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The distribution and frequency of occurrence of different cloud types affect the energy balance of the Earth. Automatic cloud type classification of images continuously observed by the ground-based imagers could help climate researchers find the relationship between cloud type variations and climate change. However, by far it is still a huge challenge to design a powerful discriminative classifier for cloud categorization. To tackle this difficulty, in this paper, we present an improved method with region covariance descriptors (RCovDs) and the Riemannian bag-of-feature (BoF) method. RCovDs model the correlations among different dimensional features, which allows for a more discriminative representation. BoF is extended from Euclidean space to Riemannian manifold by k -means clustering, in which Stein divergence is adopted as a similarity metric. The histogram feature is extracted by encoding RCovDs of the cloud image blocks with a BoF-based codebook. The multiclass support vector machine (SVM) is utilized for the recognition of cloud types. The experiments on the ground-based cloud image datasets show that a very high prediction accuracy (more than 98 % on two datasets) can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

For the last 25 years, CO-PDD (Cézeaux-Aulnat-Opme-puy de Dôme) has evolved to become a full instrumented platform for atmospheric research. It has received credentials as a national observing platform in France and is internationally recognized as a global station in the GAW (Global Atmosphere Watch) network. It is a reference site of European and national research infrastructures ACTRIS (Aerosol Cloud and Trace gases Research Infrastructure) and ICOS (Integrated Carbon Observing System). The site located on top of the puy de Dôme mountain (1465 m a.s.l.) is completed by additional sites located at lower altitudes and adding the vertical dimension to the atmospheric observations: Opme (660 m a.s.l.), Cézeaux (410 m), and Aulnat (330 m). The integration of different sites offers a unique combination of in situ and remote sensing measurements capturing and documenting the variability of particulate and gaseous atmospheric composition, but also the optical, biochemical, and physical properties of aerosol particles, clouds, and precipitations. Given its location far away from any major emission sources, its altitude, and the mountain orography, the puy de Dôme station is ideally located to sample different air masses in the boundary layer or in the free troposphere depending on time of day and seasons. It is also an ideal place to study cloud properties with frequent presence of clouds at the top in fall and winter. As a result of the natural conditions prevailing at the site and of the very exhaustive instrumental deployment, scientific studies at the puy de Dôme strongly contribute to improving knowledge in atmospheric sciences, including the characterization of trends and variability, the understanding of complex and interconnected processes (microphysical, chemical, biological, chemical and dynamical), and the provision of reference information for climate/chemistry models. In this context, CO-PDD is a pilot site to conduct instrumental development inside its wind tunnel for testing liquid and ice cloud probes in natural conditions, or in situ systems to collect aerosol and cloud. This paper reviews 25 years (1995–2020) of atmospheric observation at the station and related scientific research contributing to atmospheric and climate science. [ABSTRACT FROM AUTHOR]

Atmospheric ozone plays a key role in air quality and the radiation budget of the Earth, both directly and through its chemical influence on other trace gases. Assessments of the atmospheric ozone distribution and associated climate change therefore demand accurate vertically resolved ozone observations with both stratospheric and tropospheric sensitivity, on both global and regional scales, and both in the long term and at shorter timescales. Such observations have been acquired by two series of European nadirviewing ozone profilers, namely the scattered-light UV--visible spectrometers of the GOME family, launched regularly since 1995 (GOME, SCIAMACHY, OMI, GOME-2A/B, TROPOMI, and the upcoming Sentinel-5 series), and the thermal infrared emission sounders of the IASI type, launched regularly since 2006 (IASI on Metop platforms and the upcoming IASI-NG on Metop-SG). In particular, several Level-2 retrieved, Level-3 monthly gridded, and Level-4 assimilated nadir ozone profile data products have been improved and harmonized in the context of the ozone project of the European Space Agency's Climate Change Initiative (ESA Ozone_cci). To verify their fitness for purpose, these ozone datasets must undergo a comprehensive quality assessment (QA), including (a) detailed identification of their geographical, vertical, and temporal domains of validity; (b) quantification of their potential bias, noise, and drift and their dependences on major influence quantities; and (c) assessment of the mutual consistency of data from different sounders. For this purpose we have applied to the Ozone_cci Climate Research Data Package (CRDP) released in 2017 the versatile QA and validation system Multi-TASTE, which has been developed in the context of several heritage projects (ESA's Multi-TASTE, EUMETSAT's O3M-SAF, and the European Commission's FP6 GEOmon and FP7 QA4ECV). This work, as the second in a series of four Ozone_cci validation papers, reports for the first time on data content studies, information content studies and ground-based validation for both the GOME- and IASI-type climate data records combined. The ground-based reference measurements have been provided by the Network for the Detection of Atmospheric Composition Change (NDACC), NASA's Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other ozonesonde and lidar stations contributing to the World Meteorological Organisation's Global Atmosphere Watch (WMO GAW). The nadir ozone profile CRDP quality assessment reveals that all nadir ozone profile products under study fulfil the GCOS user requirements in terms of observation frequency and horizontal and vertical resolution. Yet all L2 observations also show sensitivity outliers in the UTLS and are strongly correlated vertically due to substantial averaging kernel fluctuations that extend far beyond the kernel's 15 km FWHM. The CRDP typically does not comply with the GCOS user requirements in terms of total uncertainty and decadal drift, except for the UV--visible L4 dataset. The drift values of the L2 GOME and OMI, the L3 IASI, and the L4 assimilated products are found to be overall insignificant, however, and applying appropriate altitude-dependent bias and drift corrections make the data fit for climate and atmospheric composition monitoring and modelling purposes. Dependence of the Ozone_cci data quality on major influence quantities -- resulting in data screening suggestions to users -- and perspectives for the Copernicus Sentinel missions are additionally discussed. [ABSTRACT FROM AUTHOR]

Limb scatter instruments in the UV--vis spectral range have provided long-term global records of stratospheric aerosol extinction important for climate records and modelling. While comparisons with occultation instruments show generally good agreement, the source and magnitude of the biases arising from retrieval assumptions, approximations in the radiative transfer modelling and inversion techniques have not been thoroughly characterized. This paper explores the biases between SCIAMACHY v1.4, OSIRIS v5.07 and SAGE II v7.00 aerosol extinctions through a series of coincident comparisons as well as simulation and retrieval studies to investigate the cause and magnitude of the various systematic differences. The effect of a priori profiles, particle size assumptions, radiative transfer modelling, inversion techniques and the different satellite datasets are explored. It is found that the assumed a priori profile can have a large effect near the normalization point, as well as systematic influence at lower altitudes. The error due to particle size assumptions is relatively small when averaged over a range of scattering angles, but individual errors depend on the particular scattering angle, particle size and measurement vector definition. Differences due to radiative transfer modelling introduce differences between the retrieved products of less than 10% on average, but can introduce vertical structure. The combination of the different scenario simulations and the application of both algorithms to both datasets enable the origin of some of the systematic features such as high-altitude differences when compared to SAGE II to be explained. [ABSTRACT FROM AUTHOR]

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %. [ABSTRACT FROM AUTHOR]

Critical data selection is essential for determining representative baseline levels of atmospheric trace gases even at remote measurement sites. Different data selection techniques have been used around the world, which could potentially lead to reduced compatibility when comparing data from different stations. This paper presents a novel statistical data selection method named adaptive diurnal minimum variation selection (ADVS) based on CO2 diurnal patterns typically occurring at elevated mountain stations. Its capability and applicability were studied on records of atmospheric CO2 observations at six Global Atmosphere Watch stations in Europe, namely, Zugspitze-Schneefernerhaus (Germany), Sonnblick (Austria), Jungfraujoch (Switzerland), Izaña (Spain), Schauinsland (Germany), and Hohenpeissenberg (Germany). Three other frequently applied statistical data selection methods were included for comparison. Among the studied methods, our ADVS method resulted in a lower fraction of data selected as a baseline with lower maxima during winter and higher minima during summer in the selected data. The measured time series were analyzed for long-term trends and seasonality by a seasonal-trend decomposition technique. In contrast to unselected data, mean annual growth rates of all selected datasets were not significantly different among the sites, except for the data recorded at Schauinsland. However, clear differences were found in the annual amplitudes as well as the seasonal time structure. Based on a pairwise analysis of correlations between stations on the seasonal-trend decomposed components by statistical data selection, we conclude that the baseline identified by the ADVS method is a better representation of lower free tropospheric (LFT) conditions than baselines identified by the other methods. [ABSTRACT FROM AUTHOR]

A network of three tall tower measurement stations was set up in 2012 across the United Kingdom to expand measurements made at the long-term background northern hemispheric site, Mace Head, Ireland. Reliable and precise in situ greenhouse gas (GHG) analysis systems were developed and deployed at three sites in the UK with automated instrumentation measuring a suite of GHGs. The UK Deriving Emissions linked to Climate Change (UK DECC) network uses tall (165-230m) open-lattice telecommunications towers, which provide a convenient platform for boundary layer trace gas sampling. In this paper we describe the automated measurement system and first results from the UK DECC network for CO2, CH4, N2O, SF6, CO and H2. CO2 and CH4 are measured at all of the UK DECC sites by cavity ring-down spectroscopy (CRDS) with multiple inlet heights at two of the three tall tower sites to assess for boundary layer stratification. The short-term precisions (1s on 1 min means) of CRDS measurements at background mole fractions for January 2012 to September 2015 is < 0.05 µmol mol-1 for CO2 and < 0.3 nmol mol-1 for CH4. Repeatability of standard injections (1s) is < 0.03 µmol mol-1 for CO2 and <0.3nmolmol-1 for CH4 for the same time period. N2O and SF6 are measured at three of the sites, and CO and H2 measurements are made at two of the sites, from a single inlet height using gas chromatography (GC) with an electron capture detector (ECD), flame ionisation detector (FID) or reduction gas analyser (RGA). Repeatability of individual injections (1s) on GC and RGA instruments between January 2012 and September 2015 for CH4, N2O, SF6, CO and H2 measurements were < 2.8 nmol mol-1, < 0.4 nmol mol-1, < 0.07 pmol mol-1, < 2 nmol mol-1 and < 3 nmol mol-1, respectively. Instrumentation in the network is fully automated and includes sensors for measuring a variety of instrumental parameters such as flow, pressures, and sampling temperatures. Automated alerts are generated and emailed to site operators when instrumental parameters are not within defined set ranges. Automated instrument shutdowns occur for critical errors such as carrier gas flow rate deviations. Results from the network give good spatial and temporal coverage of atmospheric mixing ratios within the UK since early 2012. Results also show that all measured GHGs are increasing in mole fraction over the selected reporting period and, except for SF6, exhibit a seasonal trend. CO2 and CH4 also show strong diurnal cycles, with night-time maxima and daytime minima in mole fractions. [ABSTRACT FROM AUTHOR]

The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2016, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. It is based on level-2 total ozone data produced by the GODFIT (GOME-type Direct FITting) v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/Metop-A and Metop-B observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate-chemistry modelling community for decadal stability long-term and short-term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC) at both level 2 and level 3. The inter-sensor consistency of the individual level-2 data sets has mean differences generally within 0.5% at moderate latitudes (±50°), whereas the level-3 data sets show mean differences with respect to the OMI reference data record that span between -0.2±0.9% (for GOME-2B) and 1.0±1.4% (for SCIAMACHY). Very similar findings are reported for the level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments: the mean bias between GODFIT v4 satellite TOC and the ground instrument is well within 1.0±1.0% for all sensors, the drift per decade spans between -0.5% and 1.0±1.0% depending on the sensor, and the peak-to-peak seasonality of the differences ranges from ~1% for GOME and OMI to ~2% for SCIAMACHY. For the level-3 validation, our first goal was to show that the level-3 CRDP produces findings consistent with the level-2 individual sensor comparisons. We show a very good agreement with 0.5 to 2% peak-topeak amplitude for the monthly mean difference time series and a negligible drift per decade of the differences in the Northern Hemisphere of -0.11±0.10%decade-1 for Dobson and C0.22±0.08%decade-1 for Brewer collocations. The exceptional quality of the level-3 GTO-ECV v3 TOC record temporal stability satisfies well the requirements for the total ozone measurement decadal stability of 1-3% and the short-term and long-term accuracy requirements of 2 and 3 %, respectively, showing a remarkable inter-sensor consistency, both in the level-2 GODFIT v4 and in the level-3 GTO-ECV v3 datasets, and thus can be used for longerterm analysis of the ozone layer, such as decadal trend studies, chemistry-climate model evaluation and data assimilation applications. [ABSTRACT FROM AUTHOR]

Particle depolarization ratio retrieved from lidar measurements are commonly used for aerosol-typing studies, microphysical inversion, or mass concentration retrievals. The particle depolarization ratio is one of the primary parameters that can differentiate several major aerosol components but only if the measurements are accurate enough. The accuracy related to the retrieval of particle depolarization ratios is the driving factor for assessing and improving the uncertainties of the depolarization products. This paper presents different depolarization calibration procedures used to improve the quality of the depolarization data. The results illustrate a significant improvement of the depolarization lidar products for all the selected lidar stations that have implemented depolarization calibration procedures. The calibrated volume and particle depolarization profiles at 532 nm show values that fall within a range that is generally accepted in the literature. [ABSTRACT FROM AUTHOR]

The unambiguous retrieval of cloud phase from polarimetric lidar observations is dependent on the assumption that only cloud scattering processes affect polarization measurements. A systematic bias of the traditional lidar depolarization ratio can occur due to a lidar system's inability to accurately measure the entire backscattered signal dynamic range, and these biases are not always identifiable in traditional polarimetric lidar systems. This results in a misidentification of liquid water in clouds as ice, which has broad implications on evaluating surface energy budgets. The Clouds Aerosol Polarization and Backscatter Lidar at Summit, Greenland employs multiple planes of linear polarization, and photon counting and analog detection schemes, to self evaluate, correct, and optimize signal combinations to improve cloud classification. Using novel measurements of diattenuation that are sensitive to both horizontally oriented ice crystals and counting system nonlinear effects, unambiguous measurements are possible by over constraining polarization measurements. This overdetermined capability for cloud-phase determination allows for system errors to be identified and quantified in terms of their impact on cloud properties. It is shown that lidar system dynamic range effects can cause errors in cloud-phase fractional occurrence estimates on the order of 30% causing errors in attribution of cloud radiative effects on the order of 10-30 %. This paper presents a method to identify and remove lidar system effects from atmospheric polarization measurements and uses colocated sensors at Summit to evaluate this method. Enhanced measurements are achieved in this work with non-orthogonal polarization retrievals as well as analog and photon counting detection facilitating a more complete attribution of radiative effects linked to cloud properties. [ABSTRACT FROM AUTHOR]

The effective radiative forcing (ERF) due to aerosol–cloud interactions (ACIs) and rapid adjustments (ERFaci) still causes the largest uncertainty in the assessment of climate change. It is understood only with medium confidence and is studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C × C) approach for studying ACI in satellite observations that combines the concentration of cloud condensation nuclei (nCCN) and ice nucleating particles (nINP) from polar-orbiting lidar measurements with the development of the properties of individual clouds by tracking them in geostationary observations. We present a step-by-step description for obtaining matched aerosol–cloud cases. The application to satellite observations over central Europe and northern Africa during 2014, together with rigorous quality assurance, leads to 399 liquid-only clouds and 95 ice-containing clouds that can be matched to surrounding nCCN and nINP respectively at cloud level. We use this initial data set for assessing the impact of changes in cloud-relevant aerosol concentrations on the cloud droplet number concentration (Nd) and effective radius (reff) of liquid clouds and the phase of clouds in the regime of heterogeneous ice formation. We find a Δln⁡Nd/Δln⁡nCCN of 0.13 to 0.30, which is at the lower end of commonly inferred values of 0.3 to 0.8. The Δln⁡reff/Δln⁡nCCN between -0.09 and -0.21 suggests that reff decreases by -0.81 to -3.78 nm per increase in nCCN of 1 cm -3. We also find a tendency towards more cloud ice and more fully glaciated clouds with increasing nINP that cannot be explained by the increasingly lower cloud top temperature of supercooled-liquid, mixed-phase, and fully glaciated clouds alone. Applied to a larger number of observations, the C × C approach has the potential to enable the systematic investigation of warm and cold clouds. This marks a step change in the quantification of ERFaci from space. [ABSTRACT FROM AUTHOR]

Mitigating the global climate crisis and its consequences, such as more frequent and severe droughts, is one of the major challenges for future agriculture. Therefore, identifying land use systems and management practices that reduce greenhouse gas (GHG) emissions and promote water use efficiency (WUE) is crucial. This, however, requires accurate and precise measurements of carbon dioxide (CO 2) fluxes and evapotranspiration (ET). Despite that, commercial systems to measure CO 2 and ET fluxes are expensive and thus often exclude research in ecosystems within the Global South. This is especially true for research and data of agroecosystems in these areas, which are to date still widely underrepresented. Here, we present a newly developed low-cost, non-dispersive infrared (NDIR)-based CO 2 and ET flux measurement device (∼ EUR 200) that provides reliable, accurate and precise CO 2 and ET flux measurements in conjunction with manually operated closed chambers. To validate the system, laboratory and field validation experiments were performed, testing multiple different low-cost sensors. We demonstrate that the system delivers accurate and precise CO 2 and ET flux measurements using the K30 FR NDIR (CO 2) and SHT31 (RH, relative humidity) sensor. An additional field trial application demonstrated its longer-term stability (> 3 months) and ability to obtain valid net ecosystem C balances (NECBs) and WUE. This was the case, even though environmental conditions at the field trial application site in sub-Saharan Africa were rather challenging (e.g., extremely high temperatures, humidity and rainfall). Consequently, the developed low-cost CO 2 and ET flux measurement device not only provides reasonable results but also might help with democratizing science and closing current data gaps. [ABSTRACT FROM AUTHOR]

Cirrus ice particle sedimentation velocity (vs) is one of the critical variables for the parameterization of cirrus properties in a global climate model (GCM). In this study a methodology to estimate cirrus properties, such as crystal mean fall speed, through successive lidar measurements is evaluated. This "Match" technique has been applied on cirrus cloud observations and then tested with measurements from two ground-based lidars located in the Mediterranean area. These systems, with similar instrumental characteristics, are installed at the Observatory of Haute Provence (OHP, 43.9° N, 5.7° E) in France and at Rome Tor Vergata (RTV, 41.8° N, 12.6° E) in Italy. At a distance of approximately 600 km, the two lidar stations have provided systematic measurements for several years and are along a typical direction of an air path. A test case of an upper tropospheric cirrus, observed over both sites during the night between 13 and 14 March 2008, has been selected and the feasibility of the Match-cirrus approach investigated through this case. The analysis through lidar principal parameters (vertical location, geometrical thickness and optical depth) reveals a case of a thin sub-visible cirrus (SVC) located around the tropopause. A first range of values for vs (1.4-1.9 cm s-1, consistent with simple-shaped small crystals) has been retrieved with a simplified approach (adiabatic transport and "frozen" microphysical conditions inside the cirrus). The backward trajectory analysis suggests a type of cirrus formed by large-scale transport processes (adiabatic cooling of moist air masses coming from the subtropical area around Mexico gulf), which is characterized by a long atmospheric lifetime and horizontal extension of several hundred km. The analysis of this case study reveals that many uncertainties reduce the confidence of the retrieved estimates of the crystal fall velocity. However, this paper allows for assessing the technique feasibility by identifying the main critical issues for future similar investigations. This study shows that such approach is feasible; however, the methodology should be improved and some directions have been suggested for future campaigns. [ABSTRACT FROM AUTHOR]

The Canadian Network for the Detection of Atmospheric Change (CANDAC) Rayleigh-Mie-Raman lidar (CRL) at Eureka, Nunavut, has measured tropospheric clouds, aerosols, and water vapour since 2007. In remote and meteorologically significant locations, such as the Canadian High Arctic, the ability to add new measurement capability to an existing well-tested facility is extremely valuable. In 2010, linear depolarization 532 nm measurement hardware was installed in the lidar's receiver. To minimize disruption in the existing lidar channels and to preserve their existing characterization so far as is possible, the depolarization hardware was placed near the end of the receiver cascade. The upstream optics already in place were not optimized for preserving the polarization of received light. Calibrations and Mueller matrix calculations are used to determine and mitigate the contribution of these upstream optics on the depolarization measurements. The results show that with appropriate calibration, indications of cloud particle phase (ice vs. water) through the use of the depolarization parameter are now possible to a precision of ±0:05 absolute uncertainty (≤ 10% relative uncertainty) within clouds at time and altitude resolutions of 5 min and 37.5m respectively, with higher precision and higher resolution possible in select cases. The uncertainty is somewhat larger outside of clouds at the same altitude, typically with absolute uncertainty ≤ 0:1. Monitoring changes in Arctic cloud composition, including particle phase, is essential for an improved understanding of the changing climate locally and globally. [ABSTRACT FROM AUTHOR]

The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84 ∘ N, 24.29 ∘ E), North Slope of Alaska (NSA; 71.32 ∘ N, 156.61 ∘ W), Ny-Ålesund (Ny-Å; 78.92 ∘ N, 11.93 ∘ E), and Summit (72.59 ∘ N, 38.42 ∘ W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 3 optical depth (OD) units. When ice clouds are included, the underestimation increases to about 5 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈7 g m -2 , over NSA by ≈16 g m -2 , and over Ny-Å by ≈24 g m -2. Over Summit, CCI LWP is overestimated for values ≤20 g m -2 and underestimated for values >20 g m -2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. To understand the effects of multi-layer clouds on the CTH retrievals, the statistics are compared between the single-layer clouds and all types (single-layer + multi-layer). For CTH retrievals, the Cloud_CCI product overestimates the CTH for single-layer clouds. When the multi-layer clouds are included (i.e., all types), the observed CTH overestimation becomes an underestimation of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland–Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites. [ABSTRACT FROM AUTHOR]

The Brewer ozone spectrophotometer (the Brewer) is one of the World Meteorological Organization (WMO) Global Atmosphere Watch (GAW)'s standard ozone-monitoring instruments since the 1980s. The entire global Brewer ozone-monitoring network is operated and maintained via a hierarchical calibration chain, which started from world reference instruments that are independently calibrated via the primary calibration method (PCM) at a premium site (National Oceanic and Atmospheric Administration's (NOAA) Mauna Loa Observatory, Hawaii). These world reference instruments have been maintained by Environment and Climate Change Canada (ECCC) in Toronto for the last 4 decades. Their calibration is transferred to the travelling standard instrument and then to network (field) Brewer instruments at their monitoring sites (all via the calibration transfer method; CTM). Thus, the measurement accuracy for the entire global network is dependent on the calibration of world reference instruments. In 2003, to coordinate regional calibration needs, the Regional Brewer Calibration Center for Europe (RBCC-E) was formed in Izaña, Spain. From that point, RBCC-E began calibrating regional references also via PCM instead of CTM. The equivalency and consistency of world and regional references are then assured during international calibration campaigns. In practice, these two calibration methods have different physical requirements, e.g., the PCM requires a stable ozone field in the short term (i.e., half-day), while the CTM would benefit from larger changes in slant ozone conditions for the calibration periods. This difference dictates that the PCM can only be implemented on Brewer instruments at certain sites and even in certain months of the year. This work is the first effort to use long-term observation records from 11 Brewer instruments at four sites to reveal the challenges in performing the PCM. By utilizing a new calibration simulation model and reanalysis ozone data, this work also quantifies uncertainties in the PCM due to short-term ozone variability. The results are validated by real-world observations and used to provide scientific advice on where and when the PCM can be performed and how many days of observations are needed to achieve the calibration goal (i.e., ensure the calibration uncertainty is within a determined criterion, i.e., ≤5 R6 units; R6 is a measurement-derived double ratio in the actual Brewer processing algorithm). This work also suggests that even if the PCM cannot be used to deliver final calibration results for mid- or high-latitude sites, the statistics of the long-term PCM fitting results can still provide key information for field Brewer instruments as stability indicators (which would provide performance monitoring and data quality assurance). [ABSTRACT FROM AUTHOR]

Data from 242 ozonesondes launched from ARIES, Nainital (29.40 ∘ N, 79.50 ∘ E; 1793 m elevation), are used to evaluate the Atmospheric Infrared Sounder (AIRS) version 6 ozone profiles and total column ozone during the period 2011–2017 over the central Himalayas. The AIRS ozone products are analysed in terms of retrieval sensitivity, retrieval biases/errors, and ability to retrieve the natural variability in columnar ozone, which has not been done so far from the Himalayan region, having complex topography. For a direct comparison, averaging kernel information is used to account for the sensitivity difference between the AIRS and ozonesonde data. We show that AIRS has more minor differences from ozonesondes in the lower and middle troposphere and stratosphere with nominal underestimations of less than 20 %. However, in the upper troposphere and lower stratosphere (UTLS), we observe a considerable overestimation of the magnitude, as high as 102 %. The weighted statistical error analysis of AIRS ozone shows a higher positive bias and standard deviation in the upper troposphere of about 65 % and 25 %, respectively. Similarly to AIRS, the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-track Infrared Sounder (CrIS) are also able to produce ozone peak altitudes and gradients successfully. However, the statistical errors are again higher in the UTLS region, which are likely related to larger variability in ozone, lower ozone partial pressure, and inadequate retrieval information on the surface parameters. Furthermore, AIRS fails to capture the monthly variation in the total column ozone, with a strong bimodal variation, unlike unimodal variation seen in ozonesondes and the Ozone Monitoring Instrument (OMI). In contrast, the UTLS and the tropospheric ozone columns are in reasonable agreement. Increases in the ozone values of 5 %–20 % after biomass burning and during events of downward transport are captured well by AIRS. Ozone radiative forcing (RF) derived from total column ozone using ozonesonde data (4.86 mW m -2) matches well with OMI (4.04 mW m -2), while significant RF underestimation is seen in AIRS (2.96 mW m -2). The fragile and complex landscapes of the Himalayas are more sensitive to global climate change, and establishing such biases and error analysis of space-borne sensors will help us study the long-term trends and estimate accurate radiative budgets. [ABSTRACT FROM AUTHOR]

Methane, a major contributor to climate change, is emitted by a variety of natural and anthropogenic sources. Commercially available lab-grade instruments for sensing trace methane are expensive, and previous efforts to develop inexpensive, field-deployable trace methane sensors have had mixed results. Industrial and commercial metal oxide (MOx) methane sensors, which are intended for leak detection and safety monitoring, can potentially be repurposed and adapted for low-concentration sensing. As an initial step towards developing a low-cost sensing system, we characterize the performance of five off-the-shelf MOx sensors for 2–10 ppm methane detection in a laboratory setting (Figaro Engineering TGS2600, TGS2602, TGS2611-C00, TGS2611-E00, and Henan Hanwei Electronics MQ4). We identify TGS2611-C00, TGS2611-E00, and MQ4 as promising for trace methane sensing but show that variations in ambient humidity and temperature pose a challenge for the sensors in this application. [ABSTRACT FROM AUTHOR]

Aerosol spatial distribution obtained from satellite sensors is critical for understanding regional aerosol environments, anthropogenic aerosol emissions, and global climate change. The Directional Polarimetric Camera (DPC) is the first generation of multi-angle polarized sensor developed by China. It is on-board the GaoFen-5 satellite, running in 705 km sun-synchronous orbit with a 13:30 LT (local time) ascending node. The sensor has three polarized channels at 490, 670, and 865 nm and ∼ 9 viewing angles, mainly used for observing aerosols. The spatial resolution is ∼ 3.3 km at nadir, and global coverage is ∼ 2 d. In this study, the performance of aerosol optical depth (AOD) retrievals from the DPC/GaoFen-5 using the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm were evaluated on a global basis for the first time. The results showed that the DPC GRASP/Model scheme, which used several forms of aerosol-type mixing, achieved good performance. By comparing with Aerosol Robotic Network (AERONET) observations, the correlation coefficient (R), root-mean-square error (RMSE), and expected error (EE%, ± (0.05+0.15×AOD)) were 0.9007 %, 0.0662 %, and 82.54 %, respectively. The scattering angle, number of averaged pixels, length of time steps, and radiative and polarized fitting residuals showed impacts on the results of AOD retrieval in the DPC GRASP/Model scheme. From the most AERONET sites, the R and EE% were larger than ∼ 0.9 % and ∼ 80%. Compared with Moderate resolution Imaging Spectroradiometer (MODIS) products, the spatial and temporal variations of aerosol could be caught by the DPC with the GRASP/Model scheme, showing a good performance. However, values of AOD were also underestimated by DPC, probably due to an overly strict cloud mask. The above findings validated the ability of the DPC sensor to monitor aerosols. It should contribute to the development of aerosol parameter retrieval from multi-angle polarized sensors in the future. [ABSTRACT FROM AUTHOR]

The change of the global climate is most pronounced in the Arctic, where the air temperature increases 2 to 3 times faster than the global average. This process is associated with an increase in the concentration of greenhouse gases in the atmosphere. There are publications predicting the sharp increase in methane emissions into the atmosphere due to permafrost thawing. Therefore, it is important to study how the air composition in the Arctic changes in the changing climate. In the Russian sector of the Arctic, the air composition was measured only in the surface atmospheric layer at the coastal stations or earlier at the drifting stations. Vertical distributions of gas constituents of the atmosphere and aerosol were determined only in a few small regions. That is why the integrated experiment was carried out to measure the composition of the troposphere in the entire Russian sector of the Arctic from on board the Optik Tu-134 aircraft laboratory in the period of ​​​​​​​4 to 17 September of 2020. The aircraft laboratory was equipped with contact and remote measurement facilities. The contact facilities were capable of measuring the concentrations of CO 2 , CH 4 , O 3 , CO, NO x ​​​​​​​, and SO 2 , as well as the disperse composition of particles in the size range from 3 nm to 32 µ m, black carbon, and organic and inorganic components of atmospheric aerosol. The remote facilities were operated to measure the water transparency in the upper layer of the ocean, the chlorophyll content in water, and spectral characteristics of the underlying surface. The measured data have shown that the ocean continues absorbing CO 2. This process is most intense over the Barents and Kara seas. The recorded methane concentration was increased over all the Arctic seas, reaching 2090 ppb in the near-water layer over the Kara Sea. The contents of other gas components and black carbon were close to the background level. In bioaerosol, bacteria predominated among the identified microorganisms. In most samples, they were represented by coccal forms, less often spore-forming and non-spore-bearing rod-shaped bacteria. No dependence of the representation of various bacterial genera on the height and the sampling site was revealed. The most turbid during the experiment was the upper layer of the Chukchi and Bering seas. The Barents Sea turned out to be the most transparent. The differences in extinction varied by more than a factor of 1.5. In all measurements, except for the Barents Sea, the tendency of an increase in chlorophyll fluorescence in more transparent waters was observed. [ABSTRACT FROM AUTHOR]

A methodology for the round-robin evaluation and the geophysical validation of ozone profile data retrieved from nadir UV backscatter satellite measurements is detailed and discussed, consisting of data set content studies, information content studies, co-location studies, and comparisons with reference measurements. Within the European Space Agency's Climate Change Initiative on ozone (Ozone_cci project), the proposed round-robin procedure is applied to two nadir ozone profile data sets retrieved at the Royal Netherlands Meteorological Institute (KNMI) and the Rutherford Appleton Laboratory (RAL, United Kingdom), using their respective OPERA v1.26 and RAL v2.1 optimal estimation algorithms, from MetOp-A GOME-2 (i.e. the second generation Global Ozone Monitoring Experiment on the first Meteorological Operational Satellite) measurements taken in 2008. The ground-based comparisons use ozonesonde and lidar profiles as reference data, acquired by the Network for the Detection of Atmospheric Composition Change (NDACC), Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other stations of theWorld Meteorological Organisation's Global Atmosphere Watch (WMO GAW). This direct illustration highlights practical issues that inevitably emerge from discrepancies in e.g. profile representation and vertical smoothing, for which different recipes are investigated and discussed. Several approaches for information content quantification, vertical resolution estimation, and reference profile resampling are compared and applied as well. The paper concludes with compliance estimates of the two GOME-2 ozone profile data sets with user requirements from the Global Climate Observing System (GCOS) and from climate modellers. [ABSTRACT FROM AUTHOR]

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2 , i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2 , MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets. [ABSTRACT FROM AUTHOR]

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide (XCO2) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases. [ABSTRACT FROM AUTHOR]

Visibility is an indicator of atmospheric transparency, and it is widely used in many research fields, including air pollution, climate change, ground transportation, and aviation. Although efforts have been made to improve the performance of visibility meters, a significant error exists in measured visibility data. This study conducts a well-designed simulation calibration of visibility meters, which proves that current methods of visibility measurement include a false assumption, leading to the long-term neglect of an important source of visibility error caused by erroneous values of Ångström exponents. This error has two characteristics, namely (1) independence, which means that the magnitude of the error is independent of the performance of the visibility meter. It is impossible to reduce this error by improving the performance of visibility meters. The second characteristic is (2) uncertainty, which means the magnitude of the error does not show a clear change pattern but can be substantially larger than the measurement error of visibility meters. It is impossible to accurately estimate the magnitude of this error or its influence on visibility measurements. Our simulations indicate that, as errors in visibility caused by erroneous values of Ångström exponents are inevitable using current methods of visibility measurement, reliable visibility data cannot be obtained without major adjustments to current measurement methods. [ABSTRACT FROM AUTHOR]

The Brewer ozone spectrophotometer (the Brewer) was designed at Environment and Climate Change Canada (ECCC) in the 1970s to make accurate automated total ozone column measurements. Since the 1980s, the Brewer instrument has become a World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) standard ozone monitoring instrument. Now, more than 230 Brewers have been produced. To assure the quality of the Brewer measurements, a calibration chain is maintained, i.e., first, the reference instruments are independently absolutely calibrated, and then the calibration is transferred from the reference instrument to the travelling standard, and subsequently from the travelling standard to field instruments. ECCC has maintained the world Brewer reference instruments since the 1980s to provide transferable calibration to field instruments at monitoring sites. Three single-monochromator (Mark II) type instruments (serial numbers 008, 014, and 015) formed this world Brewer reference triad (BrT) and started their service in Toronto, Canada, in 1984. In the 1990s, the Mark III type Brewer (known as the double Brewer) was developed, which has two monochromators to reduce the internal instrumental stray light. The double-Brewer world reference triad (BrT-D) was formed in 2013 (serial numbers 145, 187 and 191), co-located with the BrT. The first assessment of the BrT's performance was made in 2005, covering the period between 1984 and 2004 (Fioletov et al., 2005). The current work provides an updated assessment of the BrT's performance (from 1999 to 2019) and the first comprehensive assessment of the BrT-D. The random uncertainties of individual reference instruments are within the WMO/GAW requirement of 1 % (WMO, 2001): 0.49 % and 0.42 % for BrT and BrT-D, respectively, as estimated in this study. The long-term stability of the reference instruments is also evaluated in terms of uncertainties of the key instrument characteristics: the extraterrestrial calibration constant (ETC) and effective ozone absorption coefficients (both having an effect of less than 2 % on total column ozone). Measurements from a ground-based instrument (Pandora spectrometer), satellites (11 datasets, including the most recent high-resolution satellite, TROPOspheric Monitoring Instrument), and reanalysis model (the second Modern-Era Retrospective analysis for Research and Applications, MERRA-2) are used to further assess the performance of world Brewer reference instruments and to provide a context for the requirements of stratospheric ozone observations during the last two decades. [ABSTRACT FROM AUTHOR]

Global climate change is one of the most important scientific, societal and economic contemporary challenges. Fundamental understanding of the major processes driving climate change is the key problem which is to be solved not only on a global but also on a regional scale. The accuracy of regional climate modelling depends on a number of factors. One of these factors is the adequate and comprehensive information on the anthropogenic impact which is highest in industrial regions and areas with dense population – modern megacities. Megacities are not only "heat islands", but also significant sources of emissions of various substances into the atmosphere, including greenhouse and reactive gases. In 2019, the mobile experiment EMME (Emission Monitoring Mobile Experiment) was conducted within the St. Petersburg agglomeration (Russia) aiming to estimate the emission intensity of greenhouse (CO2 , CH4) and reactive (CO, NOx) gases for St. Petersburg, which is the largest northern megacity. St. Petersburg State University (Russia), Karlsruhe Institute of Technology (Germany) and the University of Bremen (Germany) jointly ran this experiment. The core instruments of the campaign were two portable Bruker EM27/SUN Fourier transform infrared (FTIR) spectrometers which were used for ground-based remote sensing measurements of the total column amount of CO2 , CH4 and CO at upwind and downwind locations on opposite sides of the city. The NO2 tropospheric column amount was observed along a circular highway around the city by continuous mobile measurements of scattered solar visible radiation with an OceanOptics HR4000 spectrometer using the differential optical absorption spectroscopy (DOAS) technique. Simultaneously, air samples were collected in air bags for subsequent laboratory analysis. The air samples were taken at the locations of FTIR observations at the ground level and also at altitudes of about 100 m when air bags were lifted by a kite (in case of suitable landscape and favourable wind conditions). The entire campaign consisted of 11 mostly cloudless days of measurements in March–April 2019. Planning of measurements for each day included the determination of optimal location for FTIR spectrometers based on weather forecasts, combined with the numerical modelling of the pollution transport in the megacity area. The real-time corrections of the FTIR operation sites were performed depending on the actual evolution of the megacity NOx plume as detected by the mobile DOAS observations. The estimates of the St. Petersburg emission intensities for the considered greenhouse and reactive gases were obtained by coupling a box model and the results of the EMME observational campaign using the mass balance approach. The CO2 emission flux for St. Petersburg as an area source was estimated to be 89 ± 28 ktkm-2yr-1 , which is 2 times higher than the corresponding value in the EDGAR database. The experiment revealed the CH4 emission flux of 135 ± 68 tkm-2yr-1 , which is about 1 order of magnitude greater than the value reported by the official inventories of St. Petersburg emissions (∼ 25 tkm-2yr-1 for 2017). At the same time, for the urban territory of St. Petersburg, both the EMME experiment and the official inventories for 2017 give similar results for the CO anthropogenic flux (251 ± 104 tkm-2yr-1 vs. 410 tkm-2yr-1) and for the NOx anthropogenic flux (66 ± 28 tkm-2yr-1 vs. 69 tkm-2yr-1). [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO2). For ground validation of these satellite measurements, Pandora spectrometers, which retrieve high-quality NO2 total columns via direct-sun measurements, are widely used. In this study, Pandora NO2 measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO2 data products, including a standard Royal Netherlands Meteorological Institute (KNMI) tropospheric and stratospheric NO2 data product and a TROPOMI research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (in the air mass factor calculation). It is found that these current TROPOMI tropospheric NO2 data products (standard and ECCC) met the TROPOMI design bias requirement (< 10 %). Using the statistical uncertainty estimation method, the estimated TROPOMI upper-limit precision falls below the design requirement at a rural site but above in the other two urban and suburban sites. The Pandora instruments are found to have sufficient precision (< 0.02 DU) to perform TROPOMI validation work. In addition to the traditional satellite validation method (i.e., pairing ground-based measurements with satellite measurements closest in time and space), we analyzed TROPOMI pixels located upwind and downwind from the Pandora site. This makes it possible to improve the statistics and better interpret the high-spatial-resolution measurements made by TROPOMI. By using this wind-based validation technique, the number of coincident measurements can be increased by about a factor of 5. With this larger number of coincident measurements, this work shows that both TROPOMI and Pandora instruments can reveal detailed spatial patterns (i.e., horizontal distributions) of local and transported NO2 emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO2 research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO2 data product (e.g., lower multiplicative bias at the suburban and urban sites by about 10 %), demonstrating benefits from the high-resolution regional air quality forecast model. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols play a crucial role in regional radiative budgets. Previous studies on clear-sky aerosol direct radiative forcing (ADRF) have mainly been limited to site-scale observations or model simulations for short-term cases, and long-term distributions of ADRF in China have not been portrayed yet. In this study, an accurate fine-resolution ADRF estimate at the surface was proposed. Multiplatform datasets, including satellite (MODIS aboard Terra and Aqua) and reanalysis datasets, served as inputs to the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model for ADRF simulation with consideration of the aerosol vertical profile over eastern China during 2000–2016. Specifically, single-scattering albedo (SSA) from the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2) was validated with sun photometers over eastern China. The gridded asymmetry parameter (ASY) was then simulated by matching the calculated top-of-atmosphere (TOA) radiative fluxes from the radiative transfer model with satellite observations (Clouds and the Earth's Radiant Energy System, CERES). The high correlation and small discrepancy (6–8 W m -2) between simulated and observed radiative fluxes at three sites (Baoshan, Fuzhou, and Yong'an) indicated that ADRF retrieval is feasible and has high accuracy over eastern China. Then this method was applied in each grid of eastern China, and the overall picture of ADRF distributions over eastern China during 2000–2016 was displayed. ADRF ranges from -220 to -20 W m -2 , and annual mean ADRF is -100.21 W m -2 , implying that aerosols have a strong cooling effect at the surface in eastern China. With the economic development and rapid urbanization, the spatiotemporal changes of ADRF during the past 17 years are mainly attributed to the changes of anthropogenic emissions in eastern China. Our method provides the long-term ADRF distribution over eastern China for the first time, highlighting the importance of aerosol radiative impact under climate change. [ABSTRACT FROM AUTHOR]

Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities at the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information on how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best-practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2 , a 1 ∘ × 1 ∘ TM5-MP data assimilation scheme to estimate the stratospheric background and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm to the most current level-1 data sets to produce a complete 22-year data record that includes GOME (1995–2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. The first validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai'an, China, in June 2006 suggest a small bias (- 2 %) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale. [ABSTRACT FROM AUTHOR]

The concentrations of cloud condensation nuclei (CCN) modulate cloud properties, rainfall location and intensity, and climate forcings. This work assesses uncertainties in CCN measurements and the apparent hygroscopicity parameter (κapp), which is widely used to represent CCN populations in climate models. CCN measurements require accurate operation of three instruments: the CCN instrument, the differential mobility analyzer (DMA), and the condensation particle counter (CPC). Assessment of DMA operation showed that varying the ratio of aerosol to sheath flow from 0.05 to 0.30 resulted in discrepancies between the κapp values calculated from CCN measurements and the literature value. Discrepancies were found to increase from <1 % to 13 % for both sodium chloride and ammonium sulfate. The ratio of excess to sheath flow was also varied, which shifted the downstream aerosol distribution towards smaller particle diameters (for excess flow < sheath flow) or larger particle diameters (for excess flow > sheath flow) than predicted. For the CPC instrument, undercounting occurred at high concentrations, resulting in calculated κapp lower than the literature values. Lastly, undercounting by CCN instruments at high concentration was also assessed, taking the effect of supersaturation on counting efficiency into account. Under recommended operating conditions, the combined DMA, CPC, and CCN uncertainties in κapp are 1.2 % or less for 25 to 200 nm diameter aerosols. [ABSTRACT FROM AUTHOR]

The North America-based Tropospheric Ozone Lidar Network (TOLNet) was recently established to provide high spatiotemporal vertical profiles of ozone, to better understand physical processes driving tropospheric ozone variability and to validate the tropospheric ozone measurements of upcoming spaceborne missions such as Tropospheric Emissions: Monitoring Pollution (TEMPO). The network currently comprises six tropospheric ozone lidars, four of which are mobile instruments deploying to the field a few times per year, based on campaign and science needs. In August 2016, all four mobile TOLNet lidars were brought to the fixed TOLNet site of JPL Table Mountain Facility for the 1-week-long Southern California Ozone Observation Project (SCOOP). This intercomparison campaign, which included 400 h of lidar measurements and 18 ozonesonde launches, allowed for the unprecedented simultaneous validation of five of the six TOLNet lidars. For measurements between 3 and 10 km a.s.l., a mean difference of 0.7 ppbv (1.7 %), with a root-mean-square deviation of 1.6 ppbv or 2.4 %, was found between the lidars and ozonesondes, which is well within the combined uncertainties of the two measurement techniques. The few minor differences identified were typically associated with the known limitations of the lidars at the profile altitude extremes (i.e., first 1 km above ground and at the instruments' highest retrievable altitude). As part of a large homogenization and quality control effort within the network, many aspects of the TOLNet in-house data processing algorithms were also standardized and validated. This thorough validation of both the measurements and retrievals builds confidence as to the high quality and reliability of the TOLNet ozone lidar profiles for many years to come, making TOLNet a valuable ground-based reference network for tropospheric ozone profiling. [ABSTRACT FROM AUTHOR]

The Yongxing air–sea flux tower (YXASFT), which was specially designed for air–sea boundary layer observations, was constructed on Yongxing Island in the South China Sea (SCS). Surface bulk variable measurements were collected during a 1-year period from 1 February 2016 to 31 January 2017. The sensible heat flux (SHF) and latent heat flux (LHF) were further derived via the Coupled Ocean–Atmosphere Response Experiment version 3.0 (COARE3.0). This study employed the YXASFT in situ observations to evaluate the Woods Hole Oceanographic Institute (WHOI) Objectively Analyzed Air–Sea Fluxes (OAFlux) reanalysis data products. First, the reliability of COARE3.0 data in the SCS was validated using direct turbulent heat flux measurements via an eddy covariance flux (ECF) system. The LHF data derived from COARE3.0 are highly consistent with the ECF with a coefficient of determination (R2) of 0.78. Second, the overall reliabilities of the bulk OAFlux variables were diminished in the order of Ta (air temperature), U (wind speed), Qa (air humidity) and Ts (sea surface temperature) based on a combination of R2 values and biases. OAFlux overestimates (underestimates) U (Qa) throughout the year and provides better estimates for winter and spring than in the summer–autumn period, which seems to be highly correlated with the monsoon climate in the SCS. The lowest R2 is between the OAFlux-estimated and YXASFT-observed Ts , indicating that Ts is the least reliable dataset and should thus be used with considerable caution. In terms of the heat fluxes, OAFlux considerably overestimates LHF with an ocean heat loss bias of 52 w m -2 in the spring, and the seasonal OAFlux LHF performance is consistent with U and Qa. The OAFlux-estimated SHF appears to be a poor representative, with enormous overestimations in the spring and winter, while its performance is much better during the summer–autumn period. Third, analysis reveals that the biases in Qa are the most dominant factor on the LHF biases in the spring and winter, and that the biases in both Qa and U are responsible for controlling the biases in LHF during the summer–autumn period. The biases in Ts are responsible for controlling the SHF biases, and the effects of biases in Ts on the biases in SHF during the spring and winter are much greater than that in the summer–autumn period. [ABSTRACT FROM AUTHOR]

An experimental retrieval of oceanic warm rain is presented, extending a previous variational algorithm to provide a suite of retrieved variables spanning non-raining through predominantly warm raining conditions. The warm rain retrieval is underpinned by hydrometeor covariances and drizzle onset data derived from CloudSat. Radiative transfer modelling and analysis of drop size variability from disdrometer observations permit state-dependent observation error covariances that scale with columnar rainwater during iteration. The state-dependent errors and nuanced treatment of drop distributions in precipitating regions are novel and may be applicable for future retrievals and all-sky data assimilation methods. This retrieval method can effectively increase passive microwave sensors' sensitivity to light rainfall that might otherwise be missed. Comparisons with space-borne and ground radar estimates are provided as a proof of concept, demonstrating that a passive-only variational retrieval can be sufficiently constrained from non-raining through warm rain conditions. Significant deviations from forward model assumptions cause non-convergence, usually a result of scattering hydrometeors above the freezing level. However, for cases with liquidonly precipitation, this retrieval displays greater sensitivity than a benchmark operational retrieval. Analysis against passive and active products from the Global Precipitation Measurement (GPM) satellite shows substantial discrepancies in precipitation frequency, with the experimental retrieval observing more frequent light rain. This approach may be complementary to other precipitation retrievals, and its potential synergy with the operational passive GPM retrieval is briefly explored. There are also implications for data assimilation, as all 13 channels on the GPM Microwave Imager (GMI) are simulated over ocean with fidelity in warm raining conditions. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2/and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (sO3/and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8x1015 molec. cm-2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8-12x1015 molec. cm-2/, but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1-2%year-1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase <1%year-1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7-9 and 11-15%year-1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to <3%year-1 for 2009-2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component 65% of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component (35% of the total uncertainty) mainly from "stripe effects". Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step. [ABSTRACT FROM AUTHOR]

Gravity waves (GWs) and convective systems play a fundamental role in atmospheric circulation, weather, and climate. Two usual main sources of GWs are orographic effects triggering mountain waves and convective activity. In addition, GW generation by fronts and geostrophic adjustment must also be considered. The utility of Global Positioning System (GPS) radio occultation (RO) observations for the detection of convective systems is tested. A collocation database between RO events and convective systems over subtropical to midlatitude mountain regions close to the Alps and Andes is built. From the observation of large-amplitude GW structures in the absence of jets and fronts, subsets of RO profiles are sampled. A representative case study among those considered at each region is selected and analyzed. The case studies are investigated using mesoscale Weather Research and Forecasting (WRF) simulations, ERA-Interim reanalysis data, and measured RO temperature profiles. The absence of fronts or jets during both case studies reveals similar relevant GW features (main parameters, generation, and propagation). Orographic and convective activity generates the observed GWs. Mountain waves above the Alps reach higher altitudes than close to the Andes. In the Andes case, a critical layer prevents the propagation of GW packets up to stratospheric heights. The case studies are selected also because they illustrate how the observational window for GW observations through RO profiles admits a misleading interpretation of structures at different altitude ranges. From recent results, the distortion introduced in the measured atmospheric vertical wavelengths by one of the RO events is discussed as an illustration. In the analysis, both the elevation angle of the sounding path (line of tangent points) and the gravity wave aspect ratio estimated from the simulations and the line of sight are taken into account. In both case studies, a considerable distortion, over- and underestimation of the vertical wavelengths measured by RO, may be expected. [ABSTRACT FROM AUTHOR]

Glass transitions from liquid to semi-solid and solid phase states have important implications for reactivity, growth, and cloud-forming (cloud condensation nuclei and ice nucleation) capabilities of secondary organic aerosols (SOAs). The small size and relatively low mass concentration of SOAs in the atmosphere make it difficult to measure atmospheric SOA glass transitions using conventional methods. To circumvent these difficulties, we have adapted a new technique for measuring glass-forming properties of atmospherically relevant organic aerosols. Aerosol particles to be studied are deposited in the form of a thin film onto an interdigitated electrode (IDE) using electrostatic precipitation. Dielectric spectroscopy provides dipole relaxation rates for organic aerosols as a function of temperature (373 to 233 K) that are used to calculate the glass transition temperatures for several cooling or heating rates. IDE-enabled broadband dielectric spectroscopy (BDS) was successfully used to measure the kinetically controlled glass transition temperatures of aerosols consisting of glycerol and four other compounds with selected cooling and heating rates. The glass transition results agree well with available literature data for these five compounds. The results indicate that the IDE-BDS method can provide accurate glass transition data for organic aerosols under atmospheric conditions. The BDS data obtained with the IDE-BDS technique can be used to characterize glass transitions for both simulated and ambient organic aerosols and to model their climate effects. [ABSTRACT FROM AUTHOR]

We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud- Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea--West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling. [ABSTRACT FROM AUTHOR]

Sensor networks are being more widely used to characterize and understand compounds in the atmosphere like ozone (O3). This study employs a measurement tool, called the U-Pod, constructed at the University of Colorado Boulder, to investigate spatial and temporal variability of O3 in a 200 km² area of Riverside County near Los Angeles, California. This tool contains low-cost sensors to collect ambient data at non-permanent locations. The U-Pods were calibrated using a pre-deployment field calibration technique; all the U-Pods were collocated with regulatory monitors. After collocation, the U-Pods were deployed in the area mentioned. A subset of pods was deployed at two local regulatory air quality monitoring stations providing validation for the collocation calibration method. Field validation of sensor O3 measurements to minute-resolution reference observations resulted in R² and root mean squared errors (RMSEs) of 0.95-0.97 and 4.4-5.9 ppbv, respectively. Using the deployment data, ozone concentrations were observed to vary on this small spatial scale. In the analysis based on hourly binned data, the median R² values between all possible UPod pairs varied from 0.52 to 0.86 for ozone during the deployment. The medians of absolute differences were calculated between all possible pod pairs, 21 pairs total. The median values of those median absolute differences for each hour of the day varied between 2.2 and 9.3 ppbv for the ozone deployment. Since median differences between U-Pod concentrations during deployment are larger than the respective root mean square error values, we can conclude that there is spatial variability in this criteria pollutant across the study area. This is important because it means that citizens may be exposed to more, or less, ozone than they would assume based on current regulatory monitoring. [ABSTRACT FROM AUTHOR]

Latent heat flux (LHF) is one of the main contributors to the global energy budget. As the density of in situ LHF measurements over the global oceans is generally poor, the potential of remotely sensed LHF for meteorological applications is enormous. However, to date none of the available satellite products have included estimates of systematic, random, and sampling uncertainties, all of which are essential for assessing their quality. Here, the challenge is taken on by matching LHF-related pixel-level data of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) climatology (version 3.3) to in situ measurements originating from a high-quality data archive of buoys and selected ships. Assuming the ground reference to be biasfree, this allows for deriving instantaneous systematic uncertainties as a function of four atmospheric predictor variables. The approach is regionally independent and therefore overcomes the issue of sparse in situ data densities over large oceanic areas. Likewise, random uncertainties are derived, which include not only a retrieval component but also contributions from in situ measurement noise and the collocation procedure. A recently published random uncertainty decomposition approach is applied to isolate the random retrieval uncertainty of all LHF-related HOAPS parameters. It makes use of two combinations of independent data triplets of both satellite and in situ data, which are analysed in terms of their pairwise variances of differences. Instantaneous uncertainties are finally aggregated, allowing for uncertainty characterizations on monthly to multi-annual timescales. Results show that systematic LHF uncertainties range between 15 and 50Wm-2 with a global mean of 25Wm-2. Local maxima are mainly found over the subtropical ocean basins as well as along the western boundary currents. Investigations indicate that contributions from qa (U) to the overall LHF uncertainty are on the order of 60% (25 %). From an instantaneous point of view, random retrieval uncertainties are specifically large over the subtropics with a global average of 37Wm-2. In a climatological sense, their magnitudes become negligible, as do respective sampling uncertainties. Regional and seasonal analyses suggest that largest total LHF uncertainties are seen over the Gulf Stream and the Indian monsoon region during boreal winter. In light of the uncertainty measures, the observed continuous global mean LHF increase up to 2009 needs to be treated with caution. The demonstrated approach can easily be transferred to other satellite retrievals, which increases the significance of the present work. [ABSTRACT FROM AUTHOR]

We provide atmospheric temperature variability indices for the tropical troposphere and stratosphere based on global navigation satellite system (GNSS) radio occultation (RO) temperature measurements. By exploiting the high vertical resolution and the uniform distribution of the GNSS RO temperature soundings we introduce two approaches, both based on an empirical orthogonal function (EOF) analysis. The first method utilizes the whole vertical and horizontal RO temperature field from 30° S to 30° N and from 2 to 35 km altitude. The resulting indices, the leading principal components, resemble the well-known patterns of the Quasi-Biennial Oscillation (QBO) and the El Niño-Southern Oscillation (ENSO) in the tropics. They provide some information on the vertical structure; however, they are not vertically resolved. The second method applies the EOF analysis on each altitude level separately and the resulting indices contain information on the horizontal variability at each densely available altitude level. They capture more variability than the indices from the first method and present a mixture of all variability modes contributing at the respective altitude level, including the QBO and ENSO. Compared to commonly used variability indices from QBO winds or ENSO sea surface temperature, these new indices cover the vertical details of the atmospheric variability. Using them as proxies for temperature variability is also of advantage because there is no further need to account for response time lags. Atmospheric variability indices as novel products from RO are expected to be of great benefit for studies on atmospheric dynamics and variability, for climate trend analysis, as well as for climate model evaluation. [ABSTRACT FROM AUTHOR]

Linear depolarization measurement capabilities were added to the CANDAC Rayleigh-Mie-Raman lidar (CRL) at Eureka, Nunavut, in the Canadian High Arctic in 2010. This upgrade enables measurements of the phases (liquid versus ice) of cold and mixed-phase clouds throughout the year, including during polar night. Depolarization measurements were calibrated according to existing methods using parallel- and perpendicular-polarized profiles as discussed in McCullough et al. (2017). We present a new technique that uses the polarization-independent Rayleigh elastic channel in combination with one of the new polarizationdependent channels, and we show that for a lidar with low signal in one of the polarization-dependent channels this method is superior to the traditional method. The optimal procedure for CRL is to determine the depolarization parameter using the traditional method at low resolution (from parallel and perpendicular signals) and then to use this value to calibrate the high-resolution new measurements (from parallel and polarization-independent Rayleigh elastic signals). Due to its use of two high-signal-rate channels, the new method has lower statistical uncertainty and thus gives depolarization parameter values at higher spatial-temporal resolution by up to a factor of 20 for CRL. This method is easily adaptable to other lidar systems which are considering adding depolarization capability to existing hardware. [ABSTRACT FROM AUTHOR]

Atmospheric particles of biological origin, also referred to as bioaerosols or primary biological aerosol particles (PBAP), are important to various human health and environmental systems. There has been a recent steep increase in the frequency of published studies utilizing commercial instrumentation based on ultraviolet laser/light-induced fluorescence (UV-LIF), such as the WIBS (wideband integrated bioaerosol sensor) or UV-APS (ultraviolet aerodynamic particle sizer), for bioaerosol detection both outdoors and in the built environment. Significant work over several decades supported the development of the general technologies, but efforts to systematically characterize the operation of new commercial sensors have remained lacking. Specifically, there have been gaps in the understanding of how different classes of biological and non-biological particles can influence the detection ability of LIF instrumentation. Here we present a systematic characterization of the WIBS- 4A instrument using 69 types of aerosol materials, including a representative list of pollen, fungal spores, and bacteria as well as the most important groups of non-biological materials reported to exhibit interfering fluorescent properties. Broad separation can be seen between the biological and non-biological particles directly using the five WIBS output parameters and by taking advantage of the particle classification analysis introduced by Perring et al. (2015). We highlight the importance that particle size plays on observed fluorescence properties and thus in the Perring-style particle classification. We also discuss several particle analysis strategies, including the commonly used fluorescence threshold defined as the mean instrument background (forced trigger; FT) plus 3 standard deviations (σ/of the measurement. Changing the particle fluorescence threshold was shown to have a significant impact on fluorescence fraction and particle type classification. We conclude that raising the fluorescence threshold from FT C 3σ to FT C 9σ does little to reduce the relative fraction of biological material considered fluorescent but can significantly reduce the interference from mineral dust and other non-biological aerosols. We discuss examples of highly fluorescent interfering particles, such as brown carbon, diesel soot, and cotton fibers, and how these may impact WIBS analysis and data interpretation in various indoor and outdoor environments. The performance of the particle asymmetry factor (AF) reported by the instrument was assessed across particle types as a function of particle size, and comments on the reliability of this parameter are given. A comprehensive online supplement is provided, which includes size distributions broken down by fluorescent particle type for all 69 aerosol materials and comparing threshold strategies. Lastly, the study was designed to propose analysis strategies that may be useful to the broader community of UV-LIF instrumentation users in order to promote deeper discussions about how best to continue improving UV-LIF instrumentation and results. [ABSTRACT FROM AUTHOR]

Cavity ring-down spectrometers have generally been designed to operate under conditions in which the background gas has a constant composition. However, there are a number of observational and experimental situations of interest in which the background gas has a variable composition. In this study, we examine the effect of background gas composition on a cavity ring-down spectrometer that measures δ18O-H2O and δ²H-H2O values based on the amplitude of water isotopologue absorption features around 7184 cm-11 (L2120-i, Picarro, Inc.). For background mixtures balanced with N2, the apparent δ18O values deviate from true values by -10.50±0.001‰ O2 %-1 and -10.57±0.001‰ Ar%-1, and apparent δ2H values deviate from true values by 0.26±0.004‰ O2 %-1 and 0.42±0.004‰ Ar %-1. The artifacts are the result of broadening, narrowing, and shifting of both the target absorption lines and strong neighboring lines. While the background-induced isotopic artifacts can largely be corrected with simple empirical or semimechanistic models, neither type of model is capable of completely correcting the isotopic artifacts to within the inherent instrument precision. The development of strategies for dynamically detecting and accommodating background variation in N2, O2, and/or Ar would facilitate the application of cavity ring-down spectrometers to a new class of observations and experiments. [ABSTRACT FROM AUTHOR]

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) is a Fourier transform spectrometer that measured mid-infrared atmospheric limb emission spectra from July 2002 to April 2012 on board the polarorbiting satellite ENVISAT. We have used MIPAS data to study the latitudinal variations of phosgene (COCl2 or carbonyl chloride) and, for the first time, its seasonal variation in the upper troposphere/lower stratosphere region (UTLS). Retrievals of phosgene were made using the 830-860 cm-1 region, corresponding to the ν5 bands of COCl2. Unfortunately, in that region, the ν4 band of CFC-11, which is much stronger than COCl2 ν5, hides the phosgene emission. In order to evaluate seasonality and latitudinal distribution of phosgene we have analysed all the measurements made by MIPAS on days 18 and 20 of each month of 2008 with the optimized retrieval model (ORM) recently upgraded with the multi-target retrieval technique and with the optimal estimation functionality to apply external constraints to the state vector. Average seasonal profiles of phosgene show an evident latitudinal variability with the largest values observed in the tropical regions (maximum 35 parts per trillion by volume (pptv) at about 300 hPa). In the midlatitude and polar regions, the volume mixing ratio (VMR) values do not exceed 30 pptv and the vertical distributions are less peaked. Our analysis highlights that COCl2 seasonal variability is fairly low, apart from the polar regions. [ABSTRACT FROM AUTHOR]

Accurate multi-point monitoring systems are required to derive atmospheric measurements of greenhouse gas concentrations both for the calculation of surface fluxes with inversion transport models and for the estimation of non-turbulent components of the mass balance equation (i.e. advection and storage fluxes) at eddy covariance sites. When a single analyser is used to monitor multiple sampling points, the deployment of buffer volumes (BVs) along sampling lines can reduce the uncertainty due to the discrete temporal sampling of the signal. In order to optimize the use of buffer volumes we explored various set-ups by simulating their effect on time series of high-frequency CO2 concentration collected at three Fluxnet sites. Besides, we proposed a novel scheme to calculate half-hourly weighted arithmetic means from discrete point samples, accounting for the probabilistic fraction of the signal generated in the averaging period. Results show that the use of BVs with the new averaging scheme reduces the mean absolute error (MAE) up to 80% compared to a set-up without BVs and up to 60% compared to the case with BVs and a standard, non-weighted averaging scheme. The MAE of CO2 concentration measurements was observed to depend on the variability of the concentration field and on the size of BVs, which therefore have to be carefully dimensioned. The optimal volume size depends on two main features of the instrumental set-up: the number of measurement points and the time needed to sample at one point (i.e. line purging plus sampling time). A linear and consistent relationship was observed at all sites between the sampling frequency, which summarizes the two features mentioned above, and the renewal frequency associated with the volume. Ultimately, this empirical relationship can be applied to estimate the optimal volume size according to the technical specifications of the sampling system. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel- 5 Precursor mission is scheduled for launch in the last quarter of 2016. As part of its operational processing the mission will provide CH4 and CO total columns using backscattered sunlight in the shortwave infrared band (2.3 µm). By adapting the CO retrieval algorithm, we have developed a non-scattering algorithm to retrieve total column HDO and H2O from the same measurements under clear-sky conditions. The isotopologue ratio HDO/H2O is a powerful diagnostic in the efforts to improve our understanding of the hydrological cycle and its role in climate change, as it provides an insight into the source and transport history of water vapour, nature's strongest greenhouse gas. Due to the weak reflectivity over water surfaces, we need to restrict the retrieval to cloud-free scenes over land. We exploit a novel 2-band filter technique, using strong vs. weak water or methane absorption bands, to prefilter scenes with medium-to-high-level clouds, cirrus or aerosol and to significantly reduce processing time. Scenes with cloud top heights ≳1 km, very low fractions of high-level clouds or an aerosol layer above a high surface albedo are not filtered out. We use an ensemble of realistic measurement simulations for various conditions to show the efficiency of the cloud filter and to quantify the performance of the retrieval. The single-measurement precision in terms of δD is better than 15-25? for even the lowest surface albedo (2-4? for high albedos), while a small bias remains possible of up to ~ 20? due to remaining aerosol or up to ~ 70? due to remaining cloud contamination. We also present an analysis of the sensitivity towards prior assumptions, which shows that the retrieval has a small but significant sensitivity to the a priori assumption of the atmospheric trace gas profiles. Averaging multiple measurements over time and space, however, will reduce these errors, due to the quasi-random nature of the profile uncertainties. The sensitivity of the retrieval with respect to instrumental parameters within the expected instrument performance is < 3, which represents only a small contribution to the overall error budget. Spectroscopic uncertainties of the water lines, however, can have a larger and more systematic impact on the performance of the retrieval and warrant further reassessment of the water line parameters. With TROPOMI's high radiometric sensitivity, wide swath (resulting in daily global coverage) and efficient cloud filtering, in combination with a spatial resolution of 7×7 km², we will greatly increase the amount of useful data on HDO, H2O and their ratio HDO/H2O. We showcase the overall performance of the retrieval algorithm and cloud filter with an accurate simulation of TROPOMI measurements from a single overpass over parts of the USA and Mexico, based on MODIS satellite data and realistic conditions for the surface, atmosphere and chemistry (including isotopologues). This shows that TROPOMI will pave the way for new studies of the hydrological cycle, both globally and locally, on timescales of mere days and weeks instead of seasons and years and will greatly extend the HDO/H2O datasets from the SCIAMACHY and GOSAT missions. [ABSTRACT FROM AUTHOR]

Long time series of observations of essential climate variables in the troposphere and stratosphere are often impacted by inconsistencies in instrumentation and ambiguities in the interpretation of the data. To reduce these problems of long-term data series, all measurements should include an estimate of their uncertainty and a description of their sources. Here we present an update of the uncertainties for tropospheric and stratospheric water vapor observations using the cryogenic frost point hygrometer (CFH). The largest source of measurement uncertainty is the controller stability, which is discussed here in detail. We describe a method to quantify this uncertainty for each profile based on the measurements. We also show the importance of a manufacturerindependent ground check, which is an essential tool to continuously monitor the uncertainty introduced by instrument variability. A small bias, which has previously been indicated in lower tropospheric measurements, is described here in detail and has been rectified. Under good conditions, the total from all sources of uncertainty of frost point or dew point measurements using the CFH can be better than 0.2 K. Systematic errors, which are most likely to impact long-term climate series, are verified to be less than 0.1 K. The impact of the radiosonde pressure uncertainty on the mixing ratio for properly processed radiosondes is considered small. The mixing ratio uncertainty may be as low as 2 to 3%. The impact of the ambient temperature uncertainty on relative humidity (RH) is generally larger than that of the frost point uncertainty. The relative RH uncertainty may be as low as 2% in the lower troposphere and 5% in the tropical tropopause region. [ABSTRACT FROM AUTHOR]

In order to avoid problems connected with the content of a priori information in volume mixing ratio vertical profiles measured with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), a user-friendly representation of the data has been developed which will be made available in addition to the regular data product. In this representation, the data will be provided on a fixed pressure grid coarse enough to allow a virtually unconstrained retrieval. To avoid data interpolation, the grid is chosen to be a subset of the pressure grids used by the Chemistry- Climate Model Initiative and the Data Initiative within the Stratosphere-troposphere Processes And their Role in Climate (SPARC) project as well as the Intergovernmental Panel of Climate Change climatologies and model calculations. For representation, the profiles have been transformed to boxcar base functions, which means that volume mixing ratios are constant within a layer. This representation is thought to be more adequate for comparison with model data. While this method is applicable also to vertical profiles of other species, the method is discussed using ozone as an example. [ABSTRACT FROM AUTHOR]

Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003-2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of -35±30∞ compared to TCCON and -69±15∞ compared to MUSICA (the uncertainty indicating the stationto- station standard deviation). The bias shows a latitudinal dependency, being largest (~-60 to -80 ∞) at the highest latitudes and smallest (~-20 to -30 ∞) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27 ∞, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY-FTS differences is ~115 ∞, which is reduced by a factor ~ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7-0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H2O measurements provide when used in combination with variations in humidity. [ABSTRACT FROM AUTHOR]