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The sensitivity (S) of cloud parameters to the influence of different aerosol and meteorological parameters has in most previous aerosol–cloud interaction (aci) studies been addressed using traditional statistical methods. In the current study, relationships between cloud droplet effective radius (CER) and aerosol optical depth (AOD, used as a proxy for cloud condensation nuclei, CCN), i.e., the sensitivity (S) of CER to AOD, are investigated with different constraints of AOD and cloud liquid water path (LWP). In addition to traditional statistical methods, the geographical detector method (GDM) is applied in this study to quantify the relative importance of the effects of aerosol and meteorological parameters, as well as their interaction, on S. Moderate Resolution Imaging Spectroradiometer (MODIS) C6 L3 data and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 reanalysis data, for the period from 2008 to 2022, were used to investigate aci over eastern China. Two contrasting areas were selected: the heavily polluted Yangtze River Delta (YRD) and a relatively clean area over the East China Sea (ECS). Linear regression analysis shows that CER decreases with the increase in AOD (negative S) in the moderately polluted atmosphere (0.10.3) over the YRD. Evaluation of S as function of the LWP shows that in the moderately polluted atmosphere over the ECS, S is negative in the LWP interval [40 g m−2, 200 g m−2], and the sensitivity of CER to AOD is substantially stronger as LWP is larger. In contrast, in the polluted atmosphere over the YRD, S is positive in the LWP interval [0 g m−2, 120 g m−2] and does not change notably as function of LWP in this interval. The study further shows that over the ECS, the CER is larger for higher low tropospheric stability (LTS) and relative humidity (RH) but lower for higher pressure vertical velocity (PVV). Over the YRD, there is no significant influence of LTS on the relationship between CER and AOD. The GDM has been used as an independent method to analyze the sensitivity of cloud parameters to AOD and meteorological parameters (RH, LTS and PVV). The GDM has also been used to analyze the effects of interactions between two parameters and thus obtain information on confounding meteorological effects on the aci. Over the ECS, cloud parameters are sensitive to almost all parameters considered except for cloud top pressure (CTP), and the sensitivity to AOD is larger than that to any of the meteorological factors. Among the meteorological factors, the cloud parameters are most sensitive to PVV and least sensitive to RH. Over the YRD, the explanatory power of the sensitivity of cloud parameters to AOD and meteorological parameters is much smaller than over the ECS, except for RH, which has a statistically significant influence on CTP and can explain 74 % of the variation of CTP. The results from the GDM analysis show that cloud parameters are more sensitive to the combination of aerosol and a meteorological parameter than to each parameter alone, but confounding effects due to co-variation of both parameters cannot be excluded.

Nitrogen dioxide (NO2) is a highly photochemically reactive gas, has a lifetime of only a few hours, and at high concentrations is harmful to human beings. Therefore, it is important to monitor NO2 with high-precision, time-resolved instruments. To this end, a Pandora spectrometer has been installed on the roof of the laboratory building of the Aerospace Information Research Institute of the Chinese Academy of Sciences in the Olympic Park, Beijing, China. The concentrations of trace gases (including NO2, HCHO, O3) measured with Pandora are made available through the open-access Pandora database (https://data.pandonia-global-network.org/Beijing-RADI/Pandora171s1/, last access: 11 July 2023). In this paper, an overview is presented of the Pandora total and tropospheric NO2 vertical column densities (VCDs) and surface concentrations collected during the first year of operation, i.e., from August 2021 to July 2022. The data show that NO2 concentrations were high in the winter and low in the summer, with a diurnal cycle where the concentrations reached a minimum during the daytime. The concentrations were significantly lower during the 2022 Winter Olympics in Beijing, showing the effectiveness of the emission control measures during that period. The Pandora observations show that during northerly winds, clean air is transported to Beijing with low NO2 concentrations, whereas during southerly winds, pollution from surrounding areas is transported to Beijing and NO2 concentrations are high. The contribution of tropospheric NO2 to the total NO2 VCD varies significantly on daily to seasonal timescales; i.e., monthly averages vary between 50 % and 60 % in the winter and between 60 % and 70 % in the spring and autumn. A comparison of Pandora-measured surface concentrations with collocated in situ measurements using a Thermo Scientific 42i-TL analyzer shows that the Pandora data are low and that the relationship between Pandora-derived surface concentrations and in situ measurements is different for low and high NO2 concentrations. Explanations for these differences are offered in terms of measurement techniques and physical (transport) phenomena. The use of Pandora total and tropospheric NO2 VCDs for validation of collocated TROPOspheric Monitoring Instrument (TROPOMI) data, resampled to 100 m × 100 m, shows that although on average the TROPOMI VCDs are slightly lower, they are well within the expected error for TROPOMI of 0.5 Pmolec.cm-2 + (0.2 to 0.5) ⋅ VCDtrop (1 Pmolec.cm-2 = 1 × 1015 molec cm−2). The location of the Pandora instrument within a sub-orbital TROPOMI pixel of 3.5 km × 5.5 km may result in an error in the TROPOMI-derived tropospheric NO2 VCD between 0.223 and 0.282 Pmolec.cm-2, i.e., between 1.7 % and 2 %. In addition, the data also show that the Pandora observations at the Beijing-RADI site are representative of an area with a radius of 10 km.

Urban heat island (UHI) and urban air pollution are two major environmental problems faced by many metropolises. The UHI affects air pollution by changing the local circulation and the chemical reaction environment, e.g. air temperature and relative humidity. In this study, the WRF-CMAQ (Weather Research and Forecasting Community Multiscale Air Quality) model was used to investigate the impact of an UHI on the vertical distribution of aerosol particles, especially secondary inorganic aerosol (SIA), taking the strong UHI in Hangzhou, China, as an example. The results show that during the day with the strongest UHI case of the 8 d simulation, the UHI effect resulted in a decrease in the PM2.5 concentrations in the boundary layer (BL) by about 33 %, accompanied by an increase in the lower free troposphere (LFT) by about 19 %. This is mostly attributed to the UHI circulation (UHIC) effect, which accounted for 91 % of the UHI-induced variations in PM2.5, rather than the UHI temperature or humidity effects, which contributed only 5 % and 4 %, respectively. The UHIC effect plays a dominant role, ranging from 72 % to 93 %, in UHI-induced PM2.5 variation in all eight UHI cases. The UHIC not only directly transports aerosol particles from ground level to the LFT but also redistributes aerosol precursors. During the strongest UHI case, about 80 % of the UHIC-induced increase in the aerosol particles in the LFT is due to direct transport of aerosol particles, whereas the other 20 % is due to secondary aerosol formation resulting from the transport of aerosol precursor gases. Of this 20 %, 91 % is contributed by SIA, especially ammonium nitrate aerosol formed from ammonia and nitric acid. In the atmosphere, ammonium nitrate is in equilibrium with ammonia and nitric acid, and the equilibrium depends on the ambient temperature. In the lower urban BL, the temperature is higher than in the LFT, and the ammonium nitrate equilibrium in the lower BL is more toward the gas phase than in the LFT; when these gases are transported by the UHIC into the colder LFT, the equilibrium shifts to the aerosol phase. Hence, the UHIC changes the vertical distribution of SIA, which may have potential implications on the radiation budget, cloud formation, and precipitation in the urban and surrounding areas.

Using 14 years (2007–2020) of data from passive (MODIS/Aqua) and active (CALIOP/CALIPSO) satellite measurements over China, we investigate (1) the temporal and spatial variation of aerosol properties over the Beijing–Tianjin–Hebei (BTH) region, the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) and (2) the vertical distribution of aerosol types and extinction coefficients for different aerosol optical depth (AOD) and meteorological conditions. The results show the different spatial patterns and seasonal variations of the AOD over the three regions. Annual time series reveal the occurrence of AOD maxima in 2011 over the YRD and in 2012 over the BTH and PRD; thereafter the AOD decreases steadily. Using the CALIOP vertical feature mask, the relative frequency of occurrence (rFO) of each aerosol type in the atmospheric column is analyzed: rFOs of dust and polluted dust decrease from north to south; rFOs of clean ocean, polluted continental, clean continental and elevated smoke aerosol increase from north to south. In the vertical, the peak frequency of occurrence (FO) for each aerosol type depends on region and season and varies with AOD and meteorological conditions. In general, three distinct altitude ranges are observed with the peak FO at the surface (clean continental and clean marine aerosol), at ∼1 km (polluted dust and polluted continental aerosol) and at ∼3 km (elevated smoke aerosol), whereas dust aerosol may occur over the whole altitude range considered in this study (from the surface up to 8 km). The designation of the aerosol type in different height ranges may to some extent reflect the CALIOP aerosol type classification approach. Air mass trajectories indicate the different source regions for the three study areas and for the three different altitude ranges over each area. In this study nighttime CALIOP profiles are used. The comparison with daytime profiles shows substantial differences in the FO profiles with altitude, which suggest effects of boundary layer dynamics and aerosol transport on the vertical distribution of aerosol types, although differences due to day–night CALIOP performance cannot be ruled out.

Global measurements of absorbing aerosol optical depth (AAOD) are scarce and mostly provided by the ground network AERONET (AErosol RObotic NETwork). In recent years, several satellite products of AAOD have been developed. This study's primary aim is to establish the usefulness of these datasets for AEROCOM (Aerosol Comparisons between Observations and Models) model evaluation with a focus on the years 2006, 2008 and 2010. The satellite products are super-observations consisting of 1∘×1∘×30 min aggregated retrievals. This study consists of two papers, the current one that deals with the assessment of satellite observations and a second paper (Schutgens et al., 2021) that deals with the evaluation of models using those satellite data. In particular, the current paper details an evaluation with AERONET observations from the sparse AERONET network as well as a global intercomparison of satellite datasets, with a focus on how minimum AOD (aerosol optical depth) thresholds and temporal averaging may improve agreement between satellite observations. All satellite datasets are shown to have reasonable skill for AAOD (three out of four datasets show correlations with AERONET in excess of 0.6) but less skill for SSA (single-scattering albedo; only one out of four datasets shows correlations with AERONET in excess of 0.6). In comparison, satellite AOD shows correlations from 0.72 to 0.88 against the same AERONET dataset. However, we show that performance vs. AERONET and inter-satellite agreements for SSA improve significantly at higher AOD. Temporal averaging also improves agreements between satellite datasets. Nevertheless multi-annual averages still show systematic differences, even at high AOD. In particular, we show that two POLDER (Polarization and Directionality of the Earth's Reflectances) products appear to have a systematic SSA difference over land of ∼0.04, independent of AOD. Identifying the cause of this bias offers the possibility of substantially improving current datasets. We also provide evidence that suggests that evaluation with AERONET observations leads to an underestimate of true biases in satellite SSA. In the second part of this study we show that, notwithstanding these biases in satellite AAOD and SSA, the datasets allow meaningful evaluation of AEROCOM models.

The variation of NO2 concentrations in mainland China is analyzed on different timescales, from decadal to weekly, using both satellite data and data from ground-based monitoring networks. TROPOMI (TROPOspheric Monitoring Instrument) data were used to study the spatial variations of tropospheric NO2 vertical column densities (TVCDs) over the study area during 16–20 weeks after the Chinese Spring Festival (25 January 2020). These data were used to select 11 regions for more detailed analysis of the variation of NO2 TVCDs on a decadal timescale. In this analysis, monthly and annual averaged NO2 TVCDs derived from OMI (Ozone Monitoring Instrument) observations were used for the years 2011 to 2019. The results show the NO2 TVCD trends for different regions, all decreasing in response to emission reduction policies but with a different onset and a possible halt of the decrease in recent years; trends and period in the south of the study area are different from those in the north. Variations of NO2 TVCDs on shorter timescales, monthly and weekly, were analyzed using TROPOMI data. In addition, the variations of weekly-averaged ground-based NO2 concentrations in 11 major cities were analyzed together with those for O3 and PM2.5. In particular these data were used to determine their effect on the air quality as expressed by the air quality index (AQI). For quantitative estimates, the use of weekly concentrations is more accurate than the use of monthly values, and the effects of long-term trends and their reversal needs to be taken into account for the separation of effects of the lockdown and the Spring Festival. Neglecting the possible reversal of the trends leads to overestimation of the lockdown effect in the south and underestimation in the north. The ground-based data confirm earlier reports, based on satellite observations, that the expected improvement of air quality due to the reduction of NO2 concentrations was offset by the increase of the concentrations of O3 and the different effects of the lockdown measures on PM2.5, as well as effects of meteorological influences and heterogeneous chemistry. The AQI seems to be mostly influenced by PM2.5 rather than NO2. A qualitative comparison between time series of satellite and ground-based NO2 observations shows both similarities and differences. The study further shows the different behaviors in city clusters in the north and south of China, as well as inland in the Sichuan and Guanzhong basins. Effects of other holidays and events are small, except in Beijing where the air quality in 2020 was notably better than in previous years. This study was undertaken for China, but the methodology and results have consequences for air quality studies in other areas, and part of the conclusions are generally applicable.

Aeolus is the first satellite mission to directly observe wind profile information on a global scale. After implementing a set of bias corrections, the Aeolus data products went public on 12 May 2020. However, Aeolus wind products over China have thus far not been evaluated extensively by ground-based remote sensing measurements. In this study, the Mie-cloudy and Rayleigh-clear wind products from Aeolus measurements are validated against wind observations from the radar wind profiler (RWP) network in China. Based on the position of each RWP site relative to the closest Aeolus ground tracks, three matchup categories are proposed, and comparisons between Aeolus wind products and RWP wind observations are performed for each category separately. The performance of Mie-cloudy wind products does not change much between the three matchup categories. On the other hand, for Rayleigh-clear and RWP wind products, categories 1 and 2 are found to have much smaller differences compared with category 3. This could be due to the RWP site being sufficiently approximate to the Aeolus ground track for categories 1 and 2. In the vertical, the Aeolus wind products are similar to the RWP wind observations, except for the Rayleigh-clear winds in the height range of 0–1 km. The mean absolute normalized differences between the Mie-cloudy (Rayleigh-clear) and the RWP wind components are 3.06 (5.45), 2.79 (4.81), and 3.32 (5.72) m/s at all orbit times and ascending and descending Aeolus orbit times, respectively. This indicates that the wind products for ascending orbits are slightly superior to those for descending orbits, and the observation time has a minor effect on the comparison. From the perspective of spatial differences, the Aeolus Mie-cloudy winds are consistent with RWP winds in most of east China, except in coastal areas where the Aeolus Rayleigh-clear winds are more reliable. Overall, the correlation coefficient R between the Mie-cloudy (Rayleigh-clear) wind and RWP wind component observation is 0.94 (0.81), suggesting that Aeolus wind products are in good agreement with wind observations from the RWP network in China. The findings give us sufficient confidence in assimilating the newly released Aeolus wind products in operational weather forecasting in China.

Knowledge of the composition of atmospheric aerosols is important for reducing uncertainty in climate assessment. In this study, an improved algorithm is developed for the retrieval of atmospheric columnar aerosol components from optical remote sensing data. This is achieved by using the complex refractive index (CRI) of a multicomponent liquid system in the forward model and minimizing the differences with the observations. The aerosol components in this algorithm comprise five species, combining eight subcomponents including black carbon (BC), water-soluble organic matter (WSOM) and water-insoluble organic matter (WIOM), ammonium nitrate (AN), sodium chloride (SC), dust-like content (DU), and aerosol water content in the fine and coarse modes (AWf and AWc). The calculation of the CRI in the multicomponent liquid system allows for the separation of the water-soluble components (AN, WSOM and AWf) in the fine mode and SC and AWc in the coarse mode. The uncertainty in the retrieval results is analyzed based on the simulation of typical models, showing that the complex refractive index obtained from instantaneous optical–physical inversion compares well with that obtained from chemical estimation. The algorithm was used to retrieve the columnar aerosol components over China using the ground-based remote sensing measurements from the Sun–sky radiometer Observation NETwork (SONET) in the period from 2010 to 2016. The results were used to analyze the regional distribution and interannual variation. The analysis shows that the atmospheric columnar DU component is dominant in the northern region of China, whereas the AW is higher in the southern coastal region. The SC component retrieved over the desert in northwest China originates from a paleomarine source. The AN significantly decreased from 2011 to 2016, by 21.9 mg m−2, which is inseparable from China's environmental control policies.

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1∘×1∘ daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1∘×1∘ grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

In March 2017, measurements of downward global irradiance of ultraviolet (UV) radiation were started with a multichannel GUV-2511 radiometer in Marambio, Antarctica (64.23∘ S; 56.62∘ W), by the Finnish Meteorological Institute (FMI) in collaboration with the Servicio Meteorológico Nacional (SMN). These measurements were analysed and the results were compared to previous measurements performed at the same site with the radiometer of the Antarctic NILU-UV network during 2000–2008 and to data from five stations across Antarctica. In 2017/2018 the monthly-average erythemal daily doses from October to January were lower than those averaged over 2000–2008 with differences from 2.3 % to 25.5 %. In 2017/2018 the average daily erythemal dose from September to March was 1.88 kJ m−2, while in 2018/2019 it was 23 % larger (2.37 kJ m−2). Also at several other stations in Antarctica the UV radiation levels in 2017/2018 were below average. The maximum UV indices (UVI) in Marambio were 6.2 and 9.5 in 2017/2018 and 2018/2019, respectively, whereas during years 2000–2008 the maximum was 12. Cloud cover, the strength of the polar vortex and the stratospheric ozone depletion are the primary factors that influence the surface UV radiation levels in Marambio. The lower UV irradiance values in 2017/2018 are explained by the high ozone concentrations in November, February and for a large part of October. The role of cloud cover was clearly seen in December, and to a lesser extent in October and November, when cloud cover qualitatively explains changes which could not be ascribed to changes in total ozone column (TOC). In this study, the roles of aerosols and albedo are of minor influence because the variation of these factors in Marambio was small from one year to the other. The largest variations of UV irradiance occur during spring and early summer when noon solar zenith angle (SZA) is low and the stratospheric ozone concentration is at a minimum (the so-called ozone hole). In 2017/2018, coincident low total ozone column and low cloudiness near solar noon did not occur, and no extreme UV indices were measured.

A ground-based ultraviolet (GUV) multi-filter radiometer was set up at Marambio, 64∘ S, 56∘ W, Antarctica, in 2017. The instrument continuously measures ultraviolet (UV) radiation, visible (VIS) radiation and photosynthetically active radiation (PAR). The measurements are designed for providing high-quality long-term time series that can be used to assess the impact of global climate change in the Antarctic region. The quality assurance includes regular absolute calibrations and solar comparisons performed at Marambio and at Sodankylä, Finland. The measurements continue observations at Marambio that were performed with (Norwegian Institute for Air Re-search UV Radiometer (NILU-UV) radiometers between 2000 and 2010 as part of the Antarctic NILU-UV network. These measurements are ideally suited for assessing the effects of the ongoing stratospheric ozone recovery on the ecosystem, as the data products include information on radiation at various wavelengths ranging from UV to VIS so that changes on biologically effective radiation due to ozone can be separated from those due to other factors. Data products include total ozone, photosynthetically active radiation (PAR), visible (VIS) radiation at 555 nm, UV index, UV irradiance at 5 channels, UVB and UVA dose rate and daily dose, and biologically weighted UV dose rate and daily dose, calculated with 10 different action spectra. The data from the last 5 d and the daily maximum UV index time series are plotted and updated daily on the following web page: http://fmiarc.fmi.fi/sub_sites/GUVant/ (last access: 17 April 2020). The first 2 years of UV measurements were very different in terms of the results: for October, November and December the monthly average of daily maximum UVB dose rates were clearly higher in 2018 than in 2017. The largest difference was observed in October, when the average of daily maximum UVB dose rates was 76 and 102 µW cm−2 in 2017 and 2018, respectively. Monthly averages of the 3 months were similar in 2018, while in 2017 the monthly average of October was lower than those of November and December. The VIS and PAR time series show that daily maxima in 2018–2019 exceed those in 2017–2018 during late spring and summer (mid-November–January). The studied dataset, including daily maximum irradiances at five UV channels and one VIS channel; daily maximum UVB, UVA, and PAR dose rates; noon UVB, UVA, and PAR dose rates; noon total column ozone; and UVB and UVA daily doses, is freely accessible at https://doi.org/10.5281/zenodo.3688700 (Lakkala et al., 2019).

The evaluation of modelling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models (ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM) with satellite observations using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model-to-model and model-to-satellite comparisons. Cloud droplet number concentrations (CDNCs) are derived identically from MODIS-COSP-simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distributions of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases in the Northern Hemisphere. We evaluate the aerosol–cloud interactions by computing the sensitivity parameter ACICDNC=dln⁡(CDNC)/dln⁡(AI) on a global scale. However, 1 year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies, which are necessary steps to further improve the parameterisation in climate models.

Satellite instruments provide a vantage point for studying aerosol loading consistently over different regions of the world. However, the typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors should be considered. Discrepancies exist between aerosol optical depth (AOD) products due to differences in their information content, spatial and temporal sampling, calibration, cloud masking, and algorithmic assumptions. Users of satellite-based AOD time-series are confronted with the challenge of choosing an appropriate dataset for the intended application. In this study, 16 monthly AOD products obtained from different satellite sensors and with different algorithms were inter-compared and evaluated against Aerosol Robotic Network (AERONET) monthly AOD. Global and regional analyses indicate that products tend to agree qualitatively on the annual, seasonal and monthly timescales but may be offset in magnitude. Several approaches were then investigated to merge the AOD records from different satellites and create an optimised AOD dataset. With few exceptions, all merging approaches lead to similar results, indicating the robustness and stability of the merged AOD products. We introduce a gridded monthly AOD merged product for the period 1995–2017. We show that the quality of the merged product is as least as good as that of individual products. Optimal agreement of the AOD merged product with AERONET further demonstrates the advantage of merging multiple products. This merged dataset provides a long-term perspective on AOD changes over different regions of the world, and users are encouraged to use this dataset.

Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor and the use of a series of different sensors may lead to substantial differences, and no single sensor or algorithm is better than any other everywhere and at all times. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the Advanced ATSR (AATSR) aerosol optical depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17 years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor. To investigate the possibility of extending the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern China, encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable when high AOD values have been observed over the last decade. In these cases, ATSR performs much better for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with BEM AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses that need to be accounted for when combining them in a single multi-decadal climate data record.

The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date, aiming to strengthen research and innovation regarding climate change issues in the Nordic region. CRAICC gathered more than 100 scientists from all Nordic countries in a virtual centre with the objectives of identifying and quantifying the major processes controlling Arctic warming and related feedback mechanisms, outlining strategies to mitigate Arctic warming, and developing Nordic Earth system modelling with a focus on short-lived climate forcers (SLCFs), including natural and anthropogenic aerosols. The outcome of CRAICC is reflected in more than 150 peer-reviewed scientific publications, most of which are in the CRAICC special issue of the journal Atmospheric Chemistry and Physics. This paper presents an overview of the main scientific topics investigated in the centre and provides the reader with a state-of-the-art comprehensive summary of what has been achieved in CRAICC with links to the particular publications for further detail. Faced with a vast amount of scientific discovery, we do not claim to completely summarize the results from CRAICC within this paper, but rather concentrate here on the main results which are related to feedback loops in climate change–cryosphere interactions that affect Arctic amplification.

Aerosol–cloud interaction (ACI) is examined using 10 years of data from the MODIS/Terra (morning orbit) and MODIS/Aqua (afternoon orbit) satellites. Aerosol optical depth (AOD) and cloud properties retrieved from both sensors are used to explore in a statistical sense the morning-to-afternoon variation of cloud properties in conditions with low and high AOD, over both land and ocean. The results show that the interaction between aerosol particles and clouds is more complex and of greater uncertainty over land than over ocean. The variation in d(Cloud_X), defined as the mean change in cloud property Cloud_X between the morning and afternoon overpasses in high-AOD conditions minus that in low-AOD conditions, is different over land and ocean. This applies to cloud droplet effective radius (CDR), cloud fraction (CF) and cloud top pressure (CTP), but not to cloud optical thickness (COT) and cloud liquid water path (CWP). Both COT and CWP increase over land and ocean after the time step, irrespective of the AOD. However, the initial AOD conditions can affect the amplitude of variation of COT and CWP. The effects of initial cloud fraction and meteorological conditions on the change in CF under low- and high-AOD conditions after the 3 h time step over land are also explored. Two cases are considered: (1) when the cloud cover increases and (2) when the cloud cover decreases. For both cases, we find that almost all values of d(CF) are positive, indicating that the variations of CF are larger in high AOD than that in low AOD after the 3 h time step. The results also show that a large increase in cloud fraction occurs when scenes experience large AOD and stronger upward motion of air parcels. Furthermore, the increase rate of cloud cover is larger for high AOD with increasing relative humidity (RH) when RH is larger than 20 %. We also find that a smaller increase in cloud fraction occurs when scenes experience larger AOD and larger initial cloud cover. Overall, the analysis of the diurnal variation of cloud properties provides a better understanding of aerosol–cloud interaction over land and ocean.

Understanding long-term variations in aerosol loading is essential for evaluating the health and climate effects of airborne particulates as well as the effectiveness of pollution control policies. The expected satellite lifetime is about 10 to 15 years. Therefore, to study the variations of atmospheric constituents over longer periods information from different satellites must be utilized.Here we introduce a method to construct a combined annual and seasonal long time series of AOD at 550 nm using the Along-Track Scanning Radiometers (ATSR: ATSR-2 and AATSR combined) and the MODerate resolution Imaging Spectroradiometer on Terra (MODIS/Terra), which together cover the 1995–2017 period. The long-term (1995–2017) combined AOD time series are presented for all of mainland China, for southeastern (SE) China and for 10 selected regions in China. Linear regression was applied to the combined AOD time series constructed for individual L3 (1°  ×  1°) pixels to estimate the AOD tendencies for two periods: 1995–2006 (P1) and 2011–2017 (P2), with respect to the changes in the emission reduction policies in China.During P1, the annually averaged AOD increased by 0.006 (or 2 % of the AOD averaged over the corresponding period) per year across all of mainland China, reflecting increasing emissions due to rapid economic development. In SE China, the annual AOD positive tendency in 1995–2006 was 0.014 (3 %) per year, reaching maxima (0.020, or 4 %, per year) in Shanghai and the Pearl River Delta regions. After 2011, during P2, AOD tendencies reversed across most of China with the annually averaged AOD decreasing by −0.015 (−6 %) per year in response to the effective reduction of the anthropogenic emissions of primary aerosols, SO2 and NOx. The strongest AOD decreases were observed in the Chengdu (−0.045, or −8 %, per year) and Zhengzhou (−0.046, or −9 %, per year) areas, while over the North China Plain and coastal areas the AOD decrease was lower than −0.03 (approximately −6 %) per year. In the less populated areas the AOD decrease was small.The AOD tendency varied by both season and region. The increase in the annually averaged AOD during P1 was mainly due to an increase in summer and autumn in SE China (0.020, or 4 %, and 0.016, or 4 %, per year, respectively), while during winter and spring the AOD actually decreased over most of China. The AOD negative tendencies during the 2011–2017 period were larger in summer than in other seasons over the whole of China (ca. −0.021, or −7 %, per year) and over SE China (ca. −0.048, or −9 %, per year).The long-term AOD variations presented here show a gradual decrease in the AOD after 2011 with an average reduction of 30 %–50 % between 2011 and 2017. The effect is more visible in the highly populated and industrialized regions in SE China, as expected.

Solar ultraviolet (UV) irradiances were measured with NILU-UV multichannel radiometers at Ushuaia (54° S) and Marambio (64° S) between 2000 and 2013. The measurements were part of the Antarctic NILU-UV network, which was started in cooperation between Spain, Argentina and Finland. The erythemally weighted UV irradiance time series of both stations were analysed for the first time. The quality assurance procedures included a travelling reference instrument to transfer the irradiance scale to the stations. The time series were homogenized and high quality measurements were available for the period 2000–2010. During this period UV indices of 11 or more were measured on 5 and 35 days at Marambio and Ushuaia, respectively. At Marambio, the peak daily maximum UV index of 12 and daily doses of around 7 kJ m−2 were measured in November 2007. The highest UV daily doses at both stations were typically around 6 kJ m−2 and occurred when the stations were inside the polar vortex, resulting in very low total ozone amount. At both stations, daily doses in late November could even exceed those in the summer. At Marambio, in some years, also daily doses in October can be as high as those during the summer. At Ushuaia, the peak daily maximum UV index of 13 was measured twice: in November 2003 and 2009. Also during those days, the station of Ushuaia was inside the polar vortex.

Aerosol optical depth (AOD) patterns and interannual and seasonal variations over China are discussed based on the AOD retrieved from the Along-Track Scanning Radiometer (ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV) v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and deep blue (DB) AOD product. Together these datasets provide an AOD time series for 23 years, from 1995 to 2017. The difference between the AOD values retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and monthly aggregate comparisons as well as validation results. This allows for the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset correction.ADV and MODIS AOD validation results show similar high correlations with the Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while the corresponding bias is positive for MODIS (0.06) and negative for ADV (−0.07). Validation of the AOD products in similar conditions, when ATSR and MODIS/Terra overpasses are within 90 min of each other and when both ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs better than MODIS in autumn, while MODIS performs slightly better in spring and summer. In winter, both ADV and MODIS underestimate the AERONET AOD.Similar AOD patterns are observed by ADV and MODIS in annual and seasonal aggregates as well as in time series. ADV–MODIS difference maps show that MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north.The agreement between sensors regarding year-to-year AOD changes is quite good. During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between 2006 and 2011 AOD did not change much, showing minor minima in 2008–2009. From 2011 onward AOD decreased in the SE of China. Similar patterns exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping period. However, regional differences between the ATSR and MODIS AODs are quite large. The consistency between ATSR and MODIS with regards to the AOD tendencies in the overlapping period is rather strong in summer, autumn and overall for the yearly average; however, in winter and spring, when there is a difference in coverage between the two instruments, the agreement between ATSR and MODIS is lower.AOD tendencies in China during the 1995–2017 period will be discussed in more detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and MODIS is introduced, and combined AOD time series are analyzed.

We present a 3-D climatology of the desert dust distribution over South and East Asia derived using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) data. To distinguish desert dust from total aerosol load we apply a methodology developed in the framework of EARLINET (European Aerosol Research Lidar Network). The method involves the use of the particle linear depolarization ratio and updated lidar ratio values suitable for Asian dust, applied to multiyear CALIPSO observations (January 2007–December 2015). The resulting dust product provides information on the horizontal and vertical distribution of dust aerosols over South and East Asia along with the seasonal transition of dust transport pathways. Persistent high D_AOD (dust aerosol optical depth) values at 532 nm, of the order of 0.6, are present over the arid and semi-arid desert regions. Dust aerosol transport (range, height and intensity) is subject to high seasonality, with the highest values observed during spring for northern China (Taklimakan and Gobi deserts) and during summer over the Indian subcontinent (Thar Desert). Additionally, we decompose the CALIPSO AOD (aerosol optical depth) into dust and non-dust aerosol components to reveal the non-dust AOD over the highly industrialized and densely populated regions of South and East Asia, where the non-dust aerosols yield AOD values of the order of 0.5. Furthermore, the CALIPSO-based short-term AOD and D_AOD time series and trends between January 2007 and December 2015 are calculated over South and East Asia and over selected subregions. Positive trends are observed over northwest and east China and the Indian subcontinent, whereas over southeast China trends are mostly negative. The calculated AOD trends agree well with the trends derived from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), although significant differences are observed over specific regions.

Satellite-based aerosol products are routinely validated against ground-based reference data, usually obtained from sun photometer networks such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground-based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativeness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along-Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences. We find that the spatial AOD variability in the satellite data is approximately 2 times larger than in the ground-based data, and the spatial variability correlates only weakly with that of AERONET for short distances. We interpreted that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (∼ 0.5°) the correlation is improved as the noise is averaged out, and the day-to-day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates including the CMU in the validation. We find that accounting for CMU increases the fraction of consistent observations.

The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed. The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a comparison of the data sets for the overlapping period to show their complementarity. ATSR precedes the MODIS time series between 1995 and 2000 and shows a distinct increase in the AOD over this period. The two data series show similar variations during the overlapping period between 2000 and 2011, with minima and maxima in the same years. MODIS extends this time series beyond the end of the Envisat period in 2012, showing decreasing AOD.

The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

Aerosol effects on low warm clouds over the Yangtze River Delta (YRD, eastern China) are examined using co-located MODIS, CALIOP and CloudSat observations. By taking the vertical locations of aerosol and cloud layers into account, we use simultaneously observed aerosol and cloud data to investigate relationships between cloud properties and the amount of aerosol particles (using aerosol optical depth, AOD, as a proxy). Also, we investigate the impact of aerosol types on the variation of cloud properties with AOD. Finally, we explore how meteorological conditions affect these relationships using ERA-Interim reanalysis data. This study shows that the relation between cloud properties and AOD depends on the aerosol abundance, with a different behaviour for low and high AOD (i.e. AOD 0.35). This applies to cloud droplet effective radius (CDR) and cloud fraction (CF), but not to cloud optical thickness (COT) and cloud top pressure (CTP). COT is found to decrease when AOD increases, which may be due to radiative effects and retrieval artefacts caused by absorbing aerosol. Conversely, CTP tends to increase with elevated AOD, indicating that the aerosol is not always prone to expand the vertical extension. It also shows that the COT–CDR and CWP (cloud liquid water path)–CDR relationships are not unique, but affected by atmospheric aerosol loading. Furthermore, separation of cases with either polluted dust or smoke aerosol shows that aerosol–cloud interaction (ACI) is stronger for clouds mixed with smoke aerosol than for clouds mixed with dust, which is ascribed to the higher absorption efficiency of smoke than dust. The variation of cloud properties with AOD is analysed for various relative humidity and boundary layer thermodynamic and dynamic conditions, showing that high relative humidity favours larger cloud droplet particles and increases cloud formation, irrespective of vertical or horizontal level. Stable atmospheric conditions enhance cloud cover horizontally. However, unstable atmospheric conditions favour thicker and higher clouds. Dynamically, upward motion of air parcels can also facilitate the formation of thicker and higher clouds. Overall, the present study provides an understanding of the impact of aerosols on cloud properties over the YRD. In addition to the amount of aerosol particles (or AOD), evidence is provided that aerosol types and ambient environmental conditions need to be considered to understand the observed relationships between cloud properties and AOD.

Retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Aqua satellite, 12 years (2003–2014) of aerosol and cloud properties were used to statistically quantify aerosol–cloud interaction (ACI) over the Baltic Sea region, including the relatively clean Fennoscandia and the more polluted central–eastern Europe. These areas allowed us to study the effects of different aerosol types and concentrations on macro- and microphysical properties of clouds: cloud effective radius (CER), cloud fraction (CF), cloud optical thickness (COT), cloud liquid water path (LWP) and cloud-top height (CTH). Aerosol properties used are aerosol optical depth (AOD), Ångström exponent (AE) and aerosol index (AI). The study was limited to low-level water clouds in the summer. The vertical distributions of the relationships between cloud properties and aerosols show an effect of aerosols on low-level water clouds. CF, COT, LWP and CTH tend to increase with aerosol loading, indicating changes in the cloud structure, while the effective radius of cloud droplets decreases. The ACI is larger at relatively low cloud-top levels, between 900 and 700 hPa. Most of the studied cloud variables were unaffected by the lower-tropospheric stability (LTS), except for the cloud fraction. The spatial distribution of aerosol and cloud parameters and ACI, here defined as the change in CER as a function of aerosol concentration for a fixed LWP, shows positive and statistically significant ACI over the Baltic Sea and Fennoscandia, with the former having the largest values. Small negative ACI values are observed in central–eastern Europe, suggesting that large aerosol concentrations saturate the ACI.

Cloud misclassification is a serious problem in the retrieval of aerosol optical depth (AOD), which might considerably bias the AOD results. On the one hand, residual cloud contamination leads to AOD overestimation, whereas the removal of high-AOD pixels (due to their misclassification as clouds) leads to underestimation. To remove cloud-contaminated areas in AOD retrieved from reflectances measured with the (Advanced) Along Track Scanning Radiometers (ATSR-2 and AATSR), using the ATSR dual-view algorithm (ADV) over land or the ATSR single-view algorithm (ASV) over ocean, a cloud post-processing (CPP) scheme has been developed at the Finnish Meteorological Institute (FMI) as described in Kolmonen et al. (2016). The application of this scheme results in the removal of cloud-contaminated areas, providing spatially smoother AOD maps and favourable comparison with AOD obtained from the ground-based reference measurements from the AERONET sun photometer network. However, closer inspection shows that the CPP also removes areas with elevated AOD not due to cloud contamination, as shown in this paper. We present an improved CPP scheme which better discriminates between cloud-free and cloud-contaminated areas. The CPP thresholds have been further evaluated and adjusted according to the findings. The thresholds for the detection of high-AOD regions (> 60 % of the retrieved pixels should be high-AOD (> 0.6) pixels), and cloud contamination criteria for low-AOD regions have been accepted as the default for AOD global post-processing in the improved CPP. Retaining elevated AOD while effectively removing cloud-contaminated pixels affects the resulting global and regional mean AOD values as well as coverage. Effects of the CPP scheme on both spatial and temporal variation for the period 2002–2012 are discussed. With the improved CPP, the AOD coverage increases by 10–15 % with respect to the existing scheme. The validation versus AERONET shows an improvement of the correlation coefficient from 0.84 to 0.86 for the global data set for the period 2002–2012. The global aggregated AOD over land for the period 2003–2011 is 0.163 with the improved CPP compared to 0.144 with the existing scheme. The aggregated AOD over ocean and globally (land and ocean together) is 0.164 with the improved CPP scheme (compared to 0.152 and 0.150 with the existing scheme, for ocean and globally respectively). Effects of the improved CPP scheme on the 10-year time series are illustrated and seasonal and temporal changes are discussed. The improved CPP method introduced here is applicable to other aerosol retrieval algorithms. However, the thresholds for detecting the high-AOD regions, which were developed for AATSR, might have to be adjusted to the actual features of the instruments.

The northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The Arctic–boreal natural environments play a crucial role in the global climate via albedo change, carbon sources and sinks as well as atmospheric aerosol production from biogenic volatile organic compounds. Furthermore, it is expected that global trade activities, demographic movement, and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but also is able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX sets a research approach by which large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the northern Eurasian region. We introduce here the state of the art for the key topics in the PEEX research agenda and present the future prospects of the research, which we see relevant in this context.

The two Brewer spectrophotometers of the Finnish Meteorological Institute at Jokioinen and Sodankylä have been operated according to the highest levels of the WMO∕GAW (World Meteorological Organization∕Global Atmosphere Watch) recommendations with rigorous quality control and quality assurance. The calibration of the instruments is based on annual recalibrations of primary standard lamps in the VTT MIKES Metrology National Standards Laboratory in Finland and an exhaustive measurement program with measurements of standard and working lamps in the on-site optical laboratories. Over the years, the maintenance of the calibration has produced data sets of approximately 2000 lamp scans for both instruments. An extensive re-examination of the lamp measurements and the response of the spectrophotometers was carried out. The primary standard lamps were found to age on an average rate of 0.3 % per burn. The responsivity at wavelength 311 nm was found to exhibit both long-term and short-term changes. The overall long-term change was declining. In addition, abrupt changes of as large as 25 % were detected. The short-term changes were found to fluctuate on time frames shorter than the interval between the measurements of the primary standard lamps. This underlines the importance of the use of more frequently measured working standard lamps.

Aerosol–cloud interactions are the largest source of uncertainty in the radiative forcing of the global climate. A phenomenon not included in the estimates of the total net forcing is the potential increase in upper tropospheric humidity (UTH) by anthropogenic aerosols via changes in the microphysics of deep convection. Using remote sensing data over the ocean east of China in summer, we show that increased aerosol loads are associated with an UTH increase of 2.2 ± 1.5 in units of relative humidity. We show that humidification of aerosols or other meteorological covariation is very unlikely to be the cause of this result, indicating relevance for the global climate. In tropical moist air such an UTH increase leads to a regional radiative effect of 0.5 ± 0.4 W m−2. We conclude that the effect of aerosols on UTH should be included in future studies of anthropogenic climate change and climate sensitivity.

In this study, the utility of satellite-based whitecap fraction (W) data for the prediction of sea spray aerosol (SSA) emission rates is explored. More specifically, the study aims at evaluating how an account for natural variability of whitecaps in the W parameterization would affect SSA mass flux predictions when using a sea spray source function (SSSF) based on the discrete whitecap method. The starting point is a data set containing W data for 2006 together with matching wind speed U10 and sea surface temperature (SST) T. Whitecap fraction W was estimated from observations of the ocean surface brightness temperature TB by satellite-borne radiometers at two frequencies (10 and 37 GHz). A global-scale assessment of the data set yielded approximately quadratic correlation between W and U10. A new global W(U10) parameterization was developed and used to evaluate an intrinsic correlation between W and U10 that could have been introduced while estimating W from TB. A regional-scale analysis over different seasons indicated significant differences of the coefficients of regional W(U10) relationships. The effect of SST on W is explicitly accounted for in a new W(U10, T) parameterization. The analysis of W values obtained with the new W(U10) and W(U10, T) parameterizations indicates that the influence of secondary factors on W is for the largest part embedded in the exponent of the wind speed dependence. In addition, the W(U10, T) parameterization is able to partially model the spread (or variability) of the satellite-based W data. The satellite-based parameterization W(U10, T) was applied in an SSSF to estimate the global SSA emission rate. The thus obtained SSA production rate for 2006 of 4.4 × 1012 kg year−1 is within previously reported estimates, however with distinctly different spatial distribution.

A first direct intercomparison of aerosol vertical profiles from Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations, performed during the Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments (CINDI) in summer 2009, is presented. Five out of 14 participants of the CINDI campaign reported aerosol extinction profiles and aerosol optical thickness (AOT) as deduced from observations of differential slant column densities of the oxygen collision complex (O4) at different elevation angles. Aerosol extinction vertical profiles and AOT are compared to backscatter profiles from a ceilometer instrument and to sun photometer measurements, respectively. Furthermore, the near-surface aerosol extinction coefficient is compared to in situ measurements of a humidity-controlled nephelometer and dry aerosol absorption measurements. The participants of this intercomparison exercise use different approaches for the retrieval of aerosol information, including the retrieval of the full vertical profile using optimal estimation and a parametrised approach with a prescribed profile shape. Despite these large conceptual differences, and also differences in the wavelength of the observed O4 absorption band, good agreement in terms of the vertical structure of aerosols within the boundary layer is achieved between the aerosol extinction profiles retrieved by the different groups and the backscatter profiles observed by the ceilometer instrument. AOTs from MAX-DOAS and sun photometer show a good correlation (R>0.8), but all participants systematically underestimate the AOT. Substantial differences between the near-surface aerosol extinction from MAX-DOAS and from the humidified nephelometer remain largely unresolved.

The data flow involved in a long-term continuous solar spectral UV irradiance monitoring program is investigated and structured to provide an overall view on the multiphase process from data acquisition to the final products. The program employing Brewer spectrophotometers as measuring instruments is maintained by the Finnish Meteorological Institute (FMI) ever since the 1990s at two sites in Finland: Sodankylä (67° N) and Jokioinen (61° N). It is built upon rigorous operation routines, processing procedures, and tools for quality control (QC) and quality analysis (QA) under continuous development and evaluation. Three distinct levels of data emerge, each after certain phase in the data flow: Level 0 denoting raw data, Level 1 meaning calibrated data processed in near-real time, and Level 2 comprising of postprocessed data corrected for all distinguishable errors and known inaccuracies. The final products disseminated to the users are demonstrated to result from a process with a multitude of separate steps, each required in the production of high-quality data on solar UV radiation at the Earth's surface.

In order to quantify the effects of absorbing contaminants on snow, a series of spectral reflectance measurements were conducted. Chimney soot, volcanic sand, and glaciogenic silt were deposited on a natural snow surface in a controlled way as a part of the Soot on Snow (SoS) campaign. The bidirectional reflectance factors of these soiled surfaces and untouched snow were measured using the Finnish Geodetic Institute's Field Goniospectropolariradiometer, FIGIFIGO. A remarkable feature is the fact that the absorbing contaminants on snow enhanced the metamorphism of snow under strong sunlight in our experiments. Immediately after deposition, the contaminated snow surface appeared darker than the natural snow in all viewing directions, but the absorbing particles sank deep into the snow in minutes. The nadir measurement remained the darkest, but at larger zenith angles, the surface of the contaminated snow changed back to almost as white as clean snow. Thus, for a ground observer the darkening caused by impurities can be completely invisible, overestimating the albedo, but a nadir-observing satellite sees the darkest points, underestimating the albedo. Through a reciprocity argument, we predict that at noon, the albedo perturbation should be lower than in the morning or afternoon. When sunlight stimulates sinking more than melting, the albedo should be higher in the afternoon than in the morning, and vice versa when melting dominates. However, differences in the hydrophobic properties, porosity, clumping, or size of the impurities may cause different results than observed in these measurements.

Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

The Advanced Along-Track Scanning Radiometer (AATSR) on board the ENVISAT satellite is used to study aerosol properties. The retrieval of aerosol properties from satellite data is based on the optimized fit of simulated and measured reflectances at the top of the atmosphere (TOA). The simulations are made using a radiative transfer model with a variety of representative aerosol properties. The retrieval process utilizes a combination of four aerosol components, each of which is defined by their (lognormal) size distribution and a complex refractive index: a weakly and a strongly absorbing fine-mode component, coarse mode sea salt aerosol and coarse mode desert dust aerosol). These components are externally mixed to provide the aerosol model which in turn is used to calculate the aerosol optical depth (AOD). In the AATSR aerosol retrieval algorithm, the mixing of these components is decided by minimizing the error function given by the sum of the differences between measured and calculated path radiances at 3–4 wavelengths, where the path radiances are varied by varying the aerosol component mixing ratios. The continuous variation of the fine-mode components allows for the continuous variation of the fine-mode aerosol absorption. Assuming that the correct aerosol model (i.e. the correct mixing fractions of the four components) is selected during the retrieval process, also other aerosol properties could be computed such as the single scattering albedo (SSA). Implications of this assumption regarding the ratio of the weakly/strongly absorbing fine-mode fraction are investigated in this paper by evaluating the validity of the SSA thus obtained. The SSA is indirectly estimated for aerosol plumes with moderate-to-high AOD resulting from wildfires in Russia in the summer of 2010. Together with the AOD, the SSA provides the aerosol absorbing optical depth (AAOD). The results are compared with AERONET data, i.e. AOD level 2.0 and SSA and AAOD inversion products. The RMSE (root mean square error) is 0.03 for SSA and 0.02 for AAOD lower than 0.05. The SSA is further evaluated by comparison with the SSA retrieved from the Ozone Monitoring Instrument (OMI). The SSA retrieved from both instruments show similar features, with generally lower AATSR-estimated SSA values over areas affected by wildfires.

Ambient aerosol particles can take up water and thus change their optical properties depending on the hygroscopicity and the relative humidity (RH) of the surrounding air. Knowledge of the hygroscopicity effect is of crucial importance for radiative forcing calculations and is also needed for the comparison or validation of remote sensing or model results with in situ measurements. Specifically, particle light scattering depends on RH and can be described by the scattering enhancement factor f(RH), which is defined as the particle light scattering coefficient at defined RH divided by its dry value (RH Here, we present results of an intensive field campaign carried out in summer 2013 at the SMEAR II station at Hyytiälä, Finland. Ground-based and airborne measurements of aerosol optical, chemical and microphysical properties were conducted. The f(RH) measured at ground level by a humidified nephelometer is found to be generally lower (e.g. 1.63±0.22 at RH = 85 % and λ = 525 nm) than observed at other European sites. One reason is the high organic mass fraction of the aerosol encountered at Hyytiälä to which f(RH) is clearly anti-correlated (R2≈0.8). A simplified parametrization of f(RH) based on the measured chemical mass fraction can therefore be derived for this aerosol type. A trajectory analysis revealed that elevated values of f(RH) and the corresponding elevated inorganic mass fraction are partially caused by transported hygroscopic sea spray particles. An optical closure study shows the consistency of the ground-based in situ measurements. Our measurements allow to determine the ambient particle light extinction coefficient using the measured f(RH). By combining the ground-based measurements with intensive aircraft measurements of the particle number size distribution and ambient RH, columnar values of the particle extinction coefficient are determined and compared to columnar measurements of a co-located AERONET sun photometer. The water uptake is found to be of minor importance for the column-averaged properties due to the low particle hygroscopicity and the low RH during the daytime of the summer months. The in situ derived aerosol optical depths (AOD) clearly correlate with directly measured values of the sun photometer but are substantially lower compared to the directly measured values (factor of ~ 2–3). The comparison degrades for longer wavelengths. The disagreement between in situ derived and directly measured AOD is hypothesized to originate from losses of coarse and fine mode particles through dry deposition within the canopy and losses in the in situ sampling lines. In addition, elevated aerosol layers (above 3 km) from long-range transport were observed using an aerosol lidar at Kuopio, Finland, about 200 km east-north-east of Hyytiälä. These elevated layers further explain parts of the disagreement.

Proxies for estimating nucleation mode number concentrations and further simplification for their use with satellite data have been presented in Kulmala et al. (2011). In this paper we discuss the underlying assumptions for these simplifications and evaluate the resulting proxies over an area in South Africa based on a comparison with a suite of ground-based measurements available from four different stations. The proxies are formulated in terms of sources (concentrations of precursor gases (NO2 and SO2) and UVB radiation intensity near the surface) and a sink term related to removal of the precursor gases due to condensation on pre-existing aerosols. A-Train satellite data are used as input to compute proxies. Both the input data and the resulting proxies are compared with those obtained from ground-based measurements. In particular, a detailed study is presented on the substitution of the local condensation sink (CS) with satellite aerosol optical depth (AOD), which is a column-integrated parameter. One of the main factors affecting the disagreement between CS and AOD is the presence of elevated aerosol layers. Overall, the correlation between proxies calculated from the in situ data and observed nucleation mode particle number concentrations (Nnuc) remained low. At the time of the satellite overpass (13:00–14:00 LT) the highest correlation is observed for SO2/CS (R2 = 0.2). However, when the proxies are calculated using satellite data, only NO2/AOD showed some correlation with Nnuc (R2 = 0.2). This can be explained by the relatively high uncertainties related especially to the satellite SO2 columns and by the positive correlation that is observed between the ground-based SO2 and NO2 concentrations. In fact, results show that the satellite NO2 columns compare better with in situ SO2 concentration than the satellite SO2 column. Despite the high uncertainties related to the proxies calculated using satellite data, the proxies calculated from the in situ data did not better predict Nnuc. Hence, overall improvements in the formulation of the proxies are needed.

In this study, a method is presented to retrieve the surface reflectance using the radiances measured at the top of the atmosphere for the two views provided by the Advanced Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual-view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated versus surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (Moderate Resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with somewhat higher values (up to 0.05) obtained by ADV over bright surfaces. The difference between the ADV- and MODIS-retrieved surface reflectances is smaller than ±0.025 for 68.3% of the collocated pixels at 555 nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation in the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for both the AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover a 17-year period of measurements (1995–2012), as well as a prototype for the Sea and Land Surface Temperature Radiometer (SLSTR) planned to be launched in the fall of 2015 onboard the Sentinel-3 satellite.

A satellite-based approach to derive the aerosol direct shortwave (SW) radiative effect (ADRE) was studied in an environment with highly variable aerosol conditions over eastern China from March to October 2009. The method is based on using coincident SW top-of-the-atmosphere (TOA) fluxes from the Clouds and the Earth's Radiant Energy System (CERES) and aerosol optical depths (AODs) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to derive SW clear-sky ADRE. The estimate for the aerosol-free TOA flux (F0,TOA) is obtained by establishing linear regression between CERES SW TOA fluxes and MODIS AODs. A normalization procedure to a fixed solar zenith angle, Earth–Sun distance and atmospheric water vapor content was applied to the CERES fluxes prior to the linear fit against AOD to reduce the flux variation not related to aerosols. In the majority of the cases, the normalization increased positive correlation between observed SW TOA fluxes and AODs, and it decreased RMSE. The key question in the satellite-based approach is the accuracy of the estimated F0,TOA. Comparison with simulated F0,TOA showed that both the satellite method and the model produced qualitatively similar spatial patterns, but absolute values differed. In 58 % of the cases the satellite-based F0,TOA was within ±10 W m−2 of the modeled value (about 7–8 % difference in flux values). Over bright surfaces, the satellite-based method tend to produce lower F0,TOA than the model. The satellite-based clear-sky estimates for median instantaneous and diurnally averaged ADRE over the study area were −8.8 W m−2 and −5.1 W m−2, respectively. Over heavily industrialized areas, the cooling at TOA was 2 to more than 3 times the median value, and associated with high AODs (> 0.5). Especially during the summer months, positive ADREs were observed locally over dark surfaces. This was most probably a method artifact related to systematic change of aerosol type, sub-visual cloud contamination or both.

Recently developed parameterizations for the sea spray aerosol source flux, encapsulating wave state, and its organic fraction were incorporated into the aerosol–climate model ECHAM-HAMMOZ to investigate the direct and indirect radiative effects of sea spray aerosol particles. Our simulated global sea salt emission of 805 Tg yr−1 (uncertainty range 378–1233 Tg yr−1) was much lower than typically found in previous studies. Modelled sea salt and sodium ion concentrations agreed relatively well with measurements in the smaller size ranges at Mace Head (annual normalized mean model bias −13% for particles with vacuum aerodynamic diameter Dva < 1 μm), Point Reyes (−29% for particles with aerodynamic diameter Da < 2.5 μm) and Amsterdam Island (−52% for particles with Da < 1 μm) but the larger sizes were overestimated (899% for particles with 2.5 μm < Da < 10 μm) at Amsterdam Island. This suggests that at least the high end of the previous estimates of sea spray mass emissions is unrealistic. On the other hand, the model clearly underestimated the observed concentrations of organic or total carbonaceous aerosol at Mace Head (−82%) and Amsterdam Island (−68%). The large overestimation (212%) of organic matter at Point Reyes was due to the contribution of continental sources. At the remote Amsterdam Island site, the organic concentration was underestimated especially in the biologically active months, suggesting a need to improve the parameterization of the organic sea spray fraction. Globally, the satellite-retrieved AOD over the oceans, using PARASOL data, was underestimated by the model (means over ocean 0.16 and 0.10, respectively); however, in the pristine region around Amsterdam Island the measured AOD fell well within the simulated uncertainty range. The simulated sea spray aerosol contribution to the indirect radiative effect was positive (0.3 W m−2), in contrast to previous studies. This positive effect was ascribed to the tendency of sea salt aerosol to suppress both the in-cloud supersaturation and the formation of cloud condensation nuclei from sulfate. These effects can be accounted for only in models with sufficiently detailed aerosol microphysics and physics-based parameterizations of cloud activation. However, due to a strong negative direct effect, the simulated effective radiative forcing (total radiative) effect was −0.2 W m−2. The simulated radiative effects of the primary marine organic emissions were small, with a direct effect of 0.03 W m−2 and an indirect effect of −0.07 W m−2.

The Advanced Very High Resolution Radiometer (AVHRR) provides a global, long-term, consistent time series of radiance data in several wavebands which are used for the retrieval of surface spectral reflectance, albedo and surface temperature. Long-term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. AVHRR radiances have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land is challenging because of the limited information content in the data which renders the inversion problem ill defined. Solving the radiative transfer equations requires additional information to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following the MODIS dark target approach, this relationship is used to obtain the surface reflectance at 0.64 μm. The comparison of the estimated surface reflectances with MODIS reflectance products (MOD09) shows a strong correlation. Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to AVHRR data. A simplified look-up table (LUT) method, adopted from the Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The aerosol optical depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with 71.8% of the points located within ±(0.1 + 0.15τ) (τ is the AOD) of the identity line. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to be used on geostationary satellites.

An algorithm is presented for the estimation of volcanic ash plume top height using the stereo view of the Advanced Along Track Scanning Radiometer (AATSR) aboard Envisat. The algorithm is based on matching top of the atmosphere (TOA) reflectances and brightness temperatures of the nadir and 55° forward views, and using the resulting parallax to obtain the height estimate. Various retrieval parameters are discussed in detail, several quality parameters are introduced, and post-processing methods for screening out unreliable data have been developed. The method is compared to other satellite observations and in situ data. The proposed algorithm is designed to be fully automatic and can be implemented in operational retrieval algorithms. Combined with automated ash detection using the brightness temperature difference between the 11 and 12 μm channels, the algorithm allows efficient simultaneous retrieval of the horizontal and vertical dispersion of volcanic ash. A case study on the eruption of the Icelandic volcano Eyjafjallajökull in 2010 is presented.

A prescribed fire experiment was conducted on 26 June 2009 in Hyytiälä, Finland, to study aerosol and trace gas emissions from prescribed fires of slash fuels and the effects of fire on soil properties in a controlled environment. A 0.8 ha forest near the SMEAR II measurement station (Station for Measuring Ecosystem-Atmosphere Relations) was cut clear; some tree trunks, all tree tops and branches were left on the ground and burned. The amount of burned organic material was ~46.8 tons (i.e., ~60 tons ha−1). The flaming phase lasted 2 h 15 min, the smoldering phase 3 h. Measurements were conducted on the ground with both fixed and mobile instrumentation, and in the air from a research aircraft. In the middle of the burning area, CO2 concentration peaked around 2000–3000 ppm above the baseline, and peak vertical flow velocities were ~9 m s−1, as measured with a 10 Hz 3-D sonic anemometer placed within the burn area. In the mobile measurements the peak particle number concentrations were approximately 1–2 × 106 cm−3 in the plume at a distance of 100–200 m from the burn area. On the ground at the SMEAR II station the geometric mean diameter of the mode with the highest concentration was 80 ± 1 nm during the flaming phase and in the middle of the smoldering phase, but, at the end of the smoldering phase, the largest mode was 122 nm. In the volume size distributions, geometric mean diameter of the largest volume mode was 153 nm during the flaming phase and 300 nm during the smoldering phase. The lowest single-scattering albedo of the ground-level measurements was 0.7 in the flaming-phase plume and ~0.9 in the smoldering phase. Elevated concentrations of several volatile organic compounds (VOC) (including acetonitrile, a biomass burning marker) were observed in the smoke plume at ground level. Measurements at the forest floor (i.e., a richly organic layer of soil and debris, characteristic of forested land) showed that VOC fluxes were generally low and consisted mainly of monoterpenes, and VOC flux peaked after the burning. After one year, the fluxes had nearly stabilized close to the level before the burning. The clear-cutting and burning of slash increased the total long-term CO2 release from the soil, and altered the physical, chemical and biological properties of the soil, such as increased the available nitrogen contents of the soil, which in turn, affected the long-term fluxes of greenhouse gases.

Climatic effects of black carbon (BC) deposition on snow have been proposed to result from reduced snow albedo and increased melt due to light-absorbing particles. In this study, we hypothesize that BC may decrease the liquid-water retention capacity of melting snow, and present our first data, where both the snow density and elemental carbon content were measured. In our experiments, artificially added light-absorbing impurities decreased the density of seasonally melting natural snow. No relationship was found in case of natural non-melting snow. We also suggest three possible processes that might lead to lower snow density.

Cluster~analysis of particle number size distributions from~background sites across Europe~is presented. This generated a total of nine clusters of particle size distributions which could be further combined into two main groups, namely: a south-to-north category (four clusters) and a west-to-east category (five clusters). The first group was identified as most frequently being detected inside and around northern Germany and neighbouring countries, showing clear evidence of local afternoon nucleation and growth events that could be linked to movement of air masses from south to north arriving ultimately at the Arctic contributing to Arctic haze.~The second group of particle size spectra proved to have narrower size distributions and collectively showed a dependence of modal diameter upon the longitude of the site (west to east) at which they were most frequently detected.~These clusters indicated regional nucleation (at the coastal sites) growing to larger modes further inland. The apparent growth rate of the modal diameter was around 0.6–0.9 nm h−1. Four specific air mass back-trajectories were successively taken as case studies to examine in real time the evolution of aerosol size distributions across Europe. ~While aerosol growth processes can be observed as aerosol traverses Europe, the processes are often obscured by the addition of aerosol by emissions en route. This study revealed that some of the 24 stations exhibit more complex behaviour than others, especially when impacted by local sources or a variety of different air masses. Overall, the aerosol size distribution clustering analysis greatly simplifies the complex data set and allows a description of aerosol aging processes, which reflects the longer-term average development of particle number size distributions as air masses advect across Europe.

The climate in the Arctic is changing faster than anywhere else on earth. Poorly understood feedback processes relating to Arctic clouds and aerosol–cloud interactions contribute to a poor understanding of the present changes in the Arctic climate system, and also to a large spread in projections of future climate in the Arctic. The problem is exacerbated by the paucity of research-quality observations in the central Arctic. Improved formulations in climate models require such observations, which can only come from measurements in situ in this difficult-to-reach region with logistically demanding environmental conditions. The Arctic Summer Cloud Ocean Study (ASCOS) was the most extensive central Arctic Ocean expedition with an atmospheric focus during the International Polar Year (IPY) 2007–2008. ASCOS focused on the study of the formation and life cycle of low-level Arctic clouds. ASCOS departed from Longyearbyen on Svalbard on 2 August and returned on 9 September 2008. In transit into and out of the pack ice, four short research stations were undertaken in the Fram Strait: two in open water and two in the marginal ice zone. After traversing the pack ice northward, an ice camp was set up on 12 August at 87°21' N, 01°29' W and remained in operation through 1 September, drifting with the ice. During this time, extensive measurements were taken of atmospheric gas and particle chemistry and physics, mesoscale and boundary-layer meteorology, marine biology and chemistry, and upper ocean physics. ASCOS provides a unique interdisciplinary data set for development and testing of new hypotheses on cloud processes, their interactions with the sea ice and ocean and associated physical, chemical, and biological processes and interactions. For example, the first-ever quantitative observation of bubbles in Arctic leads, combined with the unique discovery of marine organic material, polymer gels with an origin in the ocean, inside cloud droplets suggests the possibility of primary marine organically derived cloud condensation nuclei in Arctic stratocumulus clouds. Direct observations of surface fluxes of aerosols could, however, not explain observed variability in aerosol concentrations, and the balance between local and remote aerosols sources remains open. Lack of cloud condensation nuclei (CCN) was at times a controlling factor in low-level cloud formation, and hence for the impact of clouds on the surface energy budget. ASCOS provided detailed measurements of the surface energy balance from late summer melt into the initial autumn freeze-up, and documented the effects of clouds and storms on the surface energy balance during this transition. In addition to such process-level studies, the unique, independent ASCOS data set can and is being used for validation of satellite retrievals, operational models, and reanalysis data sets.

A new sea spray source function (SSSF), termed Oceanflux Sea Spray Aerosol or OSSA, was derived based on in-situ sea spray aerosol measurements along with meteorological/physical parameters. Submicron sea spray aerosol fluxes derived from particle number concentration measurements at the Mace Head coastal station, on the west coast of Ireland, were used together with open-ocean eddy correlation flux measurements from the Eastern Atlantic Sea Spray, Gas Flux, and Whitecap (SEASAW) project cruise. In the overlapping size range, the data for Mace Head and SEASAW were found to be in a good agreement, which allowed deriving the new SSSF from the combined dataset spanning the dry diameter range from 15 nm to 6 μm. The OSSA source function has been parameterized in terms of five lognormal modes and the Reynolds number instead of the more commonly used wind speed, thereby encapsulating important influences of wave height, wind history, friction velocity, and viscosity. This formulation accounts for the different flux relationships associated with rising and waning wind speeds since these are included in the Reynolds number. Furthermore, the Reynolds number incorporates the kinematic viscosity of water, thus the SSSF inherently includes dependences on sea surface temperature and salinity. The temperature dependence of the resulting SSSF is similar to that of other in-situ derived source functions and results in lower production fluxes for cold waters and enhanced fluxes from warm waters as compared with SSSF formulations that do not include temperature effects.

The discrimination of cloudy from cloud-free pixels is required in almost any estimate of a parameter retrieved from satellite data in the ultraviolet (UV), visible (VIS) or infrared (IR) parts of the electromagnetic spectrum. In this paper we report on the development of a neural network (NN) algorithm to estimate cloud fractions using radiances measured at the top of the atmosphere with the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained from the application of two different types of neural networks, i.e., extreme learning machine (ELM) and back propagation (BP). The NNs were trained with an OMI data sets existing of six orbits, tested with three other orbits and validated with another two orbits. The results were evaluated by comparison with cloud fractions available from the MODerate Resolution Imaging Spectrometer (MODIS) flying on Aqua in the same constellation as OMI, i.e., with minimal time difference between the OMI and MODIS observations. The results from the ELM and BP NNs are compared. They both deliver cloud fraction estimates in a fast and automated way, and they both performs generally well in the validation. However, over highly reflective surfaces, such as desert, or in the presence of dust layers in the atmosphere, the cloud fractions are not well predicted by the neural network. Over ocean the two NNs work equally well, but over land ELM performs better.

Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust).

Simultaneous measurements of near-surface aerosol (0.12 < R < 9.25 μm) and bubble spectra (13 < R < 620 μm) were made during five buoy deployments in the open ocean of the North Atlantic and used to estimate aerosol fluxes per unit area of whitecap. The measurements were made during two cruises as part of the Sea Spray, Gas Flux, and Whitecaps (SEASAW) project, a UK contribution to the international Surface Ocean Lower Atmosphere Study (SOLAS) program. The mean bubble number concentrations for each deployment are in broad agreement with other open ocean spectra and are consistently one to two orders of magnitude lower than surf zone studies. Production fluxes per unit area of whitecap are estimated from the mean aerosol concentration for each buoy deployment. They are found to increase with wind speed, and span the range of values found by previous laboratory and surf-zone studies for particles with radius at 80% relative humidity, R80 < 1 μm, but to drop off more rapidly with increasing particle size for larger particles. Estimates of the mean sea spray flux were made by scaling the whitecap production fluxes with in situ estimates of whitecap fraction. The sea spray fluxes are also compared with simultaneous individual eddy covariance flux estimates, and with a sea spray source function derived from them.