dataesr/scientific-paragraphs-categorization huggingface dataset:
A few gaps still remain: in particular, data for 15 ≤ ν/GHz ≤ 300 (mm-to-cm range) is limited to the Milky Way (MW) and a few bright objects. The high-frequency side of this gap, at ν ≈ 200 GHz, is still dominated by dust emission: radiation could come from F ν [ Jy ] ν [ GHz ] λ [ µm ] NGC3627 SRT K band SRT W band SRT Q band IRAM30 1. 2mm IRAM30 2mm Figure 1. The SED of NGC3627. The total SED (black) results from the sum of: 1) CIGALE fits of the stellar (blue line) and dust (red line) components to the DustPedia datapoints (black dots;  [4, 10] ); 2) synchrotron and free-free radiation fits to the mid-radio continuum emission (green line and grey dots;  [9] ); 3) a prediction for AME (purple line), obtained using a model spectral template  [11]  normalised to the MW AME(30 GHz)/IR(100 µm) ratio  [12].
A pilot K-band program at SRT As a preliminary project, at the beginning of 2021 we observed four galaxies at SRT in the K band. They are: NGC 3627 (M 66), NGC 4254 (M 99), NGC4736 (M 94) and NGC5055 (M63). These galaxies are part of the NIKA2 IMEGIN sample, of the DustPedia database and of the KINGFISHER project. DustPedia provides homogenised images, full UV-tosubmm global photometry and SED models  [4, 10] , while KINGFISHER provides radio continuum data and models at frequencies between 1 and 10 GHz  [9]. The first objective of the observations was the detection of AME, by comparing the SRT data with the available fits for the free-free and synchrotron components. Ultimately, the targets could become the first nucleus for a study of the complete UV-to-radio SED, once the NIKA2 data and the future SRT instruments cover the frequency gap between the submm and the K band (Fig.
Preliminary results Using the apertures shown in Fig. 2 , we measured the global flux density for our targets at 18. 6 GHz and 24. 6 GHz. These new datapoints were analized together with the mid-radio continuum from KINGFISHER. For NGC 4254 and NGC 4736, the SRT observations are consistent with the synchrotron and free-free fits to data for ν ≤ 10 GHz. For NGC 3627 and NGC 5055, the 18. 6 GHz flux density is still compatible with the fit in  [9] ; the 24. 6 GHz flux density, instead, is in excess of the fit extrapolation and larger than that at 18. 6 GHz (see Fig. 1  for NGC 3627). With the current data, it is difficult to asses if the 24. 6 GHz excess seen in NGC 3627 and NGC 5055 is due to an additional component, such as AME, or if it is just a statistical fluctuation. Observations at larger frequencies, like those that will be possible with the forthcoming Q-band receiver for SRT, are needed to confirm this finding.
, 2016). To the best of our knowledge, no previous study has investigated the role of irrigation on historical climate using an ensemble of ESMs. Here, we take advantage of the newly available CMIP6 simulations from the most recent generation of climate models to re-examine the impact of irrigation activities at the global scale by investigating long-term trends of five essential climate variables (ECVs) that characterize the Earth's near-surface climate (evapotranspiration [ET], or the corresponding surface energy term, the LHF; precipitation; soil moisture; radiation components; and air temperature) over the 1900-2014 period. Within this study, we (a) compare models including and excluding irrigation (hereinafter referred to as CMIP6. irr and CMIP6. noirr, respectively) using CMIP6 historical (fully coupled) simulations; and (b) evaluate simulated trends against existing observational data sets.
Irrigation Data Sets To study historical irrigation expansion, we use the Historical Irrigation Dataset (HID) version 1. 0  (Siebert et al. , 2015). This data set provides gridded maps of the area equipped for irrigation expressed in hectares per grid cell based on national and subnational statistics at a 5 arc-minute resolution and a temporal resolution of 10 years from 1900 to 1980, increasing to 5 years from 1985 to 2005. In addition, we use the latest version (5. 0) of the FAO "Global Map of Irrigation Areas"  (Siebert et al. , 2013). This map (Figure  1a ) quantifies the areas equipped for irrigation circa 2005 at a 5 arc-minute resolution (equal to 0. 083°, i. e. , ∼8 to 9 km approximately). It is a static map of irrigated areas, which are expressed as the fraction of each 5 arc-minute pixel area that is equipped for irrigation.
Overview of CMIP6 Historical Simulations CMIP6 is a primary source of data for climate change studies. In this study, historical simulations from 18 fully coupled climate models (listed in Table  S1  in Supporting Information S1; constrained by data availability) were downloaded from the Earth System Grid Federation (ESGF) database (available at https://esgf-node. llnl. gov/search/cmip6/). These simulations were run under standardized protocols with uniform output and a consistent structure  (Eyring et al. , 2016). In this study, we use historical fully coupled (ocean-atmosphere-land) runs that are driven as much as possible by historical forcing data sets based on observations over 1850-2014  (Eyring et al. , 2016). They include time-evolving data sets of atmospheric composition, solar forcing, and gridded LULCC, following the Land-Use Harmonization database (LUH2;  Hurtt et al.
, 2020). However, not all ESMs represent the effects of LULCC with the same level of complexity, and most models are not able to make full use of the information provided in the LULCC data sets produced for CMIP6  (Hurtt et al. , 2020; Lawrence et al. , 2016). The CMIP6 historical simulations start in 1850 until 2014, from which we examined the 1900-2014 period, which overlaps with the availability of historical irrigation data sets. These simulations with and without irrigation will be hereinafter referred to as CMIP6. irr and CMIP6. noirr, respectively. Historical simulations by CMIP6 models offer several members, and we focus on a single member for each model, the ones archived as "r1i1p1f1" within the ESGF databases, that is, with realization number 1, initialization method indicator 1, perturbed physics number 1, and forcing number 1.
Main Features of the CMIP6. irr Models Among the 18 selected climate models (Table  S1  in Supporting Information S1), only three of them, namely CESM2, GISS-E2-1-G, and NorESM2-LM, include time-varying irrigation. These three CMIP6. irr models all respond to prescribed time-varying irrigated areas, which are prescribed as input data from the CMIP6 LULCC database  (Hurtt et al. , 2020). They were taken from the HYDE 3. 2 database  (Goldewijk et al. , 2017) , where irrigation is based on the HID data set  (Siebert et al. , 2015) , shown in Figure  S1  in Supporting Information S1. Therefore, the delineation of the areas where irrigation is occurring (current extent as well as historical expansion) is documented in the three CMIP6 models from the best available data sets. CESM2 and NorESM2-LM use the same Community Land Model version 5 (CLM5)  (Lawrence et al.
Although the representation of irrigation is identical in NorESM2-LM and CESM2, the simulated trends such as the T max trends are not. This suggests that the impact of irrigation on the simulated climate can differ between models despite identical irrigation implementation. These differences may originate from substantial differences in the model setup, structure and atmospheric parametrizations (  de Vrese & Hagemann, 2018). Although CESM2, GISS-E2-1-G, and NorESM2-LM implement an irrigation scheme, GISS. E2. 1. G shows the strongest increase in LHF, SM, and LW net over all irrigation classes, followed by NorESM2-LM and CESM2. This is not surprising owing to the different quantities of water applied for irrigation, as there are no common data sets of irrigation forcing, defined standards, or implementation protocols for irrigation in the three climate models (see a full description of the irrigations schemes and implementations in the models in Section 2.
Observational Validation Our results show that the differences in LHF and T max between the CMIP6. irr and CMIP6. noirr simulations can be highly linked to irrigation influences. Therefore, to validate whether they make the CMIP6. irr climate models more realistic, we compared the simulated time series of LHF and T max to those of observation-based data sets. We first focused on heavily irrigated regions (i. e. , 50%-100% class in Figure  1a ) and used three observation-based estimates (Fluxnet-MTE, GLASS, and GLEAM) for LHF, with a common availability period over 1983-2014. The validation period for T max is longer, and we consider the 1960-2014 period (used in Figure  4 ) against CRU and Willmott. For both variables, simulated and observed time series are offset to share the same mean over the beginning of the analyzed period for easier trend comparison.
CMIP6. irr and CMIP6. noirr do not show any particular behavior over this period. Such short terms variations are mostly driven by inter-annual variability but in the case of GLASS data it could be related to the merging of several algorithms and data where MODIS, for instance, started in 2000. To expand this analysis, Figure  7  shows the 1983-2014 trends for both LHF and T max over all irrigation classes. Except for the least irrigated class, divergence is detectable starting with irrigation classes 5%-10%: the simulated LHF trends agree better with observational data in CMIP6. irr than in CMIP6. noirr. They remain underestimated in CMIP6. irr, however, for the most highly irrigated class (50%-100%), in which the mean trend of CMIP6. irr simulations (2. 57 W/m 2 /decade) very satisfactorily matches the observed ones (GLASS with 2.
noirr), especially given the large model spread. In the models implementing irrigation (CMIP6. irr), the warming trend is reduced with extensive irrigation, but was excessive compared to observations, that may lead to an unrealistic cooling trend during 1983-2014 in the mostly highly irrigated class. The reduced warming trends found in CMIP6. irr and observations over highly irrigated areas are consistent with the alleviation of hot extremes by expanding irrigation already found by  Thiery et al. (2020)  by combining observed temperature and irrigation data with historical simulations with a previous version of CESM2. A likely explanation is that irrigation increases LHF, which alleviates extreme temperatures, a relationship that has been observed in India  (Bonfils & Lobell, 2007; Chou et al.
noirr and CMIP6. irr models in heavily irrigated regions. Based on time series anomalies and Sen's trend results, a distinct behavior of the three models was observed, with annual LHF and SM generally increasing over heavily irrigated regions. The trend in the corresponding annual maximum of monthly temperature over the irrigated areas showed a cooling trend. We also find that neglecting irrigation reduces the accuracy of LHF trend estimates over heavily irrigated regions, as the behavior of models with irrigation was consistent and confirmed by observational LHF products. In the intensively irrigated areas, the CMIP6. irr models capture the decrease in air temperature that started in 1960, observed by observational data sets. However, the cooling shown by CMIP6. irr after 2010 was not confirmed by the observational air temperature products.
Acknowledgments This research work was funded by the  CNES  ( Centre National d'Etudes Spatiales)  through the  Science TOSCA (Terre Océan Surfaces Continentales et Atmosphère) program. The authors wish to thank  Prof Stefan Siebert  and  Prof Matti Kummu  for the useful information about the global FAO irrigation map. The authors wish to thank  Dr. Benjamin I. Cook  for the useful information about the GISS model. Agnes Ducharne gratefully acknowledges the  Belmont Forum  support through a grant by the  Agence Nationale de la Recherche  ( ANR ) for the  BLUE-GEM  project ( ANR-21-SOIL-0001 ).
Data Availability Statement The data that support the findings of this study are openly available. The Historical Irrigation Dataset version 1. 0 can be freely obtained from https://mygeohub. org/publications/8/2. The latest version (5. 0) of the FAO "Global Map of Irrigation Areas"  (Siebert et al. , 2013 ) is available at http://www. fao. org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/. The Global Land Evaporation Amsterdam Model data is publicly and freely available, it can be obtained from the SFTP-site sftp://hydras. ugent. be, a password to the SFTP can be received within a few minutes after simply filling an email at https://www. gleam. eu/. The Global Land Surface Satellite evapotranspiration product is available at http://www. glass. umd. edu/GPP/AVHRR/.
This global FLUXNET-MTE latent heat flux product is available at https://www. bgc-jena. mpg. de/geodb/projects/ Home. php. The updated version of the CRU TS v4 monthly temperature data is available from https://crudata. uea. ac. uk/cru/data/hrg/. The monthly air temperature data from the University of Delaware can be accessed from https://psl. noaa. gov/data/gridded/data. UDel_AirT_Precip. html.
Acknowledgments This project is part of the  NSERC-Manitoba Hydro funded Collaborative Research and Development (CRD ) program known as BaySys. Data collection for this research would not have been possible without the support and hospitality of the CCGS Amundsen crew during the 2017 and 2018 field season. D. G. Babb  was supported through an  NSERC PGS-D , the  Canadian Meteorological and Oceanographic Society (CMOS)  and  D. Barber 's  Canada Research Chair funding. NSERC discovery grant funding (DI)  supported research specific to this work, as did the  University of Manitoba Graduate Fellowship (UMGF)  and the  Northern Scientific Training Program (NSTP). This work is a contribution to the  ArcticNet Networks of Centres of Excellence  and the  Arctic Science Partnership (ASP , asp-net.
, 1998; Kring et al. , 2001)  or even millions of years (some hot deserts, cold deserts;  Cassidy, 2003; Gattacceca et al. , 2011; Jull, 2006). The conditions supporting or limiting meteorite preservation differ from one desert to another-meteorites in the deserts of the Sultanate of Oman, North Africa, United States, and Australia survive 30-50 ka on average, whereas samples found in the Atacama Desert and in Antarctica can have terrestrial ages of as high as 2 Ma  (Drouard et al. , 2019; Jull, 2006). Of the 69,838 approved meteorites listed in the Meteoritical Bulletin Database (as of July 1, 2022), 62. 1% are finds from Antarctica. The vast majority of the rest are hot desert finds: 19. 9% are from North Africa, 6. 0% from Arabia, 3. 3% from Chile, and 0. 5% from Iran. This demonstrates the importance of hot desert finds as a source of meteorites.
Since 2014-2015, the major focus of the campaigns has been on the dune-rich areas in the southwest of Oman, where sediment age data indicate surface ages that range from <1 ka to approximately 100 ka  (Matter et al. , 2015). Current and future research are focused on the so-called "meteoriterich" areas, that is, areas with a high density of meteorites. In recent fieldwork, strongly deflated small areas close to large star dunes, the so-called blowouts, were also a major target of the search campaigns. The find density in these areas is approximately 30 meteorites per km 2 , that is, well above the previously determined average density of 1-2 finds per km 2 typical for Oman (data used from 2001 to 2014 search period; OSMS database;  Hofmann et al. , 2014 Hofmann et al. , , 2018;; Rosén et al.
, 2021). The 14 C extraction line at the University of Bern  (Mészáros et al. , 2018; Sliz et al. , 2018 Sliz et al. , , 2020) )  has been built to determine terrestrial ages of meteorites. Doing so, the 14 C activity concentrations are measured in Bern and the 10 Be activity concentrations are measured at CEREGE (Aix-en-Provence, France;  Arnold et al. , 2010 Arnold et al. , , 2013)). Here, we present terrestrial ages for seven ordinary chondrite strewn fields and two single unpaired ordinary chondrites, all from Oman. The ages are determined via 14 C/ 10 Be ratios measured in purified silicate fractions. We anticipate that the data will help us to better understand the time scales of meteorite accumulation and losses in the deserts of Oman (and probably in a more general term also in other hot deserts).
An additional 78 individuals (1. 29 kg) were collected during the Omani-Swiss search campaign in 2001. Our samples are from an accumulation of stones on an area of 0. 9 × 0. 7 km (Fig. S1b ). A single 14 C age of 12. 7 AE 1. 3 ka was published by  Al-Kathiri et al. (2005). Jiddat al Harasis (JaH) 073 is an L6 ordinary chondrite strewn field with a length of 25. 6 km (fall azimuth 340°). In total, 3463 individual stones (<1 g to 52. 2 kg) were found (Fig. S1c ). The total recovered mass is approx. 650 kg  (Gnos et al. , 2009). 14 C and 10 Be data were reported by  Gnos et al. (2009)  and  Huber et al. (2008). Note, however, that  Mészáros et al. (2018)  already discussed that for JaH 073, the 14 C-10 Be terrestrial age is likely not reliable because 10 Be was probably not saturated at the time of fall.
Huber et al. (2008)  concluded that JaH 073 had a complex exposure history with a first-stage exposure under 2π conditions for ≤5 Ma followed by a second irradiation stage (4π) of 0. 5-0. 7 Ma. While the 10 Be data are a record of the first and the second irradiation stage, the 14 C data record only the second irradiation stage. JaH 091 is an L5 ordinary chondrite strewn field with a length of 52. 2 km (fall azimuth 126°), 702 recovered individuals, and a total meteorite mass of ~4500 kg  (Gnos et al. , 2006; Weber et al. , 2017; Zurfluh et al. , 2011)  (Fig. S1 ). This makes JaH 091 the largest known strewn field in Oman. The sample measured in this study is from the first recovered individual (0210-0011; 123. 37 kg). A single 14 C terrestrial age of 19. 3 AE 1. 3 ka and a cosmic ray exposure age of 15.
(2005) , the latter indicating that SaU 163 is older than 50 ka. This meteorite was selected for our study to enable comparison with previous age data. Shis ¸r 015 is a strewn field with a length of 21. 1 km (fall azimuth 265°) that consists of 43 individual stones with a total mass of 23. 7 kg (OSMS database, Fig. S1 ). Available is a single 14 C terrestrial age of 36. 5 AE 2. 4 ka  (Al-Kathiri et al. , 2005). Ramlat as Sahmah (RaS) 267 is a single, unpaired LL6 ordinary chondrite; the degree of weathering is W4. 0. Zurfluh et al. (2016)  measured a 14 C age of 30. 5 AE 4. 4 ka for a leached sample of RaS 267. This meteorite was selected in our study for comparison with previous age data. Ramlat as Sahmah (RaS) 418 is an L6 ordinary chondrite strewn field (19. 5 km length, fall azimuth 230°), with a total mass of 69.
Sample Preparation Samples were milled and 1-2 g of the 125-200 μm grain size fractions was leached to remove terrestrial alteration products without affecting the primary minerals hosting the cosmogenic 14 C. A first leaching was done with 10 mL of 6. 0 M hydrochloric acid for 30 min. After washing and drying, a second leaching was performed using monoethanolamine thioglycolate (EATG) 65%  (Cornish & Doyle, 1984)  for 48 h. All leaching steps were done at room temperature (approx. 24 °C). The leaching procedure primarily results in losses of oxidation products of metal and troilite, resulting in a sample enriched in silicates. Typically, a 50 mg sample of leached weathered chondrite corresponds to about 55 mg of bulk meteorite, Table  1. Information of the studied meteorite strewn fields (information as of June 16, 2020 from Meteoritical Bulletin Database, and OSMS database).
The identification of the crystallized compounds was performed using the ICDD PDF2 database. All solid residues gave very similar X-ray diffraction spectra, showing almost exclusively fluoride compounds in the form of KNiCrF 6 and MgF 2. For MgF 2 , a slight shift of the peaks was observed, which we attribute to a substitution of the Mg by other cations. In addition, one sample may contain small amounts of magnetite. No traces of initial main minerals (olivine, pyroxene, plagioclase) have been detected. From this result, we can safely conclude that all 10 Be in the samples was extracted during the dissolutions. To this end, 10 Be measurements were normalized by the initial sample mass rather than the dissolved mass (initial mass minus mass of precipitates). A detailed description of the ASTER 5 MV AMS facility is given by  Arnold et al.
Jiddat al Harasis 091 Table  S3  compiles the 14 C data of three replicate measurements of a homogenized ground sample from the JaH 091 strewn field. Unfortunately, one sample was lost during the AMS measurement; hence, only two successful measurements can be used for discussion. The 14 C activity concentrations agree within 16%. The 14 C terrestrial ages of 12. 7 AE 2. 3 ka and 11. 5 AE 3. 1 ka (Table  3 ) are significantly lower than the age of 19. 3 AE 1. 3 ka reported earlier by  Al-Kathiri et al. (2005)  for the same sample. The source of the discrepancy is unclear. We calculate a mean 14 C age for JaH 091 of 12. 3 AE 1. 9 ka. Four aliquots of a homogenized ground sample of JaH 091 were measured for 10 Be, yielding an average 10 Be activity concentration of 17. 7 AE 0. 2 dpm kg -1. Leya et al.
Dhofar 020 Table  S4  summarizes the 14 C data of the three measured Dho 020 samples. Two 14 C activity concentrations were measured per sample, resulting in six data points. The reproducibility is ~16% for stone 90, ~1% for stone 79, and ~10% for stone 71 (Table  S4 ). By averaging the 14 C activity concentrations for the individual stones, we calculate 14 C ages in the range 13. 4 AE 1. 7 ka to 14. 4 AE 1. 8 ka for the three samples (mean 13. 9 AE 1. 0 ka), that is, the variability is ~7%. Considering the advanced degree of weathering (W 3. 6) of Dho 020, the ages are quite low. The published 14 C age of 12. 7 AE 1. 3 ka for stone 52  (Al-Kathiri et al. , 2005)  is somewhat lower than our new ages; however, all ages agree with the published age within their uncertainties. Again, the slight discrepancy could result from the different sample preparation procedures.
Sayh al Uhaymir 001 Three samples of SaU 001 (04, 243, and 290) were studied for 14 C (Table  S5 ) and 10 Be. For 14 C, we performed two measurements per sample, resulting in six data points. For 10 Be, we measured two aliquots for stone 04, and one aliquot each for stones 290 and 243. The variability of the two 14 C measurements performed for each aliquot is ~18%, ~90%, and ~84% for sample 290, 243, and 04, respectively, that is, the reproducibility for samples 243 and 04 is bad. Interestingly, the 14 C activity concentrations also vary significantly among the studied samples. For example, while the 14 C activity concentrations for sample 290 vary by ~18%, they are both significantly higher than the four 14 C activity concentrations measured for the other two samples. Considering the good reproducibility of all measurements performed in this study, the observed variability among the different samples is likely not caused by analytical artifacts.
Sayh al Uhaymir 163 Two replicate measurements of SaU 163 yielded 14 C specific activity concentrations of 2. 80 AE 0. 04 dpm kg -1 and 2. 99 AE 0. 05 dpm kg -1 (Table  3 ), that is, the variability is ~7%. The calculated 14 C terrestrial ages are 23. 0 AE 3. 1 ka and 22. 5 AE 3. 1 ka, respectively, with a mean value of 22. 8 AE 2. 2 ka. Al-Kathiri et al. (2005)  measured a 14 C age of >50 ka for a leached aliquot of SaU 163. Interestingly, and as was also the case for Dho 005, the 14 C activity concentration of SaU 163 measured by  Al-Kathiri et al. (2005)  for the bulk sample is consistent with the 14 C activity concentration measured by us for the leached sample. In contrast, Al-Kathiri et al. 's data for leached aliquots are significantly different, that is (-0. 47) AE 0. 20 dpm kg -1 and 3.