dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Gultepe and Isaac, 1999) , or directly to the relative change of the CCN number concentration  (Platnick and Twomey, 1994). Based on these reasons, we concentrated on the relative trends of the N, calculated using log 10 (N ) datasets. The additional MK trends were calculated using a linear scale, resulting in cm -3 yr -1 trends, which were then changed to relative trends by dividing by the sample median. Another issue is the climate relevance of the resulting trend. CCN are only a small part of the total aerosol particle number concentration, and accurate knowledge of concentrations in the accumulation mode and at the larger end of Aitken mode can not be directly obtained from the N data only. As mentioned previously, strong connections exist between modelled number concentrations of particles and their concentrations in the CCN particle size range  (Spracklen et al.
, 2010; Reddington et al. , 2011). In this paper, we have used stations reporting size distribution datasets, and we studied the size-dependence of the observed trends by calculating both N20 concentrations (particle diameters from 20 to 500 nm) and N100 concentrations (diameters from 100 to 500 nm), with the assumption that N100 will represent the CCN sized particle number concentration.
Data handling and pre-processing The data were divided into several datasets for the use of this study. In GLS trend analyses (Sect. 2. 5. 1) two kinds of daily means were calculated for logarithmic datasets, one with the whole day data, and the other using only the periods between 21:00-09:00 of local solar time. This was done to test the sensitivity of trends to day-time nucleation bursts, and to potentially generate trends that are more dominated by trends in the Aitken and/or accumulation mode. The station at Pallas (PAL) has frequent inside-cloud situations, which can affect the aerosol particle number concentrations, as the data used were from instruments that did not use a total aerosol inlet. For this reason, we followed the approach from  Kivekäs et al. (2009) , and pre-screened the data for only the periods where the local visibility was above 3000 m, indicating cloud-free conditions.
At other stations with frequent inside-cloud situations, a heated total aerosol inlet was used, and the in-cloud situations should not affect the detected concentrations. The daily means were, in the case of N, a suitable parameter to use, as the tests done with the full hourly time series did not show strong variation between hourly and daily mean values for the N trends. This is not necessarily true for many other aerosol properties and thus we do not support this methodology without checking the dataset sensitivity for such averaging. For MK trends, hourly values were used for consistency with the companion paper methodology.
Quality assurance The quality assurance protocol for this study was similar to the companion paper. As some of the data were from non-GAW stations, a similar procedure as in the GAW station quality assurance was adapted for this data. The overall procedure of the data checking was -The data were initially collected and pre-screened by the institutions responsible for the dataset. These data were submitted to the GAW database at NILU and then downloaded for the use in this study. -A questionnaire about changes in instruments, operating procedures, and data handling was sent to the data providers. The information from this questionnaire was used to detect potential disruptions in the dataset. -The submitted data were independently visually inspected in log and in linear scales by the main author of this work.
Questions, especially concerning rapid or un-characteristic changes in concentration or variability were then sent to the data providers for resolution. -If a change in the instrumental conditions was coincident with a clear change in the concentration level or variance, the data of the changed period were not included in the analysis. In many cases, the disruption was only temporary, and the data were again accepted when the signal returned to close-to-normal level after the next instrument check-up. Notably, this quality assurance removed significant parts of the data from some of the stations. The updated versions of the datasets were submitted to the GAW-WDCA database in NILU EBAS (http://ebas. nilu. no/) for more general use by other studies. The resulting improvement in the quality of data highlights the benefits of close interaction between data users and data providers to long-term monitoring.
Trends and confidence intervals Aerosol number concentrations are not usually normally distributed in either concentration or size space. Traditionally, the size distribution function is assumed to consist of relatively log-normal modes  (Seinfeld and Pandis, 2006). Similarly, the aerosol number and mass concentration are also generally relatively log-normally distributed  (van Dingenen et al. , 2004; Asmi et al. , 2011). There are some cases however, where this does not necessarily hold, such as mountain and coastal sites, where particle number histograms can be formed from several (semi)lognormal modes  (Asmi et al. , 2011). A common feature in almost all atmospheric datasets, and in particular aerosol number concentrations, is a high autocorrelation (persistence) of the concentrations  (Asmi et al.
, 2011). In essence this means that the variability of concentration time series is affected by long wavelength (slow) Table  2. Trends of aerosol number concentrations for the entire period available for each dataset. Trends for daily means are calculated in logarithmic space for the GLS method (Generalized Least Squares). The confidence intervals (CIs) for the GLS method are calculated using an autoregressive bootstrap (ARB) method, showing the 5th and 95th percentile CIs. Mann-Kendall trends and significances are calculated in linear space, and the relative trends are obtained by dividing the absolute trend by the sample median concentration. Night time trends are calculated from night time (21:00-09:00 local (solar) time) means. The bolded trends have statistically significant sign (CIs are the same sign for GLS trends, for MK trends the trend passes the significance test with p < 0.
Trend of daily means (yr -1 ) Trend of night-time means (yr -1 ) Dataset Region Period GLS CI (ARB) MK GLS CI (ARB) Notes NMY ANT 1995-2010 0. 2 % -0. 5 % 1. 0 % 0. 8 % 0. 4 % -0. 3 % 1. 1 % SPO1 ANT 1974-1988 -2. 2 % -3. 4 % -1. 0 % -4. 6 % -2. 2 % -3. 4 % -1. 0 % HA SPO2 1989-2011 -1. 5 % -2. 4 % -0. 5 % -3. 4 % -1. 5 % -2. 3 % -0. 6 % HPB EUR 1995-2011 -0. 2 % -1. 1 % -0. 2 % 0. 3 % -0. 1 % -0. 5 % 0. 4 % JFJ EUR 1997-2010 -1. 0 % -2. 6 % 0. 5 % 0. 5 % -0. 4 % -1. 1 % 0. 2 % HA MHD EUR 2000-2010 0. 7 % -2. 7 % 3. 9 % -0. 5 % 1. 3 % -2. 5 % 4. 7 % 1 PAL EUR 1996-2010 -1. 5 % -2. 6 % -0. 4 % -1. 0 % -1. 3 % -2. 2 % -0. 2 % BND NCA 1994-2011 -2. 7 % -3. 5 % -1. 9 % -4. 8 % -2. 9 % -3. 7 % -2. 2 % BRW NCA 1998-2011 0. 4 % -1. 0 % 2. 4 % 1. 1 % 0. 9 % -0. 7 % 2. 5 % SGP NCA 1997-2011 -2. 6 % -3.
3 % -1. 8 % -2. 3 % -2. 6 % -3. 3 % -2. 0 % SPL SPL 1998-2010 -2. 4 % -4. 5 % -1. 1 % -2. 6 % -2. 5 % -4. 3 % -1. 3 % HA MLO SWP 1999-2011 -4. 0 % -4. 7 % -3. 3 % -3. 1 % -4. 5 % -5. 3 % -3. 6 % HA SMO1 SWP 1977-1992 -3. 7 % -4. 3 % -3. 0 % -4. 0 % -3. 5 % -4. 0 % -2. 9 % SMO2 1992-2011 -1. 1 % -1. 7 % -0. 4 % -0. 3 % -1. 2 % -1. 9 % -0. 4 % 2 Size distribution datasets HYY20 EUR 1996-2011 -1. 3 % -1. 9 % -0. 8 % -1. 0 % -1. 2 % -1. 8 % -0. 6 % HYY100 -1. 5 % -2. 5 % -0. 5 % -1. 9 % -1. 6 % -2. 6 % -0. 7 % VAR20 EUR 1997-2011 -2. 9 % -3. 7 % -2. 1 % -4. 6 % -2. 8 % -3. 7 % -1. 8 % VAR100 -3. 5 % -4. 8 % -2. 4 % -3. 2 % -3. 6 % -4. 8 % -2. 6 % PAL20 EUR 2000-2010 0. 1 % -1. 0 % 2. 6 % 0. 0 % 0. 7 % -1. 0 % 2. 1 % 3,4 PAL100 2. 6 % 0. 5 % 5. 3 % 5. 0 % 2. 9 % 0. 7 % 5. 2 % 3,4 MPZ20 EUR 1997-1998 and 0.
4 % -0. 4 % 1. 2 % 1. 6 % 0. 5 % -0. 2 % 1. 4 % 5 MPZ100 2004-2010 0. 2 % -0. 8 % 1. 3 % 2. 7 % 0. 5 % -0. 6 % 1. 9 % 5 VHL20 EUR 2001-2011 -1. 5 % -2. 9 % -0. 1 % -0. 4 % -1. 5 % -3. 1 % -0. 1 % VHL100 -2. 6 % -4. 0 % -0. 1 % -0. 8 % -2. 2 % -4. 3 % -0. 2 % HA = High altitude station, ANT = Antarctica, EUR = Europe, NCA = North America and Caribbean, SWP = South West Pacific. 1 Several instances of exceeding the instrumental measurement limits in the daytime datasets due to coastal nucleation -gap in 2001. 2 Long gap from 1994 to 2004 -significant chance of error or bias in the analysis, especially as the instrument changed from 2004 on. See Table  1. 3 Several data gaps in the last years of dataset, potential bias to trend. 4 Station commonly in-cloud. Trends given for cloud-screened datasets.
In our case, the solution for the minimization of Eq. (  2 ) was obtained using the Matlab function lscov, which uses either Cholesky or orthogonal decomposition of V, depending on the condition of V (MathWorks, Inc. , 2010). The GLS fit first approximated the β factors directly from the sample, using Eq. (  2 ) with autocorrelation and standard deviation of the time series. Using the obtained trend and seasonal signal, the time series was then split into trend, seasonal signal and the residuals (Eq. 1). The GLS fitting was then iterated using the autocorrelation and standard deviation of the residuals to update the V, until the relative difference between the trends from the last two iterations was below 1 % of the trend slope.
Bootstrap confidence intervals (CI) Fitting a trend does not itself tell if there is a significant linear (or log-linear) change in the time series. Strong seasonality and noise of the datasets makes the trend detection difficult, potentially generating spurious trends and tendencies, even when using non-parametric methods. One approach to estimate the significance of the detected trends is by using statistical significance tests. The traditional statistical tests, assuming a specific shape of the distribution and independent measurements, are not directly applicable to aerosol particle number concentration datasets. In this paper, we concentrate on the bootstrap methods to evaluate the trend significance. In this context, "bootstrapping" means that we estimate the robustness of the observed trend by calculating similar trends from re-sampled datasets, which are generated by randomly sampling (with replacement) the noise terms of the data model (Eq.
1). Figure  1  shows the two methods used for such re-sampled dataset generation in a schematic way. The autoregressive bootstrap (ARB) approach used is directly applied from the Mudelsee (2010), algorithm 3. 5. By randomly selecting (with replacement) the last part of the Eq. (  1 ) (e) and re-building the datasets with the original autocorrelation, trend and seasonality, a semi-random realization of the time series is created. For studies of individual monthly trends, we used a moving block substitution bootstrap process (MBB)  (Mudelsee, 2010, algorithm 3. 3 ). This approach is natural for the monthly data, as the monthly datasets provide compact blocks of the time series that contain a significant part of the time series autocorrelation (persistence), and are short enough to have large enough pool of blocks for the resampling.
This indicates that the lack of a detected trend is robust with respect to nucleation events, or that the signals from the day-time nucleation peaks were diminished by the upper detection limit of the instrument. The MK and GLS/ARB methods of trend/significance testing agreed closely, especially in the sign and significance of the detected trends. The MK method gave s. s. trends, which the GLS/ARB method did not only in two cases (BRW and MHD), even though the MK trends were within the ARB confidence interval. The MK method gives slightly larger absolute trend values than GLS/ARB for the N datasets, although this result is not universal. Overall, the relatively good agreement between the two methods on the trend sign, and to 0. 4 % -0. 2 % 1. 1 % 0. 3 % JFJ -2. 2 % -3. 3 % -1. 0 % -1. 6 % MHD 0.
9 % -2. 6 % 4. 8 % 0. 9 % PAL -2. 6 % -4. 3 % -0. 9 % -3. 0 % BND -4. 6 % -6. 0 % -3. 3 % -7. 2 % BRW -0. 4 % -2. 9 % 2. 5 % -1. 3 % SGP -5. 7 % -6. 6 % -4. 5 % -5. 3 % SPL -5. 6 % -7. 7 % -4. 3 % -7. 3 % MLO -2. 1 % -3. 0 % -1. 3 % -3. 5 % Size distribution datasets HYY20 -2. 8 % -3. 7 % -1. 9 % -3. 3 % HYY100 -2. 4 % -3. 8 % -0. 7 % -1 % PAL20 0. 4 % -0. 3 % 1. 3 % 0. 0 % PAL100 0. 3 % -0. 8 % 1. 4 % 5. 0 % VAR20 -3. 0 % -4. 2 % -1. 3 % -1. 6 % VAR100 -3. 2 % -5. 1 % -1. 2 % -2. 9 % VHL20 -1. 5 % -2. 9 % -0. 1 % -0. 4 % VHL100 -2. 2 % -4. 2 % -0. 1 % -0. 8 % a lesser degree the trend magnitude, gives credibility to the technical robustness of the observed trends.
(2011). The Pacific stations have very stable decreasing trends with no clear seasonal effects. In Antarctica, the apparent decrease of the South Pole datasets is especially clear from September to May (SH summer) for SPO1, but no clear seasonal differences in the trends could be seen at NMY. In Northern Europe, the decreases are concentrated to wintertime for all PAL, VAR20 and HYY20 datasets. The PAL20 dataset does not agree in its seasonal or overall trend with the other datasets, most likely due the data gaps (see next section). The VHL station in Southern Sweden shows much weaker winter-time decreasing signal than other Nordic stations, probably due the influence of longrange transport from Central Europe. In Central Europe, both MPZ and HPB seem to have decreases of concentrations during wintertime, but as this is balanced by increases in other seasons (especially in the MPZ spring-summer trends), the overall trends are minimal.
The N20 and N100 GLS/ARB trends agreed almost perfectly on most available datasets on trend direction, magnitude and significance. For Northern European conditions, the trends seem to be uniform over the size distributions, and the observed decrease in the N20 is applicable to "CCN" concentrations in this area and for this period. The general lack of difference between all-day and night-time mean trends demonstrates that the observed trends are robust representations of the relatively long-lived part of the size distribution, or that the frequency and intensity of new particle formation events are relatively constant. The similar longterm behaviour of N20 and N100 is in agreement with earlier studies of short time (2 yr) comparisons of 30-100 nm and 100-500 nm size class concentrations at European stations, where both concentrations showed a general tendency to follow each other (Fig.
13  in  Asmi et al. , 2011). Measurements at PAL are a particular case in this context, as the station has both N and size distribution (N20 and N100) measurements. The calculated trends do not agree well (Table  2 ), as the size distribution trends are either increasing or indicate no trend, while the total N is decreasing. This could be problematic, as the size cut-off difference between N20 and N concentrations is relatively small, 10 nm, and a large difference in trends suggests high sensitivity to the smallest particle sizes. However, we found that a possible reason for this apparent discrepancy is data availability, as the later years of the Pallas DMPS measurements had long gaps during periods with unusually low concentrations for the season, which effectively removed these low concentrations from the trend analysis.
Trend reliability The sensitivity of trend fitting to data gaps, as evidenced in the PAL station data above, can be problematic in some of the observed trends. As we do not have similar comparison datasets for other stations, different amounts of gaps in different seasons and different parts of the datasets can cause a bias in the observed trends. Graphs under each station's time series in Figs. 2  and 4  show the seasonal data coverage for each dataset. Highly seasonal datasets (usually located in extreme latitudes) are perhaps more sensitive to data gaps, especially in the monthly trends (Fig. 3 ). In the overall trend calculation, both MK and GLS methods included a fitted seasonal component. This seasonal component fitting makes the trends less sensitive to seasonal gaps, although the trends will still be sensitive to gaps that happen during unusually low or high concentrations for that time of the year.
This was the reason for the trend sign difference at PAL, and there is no clear way to make sure the same has not happened at stations with many (or long) data gaps. Even in the best case, the lack of one season's data could still affect the fitting of the seasonal component, influencing the detected trends indirectly. The monthly trends do not have seasonal components for obvious reasons. This makes them more sensitive to the lack of data for specific years, especially at either end of the dataset. By going through the data coverage per season in Figs. 2  and 4  and trying to find seasons with particularly low representation in the dataset, or very one-sided datasets (e. g. , consisting of mostly data from the beginning of the measurement period), we identified some specific times of the year when the monthly trends could be affected by data gaps.
(ii) Aerosol and aerosol precursor emissions are generally at ground level, but the concentration is affected by the height of the mixed layer. If there is a consistent relative change in the mixing layer height, this could lead to relative changes in aerosol concentrations, even when surface emissions are constant. An increase in the general boundary layer heights could also act as a proxy of possibility of incursions of boundary layer air to high altitude stations. To study these two parameters, we collected the "synoptic monthly means" 2001-2010 datasets of both total precipitation and boundary layer heights 3  from the ECMWF ERA-INTERIM reanalysis at http://data-portal. ecmwf. int/data/d/interim full mnth/  (Dee et al. , 2011). The monthly means of daily-accumulated total precipitation were assumed to represent changes in regional precipitation of the station, and the boundary layer heights were assumed to represent the changes in the mixing layer behaviour.
, 2001). Thus an increase of precipitation could also increase N. For all the datasets described above, we did a simple ordinary least squares (OLS) trend fit for each map grid point over the period  2001 -2010 (2001 -2008 for EDGAR emissions) for EDGAR emissions) , i. e. we assumed that the comparison series are independent and normally distributed. For relative trends, the fit was done to the logarithm of the data. No tests of statistical significance were done for these comparison datasets, and thus the main idea was to identify possible qualitative similarities between N trends and these potential controlling factors. The resulting trends are shown in Fig. 5 ; as aerosol particles have a life time of approximately one week, we will concentrate on the changes in the regional behaviour of the potential trend drivers.
The surface temperature trends (Fig. 5b ) resemble the N trends at continental sites where mechanism (2) could apply, especially considering that the prevailing winds in Northern Europe are from the western direction. The marine sites do not have visible temperature trends in the GISS dataset, and the similarity between Antarctic N and T trends is most likely co-incidental, as there is no vegetation to emit VOCs in that region. Overall, of the temperature-based trend mechanisms, case (2) seems to have better agreement with the measurements for this time period. However, when the time period studied is increased to cover the period from 1995-2010 (Fig. 6 ), the picture for the case (2) changes: even though the number concentration trends for this time period are still negative in Northern Europe and the continental US, the surface temperature has actually increased in Northern Europe over the period.
The inventoried anthropogenic emissions are zero for Antarctica, but there are indications that emissions from the South Pole base itself might affect number concentrations at SPO 5. Overall, anthropogenic SO 2 emissions generally have similar behaviour as N trends in this period, and we could not find opposing trends between N concentrations and PM 10 emissions, making the trend model (III) for SO 2 a possible partial explanation of the aerosol particle number concentration trends. Notably, the trends of the SO 2 emission datasets are also decreasing in the 1995-2008 period (not shown), making the anthropogenic influ-ences a potential explanation of the observed N trends on that timescale as well. However, the absence of a decreasing N trend at MPZ and HPB, even with the decrease in Central European SO 2 emissions, shows that this process is not necessarily linearly correlated in polluted environments, presumably due to multiple complex feedbacks in aerosol growth and dynamics  (Hamed et al.
Conclusions Trend analyses showed that the near-surface concentrations of atmospheric aerosol particles decreased at most observation points since the 1990's. These changes are visible and statistically significant in most locations studied in this paper, covering Northern Europe, North America, Antarctica, and the Pacific Ocean. The derived negative relative trends are generally slightly lower in magnitude than the reduction of anthopogenic SO 2 emissions over the last decades in EU and in US. However, the low number of stations with long datasets does not allow us yet to conclude that such decreasing trends are universal, regionally or globally. Aerosol number concentration trends showed clear seasonal variations, with Northern European stations especially showing decreasing N trends during wintertime.
We also observed that gaps in the dataset can affect the resulting trends, especially at locations with large seasonal cycles. Based on this, we can not recommend using short time campaign-type measurements as the basis for trend analyses of aerosol properties, at least without further knowledge of the seasonal and inter-annual variability of N concentrations in the area. The high level of agreement between the two fitting methods used in this paper demonstrates the technical reliability of the trend fitting procedures. However, the overall reliability of the trends is also dependent on the data availability, which was unfortunately not optimal at some stations. However, due the overall lack of long duration data series, these are the pieces of information we have and they provide us with the only qualified estimations of long-term N behaviour in these environments.
However, further analysis with longer time periods and seasonal variation did not support such a temperature-based effect. We could not find evidence of temperature-nucleation feedback suggested by  Yu et al. (2012)  based on free tropospheric or surface temperature trends. This does not mean that such processes could not affect the atmosphere in different regions, over different timescales and different periods of time. We could also not find clear similarity between N trends and trends of total precipitation or boundary layer height. The two Central European low-land stations did not show statistically significant trends. However, there are decreasing trends in monthly-analysed winter-time concentrations measured at these stations. As the role of biogenic emissions should be lessened during wintertime (making the anthropogenic emissions more prominent), this could indicate a role of human-induced emission decreases also in HPB and MPZ dataset trends, which are masked by biogenic emission increases during the summer.