Stratigraphic templates for ice core records of the past 1.5 million years

The international ice core community has a target to obtain continuous ice cores stretching back as far as 1.5 million years. This would provide vital data (including a CO2 profile) allowing us to assess ideas about the cause of the Mid-Pleistocene Transition (MPT). The European Beyond EPICA project and the Australian Million Year Ice Core project each plan to drill such a core in the region known as Little Dome C. Dating the cores will be challenging, and one approach will be to match some of the records obtained with existing marine sediment datasets, informed by similarities in the existing 800 kyr period. Water isotopes in Antarctica have been shown to closely mirror deepwater temperature, estimated from Mg / Ca ratios of benthic foraminifera, in a marine core on the Chatham Rise near to New Zealand. The dust record in ice cores resembles very closely a South Atlantic marine record of iron accumulation rate. By assuming these relationships continue beyond 800 ka, our ice core record could be synchronised to dated marine sediments. This could be supplemented, and allow synchronisation at higher resolution, by the identification of rapid millennial scale-events that are observed both in Antarctic methane records and in emerging records of planktic oxygen isotopes and alkenone sea surface temperature (SST) from the Portuguese Margin. Although published data remain quite sparse, it should also be possible to match 10Be from ice cores to records of geomagnetic palaeointensity and authigenic 10Be/9Be in marine sediments. However, there are a number of issues that have to be resolved before the ice core 10Be record can be used. The approach of matching records to a template will be most successful if the new core is in stratigraphic order, but should also provide constraints on disordered records, if used in combination with absolute radiogenic ages.

Retrieval of atmospheric CO₂ vertical profiles from ground-based near-infrared spectra

We evaluate vertical profile retrievals of CO2 from 0.02 cm−1 resolution ground-based near-infrared solar absorption spectra with the GFIT2 algorithm, using improved spectroscopic line lists and line shapes. With these improvements, CO2 profiles were obtained from sequential retrievals in five spectral windows with different vertical sensitivities using synthetic and real spectra. A sensitivity study using synthetic spectra shows that the leading source of uncertainty in the retrieved CO2 profiles is the error in the a priori temperature profile, even with 3-hourly reanalysis a priori profiles. A 2 ∘C error in the temperature profile in the lower troposphere between 0.6 and 0.85 atm causes deviations in the retrieved CO2 profiles that are larger than the typical vertical variations of CO2. To distinguish the effect of errors in the a priori meteorology and trace gas concentration profiles from those in the instrument alignment and spectroscopic parameters, we retrieve CO2 profiles from atmospheric spectra while using an a priori profile built from coincident AirCore, radiosonde, and surface in situ measurements at the Lamont, Oklahoma (USA), Total Carbon Column Observing Network station. In those cases, the deviations in retrieved CO2 profiles are also larger than typical vertical variations of CO2, suggesting that remaining errors in the forward model limit the accuracy of the retrieved profiles. Implementing a temperature retrieval or correction and quantifying and modeling an imperfect instrument alignment are critical to improve CO2 profile retrievals. Without significant advances in modeling imperfect instrument alignment, and improvements in the accuracy of the temperature profile, the CO2 profile retrieval with GFIT2 presents no clear advantage over scaling retrievals for the purpose of ascertaining the total column.

Retrieval of atmospheric CO₂ vertical profiles from ground-based near-infrared spectra

We evaluate vertical profile retrievals of CO2 from 0.02 cm−1 resolution ground-based near-infrared solar absorption spectra with the GFIT2 algorithm, using improved spectroscopic linelists and line shapes. With these improvements, CO2 profiles were obtained from sequential retrievals in five spectral windows with different vertical sensitivities. A sensitivity study using synthetic spectra shows that the leading source of uncertainty in the retrieved CO2 profiles is the error in the a priori temperature profile, even with 3-hourly reanalysis a priori profiles. A 2 °C error in the temperature profile in the lower troposphere between 0.6 and 0.85 atm causes deviations in the retrieved CO2 profiles that are larger than the typical vertical variations of CO2. To distinguish the effect of errors in the a priori meteorology and trace gas concentration profiles from those in the instrument alignment and spectroscopic parameters, we retrieve CO2 profiles from atmospheric spectra while using an a priori built from coincident AirCore, radiosonde, and surface in situ measurements at the Lamont, Oklahoma (USA) Total Carbon Column Observing Network station. In those cases, the deviations in retrieved CO2 profiles are also larger than typical vertical variations of CO2, suggesting that remaining errors in the forward model limit the accuracy of the retrieved profiles. Implementing a temperature retrieval or correction, and quantifying and modeling an imperfect instrument alignment, are critical to improve CO2 profile retrievals. Without significant advances in modeling imperfect instrument alignment, and improvements in the accuracy of the temperature profile, the CO2 profile retrieval with GFIT2 presents no clear advantage over scaling retrievals for the purpose of ascertaining the total column.

GFIT2: an experimental algorithm for vertical profile retrieval from near-IR spectra

An algorithm for retrieval of vertical profiles from ground-based spectra in the near IR is described and tested. Known as GFIT2, the algorithm is primarily intended for CO2, and is used exclusively for CO2 in this paper. Retrieval of CO2 vertical profiles from ground-based spectra is theoretically possible, would be very beneficial for carbon cycle studies and the validation of satellite measurements, and has been the focus of much research in recent years. GFIT2 is tested by application both to synthetic spectra and to measurements at two Total Carbon Column Observing Network (TCCON) sites. We demonstrate that there are approximately 3° of freedom for the CO2 profile, and the algorithm performs as expected on synthetic spectra. We show that the accuracy of retrievals of CO2 from measurements in the 1.61μ (6220 cm−1) spectral band is limited by small uncertainties in calculation of the atmospheric spectrum. We investigate several techniques to minimize the effect of these uncertainties in calculation of the spectrum. These techniques are somewhat effective but to date have not been demonstrated to produce CO2 profile retrievals with sufficient precision for applications to carbon dynamics. We finish by discussing ongoing research which may allow CO2 profile retrievals with sufficient accuracy to significantly improve the scientific value of the measurements from that achieved with column retrievals.

GFIT2: an experimental algorithm for vertical profile retrieval from near IR spectra

An algorithm for retrieval of vertical profiles from ground-based spectra in the near IR is described and tested. Known as GFIT2, the algorithm is primarily intended for CO2, and is used exclusively for CO2 in this paper. Retrieval of CO2 vertical profiles from ground-based spectra is theoretically possible, would be very beneficial for carbon cycle studies and the validation of satellite measurements, and has been the focus of much research in recent years. GFIT2 is tested by application both to synthetic spectra, and to measurements at two TCCON sites. We demonstrate that there are approximately 3° of freedom for the CO2 profile, and the algorithm performs as expected on synthetic spectra. We show that the accuracy of retrievals of CO2 from measurements in the 1.6 μ spectral band is limited by small uncertainties in calculation of the atmospheric spectrum. We investigate several techniques to minimize the effect of these uncertainties in calculation of the spectrum. These techniques are somewhat effective, but to date have not been demonstrated to produce CO2 profile retrievals superior to existing techniques for retrieval of column abundance. We finish by discussing on-going research which may allow CO2 profile retrievals with sufficient accuracy to significantly improve on the results of column retrievals, both in total column abundance and in profile shape.

The use of IASI data to identify systematic errors in the ECMWF forecasts of temperature in the upper stratosphere

Since data from the Infrared Atmospheric Sounding Interferometer (IASI) became available in 2007, a number of papers have appeared in the literature which have reported relatively large discrepancies between IASI spectra and forward calculations in the centre of the CO2 Q-branch at 667 cm−1. In this paper we show that these discrepancies are primarily due to errors in the temperature profiles used in the forward calculations. In particular, we have used forecasts of temperature profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) to demonstrate that, for the case study considered in this paper, these profiles are affected by systematic errors of the order of ≈10 K at the level of the stratopause. To derive the magnitude and the spatial location of the systematic errors in the temperature profile, we have carried out forward/inverse calculations for a number of clear-sky, daytime, IASI tropical soundings over the sea. The forward calculations have been performed using atmospheric state vectors which have been obtained either from the direct inversion of the IASI radiances or from space-time co-located profiles derived from radiosonde observations and from the ECMWF model. To rule out any effect due to the accuracy of the forward model, we have performed the forward calculations using two independent models. The sensitivity of the temperature biases to the variability of the CO2 profile and to spectroscopy errors has also been studied.

The use of IASI data to identify systematic errors in the ECMWF temperature analysis in the upper stratosphere

Since data from the Infrared Atmospheric Sounding Interferometer (IASI) became available in 2007, a number of papers have appeared in the literature which have reported relatively large discrepancies between IASI spectra and forward calculations in the centre of the CO2 Q-branch at 667 cm−1. In this paper we argue that these discrepancies are primarily due to errors in the temperature profiles used in the forward calculations. In particular, we have used temperature profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis to demonstrate that for the case study considered in this paper, these profiles are affected by systematic errors of the order of 7–10 K at the level of the stratopause. To derive the magnitude and the spatial location of the systematic errors in the temperature profile, we have carried out forward/inverse calculations for a number of clear-sky, daytime, IASI tropical soundings over the sea. The forward calculations have been performed using atmospheric state vectors which have been obtained either from the direct inversion of the IASI radiances or from space-time co-located profiles derived from radiosonde observations and from the ECMWF analysis. To rule out any effect due to the accuracy of the forward model, we have performed the forward calculations using two independent models. The sensitivity of the temperature biases to the variability of the CO2 profile and to spectroscopy errors has also been studied.

CO₂ column-averaged volume mixing ratio derived over Tsukuba from measurements by commercial airlines

Column-averaged volume mixing ratios of carbon dioxide (XCO2) during the period from January 2007 to May 2008 over Tsukuba, Japan, were derived using CO2 concentrations measured by Continuous CO2 Measuring Equipment (CME). The CMEs were installed on Japan Airlines Corporation (JAL) commercial airliners, which frequently fly to and from Narita Airport. It was assumed that CO2 profiles over Tsukuba and Narita are the same. CO2 profile data for 493 flights on clear-sky days were analyzed in order to calculate XCO2 with one of two ancillary datasets: "Tsukuba observational" data (rawinsonde and meteorological tower), or "global" forecast/reanalysis and climatological data (NCEP and CIRA-86). The amplitude of the seasonal variation of XCO2 using the ancillary data measured in Tsukuba (XCO2 (Tsukuba observational)) was determined by a least squares fit using a harmonic function to roughly evaluate the seasonal variation over Tsukuba. The highest and lowest values of the obtained fitted curve in 2007 for XCO2 (Tsukuba observational) were 386.4 ± 1.0 and 381.7 ± 1.0 ppm in May and September, respectively, where the errors represent 1 standard deviation of the fit residuals. The dependence of XCO2 on the type of ancillary dataset was evaluated. The average difference between XCO2 from global climatological data, XCO2 (global), and XCO2 (Tsukuba observational), i.e., the bias of XCO2 (global) based on XCO2 (Tsukuba observational), was found to be −0.621 ppm with a standard deviation of 0.682 ppm. The uncertainty of XCO2 (global) based on XCO2 (Tsukuba observational) was estimated to be 0.922 ppm. This small uncertainty relative to the GOSAT precision suggests that calculating XCO2 using data from airliners and global climatological data can be applied to the validation of GOSAT products for XCO2 over airports worldwide.

An inverse analysis reveals limitations of the soil-CO₂ profile method to calculate CO₂ production and efflux for well-structured soils

Soil respiration is the second largest flux in the global carbon cycle, yet the underlying below-ground process, carbon dioxide (CO2) production, is not well understood because it can not be measured in the field. CO2 production has frequently been calculated from the vertical CO2 diffusive flux divergence, known as "soil-CO2 profile method". This relatively simple model requires knowledge of soil CO2 concentration profiles and soil diffusive properties. Application of the method for a tropical lowland forest soil in Panama gave inconsistent results when using diffusion coefficients (D) calculated based on relationships with soil porosity and moisture ("physically modeled" D). Our objective was to investigate whether these inconsistencies were related to (1) the applied interpolation and solution methods and/or (2) uncertainties in the physically modeled profile of D. First, we show that the calculated CO2 production strongly depends on the function used to interpolate between measured CO2 concentrations. Secondly, using an inverse analysis of the soil-CO2 profile method, we deduce which D would be required to explain the observed CO2 concentrations, assuming the model perception is valid. In the top soil, this inversely modeled D closely resembled the physically modeled D. In the deep soil, however, the inversely modeled D increased sharply while the physically modeled D did not. When imposing a constraint during the fit parameter optimization, a solution could be found where this deviation between the physically and inversely modeled D disappeared. A radon (Rn) mass balance model, in which diffusion was calculated based on the physically modeled or constrained inversely modeled D, simulated observed Rn profiles reasonably well. However, the CO2 concentrations which corresponded to the constrained inversely modeled D were too small compared to the measurements. We suggest that, in well-structured soils, a missing description of steady state CO2 exchange fluxes across water-filled pores causes the soil-CO2 profile method to fail. These fluxes are driven by the different diffusivities in inter- vs. intra-aggregate pores which create permanent CO2 gradients if separated by a "diffusive water barrier". These results corroborate other studies which have shown that the theory to treat gas diffusion as homogeneous process, a precondition for use of the soil-CO2 profile method, is inaccurate for pore networks which exhibit spatial separation between CO2 production and diffusion out of the soil.