scholarly journals Biases in atmospheric CO<sub>2</sub> estimates from correlated meteorology modeling errors

2015 ◽  
Vol 15 (5) ◽  
pp. 2903-2914 ◽  
Author(s):  
S. M. Miller ◽  
M. N. Hayek ◽  
A. E. Andrews ◽  
I. Fung ◽  
J. Liu
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Meteorological Parameter ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Top Down ◽  
First Case

Abstract. Estimates of CO2 fluxes that are based on atmospheric measurements rely upon a meteorology model to simulate atmospheric transport. These models provide a quantitative link between the surface fluxes and CO2 measurements taken downwind. Errors in the meteorology can therefore cause errors in the estimated CO2 fluxes. Meteorology errors that correlate or covary across time and/or space are particularly worrisome; they can cause biases in modeled atmospheric CO2 that are easily confused with the CO2 signal from surface fluxes, and they are difficult to characterize. In this paper, we leverage an ensemble of global meteorology model outputs combined with a data assimilation system to estimate these biases in modeled atmospheric CO2. In one case study, we estimate the magnitude of month-long CO2 biases relative to CO2 boundary layer enhancements and quantify how that answer changes if we either include or remove error correlations or covariances. In a second case study, we investigate which meteorological conditions are associated with these CO2 biases. In the first case study, we estimate uncertainties of 0.5–7 ppm in monthly-averaged CO2 concentrations, depending upon location (95% confidence interval). These uncertainties correspond to 13–150% of the mean afternoon CO2 boundary layer enhancement at individual observation sites. When we remove error covariances, however, this range drops to 2–22%. Top-down studies that ignore these covariances could therefore underestimate the uncertainties and/or propagate transport errors into the flux estimate. In the second case study, we find that these month-long errors in atmospheric transport are anti-correlated with temperature and planetary boundary layer (PBL) height over terrestrial regions. In marine environments, by contrast, these errors are more strongly associated with weak zonal winds. Many errors, however, are not correlated with a single meteorological parameter, suggesting that a single meteorological proxy is not sufficient to characterize uncertainties in atmospheric CO2. Together, these two case studies provide information to improve the setup of future top-down inverse modeling studies, preventing unforeseen biases in estimated CO2 fluxes.

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Variability and budget of CO<sub>2</sub> in Europe: analysis of the CAATER airborne campaigns – Part 2: Comparison of CO<sub>2</sub> vertical variability and fluxes from observations and a modeling framework

2010 ◽  
Vol 10 (2) ◽  
pp. 4271-4304 ◽  
Author(s):  
I. Xueref-Remy ◽  
P. Bousquet ◽  
C. Carouge ◽  
L. Rivier ◽  
N. Viovy ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Transport Model ◽  
Regional Scale ◽  
Modeling Method ◽  
Atmospheric Co2 ◽  
Future Climate Change ◽  
Co2 Fluxes ◽  
Free Troposphere ◽  
Modeling Framework

Abstract. Our ability to predict future climate change relies on our understanding of current and future CO2 fluxes, particularly at the scale of regions (100–1000 km). Nowadays, CO2 regional sources and sinks are still poorly known. Inverse transport modeling, a method often used to quantify these fluxes, relies on atmospheric CO2 measurements. One of the main challenge for the transport models used in the inversions is to reproduce properly CO2 vertical gradients between the boundary layer and the free troposphere, as these gradients impact on the partitioning ot the calculated fluxes between the different model regions. Vertical CO2 profiles are very well suited to assess the performances of the models. In this paper, we conduct a comparison between observed and modeled CO2 profiles recorded during two CAATER campaigns that occurred in May 2001 and October 2002 over western Europe, and that we have described in a companion paper. We test different combinations between a global transport model (LMDZt), a mesoscale transport model (CHIMERE), and different sets of biospheric fluxes, those latter all chosen to have a diurnal cycle (CASA, SiB2 and ORCHIDEE). The vertical profile comparison shows that: (1) in most cases the influence of the biospheric flux is small but sometimes not negligeable, ORCHIDEE giving the best results in the present study; (2) LMDZt is most of the time too diffusive, as it simulates a too high boundary layer height; (3) CHIMERE reproduces better the observed gradients between the boundary layer and the free troposphere, but is sometimes too variable and gives rise to incoherent structures. We conclude there is a need for more vertical profiles to conduct further studies that will help to improve the parameterization of vertical transport in the models used for CO2 flux inversions. Furthermore, we use a modeling method to quantify CO2 fluxes at the regional scale from any observing point, coupling influence functions from the transport model LMDZt (that works quite well at the synoptic scale) with information on the space-time distribution of fluxes. This modeling method is compared to a dual tracer method (the so-called Radon method) for a case study on 25 May 2001 during which simultaneous well-correlated in-situ CO2 and Radon 222 measurements have been collected. Both methods give a similar flux within the Radon 222 method uncertainty (35%), that is an atmospheric CO2 sink of −4.2 to −4.4 gC m−2 day−1. We have estimated the uncertainty of the modeling method to be at least 33% when considering averages, even much more on individual events. This method allows the determination of the area that contributed to the CO2 observed concentration. In our case, the observation point located at 1700 m a.s.l. in the North of France, is influenced by an area of 1500×700 km2 that covers the Benelux region, part of Germany and western Poland. Furthermore, this method allows deconvolution between the different contributing fluxes. In this case study, the biospheric sink contributes for 73% of the total flux, fossil fuel emissions for 27%, the oceanic flux being negligeable. However, the uncertainties of the influence function method must be better assessed. This could be possible by applying it to other cases where the calculated fluxes can be checked independantly, for example at tall towers where simultaneous CO2 and Radon 222 measurements can be conducted. The use of optimized fluxes (from atmospheric inversions) and of mesoscale models for atmospheric transport may also significantly reduce the uncertainties.

scholarly journals Variability and budget of CO<sub>2</sub> in Europe: analysis of the CAATER airborne campaigns – Part 2: Comparison of CO<sub>2</sub> vertical variability and fluxes between observations and a modeling framework

2011 ◽  
Vol 11 (12) ◽  
pp. 5673-5684 ◽  
Author(s):  
I. Xueref-Remy ◽  
P. Bousquet ◽  
C. Carouge ◽  
L. Rivier ◽  
P. Ciais
Keyword(s):  
Boundary Layer ◽  
Transport Model ◽  
Regional Scale ◽  
Modeling Method ◽  
Atmospheric Co2 ◽  
Future Climate Change ◽  
Co2 Fluxes ◽  
Free Troposphere ◽  
Modeling Framework

Abstract. Our ability to predict future climate change relies on our understanding of current and future CO2 fluxes, particularly on a regional scale (100–1000 km). CO2 regional sources and sinks are still poorly understood. Inverse transport modeling, a method often used to quantify these fluxes, relies on atmospheric CO2 measurements. One of the main challenges for the transport models used in the inversions is to properly reproduce CO2 vertical gradients between the boundary layer and the free troposphere, as these gradients impact on the partitioning of the calculated fluxes between the different model regions. Vertical CO2 profiles are very well suited to assess the performances of the models. In this paper, we conduct a comparison between observed and modeled CO2 profiles recorded during two CAATER campaigns that occurred in May 2001 and October 2002 over Western Europe, as described in a companion paper. We test different combinations between a global transport model (LMDZt), a mesoscale transport model (CHIMERE), and different sets of biospheric fluxes, all chosen with a diurnal cycle (CASA, SiB2 and ORCHIDEE). The vertical profile comparison shows that: 1) in most cases the influence of the biospheric flux is small but sometimes not negligible, ORCHIDEE giving the best results in the present study; 2) LMDZt is most of the time too diffuse, as it simulates a too high boundary layer height; 3) CHIMERE better reproduces the observed gradients between the boundary layer and the free troposphere, but is sometimes too variable and gives rise to incoherent structures. We conclude there is a need for more vertical profiles to conduct further studies to improve the parameterization of vertical transport in the models used for CO2 flux inversions. Furthermore, we use a modeling method to quantify CO2 fluxes at the regional scale from a chosen observing point, coupling influence functions from the transport model LMDZt (that works quite well at the synoptic scale) with information on the space-time distribution of fluxes. This modeling method is compared to a dual tracer method (the so-called Radon method) for a case study on 25 May 2001 during which simultaneous well-correlated in situ CO2 and Radon 222 measurements have been collected. Both methods give a similar result: a flux within the Radon 222 method uncertainty (35%), that is an atmospheric CO2 sink of −4.2 to −4.4 gC m−2 day−1. We have estimated the uncertainty of the modeling method to be at least 33% on average, and even more for specific individual events. This method allows the determination of the area that contributed to the CO2 observed concentration. In our case, the observation point located at 1700 m a.s.l. in the north of France, is influenced by an area of 1500×700 km2 that covers the Benelux region, part of Germany and western Poland. Furthermore, this method allows deconvolution between the different contributing fluxes. In this case study, the biospheric sink contributes 73% of the total flux, fossil fuel emissions for 27%, the oceanic flux being negligible. However, the uncertainties of the influence function method need to be better assessed. This could be possible by applying it to other cases where the calculated fluxes can be checked independently, for example at tall towers where simultaneous CO2 and Radon 222 measurements can be conducted. The use of optimized fluxes (from atmospheric inversions) and of mesoscale models for atmospheric transport may also significantly reduce the uncertainties.

scholarly journals Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO<sub>2</sub> mole fractions in the US Midwest

2018 ◽  
Vol 18 (20) ◽  
pp. 14813-14835 ◽  
Author(s):  
Liza I. Díaz-Isaac ◽  
Thomas Lauvaux ◽  
Kenneth J. Davis
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Boundary Conditions ◽  
Planetary Boundary Layer ◽  
Initial Conditions ◽  
Atmospheric Transport ◽  
Atmospheric Co2 ◽  
The Us ◽  
Physics Parameterizations ◽  
Physical Parameterizations

Abstract. Atmospheric transport model errors are one of the main contributors to the uncertainty affecting CO2 inverse flux estimates. In this study, we determine the leading causes of transport errors over the US upper Midwest with a large set of simulations generated with the Weather Research and Forecasting (WRF) mesoscale model. The various WRF simulations are performed using different meteorological driver datasets and physical parameterizations including planetary boundary layer (PBL) schemes, land surface models (LSMs), cumulus parameterizations and microphysics parameterizations. All the different model configurations were coupled to CO2 fluxes and lateral boundary conditions from the CarbonTracker inversion system to simulate atmospheric CO2 mole fractions. PBL height, wind speed, wind direction, and atmospheric CO2 mole fractions are compared to observations during a month in the summer of 2008, and statistical analyses were performed to evaluate the impact of both physics parameterizations and meteorological datasets on these variables. All of the physical parameterizations and the meteorological initial and boundary conditions contribute 3 to 4 ppm to the model-to-model variability in daytime PBL CO2 except for the microphysics parameterization which has a smaller contribution. PBL height varies across ensemble members by 300 to 400 m, and this variability is controlled by the same physics parameterizations. Daily PBL CO2 mole fraction errors are correlated with errors in the PBL height. We show that specific model configurations systematically overestimate or underestimate the PBL height averaged across the region with biases closely correlated with the choice of LSM, PBL scheme, and cumulus parameterization (CP). Domain average PBL wind speed is overestimated in nearly every model configuration. Both planetary boundary layer height (PBLH) and PBL wind speed biases show coherent spatial variations across the Midwest, with PBLH overestimated averaged across configurations by 300–400 m in the west, and PBL winds overestimated by about 1 m s−1 on average in the east. We find model configurations with lower biases averaged across the domain, but no single configuration is optimal across the entire region and for all meteorological variables. We conclude that model ensembles that include multiple physics parameterizations and meteorological initial conditions are likely to be necessary to encompass the atmospheric conditions most important to the transport of CO2 in the PBL, but that construction of such an ensemble will be challenging due to ensemble biases that vary across the region.

scholarly journals Characterizing biospheric carbon balance using CO<sub>2</sub> observations from the OCO-2 satellite

2017 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak ◽  
Vineet Yadav ◽  
Jovan M. Tadic
Keyword(s):  
Carbon Balance ◽  
Atmospheric Transport ◽  
Synthetic Data ◽  
Initial Step ◽  
Co2 Fluxes ◽  
Top Down ◽  
Future Improvement ◽  
The Individual ◽  
Hemisphere Winter ◽  
Atmospheric Transport Modeling

Abstract. NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite launched in summer of 2014. Its observations could allow scientists to constrain CO2 fluxes across regions or continents that were previously difficult to monitor. This study explores an initial step toward that goal; we evaluate the extent to which current OCO-2 observations can detect patterns in biospheric CO2 fluxes and constrain monthly CO2 budgets. Our goal is to guide top-down, inverse modeling studies and identify areas for future improvement. We find that uncertainties and biases in the individual OCO-2 observations are comparable to the atmospheric signal from biospheric fluxes, particularly during northern hemisphere winter when biospheric fluxes are small. A series of top-down experiments indicate how these errors affect our ability to constrain monthly biospheric CO2 budgets. We are able to constrain budgets for between two and four global regions using OCO-2 observations, depending on the month, and we can constrain CO2 budgets at the regional level (i.e., smaller than seven global biomes) in only a handful of cases (16 % of all regions and months). The potential of the OCO-2 observations, however, is greater than these results might imply. A set of synthetic data experiments suggests that observation or retrieval errors have a salient effect. Advances in retrieval algorithms and to a lesser extent atmospheric transport modeling will improve the results. In the interim, top-down studies that use current satellite observations are best-equipped to constrain the biospheric carbon balance across only continental or hemispheric regions.

scholarly journals Forecasting global atmospheric CO<sub>2</sub>

2014 ◽  
Vol 14 (9) ◽  
pp. 13909-13962 ◽  
Author(s):  
A. Agustí-Panareda ◽  
S. Massart ◽  
F. Chevallier ◽  
S. Boussetta ◽  
G. Balsamo ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Real Time ◽  
Diurnal Cycle ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Physical Models ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Near Surface ◽  
Forecasting System

Abstract. A new global atmospheric carbon dioxide (CO2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO2 analyses based on the assimilation of CO2 satellite retrievals, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO2 forecast will be reduced. Improvements in the CO2 forecast are also expected with the continuous developments in the operational IFS.

scholarly journals Evaluating a 3-D transport model of atmospheric CO<sub>2</sub> using ground-based, aircraft, and space-borne data

2010 ◽  
Vol 10 (7) ◽  
pp. 18025-18061 ◽  
Author(s):  
L. Feng ◽  
P. I. Palmer ◽  
Y. Yang ◽  
R. M. Yantosca ◽  
S. R. Kawa ◽  
...  
Keyword(s):  
Boundary Layer ◽  
North America ◽  
Seasonal Cycle ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Co2 Fluxes ◽  
A Posteriori ◽  
Upper Troposphere ◽  
Co2 Concentrations ◽  
Geographical Regions

Abstract. We evaluate the GEOS-Chem atmospheric transport model (v8-02-01) of CO2 over 2003–2006, driven by GEOS-4 and GEOS-5 meteorology from the NASA Goddard Global Modelling and Assimilation Office, using surface, aircraft and space-borne concentration measurements of CO2. We use an established ensemble Kalman filter to estimate a posteriori biospheric+biomass burning (BS+BB) and oceanic (OC) CO2 fluxes from 22 geographical regions, following the TransCom 3 protocol, using boundary layer CO2 data from a subset of GLOBALVIEW surface sites. Global annual net BS+BB+OC CO2 fluxes over 2004–2006 for GEOS-4 (GEOS-5) meteorology are −4.4±0.9 (−4.2±0.9), −3.9±0.9 (−4.5±0.9), and −5.2±0.9 (−4.9±0.9) Pg C yr−1 , respectively. The regional a posteriori fluxes are broadly consistent in the sign and magnitude of the TransCom-3 study for 1992–1996, but we find larger net sinks over northern and southern continents. We find large departures from our a priori over Europe during summer 2003, over temperate Eurasia during 2004, and over North America during 2005, reflecting an incomplete description of terrestrial carbon dynamics. We find GEOS-4 (GEOS-5) a posteriori CO2 concentrations reproduce the observed surface trend of 1.91–2.43 ppm yr−1, depending on latitude, within 0.15 ppm yr−1 (0.2 ppm yr−1) and the seasonal cycle within 0.2 ppm (0.2 ppm) at all latitudes. We find the a posteriori model reproduces the aircraft vertical profile measurements of CO2 over North America and Siberia generally within 1.5 ppm in the free and upper troposphere but can be biased by up to 4–5 ppm in the boundary layer at the start and end of the growing season. The model has a small negative bias in the free troposphere CO2 trend (1.95–2.19 ppm yr−1) compared to AIRS data which has a trend of 2.21–2.63 ppm yr−1 during 2004–2006, consistent with surface data. Model CO2 concentrations in the upper troposphere, evaluated using CONTRAIL (Comprehensive Observation Network for TRace gases by AIrLiner) aircraft measurements, reproduce the magnitude and phase of the seasonal cycle of CO2 in both hemispheres. We generally find that the GEOS meteorology reproduces much of the observed tropospheric CO2 variability, suggesting that these meteorological fields will help make significant progress in understanding carbon fluxes as more data become available.

scholarly journals Modelling regional scale surface fluxes, meteorology and CO<sub>2</sub> mixing ratios for the Cabauw tower in the Netherlands

2009 ◽  
Vol 6 (10) ◽  
pp. 2265-2280 ◽  
Author(s):  
L. F. Tolk ◽  
W. Peters ◽  
A. G. C. A. Meesters ◽  
M. Groenendijk ◽  
A. T. Vermeulen ◽  
...  
Keyword(s):  
The Netherlands ◽  
Surface Energy ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Mixing Ratio ◽  
Surface Energy Fluxes ◽  
Mixing Ratios ◽  
Flux Model

Abstract. We simulated meteorology and atmospheric CO2 transport over the Netherlands with the mesoscale model RAMS-Leaf3 coupled to the biospheric CO2 flux model 5PM. The results were compared with meteorological and CO2 observations, with emphasis on the tall tower of Cabauw. An analysis of the coupled exchange of energy, moisture and CO2 showed that the surface fluxes in the domain strongly influenced the atmospheric properties. The majority of the variability in the afternoon CO2 mixing ratio in the middle of the domain was determined by biospheric and fossil fuel CO2 fluxes in the limited area domain (640×640 km). Variation of the surface CO2 fluxes, reflecting the uncertainty of the parameters in the CO2 flux model 5PM, resulted in a range of simulated atmospheric CO2 mixing ratios of on average 11.7 ppm in the well-mixed boundary layer. Additionally, we found that observed surface energy fluxes and observed atmospheric temperature and moisture could not be reconciled with the simulations. Including this as an uncertainty in the simulation of surface energy fluxes changed simulated atmospheric vertical mixing and horizontal advection, leading to differences in simulated CO2 of on average 1.7 ppm. This is an important source of uncertainty and should be accounted for to avoid biased calculations of the CO2 mixing ratio, but it does not overwhelm the signal in the CO2 mixing ratio due to the uncertainty range of the surface CO2 fluxes.

scholarly journals Airborne verification of CALIPSO products over the Amazon: a case study of daytime observations in a complex atmospheric scene

2014 ◽  
Vol 14 (7) ◽  
pp. 9203-9224
Author(s):  
F. Marenco ◽  
V. Amiridis ◽  
E. Marinou ◽  
A. Tsekeri ◽  
J. Pelon
Keyword(s):  
Boundary Layer ◽  
Extinction Coefficient ◽  
Airborne Lidar ◽  
Aerosol Extinction ◽  
Atmospheric Measurements ◽  
Complex Scene ◽  
Detection Scheme ◽  
Stratocumulus Clouds ◽  
Retrieval Scheme

Abstract. A daytime underflight of CALIPSO with the Facility for Airborne Atmospheric Measurements has been performed on 20 September 2012 in the Amazon region, during the biomass burning season. The scene is dominated by a thin elevated layer (aerosol optical depth 0.03 at 532 nm) and a moderately turbid boundary layer (aerosol extinction coefficient ∼110 Mm−1). The boundary layer is topped with small broken stratocumulus clouds. In this complex scene, a comparison of observations from the airborne and spaceborne lidars reveals a few discrepancies. The CALIPSO detection scheme tends to miss the elevated thin layer, and also shows several gaps (∼30%) in the boundary layer. The small clouds are not correctly detected in the atmospheric volume description flags, and are therefore not removed from the signals; this causes the CALIPSO aerosol subtype to oscillate between smoke and polluted dust and may introduce distorsion in the aerosol retrieval scheme. The magnitude of the average extinction coefficient estimated from CALIPSO level 2 data in the boundary layer is as expected, when compared to the aircraft lidar and accounting for wavelength scaling. However, when the gaps in aerosol detection mentioned above are accounted for, we are left with an overall estimate of aerosol extinction for this particular scene that is of the order of two thirds of that determined with the airborne lidar.

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