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One of the largest uncertainties in estimating the anthropogenic radiative forcing is related to the impact of atmospheric aerosols on cloud properties. This uncertainty originates mainly from the complicated nature of aerosol-cloud interaction as it is much stronger and more difficult to observe than the aerosol-radiation interaction. The estimates of radiative forcing due to changes in cloud properties vary significantly between different global climate models, highlighting the need for constraining this forcing by using observations. Moreover, it is challenging to determine the impact of aerosols on clouds from satellite observations only. In this study, we aim to improve that by combining the in-situ observations with satellite retrievals, in order to reduce uncertainties in the anthropogenic impact on clouds and the climate. The study is performed for the low-level liquid clouds over the Southern Great Plains (SGP), Oklahoma. The in-situ data on particle number concentration, large enough to act as cloud condensation nuclei (CCN), with diameter larger than 100 nm (N100) were collected for a 5-year period from the Atmospheric Radiation Measurement (ARM) observatory at SGP site. In this analysis, the level-2 collection-6 MODIS cloud property dataset (MYD06_L2) with a 1x1 km resolution was used, where only liquid, single-layer clouds with a cloud top warmer than 268 K were included. It was observed that both CER and COT increased with increasing CWP. However, at a same level of CWP, the CER was smaller, and COT was larger at high N100 concentrations, as compared to the lower N100 concentrations. This result is consistent with the hypothesis of enhanced aerosol load increasing the number concentration of CCN, which in turn leads to smaller cloud droplets.

Interactions in urban environment were investigated using a multidisciplinary model combination, with focus on traffic, emissions and atmospheric particles. An agent-based model was applied to simulate the evolution of unsustainable human behavior (usage of combustion-based personal vehicles) as a function of pro-environmental affordances (opportunities for sustainable choices). Scenarios regarding changes in multi-pollutant emissions were derived, and the non-linear implications to atmospheric particles were simulated with a box model. Based on the results for a Nordic city, increasing pro-environmental affordances by 10%, 50% or 100% leads to emission reductions of 15%, 30% and 40% within 2 years. To reduce ambient particle mass, emissions from traffic should decrease by > 15%, while the lung deposited surface area decreases in all scenarios ($$-\,23\%$$ - 23 % , $$-\,32\%$$ - 32 % and $$-\,36\%$$ - 36 % , correspondingly). The presented case is representative of one season, but the approach is generic and applicable to simulating a full year, given meteorological and pollution data that reflects seasonal variation. This work emphasizes the necessity to consider feedback mechanisms and non-linearities in both human behavior and atmospheric processes, when predicting the outcomes of changes in an urban system.

A quantitative comparison study for Raman lidar and ceilometer observations, and for model simulations of mass concentration estimates of smoke particles is presented. Layers of biomass burning aerosol particles were observed in the lower troposphere, at 2 to 5 km height on 4 to 6 June 2019, over Kuopio, Finland. These long-range-transported smoke particles originated from a Canadian wildfire event. The most pronounced smoke plume detected on 5 June was intensively investigated. Optical properties were retrieved from the multi-wavelength Raman polarization lidar PollyXT. Particle linear depolarization ratios (PDRs) of this plume were measured to be 0.08±0.02 at 355 nm and 0.05±0.01 at 532 nm, suggesting the presence of partly coated soot particles or particles that have mixed with a small amount of dust or other non-spherical aerosol type. The layer-mean PDR at 355 nm (532 nm) decreased during the day from ∼ 0.11 (0.06) in the morning to ∼ 0.05 (0.04) in the evening; this decrease with time could be linked to the particle aging and related changes in the smoke particle shape properties. Lidar ratios were derived as 47±5 sr at 355 nm and 71±5 sr at 532 nm. A complete ceilometer data processing for a Vaisala CL51 ceilometer is presented from a sensor-provided attenuated backscatter coefficient to particle mass concentration (including the water vapor correction for high latitude for the first time). Aerosol backscatter coefficients (BSCs) were measured at four wavelengths (355, 532, 1064 nm from PollyXT and 910 nm from CL51). Two methods, based on a combined lidar and sun-photometer approach, are applied for mass concentration estimations from both PollyXT and the ceilometer CL51 observations. In the first method, no. 1, we used converted BSCs at 532 nm (from measured BSCs) by corresponding measured backscatter-related Ångström exponents, whereas in the second method, no. 2, we used measured BSCs at each wavelength independently. A difference of ∼ 12 % or ∼ 36 % was found between PollyXT and CL51 estimated mass concentrations using method no. 1 or no. 2, showing the potential of mass concentration estimates from a ceilometer. Ceilometer estimations have an uncertainty of ∼ 50 % in the mass retrieval, but the potential of the data lies in the great spatial coverage of these instruments. The mass retrievals were compared with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorological and aerosol reanalysis. The inclusion of dust (as indicated by MERRA-2 data) in the retrieved mass concentration is negligible considering the uncertainties, which also shows that ceilometer observations for mass retrievals can be used even without exact knowledge of the composition of the smoke-dominated aerosol plume in the troposphere.

Satellite-based aerosol retrievals provide a timely view of atmospheric aerosol properties, having a crucial role in the subsequent estimation of air quality indicators, atmospherically corrected satellite data products, and climate applications. However, current aerosol data products based on satellite data often have relatively large biases compared to accurate ground-based measurements and distinct uncertainty levels associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become tedious and computationally expensive. To overcome this burden, we have developed a model-enforced post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine-learning-based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets without fully re-processing the much larger original radiance data. We demonstrate with over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. For instance, in our evaluation, the number of AOD samples within the MODIS Dark Target expected error envelope increased from 63 % to 85 % when the post-process correction was applied. In addition to method description and accuracy results, we also give recommendations for validating machine-learning-based satellite data products.

Uncertainty in the representation of biomass burning (BB) aerosol composition and optical properties in climate models contributes to a range in modeled aerosol effects on incoming solar radiation. Depending on the model, the top-of-the-atmosphere BB aerosol effect can range from cooling to warming. By relating aerosol absorption relative to extinction and carbonaceous aerosol composition from 12 observational datasets to nine state-of-the-art Earth system models/chemical transport models, we identify varying degrees of overestimation in BB aerosol absorptivity by these models. Modifications to BB aerosol refractive index, size, and mixing state improve the Community Atmosphere Model version 5 (CAM5) agreement with observations, leading to a global change in BB direct radiative effect of −0.07 W m−2, and regional changes of −2 W m−2 (Africa) and −0.5 W m−2 (South America/Temperate). Our findings suggest that current modeled BB contributes less to warming than previously thought, largely due to treatments of aerosol mixing state., Wildfires produce aerosols known to impact the climate, but the wider-reaching effects of this biomass burning are poorly constrained in models. Here the authors use a suite of observations from 12 campaigns around the globe to determine that the values used by most climate models overestimate the contribution of biomass burning aerosols.

As the share of photovoltaics (PV) in electricity production increases, accurate modeling and forecasting of its output becomes critical. PV output is however affected by multiple factors, making accurate modeling of these systems a non-trivial task. This study provides new insight on the performance of a popular parametric PV output model in a Nordic context. The model and its subcomponents – including estimations on plane-of-array radiation, PV module temperature, and PV output – are here evaluated against dedicated measurements at two Finnish sites. In addition, a novel Quality Control (QC) approach is introduced for handling calculated direct normal irradiance (DNI) values. All reviewed methods show good agreement with the references. The proposed QC efficiently filters unrealistic calculated DNI data, providing a potential approach for DNI QC implementation. In snow-free conditions, the selected PV module temperature scheme has a mean absolute error (MAE) around 2 °C, while the bias is below 1 °C. The plane-of-array radiation model has a MAE of 10–15 W/m2 with a bias of smaller than ±10 W/m2, depending on the site location and available input data. Altogether, the PV model is shown to provide relevant PV system performance information while demonstrating precise performance in snow-free conditions. By implementing site-specific parameter optimization, MAE was reduced from 19 to 14 W/kWp and bias lowered from 15 to 7 W/kWp. For the locations studied, the estimated PV output losses due to snow cover for the winter period 2017–2018 are estimated to be up to 1.5 months of summer production.

Biogenic secondary organic aerosol (BSOA) constitutes a major fraction of aerosol over boreal forests. As the emissions of BSOA precursors are temperature dependent, changes in temperature are likely to have substantial implications on regional aerosol radiative forcing. In this work, we have used a century long aerosol-climate model simulation to investigate the effect of increasing temperature on organic aerosol mass loadings, and further on aerosol-cloud interaction. The analysis was based on a nudged simulation done with ECHAM6-SALSA covering the period from 1905 to 2010. We limited the analysis to summer months to isolate the temperature dependence of biogenic emissions from the seasonal cycle of vegetation growth. We concentrated on three regions in Russia and three in Canada to analyze the spatial variability of the climatic impacts of BSOA. Our analysis showed that BSOA loadings increased with surface temperature and higher BSOA loads were connected to higher cloud condensation nuclei concentrations in all the regions. However, the relationship between BSOA and cloud optical thickness or cloud droplet size was not that clear in all the regions. These regional differences highlight the need to have accurate aerosol and cloud observations from various locations in the boreal region in order to estimate the climatic significance of biogenic aerosols.

Despite a large number of studies, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in different sizes in total of for 21 measurement sites in Europe and Arctic. For 13 of those, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in-situ observations from Europe and five earth system models (ESM). We found that the trends of particle number concentrations were mostly consistent and decreasing in both, measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, were typically stronger in northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories. For EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, for others, the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showing clear differences in concentrations between remote and urban sites. To conclude, although anthropogenic mass emissions are harmonized in models, trends in different sized particles vary among the model due to assumptions in emission sizes and differences in how models treat size dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol-cloud interactions.

PURPOSE: Tehran, Iran, is an interesting location for aerosol studies because it is affected by anthropogenic pollution and desert dust aerosols. The aim of this study was to discriminate the aerosol types using satellite data over the city. METHOD: The study was performed using Level-2 daily Aerosol Optical Depth (AOD) and Ångström Exponent (AE) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua satellites for the years 2011 to 2015. As the Deep Blue (DB) AE retrievals are more reliable than the Dark Target (DT) AE retrievals, the study was performed using DB data. RESULTS: The number of granules with successful retrievals (at least in two pixels with AODs >0.2 over Tehran with high quality assurance) was 200, which indicates that aerosols could be observed in 5.47% (200 from 3652 of Terra and Aqua granules) of the overpasses during the study period. The maximum and minimum values of AOD occurred during May (0.32 ± 0.27) and August (0.18 ± 0.07), respectively. Based on the AOD vs. AE data, aerosols were classified into three different categories: urban/industry (UI), Desert Dust (DD) and Mixed (Mix). To improve the accuracy of the aerosol classification, the analysis was limited to retrievals with AOD values larger than 0.2. The DD, UI and Mix types had 48.5%, 30.5% and 21% contribution in the aerosol days, respectively. CONCLUSIONS: The maximum DD frequency was observed in the spring and summer seasons, while the UI type had its maximum during the cold season. The AOD of the DD type (over Tehran) correlated well with the AOD observations done at the Aerosol Robotic Network (AERONET) site in Zanjan (300 km northwest from Tehran). For the UI type, no relationship with the AERONET AOD was detected. This gives confidence in our aerosol typing as the contribution of dust in the aerosol load is mainly from long-range transport, whereas the urban aerosols originate from local sources. Back trajectories ending in Tehran show that the northeast and west trajectories are two main transport routes for the dust to the study area.

Iran is located in the dust-belt region and so dust is a main environmental issue over the country. The main purpose of this study is to better understand of the spatial and temporal properties of the Dust Aerosol Frequency (DAF) over Iran using satellite data. Daily Moderate Resolution Imaging Spectroradiometer (MODIS) deep blue Aerosol Optical Depth (AOD), Angstrom Exponent (AE) (both Terra and Aqua, Level 3, Collection 6.1) and Ozone Monitoring Instrument (OMI) Absorption Aerosol Index (AAI) were obtained at 1° × 1° spatial resolution over Iran for the 10 years from 2006 to 2015. In this study observations with AOD higher than 0.3, AE 0.7 are considered to be dust aerosols. Results showed that the maximum areas with high DAF are located in the Khuzestan province in the southwest and Sistan in the east of the country. The other areas with peak DAF were observed in the low elevation areas of the country, namely coastal plain of the Oman Sea, Jaz Murian depression and Lut desert in the southeast, and Southern Dasht-e Kavir in central northern. DAF is decreased significantly at latitudes higher than 34.5° N. The maximum and minimum DAFs over Iran occur in July (Terra = 2.02, Aqua = 2.1) and November (Terra = 0.1, Aqua = 0.05), respectively. We found good spatial relationship between DAF with the meteorological codes frequency related to the dust from weather stations as well as with the fraction of dust AOD and from Monitoring Atmospheric Composition and Climate (MACC) project. In addition, results showed that dust frequencies based on both weather station and satellite data exhibited corresponding relationships with latitude, elevation, vegetation cover, precipitation and temperature.

Layers of biomass burning aerosol particles were observed in the lower troposphere, at 2 to 5 km height on 4 to 6 June 2019, over Kuopio, Finland. These long-range-transported smoke particles originated from a Canadian wildfire event. The most pronounced smoke plume detected on 5 June was intensively investigated. Optical properties were retrieved from the multi-wavelength Raman polarization lidar PollyXT. Particle linear depolarization ratios of this plume were measured to be 0.08 ± 0.02 at 355 nm and 0.05 ± 0.01 at 532 nm which were slightly higher than the values given in the literature. Non-spherical shaped aged smoke particles and/or the mixing with a small amount of fine dust particles could cause the observed increase in the particle linear depolarization ratios. Lidar ratios were derived as 47 ± 5 sr at 355 nm and 71 ± 5 sr at 532 nm. A complete ceilometer data processing for a Vaisala CL51 is presented, including the water vapor correction for high latitude for the first time, from sensor provided attenuated backscatter coefficient to particle mass concentration. A combined lidar and sun-photometer approach (based on AERONET products) is applied for mass concentration estimations. Mass concentrations were estimated from both PollyXT and the ceilometer CL51 observations, which were of the order of ~ 30 µg m−3 in the morning and decreased to ~ 20 µg m−3 in the night. A difference of ~ 30% was found between PollyXT and CL51 estimated mass concentrations. The mass retrievals were discussed and compared with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorological and aerosol reanalysis. The inclusion of dust in the retrieved mass concentration slightly improved the correspondence between the observations and the MERRA-2 simulations.

Biogenic secondary organic aerosol (BSOA) constitutes a major fraction of aerosol over boreal forests. As the emissions of BSOA precursors are temperature dependent, changes in temperature have potentially important implications on regional aerosol radiative forcing. Here, we have used long-term aerosol composition and temperature data measurements from a boreal forest site together with remote sensing observations of aerosol and cloud properties to investigate the effect of increasing temperature on organic aerosol mass loadings, and further on aerosol direct and indirect radiative effects. The analysis was based on 7 years of measurements done at Hyytiälä, Southern Finland, and they cover the summer months (July-August) between 2012-2018. We limited the analyses to these summer months to isolate the temperature dependence of the organic mass loadings from the seasonal effects arising from the vegetation growth cycle. Our analysis showed that organic aerosol loadings and cloud condensation nuclei concentrations increased in concert with surface temperature. Furthermore, we found that cloud reflectivity increased when the organic aerosol loadings increased. This research presents the first direct observational evidence on the effect of BSOA on cloud properties and their climatic significance.

Monitoring spatio-temporal changes of aerosols is necessary to better understand atmospheric processes. Here, the vertical distribution of aerosols and how it has changed from 2006 to 2017 is studied using time series data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Aerosol centroid altitude, a variable representing the aerosol vertical extent, is estimated using the aerosol flags of the Vertical Feature Mask (VFM) product of CALIOP. The case study includes four overpasses, with various aerosol types and densities, covering the Middle East and North Africa (MENA), and India regions characterized by frequent dust storms and smoke emissions, respectively, and Europe, a pioneer in establishing air quality policies. The values of aerosol centroid altitude are examined to determine the effects of climate (space) and season (time) on aerosol vertical distribution. For this purpose, 10-year mean values of aerosol centroid altitude were calculated over climate regions extracted from the Food and Agriculture Organization (FAO) climate map as well as over each season, and the resultant values were then statistically analyzed. The results indicate that environmental heat, steam from climate or season, leads to aerosols increasing in altitude. The time series was imported in a version of the Breaks For Additive Season and Trend (BFAST) algorithm, so-called BFAST01, allowing the magnitude of gradual changes along with the time and type of the major abrupt change to be determined. The strongest positive trends in aerosol centroid altitude were recorded in the desert regions. Positive trends were also observed in the mid-latitudes steppes and the North Sea. India was another region with a relatively positive trend which could be mainly caused by the increasing anthropogenic activities and smoke emission. The strongest negative trend detected in Europe is a result of strict environmental regulation policies and efforts for air quality improvement. The results show that a small fraction of granules (12 out of 4036) mainly in the northern part of overpass A, over Europe, the mid part of overpasses B and C, over the Middle East, and southern part of overpass D, over India, experienced structural changes. The reversal changes type 8 (50% of the abrupt changes) and interruption type 6 (33%) are the dominant types, mostly occurred around 2014 and 2012, respectively. Changing environmental factors such as temperature, wind speed, and anthropogenic activities are the main drivers of these changes. The study demonstrates the applicability of BFAST01 and the usability of CALIOP VFM time series data to monitor the vertical structure of the atmosphere.

Satellite-based aerosol retrievals provide a timely global view of atmospheric aerosol properties for air quality, atmospheric characterization, and correction of satellite data products and climate applications. Current aerosol data products based on satellite data, however, often have relatively large biases relative to accurate ground-based measurements and distinct levels of uncertainty associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become a tedious and computationally expensive task. To overcome this burden, we have developed a model enforced post-process correction approach that can be used to correct the existing and operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine learning based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data. We demonstrate with over land aerosol optical depth (AOD) and Angstrom exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. We also give recommendations for the validation of satellite data products that are constructed using machine learning based models.

We took science to the streets of Helsinki using urban art in order to foster communication between scientists and the general public.Researchers from the Institute for Atmospheric and Earth System Research (INAR) and Finnish Meteorological Institute, together with artists from Helsinki Urban Art co-designed a public mural that joined climate research, art and literary characters of Alice in Wonderland. The Climate Wall illustrates the 1.5°C and 4°C pathways and highlights the need for a transition from a greenhouse-gas spewing caterpillar into a sustainable butterfly. The wall includes the hashtag #ScienceInHelsinki and INAR’s twitter handle inviting passers-by to ask scientists questions anytime.The design of the mural was a joint effort, from posing the starting question, “what is the key message we want to deliver?”, to the final imagery used on the wall (and some actual painting!). Methods used for the co-design process included in-person workshops, a facebook group, and online virtual ‘whiteboards’, which resulted in a collateral result of this project: how scientists and artists can work together effectively.The Climate Wall was inaugurated in a public event at Helsinki Central Library Oodi auditorium in December 2019 with a short presentation, a panel discussion with some of the scientists and artists, and a children’s workshop (“The Art of Asking Questions”).The project took place from May 2019 until the mural’s completion on October 2019, and was funded by the Finnish Arts Promotion Center (Taike).

We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

The TROPOspheric Monitoring Instrument (TROPOMI) is the only payload of the Sentinel-5 Precursor (S5P), which is a polar-orbiting satellite mission of the European Space Agency (ESA). TROPOMI is a nadir-viewing spectrometer measuring in the ultraviolet, visible, near-infrared, and the shortwave infrared that provides near-global daily coverage. Among other things, TROPOMI measurements will be used for calculating the UV radiation reaching the Earth's surface. Thus, the TROPOMI surface UV product will contribute to the monitoring of UV radiation by providing daily information on the prevailing UV conditions over the globe. The TROPOMI UV algorithm builds on the heritage of the Ozone Monitoring Instrument (OMI) and the Satellite Application Facility for Atmospheric Composition and UV Radiation (AC SAF) algorithms. This paper provides a description of the algorithm that will be used for estimating surface UV radiation from TROPOMI observations. The TROPOMI surface UV product includes the following UV quantities: the UV irradiance at 305, 310, 324, and 380 nm; the erythemally weighted UV; and the vitamin-D weighted UV. Each of these are available as (i) daily dose or daily accumulated irradiance, (ii) overpass dose rate or irradiance, and (iii) local noon dose rate or irradiance. In addition, all quantities are available corresponding to actual cloud conditions and as clear-sky values, which otherwise correspond to the same conditions but assume a cloud-free atmosphere. This yields 36 UV parameters altogether. The TROPOMI UV algorithm has been tested using input based on OMI and the Global Ozone Monitoring Experiment-2 (GOME-2) satellite measurements. These preliminary results indicate that the algorithm is functioning according to expectations.

Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.

The magnitude of aerosol radiative effects remains the single largest uncertainty in current estimates of anthropogenic radiative forcing. One of the key quantities needed for accurate estimates of...

Satellite data suggest that summertime aerosol optical depth (AOD) over the southeastern USA depends on the air/land surface temperature, but the magnitude of the radiative effects caused by this dependence remains unclear. To quantify these radiative effects, we utilized several remote sensing datasets and ECMWF reanalysis data for the years 2005–2011. In addition, the global aerosol–climate model ECHAM-HAMMOZ was used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. The satellite-based observations suggest that changes in the total summertime AOD in the southeastern USA are mainly governed by changes in anthropogenic emissions. In addition, summertime AOD exhibits a dependence on southerly wind speed and land surface temperature (LST). Transport of sea salt and Saharan dust is the likely reason for the wind speed dependence, whereas the temperature-dependent component is linked to temperature-induced changes in the emissions of biogenic volatile organic compounds (BVOCs) over forested regions. The remote sensing datasets indicate that the biogenic contribution increases AOD with increasing temperature by approximately (7 ± 6) × 10−3 K−1 over the southeastern USA. In the model simulations, the increase in summertime AOD due to temperature-enhanced BVOC emissions is of a similar magnitude, i.e., (4 ± 1) × 10−3 K−1. The largest source of BVOC emissions in this region is broadleaf trees, thus if the observed temperature dependence of AOD is caused by biogenic emissions the dependence should be the largest in the vicinity of forests. Consequently, the analysis of the remote sensing data shows that over mixed forests the biogenic contribution increases AOD by approximately (27 ± 13) × 10−3 K−1, which is over four times higher than the value for over the whole domain, while over other land cover types in the study region (woody savannas and cropland/natural mosaic) there is no clear temperature dependence. The corresponding clear-sky direct radiative effect (DRE) of the observation-based biogenic AOD is −0.33 ± 0.29 W/m2/K for the whole domain and −1.3 ± 0.7 W/m2/K over mixed forests only. The model estimate of the regional clear-sky DRE for biogenic aerosols is similar to the observational estimate for the whole domain: −0.29 ± 0.09 W/m2/K. Furthermore, the model simulations showed that biogenic emissions have a significant effective radiative forcing (ERF) in this region: −1.0 ± 0.5 W/m2/K.

We have investigated what would be the climate and PM-induced air quality consequences if all nuclear reactors worldwide were closed down and replaced by coal combustion. In a way, this presents a “worst-case scenario” since less polluting energy sources are available. We studied simultaneously the radiative and health effects of coal power emissions using a global 3-D aerosol-climate model (ECHAM-HAMMOZ). This approach allowed us to estimate the effects of a major global energy production change from low carbon source to a high carbon one using detailed spatially resolved population density information. We included the radiative effects of both CO2 and PM2.5 but limited the study of health effects to PM2.5 only. Our results show that the replacement of nuclear power with coal power would have globally caused an average of 150,000 premature deaths per year during the period 2005–2009 with two thirds of them in Europe. For 37 years the aerosol emissions from the additional coal power plants would cool the climate but after that the accumulating CO2 emissions would accelerate the warming of the climate.

Article, The importance of light-absorbing organic aerosols, often called brown carbon (BrC), has become evident in recent years. However, there have been relatively few measurement-based estimates for the direct radiative effect of BrC so far. In earlier studies, the AErosol RObotic NETwork (AERONET)-measured aerosol absorption optical depth (AAOD) and absorption Angstrom exponent (AAE) were exploited. However, these two pieces of information are clearly not sufficient to separate properly carbonaceous aerosols from dust, while imaginary indices of refraction would contain more and better justified information for this purpose. This is first time that the direct radiative effect (DRE) of BrC is estimated by exploiting the AERONET-retrieved imaginary indices. We estimated it for four sites in the Indo-Gangetic Plain (IGP), Karachi, Lahore, Kanpur and Gandhi College. We found a distinct seasonality, which was generally similar among all the sites, but with slightly different strengths. The monthly warming effect up to 0.5 W m−2 takes place during the spring season. On the other hand, BrC results in an overall cooling effect in the winter season, which can reach levels close to −1 W m−2. We then estimated similarly also the DRE of black carbon and total aerosol, in order to assess the relative significance of the BrC radiative effect in the radiative effects of other components. Even though BrC impact seems minor in this context, we demonstrated that it is not insignificant. Moreover, we demonstrated that it is crucial to perform spectrally resolved radiative transfer calculations to obtain good estimates for the DRE of BrC., published version, http://purl.org/eprint/status/PeerReviewed

Amines have recently been found to be an important ingredient in the nucleation and initial growth of atmospheric aerosols; however, global estimates of the spatial and temporal extent of amine-enhanced nucleation are currently missing. We utilize two recently published laboratory data sets of amine-sulfuric acid nucleation to evaluate the accuracy of previously published nucleation parameterizations and to produce a new amine-enhanced new particle formation (NPF) parameterization that better reproduces the laboratory observations at atmospherically relevant sulfuric acid concentrations. We implement and compare the amine-enhanced NPF parameterizations and a kinetic nucleation parameterization within the global aerosol-climate model ECHAM-HAMMOZ and find that the spatial features of amine-enhanced and kinetic NPF are clearly different. Amine-enhanced NPF is limited to areas near the source regions of amine due to its short gas phase residence time of 6.9 h, whereas kinetic nucleation (which depends only on sulfuric acid concentration) produces particles more uniformly across the globe due to long-range transport of SO2. The notably stronger land-sea contrast in amine-enhanced nucleation simulations is in line with relatively rare atmospheric observations of NPF over open oceans. However, when the uptake of gas phase amine molecules to aerosol particles is limited according to previously published estimates (0.2% of collisions leading to uptake), the amine-enhanced NPF parameterization predicts in some regions unrealistically high NPF rates (∼1000 cm−3 s−1) compared to typical observations. Our results indicate that amine-enhanced nucleation may be an important particle formation mechanism near amine source regions but also highlights the need for more tightly defined constraints on the spatial and temporal distribution of amine emissions, gas-to-particle partitioning mechanisms of amines, and condensation and coagulation sinks in global models.

Article, Raman lidar data obtained over a 1 year period has been analysed in relation to aerosol layers in the free troposphere over the Highveld in South Africa. In total, 375 layers were observed above the boundary layer during the period 30 January 2010 to 31 January 2011. The seasonal behaviour of aerosol layer geometrical characteristics, as well as intensive and extensive optical properties were studied. The highest centre heights of free-tropospheric layers were observed during the South African spring (2520 ± 970 m a.g.l., also elsewhere). The geometrical layer depth was found to be maximum during spring, while it did not show any significant difference for the rest of the seasons. The variability of the analysed intensive and extensive optical properties was high during all seasons. Layers were observed at a mean centre height of 2100 ± 1000 m with an average lidar ratio of 67 ± 25 sr (mean value with 1 standard deviation) at 355 nm and a mean extinction-related Ångström exponent of 1.9 ± 0.8 between 355 and 532 nm during the period under study. Except for the intensive biomass burning period from August to October, the lidar ratios and Ångström exponents are within the range of previous observations for urban/industrial aerosols. During Southern Hemispheric spring, the biomass burning activity is clearly reflected in the optical properties of the observed free-tropospheric layers. Specifically, lidar ratios at 355 nm were 89 ± 21, 57 ± 20, 59 ± 22 and 65 ± 23 sr during spring (September–November), summer (December–February), autumn (March–May) and winter (June–August), respectively. The extinction-related Ångström exponents between 355 and 532 nm measured during spring, summer, autumn and winter were 1.8 ± 0.6, 2.4 ± 0.9, 1.8 ± 0.9 and 1.8 ± 0.6, respectively. The mean columnar aerosol optical depth (AOD) obtained from lidar measurements was found to be 0.46 ± 0.35 at 355 nm and 0.25 ± 0.2 at 532 nm. The contribution of free-tropospheric aerosols on the AOD had a wide range of values with a mean contribution of 46%., published version, http://purl.org/eprint/status/PeerReviewed

We have developed a Bayesian Dark Target (BDT) algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval over land. In the BDT algorithm, we simultaneously retrieve all pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 550 nm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (466, 550, 644, and 2100 nm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BDT significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BDT was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05 + 15 %) expected error envelope increased from 55 % to 76 %. In addition to retrieving the values of AOD, FMF and surface reflectance, the BDT also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BDT algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

Atmospheric lidar measurements were carried out at Elandsfontein measurement station, on the eastern Highveld approximately 150 km east of Johannesburg in South Africa (SA) throughout 2010. The height of the planetary boundary layer (PBL) top was continuously measured using a~Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended). High atmospheric variability together with a large surface temperature range and significant seasonal changes in precipitation were observed, which had an impact on the vertical mixing of particulate matter (PM), and hence, on the PBL evolution. The results were compared to radio soundings, CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) space-borne lidar measurements and three atmospheric models that followed different approaches to determine the PBL top height. These models included two weather forecast models operated by ECMWF (European Centre for Medium-range Weather Forecasts) and SAWS (South African Weather Service) and one mesoscale prognostic meteorological and air pollution regulatory model TAPM (The Air Pollution Model). The ground-based lidar used in this study was operational for 4935 h during 2010 (49% of the time). The PBL top height was detected 86% of the total measurement time (42% of the total time). Large seasonal and diurnal variations were observed between the different methods utilised. Comparison of lidar measurements to the models indicated that the ECMWF model agreed the best with mean absolute difference of 15.4%, while the second best correlation was with the SAWS model with corresponding difference of 20.1%. TAPM was found to have a tendency to underestimate the PBL top height. The wind speeds in SAWS operated and TAPM models were strongly underestimated which probably led to underestimation of the vertical wind and turbulence and thus underestimation of the PBL top height. High variation was found when lidar measurements were compared to radiosonde measurements. This could be partially due to the distance between the lidar measurements and the radiosondes, which were 120 km apart. Comparison between ground-based and satellite lidar shows good agreement with a correlation coefficient of 0.88. On average the daily maximum PBL top height in October (spring) and June (winter) were 2260 m and 1480 m, respectively. To our knowledge, this study is the first long term study of PBL top heights and PBL growth rates in the Southern Hemisphere. Only a few studies have been performed in Europe and Asia, most of them with less data coverage.

A smoke plume originating from the massive wildfires near Moscow was clearly detected in northern Finland on 30 July 2010. Measurements made with remote sensing instruments demonstrated how the biomass burning aerosols affected the chemical and optical characteristics of the atmosphere in regions hundreds of kilometers away from the actual fires. In this study, we used MODIS, AIRS, CALIOP, PFR, ceilometers, FTS and Brewer data to quantify the properties of the transported smoke plume. In addition, in situ measurements of aerosol concentration (DMPS), absorption (aethalometer) and scattering (nephelometer) are presented. We found that due to the smoke plume in northern Finland, the daily averaged optical thickness of aerosols increased fourfold, and MODIS retrieved AOD as high as 4.5 for the thickest part of the plume. FTS measurements showed that CO concentration increased by 100% during the plume. CALIOP and ceilometer measurements revealed that the smoke plume was located close to the surface, below 3 km, and that the plume was not homogeneously mixed. In addition, in situ measurements showed that the scattering and absorption coefficients were almost 20 times larger in the smoke plume than on average, and that the number of particles larger than 320 nm increased 14-fold. Moreover, a comparison with in situ measurements recorded in eastern Finland on the previous day showed that the transport from eastern to northern Finland decreased the scattering coefficient, black carbon concentration, and total number concentration 0.5%/h, 1.5%/h and 2.0%/h, respectively.

Previous studies have indicated that summer-time aerosol optical depths (AOD) over the southeastern US are dependent on temperature but the reason for this dependence and its radiative effects have so far been unclear. To quantify these effects we utilized AOD and land surface temperature (LST) products from the Advanced Along-Track Scanning Radiometer (AATSR) with observations of tropospheric nitrogen dioxide (NO2) column densities from the Ozone Monitoring Instrument (OMI). Furthermore, simulations of the global aerosol-climate model ECHAM-HAMMOZ have been used to identify the possible processes affecting aerosol loads and their dependence on temperature over the studied region. Our results showed that the level of AOD in the southeastern US is mainly governed by anthropogenic emissions but the observed temperature dependent behaviour is most likely originating from non-anthropogenic emissions. Model simulations indicated that biogenic emissions of volatile organic compounds (BVOC) can explain the observed temperature dependence of AOD. According to the remote sensing data sets, the non-anthropogenic contribution increases AOD by approximately 0.009 ± 0.018 K−1 while the modelled BVOC emissions increase AOD by 0.022 ± 0.002 K−1. Consequently, the regional direct radiative effect (DRE) of the non-anthropogenic AOD is −0.43 ± 0.88 W/m2/K and −0.17 ± 0.35 W/m2/K for clear- and all-sky conditions, respectively. The model estimate of the regional clear-sky DRE for biogenic aerosols is also in the same range: −0.86 ± 0.06 W/m2/K. These DRE values indicate significantly larger cooling than the values reported for other forested regions. Furthermore, the model simulations showed that biogenic emissions increased the number of biogenic aerosols with radius larger than 100 nm (N100, proxy for cloud condensation nuclei) by 28 % per one degree temperature increase. For the total N100, the corresponding increase was 4 % which implies that biogenic emissions could also have a small effect on indirect radiative forcing in this region.