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The long-term frequency of atmospheric dust observations was investigated for the southern part of Iceland and interpreted together with earlier results obtained from northeastern (NE) Iceland (Dagsson-Waldhauserova et al., 2013). In total, over 34 dust days per year on average occurred in Iceland based on conventionally used synoptic codes for dust observations. However, frequent volcanic eruptions, with the re-suspension of volcanic materials and dust haze, increased the number of dust events fourfold (135 dust days annually). The position of the Icelandic Low determined whether dust events occurred in the NE (16.4 dust days annually) or in the southern (S) part of Iceland (about 18 dust days annually). The decade with the most frequent dust days in S Iceland was the 1960s, but the 2000s in NE Iceland. A total of 32 severe dust storms (visibility < 500 m) were observed in Iceland with the highest frequency of events during the 2000s in S Iceland. The Arctic dust events (NE Iceland) were typically warm, occurring during summer/autumn (May–September) and during mild southwesterly winds, while the subarctic dust events (S Iceland) were mainly cold, occurring during winter/spring (March–May) and during strong northeasterly winds. About half of the dust events in S Iceland occurred in winter or at sub-zero temperatures. A good correlation was found between particulate matter (PM<sub>10</sub>) concentrations and visibility during dust observations at the stations Vík and Stórhöfði. This study shows that Iceland is among the dustiest areas of the world and that dust is emitted year-round.
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Ice core nitrate concentrations peak in the summer in both Greenland and Antarctica. Two nitrate concentration peaks in one annual layer have been observed some years in ice cores in Greenland from samples dating post-1900, with the additional nitrate peak occurring in the spring. The origin of the spring nitrate peak was hypothesized to be pollution transport from the mid-latitudes in the industrial era. We performed a case study on the origin of a spring nitrate peak in 2005 measured from a snowpit at Summit, Greenland, covering 3 years of snow accumulation. The effect of long-range transport of nitrate on this spring peak was excluded by using sulfate as a pollution tracer. The isotopic composition of nitrate (δ<sup>15</sup>N, δ<sup>18</sup>O and Δ<sup>17</sup>O) combined with photochemical calculations suggest that the occurrence of this spring peak is linked to a significantly weakened stratospheric ozone (O<sub>3</sub>) layer. The weakened O<sub>3</sub> layer resulted in elevated UVB (ultraviolet-B) radiation on the snow surface, where the production of OH and NO<sub>x</sub> from the photolysis of their precursors was enhanced. Elevated NO<sub>x</sub> and OH concentrations resulted in enhanced nitrate production mainly through the NO<sub>2</sub> + OH formation pathway, as indicated by decreases in δ<sup>18</sup>O and Δ<sup>17</sup>O of nitrate associated with the spring peak. We further examined the nitrate concentration record from a shallow ice core covering the period from 1772 to 2006 and found 19 years with double nitrate peaks after the 1950s. Out of these 19 years, 14 of the secondary nitrate peaks were accompanied by sulfate peaks, suggesting long-range transport of nitrate as their source. In the other 5 years, low springtime O<sub>3</sub> column density was observed, suggesting enhanced local production of nitrate as their source. The results suggest that, in addition to direct transport of nitrate from polluted regions, enhanced local photochemistry can also lead to a spring nitrate peak. The enhanced local photochemistry is probably associated with the interannual variability of O<sub>3</sub> column density in the Arctic, which leads to elevated surface UV radiation in some years. In this scenario, enhanced photochemistry caused increased local nitrate production under the condition of elevated local NO<sub>x</sub> abundance in the industrial era.
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Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modelling enterprise. One method to estimate these emissions is to convert fire radiative power (FRP) analysis into dry matter burnt and emissions of smoke constituents using land-cover-dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments onboard the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. <br><br> We develop a method to correct this bias in daily FRP observations from any low Earth orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, for example, GFAS in case of failure of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It decreases the bias in daily average FRP from Terra and Aqua by more than 95%, and RMSE by 75% for Aqua and 55% for Terra. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.
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This paper details a new method of regression for sparsely sampled data sets for use with time-series analysis, in particular the Stratospheric Aerosol and Gas Experiment (SAGE) II ozone data set. Non-uniform spatial, temporal, and diurnal sampling present in the data set result in biased values for the long-term trend if not accounted for. This new method is performed close to the native resolution of measurements and is a simultaneous temporal and spatial analysis that accounts for potential diurnal ozone variation. Results show biases, introduced by the way data are prepared for use with traditional methods, can be as high as 10%. Derived long-term changes show declines in ozone similar to other studies but very different trends in the presumed recovery period, with differences up to 2% per decade. The regression model allows for a variable turnaround time and reveals a hemispheric asymmetry in derived trends in the middle to upper stratosphere. Similar methodology is also applied to SAGE II aerosol optical depth data to create a new volcanic proxy that covers the SAGE II mission period. Ultimately this technique may be extensible towards the inclusion of multiple data sets without the need for homogenization.
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The contribution of dynamical forcing to variations and trends in tropical lower stratospheric 70 hPa temperature for the period 1980–2011 is estimated based on ERA-Interim and Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis data. The dynamical forcing is estimated from the tropical mean residual upwelling calculated with the momentum balance equation, and with a simple proxy based on eddy heat fluxes averaged between 25° and 75° in both hemispheres. The thermodynamic energy equation with Newtonian cooling is used to relate the dynamical forcing to temperature. The deseasonalised, monthly mean time series of all four calculations are highly correlated (~ 0.85) with temperature for the period 1995–2011 when variations in radiatively active tracers are small. All four calculations provide additional support to previously noted prominent aspects of the temperature evolution 1980–2011: an anomalously strong dynamical cooling (~ −1 to −2 K) following the Pinatubo eruption that partially offsets the warming from enhanced aerosol, and a few years of enhanced dynamical cooling (~ −0.4 K) after October 2000 that contributes to the prominent drop in water entering the stratosphere at that time. The time series of dynamically forced temperature calculated with the same method are more highly correlated and have more similar trends than those from the same reanalysis but with different methods. For 1980–2011 (without volcanic periods), the eddy heat flux calculations give a dynamical cooling of ~ −0.1 to ~ −0.25 K decade<sup>−1</sup> (magnitude sensitive to latitude belt considered and reanalysis), largely due to increasing high latitude eddy heat flux trends in September and December–January. The eddy heat flux trends also explain the seasonality of temperature trends very well, with maximum cooling in January–February. Trends derived from momentum balance calculations show near-zero annual mean dynamical cooling, with weaker seasonal trends especially in December–January. These contradictory results arising from uncertainties in data and methods are discussed and put in context to previous analyses.
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This study investigates the diurnal evolution of sea-breeze (SB) rotation over an island at the middle latitudes. Earlier research on sea breezes in Sardinia shows that the onshore winds around various coasts of the island exhibit both the theoretically predicted clockwise rotation as well as seemingly anomalous anticlockwise rotation. A non-hydrostatic fully compressible numerical model (WRF) is used to simulate wind fields on and around the island on previously studied sea-breeze days, and is shown to capture the circulation on all coasts accurately. Diurnal rotation of wind is examined, and patterns of clockwise and anticlockwise rotation are identified. A dynamical analysis is performed by extracting individual forcing terms from the horizontal momentum equations. Analysis of several regions around the island shows that the direction of rotation is a result of a complex interaction between near-surface and synoptic pressure gradient, Coriolis and advection forcings. An idealized simulation is performed over an artificial island with dramatically simplified topography yet similar dimensions and latitude to Sardinia. Dynamical analysis of the idealized case reveals a rather different pattern of hodograph rotation to the real Sardinia, yet similar underlying dynamics. The research provides new insights into the dynamics underlying sea-breeze hodograph rotation, especially in coastal zones with a complex topography and/or coastline.
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The Puijo aerosol–cloud observation station is a unique measurement site for its location in the mixed region between the boreal forestland and the municipality of Kuopio, Finland. A measurement campaign was carried out at the station during fall 2010. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-Tof-AMS) was deployed to characterize the atmospheric submicron aerosols. Positive matrix factorization (PMF) was applied to the unified high-resolution mass spectra organic species with NO<sup>+</sup> and NO<sub>2</sub><sup>+</sup> ions to discover the intrinsic relationships between the organic and inorganic species and their daily cycles. On average, the submicron aerosols in this study were dominated by organic and sulfate species, composing 48.2 and 28.7% of total observed aerosol mass, respectively, with smaller contributions from ammonium (9.3%), nitrate (4.9%), chloride (0.8%) and BC (8.1%). The sources of these species included the primary emissions originating from the city area, secondary formation from both natural and anthropogenic emissions and regional transport. The PMF analysis succeeded in separating the mixed organic and inorganic spectra into three distinct organic and one inorganic factors. For organic factors, the semi-volatile oxygenated organic aerosol (SVOOA) and low-volatility oxygenated OA (LVOOA) accounted for 54.8 and 36.3% of total organic masses, respectively, while the hydrocarbon-like organic aerosol (HOA) accounted for 8.9% of total organics, with its main source from urban emissions. The inorganic factor is identified as NH<sub>4</sub>NO<sub>3</sub>, comprising 6.9% of the fitted aerosol mass by PMF. Based on the PMF results, the nitrate species were separated into organic and inorganic components, with the organic nitrates contributing one-third of the total nitrate mass. The results highlight both anthropogenic and biogenic emissions as important atmospheric aerosol sources in a forest–urban mixed region.
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In this study, the effect of CO<sub>2</sub> observations on an analysis of surface CO<sub>2</sub> flux was calculated using an influence matrix in the CarbonTracker, which is an inverse modeling system for estimating surface CO<sub>2</sub> flux based on an ensemble Kalman filter. The influence matrix represents a sensitivity of the analysis to observations. The experimental period was from January 2000 to December 2009. The diagonal element of the influence matrix (i.e., analysis sensitivity) is globally 4.8% on average, which implies that the analysis extracts 4.8% of the information from the observations and 95.2% from the background each assimilation cycle. Because the surface CO<sub>2</sub> flux in each week is optimized by 5 weeks of observations, the cumulative impact over 5 weeks is 19.1%, much greater than 4.8%. The analysis sensitivity is inversely proportional to the number of observations used in the assimilation, which is distinctly apparent in continuous observation categories with a sufficient number of observations. The time series of the globally averaged analysis sensitivities shows seasonal variations, with greater sensitivities in summer and lower sensitivities in winter, which is attributed to the surface CO<sub>2</sub> flux uncertainty. The time-averaged analysis sensitivities in the Northern Hemisphere are greater than those in the tropics and the Southern Hemisphere. The trace of the influence matrix (i.e., information content) is a measure of the total information extracted from the observations. The information content indicates an imbalance between the observation coverage in North America and that in other regions. Approximately half of the total observational information is provided by continuous observations, mainly from North America, which indicates that continuous observations are the most informative and that comprehensive coverage of additional observations in other regions is necessary to estimate the surface CO<sub>2</sub> flux in these areas as accurately as in North America.
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A better understanding of aerosol radiative properties is a crucial challenge for climate change studies. This study aims at providing a complete characterization of aerosol radiative effects in different spectral ranges within the shortwave (SW) solar spectrum. For this purpose, long-term data sets of aerosol properties from six AERONET stations located in the Iberian Peninsula (southwestern Europe) have been analyzed in terms of climatological characterization and inter-annual changes. Aerosol information was used as input for the libRadtran model in order to determine the aerosol radiative effect (ARE) at the surface in the ultraviolet (ARE<sub>UV</sub>), visible (ARE<sub>VIS</sub>), near-infrared (ARE<sub>NIR</sub>), and the entire SW range (ARE<sub>SW</sub>) under cloud-free conditions. Over the whole Iberian Peninsula, yearly aerosol radiative effects in the different spectral ranges were found to be −1.1 < ARE<sub>UV</sub> < −0.7, −5.7 < ARE<sub>VIS</sub> < −3.5, −2.6 < ARE<sub>NIR</sub> < −1.6, and −8.8 < ARE<sub>SW</sub> < −5.7 (in W m<sup>−2</sup>). Monthly means of ARE showed a seasonal pattern with larger values in spring and summer. The aerosol forcing efficiency (AFE), ARE per unit of aerosol optical depth, has also been evaluated in the four spectral ranges. AFE exhibited a dependence on single scattering albedo as well as a weaker one on the Ångström exponent. AFE is larger (in absolute value) for small and absorbing particles. The contributions of the UV, VIS, and NIR ranges to the SW efficiency varied with the aerosol types. The predominant aerosol size determined the fractions AFE<sub>VIS</sub>/AFE<sub>SW</sub> and AFE<sub>NIR</sub>/AFE<sub>SW</sub>. The AFE<sub>VIS</sub> was the dominant contributor for all aerosol types, although non-absorbing large particles caused more even contribution of VIS and NIR intervals. The AFE<sub>UV</sub> / AFE<sub>SW</sub> ratio showed a higher value in the case of absorbing fine particles.
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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. <br><br> 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. <br><br> 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.
