The first EMEP intensive measurement periods were held in June 2006 and January 2007. The measurements aimed to characterize the aerosol chemical compositions, including the gas/aerosol partitioning of inorganic compounds. The measurement program during these periods included daily or hourly measurements of the secondary inorganic components, with additional measurements of elemental- and organic carbon (EC and OC) and mineral dust in PM1, PM2.5 and PM10. These measurements have provided extended knowledge regarding the composition of particulate matter and the temporal and spatial variability of PM, as well as an extended database for the assessment of chemical transport models. This paper summarise the first experiences of making use of measurements from the first EMEP intensive measurement periods along with EMEP model results from the updated model version to characterise aerosol composition. We investigated how the PM chemical composition varies between the summer and the winter month and geographically. The observation and model data are in general agreement regarding the main features of PM10 and PM2.5 composition and the relative contribution of different components, though the EMEP model tends to give slightly lower estimates of PM10 and PM2.5 compared to measurements. The intensive measurement data has identified areas where improvements are needed. Hourly concurrent measurements of gaseous and particulate components for the first time facilitated testing of modelled diurnal variability of the gas/aerosol partitioning of nitrogen species. In general, the modelled diurnal cycles of nitrate and ammonium aerosols are in fair agreement with the measurements, but the diurnal variability of ammonia is not well captured. The largest differences between model and observations of aerosol mass are seen in Italy during winter, which to a large extent may be explained by an underestimation of residential wood burning sources. It should be noted that both primary and secondary OC has been included in the calculations for the first time, showing promising results. Mineral dust is important, especially in southern Europe, and the model seems to capture the dust episodes well. The lack of measurements of mineral dust hampers the possibility for model evaluation for this highly uncertain PM component. There are also lessons learnt regarding improved measurements for future intensive periods. There is a need for increased comparability between the measurements at different sites. For the nitrogen compounds it is clear that more measurements using artefact free methods based on continuous measurement methods and/or denuders are needed. For EC/OC, a reference methodology (both in field and laboratory) was lacking during these periods giving problems with comparability, though measurement protocols have recently been established and these should be followed by the Parties to the EMEP Protocol. For measurements with no defined protocols, it might be a good solution to use centralised laboratories to ensure comparability across the network. To cope with the introduction of these new measurements, new reporting guidelines have been developed to ensure that all proper information about the methodologies and data quality is given.
We have evaluated tropospheric ozone enhancement in air dominated by biomass burning emissions at high latitudes (>50 degrees N) in July 2008, using 10 global chemical transport model simulations from the POLMIP multimodel comparison exercise. In model air masses dominated by fire emissions, Delta O-3/Delta CO values ranged between 0.039 and 0.196 ppbv ppbv(-1) (mean: 0.113 ppbv ppbv(-1)) in freshly fire-influenced air, and between 0.140 and 0.261 ppbv ppb(-1) (mean: 0.193 ppbv) in more aged fire-influenced air. These values are in broad agreement with the range of observational estimates from the literature. Model Delta PAN/Delta CO enhancement ratios show distinct groupings according to the meteorological data used to drive the models. ECMWF-forced models produce larger Delta PAN/Delta CO values (4.47 to 7.00 pptv ppbv(-1)) than GEOS5-forced models (1.87 to 3.28 pptv ppbv(-1)), which we show is likely linked to differences in efficiency of vertical transport during poleward export from mid-latitude source regions. Simulations of a large plume of biomass burning and anthropogenic emissions exported from towards the Arctic using a Lagrangian chemical transport model show that 4-day net ozone change in the plume is sensitive to differences in plume chemical composition and plume vertical position among the POLMIP models. In particular, Arctic ozone evolution in the plume is highly sensitive to initial concentrations of PAN, as well as oxygenated VOCs (acetone, acetaldehyde), due to their role in producing the peroxyacetyl radical PAN precursor. Vertical displacement is also important due to its effects on the stability of PAN, and subsequent effect on NOx abundance. In plumes where net ozone production is limited, we find that the lifetime of ozone in the plume is sensitive to hydrogen peroxide loading, due to the production of HOx from peroxide photolysis, and the key role of HO2 + O-3 in controlling ozone loss. Overall, our results suggest that emissions from biomass burning lead to large-scale photochemical enhancement in high-latitude tropospheric ozone during summer.
Although the links between stratospheric dynamics, climate and weather have been demonstrated, direct observations of stratospheric winds are lacking, in particular at altitudes above 30 km. We report observations of winds between 8 and 0.01 hPa (similar to 35-80 km) from October 2009 to April 2010 by the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the International Space Station. The altitude range covers the region between 35-60 km where previous space-borne wind instruments show a lack of sensitivity. Both zonal and meridional wind components were obtained, though not simultaneously, in the latitude range from 30 degrees S to 55 degrees N and with a single profile precision of 7-9 ms(-1) between 8 and 0.6 hPa and better than 20 ms(-1) at altitudes above. The vertical resolution is 5-7 km except in the upper part of the retrieval range (10 km at 0.01 hPa). In the region between 1-0.05 hPa, an absolute value of the mean difference <2 ms(-1) is found between SMILES profiles retrieved from different spectroscopic lines and instrumental settings. Good agreement (absolute value of the mean difference of similar to 2 ms(-1)) is also found with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis in most of the stratosphere except for the zonal winds over the equator (difference >5 ms(-1)). In the mesosphere, SMILES and ECMWF zonal winds exhibit large differences (>20 ms(-1)), especially in the tropics. We illustrate our results by showing daily and monthly zonal wind variations, namely the semi-annual oscillation in the tropics and reversals of the flow direction between 50-55 degrees N during sudden stratospheric warmings. The daily comparison with ECMWF winds reveals that in the beginning of February, a significantly stronger zonal westward flow is measured in the tropics at 2 hPa compared to the flow computed in the analysis (difference of similar to 20 ms(-1)). The results show that the comparison between SMILES and ECMWF winds is not only relevant for the quality assessment of the new SMILES winds, but it also provides insights on the quality of the ECMWF winds themselves. Although the instrument was not specifically designed for measuring winds, the results demonstrate that space-borne sub-mm wave radiometers have the potential to provide good quality data for improving the stratospheric winds in atmospheric models.
This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (-23 %), surface stations (-43 %), or models (-32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (-37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.
We have investigated the potential impact on organic aerosol formation from biotic stress-induced emissions (SIE) of organic molecules from forests in Europe (north of lat. 45 degrees N). Emission estimates for sesquiterpenes (SQT), methyl salicylate (MeSA) and unsaturated C-17 compounds, due to different stressors, are based on experiments in the Julich Plant Atmosphere Chamber (JPAC), combined with estimates of the fraction of stressed trees in Europe based on reported observed tree damage. SIE were introduced in the EMEP MSC-W chemical transport model and secondary organic aerosol (SOA) yields from the SIE were taken from the JPAC experiments. Based on estimates of current levels of infestation and the JPAC aerosol yields, the model results suggest that the contribution to SOA in large parts of Europe may be substantial. It is possible that SIE contributes as much, or more, to organic aerosol than the constitutive biogenic VOC emissions, at least during some periods. Based on the assumptions in this study, SIE-SOA are estimated to constitute between 50 and 70% of the total biogenic SOA (BSOA) in a current-situation scenario where the biotic stress in northern and central European forests causes large SIE of MeSA and SQT. An alternative current-situation scenario with lower SIE, consisting solely of SQT, leads to lower SIE-SOA, between 20 and 40% of the total BSOA. Hypothetical future scenarios with increased SIE, due to higher degrees of biotic stress, show that SOA formation due to SIE can become even larger. Unsaturated C17 BVOC (biogenic volatile organic compounds) emitted by spruce infested by the forest-honey generating bark louse, Cinara pilicornis, have a high SOA-forming potential. A model scenario investigating the effect of a regional, episodic infestation of Cinara pilicornis in Baden-Wurttemberg, corresponding to a year with high production of forest honey, shows that these types of events could lead to very large organic aerosol formation in the infested region. We have used the best available laboratory data on biotic SIE applicable to northern and central European forests. Using these data and associated assumptions, we have shown that SIE are potentially important for SOA formation but the magnitude of the impact is uncertain and needs to be constrained by further laboratory, field and modelling studies. As an example, the MeSA, which is released as a consequence of various types of biotic stress, is found to have a potentially large impact on SIE-SOA in Europe, but different assumptions regarding the nighttime chemistry of MeSA can change its SOA potential substantially. Thus, further investigations of the atmospheric chemistry of MeSA and observational field studies are needed to clarify the role of this compound in the atmosphere.
A new organic aerosol module has been implemented into the EMEP chemical transport model. Four different volatility basis set (VBS) schemes have been tested in long-term simulations for Europe, covering the six years 2002-2007. Different assumptions regarding partitioning of primary organic aerosol and aging of primary semi-volatile and intermediate volatility organic carbon (S/IVOC) species and secondary organic aerosol (SOA) have been explored. Model results are compared to filter measurements, aerosol mass spectrometry (AMS) data and source apportionment studies, as well as to other model studies. The present study indicates that many different sources contribute significantly to organic aerosol in Europe. Biogenic and anthropogenic SOA, residential wood combustion and vegetation fire emissions may all contribute more than 10% each over substantial parts of Europe. This study shows smaller contributions from biogenic SOA to organic aerosol in Europe than earlier work, but relatively greater anthropogenic SOA. Simple VBS based organic aerosol models can give reasonably good results for summer conditions but more observational studies are needed to constrain the VBS parameterisations and to help improve emission inventories. The volatility distribution of primary emissions is one important issue for further work. Emissions of volatile organic compounds from biogenic sources are also highly uncertain and need further validation. We can not reproduce winter levels of organic aerosol in Europe, and there are many indications that the present emission inventories substantially underestimate emissions from residential wood combustion in large parts of Europe.
Observations made during late summer in the central Arctic Ocean, as part of the Arctic Summer Cloud Ocean Study (ASCOS), are used to evaluate cloud and vertical temperature structure in the Met Office Unified Model (MetUM). The observation period can be split into 5 regimes; the first two regimes had a large number of frontal systems, which were associated with deep cloud. During the remainder of the campaign a layer of low-level cloud occurred, typical of central Arctic summer conditions, along with two periods of greatly reduced cloud cover. The short-range operational NWP forecasts could not accurately reproduce the observed variations in near-surface temperature. A major source of this error was found to be the temperature-dependant surface albedo parameterisation scheme. The model reproduced the low-level cloud layer, though it was too thin, too shallow, and in a boundary-layer that was too frequently well-mixed. The model was also unable to reproduce the observed periods of reduced cloud cover, which were associated with very low cloud condensation nuclei (CCN) concentrations (< 1 cm(-3)). As with most global NWP models, the MetUM does not have a prognostic aerosol/cloud scheme but uses a constant CCN concentration of 100 cm(-3) over all marine environments. It is therefore unable to represent the low CCN number concentrations and the rapid variations in concentration frequently observed in the central Arctic during late summer. Experiments with a single-column model configuration of the MetUM show that reducing model CCN number concentrations to observed values reduces the amount of cloud, increases the near-surface stability, and improves the representation of both the surface radiation fluxes and the surface temperature. The model is shown to be sensitive to CCN only when number concentrations are less than 10-20 cm(-3).
Linear particle depolarization ratio is presented for three case studies from the NASA Langley airborne High Spectral Resolution Lidar-2 (HSRL-2). Particle depolarization ratio from lidar is an indicator of non-spherical particles and is sensitive to the fraction of non-spherical particles and their size. The HSRL-2 instrument measures depolarization at three wavelengths: 355, 532, and 1064 nm. The three measurement cases presented here include two cases of dust-dominated aerosol and one case of smoke aerosol. These cases have partial analogs in earlier HSRL-1 depolarization measurements at 532 and 1064 nm and in literature, but the availability of three wavelengths gives additional insight into different scenarios for non-spherical particles in the atmosphere. A case of transported Saharan dust has a spectral dependence with a peak of 0.30 at 532 nm with smaller particle depolarization ratios of 0.27 and 0.25 at 1064 and 355 nm, respectively. A case of aerosol containing locally generated wind-blown North American dust has a maximum of 0.38 at 1064 nm, decreasing to 0.37 and 0.24 at 532 and 355 nm, respectively. The cause of the maximum at 1064 nm is inferred to be very large particles that have not settled out of the dust layer. The smoke layer has the opposite spectral dependence, with the peak of 0.24 at 355 nm, decreasing to 0.09 and 0.02 at 532 and 1064 nm, respectively. The depolarization in the smoke case may be explained by the presence of coated soot aggregates. We note that in these specific case studies, the linear particle depolarization ratio for smoke and dust-dominated aerosol are more similar at 355 nm than at 532 nm, having possible implications for using the particle depolarization ratio at a single wavelength for aerosol typing.
This paper presents the aerosol budget over Europe in 2006 calculated with the global transport model TM5 coupled to the size-resolved aerosol module M7. Comparison with ground observations indicates that the model reproduces the observed concentrations quite well with an expected slight underestimation of PM10 due to missing emissions (e.g. resuspension). We model that a little less than half of the anthropogenic aerosols emitted in Europe are exported and the rest is removed by deposition. The anthropogenic aerosols are removed mostly by rain (95%) and only 5% is removed by dry deposition. For the larger natural aerosols, especially sea salt, a larger fraction is removed by dry processes (sea salt: 70%, mineral dust: 35%). We model transport of aerosols in the jet stream in the higher atmosphere and an import of Sahara dust from the south at high altitudes. Comparison with optical measurements shows that the model reproduces the Angstrom parameter very well, which indicates a correct simulation of the aerosol size distribution. However, we underestimate the aerosol optical depth. Because the surface concentrations are close to the observations, the shortage of aerosol in the model is probably at higher altitudes. We show that the discrepancies are mainly caused by an overestimation of wet-removal rates. To match the observations, the wet-removal rates have to be scaled down by a factor of about 5. In that case the modelled ground-level concentrations of sulphate and sea salt increase by 50% (which deteriorates the match), while other components stay roughly the same. Finally, it is shown that in particular events, improved fire emission estimates may significantly improve the ability of the model to simulate the aerosol optical depth. We stress that discrepancies in aerosol models can be adequately analysed if all models would provide (regional) aerosol budgets, as presented in the current study.
The impact of very deep convection on the water budget and thermal structure of the tropical tropopause layer is still not well quantified, not least because of limitations imposed by the available observation techniques. Here, we present detailed analysis of the climatology of the cloud top brightness temperatures as indicators of deep convection during the Indian summer monsoon, and the variations therein due to active and break periods. We make use of the recently newly processed data from the Advanced Very High Resolution Radiometer (AVHRR) at a nominal spatial resolution of 4 km. Using temperature thresholds from the Atmospheric Infrared Sounder (AIRS), the AVHRR brightness temperatures are converted to climatological mean (2003-2008) maps of cloud amounts at 200, 150 and 100 hPa. Further, we relate the brightness temperatures to the level of zero radiative heating, which may allow a coarse identification of convective detrainment that will subsequently ascend into the stratosphere. The AVHRR data for the period 1982-2006 are used to document the differences in deep convection between active and break conditions of the monsoon. The analysis of AVHRR data is complemented with cloud top pressure and optical depth statistics (for the period 2003-2008) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite. Generally, the two sensors provide a very similar description of deep convective clouds. Our analysis shows that most of the deep convection occurs over the Bay of Bengal and central northeast India. Very deep convection over the Tibetan plateau is comparatively weak, and may play only a secondary role in troposphere-to-stratosphere transport. The deep convection over the Indian monsoon region is most frequent in July/August, but the very highest convection (coldest tops, penetrating well into the TTL) occurs in May/June. Large variability in convection reaching the TTL is due to monsoon break/active periods. During the monsoon break period, deep convection reaching the TTL is almost entirely absent in the western part of the study area (i.e. 60 E-75 E), while the distribution over the Bay of Bengal and the Tibetan Plateau is less affected. Although the active conditions occur less frequently than the break conditions, they may have a larger bearing on the composition of the TTL within the monsoonal anticyclone, and tracer transport into the stratosphere because of deep convection occurring over anthropogenically more polluted regions.
A daytime climatological spatio-temporal distribution of high opaque ice cloud (HOIC) classes over the Indian subcontinent (0-40 degrees N, 60 degrees E-100 degrees E) is presented using 25-year data from the Advanced Very High Resolution Radiometers (AVHRRs) for the summer monsoon months. The HOICs are important for regional radiative balance, precipitation and troposphere-stratosphere exchange. In this study, HOICs are sub-divided into three classes based on their cloud top brightness temperatures (BT). Class I represents very deep convection (BT < 220 K). Class II represents deep convection (220 K <=BT < 233 K) and Class III background convection (233 K <=BT < 253 K). Apart from presenting finest spatial resolution (0.1x0.1 degrees) and long-term climatology of such cloud classes from AVHRRs to date, this study for the first time illustrates on (1) how these three cloud classes are climatologically distributed during monsoon months, and (2) how their distribution changes during active and break monsoon conditions. It is also investigated that how many deep convective clouds reach the tropopause layer during individual monsoon months. It is seen that Class I and Class II clouds dominate the Indian subcontinent during monsoon. The movement of monsoon over continent is very well reflected in these cloud classes. During monsoon breaks strong suppression of convective activity is observed over the Arabian Sea and the western coast of India. On the other hand, the presence of such convective activity is crucial for active monsoon conditions and all-India rainfall. It is found that a significant fraction of HOICs (3-5%) reach the tropopause layer over the Bay of Bengal during June and over the north and northeast India during July and August. Many cases are observed when clouds penetrate the tropopause layer and reach the lower stratosphere. Such cases mostly occur during June compared to the other months.
The record sea ice minimum (SIM) extents observed during the summers of 2007 and 2012 in the Arctic are stark evidence of accelerated sea ice loss during the last decade. Improving our understanding of the Arctic atmosphere and accurate quantification of its characteristics becomes ever more crucial, not least to improve predictions of such extreme events in the future. In this context, the Atmospheric Infrared Sounder (AIRS) instrument onboard NASA's Aqua satellite provides crucial insights due to its ability to provide 3-D information on atmospheric thermodynamics. Here, we facilitate comparisons in the evolution of the thermodynamic state of the Arctic atmosphere during these two SIM events using a decade-long AIRS observational record (2003-2012). It is shown that the meteorological conditions during 2012 were not extreme, but three factors of preconditioning from winter through early summer played an important role in accelerating sea ice melt. First, the marginal sea ice zones along the central Eurasian and North Atlantic sectors remained warm throughout winter and early spring in 2012 preventing thicker ice build-up. Second, the circulation pattern favoured efficient sea ice transport out of the Arctic in the Atlantic sector during late spring and early summer in 2012 compared to 2007. Third, additional warming over the Canadian archipelago and southeast Beaufort Sea from May onward further contributed to accelerated sea ice melt. All these factors may have lead the already thin and declining sea ice cover to pass below the previous sea ice extent minimum of 2007. In sharp contrast to 2007, negative surface temperature anomalies and increased cloudiness were observed over the East Siberian and Chukchi seas in the summer of 2012. The results suggest that satellite-based monitoring of atmospheric preconditioning could be a critical source of information in predicting extreme sea ice melting events in the Arctic.
An accurate characterization of the vertical structure of the Arctic atmosphere is useful in climate change and attribution studies as well as for the climate modelling community to improve projections of future climate over this highly sensitive region. Here, we investigate one of the dominant features of the vertical structure of the Arctic atmosphere, i.e. water-vapour inversions, using eight years of Atmospheric Infrared Sounder data (2002-2010) and radiosounding profiles released from the two Arctic locations (North Slope of Alaska at Barrow and during SHEBA). We quantify the characteristics of clear-sky water vapour inversions in terms of their frequency of occurrence, strength and height covering the entire Arctic for the first time. We found that the frequency of occurrence of water-vapour inversions is highest during winter and lowest during summer. The inversion strength is, however, higher during summer. The observed peaks in the median inversion-layer heights are higher during the winter half of the year, at around 850 hPa over most of the Arctic Ocean, Siberia and the Canadian Archipelago, while being around 925 hPa during most of the summer half of the year over the Arctic Ocean. The radiosounding profiles agree with the frequency, location and strength of water-vapour inversions in the Pacific sector of the Arctic. In addition, the radiosoundings indicate that multiple inversions are the norm with relatively few cases without inversions. The amount of precipitable water within the water-vapour inversion structures is estimated and we find a distinct, two-mode contribution to the total column precipitable water. These results suggest that water-vapour inversions are a significant source to the column thermodynamics, especially during the colder winter and spring seasons. We argue that these inversions are a robust metric to test the reproducibility of thermodynamics within climate models. An accurate statistical representation of water-vapour inversions in models would mean that the large-scale coupling of moisture transport, precipitation, temperature and water-vapour vertical structure and radiation are essentially captured well in such models.
Simulating the radiative impacts of aerosols located above liquid water clouds presents a significant challenge. In particular, absorbing aerosols, such as smoke, may have significant impact in such situations and even change the sign of net radiative forcing. It is not possible to reliably obtain information on such overlap events from existing passive satellite sensors. However, the CALIOP instrument onboard NASA's CALIPSO satellite allows us to examine these events with unprecedented accuracy. Using four years of collocated CALIPSO 5 km Aerosol and Cloud Layer Version 3 Products (June 2006 May 2010), we quantify, for the first time, the characteristics of overlapping aerosol and water cloud layers globally. We investigate seasonal variability in these characteristics over six latitude bands to understand the hemispheric differences when all aerosol types are included in the analysis (the AAO case). We also investigate frequency of smoke aerosol-cloud overlap (the SAO case). Globally, the frequency is highest during the JJA months in the AAO case, while for the SAO case, it is highest in the SON months. The seasonal mean overlap frequency can regionally exceed 20% in the AAO case and 10% in the SAO case. In about 5-10% cases the vertical distance between aerosol and cloud layers is less than 100 m, while about in 45-60% cases it less than a kilometer in the annual means for different latitudinal bands. In about 70-80% cases, aerosol layers are less than a kilometer thick, while in about 18-22% cases they are 1-2 km thick. The frequency of aerosol layers 2-3 km thick is about 4-5% in the tropical belts during overlap events. Over the regions where high aerosol loadings are present, the overlap frequency can be up to 50% higher when quality criteria on aerosol/cloud feature detection are relaxed. Over the polar regions, more than 50% of the overlapping aerosol layers have optical thickness less than 0.02, but the contribution from the relatively optically thicker aerosol layers increases towards the equatorial regions in both hemispheres. The results suggest that the frequency of occurrence of overlap events is far from being negligible globally.
The main purpose of this study is to investigate the influence of the Arctic Oscillation (AO), the dominant mode of natural variability over the northerly high latitudes, on the spatial (horizontal and vertical) distribution of clouds in the Arctic. To that end, we use a suite of sensors on-board NASA's A-Train satellites that provide accurate observations of the distribution of clouds along with information on atmospheric thermodynamics. Data from three independent sensors are used (AQUA-AIRS, CALIOP-CALIPSO and CPR-CloudSat) covering two time periods (winter half years, November through March, of 2002-2011 and 2006-2011, respectively) along with data from the ERA-Interim reanalysis. We show that the zonal vertical distribution of cloud fraction anomalies averaged over 67-82 degrees N to a first approximation follows a dipole structure (referred to as "Greenland cloud dipole anomaly", GCDA), such that during the positive phase of the AO, positive and negative cloud anomalies are observed eastwards and westward of Greenland respectively, while the opposite is true for the negative phase of AO. By investigating the concurrent meteorological conditions (temperature, humidity and winds), we show that differences in the meridional energy and moisture transport during the positive and negative phases of the AO and the associated thermodynamics are responsible for the conditions that are conducive for the formation of this dipole structure. All three satellite sensors broadly observe this large-scale GCDA despite differences in their sensitivities, spatio-temporal and vertical resolutions, and the available lengths of data records, indicating the robustness of the results. The present study also provides a compelling case to carry out process-based evaluation of global and regional climate models.
Temperature inversions are one of the dominant features of the Arctic atmosphere and play a crucial role in various processes by controlling the transfer of mass and moisture fluxes through the lower troposphere. It is therefore essential that they are accurately quantified, monitored and simulated as realistically as possible over the Arctic regions. In the present study, the characteristics of inversions in terms of frequency and strength are quantified for the entire Arctic Ocean for summer and winter seasons of 2003 to 2008 using the AIRS data for the clear-sky conditions. The probability density functions (PDFs) of the inversion strength are also presented for every summer and winter month. Our analysis shows that although the inversion frequency along the coastal regions of Arctic decreases from June to August, inversions are still seen in almost each profile retrieved over the inner Arctic region. In winter, inversions are ubiquitous and are also present in every profile analysed over the inner Arctic region. When averaged over the entire study area (70 degrees N-90 degrees N), the inversion frequency in summer ranges from 69 to 86% for the ascending passes and 72-86% for the descending passes. For winter, the frequency values are 88-91% for the ascending passes and 89-92% for the descending passes of AIRS/AQUA. The PDFs of inversion strength for the summer months are narrow and right-skewed (or positively skewed), while in winter, they are much broader. In summer months, the mean values of inversion strength for the entire study area range from 2.5 to 3.9 K, while in winter, they range from 7.8 to 8.9 K. The standard deviation of the inversion strength is double in winter compared to summer. The inversions in the summer months of 2007 were very strong compared to other years. The warming in the troposphere of about 1.5-3.0K vertically extending up to 400 hPa was observed in the summer months of 2007.
Sea ice is the central component and most sensitive indicator of the Arctic climate system. Both the depletion and areal decline of the Arctic sea ice cover, observed since the 1970s, have accelerated since the millennium. While the relationship of global warming to sea ice reduction is evident and underpinned statistically, it is the connecting mechanisms that are explored in detail in this review. Sea ice erodes both from the top and the bottom. Atmospheric, oceanic and sea ice processes interact in non-linear ways on various scales. Feedback mechanisms lead to an Arctic amplification of the global warming system: the amplification is both supported by the ice depletion and, at the same time, accelerates ice reduction. Knowledge of the mechanisms of sea ice decline grew during the 1990s and deepened when the acceleration became clear in the early 2000s. Record minimum summer sea ice extents in 2002, 2005, 2007 and 2012 provide additional information on the mechanisms. This article reviews recent progress in understanding the sea ice decline. Processes are revisited from atmospheric, oceanic and sea ice perspectives. There is strong evidence that decisive atmospheric changes are the major driver of sea ice change. Feedbacks due to reduced ice concentration, surface albedo, and ice thickness allow for additional local atmospheric and oceanic influences and self-supporting feedbacks. Large-scale ocean influences on Arctic Ocean hydrology and circulation are highly evident. Northward heat fluxes in the ocean are clearly impacting the ice margins, especially in the Atlantic sector of the Arctic. There is little indication of a direct and decisive influence of the warming ocean on the overall sea ice cover, due to an isolating layer of cold and fresh water underneath the sea ice.
The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008-2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January-March underestimated by 59 and 37% for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44% for July-September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.
Convective and stratiform precipitation events have fundamentally different physical causes. Using a radar composite over Germany, this study separates these precipitation types and compares extremes at different spatial and temporal scales, ranging from 1 to 50 km and 5 min to 6 h, respectively. Four main objectives are addressed. First, we investigate extreme precipitation intensities for convective and stratiform precipitation events at different spatial and temporal resolutions to identify type-dependent space and time reduction factors and to analyze regional and seasonal differences over Germany. We find strong differences between the types, with up to 30% higher reduction factors for convective compared to stratiform extremes, exceeding all other observed seasonal and regional differences within one type. Second, we investigate how the differences in reduction factors affect the contribution of each type to extreme events as a whole, again dependent on the scale and the threshold chosen. A clear shift occurs towards more convective extremes at higher resolution or higher percentiles. For horizontal resolutions of current climate model simulations, i.e., similar to 10 km, the temporal resolution of the data as well as the chosen threshold have profound influence on which type of extreme will be statistically dominant. Third, we compare the ratio of area to duration reduction factor for convective and stratiform events and find that convective events have lower effective advection velocities than stratiform events and are therefore more strongly affected by spatial than by temporal aggregation. Finally, we discuss the entire precipitation distribution regarding data aggregation and identify matching pairs of temporal and spatial resolutions where similar distributions are observed. The information is useful for planning observational networks or storing model data at different temporal and spatial scales.
A model intercomparison activity was inspired by the large suite of observations of atmospheric composition made during the International Polar Year (2008) in the Arctic. Nine global and two regional chemical transport models participated in this intercomparison and performed simulations for 2008 using a common emissions inventory to assess the differences in model chemistry and transport schemes. This paper summarizes the models and compares their simulations of ozone and its precursors and presents an evaluation of the simulations using a variety of surface, balloon, aircraft and satellite observations. Each type of measurement has some limitations in spatial or temporal coverage or in composition, but together they assist in quantifying the limitations of the models in the Arctic and surrounding regions. Despite using the same emissions, large differences are seen among the models. The cloud fields and photolysis rates are shown to vary greatly among the models, indicating one source of the differences in the simulated chemical species. The largest differences among models, and between models and observations, are in NOy partitioning (PAN vs. HNO3) and in oxygenated volatile organic compounds (VOCs) such as acetaldehyde and acetone. Comparisons to surface site measurements of ethane and propane indicate that the emissions of these species are significantly underestimated. Satellite observations of NO2 from the OMI (Ozone Monitoring Instrument) have been used to evaluate the models over source regions, indicating anthropogenic emissions are underestimated in East Asia, but fire emissions are generally overestimated. The emission factors for wildfires in Canada are evaluated using the correlations of VOCs to CO in the model output in comparison to enhancement factors derived from aircraft observations, showing reasonable agreement for methanol and acetaldehyde but underestimate ethanol, propane and acetone, while overestimating ethane emission factors.
Artificial Neural Networks (ANN) are efficient tools to derive solar UV radiation from measured meteorological parameters such as global radiation, aerosol optical depths and atmospheric column ozone. The ANN model has been tested with different combinations of data from the two sites Potsdam and Lindenberg, and used to reconstruct solar UV radiation at eight European sites by more than 100 years into the past. Special emphasis will be given to the discussion of small-scale characteristics of input data to the ANN model. Annual totals of UV radiation derived from reconstructed daily UV values reflect interannual variations and long-term patterns that are compatible with variabilities and changes of measured input data, in particular global dimming by about 1980/1990, subsequent global brightening, volcanic eruption effects such as that of Mt. Pinatubo, and the long-term ozone decline since the 1970s. Patterns of annual erythemal UV radiation are very similar at sites located at latitudes close to each other, but different patterns occur between UV radiation at sites in different latitude regions.
This article investigates the annual cycle observed in the Antarctic baseline aerosol scattering coefficient, total particle number concentration, and particle number size distribution (PNSD), as measured at Troll Atmospheric Observatory. Mie theory shows that the annual cycles in microphysical and optical aerosol properties have a common cause. By comparison with observations at other Antarctic stations, it is shown that the annual cycle is not a local phenomenon, but common to central Antarctic baseline air masses. Observations of ground-level ozone at Troll as well as backward plume calculations for the air masses arriving at Troll demonstrate that the baseline air masses originate from the free troposphere and lower stratosphere region, and descend over the central Antarctic continent. The Antarctic summer PNSD is dominated by particles with diameters < 100 nm recently formed from the gas-phase despite the absence of external sources of condensible gases. The total particle volume in Antarctic baseline aerosol is linearly correlated with the integral insolation the aerosol received on its transport pathway, and the photooxidative production of particle volume is mostly limited by photooxidative capacity, not availability of aerosol precursor gases. The photooxidative particle volume formation rate in central Antarctic baseline air is quantified to 207 +/- 4 mu m(3)/(MJ m). Further research is proposed to investigate the applicability of this number to other atmospheric reservoirs, and to use the observed annual cycle in Antarctic baseline aerosol properties as a benchmark for the representation of natural atmospheric aerosol processes in climate models.
International initiatives have successfully brought down the emissions, and hence also the related negative impacts on environment and human health, from shipping in Emission Control Areas (ECAs). However, the question remains as to whether increased shipping in the future will counteract these emission reductions. The overall goal of this study is to provide an up-to-date view on future ship emissions and provide a holistic view on atmospheric pollutants and their contribution to air quality in the Nordic (and Arctic) area. The first step has been to set up new and detailed scenarios for the potential developments in global shipping emissions, including different regulations and new routes in the Arctic. The scenarios include a Baseline scenario and two additional SOx Emission Control Areas (SE-CAs) and heavy fuel oil (HFO) ban scenarios. All three scenarios are calculated in two variants involving Business-As-Usual (BAU) and High-Growth (HiG) traffic scenarios. Additionally a Polar route scenario is included with new ship traffic routes in the future Arctic with less sea ice. This has been combined with existing Current Legislation scenarios for the land-based emissions (ECLIPSE V5a) and used as input for two Nordic chemistry transport models (DEHM and MATCH). Thereby, the current (2015) and future (2030, 2050) air pollution levels and the contribution from shipping have been simulated for the Nordic and Arctic areas. Population exposure and the number of premature deaths attributable to air pollution in the Nordic area have thereafter been assessed by using the health assessment model EVA (Economic Valuation of Air pollution). It is estimated that within the Nordic region approximately 9900 persons died prematurely due to air pollution in 2015 (corresponding to approximately 37 premature deaths for every 100 000 inhabitants). When including the projected development in both shipping and land-based emissions, this number is estimated to decrease to approximately 7900 in 2050. Shipping alone is associated with about 850 premature deaths during present day conditions (as a mean over the two models), decreasing to approximately 600 cases in the 2050 BAU scenario. Introducing a HFO ban has the potential to lower the number of cases associated with emissions from shipping to approximately 550 in 2050, while the SECA scenario has a smaller impact. The "worst-case" scenario of no additional regulation of shipping emissions combined with a high growth in the shipping traffic will, on the other hand, lead to a small increase in the relative impact of shipping, and the number of premature deaths related to shipping is in that scenario projected to be around 900 in 2050. This scenario also leads to increased deposition of nitrogen and black carbon in the Arctic, with potential impacts on environment and climate.
A one-year study was performed at the Vavihill background station in southern Sweden to estimate the anthropogenic contribution to the carbonaceous aerosol. Weekly samples of the particulate matter PM10 were collected on quartz filters, and the amounts of organic carbon, elemental carbon, radiocarbon (C-14) and levoglucosan were measured. This approach enabled source apportionment of the total carbon in the PM10 fraction using the concentration ratios of the sources. The sources considered in this study were emissions from the combustion of fossil fuels and biomass, as well as biogenic sources. During the summer, the carbonaceous aerosol mass was dominated by compounds of biogenic origin (80 %), which are associated with biogenic primary and secondary organic aerosols. During the winter months, biomass combustion (32 %) and fossil fuel combustion (28 %) were the main contributors to the carbonaceous aerosol. Elemental carbon concentrations in winter were about twice as large as during summer, and can be attributed to biomass combustion, probably from domestic wood burning. The contribution of fossil fuels to elemental carbon was stable throughout the year, although the fossil contribution to organic carbon increased during the winter. Thus, the organic aerosol originated mainly from natural sources during the summer and from anthropogenic sources during the winter. The result of this source apportionment was compared with results from the EMEP MSC-W chemical transport model. The model and measurements were generally consistent for total atmospheric organic carbon, however, the contribution of the sources varied substantially. E.g. the biomass burning contributions of OC were underestimated by the model by a factor of 2.2 compared to the measurements.
The atmospheric concentration of elemental carbon (EC) in Europe during the six-year period 2005-2010 has been simulated with the EMEP MSC-W model. The model bias compared to EC measurements was less than 20% for most of the examined sites. The model results suggest that fossil fuel combustion is the dominant source of EC in most of Europe but that there are important contributions also from residential wood burning during the cold seasons and, during certain episodes, also from open biomass burning (wildfires and agricultural fires). The modelled contributions from open biomass fires to ground level concentrations of EC were small at the sites included in the present study, <3% of the long-term average of EC in PM10. The modelling of this EC source is subject to many uncertainties, and it was likely underestimated for some episodes. EC measurements and modelled EC were also compared to optical measurements of black carbon (BC). The relationships between EC and BC (as given by mass absorption cross section, MAC, values) differed widely between the sites, and the correlation between observed EC and BC is sometimes poor, making it difficult to compare results using the two techniques and limiting the comparability of BC measurements to model EC results. A new bottom-up emission inventory for carbonaceous aerosol from residential wood combustion has been applied. For some countries the new inventory has substantially different EC emissions compared to earlier estimates. For northern Europe the most significant changes are much lower emissions in Norway and higher emissions in neighbouring Sweden and Finland. For Norway and Sweden, comparisons to source-apportionment data from winter campaigns indicate that the new inventory may improve model-calculated EC from wood burning. Finally, three different model setups were tested with variable atmospheric lifetimes of EC in order to evaluate the model sensitivity to the assumptions regarding hygroscopicity and atmospheric ageing of EC. The standard ageing scheme leads to a rapid transformation of the emitted hydrophobic EC to hygroscopic particles, and generates similar results when assuming that all EC is aged at the point of emission. Assuming hydrophobic emissions and no ageing leads to higher EC concentrations. For the more remote sites, the observed EC concentration was in between the modelled EC using standard ageing and the scenario treating EC as hydrophobic. This could indicate too-rapid EC ageing in the model in relatively clean parts of the atmosphere.
We present a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ) models, and an intercomparison of the contributing RAQ models and two global models for the period July 2008 - June 2009 over Europe. The model forecasts were produced routinely on a daily basis in the context of the European GEMS ("Global and regional Earth-system (atmosphere) Monitoring using Satellite and in-situ data") project. The tropospheric vertical column of the RAQ ensemble median shows a spatial distribution which agrees well with the OMI NO2 observations, with a correlation r=0.8. This is higher than the correlations from any one of the individual RAQ models, which supports the use of a model ensemble approach for regional air pollution forecasting. The global models show high correlations compared to OMI, but with significantly less spatial detail, due to their coarser resolution. Deviations in the tropospheric NO2 columns of individual RAQ models from the mean were in the range of 20-34% in winter and 40-62% in summer, suggesting that the RAQ ensemble prediction is relatively more uncertain in the summer months. The ensemble median shows a stronger seasonal cycle of NO2 columns than OMI, and the ensemble is on average 50% below the OMI observations in summer, whereas in winter the bias is small. On the other hand the ensemble median shows a somewhat weaker seasonal cycle than NO2 surface observations from the Dutch Air Quality Network, and on average a negative bias of 14%. Full profile information was available for two RAQ models and for the global models. For these models the retrieval averaging kernel was applied. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of replacing the a priori profiles by the RAQ model profiles is on average small. However, the contrast between major hotspots and rural areas is stronger for the direct modeled vertical columns than the columns where the averaging kernels are applied, related to a larger relative contribution of the free troposphere and the coarse horizontal resolution in the a priori profiles compared to the RAQ models. In line with validation results reported in the literature, summertime concentrations in the lowermost boundary layer in the a priori profiles from the DOMINO product are significantly larger than the RAQ model concentrations and surface observations over the Netherlands. This affects the profile shape, and contributes to a high bias in OMI tropospheric columns over polluted regions. The global models indicate that the upper troposphere may contribute significantly to the total column and it is important to account for this in comparisons with RAQ models. A combination of upper troposphere model biases, the a priori profile effects and DOMINO product retrieval issues could explain the discrepancy observed between the OMI observations and the ensemble median in summer.
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (> 60 degrees N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Inter-comparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO(4)(2-)burdens decrease significantly in all simulations by 10 %-60% following the reductions of 7 %-78% in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030-2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol-radiation interactions (RFARI) of -0.39 +/- 0.01Wm(-2), which is -0.08Wm(-2) larger than the 1990-2010 mean forcing (-0.32Wm(-2)), of which -0.24 +/- 0.01Wm(-2) was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of --0.35 to -0.40Wm(-2) for the same period, which is -0.01 to -0.06Wm(-2) larger than the 1990-2010 mean forcing of 0.35Wm(-2). The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for -0.24 to -0.26Wm(-2) of the net RFARI in 2030-2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 degrees C in the Eclipse ensemble and 1.9 to 2.6 degrees C in the CMIP6 ensemble, compared to the 1990-2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.
Clouds forming during the summer monsoon over the Indian subcontinent affect its evolution through their radiative impact as well as the release of latent heat. While the latter is previously studied to some extent, comparatively little is known about the radiative impact of different cloud types and the vertical structure of their radiative heating/cooling effects. Therefore, the main aim of this study is to partly fill this knowledge gap by investigating and documenting the vertical distributions of the different cloud types associated with the Indian monsoon and their radiative heating/cooling using the active radar and lidar sensors on-board CloudSat and CALIPSO. The intraseasonal evolution of clouds from May to October is also investigated to understand pre-to-post monsoon transitioning of their radiative heating/cooling effects. The vertical structure of cloud radiative heating (CRH) follows the northward migration and retreat of the monsoon from May to October. Throughout this time period, stratiform clouds radiatively warm the middle troposphere and cool the upper troposphere by more than +/- 0.2 K day(-1) (after weighing by cloud fraction), with the largest impacts observed in June, July and August. During these months, the fraction of high thin cloud remains high in the tropical tropopause layer (TTL). Deep convective towers cause considerable radiative warming in the middle and upper troposphere, but strongly cool the base and inside of the TTL. This cooling is stronger during active (-1.23 K day(-1)) monsoon periods compared to break periods (-0.36 K day(-1)). The contrasting radiative warming effect of high clouds in the TTL is twice as largeduring active periods than in break periods. These results highlight the increasing importance of CRH with altitude, especially in the TTL. Stratiform (made up of alto- and nimbostratus clouds) and deep convection clouds radiatively cool the surface by approximately -100 and -400Wm(-2) respectively while warming the atmosphere radiatively by about 40 to 150Wm(-2). While the cooling at the surface induced by deep convection and stratiform clouds is largest during active periods of monsoon, the importance of stratiform clouds further increases during break periods. The contrasting CREs (cloud radiative effects) in the atmosphere and at surface, and during active and break periods, should have direct implications for the monsoonal circulation.
Light absorbing carbon (LAC) aerosols have a complex, fractal-like aggregate structure. Their optical and radiative properties are notoriously difficult to model, and approximate methods may introduce large errors both in the interpretation of aerosol remote sensing observations, and in quantifying the direct radiative forcing effect of LAC. In this paper a numerically exact method for solving Maxwell's equations is employed for computing the optical properties of freshly emitted, externally mixed LAC aggregates. The computations are performed at wavelengths of 440 nm and 870 nm, and they cover the entire size range relevant for modelling these kinds of aerosols. The method for solving the electromagnetic scattering and absorption problem for aggregates proves to be sufficiently stable and fast to make accurate multiple-band computations of LAC optical properties feasible. The results from the electromagnetic computations are processed such that they can readily be integrated into a chemical transport model (CTM), which is a prerequisite for constructing robust observation operators for chemical data assimilation of aerosol optical observations. A case study is performed, in which results obtained with the coupled optics/CTM model are employed as input to detailed radiative transfer computations at a polluted European location. It is found that the still popular homogeneous sphere approximation significantly underestimates the radiative forcing at top of atmosphere as compared to the results obtained with the aggregate model. Notably, the LAC forcing effect predicted with the aggregate model is less than that one obtains by assuming a prescribed mass absorption cross section for LAC.
The optical properties of externally mixed light absorbing carbon (LAC) aggregates are computed over the spectral range from 200 nm-12.2 mu m by use of the numerically exact superposition T-matrix method. The spectral computations are tailored to the 14-band radiation model employed in the Integrated Forecasting System operated at the European Centre for Medium Range Weather Forecast. The size- and wavelength dependence of the optical properties obtained with the fractal aggregate model differs significantly from corresponding results based on the homogeneous sphere approximation, which is still commonly employed in climate models. The computational results are integrated into the chemical transport model MATCH (Multiple-scale Atmospheric Transport and CHemistry modelling system) to compute 3-D fields of size-averaged aerosol optical properties. Computational results obtained with MATCH are coupled to a radiative transfer model to compute the shortwave radiative impact of LAC. It is found that the fractal aggregate model gives a shortwave forcing estimate that is twice as high as that obtained with the homogeneous sphere approximation. Thus previous estimates based on the homogeneous sphere model may have substantially underestimated the shortwave radiative impact of freshly emitted LAC.
The performance of the three cloud products cloud fractional cover, cloud type and cloud top height, derived from NOAA AVHRR data and produced by the EUMETSAT Climate Monitoring Satellite Application Facility, has been evaluated in detail over the Arctic region for four months in 2007 using CALIPSO-CALIOP observations. The evaluation was based on 142 selected NOAA/Metop overpasses allowing almost 400 000 individual matchups between AVHRR pixels and CALIOP measurements distributed approximately equally over the studied months (June, July, August and December 2007). Results suggest that estimations of cloud amounts are very accurate during the polar summer season while a substantial loss of detected clouds occurs in the polar winter. Evaluation results for cloud type and cloud top products point at specific problems related to the existence of near isothermal conditions in the lower troposphere in the polar summer and the use of reference vertical temperature profiles from Numerical Weather Prediction model analyses. The latter are currently not detailed enough in describing true conditions relevant on the pixel scale. This concerns especially the description of near-surface temperature inversions which are often too weak leading to large errors in interpreted cloud top heights.
A new satellite-derived climate dataset - denoted CLARA-A1 ("The CM SAF cLoud, Albedo and RAdiation dataset from AVHRR data") - is described. The dataset covers the 28 yr period from 1982 until 2009 and consists of cloud, surface albedo, and radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting operational meteorological satellites. Its content, anticipated accuracies, limitations, and potential applications are described. The dataset is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project. The dataset has its strengths in the long duration, its foundation upon a homogenized AVHRR radiance data record, and in some unique features, e. g. the availability of 28 yr of summer surface albedo and cloudiness parameters over the polar regions. Quality characteristics are also well investigated and particularly useful results can be found over the tropics, mid to high latitudes and over nearly all oceanic areas. Being the first CM SAF dataset of its kind, an intensive evaluation of the quality of the datasets was performed and major findings with regard to merits and shortcomings of the datasets are reported. However, the CM SAF's long-term commitment to perform two additional reprocessing events within the time frame 2013-2018 will allow proper handling of limitations as well as upgrading the dataset with new features (e. g. uncertainty estimates) and extension of the temporal coverage.
The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish-Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.
Spatial and temporal variations of summer sea ice albedo over the Arctic are analyzed using an ensemble of historical CMIP5 model simulations. The results are compared to the CLARA-SAL product that is based on long-term satellite observations. The summer sea ice albedo varies substantially among CMIP5 models, and many models show large biases compared to the CLARA-SAL product. Single summer months show an extreme spread of ice albedo among models; July values vary between 0.3 and 0.7 for individual models. The CMIP5 ensemble mean, however, agrees relatively well in the central Arctic but shows too high ice albedo near the ice edges and coasts. In most models, the ice albedo is spatially too uniformly distributed. The summer-to-summer variations seem to be underestimated in many global models, and almost no model is able to reproduce the temporal evolution of ice albedo throughout the summer fully. While the satellite observations indicate the lowest ice albedos during August, the models show minimum values in July and substantially higher values in August. Instead, the June values are often lower in the models than in the satellite observations. This is probably due to too high surface temperatures in June, leading to an early start of the melt season and too cold temperatures in August causing an earlier refreezing in the models. The summer sea ice albedo in the CMIP5 models is strongly governed by surface temperature and snow conditions, particularly during the period of melt onset in early summer and refreezing in late summer. The summer surface net solar radiation of the ice-covered Arctic areas is highly related to the ice albedo in the CMIP5 models. However, the impact of the ice albedo on the sea ice conditions in the CMIP5 models is not clearly visible. This indicates the importance of other Arctic and large-scale processes for the sea ice conditions.
Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.