess and boundary condition fields interpolated from the NOAA National Center for Environmental Prediction (NCEP) reanalysis data [Kalnay et al., 1996; Kistler et al., 2001] to the outer computational grid. The model was run with a single domain with horizontal resolution of 36 km and 18 vertical sigma layers between the surface and 5000 Pa; the time step used was 108 s, and output was generated every two hours. The two-hourly model output was used in all the analyses that follow by integrating or averaging over hourly, seasonal, or annual periods.

[17] We simulated a twelve-month period (July, 2004 through June, 2005) that encompasses the typical growing season for C3 plants (November through May). The model was then run again over the same period, but with ecosystem respiration scaled by a constant factor so that the annual net CO2 flux was zero at each grid cell [Denning et al., 1996]. The most abundant vegetation cover type inferred from the USGS 1 km surface cover map was used to identify the dominant vegetation in each 36 × 36 km grid cell. Since many grid cells are not dominated by C3 grasses, we estimated C3 gross primary production (GPP, Gp, μmol m−2 s−1) at each grid cell over the simulation period to ensure that the life history, and therefore the time history of CO2 assimilation, was properly accounted for. C3 grass GPP was estimated using the MM5 meteorological forcing, the offline version of LSM1.0, and MODIS LAI profiles (http://LPDAAC.usgs.gov) for this time period spatially averaged over California. To ensure that the LAI profiles were representative of C3 grasses, we set LAI to zero during June through October, the typical period between plant senescence and germination. Predicted grass Δ14C (Δg, ‰) changed only slightly when we used GPP calculated from the LAI time series of default vegetation in the coupled model (versus using the GPP derived from MODIS LAI time series).

2.5. Δ14C of Near-Surface CO2

[18] The Δ14C of near-surface CO2 at a particular grid cell and time depends on CO2 and 14CO2 fluxes from advection from adjacent cells, respiration, and fossil fuel combustion. Assuming that the Δ14C of respiration does not vary, a steady state mass balance gives a relationship for the Δ14C of near-surface atmospheric CO2a, ‰):

equation image

Here, the subscripts b, r, and f refer to background, heterotrophic respiration, and fossil fuel, respectively; Δ refers to Δ14C (‰); C refers to the atmospheric CO2 mixing ratio (ppm); and Δf = −1000‰. Note that because Δ14C notation normalizes for variations in fractionation using concurrent 13C observations [Stuiver and Polach, 1977], fractionation by photosynthesis does not impact Δa. Temporal variations in background CO2 mixing ratio (Cb) and Δ14C (Δb) occur over the year and probably introduce some error into our estimates of Cf derived from the observations via equation (1). For the model analysis, this error source is likely to be small compared to uncertainties arising from the fossil fuel emissions inventory and biases in model transport. Further, these variations will not impact our analysis of flow patterns of fossil fuel CO2 emitted within California.

[19] We estimated Δr by combining heterotrophic respiration impulse functions derived from the CASA model [Thompson and Randerson, 1999] and a Δ14C record of the atmosphere since 1890 [Levin and Hesshaimer, 2000; Levin and Kromer, 2004]. The impulse functions were generated for an area-weighted combination of eleven biome types present in California. The area-weighted Δ14C of heterotrophic respiration was calculated to be 112‰, with a range between 101‰ for grasslands and 118‰ for evergreen needleleaf trees. Assuming that ecosystem respiration was 50% heterotrophic and approximately 50% autotrophic [Litton et al., 2007; Waring et al., 1998], and that autotrophic respiration had a Δ14C of Δa, we estimated that Δr = equation image = 89‰.

[20] We note that Turnbull et al. [2006] (their equation (1)) and Levin et al. [2003] (their equation (3)) used different relationships than equation (1) for estimating fossil fuel CO2 mixing ratios based on atmospheric Δ14C measurements. The derivation of equation (1) assumes that the Δ14C of photosynthesis and autotrophic respiration are Δa, while that of Turnbull et al. [2006], for example, assumed a Δ14C of photosynthesis equivalent to background air (Δb). The impact of this difference is often small, but can be as high as 0.5 ppm in the inferred value of Cf.

2.6. Estimating the Δ14C of C3 Grasses

[21] To estimate Δ14C of C3 grasses (Δg), we computed the GPP-weighted sum of Δa at each grid cell:

equation image

where the integrals are evaluated over the entire year of the simulation. Thus, the predicted biomass 14C composition reflects both the atmospheric Δ14C and the temporal variation in plant C assimilation. We evaluated Δg with both default (i.e., using the default vegetation type and LAI time series used in LSM1.0) and satellite-derived C3 grass LAI time series.

2.7. Near-Surface Fossil Fuel CO2 Versus Local Emissions

[22] To characterize impacts of local (i.e., from the same model grid cell) fossil fuel CO2 emissions on Δa, we developed a non-dimensional index (I). I is calculated, for each grid cell, as the ratio of local surface fossil fuel CO2 mixing ratio to local fossil fuel CO2 emissions (Ef, kg m−2 s−1) normalized by the statewide average of these quantities:

equation image

where the overbars indicate time averaging over the year and A represents the area of California. While this index does not give a direct measure of the impact of local versus distant sources, it allows a relative comparison between regions within the state.

2.8. Santa Ana Winds

[23] Santa Ana winds are an important component of southern California meteorology, partly because they substantially increase wildfire risk [Westerling et al., 2004], but also because they cause transport that opposes the prevailing eastward flow. Santa Ana events are characterized by dry and often hot offshore winds. Raphael [2003] described a 33-year record of SA occurrences and the conditions necessary for their development: a high pressure region in the Great Basin and a surface low pressure system off the Southern California coast. SA conditions occur typically between September and April, with peak occurrences in December. Conil and Hall [2006] describe three October-March southern California wind regimes (alongshore, onshore, and offshore Santa Ana flows). They concluded that none of the large-scale teleconnection patterns (e.g., the Pacific-North American mode) are more likely than any of the others to coincide with the three southern California wind regimes.

[24] As we discuss below, Santa Ana winds substantially impact fossil fuel CO2 transport toward the south and west from September to May. Further, there is large interannual variability in the number of Santa Ana days [Raphael, 2003]. To place our results for a single year into a broader context with respect to this transport mechanism, we developed a simple method to predict the number of SA wind days (NS) using the six-hour NCEP surface pressure and wind direction predictions. We identified Santa Ana days as those with both (1) a 3 A.M. (Pacific Standard Time) pressure difference between grid cells over the Great Basin and Interior West (lower left corner: 118°W 36°N; upper right corner: 103°W 43°N) and over the Pacific Ocean (lower left corner: 126°W 29°N; upper right corner: 114°W 35°N) that was larger than 1400 Pa and (2) winds in Los Angeles from between northerly and easterly. The predicted NS compared well with the monthly and interannual variability estimated by Raphael [2003] (Figure 2).

Figure 2.

Predicted Santa Ana days per year using the NCEP reanalysis and the 28 year estimates from Raphael [2003]: (a) yearly total NS; (b) monthly average NS between 1968 and 1995. The simple method to predict NS using the NCEP reanalysis sea level pressure and wind direction data captures much of the monthly and interannual variability.

3. Results and Discussion

3.1. Measured Δ14C of C3 Grasses

[25] Measured Δg for our sample sites across California are shown in Figure 3. Additional information on site coordinates, elevation, and species type is provided in Table 1. Annual grasses growing on the coast in the northern part of the state had the highest radiocarbon levels (and thus were exposed to the least amount of locally added fossil fuel CO2). The mean of northern coastal samples from sites near Crescent City, McKinleyville, Rohnerville, and Mendocino was 59.5 ± 2.1‰. Coastal sites in the central part of the state were also relatively clean, with a mean of 58.2 ± 2.7‰ for samples collected near Carmel, Fort Hunter-Liggett, Gorda, Los Osos, and Santa Cruz Island.

Figure 3.

Measured Δ14C of California C3 grasses (Δg, ‰); (inset) expanded view of the Los Angeles Basin. Background interpolation color was built based on 13 nearest neighbors using a cokriging method (including elevation) using Geostatistics Analyst tools in ESRI's ArcMap software.

Table 1. Sample Locations, Measured Δ14C, and Measurement Precision (Standard Deviation) for Samples Collected in Californiaa
Nearest CityCollection DateLongitudeLatitudeElevation (m)Distance to City (km)SpeciesNumber of RunsΔ14C (‰)Standard Deviation Error Estimateb
  • a

    The pooled mean standard deviation across sites for which we made multiple measurements was 3.5‰.

  • b

    We used barley (FIRI G) as a secondary standard, and its standard deviation was 2.3‰ based on 20 replicates scattered across multiple batches. The two other secondary standards we used were an oxalic acid (OX-II) and an Australian National University (ANU) standard. These had standard deviations of 3.2‰ (with 5 replicates) and 2.6‰ (with 5 replicates), respectively. Based on the accuracy of these three standards (FIRI G, OX-II, and ANU), we assumed that the accuracy of an individual measured was ±2.7‰.

Adin7/6/05−120.9578541.190231295Hordeum leporinum157.5
Ahwahnee7/26/05−119.3277337.01615710unk annual grass155.6
Avalon8/8/05−118.4211733.390771911unk annual grass116.0
Baker7/12/05−116.1662335.4698220825Schizmus barbatus154.3
Bellflower8/8/05−118.1255033.88620240Bromus madritensis117.2
Benicia7/5/05−122.1952038.0657212Avena barbata137.0
Blythe2/9/06−114.5169533.734209019Schizmus barbatus160.4
Buena Park8/5/05−117.9977333.86045300unk annual grass126.6
Calimesa8/7/05−117.0531933.97124678∼3Bromus diandrus151.4
Central Weed8/1/05−122.3730641.428061093Bromus160.6
Chino8/9/05−117.6277034.019422300Bromus madritensis160.5
Corona8/8/05−117.6038033.837704061Bromus madritensis141.8
Crescent City7/72005−124.1845241.7406201.6 Avena barbata158.6
Dana Point8/8/05−117.7421233.512231015Bromus madritensis151.8
∼ Essex2/9/06−115.4529534.5900337825Schizmus barbatus155.3
Fontana8/9/05−117.4913334.106683720Avena fatua137.4
Fort Hunter-Liggett2/18/06−121.2695235.969874408unknown annual160.3
Fountain Valley7/9/05−117.9550533.7137390Bromus madritensis126.1
Freeman Jct.2/8/06−117.9417735.6139011445Schizmus barbatus157.8
in Fresno7/10/05−119.7998536.74405870Bromus147.1
Glendale7/9/05−118.3314034.147231540Lolium multiflorum59.03.0
Gorda2/18/06−121.3150235.742082124Briza maxima161.0
Happy Camp7/6/05−123.3399841.801705068Avena sativa154.8
Hemet4/22/05−117.0663933.739254949.6Bromus madritensis145.8
Hemet8/7/05−116.8275733.6731094312Bromus madritensis154.5
Hemet8/7/05−117.0636833.736935613Bromus diandrus152.3
West side of Kaiser Pass7/26/05−119.1250037.283332136Taeniatherum caput160.7
East side of Kaiser Pass7/26/05−119.0666737.316672427Taeniatherum caput158.9
Laguna Woods8/9/05−117.7500833.598671461Bromus madritensis147.1
Lake Elsinore8/7/05−117.4090633.681305531Bromus madritensis151.7
Lake Elsinore8/8/05−117.4105533.736943886Bromus madritensis139.0
Lodi7/5/05−121.2159238.1740716Avena barbata150.4
Los Osos7/10/05−120.8879735.2745748Bromus555.52.6
Ludlow2/9/06−116.0928234.711175714Schizmus barbatus155.2
Marin 9/17/05−122.5728438.1523141Avena154.9
McKinleyville7/72005−124.1144340.994080Lolium temulentum158.3
Mendocino9/17/05−123.8107339.3284713unknown annual grass158.5
Merced7/10/05−120.2747537.178606526Hordeum vulgare146.2
Mill Creek8/12/05−121.5189040.3494014845Bromus japonicus.163.7
Moreno Valley5/25/05−117.2001233.982498196.4Bromus diandrus162.3
Moreno Valley3/15/05−117.2734633.950765931Bromus madritensis145.7
Moreno Valley8/7/05−117.1397433.911775031Bromus diandrus151.0
Morgan Hill8/31/05−121.6879937.191791326.4Lolium multiflorum145.4
Morgan Hill8/31/05−121.6729637.193213286.4Lolium multiflorum146.4
Murrieta8/7/05−116.8467833.5010380525Bromus madritensis157.2
Needles2/9/06−114.6453734.882781586Schizmus barbatus250.72.5
Newport Beach8/8/05−117.8461333.607581731Bromus madritensis146.0
Norco8/9/05−117.5405833.974182169.6Bromus madritensis154.4
Norco8/7/05−117.5219133.949402600Bromus madritensis137.1
Orland7/7/05−122.2167039.7404373Digitaria sanguinalis162.4
Owens Valley6/5/05−118.3315837.300571223unk annual grass156.6
in Owens Valley8/1/05−118.3950037.363331263Bromus161.4
Palo Verde2/9/06−114.7239033.345727910Schizmus barbatus162.0
Paramint Springs7/12/05−117.4501236.337525263Schizmus barbatus155.9
Perris3/17/05−117.3535133.80392605Amsinckia menziesii154.1
Perris8/7/05−117.3337133.784296788Bromus madritensis150.3
Perris8/7/05−117.3053333.764306646Bromus diandrus156.1
Portola Valley9/15/05−122.2244037.40468182Lolium multiflorum149.8
Pt. Reyes Station8/15/05−122.9481738.033573632unk annual grass156.4
Rancho Mirage7/13/05−116.4061833.78547971Schizmus barbatus135.4
Racho Santa Margarita8/8/05−117.6198133.659733431Bromus diandrus137.6
Red Bluff7/6/05−122.1775240.2074892Avena barbata155.0
Redding7/6/05−122.3133540.596921808Avena barbata155.6
Redwood City9/18/05−122.2000837.481943Lolium multiflorum127.5
Rialto8/9/05−117.3407234.107753610unk annual grass142.2
Riverside3/17/05−117.3215433.93628436Bromus madritensis142.9
Rohnerville7/7/05−124.1116840.5395515Avena barbata162.7
Rosewood7/72005−122.4872340.2622824427Avena barbata156.3
Rubidoux3/17/05−117.3876633.98448279Avena fatua157.6
Salton City2/9/06−115.9405533.27105−601Schizmus barbatus156.1
San Bernardino8/9/05−117.2963734.110583220unk annual grass131.0
San Bernardino7/13/05−117.2899034.108633120Avena512.92.4
San Clemente8/8/05−117.5955933.419201120Bromus hordeaceus148.8
San Jose8/31/05−121.7523537.22027161Lolium multiflorum142.3
San Lucas7/10/05−121.0113236.144351443Avena154.4
San Miguel7/10/05−120.6942335.747851811Avena155.6
Santa Barbara10/14/05−119.7291733.9911121448Avena155.1
Santa Barbara10/14/05−119.6833334.01667545Avena160.3
Santa Clarita7/9/05−118.5590234.357024194Avena134.2
Santa Maria7/10/05−120.4066334.997821267Avena153.0
Sausalito7/15/05−122.5239637.860282198Avena fatua157.4
Shaver Lake7/27/05−119.3143637.124051242unk annual grass155.7
South Gate8/8/05−118.2192033.9527330Bromus madritensis110.3
Stovepipe Wells7/12/05−117.0490236.63032−295Schizmus barbatus150.9
Temecula8/7/05−117.2093733.505534654Bromus diandrus154.6
Temecula8/7/05−117.1488733.474694092Bromus madritensis143.8
Tustin8/8/05−117.6987033.756072707Bromus diandrus140.6
Vernon8/8/05−118.2385033.99487550Avena fatua1−14.3
Vidal Junction2/9/06−114.6476034.2648537410Schizmus barbatus154.3
Warner Springs7/2/05−116.5666733.3500014843.2Avena158.8
Westminister8/5/05−117.9900833.76538170Bromus hordeaceus122.4
Woodside9/12/05−122.2959237.46197135Lolium multiflorum150.4
Woodside9/12/05−122.2913837.464221683.2Lolium multiflorum141.7
Woodside9/12/05−122.2902937.464671723.2Lolium multiflorum125.5
Woodside9/12/05−122.2852737.46197203Lolium multiflorum147.9

[26] Within urban areas, Δg was substantially lower and more variable. For example, in the Los Angeles Basin, Δg ranged from −14.3 to 60.5‰, with a mean of 27.7 ± 20.0‰ (Table 2). Samples collected near the center of the Los Angeles metropolis, including those near the cities of Vernon, South Gate, Bellflower, Buena Park, and Westminister, had a mean of 19.1 ± 2.1‰. Relative to the mean Δg from the north coast (and assuming Ca equal to 380 ppm), these cities near the center of the Los Angeles metropolis had 15.1 ± 5.5 ppm of locally added CO2 (weighted by diurnally and seasonally varying photosynthetic C uptake). In contrast, urban and suburban samples collected near the coast to the west and south of Los Angeles had markedly less exposure to fossil fuel CO2. The mean Δg of samples from Newport Beach, Dana Point, and Laguna Woods was 48.3 ± 3.1‰. The relative depletion of Δg for these coastal samples as compared with those from Central and Northern California may reflect (1) local fossil fuel sources that offset the cleansing impact of onshore winds and (2) entrainment of fossil fuel CO2 from Los Angeles into the land-sea circulation and subsequent along-shore transport and onshore flow [e.g., Riley et al., 2005].

Table 2. Predicted and Measured Mean (SD) Δg and GPP-Weighted Cf Inferred From Measured Δg for Four Regions: North Coast, San Francisco Bay Area, Los Angeles Basin, and Central Valleya
 North CoastSan FranciscoCentral ValleyLos Angeles
  • a

    Measured means in Los Angeles do not include February samples measured near freeways, as described in text.

Measured mean (SD) Δ14C (‰)59.9 (2.5)44.0 (10.9)48.7 (1.9)27.7 (20.0)
Predicted mean (SD) Δ14C (‰)59.3 (0.2)39.4 (3.9)46.8 (3.0)27.6 (2.4)
Predicted mean (SD) GPP-weighted Cf (ppm)0.3 (0.08)6.1 (1.1)4.8 (0.9)13.7 (0.4)

[27] In the San Francisco Bay region, measured Δg ranged from 25.5 to 57.4‰, with a mean of 44.0 ± 10.1‰. Two samples collected along the peninsula (Redwood City and Woodside) had values below 28‰ and one sample collected along the transportation corridor near the Sacramento Delta had a Δ14C of 37.0‰. Samples from grassland parks south of San Jose were also relatively depleted with a mean of 44.7 ± 2.1‰, probably as a result of the trapping of fossil fuel CO2 from San Jose between the two roughly parallel southwest to northeast coastal mountain ranges.

[28] Within the Central Valley, measured Δg was lowest directly to the east of the San Francisco Bay area and increased both to the north and south. These gradients are consistent with transport and mixing of San Francisco Bay area and Sacramento fossil fuel CO2 sources within the valley. The mean of samples collected to the east of the Bay Area (and including those collected near Lodi, Byron, Mariposa, Merced, and Fresno) was 47.7 ± 1.9‰. Samples collected from the northern part of the Central Valley (including samples near Redding, Rosewood, Mendocino, Orland, Red Bluff, and Mill Creek) were considerably more enriched in 14C, with a mean of 58.6 ± 3.7‰.

[29] There were strong gradients in Δg for transects starting in the Central Valley and terminating in the Sierra. The first such transect started near Corcoran (elev. 55 m) in the middle of the Central Valley and ended near Kaiser Pass (elev. 2136 m). Δg increased monotonically from 50.3 to 60.7‰ for 6 samples collected across this elevation gradient. A second transect further south ran from Arvin to Welden, and spanned about a 20‰ gradient. The increase in Δg with elevation along the western slope of the Sierra was likely caused by dilution of Central Valley air (with high fossil fuel CO2 mixing ratios) with air from the free troposphere. Diurnal upslope and downslope flows along the western slope of the Sierra also probably influenced Δg [Dillon et al., 2002]. Several studies have reported analogous elevation patterns for air pollutants transported from the Los Angeles Basin, including large nitrogen deposition [Fenn and Bytnerowicz, 1997; Fenn et al., 2000] and ozone concentration [Lee et al., 2003; Miller et al., 1986] gradients across the San Bernardino Mountains. This pollution gradient has caused significant and well-documented changes in the physiology and ecology of montane forests in this region [Arbaugh et al., 1998; Fenn et al., 1996; Grulke et al., 1998; Grulke and Balduman, 1999; Grulke et al., 2001; Miller et al., 1998].

3.2. Predicted Δ14C of C3 Grasses

[30] Model estimates of Δg (Figure 4) captured much of the observed spatial variability (Figure 3). Care should be taken in comparing these two contour plots because of difficult-to-quantify uncertainties introduced from our interpolation approach. Predicted mean values of Δg for Los Angeles, San Francisco, the Central Valley, and the North Coast were similar to observed values (Table 2).

Figure 4.

Predicted C3 Δ14C (Δg, ‰) averaged over the growing season, calculated as the gross primary production-weighted Δ14C of surface-layer atmospheric CO2. Background color interpolation was generated using the same method as Figure 3.

[31] Within the Los Angeles basin, the eastward propagation of the fossil fuel CO2 plume from Los Angeles was relatively well represented. Within the San Francisco Bay region, mean predicted and measured Δg differed by 5‰. The model over predicted the fossil fuel CO2 mixing ratios (and depletion of Δg) to the east and north of the San Francisco Bay region within the Central Valley. It was not possible from our simulations to determine if the over prediction occurred because of errors associated with transport processes or CO2 emissions estimates.

[32] Model estimates of annual mean Δ14C of near-surface atmospheric CO2a; not shown) were almost the same as those of predicted Δg. In Los Angeles, where Δa and Δg were largely impacted by local emissions, the covariance of nighttime fossil fuel CO2 emissions and small PBL depths led to lower annual mean Δa than Δg. Similar mechanisms impacted Central Valley Δa and Δg. Also, some of the fossil fuel CO2 emitted during the daytime in Central Valley urban areas moves laterally into rural parts of the Central Valley during the evening and night, further enhancing the differences between Δa and Δg in this region. To illustrate these interactions, the annual average difference between midnight and noon surface fossil fuel CO2 mixing ratios were 0.02, 0.1, 2.8, and 1.4 ppm in the Coastal North, San Francisco Bay, Los Angeles, and Central Valley regions, respectively. The relatively higher nighttime fossil fuel CO2 mixing ratios in Los Angeles and the Central Valley are consistent with the lower predicted value for annual mean Δa as compared with Δg in these regions.

[33] The impacts of boundary layer dynamics on the relationship between fossil fuel CO2 emissions, Δa, and Δg are substantial. For example, during the summer in Los Angeles, when fossil fuel CO2 emissions are relatively high, the Pacific High often causes low daytime boundary layer depths. This lowering of the effective atmospheric mixing volume enhances the impact of fossil fuel emissions on Δa and Δg. Concurrent changes in mixing rates between the PBL and overlying free troposphere may also be important. Figure 5 illustrates the relationship between monthly mean noon fossil fuel CO2 emissions, PBL depth, Cf, and Δa for a single point (34 °N, 118 °W) in the Los Angeles Basin. For this point, there is a strong correlation between Cf, PBL depth, and Δa. To illustrate the impact of PBL depth, we compared April and August conditions using monthly means. Between these months, fossil fuel CO2 emissions increased about 0.8%, midday PBL depth decreased about 60%, Cf increased by ∼8 ppm (170%), and Δa decreased by 19‰ (from 47‰ to 28‰). This simple comparison indicates that, in Los Angeles, a substantial portion of the changes in Cf and Δa between these months resulted from changes in PBL properties. Therefore, the impact of intra-annual variations in PBL dynamics must be accounted for when using Δg to infer fossil fuel CO2 emissions.

Figure 5.

Comparison for a single model point in Los Angeles (34°N, 118°W) of midday fossil fuel CO2 emissions (left axis), fossil fuel CO2 mixing ratio (left axis), PBL depth (first right axis), and Δ14C of near-surface air (second right axis, Δa). Δa is largely in phase with PBL depth and out of phase with fossil fuel CO2 emissions.

[34] Measured Δg were more spatially heterogeneous than predicted Δg in urban areas (Table 2), likely because of spatial resolution limits associated with the meteorological model and the fossil fuel emissions inventory, both of which had a 36 km horizontal resolution. This relatively coarse spatial resolution would not resolve many topographical features, such as small valleys, which might trap fossil fuel CO2. Also, fine scale CO2 emissions (e.g., associated with freeways and industrial point sources) were not resolved in our emissions estimates. However, the mean predictions accurately reproduced the patterns in measured Δg, with the means differing by 0.6, 4.6, 0.1, and 1.9‰ in the North Coast, San Francisco Bay, Los Angeles, and Central Valley regions, respectively (Table 2). The mean predicted GPP-weighted fossil fuel CO2 mixing ratios are 0.3 (0.08), 6.1 (1.1), 13.7 (0.4), and 4.8 (0.9) ppm for the same regions.

3.3. Near-Surface Fossil-Fuel CO2 Mixing Ratios Versus Local Emissions

[35] The index I (equation (3)) qualitatively describes the extent to which factors (e.g., transport, local mixing conditions) other than local emissions effect local near-surface fossil fuel CO2 mixing ratios. Since fossil fuel CO2 is a good tracer (on moderate spatial and temporal scales) of primary combustion-generated pollutants, this index may also be helpful in attributing other air pollution issues (e.g., particulate matter, tropospheric O3) to local versus distant sources.

[36] Predicted values of I were relatively larger in portions of the western Central Valley, Sierra Mountains, Owens Valley, and Northern California (Figure 6). Large values of I in the Sierra Mountains and Owens Valley occurred because very little fossil fuel CO2 is emitted in these areas, yet near-surface fossil fuel CO2 mixing ratios can become elevated from CO2 transport from the urban air basins and the Central Valley. Fossil fuel CO2 was also predicted to move from Los Angeles down the Coachella and Imperial Valleys, where fossil fuel emissions are lower. A second Southern California region just east of San Diego also had relatively larger values of I, again resulting from transport from San Diego and relatively low local emissions. These results indicate that a number of areas in California are exposed to higher primary air pollution concentrations than would result from local emissions alone.

Figure 6.

The index, I, indicating the ratio of local fossil fuel CO2 (normalized by the state-wide average mixing ratio) to local fossil fuel CO2 emissions (normalized by the state-wide total inventory). Areas with I larger than one have fossil fuel CO2 contributions from other regions in the state (that exceed what would be expected from local emissions). Background color interpolation was generated using the same method as Figure 3.

3.4. Exit Pathways for California's Fossil Fuel CO2

[37] A three-dimensional representation of fossil fuel CO2 leaving the California airspace is shown in Figure 7. On an annual basis, a large fraction of fossil fuel CO2 exited California to the south and within the marine boundary layer (Figure 8a). A broad and more diffuse plume exited to the east, with relative maxima at latitudes corresponding approximately to Los Angeles, the middle of the Central Valley, and the San Francisco Bay area (Figure 8b). Annually, about 21, 39, 35, and 5% of fossil fuel CO2 left the California airspace to the north, east, south, and west, respectively. We note that, because of the limited boundary of our simulation domain, our analysis framework is unable to characterize whether CO2 exiting in any particular direction could be recirculated back into the California airspace. Given the large-scale atmospheric circulation associated with the Pacific High that results in transport of CO2 from North America to Hawaii [Lintner et al., 2006], we believe this recirculation to be small for air exiting to the west and south.

Figure 7.

Cumulative annual fossil fuel CO2 transport out of California. The figure shows contour plots on each vertical face of the cube surrounding California.

Figure 8.

Cumulative annual fossil fuel CO2 transport out of California for the (a) south and (b) east vertical faces of the cube surrounding California. Note the different altitude scales and contour intervals.

[38] The predicted large fraction of fossil fuel CO2 leaving California to the south has important implications for continental scale inversions used to infer fossil fuel and ecosystem CO2 fluxes. Future measurements of CO2 and its isotopes on the islands offshore from southern California could help better characterize this transport pathway. East-west aircraft transects in the marine boundary layer near the U.S. - Mexico border (and extending several hundred kilometers offshore) would also be helpful in this regard. A second, and smaller, predicted southward flux of fossil fuel CO2 occurred further east (approximately between 114°W and 115°W), also primarily within the boundary layer (Figure 8a). This portion of the flux resulted from eastward transport of fossil fuel CO2 out of Los Angeles and San Diego, and then southward transport down the Coachella and Imperial Valleys. Unfortunately, we lacked measurements in these valleys to corroborate model predictions.

[39] Some of the flux moving eastward out of the Los Angeles Basin escaped directly toward Arizona, resulting in coherent fossil fuel CO2 plumes centered just north of the Mexican border (Figure 8b). Much of the remaining eastward flux manifested as a broad and more diffuse plume over the Sierra Nevada. Peaks in this broad plume associated with Los Angeles (between about 33° and 36° N) and the Central Valley (between about 35° and 36° N) were discernible. In contrast to the southward fossil fuel CO2 plume, the eastward plume extended further upward into the atmosphere. Of the 21% of fossil fuel CO2 that left the California airspace via the north, most was centered on 122°W (approximately due north of the San Francisco Bay region). Very little fossil fuel CO2 (5%) exited the airspace to the west.

[40] There were distinct seasonal patterns of fossil fuel CO2 fluxes in the four compass directions (Figure 9). The fraction of the monthly flux leaving toward the south had a maximum in November, with a secondary peak in March. Northward fluxes peaked in December and January while the westward flux peaked one month later in February. The eastward fluxes peaked in the summer and were relatively smaller during winter. Between November and March, the northward fluxes were roughly out of phase with the southward fluxes, implying a trade-off in transport patterns during these months.

Figure 9.

Percent of monthly fossil fuel CO2 leaving the California airspace in each of the four directions (left axis) and the number of Santa Ana days each month (NS) predicted from the NCEP reanalysis data (right axis). Westward CO2 flux and NS were positively correlated (r = 0.70; p = 0.01) and eastward CO2 flux and NS were negatively correlated (r = −0.56; p = 0.06).

[41] The fraction of each month's fossil fuel CO2 flux leaving toward the east and west and the monthly number of Santa Ana wind days (NS) predicted from the NCEP reanalysis data were correlated; correlation coefficients (p value) were: −0.56 (0.06) and 0.70 (0.01) for the east and west directions, respectively. Almost none of the annual cumulative flux exited toward the west outside of the Santa Ana winds season. Our one-year simulation (during 2004–2005) does not allow us to directly infer interannual variability in the directional partitioning of fossil fuel CO2 fluxes out of the state. However, since intra-annual variability in partitioning was correlated to Santa Ana wind conditions, we conclude from our simple interannual NS estimates (Figure 2) that the relative proportion of fossil fuel CO2 leaving California in each of the four directions can vary substantially between years. More work needs to be performed to characterize the impact of these short duration and intermittent events on atmospheric transport of fossil fuel derived CO2.

[42] These results are also relevant to tropospheric air quality issues and for characterizing the net climate impact of fossil fuel combustion. Tropospheric air quality can be deleteriously impacted by fossil fuel combustion, with consequent impacts to human health [Peel et al., 2005; Schwartz et al., 1996], vegetation [Davison and Barnes, 1998], precipitation [Rosenfeld and Givati, 2006], the Earth's radiation budget [Ramanathan et al., 2001], and snow albedo and the timing of snowmelt [Flanner et al., 2007]. Although atmospheric pollutant generation and transport has been the focus of many California air quality studies [e.g., Blumenthal et al., 1978; Carreras-Sospedra et al., 2006; Croes and Fujita, 2003; Dillon et al., 2002; Edinger, 1973; Lu and Turco, 1995; McElroy and Smith, 1986; Rinehart et al., 2006], much less is known about transport of pollutants out of the state. Characterizing whether pollutants generated in California move toward Arizona, Nevada, the Pacific Ocean, or Mexico is important for characterizing the broader implications of California's fossil fuel combustion, including consequences for aerosol radiative forcing and the albedo of snow in the Sierra-Nevada and Rocky Mountain systems. For example, California emissions of black carbon aerosols, which can have a relatively short atmospheric residence time, will have a different impact on climate if they are lofted above the bright Arizona desert as compared with transport over the much darker Pacific Ocean.

4. Conclusions

[43] Our prediction that 21, 39, 35, and 5% of California's fossil fuel CO2 exits to the north, east, south, and west, respectively, has several important implications. Proposals have been made to use CO2 measurements on the coastal boundaries of the continental U.S. to infer CO2 emissions and exchanges [Wofsy and Harris, 2002]. Our estimate that a substantial portion of California's fossil fuel CO2 emissions exit California toward the south implies that flask networks need to sample this plume. Since there are relatively few islands in the southward transport path, regular measurements on ships or buoys may be required. Further, many current global and regional models do not accurately simulate boundary layer development and exchanges with the free troposphere [Stephens et al., 2007], processes critical to interpreting these proposed measurements.

[44] Our results are relevant to other atmospheric components of interest. Pollutants generated concurrently with CO2 or from atmospheric photochemical reactions will be impacted by the transport patterns described here. Issues relevant to tropospheric air quality include characterizing southward transport into Mexico of ozone, NOx, particulate matter, and acid compounds, and how these fluxes impact local ecosystems, visibility, and human health.

[45] Model predictions indicated that some areas within California had higher near-surface fossil fuel CO2 mixing ratios than would be expected from local emissions alone. The additional fossil fuel CO2 loading resulted from transport of fossil fuel CO2 generated in the San Francisco Bay, Sacramento, and Los Angeles air basins. Similar behavior of other contaminants co-emitted with fossil fuel CO2, or secondary pollutants associated with combustion byproducts, would analogously be expected to contribute to air pollution in these areas.

[46] It is likely that ecosystem respiratory and photosynthetic CO2 fluxes also have substantial southward flux components. Finally, given the significant correlation between southern California wildfires and Santa Ana winds, it is likely that a large fraction of wildfire CO2 exits the California airspace to the south. Overall, our results indicate that the paradigm that California's air pollutants travel predominantly from west to east across the continental U.S. needs to be reexamined.


[47] We would like to thank the following people for collecting plant samples for us: B. Adamus, P. Adamus, D. Baldocchi, M. S. Carbone, A. M. Delaney, L. Feinstein, D. T. Fischer, M. L. Fischer, J. G. Hatch, F. M. Kai, P. G. Kennedy, L. E. Koteen, E. A. Lyons, M. and R. Lyons, R. Redmond, A. V. Rocha, D. L. Serio, J. K. Shake, M. V. Talluto, S. E. Trumbore, and S. Weiss. J. G. Hatch was supported by a Summer Undergraduate Research Education internship at LBNL through the DOE Global Change Education Program. We also wish to thank M. V. Talluto, P. A. Bowler, and M. A. Elvin for identifying grass samples; and Y. Fung for characterizing the Δ14C of respiration. NCEP Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.cdc.noaa.gov/. We gratefully acknowledge support from NASA (NNG05GD126), the Office of Science, U.S. Department of Energy (DE-AC02-05CH11231), and the National Science Foundation (0620176).