dataesr/scientific-paragraphs-categorization huggingface dataset:
Open Peer Review Current Peer Review Status: This is an open access peer review report distributed under the terms of the Creative Commons , which permits unrestricted use, distribution, and reproduction in any medium, provided the original Attribution Licence work is properly cited. Version 2 Reviewer Report 27 June 2019 https://doi. org/10. 5256/f1000research. 21019. r49370 © 2019 Velayutham B.
Aerosol optical depth (AOD) has become a crucial metric for assessing global climate change. Although global and regional AOD trends have been studied extensively, it remains unclear what factors are driving the inter-decadal variations in regional AOD and how to quantify the relative contribution of each dominant factor. This study used a long-term  aerosol dataset from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellitebased AOD datasets (MODIS/Terra and MISR) from 2001 to 2016, to investigate the long-term trends in global and regional aerosol loading. Statistical models based on emission factors and meteorological parameters were developed to identify the main factors driving the inter-decadal changes of regional AOD and to quantify their contribution.
, 2015; Cohen et al. , 2017). Unlike the long-lived greenhouse gases (e. g. , CO 2 , CH 4 and N 2 O), aerosols produced via anthropogenic activity or naturally have relatively short life spans and large spatial and temporal variability. Therefore, it is essential to investigate the long-term variability and inter-decadal trends of atmospheric aerosol loadings on both regional and global scales. Aerosol optical depth (AOD), representing the attenuation of sunlight induced by aerosols and serving as an important measure of aerosol loading, has become a crucial metric in assessing global climate change and the effects of aerosols on radiation, precipitation and clouds. Through the efforts of scientists in various countries over the past 3 decades, a series of AOD datasets with different time spans derived from continuous ground-based and satellite observations have been accumulated.
These datasets have been widely employed to investigate the long-term annual and seasonal trends of AOD at global and regional scales. Although ground-based observations have limited spatial and/or temporal coverage, they can provide more detailed information on aerosol properties and long-term variations for satellite and model validation. For example, using the long-term and high-quality AOD datasets from the Aerosol Robotic Network (AERONET),  Li et al. (2014)  found that North America and Europe experienced a uniform decrease in AOD from 2000 to 2013. Che et al. (2015)  estimated the change in AOD based on AOD data at 12 long-term ground-based sites in China from the China Aerosol Remote Sensing Network (CARSNET) and found that AOD showed a downward trend from 2006 to 2009 and an upward trend from 2009 to 2013.
Considering the localized changes in anthropogenic aerosol emissions and meteorological conditions in different regions, a key question is whether these factors are responsible for the regional AOD trends or which main factors dominate the trends. Therefore, it is important to investigate the cause of regional AOD trends in terms of the variations in both anthropogenic emissions and meteorological factors for projecting the response of the Earth atmosphere system to future changes. In this study, we used a long-term  aerosol dataset obtained from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellite-based datasets (MODIS/Terra and MISR) during 2001-2016, to conduct a comprehensive estimation of global and regional AOD trends over different periods.
MERRA-2 aerosol reanalysis data MERRA-2 is the latest atmospheric reanalysis version for the modern satellite era provided by the NASA Global Modeling and Assimilation Office  (Gelaro et al. , 2017) , using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model  (Molod et al. , 2012 (Molod et al. , , 2015)) , which includes atmospheric circulation and composition, ocean circulation and land surface processes, and biogeochemistry. Note that in MERRA-2, in addition to providing assimilation of traditional meteorological observations, a series of AOD observation datasets, including bias-corrected AODs, retrieved from the Advanced Very High Resolution Radiometer (AVHRR) instrument over the oceans  (Heidinger et al. , 2014)  and MODIS (onboard both the Terra and Aqua satellites)  (Levy et al.
Satellite aerosol data Two AOD datasets during 2001-2016 retrieved from MODIS and MISR, both onboard the Terra platform, were used in this study. The MODIS sensor onboard the Terra satellite observes the Earth at multiple wavelengths (range: 410-1450 nm; 36 bands) with a 2330 km swath, which has provided near-daily global coverage since 2000  (King et al. , 2003; Levy et al. , 2015). This study employed the combined Dark Target-Deep Blue (DTB) AOD algorithm at 550 nm, with a 1 • × 1 • resolution, from the level 3 (L3) monthly global aerosol dataset for MODIS Terra, Collection 6. 1. The average mean absolute error (RMSE) of the level 3 MODIS/Terra DTB monthly AOD data have been estimated to be about 0. 075 (0. 120) over land  (Wei et al. , 2019). Note that MODIS/Aqua L3 was not used because it started late (June 2002).
, 1998) , were utilized. It should be noted that, although MISR has a much narrower swath (∼ 360 km) compared with MODIS, the multi-angle observation from MISR provides the capability for retrieving a more reliable AOD over bright surfaces, such as desert areas  (Diner et al. , 1998; Kahn et al. , 2010). The AOD retrieval in the 555 nm channel from monthly global aerosol datasets at a spatial resolution of 0. 5 • ×0. 5 • were used in this study. The uncertainty of the MISR level 2 (L2) AOD data over land and ocean has been estimated to be ±0. 05 or ±(0. 2 × AOD)  (Kahn et al. , 2005). Note that the wavelength of AOD (555 nm) reported by MISR is different from that of the MERRA-2 and MODIS/Terra datasets (550 nm); however, this slight wave-H. Che et al. :  Large contribution of meteorological factors to inter-decadal changes length difference is not expected to affect our analysis and conclusions regarding AOD annual and seasonal trends.
Emissions inventory and meteorological data The anthropogenic emissions inventories used in this study were obtained from the Peking University (PKU) website (http://inventory. pku. edu. cn/, last access: 31 March 2019), including total suspended particles (TSP)  (Huang et al. , 2014) , SO 2  (Su et al. , 2011) , BC  (Wang et al. , 2014)  and organic carbon (OC)  (Huang et al. , 2015) , with a spatial resolution of 0. 1 • × 0. 1 • and spanning the period 1980-2014. The emissions were calculated using a bottom-up approach based on fuel consumption and an emissions factor database. Huang et al. (2015)  showed that the PKU emissions inventories are broadly similar to those of EDGAR version 4. 2 (Edgar, 2011). Monthly meteorological fields from the MERRA-2 global reanalysis were also utilized, including total surface precipitation, surface wind speed, surface relative humidity (RH), mean sea level pressure, etc.
Regions of Interest (ROIs) In this study, 12 regions of interest (ROIs) dominated by different aerosol types were selected to study the long-term trends in regional aerosol loading and how they are related to local emission changes as well as the variation in meteorological variables. These 12 ROIs included three mineraldust-dominant regions, SD (3-25 AOD data from the 468 AERONET sites worldwide and the 37 CARSNET sites in China were used to evaluate the performance of the 3-hourly AOD datasets from MERRA-2. To ensure the accuracy of the assessment, instantaneous ground-based AOD observations within 1 h, obtained from AERONET and CARSNET, were averaged as the hourly mean AOD and compared with those from the MERRA-2 3hourly AOD datasets (see Fig. 2a  for the whole procedure). The errors and quality of the MERRA-2 AOD retrievals are reported using the (Pearson) correlation coefficient (R, Eq.
1), the mean absolute error (MAE, Eq. 2), root-meansquare error (RMSE, Eq. 3), the relative mean bias (RMB, Eq. 4), the mean fractional error (MFE, Eq. 5), the fractional gross error (FGE, Eq. 6), and the index of agreement (IOA, Eq. 7) for validating the reanalysis  (Yumimoto et al. , 2017). Gridded monthly mean vertical wind shear between 500 and 850 hPa a Units: g km -2 (TSP, SO 2 , BC, OC); kg m -2 s -1 (Pre); m (PBLH, GH); 1 (SM, CLF); Pa (SLP); K (T , dT ); % (RH); pa s -1 (Ome) and m s -1 (U,V, WS, VWS 500-850 ). b VWS 500-850 was calculated as (U 500 -U 850 ) 2 + (V 500 -V 850 ) 2. H. Che et al. :  Large contribution of meteorological factors to inter-decadal changes Figure  2. Flowchart with the procedure followed for (a) the evaluation of MERRA-2 global AOD, using the AERONET and CARSNET ground-based reference dataset, and (b) the evaluation of global and regional AOD trends.
Trend analysis and stepwise MLR model Long-term trend analysis of the AOD from MERRA-2, MODIS/Terra and MISR was performed on monthly time series data, using ordinary least-squares linear regressiona technique widely employed for trend analysis of aerosol data  (Hsu et al. , 2012; Pozzer et al. , 2015; Klingmüller et al. , 2016; Ma et al. , 2016; Hammer et al. , 2018). Prior to regression, these data were first deseasonalized by subtracting the monthly mean for different study periods for each grid cell to eliminate the large influence of the annual cycle. To better compare the results of the trend analysis, the MERRA-2 and MISR datasets at high spatial resolution (0. 5 • × 0. 625 • and 0. 5 • × 0. 5 • , respectively) were bilinear interpolation to the MODIS/Terra resolution of 1 • × 1 • (see Fig.
Assessing the performance of the MERRA-2 AOD datasets on the global scale Although the official documentation points out that a large number of AOD observations have been assimilated into the system  (Buchard et al. , 2017) , the global performance of MERRA-2 AOD is still unknown. In addition, since MERRA-2 assimilates a variety of AOD datasets from different observation periods (such as AVHRR before 1999, AERONET since 1999 and Earth Observing System (EOS) era satellites after 2000)  (Buchard et al. , 2017) , it is difficult to disentangle the influence of each assimilated dataset alone on the overall accuracy of MERRA-2. Strictly speaking, we need to point out that MERRA-2 and AERONET are not independent of each other (after 1999). Nevertheless, we hope that this assessment will still provide some reference for other studies using the MERRA-2 AOD dataset.
MERRA-2 vs. AERONET Using all of the collected AERONET observations, the overall performance of the MERRA-2 AOD on a global scale was validated first. The results showed significant spatial agreement between MERRA-2 and ground-based AOD on the global scale, with an acceptable bias (r = 0. 85, RMSE = 0. 12, MAE = 0. 06 and MFE = 38. 73 %) (Fig. 3a ). Moreover, Fig. 4  shows site-to-site comparisons of the 3hourly MERRA-2 AOD at 550 nm and the collocated AERONET AOD observations, and a statistical summary of the comparison and the location information for each site are given in Table  S4. Globally, the MERRA-2 AOD datasets exhibited high R values against ground-based observations: over 83. 3 %, 59. 0 % and 28. 0 % of sites had an R greater than 0. 6, 0. 7 and 0. 8, respectively; 95. 9 % and 87.
6 % of sites had an IOA greater than 0. 8 and 0. 9, respectively; 85. 3 % and 50. 4 % of sites had an MAE lower than 0. 1 and 0. 05, respectively; 22. 6 % and 59. 8 % of sites had an MFE lower than 30 % and 40 %, respectively; and more than 69. 9 % and 89. 3 % of sites had an RMSE less than 0. 1 and 0. 2, respectively. These results indicated that, although MERRA-2 does not perform well in some individual regions, it does not affect the global accuracy of MERRA-2 as the latest global aerosol reanalysis dataset, especially in comparison with other satellite datasets. In addition, the obvious regional differences in the global performance of MERRA-2 AOD should not be overlooked. According to Fig. 4c  and e , the RMB was greater than 1 and FGE was greater than 0 % in the United States, southern South America and Australia, which indicates that MERRA-2 overestimates the AOD in these regions.
To ensure the accuracy of interannual variations in AODs over different ROIs (as defined in Fig. 1 ), the regional performance of MERRA-2 AOD was evaluated by integrating all sites within each ROI (Table  2  and Fig. S2 ). Regionally, R ranged from 0. 7 to 0. 95 among the 12 ROIs, with the highest R (0. 95) occurring in the ME and the lowest (0. 7) in the EUS. Similar to the site-to-site FGE distribution, the FGE presented a systematic overestimation in the EUS of around 17. 82 %. In contrast, the FGE showed significant systematic underestimation in NC, SA, CF and SEA, with the degree of underestimation being 23. 9 %, 8. 1 %, 23. 0 % and 8. 5 %, respectively. Significant differences in these regions were also supported by small RMBs of 0. 71, 0. 87, 0. 75 and 0. 84, respectively. The MERRA-2 AOD datasets performed better over SA than over NC, which is one of the most polluted areas in the world, in terms of a smaller MAE (0.
Therefore, taking MODIS/Terra and MISR AOD as a reference, the accuracy of MERRA-2 annual-mean AOD was evaluated at global and regional scales (Figs. S4  and S5 ). Globally, the overall spatial correlations between the MERRA-2 AOD and MODIS/Terra and MISR AOD datasets was found to be quite acceptable, with no apparent disagreements in the annual AOD variations during 2001-2016 (Fig. S5 ). Besides, although an offset was found between MERRA-2, MODIS/Terra and MISR in terms of absolute values of AOD in some ROIs, the short-term tendency during the overlapping period was similar among the three datasets (Fig. S4 ). Because the aerosol retrieval algorithm based on satellite observation does not work well under cloudy conditions or for bright surfaces, there are always numerous missing values in satellite-retrieved AOD datasets.
In contrast, not only is the accuracy of the MERRA-2 AOD dataset comparable with satellite observations (Fig. S4 ), it also provides a complete AOD record from 1980 to the present day. These reasons give confidence that the MERRA-2 aerosol dataset is suitable for analysis of the variations in AOD. Thus, the AOD values from MERRA-2's aerosol analysis fields, in combination with the AOD datasets derived from two satellite sensors, were used to comprehensively analyze the spatiotemporal variability of aerosols at global and regional scales. although MISR underestimated the AOD (e. g. , in SA and eastern China), as expected because of insufficient sampling  (Mehta et al. , 2016; Kahn et al. , 2009) , the three AOD products were generally closely consistent on the global scale (also see Fig. S5 ).
Regional AOD trends To examine the spatial and temporal changes in more detail, the annual trend over the globe and in the 12 ROIs, derived based on MERRA-2 during periods 1, 2 and 3, were calculated. In addition, for comparison purposes, the regional trends in AODs from MERRA-2, MODIS and MISR during 2001-2016 were also estimated. It should be noted that different datasets may have a certain effect on global and regional trend assessment due to their own uncertainties. Nevertheless, we include them for completeness but exercise caution when interpreting the differences in trend values between different datasets. The comparisons of the magnitudes of global annual trends with these regional trends are summarized in Fig. 8  and Table  S1. In general, the annual trends derived from different datasets were small on the global scale.
As indicated by the results in Fig. 8  and Table  S1 , the trend values were -0. 00068 yr -1 for the globe during period 1, with statistical significance at the 95 % confidence level. In contrast, no statistically significant trend was detected at the global scale for period 2 (0. 00050 yr -1 ) or 3 (0. 00038 yr -1 ). Analyzing the global AOD trends during 2001-2016 from MERRA-2 and the two satellite datasets, it was found that the MERRA-2 trends were negligible, whereas significant positive (negative) trends were found for MODIS (MISR). However, the trends could be considerable on regional scales. For example, over the anthropogenic-aerosoldominant regions for periods 1, 2 and 3, strong positive trends were apparent over NEA, NC, SC and SA, while strong and statistically significant negative trends were found over WEU and EUS.
(De Meij et al. , 2012). In addition to the annual trend, the seasonal trend of AOD for different datasets in different ROIs and different historical periods was also studied (Fig. S7  and Table  S1 ). Globally, negative trends were observed throughout the four seasons during period 1, especially during summer, autumn and winter (-0. 00078, -0. 00092 and -0. 00097 yr -1 , respectively; statistically significant at the 95 % confidence level). In contrast, there was a negative trend in period 2, although it was not significant. In the subsequent period, period 3, the trend values shifted from negative to positive. The positive trend was more significant in spring and autumn (0. 00053 and 0. 00070 yr -1 ). Regionally, strong positive trends were apparent over both NC and SC throughout the four seasons during periods 1, 2 and 3.
When compared with the regional trends during 2001-2016 calculated by the two satellite datasets, we found that the seasonal trends of MERRA-2 were highly consistent with the satellite results in almost all regions, especially in spring and autumn. It is worth noting that the trend differences among the three different datasets in all four seasons still existed in NC and SC, and the differences had different seasonal characteristics. For example, over NC, the most significant difference occurred in spring and summer, whereas it occurred in summer and winter over SC. Seasonal differences in trends are mainly due to insufficient accuracy of MERRA-2 in China (see  Sect. 3. 1. 2). Since the sign of a trend value often varies with the span of the calculation period, it was necessary to evaluate the sliding trend of different periods to help examine the time node of the changes more precisely.
Conclusions and implications This paper presents a comprehensive assessment of the global and regional AOD trends over the past 37 years , based on the reanalysis MERRA-2 AOD dataset. AOD observations from both AERONET and CARSNET stations were used to assess the performance of the MERRA-2 AOD dataset on global and regional scales prior to calculating the global and regional AOD trends. Satellite retrievals from MODIS/Terra and MISR were then used to estimate the AOD annual and seasonal trends and compare them with the MERRA-2 results. Finally, the stepwise MLR and LMG methods were jointly applied to quantify the influences of emission factors and meteorological parameters on the interdecadal changes in AOD over 12 ROIs during the three periods of  1980-2014, 1980-1997 and 1998-2014. Results showed that the MERRA-2 AOD was comparable in accuracy to the satellite-retrieved AOD, albeit there was slight overestimation in the United States, southern South America and Australia and underestimation in the NC, SA, CF and SEA when compared with the groundbased AERONET and CARSNET AOD.
Acknowledgements. NASA's global modeling and assimilation office is gratefully acknowledged for making the MERRA-2 aerosol reanalysis publicly accessible. Thanks are also extended to the  PKU  emissions inventory research group and AERONET networks for making their data available online, as well as the  GES-DISC  for providing gridded AOD products of MODIS and MISR through their  Giovanni  website. Financial support. This research has been supported by the  National Science Fund for Distinguished Young Scholars  (grant no. 41825011 ), the  National Key R & D Program Pilot Projects of China  (grant nos. 2016YFA0601901  and  2016YFC0203304 ), the  National Natural Science Foundation of China  (grant no. 41590874 ), the  CAMS Basis Research Project  (grant no. 2017Z011 ), the  European Union Seventh Framework Programme ( FP7/2007-2013 ) (grant no.
We also assessed the effect of land use and land cover on weekly fire cycle significance by testing the aforementioned hypotheses separately for the Villages, Settlements, Croplands, Rangelands, Seminatural, and Wildlands anthropogenic biomes (anthromes)  25  , which map global patterns of sustained direct human interaction with ecosystems. Anthrome-specific analysis provides valuable information to elucidate the land management activities, regions, and times of the year contributing to the occurrence of weekly cycles in global vegetation burning. Previous research by  26  and 27 also detected Sunday minima in global fire activity, using spatially aggregated fire counts data from the NASA Earth Observations website (http://neo. sci. gsfc. nasa. gov/view. php?datasetId=-MOD14A1_M_FIRE). However, inconsistencies between the active fire numbers reportedly used by  26  and by  27  , and their discrepancies relatively to other published sources justify revisiting the topic of weekly cycles in global active fire data.
Data Daily fire activity data were obtained from the NASA MODIS MCD14ML Collection 5 active fire product  30  at 1 km spatial resolution, after screening for false alarms and non-vegetation fires according to the procedures detailed in  29. We analysed the period from July 8, 2002 to July 29, 2012 (TERRA and AQUA sensors), for a total of 525 full weeks and 43,416,629 fire counts. Figure  1  shows the mean + 1 sd. of total fire counts per weekday. Land use/land cover data used are from the Anthropogenic Biomes of the World (anthromes) dataset, at 5′ (ca. 9 km) spatial resolution, dated from the year 2000. Anthrome data combine information on population density, land cover and land use to derive a map of global patterns of sustained direct human interaction with ecosystems  25. Since a large majority of global vegetation burning is anthropogenic  24  , they were considered particularly appropriate for our analysis.
The relative stability of spatial patterns depicted by the anthromes dataset further justifies its use in conjunction with global fire activity data for the period 2002 to 2012. Each active fire was labeled with the anthrome class present at its geographical coordinates to create the datasets for the anthrome-specific fire weekly cycle analyses (see Table  1 ). Maps aggregated at 0. 5° spatial resolution of the weekday with the fewest fires and of Global Anthromes (v2. 0) using the modal class rule are shown in Figs 2 and 3 respectively.
Methods and Models Preliminary data analysis and sampling model identification. To search for weekly cycles in vegetation burning, we analysed a 525-week time series of global daily MODIS active fire counts. The sample auto correlation (acf) and the partial auto correlation function (pcf) together with the periodogram of the log-transformed daily global fire count data were used to search for periodical variations in the data. The periodogram was calculated to identify frequencies at which jumps occur and their significance was tested with Fisher's test and Whittle's test  31, 32. The periodogram showed statistically significant jumps at the fundamental frequency w j = 2πj/365 corresponding to 1 cycle in 365 days and its first 3 harmonics, corresponding respectively to 1 cycle in 6 months, 4 months, and 3 months.