ALBERT

Description: Atmospheric column-averaged mole fractions of carbon dioxide (XCO2) at 53 locations around the world were derived from aircraft measurements covering the altitude range of about 1–10 km. We used CO2 vertical profile measurements from three major carbon cycle programs, a global climatological data set of air number density profiles and tropopause height for calculating XCO2 for the period of 2007–2009. Vertical profiles of the CO2 mixing ratio are complemented by tall tower data up to 400 m from the earth's surface and by simulated profiles in the stratosphere from a chemistry-transport model. The amplitude of the seasonal cycle of calculated XCO2 values shows clear latitudinal dependence, and the amplitude decreases from about 10 ppm at high latitudes in the Northern Hemisphere to at most 2 ppm in the tropics and the Southern Hemisphere. The uncertainties of XCO2 were estimated from assumptions about CO2 profiles for each flight. Typically, uncertainties were less than 1 ppm; thus, this data set is within the level of uncertainty needed for primary validation of XCO2 measurements by the Greenhouse gases Observing SATellite (GOSAT) and by future satellite missions for monitoring greenhouse gases.

Description: A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of transport model errors in current inverse systems. Future inversions should include more accurately prescribed observation covariances matrices in order to limit the impact of transport model errors on estimated methane fluxes.

Description: Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

Description: A modified cumulus convection parametrisation scheme is presented. This scheme computes the mass of air transported upward in a cumulus cell using conservation of moisture and a detailed distribution of convective precipitation provided by a reanalysis dataset. The representation of vertical transport within the scheme includes entrainment and detrainment processes in convective updrafts and downdrafts. Output from the proposed parametrisation scheme is employed in the National Institute for Environmental Studies (NIES) global chemical transport model driven by JRA-25/JCDAS reanalysis. The simulated convective precipitation rate and mass fluxes are compared with observations and reanalysis data. A simulation of the short-lived tracer 222Rn is used to further evaluate the performance of the cumulus convection scheme. Simulated distributions of 222Rn are evaluated against observations at the surface and in the free troposphere, and compared with output from models that participated in the TransCom-CH4 Transport Model Intercomparison. From this comparison, we demonstrate that the proposed convective scheme in general is consistent with observed and modeled results.

Description: We introduce the effect of enhanced ionospheric conductivity into a low-order, physics-based nonlinear model of the nightside magnetosphere called WINDMI. The model uses solar wind and interplanetary magnetic field (IMF) parameters from the ACE satellite located at the L1 point to predict substorm growth, onset, expansion and recovery measured by the AL index roughly 50–60 min in advance. The dynamics introduced by the conductivity enhancement into the model behavior is described, and illustrated through using synthetically constructed solar wind parameters as input. We use the new model to analyze two well-documented isolated substorms: one that occurred on 31 July 1997 from Aksnes et al. (2002), and another on 13 April 2000 from Huang et al. (2004). These two substorms have a common feature in that the solar wind driver sharply decreases in the early part of the recovery phase, and that neither of them are triggered by northward turning of the IMF Bz. By controlling the model parameters such that the onset time of the substorm is closely adhered to, the westward auroral electrojet peaks during substorm expansion are qualitatively reproduced. Furthermore, the electrojet recovers more slowly with enhanced conductivity playing a role, which explains the data more accurately.

Description: Atmospheric CO2 inversions estimate surface carbon fluxes from an optimal fit to atmospheric CO2 measurements, usually including prior constraints on the flux estimates. Eleven sets of carbon flux estimates are compared, generated by different inversions systems that vary in their inversions methods, choice of atmospheric data, transport model and prior information. The inversions were run for at least 5 yr in the period between 1990 and 2009. Mean fluxes for 2001–2004, seasonal cycles, interannual variability and trends are compared for the tropics and northern and southern extra-tropics, and separately for land and ocean. Some continental/basin-scale subdivisions are also considered where the atmospheric network is denser. Four-year mean fluxes are reasonably consistent across inversions at global/latitudinal scale, with a large total (land plus ocean) carbon uptake in the north (−3.3 Pg Cy−1 (±0.6 standard deviation)) nearly equally spread between land and ocean, a significant although more variable source over the tropics (1.6 ± 1.0 Pg Cy−1) and a compensatory sink of similar magnitude in the south (−1.4 ± 0.6 Pg Cy−1) corresponding mainly to an ocean sink. Largest differences across inversions occur in the balance between tropical land sources and southern land sinks. Interannual variability (IAV) in carbon fluxes is larger for land than ocean regions (standard deviation around 1.05 versus 0.34 Pg Cy−1 for the 1996–2007 period), with much higher consistency amoung the inversions for the land. While the tropical land explains most of the IAV (stdev ∼ 0.69 Pg Cy−1), the northern and southern land also contribute (stdev ∼ 0.39 Pg Cy−1). Most inversions tend to indicate an increase of the northern land carbon uptake through the 2000s (around 0.11 Pg Cy−1), shared by North America and North Asia. The mean seasonal cycle appears to be well constrained by the atmospheric data over the northern land (at the continental scale), but still highly dependent on the prior flux seasonality over the ocean. Finally we provide recommendations to interpret the regional fluxes, along with the uncertainty estimates.

Description: The Indian Ocean (44° S–30° N) plays an important role in the global carbon cycle, yet remains one of the most poorly sampled ocean regions. Several approaches have been used to estimate net sea–air CO2 fluxes in this region: interpolated observations, ocean biogeochemical models, atmospheric and ocean inversions. As part of the RECCAP (REgional Carbon Cycle Assessment and Processes) project, we combine these different approaches to quantify and assess the magnitude and variability in Indian Ocean sea–air CO2 fluxes between 1990 and 2009. Using all of the models and inversions, the median annual mean sea–air CO2 uptake of −0.37 ± 0.06 Pg C yr–1, is consistent with the −0.24 ± 0.12 Pg C yr–1 calculated from observations. The fluxes from the Southern Indian Ocean (18° S–44° S; −0.43 ± 0.07 Pg C yr–1) are similar in magnitude to the annual uptake for the entire Indian Ocean. All models capture the observed pattern of fluxes in the Indian Ocean with the following exceptions: underestimation of upwelling fluxes in the northwestern region (off Oman and Somalia), over estimation in the northeastern region (Bay of Bengal) and underestimation of the CO2 sink in the subtropical convergence zone. These differences were mainly driven by a lack of atmospheric CO2 data in atmospheric inversions, and poor simulation of monsoonal currents and freshwater discharge in ocean biogeochemical models. Overall, the models and inversions do capture the phase of the observed seasonality for the entire Indian Ocean but over estimate the magnitude. The predicted sea–air CO2 fluxes by Ocean BioGeochemical Models (OBGM) respond to seasonal variability with strong phase lags with reference to climatological CO2 flux, whereas the atmospheric inversions predict an order of magnitude higher seasonal flux than OBGMs. The simulated interannual variability by the OBGMs is weaker than atmospheric inversions. Prediction of such weak interannual variability in CO2 fluxes by atmospheric inversions was mainly caused by lack of atmospheric data in the Indian Ocean. The OBGM models suggest a small strengthening of the sink over the period 1990–2009 of −0.01 Pg C decade–1. This is inconsistent with the observations in the southwest Indian Ocean that shows the growth rate of oceanic pCO2 was faster than the observed atmospheric CO2 growth, a finding attributed to the trend of the Southern Annual Mode (SAM) during the 1990s.

Description: Air-sea CO2 fluxes over the Pacific Ocean are known to be characterized by coherent large-scale structures that reflect not only ocean subduction and upwelling patterns, but also the combined effects of wind-driven gas exchange and biology. On the largest scales, a large net CO2 influx into the extra-tropics is associated with a robust seasonal cycle, and a large net CO2 efflux from the tropics is associated with substantial inter-annual variability. In this work, we have synthesized estimates of the net air-sea CO2 flux from a variety of products drawing upon a variety of approaches in three sub-basins of the Pacific Ocean, i.e., the North Pacific extra-tropics (18° N–66° N), the tropical Pacific (18° S–18° N), and the South Pacific extra-tropics (44.5° S–18° S). These approaches include those based on the measurements of CO2 partial pressure in surface seawater (pCO2sw), inversions of ocean interior CO2 data, forward ocean biogeochemistry models embedded in the ocean general circulation models (OBGCMs), a model with assimilation of pCO2sw data, and inversions of atmospheric CO2 measurements. Long-term means, inter-annual variations and mean seasonal variations of the regionally-integrated fluxes were compared in each of the sub-basins over the last two decades, spanning the period from 1990 through 2009. A simple average of the long-term mean fluxes obtained with surface water pCO2 diagnostics and those obtained with ocean interior CO2 inversions are –0.47 ± 0.13 Pg C yr–1 in the North Pacific extra-tropics, +0.44 ± 0.14 Pg C yr–1 in the tropical Pacific, and –0.37 ± 0.08 Pg C yr–1 in the South Pacific extra-tropics, where positive fluxes are into the atmosphere. This suggests that approximately half of the CO2 taken up over the North and South Pacific extra-tropics is released back to the atmosphere from the tropical Pacific. These estimates of the regional fluxes are also supported by the estimates from OBGCMs after adding the riverine CO2 flux, i.e., –0.49 ± 0.02 Pg C yr–1 in the North Pacific extra-tropics, +0.41 ± 0.05 Pg C yr–1 in the tropical Pacific, and –0.39 ± 0.11 Pg C yr–1 in the South Pacific extra-tropics. The estimates from the atmospheric CO2 inversions show large variations amongst different inversion systems, but their median fluxes are consistent with the estimates from climatological pCO2sw data and pCO2sw diagnostics. In the South Pacific extra-tropics, where CO2 variations in the surface and ocean interior are severely under-sampled, the difference in the air-sea CO2 flux estimates between the diagnostic models and ocean interior CO2 inversions is larger (0.18 Pg C yr–1). The range of estimates from forward OBGCMs is also large (−0.19 to −0.72 Pg C yr–1). Regarding inter-annual variability of air-sea CO2 fluxes, positive and negative anomalies are evident in the tropical Pacific during the cold and warm events of the El Niño Southern Oscillation in the estimates from pCO2sw diagnostic models and from OBGCMs. They are consistent in phase with the Southern Oscillation Index, but the peak-to-peak amplitudes tend to be higher in OBGCMs (0.40 ± 0.09 Pg C yr–1) than in the diagnostic models (0.27 ± 0.07 Pg C yr–1).

Description: Column-averaged volume mixing ratios of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were compared with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), and the National Institute for Environmental Studies (NIES). Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAK) and the shape of a priori profiles on the calculation of XCO2 based on aircraft measurements (aircraft-based XCO2). The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than 0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. In this study, two approaches were used to validate GOSAT products (Ver. 02.00). First, we performed a comparison of GOSAT data retrieved within ±2-degree or ±5-degree latitude/longitude boxes centered at each aircraft measurement site and aircraft-based data measured on a GOSAT overpass day (i.e. extraction of temporally matched cases). As this method resulted in no matched data for observation sites where no aircraft measurement was made on the GOSAT overpass day, we also attempted to validate GOSAT products by gap-filling the aircraft-based XCO2 time series through curve fitting. Both methods indicated that GOSAT XCO2 agreed well with aircraft-based XCO2, except that the former is negatively biased by 1–2 ppm with a standard deviation of 1–3 ppm.