Monitoring Atmospheric Composition And Climate Research Articles

A Postprocessing Methodology for Direct Normal Irradiance Forecasting Using Cloud Information and Aerosol Load Forecasts

AbstractA method for direct normal irradiance (DNI) forecasting for specific sites is proposed. It is based on the combination of a numerical weather prediction (NWP) model, which provides cloud information, with radiative transfer simulations fed with external aerosol forecasts. The NWP model used is the ECMWF Integrated Forecast System, and the radiative transfer information has been obtained from the Library of Radiative Transfer (libRadtran). Two types of aerosol forecasts have been tested: the global Monitoring Atmospheric Composition and Climate (MACC) model, which predicts five major components of aerosols, and the Dust Regional Atmospheric Model (BSC-DREAM8b) added to a fixed background calculated as the 20th percentile of the monthly mean of AERONET 2.0 observations from a different year. The methodology employed is valid for all meteorological situations, providing a stable and continuous DNI curve. The performance of the combined method has been evaluated against DNI observations and compared with the pure ECMWF forecasts at eight locations in the southern half of mainland Spain and the Canary Islands, which received high loadings of African dust for 2013 and 2014. Results for 1-day forecasts are presented. Although clouds play a major role, aerosols have a significant effect, but at shorter time scales. The combination of ECMWF and MACC forecasts gives the best global results, improving the DNI forecasts in events with high aerosol content. The regional BSC-DREAM8b yields good results for some extremely high dust conditions, although more reliable predictions, valid for any aerosol conditions, are provided by the MACC model.

The CAMS interim Reanalysis of Carbon Monoxide, Ozone and Aerosol for 2003–2015

Abstract. A new global reanalysis data set of atmospheric composition (AC) for the period 2003–2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

Detecting volcanic sulfur dioxide plumes in the Northern Hemisphere using the Brewer spectrophotometers, other networks, and satellite observations

Abstract. This study examines the adequacy of the existing Brewer network to supplement other networks from the ground and space to detect SO2 plumes of volcanic origin. It was found that large volcanic eruptions of the last decade in the Northern Hemisphere have a positive columnar SO2 signal seen by the Brewer instruments located under the plume. It is shown that a few days after the eruption the Brewer instrument is capable of detecting significant columnar SO2 increases, exceeding on average 2 DU relative to an unperturbed pre-volcanic 10-day baseline, with a mean close to 0 and σ = 0.46, as calculated from the 32 Brewer stations under study. Intercomparisons with independent measurements from the ground and space as well as theoretical calculations corroborate the capability of the Brewer network to detect volcanic plumes. For instance, the comparison with OMI (Ozone Monitoring Instrument) and GOME-2 (Global Ozone Monitoring Experiment-2) SO2 space-borne retrievals shows statistically significant agreement between the Brewer network data and the collocated satellite overpasses in the case of the Kasatochi eruption. Unfortunately, due to sparsity of satellite data, the significant positive departures seen in the Brewer and other ground networks following the Eyjafjallajökull, Bárðarbunga and Nabro eruptions could not be statistically confirmed by the data from satellite overpasses. A model exercise from the MACC (Monitoring Atmospheric Composition and Climate) project shows that the large increases in SO2 over Europe following the Bárðarbunga eruption in Iceland were not caused by local pollution sources or ship emissions but were clearly linked to the volcanic eruption. Sulfur dioxide positive departures in Europe following Bárðarbunga could be traced by other networks from the free troposphere down to the surface (AirBase (European air quality database) and EARLINET (European Aerosol Research Lidar Network)). We propose that by combining Brewer data with that from other networks and satellites, a useful tool aided by trajectory analyses and modelling could be created which can also be used to forecast high SO2 values both at ground level and in air flight corridors following future eruptions.

Saharan dust long-range transport across the Atlantic studied by an airborne Doppler wind lidar and the MACC model

Abstract. A huge amount of dust is transported every year from north Africa into the Caribbean region. This paper presents an investigation of this long-range transport process based on airborne Doppler wind lidar (DWL) measurements conducted during the SALTRACE campaign (June–July 2013), as well as an evaluation of the ability of the MACC (Monitoring Atmospheric Composition and Climate) global aerosol model to reproduce it and its associated features. Although both the modeled winds from MACC and the measurements from the DWL show a generally good agreement, some differences, particularly in the African easterly jet (AEJ) intensity, were noted. The observed differences between modeled and measured wind jet speeds are between 5 and 10 m s−1. The vertical aerosol distribution within the Saharan dust plume and the marine boundary layer is investigated during the June–July 2013 period based on the MACC aerosol model results and the CALIOP satellite lidar measurements. While the modeled Saharan dust plume extent shows a good agreement with the measurements, a systematic underestimation of the marine boundary layer extinction is observed. Additionally, three selected case studies covering different aspects of the Saharan dust long-range transport along the west African coast, over the North Atlantic Ocean and the Caribbean are presented. For the first time, DWL measurements are used to investigate the Saharan dust long-range transport. Simultaneous wind and backscatter measurements from the DWL are used, in combination with the MACC model, to analyze different features associated with the long-range transport, including an African easterly wave trough, the AEJ and the intertropical convergence zone.

Atmospheric salt deposition in a tropical mountain rainforest at the eastern Andean slopes of south Ecuador – Pacific or Atlantic origin?

Abstract. Sea salt (NaCl) has recently been proven to be of the utmost importance for ecosystem functioning in Amazon lowland forests because of its impact on herbivory, litter decomposition and, thus, carbon cycling. Sea salt deposition should generally decline as distance from its marine source increases. For the Amazon, a negative east–west gradient of sea salt availability is assumed as a consequence of the barrier effect of the Andes Mountains for Pacific air masses. However, this generalized pattern may not hold for the tropical mountain rainforest in the Andes of southern Ecuador. To analyse sea salt availability, we investigated the deposition of sodium (Na+) and chloride (Cl−), which are good proxies of sea spray aerosol. Because of the complexity of the terrain and related cloud and rain formation processes, sea salt deposition was analysed from both, rain and occult precipitation (OP) along an altitudinal gradient over a period between 2004 and 2009. To assess the influence of easterly and westerly air masses on the deposition of sodium and chloride over southern Ecuador, sea salt aerosol concentration data from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis data set and back-trajectory statistical methods were combined. Our results, based on deposition time series, show a clear difference in the temporal variation of sodium and chloride concentration and Na+ ∕ Cl− ratio in relation to height and exposure to winds. At higher elevations, sodium and chloride present a higher seasonality and the Na+ ∕ Cl− ratio is closer to that of sea salt. Medium- to long-range sea salt transport exhibited a similar seasonality, which shows the link between our measurements at high elevations and the sea salt synoptic transport. Although the influence of the easterlies was predominant regarding the atmospheric circulation, the statistical analysis of trajectories and hybrid receptor models revealed a stronger impact of the north equatorial Atlantic, Caribbean, and Pacific sea salt sources on the atmospheric sea salt concentration in southern Ecuador. The highest concentration in rain and cloud water was found between September and February when air masses originated from the north equatorial Atlantic, the Caribbean Sea and the equatorial Pacific. Together, these sources accounted for around 82.4 % of the sea salt budget over southern Ecuador.

Human-model hybrid Korean air quality forecasting system

ABSTRACTThe Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters’ capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters’ error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16.6μ/m3 and the R2 of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases.Implications: The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.

Using airborne HIAPER Pole-to-Pole Observations (HIPPO) to evaluate model and remote sensing estimates of atmospheric carbon dioxide

Abstract. In recent years, space-borne observations of atmospheric carbon dioxide (CO2) have been increasingly used in global carbon-cycle studies. In order to obtain added value from space-borne measurements, they have to suffice stringent accuracy and precision requirements, with the latter being less crucial as it can be reduced by just enhanced sample size. Validation of CO2 column-averaged dry air mole fractions (XCO2) heavily relies on measurements of the Total Carbon Column Observing Network (TCCON). Owing to the sparseness of the network and the requirements imposed on space-based measurements, independent additional validation is highly valuable. Here, we use observations from the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) flights from 01/2009 through 09/2011 to validate CO2 measurements from satellites (Greenhouse Gases Observing Satellite – GOSAT, Thermal Emission Sounder – TES, Atmospheric Infrared Sounder – AIRS) and atmospheric inversion models (CarbonTracker CT2013B, Monitoring Atmospheric Composition and Climate (MACC) v13r1). We find that the atmospheric models capture the XCO2 variability observed in HIPPO flights very well, with correlation coefficients (r2) of 0.93 and 0.95 for CT2013B and MACC, respectively. Some larger discrepancies can be observed in profile comparisons at higher latitudes, in particular at 300 hPa during the peaks of either carbon uptake or release. These deviations can be up to 4 ppm and hint at misrepresentation of vertical transport. Comparisons with the GOSAT satellite are of comparable quality, with an r2 of 0.85, a mean bias μ of −0.06 ppm, and a standard deviation σ of 0.45 ppm. TES exhibits an r2 of 0.75, μ of 0.34 ppm, and σ of 1.13 ppm. For AIRS, we find an r2 of 0.37, μ of 1.11 ppm, and σ of 1.46 ppm, with latitude-dependent biases. For these comparisons at least 6, 20, and 50 atmospheric soundings have been averaged for GOSAT, TES, and AIRS, respectively. Overall, we find that GOSAT soundings over the remote Pacific Ocean mostly meet the stringent accuracy requirements of about 0.5 ppm for space-based CO2 observations.

Assessment of fire emission inventories during the South American Biomass Burning Analysis (SAMBBA) experiment

Abstract. Fires associated with land use and land cover changes release large amounts of aerosols and trace gases into the atmosphere. Although several inventories of biomass burning emissions cover Brazil, there are still considerable uncertainties and differences among them. While most fire emission inventories utilize the parameters of burned area, vegetation fuel load, emission factors, and other parameters to estimate the biomass burned and its associated emissions, several more recent inventories apply an alternative method based on fire radiative power (FRP) observations to estimate the amount of biomass burned and the corresponding emissions of trace gases and aerosols. The Brazilian Biomass Burning Emission Model (3BEM) and the Fire Inventory from NCAR (FINN) are examples of the first, while the Brazilian Biomass Burning Emission Model with FRP assimilation (3BEM_FRP) and the Global Fire Assimilation System (GFAS) are examples of the latter. These four biomass burning emission inventories were used during the South American Biomass Burning Analysis (SAMBBA) field campaign. This paper analyzes and inter-compared them, focusing on eight regions in Brazil and the time period of 1 September–31 October 2012. Aerosol optical thickness (AOT550 nm) derived from measurements made by the Moderate Resolution Imaging Spectroradiometer (MODIS) operating on board the Terra and Aqua satellites is also applied to assess the inventories' consistency. The daily area-averaged pyrogenic carbon monoxide (CO) emission estimates exhibit significant linear correlations (r, p > 0.05 level, Student t test) between 3BEM and FINN and between 3BEM_ FRP and GFAS, with values of 0.86 and 0.85, respectively. These results indicate that emission estimates in this region derived via similar methods tend to agree with one other. However, they differ more from the estimates derived via the alternative approach. The evaluation of MODIS AOT550 nm indicates that model simulation driven by 3BEM and FINN typically underestimate the smoke particle loading in the eastern region of Amazon forest, while 3BEM_FRP estimations to the area tend to overestimate fire emissions. The daily regional CO emission fluxes from 3BEM and FINN have linear correlation coefficients of 0.75–0.92, with typically 20–30 % higher emission fluxes in FINN. The daily regional CO emission fluxes from 3BEM_FRP and GFAS show linear correlation coefficients between 0.82 and 0.90, with a particularly strong correlation near the arc of deforestation in the Amazon rainforest. In this region, GFAS has a tendency to present higher CO emissions than 3BEM_FRP, while 3BEM_FRP yields more emissions in the area of soybean expansion east of the Amazon forest. Atmospheric aerosol optical thickness is simulated by using the emission inventories with two operational atmospheric chemistry transport models: the IFS from Monitoring Atmospheric Composition and Climate (MACC) and the Coupled Aerosol and Tracer Transport model to the Brazilian developments on the Regional Atmospheric Modelling System (CCATT-BRAMS). Evaluation against MODIS observations shows a good representation of the general patterns of the AOT550 nm time series. However, the aerosol emissions from fires with particularly high biomass consumption still lead to an underestimation of the atmospheric aerosol load in both models.

Inverse modeling of GOSAT-retrieved ratios of total column CH<sub>4</sub> and CO<sub>2</sub> for 2009 and 2010

Abstract. This study investigates the constraint provided by greenhouse gas measurements from space on surface fluxes. Imperfect knowledge of the light path through the atmosphere, arising from scattering by clouds and aerosols, can create biases in column measurements retrieved from space. To minimize the impact of such biases, ratios of total column retrieved CH4 and CO2 (Xratio) have been used. We apply the ratio inversion method described in Pandey et al. (2015) to retrievals from the Greenhouse Gases Observing SATellite (GOSAT). The ratio inversion method uses the measured Xratio as a weak constraint on CO2 fluxes. In contrast, the more common approach of inverting proxy CH4 retrievals (Frankenberg et al., 2005) prescribes atmospheric CO2 fields and optimizes only CH4 fluxes. The TM5–4DVAR (Tracer Transport Model version 5–variational data assimilation system) inverse modeling system is used to simultaneously optimize the fluxes of CH4 and CO2 for 2009 and 2010. The results are compared to proxy inversions using model-derived CO2 mixing ratios (XCO2model) from CarbonTracker and the Monitoring Atmospheric Composition and Climate (MACC) Reanalysis CO2 product. The performance of the inverse models is evaluated using measurements from three aircraft measurement projects. Xratio and XCO2model are compared with TCCON retrievals to quantify the relative importance of errors in these components of the proxy XCH4 retrieval (XCH4proxy). We find that the retrieval errors in Xratio (mean = 0.61 %) are generally larger than the errors in XCO2model (mean = 0.24 and 0.01 % for CarbonTracker and MACC, respectively). On the annual timescale, the CH4 fluxes from the different satellite inversions are generally in agreement with each other, suggesting that errors in XCO2model do not limit the overall accuracy of the CH4 flux estimates. On the seasonal timescale, however, larger differences are found due to uncertainties in XCO2model, particularly over Australia and in the tropics. The ratio method stays closer to the a priori CH4 flux in these regions, because it is capable of simultaneously adjusting the CO2 fluxes. Over tropical South America, comparison to independent measurements shows that CO2 fields derived from the ratio method are less realistic than those used in the proxy method. However, the CH4 fluxes are more realistic, because the impact of unaccounted systematic uncertainties is more evenly distributed between CO2 and CH4. The ratio inversion estimates an enhanced CO2 release from tropical South America during the dry season of 2010, which is in accordance with the findings of Gatti et al. (2014) and Van der Laan et al. (2015). The performance of the ratio method is encouraging, because despite the added nonlinearity due to the assimilation of Xratio and the significant increase in the degree of freedom by optimizing CO2 fluxes, still consistent results are obtained with respect to other CH4 inversions.

Particulate matter (PM10) patterns in Europe: An exploratory data analysis using non-negative matrix factorization

In last decade space-density of monitoring stations increased, in to addition also air pollution modeling made big progress. Using diversity of big data can lead to better knowledge about air pollution at continental scale. The focus of presented study is the data-driven approach using non-negative matrix factorization to provide new insights and to study the characteristic space-time particulate-matter patterns across Europe. We analyzed the PM10 concentrations obtained from 1097 monitoring stations (AirBase data) and the Monitoring Atmospheric Composition and Climate (MACC) modeled fields for a period of 3 years. We distinguished five characteristic patterns obtained from the AirBase data and five patterns from the MACC data. A comparison between the AirBase and MACC data shows a good spatial overlap for the east Europe, central Europe and the Mediterranean patterns. However, it should be noted that an analysis of the MACC data revealed two additional marine patterns: the Celtic and the North Seas. The Po Valley and Balkan patterns were very clearly identified when analyzing the AirBase data. In order to better understand the influence of the synoptic situation on the particulate-matter concentrations the synoptic meteorological situations were additionally analyzed. The cold season, low wind and very stable conditions, which can last for several days, is the most common situation linked to high concentrations of anthropogenic air pollution with particulate matter. In contrast, for the Mediterranean pattern the most common situation (high factor loadings) is observed during the summer period. This pattern also exhibits a clearer annual cycle. A closer look at the sea-salt patterns (Celtic and North Seas) shows low time-series correlations between these two factors. Nevertheless, the physical mechanism is the same: a steep gradient between the cyclone and the anti-cyclone that causes high winds and, consequently, higher sea-salt production.

Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON

Abstract. Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to error2 = a2 + b2/n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of ∼ 0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.

Impacts of the direct radiative effect of aerosols in numerical weather prediction over Europe using the ALADIN-HIRLAM NWP system

Aerosol feedbacks are becoming more accepted as physical mechanisms that should be included in numerical weather prediction models in order to improve the accuracy of the weather forecasts. The default set-up in the Aire Limitee Adaptation dynamique Developpement INternational (ALADIN) — High Resolution Limited Area Model (HIRLAM) numerical weather prediction system includes monthly aerosol climatologies to account for the average direct radiative effect of aerosols. This effect was studied using the default aerosol climatology in the system and compared to experiments run using the more up-to-date Max-Planck-Institute Aerosol Climatology version 1 (MACv1), and time-varying aerosol data from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis aerosol dataset. Accounting for the direct radiative effect using monthly aerosol climatologies or near real-time aerosol distributions improved the accuracy of the simulated radiative fluxes and temperature and humidity forecasts in the lower troposphere. However, the dependency of forecast meteorological conditions on the aerosol dataset itself was found to be weak.

Evaluation of the MACC operational forecast system – potential and challenges of global near-real-time modelling with respect to reactive gases in the troposphere

Abstract. The Monitoring Atmospheric Composition and Climate (MACC) project represents the European Union's Copernicus Atmosphere Monitoring Service (CAMS) (http://www.copernicus.eu/), which became fully operational during 2015. The global near-real-time MACC model production run for aerosol and reactive gases provides daily analyses and 5-day forecasts of atmospheric composition fields. It is the only assimilation system worldwide that is operational to produce global analyses and forecasts of reactive gases and aerosol fields. We have investigated the ability of the MACC analysis system to simulate tropospheric concentrations of reactive gases covering the period between 2009 and 2012. A validation was performed based on carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) surface observations from the Global Atmosphere Watch (GAW) network, the O3 surface observations from the European Monitoring and Evaluation Programme (EMEP) and, furthermore, NO2 tropospheric columns, as well as CO total columns, derived from satellite sensors. The MACC system proved capable of reproducing reactive gas concentrations with consistent quality; however, with a seasonally dependent bias compared to surface and satellite observations – for northern hemispheric surface O3 mixing ratios, positive biases appear during the warm seasons and negative biases during the cold parts of the year, with monthly modified normalised mean biases (MNMBs) ranging between −30 and 30 % at the surface. Model biases are likely to result from difficulties in the simulation of vertical mixing at night and deficiencies in the model's dry deposition parameterisation. Observed tropospheric columns of NO2 and CO could be reproduced correctly during the warm seasons, but are mostly underestimated by the model during the cold seasons, when anthropogenic emissions are at their highest level, especially over the US, Europe and Asia. Monthly MNMBs of the satellite data evaluation range from values between −110 and 40 % for NO2 and at most −20 % for CO, over the investigated regions. The underestimation is likely to result from a combination of errors concerning the dry deposition parameterisation and certain limitations in the current emission inventories, together with an insufficiently established seasonality in the emissions.

LSA SAF Meteosat FRP products – Part 2: Evaluation and demonstration for use in the Copernicus Atmosphere Monitoring Service (CAMS)

Abstract. Characterising the dynamics of landscape-scale wildfires at very high temporal resolutions is best achieved using observations from Earth Observation (EO) sensors mounted onboard geostationary satellites. As a result, a number of operational active fire products have been developed from the data of such sensors. An example of which are the Fire Radiative Power (FRP) products, the FRP-PIXEL and FRP-GRID products, generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from imagery collected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) series of geostationary EO satellites. The processing chain developed to deliver these FRP products detects SEVIRI pixels containing actively burning fires and characterises their FRP output across four geographic regions covering Europe, part of South America and Northern and Southern Africa. The FRP-PIXEL product contains the highest spatial and temporal resolution FRP data set, whilst the FRP-GRID product contains a spatio-temporal summary that includes bias adjustments for cloud cover and the non-detection of low FRP fire pixels. Here we evaluate these two products against active fire data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and compare the results to those for three alternative active fire products derived from SEVIRI imagery. The FRP-PIXEL product is shown to detect a substantially greater number of active fire pixels than do alternative SEVIRI-based products, and comparison to MODIS on a per-fire basis indicates a strong agreement and low bias in terms of FRP values. However, low FRP fire pixels remain undetected by SEVIRI, with errors of active fire pixel detection commission and omission compared to MODIS ranging between 9–13 % and 65–77 % respectively in Africa. Higher errors of omission result in greater underestimation of regional FRP totals relative to those derived from simultaneously collected MODIS data, ranging from 35 % over the Northern Africa region to 89 % over the European region. High errors of active fire omission and FRP underestimation are found over Europe and South America and result from SEVIRI's larger pixel area over these regions. An advantage of using FRP for characterising wildfire emissions is the ability to do so very frequently and in near-real time (NRT). To illustrate the potential of this approach, wildfire fuel consumption rates derived from the SEVIRI FRP-PIXEL product are used to characterise smoke emissions of the 2007 "mega-fire" event focused on Peloponnese (Greece) and used within the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) as a demonstration of what can be achieved when using geostationary active fire data within the Copernicus Atmosphere Monitoring Service (CAMS). Qualitative comparison of the modelled smoke plumes with MODIS optical imagery illustrates that the model captures the temporal and spatial dynamics of the plume very well, and that high temporal resolution emissions estimates such as those available from a geostationary orbit are important for capturing the sub-daily variability in smoke plume parameters such as aerosol optical depth (AOD), which are increasingly less well resolved using daily or coarser temporal resolution emissions data sets. Quantitative comparison of modelled AOD with coincident MODIS and AERONET (Aerosol Robotic Network) AOD indicates that the former is overestimated by ~ 20–30 %, but captures the observed AOD dynamics with a high degree of fidelity. The case study highlights the potential of using geostationary FRP data to drive fire emissions estimates for use within atmospheric transport models such as those implemented in the Monitoring Atmospheric Composition and Climate (MACC) series of projects for the CAMS.

Validation of reactive gases and aerosols in the MACC global analysis and forecast system

Abstract. The European MACC (Monitoring Atmospheric Composition and Climate) project is preparing the operational Copernicus Atmosphere Monitoring Service (CAMS), one of the services of the European Copernicus Programme on Earth observation and environmental services. MACC uses data assimilation to combine in situ and remote sensing observations with global and regional models of atmospheric reactive gases, aerosols, and greenhouse gases, and is based on the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts (ECMWF). The global component of the MACC service has a dedicated validation activity to document the quality of the atmospheric composition products. In this paper we discuss the approach to validation that has been developed over the past 3 years. Topics discussed are the validation requirements, the operational aspects, the measurement data sets used, the structure of the validation reports, the models and assimilation systems validated, the procedure to introduce new upgrades, and the scoring methods. One specific target of the MACC system concerns forecasting special events with high-pollution concentrations. Such events receive extra attention in the validation process. Finally, a summary is provided of the results from the validation of the latest set of daily global analysis and forecast products from the MACC system reported in November 2014.

A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances

Abstract. Detecting the optical properties of aerosols using passive satellite-borne measurements alone is a difficult task due to the broadband effect of aerosols on the measured spectra and the influences of surface and cloud reflection. We present another approach to determine aerosol type, namely by studying the relationship of aerosol optical depth (AOD) with trace gas abundance, aerosol absorption, and mean aerosol size. Our new Global Aerosol Classification Algorithm, GACA, examines relationships between aerosol properties (AOD and extinction Ångström exponent from the Moderate Resolution Imaging Spectroradiometer (MODIS), UV Aerosol Index from the second Global Ozone Monitoring Experiment, GOME-2) and trace gas column densities (NO2, HCHO, SO2 from GOME-2, and CO from MOPITT, the Measurements of Pollution in the Troposphere instrument) on a monthly mean basis. First, aerosol types are separated based on size (Ångström exponent) and absorption (UV Aerosol Index), then the dominating sources are identified based on mean trace gas columns and their correlation with AOD. In this way, global maps of dominant aerosol type and main source type are constructed for each season and compared with maps of aerosol composition from the global MACC (Monitoring Atmospheric Composition and Climate) model. Although GACA cannot correctly characterize transported or mixed aerosols, GACA and MACC show good agreement regarding the global seasonal cycle, particularly for urban/industrial aerosols. The seasonal cycles of both aerosol type and source are also studied in more detail for selected 5° × 5° regions. Again, good agreement between GACA and MACC is found for all regions, but some systematic differences become apparent: the variability of aerosol composition (yearly and/or seasonal) is often not well captured by MACC, the amount of mineral dust outside of the dust belt appears to be overestimated, and the abundance of secondary organic aerosols is underestimated in comparison with GACA. Whereas the presented study is of exploratory nature, we show that the developed algorithm is well suited to evaluate climate and atmospheric composition models by including aerosol type and source obtained from measurements into the comparison, instead of focusing on a single parameter, e.g., AOD. The approach could be adapted to constrain the mix of aerosol types during the process of a combined data assimilation of aerosol and trace gas observations.

A regional air quality forecasting system over Europe: the MACC-II daily ensemble production

Abstract. This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 μg m−3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30–50 μg m−3. Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM10 for winter 2013–1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 μg m−3. The ensemble median fractional gross error is larger for PM10 (~ 0.52) than for ozone and the correlation is lower (~ 0.35 for PM10 and ~ 0.54 for ozone). This is related to a larger spread of the seven model scores for PM10 than for ozone linked to different levels of complexity of aerosol representation in the individual models. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

The ENSO signal in atmospheric composition fields: emission-driven versus dynamically induced changes

Abstract. The El Niño–Southern Oscillation (ENSO) not only affects meteorological fields but also has a large impact on atmospheric composition. Atmospheric composition fields from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis are used to identify the ENSO signal in tropospheric ozone, carbon monoxide, nitrogen oxide and smoke aerosols, concentrating on the months October to December. During El Niño years, all of these fields have increased concentrations over maritime South East Asia in October. The MACC Composition Integrated Forecasting System (C-IFS) model is used to quantify the relative magnitude of dynamically induced and emission-driven changes in the atmospheric composition fields. While changes in tropospheric ozone are a combination of dynamically induced and emission-driven changes, the changes in carbon monoxide, nitrogen oxides and smoke aerosols are almost entirely emission-driven in the MACC model. The ozone changes continue into December, i.e. after the end of the Indonesian fire season while changes in the other fields are confined to the fire season.

Evaluation of near-surface ozone over Europe from the MACC reanalysis

Abstract. This work is an extended evaluation of near-surface ozone as part of the global reanalysis of atmospheric composition, produced within the European-funded project MACC (Monitoring Atmospheric Composition and Climate). It includes an evaluation over the period 2003–2012 and provides an overall assessment of the modeling system performance with respect to near-surface ozone for specific European subregions. Measurements at rural locations from the European Monitoring and Evaluation Program (EMEP) and the European Air Quality Database (AirBase) were used for the evaluation assessment. The fractional gross error of near-surface ozone reanalysis is on average 24 % over Europe, the highest found over Scandinavia (27 %) and the lowest over the Mediterranean marine stations (21 %). Near-surface ozone shows mostly a negative bias in winter and a positive bias during warm months. Assimilation reduces the bias in near-surface ozone in most of the European subregions – with the exception of Britain and Ireland and the Iberian Peninsula and its impact is mostly notable in winter. With respect to the seasonal cycle, the MACC reanalysis reproduces the photochemically driven broad spring-summer maximum of surface ozone of central and south Europe. However, it does not capture adequately the early spring peak and the shape of the seasonality at northern and north-eastern Europe. The diurnal range of surface ozone, which is as an indication of the local photochemical production processes, is reproduced fairly well, with a tendency for a small overestimation during the warm months for most subregions (especially in central and southern Europe). Possible reasons leading to discrepancies between the MACC reanalysis and observations are discussed.

Towards Optimal Aerosol Information for the Retrieval of Solar Surface Radiation Using Heliosat

High quality data of surface radiation is a prerequisite for climate monitoring (Earth radiation budget) and solar energy applications. A very common method to derive solar surface irradiance is the Heliosat method, a one channel approach for the retrieval of the effective cloud albedo (CAL). This information is then used to derive the solar surface irradiance by application of a clear sky model. The results of this study are based on radiative transfer modelling, visual inspection of satellite images and evaluation of satellite based solar surface radiation with ground measurements. The respective results provide evidence that variations in Aerosol Optical depth induced by desert storms and biomass burning events lead to a significant increase of the effective cloud albedo, thus, that certain aerosol events are interpreted as clouds by the method. For the estimation of the solar surface radiation aerosol information is needed as input for the clear sky model. As the aerosol effect is partly considered by CAL, there is a need to modify external aerosol information for the use within the clear sky model, e.g., by truncation of high aerosol loads. Indeed, it has been shown that a modified version of the Monitoring Atmospheric Composition and Climate (MACC) aerosol information leads to better accuracy of the retrieved solar surface radiation than the original MACC data for the investigated 9 sites and time period (2006–2009). Further, the assumption of a constant aerosol optical depth of 0.18 provides also better accuracies of the estimated solar surface radiation than the original MACC data for the investigated sites and period. It is concluded that this is partly due to the consideration of scattering aerosols by the effective cloud albedo.