This paper presents a summary of the work done within the European Union's Seventh Framework Programme project ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). ECLIPSE had a unique systematic concept for designing a realistic and effective mitigation scenario for short-lived climate pollutants (SLCPs; methane, aerosols and ozone, and their precursor species) and quantifying its climate and air quality impacts, and this paper presents the results in the context of this overarching strategy. The first step in ECLIPSE was to create a new emission inventory based on current legislation (CLE) for the recent past and until 2050. Substantial progress compared to previous work was made by including previously unaccounted types of sources such as flaring of gas associated with oil production, and wick lamps. These emission data were used for present-day reference simulations with four advanced Earth system models (ESMs) and six chemistry transport models (CTMs). The model simulations were compared with a variety of ground-based and satellite observational data sets from Asia, Europe and the Arctic. It was found that the models still underestimate the measured seasonality of aerosols in the Arctic but to a lesser extent than in previous studies. Problems likely related to the emissions were identified for northern Russia and India, in particular. To estimate the climate impacts of SLCPs, ECLIPSE followed two paths of research: the first path calculated radiative forcing (RF) values for a large matrix of SLCP species emissions, for different seasons and regions independently. Based on these RF calculations, the Global Temperature change Potential metric for a time horizon of 20 years (GTP₂₀) was calculated for each SLCP emission type. This climate metric was then used in an integrated assessment model to identify all emission mitigation measures with a beneficial air quality and short-term (20-year) climate impact. These measures together defined a SLCP mitigation (MIT) scenario. Compared to CLE, the MIT scenario would reduce global methane (CH₄) and black carbon (BC) emissions by about 50 and 80 %, respectively. For CH₄, measures on shale gas production, waste management and coal mines were most important. For non-CH₄ SLCPs, elimination of high-emitting vehicles and wick lamps, as well as reducing emissions from gas flaring, coal and biomass stoves, agricultural waste, solvents and diesel engines were most important. These measures lead to large reductions in calculated surface concentrations of ozone and particulate matter. We estimate that in the EU, the loss of statistical life expectancy due to air pollution was 7.5 months in 2010, which will be reduced to 5.2 months by 2030 in the CLE scenario. The MIT scenario would reduce this value by another 0.9 to 4.3 months. Substantially larger reductions due to the mitigation are found for China (1.8 months) and India (11–12 months). The climate metrics cannot fully quantify the climate response. Therefore, a second research path was taken. Transient climate ensemble simulations with the four ESMs were run for the CLE and MIT scenarios, to determine the climate impacts of the mitigation. In these simulations, the CLE scenario resulted in a surface temperature increase of 0.70 ± 0.14 K between the years 2006 and 2050. For the decade 2041–2050, the warming was reduced by 0.22 ± 0.07 K in the MIT scenario, and this result was in almost exact agreement with the response calculated based on the emission metrics (reduced warming of 0.22 ± 0.09 K). The metrics calculations suggest that non-CH₄ SLCPs contribute ~ 22 % to this response and CH₄ 78 %. This could not be fully confirmed by the transient simulations, which attributed about 90 % of the temperature response to CH₄ reductions. Attribution of the observed temperature response to non-CH₄ SLCP emission reductions and BC specifically is hampered in the transient simulations by small forcing and co-emitted species of the emission basket chosen. Nevertheless, an important conclusion is that our mitigation basket as a whole would lead to clear benefits for both air quality and climate. The climate response from BC reductions in our study is smaller than reported previously, possibly because our study is one of the first to use fully coupled climate models, where unforced variability and sea ice responses cause relatively strong temperature fluctuations that may counteract (and, thus, mask) the impacts of small emission reductions. The temperature responses to the mitigation were generally stronger over the continents than over the oceans, and with a warming reduction of 0.44 K (0.39–0.49) K the largest over the Arctic. Our calculations suggest particularly beneficial climate responses in southern Europe, where surface warming was reduced by about 0.3 K and precipitation rates were increased by about 15 (6–21) mm yr⁻¹ (more than 4 % of total precipitation) from spring to autumn. Thus, the mitigation could help to alleviate expected future drought and water shortages in the Mediterranean area. We also report other important results of the ECLIPSE project.

Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a d... Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a d...

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.

We present initial validation results of the space-borne lidar CALIOP onboard CALIPSO satellite using coincidental observations from a ground-based lidar in Seoul National University (SNU), Seoul, Korea (37.46° N, 126.95° E). We analyze six selected cases between September 2006 and February 2007, including 3 daytime and 3 night-time observations and covering different types of clear and cloudy atmospheric conditions. Apparent scattering ratios calculated from the two lidar measurements of total attenuated backscatter at 532 nm show similar aerosol and cloud layer structures both under cloud-free conditions and in cases of multiple aerosol layers underlying semi-transparent cirrus clouds. Agreement on top and base heights of cloud and aerosol layers is generally within 0.10 km, particularly during night-time. This result confirms that the CALIPSO science team algorithms for the discrimination of cloud and aerosol as well as for the detection of layer top and base altitude provide reliable information in such atmospheric conditions. This accuracy of the planetary boundary layer top height under cirrus cloud appears, however, limited during daytime. Under thick cloud conditions, however, information on the cloud top (bottom) height only is reliable from CALIOP (ground-based lidar) due to strong signal attenuations. However, simultaneous space-borne CALIOP and ground-based SNU lidar (SNU-L) measurements complement each other and can be combined to provide full information on the vertical distribution of aerosols and clouds. An aerosol backscatter-to-extinction ratio (BER) estimated from lidar and sunphotometer synergy at the SNU site during the CALIOP overpass is assessed to be 0.023±0.004 sr⁻¹ (i.e. a lidar ratio of 43.2±6.2 sr) from CALIOP and 0.027±0.006 sr⁻¹ (37.4±7.2 sr) from SNU-L. For aerosols within the planetary boundary layer under cloud-free conditions, the aerosol extinction profiles from both lidars are in agreement within about 0.02 km⁻¹. Under semi-transparent cirrus clouds, such profiles also show good agreement for the night-time CALIOP flight, but large discrepancies are found for the daytime flights due to a small signal-to-noise ratio of the CALIOP data.