dataesr/scientific-paragraphs-categorization huggingface dataset:
We would also like to thank Sheryl Fontaine and Daren Winkelman from the Nevada Division of Environmental Protection Bureau of Air Quality Planning for providing the Railroad Valley, Warm Springs Summit, and Cathedral Gorge State Park measurements. We are most grateful to Zaheer Kamal for his able piloting of the SA Mooney aircraft. We would also like to thank Richard Marchbanks for his assistance during the field campaign and Cathy Burgdorf-Rasco for maintaining the FAST-LVOS data site. Finally, we would also like to thank Katherine Emma Knowland and an anonymous reviewer for their careful reading of the manuscript and helpful suggestions. The  Review statement. This paper was edited by Andreas Hofzumahaus and reviewed by Katherine Emma Knowland and one anonymous referee.
Fires, Asian, and Stratospheric Transport-Las Vegas Ozone Study (FAST-LVOS) field measurements and model simulations were funded by the Clark County Department of Air Quality (CCDAQ) under contracts CBE 604318-16 and CBE 605334-19 (NOAA CCSL), CBE 603380-17 (Scientific Aviation), and CBE 604279-16 (NOAA GFDL). The NOAA CSL lidar operations were also supported by the NOAA Climate Program Office; Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) Program; and the NASA-sponsored Tropospheric Ozone Lidar Network (TOL-Net; http://www-air. larc. nasa. gov/missions/TOLNet/, last access: 16 August 2021).
Financial support. support This research has been supported by the Clark County Department of Air Quality (CCDAQ) under contracts CBE 604318-16 and CBE 605334-19 (NOAA CCSL), CBE 603380-17 (Scientific Aviation), and CBE 604279-16 (NOAA GFDL); the NOAA Climate Program Office; Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) Program; and the NASAsponsored Tropospheric Ozone Lidar Network (TOLNet).
Ground Observations A total of 33 rain gauge stations were used to evaluate the satellite products in this study. Of these, 20 stations were from the Souss Massa Hydraulic Basin Agency (ABHSM) and the remaining stations  (13)  were from the Draa Oued Noun Hydraulic Basin Agency (ABHDON). These two agencies provide data on a daily time scale. Table  1  shows the coordinates, as well as the percentage of missing values, in each station calculated at a daily time step. A rate of 20% missing data was considered a threshold not to be exceeded for the total records  [50]. These missing values were not considered in the evaluation. To not disturb the evaluation and to guarantee its reliability, the missing days in each station were also excluded from the satellite data. The availability of observed data only at the daily scale forced us to start the evaluation at this scale, despite the availability of most satellite products in a sub-daily time scale.
Satellite Rainfall Products In this study, we evaluated six satellite products, of which three belong to the GPM (Global Precipitation Measurement) mission, two others are developed by the Center for Hydrometeorology and Remote Sensing (CHRS), and one is developed by USGS (United States Geological Survey) and CHC (Climate Hazards Center). The choice of these products was based on their availability (Open access), their high spatial-temporal resolutions and their large spatial coverages. Table  2  summarizes the characteristics of these data sources. Among these products, 3 are based on satellite data only (GPM-E, GPM-L and PDIR) and three combine satellite and gauge data (CHIRPS, GPM-F, PERSIANN-CCS-CDR).
• Basin scale In order to minimize the error due to interpolation, basin-averaged analysis  [29, 30]  was used to examine the accuracy of each product over the five selected basins. It should be noted that precipitation data measured on the ground are very scattered in our study area, so the creation of a spatialized reference dataset, via spatial interpolation methods. for instance, could be very complicated and could introduce additional bias. For both spatial scales, the evaluation was done over the period from September 2000 to August 2020, on three temporal scales (daily, monthly, annually), using quantitative (continuous) and qualitative (categorical) statistical indices (Table  3 ): Table  3. List of Continuous and Categorical metrics used in this study. Gi: the gauge observation; Si: the satellite precipitation estimation; S: the mean satellite precipitation estimation; G: the mean gauge observation; N: the sample size; A-C: see the contingency table  (Table 4 ).
Evaluation at the Basins Scale The evaluation was also carried out at the scale of five basins, selected according to their aridity and distance from the ocean. Figure  9  shows the relative bias between satellite and observed data over the entire study period. The two products, GPM-E and GPM-L, overestimated precipitation for all basins. The other products performed differently from one basin to another. Basin 4 recorded the largest bias regardless of the product. The CHIRPS product was strongly biased for this basin, followed by the two products, GPM-E and GPM-L, which showed almost similar bias and, then, the PDIR, CCS-CDR and GPM-F, respectively. The other basins showed smaller bias, ranging from -25% to 38%. Overall, the GPM-F and CCS-CDR products showed the best performance across all basins.
• At the pixel scale We used 33 rain gauges to evaluate the different satellite products in this study covering the time period from 2000 to 2020. The results showed that the corrected products with observed data had the lowest bias (Figure  2 ), compared to the near-real-time products. The lowest correlations with observed data were obtained at the daily scale (Figure  3 ), which were improved (for all products) at the monthly scale. The decrease in performance for the annual scale was possibly due to the number of observations, which corresponded to just 20 observations, compared to 240 observations for the monthly scale, in the context of strong inter-annual variability. The GPM-F product was the best performing product at all time scales, and other studies have also found that the GPM-F product performs well compared to other products.
Conclusions and Perspectives In this study, we evaluated six satellite precipitation products with a high spatial and temporal resolution, i. e. , GPM-F, CHIRPS, CCS-CDR, GPM-L, GPM-E and PDIR-Now. These products were evaluated against ground-based datasets in sub-Saharan Morocco for the period from September 2000 to August 2020. Temporally, the analysis was performed at daily, monthly and annual scales, and spatially at both basin and pixel scales. Several quantitative and qualitative statistical indices were used and the SPPs performances were evaluated according to the altitude, latitude and the continentality of the rain-gauge. The main findings of the study were that the GPM-F product showed the most satisfactory results for all time scales and for both spatial scales, followed by the CHIRPS and CCS-CDR products at the monthly and annual scales, and by the GPM-E and GPM-L products at the daily scale.
, 1996). Meyer et al. (2015)  investigated the impact on cirrus clouds from the Nabro eruption 2011 but found no significant impact on a global scale. Friberg et al. (2015)  on the other hand found lower springtime cirrus cloud reflectance in the NH midlatitudes during years with higher volcanic aerosol loading in the LMS. The aim of this study is to investigate the impact from downwelling volcanic stratospheric sulphate aerosol on cirrus clouds. More specifically, we aim to study the long-term effects of descending volcanic sulphate aerosol on cirrus clouds rather than the immediate short-term impact of ash from volcanic eruptions. In order to do this, we will use a unique satellite data set of aerosol in the LMS and combine this with two different satellite data sets of cirrus clouds. The investigation will focus on springtime cirrus clouds at midlatitudes as this is the region and season when the upper tropospheric aerosol is dominated by downwelling stratospheric aerosol.
Stratospheric Aerosol Data The aerosol data are compiled from nighttime lvl. 1b v. 4-10 backscattering data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument. The data were corrected for attenuation from molecules and particles  (Friberg et al. , 2018). Ice clouds in the stratosphere were removed using the depolarization ratio  (Friberg et al. , 2018; Vernier et al. , 2009) , and data within 2 km of the tropopause were excluded to prevent influence from tropospheric aerosol and hygroscopic growth  (Sandvik et al. , 2021). The aerosol scattering ratio (total to molecular backscattering) in the remaining LMS (SR LMS ) was used to represent the variation in aerosol load in air transported to the UT. SR is suitable since the data are averaged over a height interval that is relative to the tropopause and since the pressure at the midlatitude tropopause varies by more than a factor of two.
Extending this analysis beyond 2016 appeared unreliable due to instrumental issues. In order to isolate cirrus clouds that could be affected by volcanic aerosol, we applied a cirrus filter (described in the SI) to the Level 2 DARDAR data. From the DARDAR-CLOUD data set, we use the effective radius (r e ) and the IWC. The DARDAR-NICE provides data of the number concentration of ice crystals with diameters larger than 5, 25, and 100 μm. From these data, we calculated the ICNC between 5 and 25 μm as well as between 25 and 100 μm.
Airs Cloud Data The Atmospheric Infrared Sounder (AIRS) cloud fraction (CF) data set (Version 7;  Tian et al. , 2020)  used here is a Level 3 monthly mean product in 1° × 1° longitude/latitude grids with 12 vertical standard pressure levels  (Team & Texeira, 2013). The data span from 2008 to 2019 in daily Level 3 data compiled into arithmetic monthly mean values. The AIRS product is divided into data collected in the ascending (day) and descending (night) orbit bins. We use both to calculate weighted zonal means at 40°-60° for the NH and SH. From the standard pressure levels, we use the CFs in three vertical layers from 300 to 150 hPa. At these levels, we assume the CF to be dominated by cirrus (ice) clouds. Further processing of the cloud data sets is described in Text S2 in Supporting Information S1.
Cirrus Microstructure In this section, we will look into the volcanic stratospheric impact on midlatitude cirrus clouds microphysics. In order to study the volcanic impact on the cirrus clouds at different temperatures, we divided the DARDAR data into four temperature bins (see Figure  2 ). This division also implies that the clouds are divided according to height with the coldest clouds residing at the highest altitudes, closest to the downwelling stratospheric aerosol. The SR LMS has a significant impact on several of the microphysical cirrus properties in the NH. In particular, the IWC decreases when there is a higher aerosol load in the LMS. The relationship is stronger the colder the clouds are, that is, the closer they are to the stratosphere (Figure  2b ). The relative change in IWC at the highest/lowest SR LMS values is ±10%.
The ICNC also decreases with increasing SR LMS at all crystal sizes. The strongest correlations and largest relative changes occur for the largest crystal sizes (ICNC >100 ), which display results similar to those for the IWC. The ICNCs at the smaller sizes have smaller relative changes than ICNC >100 but also have the strongest correlations with the SR LMS at the coldest temperatures. The relative changes in r e with LMS SR are very minor and not statistically significant (Figure  2a ). Nevertheless, since the ICNC decreases at all sizes, r e should not change to a large degree. Figure  2  shows comparisons of aerosol and cirrus data from the same months of the year. However, downwelling from the higher LMS altitudes may take a few weeks to reach the UT  (Friberg et al. , 2018). We therefore performed the same analysis with aerosol data shifted to half a month earlier.
This decreased the correlations at the highest altitudes somewhat (-0. 05--0. 02), while the correlations at the lower altitudes actually increased slightly (0. 02-0. 03). These results could imply that the subsiding aerosol reaches and impacts the cirrus clouds at lower altitudes somewhat later than those at high altitudes. However, the changes are not statistically significant and the results therefore should be interpreted with caution. Shifting the aerosol data a full month or more only decreased the correlations. In an attempt to determine if there are regional differences in the effects of the sulphate aerosol on the cirrus clouds, we divided the NH cirrus data into four regions: The Atlantic, Europe-Asia, The Pacific, and America. We use one SR LMS for all regions since volcanic aerosol is expected to be homogeneously distributed over the longitudes.
We find that the relationships in Figure  2  are stronger or of similar strength over the Pacific and Atlantic (Figure  S4  in Supporting Information S1). The land areas show weaker correlations and over America, the relationships are not statistically significant. These weaker relationships could be explained by a higher frequency of liquid origin cirrus clouds over land, which are expected to be insensitive to downwelling volcanic aerosol. Moreover, the Pacific and Atlantic are the areas most strongly impacted by transport from the LMS to the UT  (Škerlak et al. , 2014). In the SH, we do not find statistically significant relationships between any of the cloud microphysical parameters and the SR LMS (Figures  2f-2j ). The SH spring data in this study are more often influenced by fresh volcanic aerosol than the NH data set and have a few points with substantially higher LMS aerosol load than the other data points (note the different scales on the x axis between the NH and SH data).
Cirrus Cloud Fraction Data from the AIRS satellite sensor are used to determine if the cirrus CF is impacted by the aerosol load in the LMS. In the NH, there is a significant decrease in CF with increasing SR LMS at all pressure levels (see Figure  3 ). The correlation is not as strong as for the IWC and ICNC >100 data but the relative change is larger or similar for the AIRS data. The correlations in the AIRS data set do not increase with altitude, as they were for some of the microphysical parameters. The weaker correlations for the CFs than the microphysical parameters are to some extent expected since the cloud fraction is strongly impacted by the synoptic weather conditions, which are highly variable from year to year. Investigating the CF for different regions did not reveal any stronger CF changes with SR LMS than using all longitudes.
Comparison With Previous Studies The results in this study agree with two previous studies of moderate-sized volcanic eruptions' impact on cirrus clouds. Friberg et al. (2015)  found decreasing cirrus cloud reflectance with increasing sulphate aerosol in the LMS. The lower IWC and CF with higher SR LMS found in this study would indeed result in a lower cirrus reflectance. Moreover,  Meyer et al. (2015)  found lower backscattering from cirrus clouds in the NH during spring 2012, the year after the Nabro eruption. Our results differ from three satellite studies of the 1991 Mount Pinatubo eruption  (Luo et al. , 2002; Song et al. , 1996; Wylie & Menzel, 1999). This could be explained by the larger size of the Mount Pinatubo Eruption and perhaps also by more limited satellite data sets available at that time.
It may be that at such high altitudes, homogeneous freezing is the dominant cirrus formation mechanism. Nevertheless, if there are INPs present at the highest altitudes, then the chance of coating these is highest here. There are currently not enough data on which freezing mechanism that is dominant for cirrus clouds at these altitudes in the NH and we therefore do not propose one explanation mechanism that we find more probable than the other. Another possible cause for the contrasting results between hemispheres could be differences in the transport from the LMS to the UT. This transport displays larger seasonal and spatial variation in the NH  (Škerlak et al. , 2014)  than in the SH. The greater variability in the NH transport implies that the cirrus clouds in the NH are exposed to high concentrations of volcanic sulphate aerosol from the LMS during certain seasons and in specific regions.
Conclusions Using three different satellite data sets, we found that cirrus cloud properties in the NH are affected by volcanic aerosol downwelling from the stratosphere during spring. The cirrus clouds in the NH have lower IWC, ICNC, and CF during years with higher volcanic impact on the LMS, in particular the clouds at the highest altitudes. The clouds in the SH did not show statistically significant changes with LMS aerosol load. We propose that the changes seen in the NH result from the cirrus clouds having larger ice crystals, which sediment faster, causing a decrease in IWC, ICNC, and CF. We identify two viable mechanisms that can explain our observations. (a) The formation of larger crystals could be a result of the volcanic aerosol forming IN, seeding cirrus clouds formed through homogeneous freezing or (b) the downwelling volcanic aerosol could coat existing INPs in the UT, providing less INPs for cirrus clouds formed through heterogeneous freezing.
Acknowledgments Aerosol products from the CALIOP sensor were produced by  NASA Langley Research Center. This research was supported by the  Swedish National Space Agency  (contracts  130/15 ,  104/17 ), FORMAS (contracts  2018-00 973  and  2020-00 997 ), and  The Crafoord Foundation  (contract  20 190 690 ). We thank  Magdalena Folestad  for her help with the data analysis. We would like to thank one anonymous reviewer and  Kerin Noone  for their comments that helped improve this manuscript.
Data Availability Statement The DARDAR data were provided by ICARE data and service center. The AIRS data were provided by Goddard Earth Sciences Data and Information Services Center (GES DISC) at https://airs. jpl. nasa. gov/. The DARDAR cirrus cloud product that we developed in this investigation and the SR LMS data are available at https://doi. org/10. 5878/xdwz-3g72.
Data availability. The original data contributions presented in this study are included in the article and attached Supplement. The GRASP-OPEN model used to perform forward calculations is publicly available on the official GRASP website (https://www. grasp-open. com/; last access: 27 May 2022; GRASP-SAS, 2015). The specific GRASP-OPEN model outputs used in the study are publicly available on Zenodo: https://doi. org/10. 5281/zenodo. 7078698 (Moallemi et al. , 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-15-5619-2022-supplement.
A transversal profile (O) was set up in 2008 (Figure  1 ). Velocities along this profile are used here for validation in the variance analysis of section 4. 4. Former transverse profiles (A-F) initiated in 1979 and 1983  35  are also displayed in Figure  1. Errors in theodolite and GNSS measurements were calculated each year from control points located on nonmoving blocks outside the rock glacier. The margin of error in annual displacements is ±0. 04-0. 06 m, which is acceptable considering observed annual displacements on the rock glacier surface from 0. 2 to 2 m. To avoid biases and ensure continuity in the data series, rock displacements were monitored by the two techniques (theodolite versus GNSS) in 2012 and 2013. The consistency is acceptable with discrepancies of ±0. 09 m between the two surveys (Appendix).
| Validation at transverse profile O Surface velocities recorded along the transverse profile O can be used as independent data to validate the time signal provided by the two models. We calculated velocity changes at blocks O3-O7 between 2008 and 2020 (other blocks unfortunately have missing values for some years). The scatter plot in Figure  7  shows the correlation between the 12 year-to-year velocity changes at these five blocks with the time signal of the linear model. The correlation is high and is better for the blocks at the central locations (O4-O6) which are close to blocks L11 and L12. The best correlations are observed for the blocks with the highest velocities (O4, O5). The lowest correlation is for block O7, which has the lowest average velocity over the period 2008-2020. For all blocks of the O profile, residuals between observed velocity changes and Δβ are between 8 and 10 cm/yr, which is approximately equal to the margin of error for measurements.
It provides a time signal that is not affected by missing values (blocks not measured in some years). Using instead a simple average of all measured velocities in each year would result in a time signal affected by an error of up to 10%. This is due to missing values whose absence skews the average up or down depending on whether they are above or below the overall average (Figure  8 ). These biases are significant only in the last years of the series, exceeding the confidence interface of v Ã ΔB t ð Þ. A variance decomposition is therefore recommended to provide a signal of velocity changes on the rock glacier scale. Year-to-year variability is also made less evident by direct averaging of the entire dataset (±0. 25 compared to ±0. 30 m/yr) and use of a variance analysis is also recommended for this purpose.
ACKNOWLEDGEMENTS We thank  B. Francou  and  L. Reynaud  who initiated the survey in 1979 and performed the early measurements of the series (1983-2000) with  C. Vincent  ( IGE, CNRS-UGA ). Field measurements were then carried out by  E. Thibert  ( Ecrins National Park  and later ETNA,  INRAE-UGA ), from  2001  to the present with  M. Bouvier  ( Ecrins National Park ) and  Mylène Bonnefoy-Demongeot (INRAE-UGA ). The last two decades of the survey were supported by  CryobsClim "Longterm Observation and Experimentation System for Environmental Research" (SOERE/All 'envi) and the PermaFrance observatory "monitoring mountain permafrost in the  French Alps " with support from  OSUG. Ecrins National Park  is warmly thanked for its support in funding and helping field surveys since the early 2000s.
FUNDING STATEMENT The last two decades of the survey were supported by  CryobsClim  " Long-term Observation and Experimentation System for Environmental Research" (SOERE/All 'envi) and the PermaFrance observatory "monitoring mountain permafrost in the French Alps. "The Ecrins National Park is warmly thanked for its support in funding and helping field surveys since the early 2000s. INRAE is a part of  Labex@Osug2020.
, 2008b] , generating deep convection that brings nutrients to the surface layer  [Marty et al. , 2002; Pasqueron de Fommervault et al. , 2015]. The objective of this study is to examine the impact of atmospheric forcing on phytoplankton bloom initiation in the northwestern Mediterranean Sea. To this aim we use concurrent hourly data of seawater carbon chemistry measured at depths less or equal to 10 m at the BOUSSOLE mooring  [Merlivat et al, 2018]  and atmospheric flux measurements at the Azur meteorological buoy 3. 1 nm (5. 8 km) away from BOUSSOLE in March and April 2016 to 2019. Additional measurements of phytoplankton chlorophyll-a (Chla), temperature and density were provided by an underwater glider deployed in March-April 2016. During this time, the initiation of biological carbon uptake is revealed by the sudden decrease in DIC measured at the BOUSSOLE buoy.