dataesr/scientific-paragraphs-categorization huggingface dataset:
In Athens there is mainly fine particles, when they are coarser in IASBS and clearly coarse in Dushanbe. ANG remains close between DFno and DFvert in Athens, mainly because the impact of the additional emissions is not important. In IASBS, ANG is less correct with DFvert: adding mineral dust induces having too coarse aerosol size distribution. In Dushanbe, the opposite happens: ANG is modeled better with DFvert. The Angstrom exponent variability is due to transport of mineral dust over this region, because in all cases, modeled aerosol are more coarse. The fact to have worst scores in IASBS is due to the fact that in the DFno configuration the model is already biased: Angstrom exponent is well modeled but it is certainly a compensation of errors. For this specific case, the biased emission then transport of mineral dust may be due to several factors: (a) the soil characteristics and its humidity is less well represented in the databases or by the meteorological model, (b) the 10 m wind speed used as dust is less well diagnosed over IASBS than Time-series comparisons were also performed for surface ozone and particulate matter, PM 2.
Aerosol Size Distribution In this section, we compare the aerosol size distributions between AERONET and the model. All modeled aerosol species are cumulated in each model bin to have a total aerosol concentration per size. The model has 10 bins when the AERONET product has 15 bins. Results are presented, in Figure  21 , for stations with available data and close as possible of the wildfires regions. Unfortunately, the closest stations of the additional dust emissions did not provide such data. Stations with data are here Athens (Greece), Lampedusa (Italia) and IASBS (Iran). Clearly, the modeling of the aerosol size distribution remains a challenge and it is true for all chemistry-transport models  (Seinfeld & Pandis, 2006). With observations, we can see two distinct modes, one peaking around the mean mass median diameter D p ≈ 0.
Acknowledgments The authors acknowledge the OASIS modeling team for their support with the OASIS coupler, the  WRF  developers team for the free use of their model. We thank the investigators and staff who maintain and provide the AERONET data (https://aeronet. gsfc. nasa. gov/). European Environmental Agency (EEA)  is acknowledged for their air quality station data that is provided and freely downloadable (https://www. eea. europa. eu/data-and-maps/data/aqereporting-8).
195 Modern oceanographic conditions over the seasonal cycle above Site U1443 were assessed using recent datasets  2). Monthly data for wind (Wind Stress, Metop-A ASCAT, 0. 25°, Global, Near Real Time, 2009-present) (Fig. 2a ),  MLD (1969 MLD ( -2010) )   (Keerthi et al. , 2013)  (Fig. 2b ) and depth-integrated NPP estimated from satellite-derived surface chlorophyll concentrations  (Primary Productivity, Aqua MODIS, NPP, Global, 2003-present, EXPERIMENTAL (Monthly 200  Composite) calculated using method of  Behrenfeld and Falkowski (1997) ;  (Erd, 2020 )) (Fig. 2c ) were extracted for a box between ~4. 5-5. 5°N latitude and 89-91°E longitude (depending on grid Fig. 1c )  (Unger et al. , 2003). In Fig. 2  we show data from the deep traps (~3000 m, ~21 day sampling intervals)  (Rixen et al. , 2019) , but seasonal patterns of biogenic particle flux are very similar in the shallow (~1000 m) SBBT traps  (Unger and Jennerjahn, 2009; Vidya et al.
Age model Seven calcareous nannofossil bio-events dated between 5. 04 Ma and 9. 53 Ma were identified at 235 high-resolution in Site U1443 splice samples (Table  S1 ) to increase the resolution of shipboard biostratigraphy  (Robinson et al. , 2016). To check for orbital periodicities prior to tuning, wavelet analyses were performed on benthic isotope records in the depth domain and on the revised nannofossil-based age model (using a 4 th order polynomial fit, Fig. S1 ) in R using the biwavelet package  (Gouhier et al. , 2016; Grinsted et al. , 2004 ) (Fig. S2a-d ). All time-series were first 240 interpolated to constant depth or age resolution, such that the maximum resolution present was preserved (2 cm and 2 kyr for benthic isotope records in the depth and age domain, respectively). Records were then filtered to remove signals with periods longer than one third of the length of the dataset using the "bandpass" function in the R package Astrochron  (Meyers, 2014).
(2004) ) and its 22-25 kyr filter and amplitude modulation (above); For panels c-e, raw datasets were filtered using a Tanner-Hilbert filter centred on 43 cycles/myr with bandwidth ±3 (designed to include both the ~24 kyr and ~22 kyr precession periods). For panel b, a Tanner-1345 Hilbert filter centred on 2. 47 cycles/myr with bandwidth ±0. 8 was applied.
Acknowledgements This research used samples and data provided by the  International Ocean Discovery Program (IODP). We thank the science party, technical staff and crew of IODP Expedition 353. Funding for this research was provided by  French   ANR  project iMonsoon  ANR-16-CE01-0004-01  (CTB), 775  IODP France (CTB) , and  Deutsche Forschungsgemeinschaft  grant  Ku649/36-1(WK ). KMG is supported by  Australian Research Council  grant  DE190100042. CTB thanks  Anna Joy Drury  and  Tim Herbert  for age model feedback, and  Luc Beaufort ,  Baptiste Suchéras-Marx , and  Ian Bailey  for discussions that helped improve the manuscript. Marta Garcia Molina ,  Jean-Charles Mazur , and  Christine Pailles  are thanked for technical laboratory support at CEREGE.
The tropospheric water vapour data record generated within the ESA Climate Change Initiative Water Vapour project (ESA TCWV-COMBI) is used to evaluate the interannual variability of global climate models (CMIP6 framework under AMIP scenarios) and reanalysis (ECMWF ERA5). The study focuses on the tropical belt, with a separation of oceanic and continental situations. The intercomparison is performed according to the probability density function (PDF) of the total column water vapour (TCWV) defined yearly from the daily scale, as well as its evolution with respect to large-scale overturning circulation. The observational diagnostic relies on the decomposition of the tropical atmosphere into percentile of the PDF and into dynamical regimes defined from the atmospheric vertical velocity. Large variations are observed in the patterns among the data records over tropical land, while oceanic situations show more similarities in both interannual variations and percentile extremes.
The signatures of El Niño and La Niña events, driven by sea surface temperatures, are obvious over the oceans. Differences also occur over land for both trends (a strong moistening is observed in the ESA TCWV-COMBI data record, which is absent in CMIP6 models and ERA5) and extreme years. The discrepancies are probably associated with the scene selection applied in the data process. Since the results are sensitive to the scene selection applied in the data process, discrepancies are observed among the datasets. Therefore, the normalization process is employed to analyse the time evolution with respect to the mean state. Other sources of differences, linked to the models and their parametrizations, are highlighted.
Many programmes have been developed to get long-term observations with high spatial and temporal resolutions. Among them, the European Space Agency (ESA) Climate Change Initiative (CCI) programme has been created to explore the full potential of its Earth observation missions and to generate a climate data record (CDR) associated with each of the 23 ECVs. Among the ESA CCI projects, the CCI Water Vapour project (hereafter CCI_WV, https://climate. esa. int/ en/projects/water-vapour, last access: 19 September 2022) started in 2018 with the objective to generate long-term coherent datasets of tropospheric and stratospheric water vapour. The tropical belt (30 • S-30 • N) is a pivotal region in the Earth's climate. In this part of the globe, regional variations of the hydrological cycle are closely related to the Hadley-Walker overturning cells, which define the tropospheric circulation, and to its long-term changes (such as its observed slowdown and poleward expansion;  Ma et al.
, 2012). However, while the models perform best for the boundary layers over the oceans, most likely thanks to the thermodynamic constraint imposed by the sea surface temperature, strong biases remain in the upper layers of the troposphere, where clouds and atmospheric dynamics have large uncertainties. A recent intercomparison work of a selection of available long-term datasets has been conducted under the auspices of the Global Energy and Water Exchanges (GEWEX) programme of the World Climate Research Programme (WCRP)  (Schröder et al. , 2019). This intercomparison not only highlighted the complementarity among the sensors, but also underlined the caveats in the studies of trends and variabilities induced by artificial break points contained in the CDRs such as calibration changes, retrieval algorithms and resolution changes that impact the sampling.
, 2016). The ESA CCI_WV CDRs are studied from the aspect of the large-scale circulation. Section 2 describes the various datasets: the ESA CCI_WV CDRs, the CMIP6 GCMs and the ERA5 reanalysis. Section 3 is dedicated to the method of evaluation itself, while Sect. 4 discusses the results. Finally, the conclusions are drawn in Sect. 5.
Data description We focus on the tropical belt (30 • S-30 • N). Data obtained from ESA CCI_WV, CMIP6 GCMs, and ERA5 reanalysis are studied at daily scale and over the period 2003-2014. The monthly atmospheric vertical velocity at 500 hPa (ω500) of each data record is used as a proxy of large-scale circulation following  Bony et al. (2004). Note that the vertical velocity from ERA5 is also used as the dynamical reference for the CCI_WV CDRs. The daily water vapour data and monthly mean ω500 are adopted in our analysis for several reasons. Firstly, our intention is to evaluate the datasets with the highest temporal resolution, as the temporal averaging will mask out the extremes, and the probability density functions (PDFs) would have been smoothened. Secondly, the cloud condition varies significantly over short timescales; therefore, quantification at high temporal resolution is required.
(2010)  and  Schröder et al. (2013)  for the MW imagers. The algorithms for NIR imagers are discussed in  Lindstrot et al. (2012) ,  Diedrich et al. (2015)  and  Preusker et al. (2021). The MW and NIR data streams are processed independently and combined afterwards so that the individual TCWV values and their uncertainties remain unchanged. Validation of the dataset against GRUAN and SuomiNet shows that the bias and corrected RMSD (cRMSD) are generally small and within ±1. 5 and 2. 5 kg m -2 , respectively. The available spatial resolutions of the combined data record (hereafter TCWV-COMBI) are 0. 5 • × 0. 5 • and 0. 05 • × 0. 05 • , where the NIR based data are averaged and the MW-based data are oversampled to produce the results with desired resolution. The daily and monthly mean data are available for the TCWV-COMBI product during July 2002-December 2017.
We focus on the AMIP (Atmospheric Model Intercomparison Project)  (Ackerley et al. , 2018)  scenario with prescribed time-varying sea surface temperature (SST) and sea-ice concentrations from observations, including variations in natural and anthropogenic external forcings  (Eyring et al. , 2016). The detailed model descriptions are listed in Table 2. In addition to the CMIP6 models, the ensemble mean of the seven models is also included in the following analysis to represent the mean state of the CMIP6 models. Since the TCWV-COMBI data are cloud-screened over the land area, it is important to carefully analyse the cloud conditions for CMIP6 models before making the quantitative comparison. The cloud screening of the model-to-observation approach must remove the cloudy pixels while maintaining enough data for further evaluation.
Since cloud fraction (cf) is the only parameter that is available for the CMIP6 models to be employed as the indicator for cloudiness, a series of threshold tests were conducted by screening the pixels with cf values larger than 5 %, 25 %, 50 % and 75 % at all pressure levels. The distribution of TCWV over tropical land from CanESM5 of the CMIP6 model with different cloud masks, along with data from TCWV-COMBI and ERA5, is shown in Fig. 1. The land area fraction product is adopted as the land-sea mask for the CanESM5 model. Here we defined the area as land where the percentage of the grid cell occupied by land is larger than 50 %. The results indicate that data with cf less than 50 % at all pressure levels show a reasonable spatial coverage comparable to TCWV-COMBI and ERA5. Therefore, this threshold is adopted as the general cloud mask for the CMIP6 models, although this cannot be considered a purely clear sky.
It is worth mentioning that since the results are dependent on the cloud masks, the normalization process is also included in the following analysis to better illustrate the time evolution with respect to the mean state instead of strict biases. The description of the datasets remaining for the following analysis after the cloud screening over the land and ocean is displayed in Table  2. Over land, the percentage of data that remained for the CMIP6 models after the cloud screening is globally in the range 46. 14 %-76. 10 %. Over tropical oceans, the percentage of data that remained after removing the pixels under heavy precipitation conditions ranges from 99. 79 % to 99. 98 %. Although the scene selection is more stringent over land, this indicates that the CMIP6 data used in the fol-lowing analysis are comparable in terms of size of sample to the data from TCWV-COMBI and ERA5.
Results and discussions This section aims to assess the degree of agreement in the TCWV climatology and interannual variations between the ESA CCI_WV TCWV-COMBI, CMIP6 models and ERA5 reanalysis data over the tropics (30 • S-30 • N). The distribution of the water vapour over tropics and its link to large-scale circulation (ω500) are discussed in detail. Figure  3  shows the time series of the TCWV of the different datasets over land (Fig. 3a ; clear skies only) and over ocean (Fig. 3b ; all weather without heavy precipitation) for the period July 2003-December 2014. There are differences in the range of daily mean TCWV, but all datasets varied with the seasons. Overall, the different data records agree well with each other despite some outliers observed over tropical land in TCWV-COMBI data that resulted from the differences in available sample size under the clear-sky condition each day.
This difference can be explained because the TCWV datasets over land areas are composed of clear-sky-only data, which are likely drier than the nearby cloud area for a given location, thus translating into a dry bias associated with moistening processes by convective clouds  (Sohn et al. , 2006). Hence, the cloud screening over land makes it difficult to compare the datasets directly. In addition, since the boundary layer is drier in the continental subtropics and the maritime stratocumulus zones are wetter at low levels, the ocean areas should appear moister than the land areas.
The distribution of tropical TCWV The normalized PDFs of the daily TCWV obtained from all data records over both land and ocean are displayed in Fig. 4. The bimodal distributions can be explained by the presence of more humid columns in the intertropical convergence zone (ITCZ) and relatively drier ones in subtropical regions. Similar to the time series analysis, the characteristics over land are significantly different to the results over the ocean area because of the data screening. Over land, all datasets reach a first maximum at around 10-13 kg m -2 and present a secondary maximum near 40-50 kg m -2. Moreover, more lower values are observed in the CMIP6 models and ERA5 than in the ESA TCWV-COMBI dataset, and this cannot be explained by the cloud-screening method alone. Indeed, the results of simulation models and ERA5 are dependent on cloud conditions, consequently leading to differences in the comparison.
Although there are changes in variability in TCWV-COMBI data over tropical land with the inclusion of MODIS from 2011, validation of the dataset against GRUAN and SuomiNet shows that the dataset is stable and accurate during the whole observation period. Over oceans, most of the TCWV data are located around 20-60 kg m -2. The main peak is around 30 kg m -2 , and a secondary peak appears near 50 kg m -2. While the mean peak of PDF is nearly identical for TCWV-COMBI, ERA5 and CMIP6, there is a divergence for the secondary peak. This secondary peak even dominates the PDF of the CMIP6 models, while ERA5 and ESA TCWV-COMBI are still quite similar.
Wet anomalies are observed in 2010 for the highest percentiles in all data records. ERA5 also reveals 2012 as a moister year, in the low range of TCWV, but this anomalous year is not present in the ESA TCWV-COMBI or CMIP6 ensemble mean. The very good agreement among the various datasets is largely due to the fact that the CMIP6 models are evaluated under the AMIP scenario, so with the same prescribed SST for all models and ERA5, and that the relationship between SST and TCWV is largely explained by the Clausius-Clapeyron law  (Stephens, 1990). Hence this explains that anomalous years are the results of El Niño-Southern Oscillation  (Trenberth et al. , 2005) : 2008 and 2011 are characterized by a very negative ENSO index, while 2010 is an intermediate year, which starts with a positive ENSO cycle and is followed by a negative one.
General assessment The interannual variability of TCWV is then analysed from its links with the large-scale atmospheric circulation and follows approach (2) described in Sect. 3. The monthly ω500 of individual data records is decomposed into intervals of 10 hPa d -1 in the range of -120 to 120 hPa d -1. Figure  7  displays the normalized PDFs of the ω500 of the CMIP6 models and ERA5. As mentioned earlier, there are no atmospheric circulation data from the ESA TCWV-COMBI data record, so the ω500 from ERA5 is also employed as the reference for this dataset. Figure  7a  and b  show that the PDFs of the ω500 from the CMIP6 ensemble mean agree well with the ERA5 data. Most of the ω500 reside in around 10-20 hPa d -1 over both the tropical-land and tropical-ocean area, which characterizes the dominance of the large-scale Hadley subsidence in the subtropical free troposphere and explains the clear-sky radiative cooling of the tropics as discussed in  Bony et al.
(2004). The TCWV of each dataset is then sorted into the vertical velocity bins by the corresponding value of ω500. Therefore, the variations of the TCWV can be analysed from the perspective of atmospheric vertical motion. As shown in Fig. 7c  and d , large-scale downward motion is associated with a dry troposphere, while large-scale ascent is associated with a moister troposphere (except for ERA5 over land, where the most humid regimes are over weak subsidence). The results are in agreement with the maps of Fig. 2. Over-  all, the CMIP6 models and ERA5 show differences with the ESA TCWV-COMBI data in the amplitude of the signal and in the gradient of moisture between the ascending and descending regions. The evaporation from the oceans is the primary source of water vapour in the atmosphere; the oceanic boundary layer is humid, even in a weak subsidence regime.
Further, as the reference ω500 for TCWV-COMBI is from ERA5, it is sensible that there are differences observed in the TCWV-COMBI data comparing to other datasets that are decomposed by the corresponding model products. While the large-scale atmospheric dynamics are consistent among the datasets, the discrepancies in the TCWV reveal difficulties in representing the moistening processes of the tropical atmosphere: lateral mixing  (Pierrehumbert and Roca, 1998; Pierrehumbert, 1998) , outflows from clouds and too high/too low precipitation efficiencies of the convective schemes https://doi. org/10. 5194/acp-22-12591-2022 Atmos. Chem. Phys. , 22, 12591-12606, 2022  (Brogniez and Pierrehumbert, 2007). It is worth mentioning that the moistest regime of TCWV from ERA5 over land areas occurred in a weak subsidence regime instead of the strong ascending region.
Different patterns of variability are observed among the various datasets, the largest discrepancies being noticed over land areas, while over the oceans, the datasets are closer to each other. -Over land, the PDFs of the ESA TCWV-COMBI present a clear moistening trend of their driest percentiles, with a tipping point in 2011, probably associated with the addition of the MODIS observation in the climate data record. The projection of the TCWV onto regimes of ω500 shows the same behaviour; dry anomalies occurred in 2008 in all of the data records (except for ERA5, where dry anomalies were observed for subsidence and weak upward motion regimes), and wet anomalies occurred in 2010. Interestingly, the CMIP6 ensemble mean and the ERA5 reanalysis are in good agreement in terms of interannual anomalies, although the ERA5 TCWV is clearly too dry.
-Over ocean, the PDFs of all datasets present the same interannual variability. The extremely dry and moist years, associated with El Niño and La Niña events, are the same. This similarities hold when using the largescale circulation as an evaluation tool, with the same transition between the dry/subsiding regimes and the moister/ascending regimes. The results show that the ESA TCWV-COMBI data and ERA5 data vary within the ensemble spread of CMIP6 models, indicating that the mean models could correctly represent the evolution of water vapour with respect to large-scale circulation. The humid area is related to the ascending motion (negative value in ω500), and the dry area is related to the subsiding motion (positive value in ω500) over both tropical-land and tropical-ocean areas. There are discrepancies observed among the data records, probably caused by the lateral mixing, outflows from clouds and the precipitation efficiencies of the convective schemes.
It is difficult to track the reasons for the differences entirely; however, the differences and similarities can be explained by several factors. 1. The use of different satellites with different accuracies and resolutions within the ESA TCWV-COMBI may explain part of the moistening trend observed for this dataset over land. 2. The cloud masks applied to the GCMs and ERA5 and defined to mimic the cloud mask of the observation can also explain the differences. 3. The parametrization of the moisture fluxes at the surface and of convection, as well as the climate efficiency of the GCMs, also contributes to the observed differences. 4. The CMIP6 models under the AMIP scenario (with prescribed sea surface temperatures) and the scene selection that is much more conservative than over land explained the agreement between the ESA TCWV-COMBI, ERA5 and the GCMs over oceans.
It is really necessary to underline the role of the cloud mask in the assessment of water vapour fields in climate models using observations, even though it is easier to compare water vapour than clouds. The water vapour profiles (and sometimes the integrated values) from climate models usually have coarser spatial resolution than satellite observations. The satellite measurements, on the other hand, are often strictly restrained by cloud contamination. This clearly shows the importance of having access to the simulated water vapour (full profiles as well as integrated values) for the clear-sky part of the meshes of the climate models. Financial support. This research has been supported by the European Space Agency (grant no. 4000123554). Review statement. This paper was edited by Martina Krämer and reviewed by three anonymous referees.
Code availability. Code is available from the corresponding author upon request (jia. he@latmos. ipsl. fr). Data availability. The CMIP6 data are available from the ESGF system (https://esgf-node. ipsl. upmc. fr/projects/esgf-ipsl/, ESGF, 2022), and the CCI Water Vapour data are available from the project website (https://climate. esa. int/en/projects/water-vapour/, ESA, 2022).