scholarly journals An intercomparison of total column-averaged nitrous oxide between ground-based FTIR TCCON and NDACC measurements at seven sites and comparisons with the GEOS-Chem model

2019 ◽  
Vol 12 (2) ◽  
pp. 1393-1408 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Kelley C. Wells ◽  
Dylan B. Millet ◽  
Corinne Vigouroux ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Southern Hemisphere ◽  
Near Infrared ◽  
A Priori ◽  
Polar Vortex ◽  
Inversion Method ◽  
Seasonal Cycles ◽  
A Posteriori ◽  
Model Simulations ◽  
N2o Fluxes

Abstract. Nitrous oxide (N2O) is an important greenhouse gas and it can also generate nitric oxide, which depletes ozone in the stratosphere. It is a common target species of ground-based Fourier transform infrared (FTIR) near-infrared (TCCON) and mid-infrared (NDACC) measurements. Both TCCON and NDACC networks provide a long-term global distribution of atmospheric N2O mole fraction. In this study, the dry-air column-averaged mole fractions of N2O (XN2O) from the TCCON and NDACC measurements are compared against each other at seven sites around the world (Ny-Ålesund, Sodankylä, Bremen, Izaña, Réunion, Wollongong, Lauder) in the time period of 2007–2017. The mean differences in XN2O between TCCON and NDACC (NDACC–TCCON) at these sites are between −3.32 and 1.37 ppb (−1.1 %–0.5 %) with standard deviations between 1.69 and 5.01 ppb (0.5 %–1.6 %), which are within the uncertainties of the two datasets. The NDACC N2O retrieval has good sensitivity throughout the troposphere and stratosphere, while the TCCON retrieval underestimates a deviation from the a priori in the troposphere and overestimates it in the stratosphere. As a result, the TCCON XN2O measurement is strongly affected by its a priori profile. Trends and seasonal cycles of XN2O are derived from the TCCON and NDACC measurements and the nearby surface flask sample measurements and compared with the results from GEOS-Chem model a priori and a posteriori simulations. The trends and seasonal cycles from FTIR measurement at Ny-Ålesund and Sodankylä are strongly affected by the polar winter and the polar vortex. The a posteriori N2O fluxes in the model are optimized based on surface N2O measurements with a 4D-Var inversion method. The XN2O trends from the GEOS-Chem a posteriori simulation (0.97±0.02 (1σ) ppb yr−1) are close to those from the NDACC (0.93±0.04 ppb yr−1) and the surface flask sample measurements (0.93±0.02 ppb yr−1). The XN2O trend from the TCCON measurements is slightly lower (0.81±0.04 ppb yr−1) due to the underestimation of the trend in TCCON a priori simulation. The XN2O trends from the GEOS-Chem a priori simulation are about 1.25 ppb yr−1, and our study confirms that the N2O fluxes from the a priori inventories are overestimated. The seasonal cycles of XN2O from the FTIR measurements and the model simulations are close to each other in the Northern Hemisphere with a maximum in August–October and a minimum in February–April. However, in the Southern Hemisphere, the modeled XN2O values show a minimum in February–April while the FTIR XN2O retrievals show different patterns. By comparing the partial column-averaged N2O from the model and NDACC for three vertical ranges (surface–8, 8–17, 17–50 km), we find that the discrepancy in the XN2O seasonal cycle between the model simulations and the FTIR measurements in the Southern Hemisphere is mainly due to their stratospheric differences.

scholarly journals An inter-comparison of total column-averaged nitrous oxide between ground-based FTIR TCCON and NDACC measurements at seven sites and comparisons with the GEOS-Chem model

2018 ◽  
Author(s):  
Minqiang Zhou ◽  
Bavo Langerock ◽  
Kelley C. Wells ◽  
Dylan B. Millet ◽  
Corinne Vigouroux ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Southern Hemisphere ◽  
Mole Fraction ◽  
Near Infrared ◽  
A Priori ◽  
Inversion Method ◽  
Seasonal Cycles ◽  
A Posteriori ◽  
Model Simulations ◽  
N2o Fluxes

Abstract. Nitrous oxide (N2O) is an important greenhouse gas and it can also generate nitric oxide, which depletes ozone in the stratosphere. It is a common target species of ground-based FTIR near-infrared (TCCON) and mid-infrared (NDACC) measurements. Both TCCON and NDACC networks provide a long-term global distribution of atmospheric N2O mole fraction. In this study, the dry-air column averaged mole fraction of N2O (XN2O) from the TCCON and NDACC measurements are compared against each other at seven sites around the world (Ny-Ålesund, Sodankylä, Bremen, Izaña, Reunion Island, Wollongong, Lauder) in the time period of 2007–2017. The mean differences in XN2O between the TCCON and NDACC (NDACC-TCCON) at these sites are between −3.32 and 1.37 ppb (−1.1–0.5 %) with the standard deviations between 1.69 and 5.01 ppb (0.5–1.6 %), which are within the uncertainties of the two datasets. The NDACC N2O retrieval has good sensitivity throughout the troposphere and stratosphere, while the TCCON retrieval underestimates a deviation from the a priori in the troposphere and overestimates it in the stratosphere. As a result, the TCCON XN2O measurement is strongly affected by its a priori profile. Trends and seasonal cycles of XN2O are derived from the TCCON and NDACC measurements and the nearby surface flask sample measurements, and compared with the results from GEOS-Chem model a priori and a posteriori simulations. The a posteriori N2O fluxes in the model are optimized based on surface N2O measurements with a 4D-Var inversion method. The XN2O trends from the GEOS-Chem a posteriori simulation are very close to those from the NDACC and the surface flask sample measurements (0.9–1.0 ppb/year). The XN2O trends from the TCCON measurements are slightly lower (0.8–0.9 ppb/year) due to the underestimation of the trend in TCCON a priori. The XN2O trends from the GEOS-Chem a priori simulation are about 1.25 ppb/year, and our study confirms that the N2O fluxes from the a priori inventories are overestimated. The seasonal cycles of XN2O from the FTIR measurements and the model simulations are close to each other in the Northern Hemisphere with a maximum in August–October and a minimum in February–April. However, in the Southern Hemisphere, the modeled XN2O shows a minimum in February–April while the FTIR XN2O retrievals shows a minimum in August–October. By comparing the partial column averaged N2O from the model and NDACC for three vertical ranges (surface–8, 8–17, 17–50 km), we find that the discrepancy in the XN2O seasonal cycle between the model simulations and the FTIR measurements in the Southern Hemisphere is mainly due to their stratospheric differences.

scholarly journals Multistation intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability

2014 ◽  
Vol 7 (12) ◽  
pp. 4081-4101 ◽  
Author(s):  
A. Ostler ◽  
R. Sussmann ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
S. Dohe ◽  
...  
Keyword(s):  
Near Infrared ◽  
A Priori ◽  
Polar Vortex ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Seasonal Cycles ◽  
Stratospheric Intrusion ◽  
Smoothing Effects ◽  
Key Factor ◽  
Source Sink

Abstract. Dry-air column-averaged mole fractions of methane (XCH4) retrieved from ground-based solar Fourier transform infrared (FTIR) measurements provide valuable information for satellite validation, evaluation of chemical-transport models, and source-sink-inversions. In this context, Sussmann et al. (2013) have shown that midinfrared (MIR) soundings from the Network for the Detection of Atmospheric Composition Change (NDACC) can be combined with near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON) without the need to apply an overall intercalibration factor. However, in spite of efforts to reduce a priori impact, some residual seasonal biases were identified, and the reasons behind remained unclear. In extension to this previous work, which was based on multiannual quasi-coincident MIR and NIR measurements from the stations Garmisch (47.48° N, 11.06° E, 743 m a.s.l.) and Wollongong (34.41° S, 150.88° E, 30 m a.s.l.), we now investigate upgraded retrievals with longer temporal coverage and include three additional stations (Ny-Ålesund, 78.92° N, 11.93° E, 20 m a.s.l.; Karlsruhe, 49.08° N, 8.43° E, 110 m a.s.l.; Izaña, 28.31° N, 16.45° W, 2.370 m a.s.l.). Our intercomparison results (except for Ny-Ålesund) confirm that there is no overall bias between MIR and NIR XCH4 retrievals, and all MIR and NIR time series reveal a quasi-periodic seasonal bias for all stations, except for Izaña. We find that dynamical variability causes MIR–NIR differences of up to ~ 30 ppb (parts per billion) for Ny-Ålesund, ~ 20 ppb for Wollongong, ~ 18 ppb for Garmisch, and ~ 12 ppb for Karlsruhe. The mechanisms behind this variability are elaborated via two case studies, one dealing with stratospheric subsidence induced by the polar vortex at Ny-Ålesund and the other with a deep stratospheric intrusion event at Garmisch. Smoothing effects caused by the dynamical variability during these events are different for MIR and NIR retrievals depending on the altitude of the perturbation area. MIR retrievals appear to be more realistic in the case of stratospheric subsidence, while NIR retrievals are more accurate in the case of stratosphere–troposphere exchange (STE) in the upper troposphere/lower stratosphere (UTLS) region. About 35% of the FTIR measurement days at Garmisch are impacted by STE, and about 23% of the measurement days at Ny-Ålesund are influenced by polar vortex subsidence. The exclusion of data affected by these dynamical situations resulted in improved agreement of MIR and NIR seasonal cycles for Ny-Ålesund and Garmisch. We found that dynamical variability is a key factor in constraining the accuracy of MIR and NIR seasonal cycles. To mitigate this impact it is necessary to use more realistic a priori profiles that take these dynamical events into account (e.g., via improved models), and/or to improve the FTIR retrievals to achieve a more uniform sensitivity at all altitudes (possibly including profile retrievals for the TCCON data).

scholarly journals Multi-station intercomparison of column-averaged methane from NDACC and TCCON: impact of dynamical variability

2014 ◽  
Vol 7 (7) ◽  
pp. 6743-6790
Author(s):  
A. Ostler ◽  
R. Sussmann ◽  
M. Rettinger ◽  
N. M. Deutscher ◽  
S. Dohe ◽  
...  
Keyword(s):  
Near Infrared ◽  
A Priori ◽  
Polar Vortex ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
Seasonal Cycles ◽  
Stratospheric Intrusion ◽  
Smoothing Effects ◽  
Key Factor ◽  
Source Sink

Abstract. Dry-air column-averaged mole fractions of methane (XCH4) retrieved from ground-based solar Fourier transform infrared (FTIR) measurements provide valuable information for satellite validation, evaluation of chemistry-transport models, and source-sink-inversions. In this context, Sussmann et al. (2013) have shown that mid-infrared (MIR) soundings from the Network for the Detection of Atmospheric Composition Change (NDACC) can be combined with near-infrared (NIR) soundings from the Total Carbon Column Observing Network (TCCON) without the need to apply an overall intercalibration factor. However, in spite of efforts to reduce a priori impact, some residual seasonal biases were identified, and the reasons behind remained unclear. In extension to this previous work, which was based on multi-annual quasi-coincident MIR and NIR measurements from the stations Garmisch (47.48° N, 11.06° E, 743 m a.s.l.) and Wollongong (34.41° S, 150.88° E, 30 m a.s.l.), we now investigate upgraded retrievals with longer temporal coverage and include three additional stations (Ny-Ålesund, 78.92° N, 11.93° E, 20 m a.s.l.; Karlsruhe, 49.08° N, 8.43° E, 110 m a.s.l.; Izaña, 28.31° N, 16.45° W, 2.370 m a.s.l.). Our intercomparison results (except for Ny-Ålesund) confirm that there is no overall bias between MIR and NIR XCH4 retrievals, and all MIR and NIR time series reveal a quasi-periodic seasonal bias for all stations, except for Izaña. We find that dynamical variability causes MIR–NIR differences of up to ~ 30 ppb for Ny-Ålesund, ~ 20 ppb for Wollongong, ~ 18 ppb for Garmisch, and ~ 12 ppb for Karlsruhe. The mechanisms behind this variability are elaborated via two case studies, one dealing with stratospheric subsidence induced by the polar vortex at Ny-Ålesund and the other with a deep stratospheric intrusion event at Garmisch. Smoothing effects caused by the dynamical variability during these events are different for MIR and NIR retrievals depending on the altitude of the perturbation area. MIR retrievals appear to be more realistic in the case of stratospheric subsidence, while NIR retrievals are more accurate in the case of stratosphere-troposphere exchange (STE) in the upper troposphere/lower stratosphere (UTLS) region. About 35% of the FTIR measurement days at Garmisch are impacted by STE, and about 23% of the measurement days at Ny-Ålesund are influenced by polar vortex subsidence. The exclusion of data affected by these dynamical situations resulted in improved agreement of MIR and NIR seasonal cycles for Ny-Ålesund and Garmisch. We found that dynamical variability is a key factor in constraining the accuracy of MIR and NIR seasonal cycles. The only way to avoid this problem is to use more realistic a priori profiles that take these dynamical events into account (e.g. via improved models), and/or to improve the FTIR retrievals to achieve a more uniform sensitivity at all altitudes (possibly including profile retrievals for the TCCON data).

Examination of the 2002 major warming in the southern hemisphere using ground-based and Odin/SMR assimilated data: stratospheric ozone distributions and tropic/mid-latitude exchange

2007 ◽  
Vol 85 (11) ◽  
pp. 1287-1300 ◽  
Author(s):  
H Bencherif ◽  
L El Amraoui ◽  
N Semane ◽  
S Massart ◽  
D Vidyaranya Charyulu ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Southern Hemisphere ◽  
Transport Model ◽  
Stratospheric Ozone ◽  
Three Dimensional ◽  
Polar Vortex ◽  
Low Latitude ◽  
Height Range ◽  
Weather Forecasts ◽  
European Centre

Following an exceptionally active winter, the 2002 Southern Hemisphere (SH) major warming occurred in late September. It was preceded by three minor warming events that occurred in late August and early September, and yielded vortex split and break-down over Antarctica. Ozone (O3 and nitrous oxide (N2O) profiles obtained during that period of time (15 August – 4 October) by the Sub-Millimetre Radiometer (SMR) aboard the Odin satellite are assimilated into MOCAGE (Modélisation Isentrope du transport Mésoéchelle de l'Ozone Stratosphérique par Advection), a global three-dimensional chemistry transport model of Météo-France. The assimilated algorithm is a three-dimensional-FGAT built by the European Centre for Research and Advance Training in Scientific Computation (CERFACS) using the PALM (Projet d'Assimilation par Logiciel Multi-méthode) software. The assimilated O3 and N2O profiles and isentropic distributions are compared to ground-based measurements (LIDAR and balloon-sonde) and to maps of advected potential vorticity (APV). The latter is computed by the MIMOSA (Modélisation Isentrope du transport Mésoéchelle de l'Ozone Stratosphérique par Advection) model, a high-resolution advection transport model, using meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). It is found that O3 concentrations retrieved by the MOCAGE–PALM assimilation system show a reasonably good agreement in the 20–28 km height range when compared with ground-based profiles. This altitude range corresponds to the intersection between the MOCAGE levels (0–28 km) and SMR O3 retrievals (20–50 km). Moreover, comparison of N2O assimilated fields with MIMOSA APV maps indicates that the dramatic split and subsequent break-down of the polar vortex, as well as the associated mixing of mid- and low-latitude stratospheric air, are well resolved and pictured by MOCAGE–PALM. The present study demonstrates also that the tremendous dynamics and associated polar vortex deformations during the 2002-austral-winter have modified ozone and nitrous oxide distributions not only at the vicinity of the polar vortex, but over topics and subtropics as well. PACS Nos.: 92.60.H–, 92.60.Hd, 92.70.Cp, 92.70.Gt

scholarly journals A new analytical inversion method for determining regional and global emissions of greenhouse gases: sensitivity studies and application to halocarbons

2008 ◽  
Vol 8 (6) ◽  
pp. 19063-19121 ◽  
Author(s):  
A. Stohl ◽  
P. Seibert ◽  
J. Arduini ◽  
S. Eckhardt ◽  
P. Fraser ◽  
...  
Keyword(s):  
A Priori ◽  
Measurement Data ◽  
Global Network ◽  
Inversion Method ◽  
A Posteriori ◽  
Inversion Algorithm ◽  
Hfc 134A ◽  
Sensitivity Studies ◽  
Global Emissions ◽  
Hfc 152A

Abstract. A new analytical inversion method has been developed to determine the regional and global emissions of long-lived atmospheric trace gases. It exploits in situ measurement data from a global network and builds on backward simulations with a Lagrangian particle dispersion model. The emission information is extracted from the observed concentration increases over a baseline that is itself objectively determined by the inversion algorithm. The method was applied to two hydrofluorocarbons (HFC-134a, HFC-152a) and a hydrochlorofluorocarbon (HCFC-22) for the period January 2005 until March 2007. Detailed sensitivity studies with synthetic as well as with real measurement data were done to quantify the influence on the results of the a priori emissions and their uncertainties as well as of the observation and model errors. It was found that the global a posteriori emissions of HFC-134a, HFC-152a and HCFC-22 all increased from 2005 to 2006. Large increases (21%, 16%, 18%, respectively) from 2005 to 2006 were found for China, whereas the emission changes in North America and Europe were modest. For Europe, the a posteriori emissions of HFC-134a and HFC-152a were slightly higher than the a priori emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). For HCFC-22, the a posteriori emissions for Europe were substantially (by almost a factor 2) higher than the a priori emissions used, which were based on HCFC consumption data reported to the United Nations Environment Programme (UNEP). Combined with the reported strongly decreasing HCFC consumption in Europe, this suggests a substantial time lag between the reported timing of the HCFC-22 consumption and the actual timing of the HCFC-22 emission. Conversely, in China where HCFC consumption is increasing rapidly according to the UNEP data, the a posteriori emissions are only about 40% of the a priori emissions. This reveals a substantial storage of HCFC-22 and potential for future emissions in China. Deficiencies in the station locations of the current global network measuring halocarbons in relation to estimating regional emissions are also discussed in the paper. Applications of the inversion algorithm to other greenhouse gases such as methane, nitrous oxide or carbon dioxide are foreseen for the future.

Joint interpretation and uncertainty analysis of petrophysical properties in unconventional shale reservoirs

2015 ◽  
Vol 3 (1) ◽  
pp. SA33-SA49 ◽  
Author(s):  
Qinshan Yang ◽  
Carlos Torres-Verdín
Keyword(s):  
Joint Inversion ◽  
A Priori ◽  
Estimation Method ◽  
Monte Carlo Algorithm ◽  
Posteriori Probability ◽  
Inversion Method ◽  
A Posteriori ◽  
A Priori Information ◽  
Core Data ◽  
Priori Information

Interpretation of hydrocarbon-bearing shale is subject to great uncertainty because of pervasive heterogeneity, thin beds, and incomplete and uncertain knowledge of saturation-porosity-resistivity models. We developed a stochastic joint-inversion method specifically developed to address the quantitative petrophysical interpretation of hydrocarbon-bearing shale. The method was based on the rapid and interactive numerical simulation of resistivity and nuclear logs. Instead of property values themselves, the estimation method delivered the a posteriori probability of each property. The Markov-chain Monte Carlo algorithm was used to sample the model space to quantify the a posteriori distribution of formation properties. Additionally, the new interpretation method allows the use of fit-for-purpose statistical correlations between water saturation, salt concentration, porosity, and electrical resistivity to implement uncertain, non-Archie resistivity models derived from core data, including those affected by total organic carbon (TOC). In the case of underdetermined estimation problems, i.e., when the number of measurements was lower than the number of unknowns, the use of a priori information enabled plausible results within prespecified petrophysical and compositional bounds. The developed stochastic interpretation technique was successfully verified with data acquired in the Barnett and Haynesville Shales. Core data (including X-ray diffraction data) were combined into a priori information for interpretation of nuclear and resistivity logs. Results consisted of mineral concentrations, TOC, and porosity together with their uncertainty. Eighty percent of the core data was located within the 95% credible interval of estimated mineral/fluid concentrations.

scholarly journals Nitrous oxide emission hotspots from organic soils in Europe

2014 ◽  
Vol 11 (6) ◽  
pp. 9135-9182
Author(s):  
T. Leppelt ◽  
R. Dechow ◽  
S. Gebbert ◽  
A. Freibauer ◽  
A. Lohila ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Greenhouse Gas ◽  
Ph Value ◽  
A Priori ◽  
Emission Factors ◽  
Organic Soils ◽  
N2o Emissions ◽  
The European Union ◽  
Study Results ◽  
N2o Fluxes

Abstract. Organic soils are a main source of direct nitrous oxide (N2O) emissions, an important greenhouse gas (GHG). Observed N2O emissions from organic soils are highly variable in space and time which causes high uncertainties in national emission inventories. Those uncertainties could be reduced when relating the upscaling process to a priori identified key drivers by using available N2O observations from plot scale in empirical approaches. We used the empirical fuzzy modelling approach MODE to identify main drivers for N2O and utilize them to predict the spatial emission pattern of European organic soils. We conducted a meta study with a total amount of 659 annual N2O measurements which was used to derive separate models for different land use types. We applied our models to available, spatial explicit input driver maps to upscale N2O emissions on European level and compared the inventory with recently published IPCC emission factors. The final statistical models explained up to 60% of the N2O variance. Our study results showed that cropland and grasslands emitted the highest N2O fluxes 0.98 ± 1.08 and 0.58 ± 1.03 g N2O-N m−2 a−1, respectively. High fluxes from cropland sites were mainly controlled by low soil pH-value and deep drained groundwater tables. Grassland hotspot emissions were strongly related to high amount of N-fertilizer inputs and warmer winter temperatures. In contrast N2O fluxes from natural peatlands were predominantly low (0.07±0.27 g N2O-N m−2 a−1) and we found no relationship with the tested drivers. The total inventory for direct N2O emissions from organic soils in Europe amount up to 149.5 Gg N2O-N a−1, which included also fluxes from forest and peat extraction sites and exceeds the inventory calculated by IPCC emission factors of 87.4 Gg N2O-N a−1. N2O emissions from organic soils represent up to 13% of total European N2O emissions reported in the European Union (EU) greenhouse gas inventory of 2011 from only 7% of the EU area. Thereby the model demonstrated that with up to 85% the major part of the inventory is induced by anthropogenic management, which shows the significant reduction potential by rewetting and extensivation of agricultural used peat soils.

scholarly journals A posteriori calculation of δ<sup>18</sup>O and δD in atmospheric water vapour from ground-based near-infrared FTIR retrievals of H<sub>2</sub><sup>16</sup>O, H<sub>2</sub><sup>18</sup>O, and HD<sup>16</sup>O

2014 ◽  
Vol 7 (8) ◽  
pp. 2567-2580 ◽  
Author(s):  
N. V. Rokotyan ◽  
V. I. Zakharov ◽  
K. G. Gribanov ◽  
M. Schneider ◽  
F.-M. Bréon ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Infrared Spectra ◽  
Linear Regression Model ◽  
Near Infrared ◽  
A Priori ◽  
A Posteriori ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Δd And Δ18o

Abstract. This paper investigates the scientific value of retrieving H218O and HDO columns in addition to H216O columns from high-resolution ground-based near-infrared spectra. We present a set of refined H216O, H218O, and HDO spectral windows. The retrieved H216O, H218O, and HDO columns are used for an a posteriori calculation of columnar δD and δ18O. We estimate the uncertainties for the so-calculated columnar δD and δ18O values. These estimations include uncertainties due to the measurement noise, errors in the a priori data, and uncertainties in spectroscopic parameters. Time series of δ18O obtained from ground-based FTIR (Fourier transform infrared) spectra are presented for the first time. For our study we use a full physics isotopic general circulation model (ECHAM5-wiso). We show that the full physics simulation of HDO and H218O can already be reasonably predicted from the H216O columns by a simple linear regression model (scatter values between full physics and linear regression simulations are 35 and 4‰ for HDO and H218O, respectively). We document that the columnar δD and δ18O values as calculated a posteriori from the retrievals of H216O, H218O, and HDO show a better agreement with the ECHAM5-wiso simulation than the δD and δ18O values as calculated from the H216O retrievals and the simple linear regression model. This suggests that the H218O and HDO column retrievals add complementary information to the H216O retrievals. However, these data have to be used carefully, because of the different vertical sensitivity of the H216O, H218O, and HDO columnar retrievals. Furthermore, we have to note that the retrievals use reanalysis humidity profiles as a priori input and the results are thus not independent of the reanalysis data.

scholarly journals Uncertainty assessment and applicability of an inversion method for volcanic ash forecasting

2017 ◽  
Author(s):  
Birthe Marie Steensen ◽  
Arve Kylling ◽  
Nina Iren Kristiansen ◽  
Michael Schulz
Keyword(s):  
Satellite Data ◽  
Volcanic Ash ◽  
A Priori ◽  
A Priori Estimate ◽  
Satellite Observations ◽  
Inversion Method ◽  
A Posteriori ◽  
Priori Estimate ◽  
Satellite Retrieval ◽  
Emission Estimate

Abstract. Significant improvements in the way we can observe and model volcanic ash clouds have been obtained since the 2010 Eyjafjallajökull eruption. One major development has been data assimilation techniques, which aim to bring models in closer agreement to satellite observations and reducing the uncertainties for the ash emission estimate. Still, questions remains to which degree the forecasting capabilities are improved by inclusion of such techniques are and how these improvements depend on the data input. This study exploits how different satellite data and different uncertainty assumptions of the satellite and a priori emissions affect the calculated volcanic ash emission estimate, which is computed by an inversion method that couples the satellite and a priori emissions with dispersion model data. Two major ash episodes over four days in April and May of the 2010 Eyjafjallajökull eruption are studied. Specifically, inversion calculations are done for four different satellite data sets with different size distribution assumptions in the retrieval. A reference satellite data set is chosen and the range between the minimum and maximum 4 day average load of hourly retrieved ash is 121 % in April and 148 % in May, compared to the reference. The corresponding a posteriori maximum and minimum emission sum found for these four satellite retrievals range from 26 % and 47 % of the a posteriori reference estimate for the same two periods. Varying the assumptions made in the satellite retrieval therefore translates into uncertainties in the calculated emissions and the modelled ash column loads. By further exploring the weighting of uncertainties connected to a priori emissions and the other-than-size uncertainties in the satellite data, the uncertainty in the a priori estimate is found to have an order of magnitude more impact on the a posteriori solution compared to the other-than-size uncertainties in the satellite. Part of this is explained by a too high a priori estimate used in this study that is reduced by around half in the a posteriori reference estimate. Setting large uncertainties connected to both a priori and satellite input data is shown to compensate each other. Because of this an inversion based emission estimate in a forecasting setting needs well tested and considered assumptions on uncertainties for the a priori emission and satellite data. The quality of using the inversion in a forecasting environment is tested by adding gradually, with time, more observations to improve the estimated height versus time evolution of Eyjafjallajökull ash emissions. We show that the initially too high a priori emissions are reduced effectively when using just 12 hours of satellite observations. More satellite observations (> 12 h), in the Eyjafjallajökull case, place the volcanic injection at higher altitudes. Adding additional satellite observations (> 36 h) changes the a posteriori emissions to only a small extent for May and minimal for the April period, because the ash is dispersed and transported effectively out of the domain after 1–2 days. A best-guess emission estimate for the forecasting period was constructed by averaging the last 12 hours of the a posteriori emission. Using this emission for a forecast simulation performs better especially compared to model simulations with no further emissions over the forecast period in the case of a continued volcanic eruption activity. Because of undetected ash in the satellite retrieval and diffusion in the model, the forecast simulations generally contain more ash than the observed fields and the model ash is more spread out. Overall, using the a posteriori emissions in our model reduces the uncertainties connected to both the satellite observations and the a priori estimate to perform a more confident forecast in both amount of ash released and emission heights.

scholarly journals Exploring causes of interannual variability in the seasonal cycles of tropospheric nitrous oxide

2011 ◽  
Vol 11 (8) ◽  
pp. 3713-3730 ◽  
Author(s):  
C. D. Nevison ◽  
E. Dlugokencky ◽  
G. Dutton ◽  
J. W. Elkins ◽  
P. Fraser ◽  
...  
Keyword(s):  
Nitrous Oxide ◽  
Interannual Variability ◽  
Empirical Evidence ◽  
Southern Hemisphere ◽  
Seasonal Cycle ◽  
Coastal Upwelling ◽  
Transport Model ◽  
Seasonal Cycles ◽  
Stratospheric Temperature

Abstract. Seasonal cycles in the mixing ratios of tropospheric nitrous oxide (N2O) are derived by detrending long-term measurements made at sites across four global surface monitoring networks. The detrended monthly data display large interannual variability, which at some sites challenges the concept of a "mean" seasonal cycle. In the Northern Hemisphere, correlations between polar winter lower stratospheric temperature and detrended N2O data, around the month of the seasonal minimum, provide empirical evidence for a stratospheric influence, which varies in strength from year to year and can explain much of the interannual variability in the surface seasonal cycle. Even at sites where a strong, competing, regional N2O source exists, such as from coastal upwelling at Trinidad Head, California, the stratospheric influence must be understood to interpret the biogeochemical signal in monthly mean data. In the Southern Hemisphere, detrended surface N2O monthly means are correlated with polar spring lower stratospheric temperature in months preceding the N2O minimum, providing empirical evidence for a coherent stratospheric influence in that hemisphere as well, in contrast to some recent atmospheric chemical transport model (ACTM) results. Correlations between the phasing of the surface N2O seasonal cycle in both hemispheres and both polar lower stratospheric temperature and polar vortex break-up date provide additional support for a stratospheric influence. The correlations discussed above are generally more evident in high-frequency in situ data than in data from weekly flask samples. Furthermore, the interannual variability in the N2O seasonal cycle is not always correlated among in situ and flask networks that share common sites, nor do the mean seasonal amplitudes always agree. The importance of abiotic influences such as the stratospheric influx and tropospheric transport on N2O seasonal cycles suggests that, at sites remote from local sources, surface N2O mixing ratio data by themselves are unlikely to provide information about seasonality in surface sources, e.g., for atmospheric inversions, unless the ACTMs employed in the inversions accurately account for these influences. An additional abioitc influence is the seasonal ingassing and outgassing of cooling and warming surface waters, which creates a thermal signal in tropospheric N2O that is of particular importance in the extratropical Southern Hemisphere, where it competes with the biological ocean source signal.

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