dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , emission, loading, and deposition are not internally consistent), our framework integrates observational constraints in a self-consistent manner. We detail our approach in Sect. 2, after which we summarize independent measurements used to evaluate our representation of the global dust cycle in Sect. 3, and present results and discussion in Sects. 4 and 5. We find that our proce-dure results in a substantially improved representation of the Northern Hemisphere global dust cycle and modest improvements for the Southern Hemisphere. We provide a dataset representing the global dust cycle in the present climate  (2004) (2005) (2006) (2007) (2008)  that is resolved by particle size and season. Because comparisons against independent measurements indicate that this dataset is more accurate than those obtained by an aerosol reanalysis product and by a large number of climate and chemical transport model simulations, this dataset can be used to obtain more accurate quantifications of the wide range of dust impacts on the Earth system.
We summed the optimal dust loadings of the nine source regions to obtain the main properties of the global dust cycle resolved by particle size, season, and location. Specifically, we obtained the dust emission flux, loading, concentration, deposition flux, and DAOD (Sect. 2. 4), which we added to the Dust Constraints from joint Experimental-Modeling-Observational Analysis (DustCOMM) dataset  (Adebiyi et al. , 2020). Throughout these calculations, we used a bootstrap procedure to propagate uncertainties in the observational constraints on dust properties and abundance, as well as uncertainties due to the spread in our ensemble of model simulations of the spatial distributions of a unit of dust loading, concentration, and deposition (Sect. 2. 5). Our methodology uses a large number of variables, which are all listed in the Glossary for clarity.
Specifically, we use simulations from the Community Earth System Model (CESM;  Hurrell et al. , 2013; Scanza et al. , 2018) , IMPACT  (Ito et al. , 2020) , ModelE2. 1  (Miller et al. , 2006; Kelley et al. , 2020) , GEOS/GOCART  (Rienecker et al. , 2008; Colarco et al. , 2010) , MONARCH  (Pérez et al. , 2011; Badia et al. , 2017; Klose et al. , 2021) , and INCA/IPSL-CM6  (Boucher et al. , 2020). These six models were forced with three different reanalysis meteorology datasets (Table  1 ), which helped sample the uncertainty due to the exact reanalysis meteorology used that past work indicates is substantial  (Largeron et al. , 2015; Smith et al. , 2017; Evan, 2018). Most of the six models were run for the years 2004-2008 or a subset thereof to coincide with the analysis period of regional DAOD in  Ridley et al.
Constraining the bulk dust loading generated by each source region The above procedure combined model simulations of the 2D spatial variability of size-resolved dust loading with constraints on dust size distribution and MEE. This procedure yielded the spatial distribution of DAOD that is produced by a unit (1 Tg) of dust loading from a given source region and season. Next, we use an inverse modeling approach to determine how many teragrams (Tg) of loading are needed from each source region to produce optimal agreement against constraints on the seasonal DAOD over areas proximal to major dust source regions. We use joint observational-modeling constraints on regional DAOD at 550 nm from  Ridley et al. (2016). This study used three different satellite AOD retrievals -from the Multiangle Imaging Radiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites -and bias-corrected those satellite data using more accurate ground-based aerosol optical depth measurements from AERONET.
2. 5). We also consider the  Ridley et al. (2016)  dataset more accurate than aerosol reanalysis products that assimilate similar AOD observations. This is because the  Ridley et al. (2016)  product includes a transparent quantification of errors that we propagated into the representation of the global dust cycle here and because the partitioning of assimilated AOD into different aerosol species in reanalysis products depends on the underlying aerosol models and is thus susceptible to the large biases in the prognostic aerosol schemes of these models (e. g. ,  Adebiyi et al. , 2020; Gliß et al. , 2021). Nonetheless, the  Ridley et al. (2016)  data are subject to some important limitations discussed further in Sect. 5. 1. Although we consider the  Ridley et al. (2016)  constraints on DAOD to be more accurate than constraints from individual satellite products, AERONET data, or aerosol reanalysis products, this study's results for the Southern Hemisphere (SH) are susceptible to substantial biases.
( 17 ) See the Glossary for further descriptions of each variable. In our companion paper  (Kok et al. , 2021a) , we further partition these fields into the originating source region. Table  2. Constraints on seasonal dust aerosol optical depth (DAOD) at 550 nm averaged over 15 regions. Regional DAOD constraints for regions 1-11 are from  Ridley et al. (2016)  and were obtained using data from AERONET, MODIS, MISR, and a model ensemble. Regional DAOD constraints for regions 12-15 are from  Adebiyi et al. (2020).
Independent dust measurements used to evaluate the inverse model We use two sets of independent measurements to evaluate the ability of the inverse model to reproduce the global dust cycle. The first dataset is a compilation of dust surface concentration measurements. Of the 27 total stations in this compilation, 22 are measurements of the bulk dust surface concentration taken in the North Atlantic from the Atmosphere-Ocean Chemistry Experiment (AEROCE;  Arimoto et al. , 1995)  and taken in the Pacific Ocean from the sea-air exchange program (SEAREX;  Prospero et al. , 1989)  for observation periods noted in Table  2  of  Wu et al. (2020). These data were obtained by drawing large volumes of air through a filter. To reduce the effects of anthropogenic aerosols, measurements were only taken when the wind was onshore and in excess of 1 m s -1  (Prospero et al.
, 1989). The mineral dust fraction of the collected particulates was determined either by burning the sample and assuming the ash residue to represent the mineral dust fraction or from their Al content (assumed to be 8 % for mineral dust, corresponding to the Al abundance in Earth's crust)  (Prospero, 1999). Note that since these measurements were taken during the period 1981-2000, the dust surface concentration "climatology" obtained from these measurements is for a different time period than that of the model simulations used in the inverse model (Table 1). Since most of the AEROCE and SEAREX stations are located far downwind of source regions, we also added a dataset of dust surface concentration from the Sahelian Dust Transect that was deployed in 2006 as part of the African Monsoon Multidisciplinary Analysis (AMMA;  Lebel et al.
, 2010; Marticorena et al. , 2010). This dataset contains measurements over 5-10 years of the surface concentration of aerosols with an aerodynamic diameter ≤ 10 µm (PM 10,aer ) at four stations in the western Sahel (M'Bour, Bambey, Cinzana, and Banizoumbou; see http://www. lisa. u-pec. fr/SDT/, last access: 13 May 2020). As with the AEROCE and SEAREX datasets, only measurements were used for which the wind direction was predominantly coming from dustdominated regions. As such, these measurements have at least two systematic errors: (i) the AMMA data reported the concentration of all particulate matter, so taking these measurements as being of dust concentration overestimates the true dust concentration, and (ii) measurements taken when wind was not coming from a dust-dominated region were omitted, which could also cause an overestimation of the dust concentration.
8 µm). The second independent dataset that we used to evaluate the inverse model results is a compilation (110 stations) of the deposition flux of dust with a geometric diameter ≤ 10 µm (PM 10 ) from  Albani et al. (2014). This study merged data from previous datasets  (Ginoux et al. , 2001; Tegen et al. , 2002; Lawrence and Neff, 2009; Mahowald et al. , 2009)  and adjusted these data to cover the 0. 1-10 µm geometric diameter range. We obtained the PM 10 deposition flux for the inverse model, the MERRA-2 data, and for each model in our ensemble following the approach above for the PM 10,aer concentration data. Note that we cannot correct the concentration and deposition flux of the AeroCom Phase I models (next section) to the PM 10,aer and PM 10 size ranges because of a lack of size-resolved simulation data.
We thus used the bulk concentration and deposition fluxes as many of these models simulated the PM 10 size range (see Table  3  in  Huneeus et al. , 2011). To assess the consistency of the inverse model results with both the independent datasets, we calculated the errorweighted mean square difference between the inverse model results and the observations. This statistic is known as the reduced chi-squared statistic and equals  (Bevington and Robinson, 2003 ) χ 2 ν = N i i (M i -O i ) 2 σ 2 i + σ 2 m , (19) where the index i sums over the N i measurements in the dataset, O i is the ith measurement in the dataset, M i is the inverse model result for the location and season of the ith measurement (if applicable), σ m is the calculated error in the inverse model result from the bootstrap procedure (see Sect.
2. 5), and σ i is the error in the measurement. For a model that matches measurements within the experimental error, χ 2 ν ≈ 1  (Bevington and Robinson, 2003). Values of χ 2 ν that are 1 indicate an overestimate of model or experimental error, whereas values of χ 2 ν 1 indicate either an underestimate of errors or substantial biases in the model or experimental data. We estimated the experimental errors in the surface concentration measurements by propagating the standard error in monthly averaged surface concentration measurements into seasonal and annual averages. Note that these errors do not include representation errors, which could be important  (Schutgens et al. , 2017). The errors in deposition data are more difficult to estimate, as these are not usually reported and because deposition fluxes can show large spatial and temporal variability  (Avila et al.
We also obtained the globally integrated annually averaged dust emission flux, dust loading, and DAOD. We obtained these variables for each of the 13 AeroCom simulations available from the online AeroCom database (see https://aerocom. met. no/, last access: 11 December 2020; this repository does not contain the 14th model simulation analyzed in  Huneeus et al. , 2011 , from the ECMWF model, which is thus omitted here). We also analyzed the MERRA-2 dust product  (Gelaro et al. , 2017)  in order to compare the inverse model's representation of the global dust cycle against a leading aerosol reanalysis product. We obtained the same variables from the MERRA-2 data as from the AeroCom data, except that we analyzed the MERRA-2 data for the years 2004-2008 to coincide with the regional DAOD constraints (Table  2 ).
We quantified the agreement of the various models against measurements using Taylor diagrams  (Taylor, 2001)  and by the correlation coefficients, bias, and root mean square errors (RMSEs). Because the surface concentration and deposition flux measurements span several orders of magnitude, their RMSEs are calculated in log space. We furthermore quantified overall model agreement against measurements by calculating the normalized error m against the available data for each hemisphere: NH m = 1 3    S NH τ,m N model n=1 S NH τ,n N model + S NH conc,m N model n=1 S NH conc,n N model + S NH dep,m N model n=1 S NH dep,n N model    , ( 20 ) SH m = 1 2    S SH conc,m N model n=1 S SH conc,n N model + S SH dep,m N model n=1 S SH dep,n N model    , (21) where n and m index the different models, which include the inverse model, MERRA-2, the six model ensemble members, and the 13 AeroCom models such that N model = 21.
Further, S denotes the RMSE of a model simulation with the DAOD (subscript τ ), surface concentration (subscript conc), and deposition flux (subscript dep) datasets on the annual timescale. These data are split into datasets for the Northern Hemisphere (superscript NH) and Southern Hemisphere (superscript SH). For the SH, there are no accurate observational constraints on DAOD available (see Sect. 2. 3), so we calculate the error relative to only the surface concentration and deposition flux datasets. Note that m is defined such that m = 1 implies that a model is average among the 21 models in reproducing the global dust cycle. The lower m is, the more accurately it reproduces measurements and observations of the various aspects of the global dust cycle.
Performance of inverse model results against independent measurements After obtaining inverse model results for key aspects of the global dust cycle, we next evaluate the accuracy of this representation of the global dust cycle using independent measurements of dust surface concentration and dust deposition fluxes (see Sect. 3. 1). We divide these results into comparisons for the NH (Sect. 4. 3. 1) and the SH (Sect. 4. 3. 2). We do this because we have observationally informed constraints on DAOD for 11 NH regions and therefore expect the inverse model results to show relatively good agreement against independent measurements in the NH. In contrast, we do not have observationally constrained DAOD for the SH; instead, the inverse model used an ensemble of reanalysis products, whose ensemble members might have similar biases as they assimilate similar remote sensing datasets.
Discussion Our results show that our framework for integrating observational constraints on dust properties and abundance yields an improved representation of the global dust cycle. Relative to the model ensemble, the inverse model results show a reduction of errors against NH dust cycle measurements of over a factor of 2 (Fig. 8e ) and modest improvements for the SH (Fig. 11e ). Moreover, we have obtained a dataset of the global dust cycle that is resolved by particle size and season and that is more accurate than the MERRA-2 dust product and any of a large number of model simulations. Below, we first discuss the main limitations of our methodology and results (Sect. 5. 1). We then discuss how our results can be used to guide improvements in the representation of the global dust cycle in climate and chemical transport models (Sect.
5. 2), after which we discuss the utility of the dataset presented here in constraining dust impacts on the Earth system (Sect. 5. 3). Shown are measurements (orange line and circles) and results from models in the AeroCom ensemble (black dotted lines and symbols) and our ensemble (brown dashed lines and symbols), as well as results from MERRA-2 (red line and diamonds) and the inverse model (blue line and squares). Also shown are the mean correlation coefficients between measurements and the different AeroCom models (r AeroCom ) and between measurements and the different models in our ensemble (r models ), as well as the correlation coefficients for MERRA-2 (r R ) and the inverse model results (r IM ). Uncertainty ranges for the inverse model results and measurements represent 1 standard error in the climatological seasonally averaged surface concentration.
Utility of the DustCOMM dataset in understanding the role of dust in the Earth system In addition to identifying mechanisms to improve individual model simulations, this study obtained an improved representation of the global dust cycle that can be used to im-prove our understanding and quantification of the impact of dust on the Earth system. This addition to the DustCOMM dataset  (Adebiyi et al. , 2020)   (Randles et al. , 2017; Wu et al. , 2020) , our method for integrating observational constraints yields a self-consistent representation of the global dust cycle. Our companion article  (Kok et al. , 2021a)  will supplement this dataset by partitioning all these fields by the originating source region. This dataset representing the seasonally resolved and sizeresolved global dust cycle can be used to more accurately quantify dust impacts on the Earth system, such as on climate, weather, the hydrological cycle, biogeochemistry, and human health.
Our dataset of an improved representation of the global dust cycle has an additional strength that amplifies its use: our dataset quantifies and propagates a range of observational and modeling uncertainties (see Sect. 2. 5). Comparisons against independent datasets indicate that the propagated error is realistic for the NH and might slightly underestimate the true errors in the SH (Figs. 7 and 10). The availability of realistic errors allows for the propagation of uncertainty into dust impacts constrained using our dataset, such as in the quantification of direct radiative effects and indirect cloud and biogeochemistry effects  (Mahowald, 2011). With a few exceptions  (Kok et al. , 2017; Regayre et al. , 2018; Di Biagio et al. , 2020) , the quantification of the uncertainty of (dust) aerosol direct and indirect radiative effects is uncommon yet critical to robustly constraining (dust) aerosol impacts on the Earth system  (Carslaw et al.
, 2010; Mahowald et al. , 2011b). Moreover, the quantification of uncertainties in aerosol effects in both the present-day and pre-industrial climates is crucial to constraining climate sensitivity  (Carslaw et al. , 2013 (Carslaw et al. , , 2018)). A second strength of our dataset representing the global dust cycle is that it uses an analytical framework that could be improved and expanded. The framework could be improved by using more accurate observational constraints of dust properties and dust abundance as inputs (see Fig. 1 ), for instance from several recent DAOD climatologies  (Pu and Ginoux, 2018; Voss and Evan, 2020; Gkikas et al. , 2021) , or by adding additional types of observational constraints, such as on the dust vertical profile  (Song et al. , 2021). The framework could be expanded by adding calculations of additional dust properties and impacts, such as dust mineralogy and radiative effects.
5a ). This large flux of coarse dust is needed to generate the ∼ 17 Tg of atmospheric (super-)coarse dust loading that in situ measurements indicate resides in the atmosphere  (Adebiyi and Kok, 2020). Accounting for this substantial loading of coarse dust is important because these particles account for a substantial fraction of absorption of shortwave radiation and both absorption and scattering of longwave radiation  (Tegen and Lacis, 1996; Ryder et al. , 2018 Ryder et al. , , 2019;; Fig. 5) , and they can also account for a large fraction of nutrients delivered to ecosystems by dust. The improved representation of the global dust cycle presented here is publicly available as part of the DustCOMM dataset  (Adebiyi and Kok, 2020; Adebiyi et al. , 2020). These data include gridded dust emission, loading, (surface) concentration, wet and dry deposition, and DAOD fields that are resolved by season and particle size, including by particle bin and for PM 2.
5 , PM 10 , and PM 20 dust. Additional strengths of this dataset are that it includes uncertainty estimates and that the data can be readily updated as improved constraints on dust properties and abundance become available. As such, our improved representation of the global dust cycle can facilitate more accurate constraints on the various critical impacts of dust on the Earth system. lr,s,k (θ, φ) Model-simulated spatial distribution of dust column loading produced by a unit of dust loading (1 Tg) in particle size bin k, emitted from source region r, averaged over season s (m -2 ). ls (θ, φ) Inverse model spatial distribution of dust bulk column loading, averaged over season s (kg m -2 ). ls,k (θ, φ) Inverse model spatial distribution of dust column loading produced by dust in particle size bin k, averaged over season s (kg m -2 ).
Error bars denote 1 standard error. (e) Taylor diagram summarizing the statistics of the comparison against the seasonally averaged regional DAOD constraints for the different models (Taylor, 2001). The different symbols represent the measurements (purple triangle), the 13 AeroCom models (black letters; A -CAM, B -GISS ModelE, C -GOCART, D -SPRINTARS, E -MATCH, F -MOZGN, G -UMI, H -LOA, I -UIO_CTM, J -LSCE, K -ECHAM5, L -MIRAGE, M -TM5), the MERRA-2 dust product (red R), the six models in the model ensemble (brown numbers, as for panels a-d), the six improved model results (green numbers with a prime), and the inverse model results (blue star). The horizontal axis shows the standard deviation of the dataset or model prediction, the curved axis shows the correlation, and the grey half-circles denote the centered root mean square difference between the observations and the model predictions.
Panel (e) shows normalized model errors (see Sect. 3. 2) relative to the DAOD (purple bars), surface concentration (green bars), and deposition flux (orange bars) datasets. Shown are results for the inverse model; the average of models in the AeroCom ensemble, our model ensemble, and our ensemble of improved models; MERRA-2; and for the individual models in our ensemble before and after applying observational constraints (see Sect. 2. 5). Hatched bars denote results of the inverse model and improved models obtained through our methodology. The reductions in bias, RMSE, and normalized error for the inverse model and improved models relative to the individual models and MERRA-2 imply that the integrational of observational constraints on dust properties and abundance improve the representation of the NH dust cycle.
Panel (e) shows normalized model errors (see Sect. 3. 2) relative to the surface concentration (green bars) and deposition flux (orange bars) datasets. Shown are results for the inverse model; the average of models in the AeroCom ensemble, our model ensemble, and our ensemble of improved models; MERRA-2; and for the individual models in our ensemble before and after applying observational constraints (see Sect. 2. 5). Hatched bars denote results of the inverse model and improved models obtained through our methodology.
contains dust loading, DAOD, (surface) concentration, and (wet and dry) deposition flux fields that are resolved by space, particle size, and season (data are available at https://dustcomm. atmos. ucla. edu/data/ K21a/, last access: 12 May 2021). Our results in Sect. 4. 3 indicate that this dataset is more accurate than both a large number of climate and chemical transport model simulations and the MERRA-2 dust product. Moreover, whereas MERRA-2 is internally inconsistent because dust loading is adjusted after emission by assimilating AOD measurements.
Acknowledgements. Martina Klose  and  Carlos Pérez García-Pando  acknowledge  PRACE  for granting access to MareNostrum at the  Barcelona Supercomputing Center  to run MONARCH. We acknowledge high-performance computing support from Cheyenne (https://doi. org/10. 5065/D6RX99HX,last access:  12 May 2021 ) provided by  NCAR's Computational and Information Systems Laboratory , sponsored by the  National Science Foundation. We further thank  Anna Benedictow  for assistance in accessing the AeroCom modeling data, the AeroCom modeling groups for making their simulations available,  Joseph Prospero  and  Nicolas Huneeus  for providing dust surface concentration data from in situ measurements from the  University of Miami Ocean Aerosol Network , and the investigators of the Sahelian Dust Transect for making their dust concentration measurements available.