dataesr/scientific-paragraphs-categorization huggingface dataset:
We choose a resolution of 5 cm for the age-modelling to obtain an individual age for each stable isotope sample. The age-modelling data are compiled in the Supplementary Data 1. The sedimentation rates for both profiles were calculated based on age modelling output at 5 cm resolution. Stable isotope analysis of earthworm calcite granules. We followed the procedure for the extraction of the ECGs and the preparation protocol from refs. 44, 45. After cleaning in an ultrasonic bath with deionized water (2 times 5 min), thirty ECGs were extracted from each layer for stable isotope analysis based on the absence of impurities and the well-preserved surface crystals. We performed 180 (30 times 6 layers of 5 cm thickness) and 690 (30 times 23 layers of 5 cm thickness) stable isotope analyses for Nussloch and Schwalbenberg, respectively.
Individual granules were crushed, and an aliquot between 200 and 300 µm of carbonate powder was digested with phosphoric acid at 70 °C. The liberated CO 2 gas was analyzed using a Thermo Finnigan GasBench II preparation device interfaced with a Thermo Delta V advantage mass spectrometer at the Max Plank Institute of Chemistry in Mainz. All δ 13 C and δ 18 O values are reported on the Vienna-Peedee Belemnite (V-PDB) scale. The long-term standard deviation of a routinely analyzed in-house CaCO 3 standard was <0. 1‰ (1σ) for both carbon and oxygen isotope ratios. The CaCO 3 standards are calibrated using NBS19, NBS18 and IAEA 603. The data are reported in Supplementary Data 2. Limits and uncertainties for the quantification of the climate parameters and temporal resolution of age models. The uncertainties of our method are related to sampling, chronology, representativeness and the past precipitation and temperature estimations.
The two profiles (Schwalbenberg RP1 and Nussloch P8) have been sampled at 5 cm resolution which represents between 50 and 150 years and the ECGs were extracted from 10 l of sediments (~15 kg of sediment). By taking 15 kg of sediment, we reduce the uncertainties associated with the distribution of ECGs inside one sample of 5 cm thickness. Furthermore, ECG concentration is well correlated with the loess palaeosol pedostratigraphy in Northern France  48  and in the Upper Rhine Valley  49. Age-depth modelling is based on 28 and 29 radiocarbon analyses for Schwalbenberg and Nussloch, respectively (Supplementary Data 1). The uncertainties for radiocarbon analysis are in the same order as the maximal counting errors in the NGRIP GICC05 chronology  3. Today earthworms daily produce granules when they are active.
We assumed that during the last glacial period, earthworms were active during the warmer half of the year from May to September  49. The concentrations of the granules (per sediment volume) produced by Lumbricus terrestris during the last glacial period is low compared to present-day data  46. This might be due to either lower productivity of the earthworms because of climate conditions or lower earthworm populations or both. Experiments are still necessary to better constrain parameters that mostly influence the granule production. We extracted and analyzed 30 individual granules per sample for the stable isotope analyses (one sample is thus made of 30 replicates) to be as representative as possible. To estimate the precipitation, we used a modern dataset between δ 13 C of C 3 plants and MAP so we assumed that this relation did not change during the last glacial period.
3  and Supplementary Data 3). We corrected the data for -0. 5 and -0. 8‰ according to the method developed by ref. 81. We reported the distribution of the 30 values for each sample of 5 cm thick at 1σ. The uncertainties are sizeable (Fig. 5 ) however, the T-test indicated that the LSTws mean values are significantly different between Schwalbenberg and Nussloch and between MIS 3 and MIS 2 (Supplementary Table  1 ). We should be aware that both methods give us a first approximation of climate parameter estimates. Wind dynamics model reconstructions. Wind and precipitation statistics are derived from the instantaneous values of 10-metre winds and total precipitation sampled at 6-hourly (synoptic) intervals from continuous 30 years output of the respective atmospheric general circulation model products.
Acknowledgements This work was supported by the  German Science Foundation (Deutsche Forschungsgemeinschaft, DFG)  in the framework of the  TERRACLIME-Project  ( FI 1941/5-1 ;  FI 1918/4-1 ;  VO 938/25-1 ) awarded to P. F. , K. E. F. , and A. V. , and by an independent  Max Planck Research Group  awarded to K. E. F. S. G. acknowledges support through the project  PalMod , funded by the  German Federal Ministry of Education and Research (BMBF). We thank  Sven Brömme  for help in the stable isotope laboratory and  Felix Hettwer  for helping to extract the earthworm calcite granules. We thank everyone who assisted with fieldwork in Remagen, including  Stefania Milano  and  Kristina Reetz. We are grateful to the three reviewers for their comments that helped us to improve the scientific content of this work.
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 the evolution of the 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, especially over tropical-land, while oceanic situations show more similarities in both interannual variations and percentile extremes.
The signatures of El Nino/La Nina events, driven by the 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 of CMIP6 models and ERA5) and extremes years. The discrepancies are probably associated with the scene selection applied in the data process. Other sources of differences, linked to the models and their parametrizations, are highlighted.
We focus on the AMIP (Atmospheric Model Inter-comparison Project)  (Ackerley et al. , 2018)  scenario with prescribed timevarying sea surface temperature (SST) and sea-ice concentrations from observations and includes 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 is cloud screened over land area, it is important to carefully analyze the cloud conditions for CMIP6 models before making the quantitative comparison. A series of tests have been done (not shown) using different screening thresholds from the cloud fraction and precipitation rate to determine the best cloud conditions for the modelobservation comparison.
The key aspect here is to have a filter on clouds and/or precipitation that is stringent enough for an apple-to-apple comparison but also that leaves enough point from a statistical point of view. Then, over land the thresholds are set with maximum cloud fraction of 50% for each vertical level and, over ocean with precipitation rate less than 0. 001 kg/m 2 /s 2  (Sohn and Bennartz, 2008). These thresholds result in a reduction of the size of the datasets that differs according to the region and the percentages of remaining data for land and for ocean are indicated in Table  2. Over land, the percentage of data remained for the CMIP6 models after the cloud screening are globally in the range 46. 14% -76. 10%. Over tropical oceans the percentage of data remained is higher and range from 99. 79% to 99.
98%. Although the scene selection is more stringent over land, this indicates that the CMIP6 data used in the following analysis are comparable in terms of size of sample to the 120 data from TCWV-COMBI and ERA5. It is worth mentioning that although the screening thresholds for the models are set to meet the criteria of the TCWV-COMBI product, the number of data retained for the comparison are not exactly the same for all models. Therefore, differences among data sets may be observed in the analysis. This is particularly true over tropical land.
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 Figure  3. 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 ESA TCWV-COMBI dataset is strictly cloud-free while some cloudy scenes remain in both ERA5 and CMIP6 (see Section 2), which could translate in a moister TCWV of the latter data records. 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.
Extremes of the distributions The data records are then evaluated following the approach (1) described in Section 3: the percentiles of the annual distributions of TCWV (at daily resolutions) are sorted into bins of 10% intervals, and this is done for each year of the period. in the ESA TCWV-COMBI or CMIP6 ensemble mean. The very good agreement among the various data sets is largely due to the fact that the CMIP6 models that 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 same, most notably those concerned by El Nino Southern Oscillation  (Trenberth et al. , 2005) : 2008 is characterized by a very strong episode of negative ENSO index, as well as 2011, while 2010 210 is an intermediate year, with the end of a positive ENSO episode followed by a negative one.
General assessment The interannual variability of TCWV is then analyzed from its links with the large-scale atmospheric circulation, and follows the approach (2) described in Section 3. The monthly ω500 of individual data records are decomposed into 10 hPa/day intervals 215 in the range of -120 to 120 hPa/day. Figure  6  displays the normalized PDFs of the ω500 of the CMIP6 models and ERA5. As mentioned earlier there is 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  6  (a) and (b) show that the PDFs of the ω500 from the CMIP6 ensemble mean agrees well with the ERA5 data. Most of the ω500 reside in around 10 -20 hPa/day over both tropical-land and tropical-ocean area, which characterizes the dominance of the large-scale Hadley subsidence in 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 analyzed from the perspective of atmospheric vertical motion. As shown in Figure  6  (c) and (d), large-scale downward motion is associated with a dry troposphere, while large-scale ascent is globally associated with a moister troposphere. The results are in agreement with the maps of Figure  1. Overall, 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. While the large-scale atmospheric dynamics are consistent among the datasets, the discrepancies in the TCWV reveal difficulties in the moistening processes of the tropical atmosphere : lateral mixing  (Pierrehumbert and Roca, 1998; Pierrehumbert, 1998) , outflows from clouds, too high/too low precipitation efficiencies of the convective schemes  (Brogniez and Pierrehumbert, 2007).
The normalized TCWV at each circulation intervals for the different data records are displayed in Figure  8. Although different patterns are observed, dry anomalies occur in 2008 and wet anomalies occur in 2010 for all of the data records. These extreme years are consistent with the previous findings (section 4. 1. 3) and this second approach provides another angle of analysis on the assessment: -The ESA TCWV-COMBI reveals a clear moistening tendency since 2004 and this tendency mostly occurs over the subsiding branch of the atmospheric circulation, with a stronger trend after 2011. Although the data record itself, merging several instruments over the period (MODIS is included in 2011), may induce a bias (from sampling for example), a steplike bias would appear and not a tendency. -The TCWV from ERA5 shows only extreme years but no distinct tendency.
The study focused over tropical-land and tropical-ocean areas at the daily frequency and over the 2003-2014 period. The variability of TCWV was analyzed according to (i) its probability density function (PDF) defined at a yearly scale over the period and (ii) to the large-scale circulation using the atmospheric vertical velocity at 500hPa (ω500) as a proxy of the tropospheric overturning circulation. 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 tiping point in 2011, probably associated to the addition of the MODIS observation in the climate data record.
The projection of the TCWV onto regimes of ω500 shows the same behavior, the drying trend being present for all regimes of ω500. 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 extreme dry and moist years, associated to El Nino and La Nina events, are the same. This similarities hold when using the large-scale 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, 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 with the ascending motion (negative value in ω500) and dry area is related with the subsiding motion (positive value in ω500) over both tropical land and tropical ocean area. There are discrepancies observed among the data records, because of the lateral mixing, outflows from clouds, and the precipitation efficiencies of the convective schemes. It is difficult to track entirely the reasons of the differences, however, the differences and similarities can be explained by several factors : 2) the cloud-masks applied to the GCMs and ERA5 and defined to mimic the cloud-mask of the observation can also explain the differencies. 3) the parametrization of the moisture fluxes at the surface and of convection, as well as the climate efficiency of the GCMs also contribute to the observed differences.
Figure2shows the climatology of the TCWV of the different datasets over land (Fig2a; clear skies only) and over ocean (Fig2b; all-weather without heavy precipitation) for the period 07/2003-12/2014. Overall, the different data records agree well with each other despite some discrepancies. Strong seasonal variations are observed over tropical land with minima reached during JFM and maxima reached during JJA and with a very weak interannual variability. Over the tropical oceans, the seasonal variations are softer (the minima still occur during JFM, but the maxima are not always reached in JJA) with a strong interannual signal. More specifically, the ESA TCWV-COMBI data is globally moister compared to the CMIP6 models and to ERA5 data, and this moist bias is even more pronounced over tropical land (Fig 2a, ∼ 10kg/m 2 over land vs ∼ 2kg/m 2 over ocean).
The role of mineral dust aerosol in the global radiative energy budget is often quantified by the dust direct radiative effect (DRE). The dust DRE strongly depends on dust aerosol optical depth (DAOD), therefore, DRE efficiency (DREE = DRE / DAOD) is widely compared across different studies to eliminate differences due to the various dust loads. Nevertheless, DREE is still influenced by the uncertainties associated with dust particle size distribution (PSD) and optical properties. In this study, we derive a global clear-sky size-resolved DREE dataset in both shortwave (SW) and longwave (LW) at top of the atmosphere (TOA) and surface based on satellite observations (i. e. , satellite-retrieved dust extinction spatial and vertical distributions). In the DREE dataset, dust geometric diameter from 0.
1 to 100 µm is divided into 10 bins and the corresponding monthly mean DREE (with respect to DAOD at 532 nm) for each size bin is derived by using the Rapid Radiative Transfer Model (RRTM). Three sets of state of the art dust refractive indices (RI) and two sets of dust shape models (sphere vs. spheroid) are adopted to investigate the sensitivity of dust DREE to dust absorption and shape. As a result, the size-resolved dust DREE dataset contains globally distributed monthly mean dust DREE at TOA and surface for each of 10 size bins with 5 • (longitude) ×2 • (latitude) resolution as well as for each dust RI and shape combination. The size-resolved dust DREE dataset can be used to readily calculate global dust DRE for any DAOD and dust PSD, including the uncertainty in the DRE induced by dust microphysical properties, (e.
For example,  Song et al. (2018)  showed that the SW DREE of a dust model with a large size and less absorptive RI is very similar to that of a dust model with a smaller size and more absorptive RI, which are both in the range of satellite-derived values in the NE Atlantic region. Not surprisingly, even DREE cannot provide sufficient constraints due to this possible compensation of effects in the dust PSD and RI. The main objective of this study is to derive a global clearsky size-resolved dust DREE dataset based on satellite observations and demonstrate its usefulness in constraining, comparing, and understanding the dust DRE estimations. As explained later on, the size-resolved DREE decomposes the DREE of dust into several size bins and therefore provides a way to explicitly take into account the effects of dust PSD.
The sensitivity of dust DRE to dust RI and shape are also assessed in this study. Due to the inhomogeneous spatiotemporal distribution of the aforementioned factors, it is thus important to consider the spatiotemporal variation of dust DREE. Therefore, we organize the DREE dataset at 5 • (longitude) ×2 • (latitude) horizontal resolution and at monthly temporal resolution. To the best of our knowledge, this work presents the first such dataset based on retrieved dust properties (i. e. , DAOD vertical and horizontal distributions) from satellite observations, although size-resolved DREE from model simulations have been used in previous studies. We will show that our size-resolved DREE can allow users to readily compute the DREE and DRE of dust based on any dust PSD (e. g. , from model simulations, satellite retrievals or in-situ measurements).
We will also carry out an intercomparison of the global dust DRE estimations based on different dust PSD and compare the results with previous studies. With these functions, we expect that the size-resolved DREE will be a useful tool for both observational and modeling studies of dust DRE. The rest of the paper is organized as follows: Section 2 provides a description of the data and models used in this study. Section 3 describes the methodology of deriving the size-resolved DREE dataset. In Section 4 we describe a methodology of calculating the dust DRE with the sizeresolved DREE dataset and its validation. In Section 5 we compare the regional and global dust DRE estimations based on different DAOD, dust PSD and compare the results with previous studies. Section 6 provides a summary of the study along with the main conclusions.
Satellite-based DAOD climatology We use the CALIOP-based DAOD climatology and dust vertical distribution derived in  Song et al. (2021)  to derive a size-resolved dust DREE. The reason for choosing CALIOP-based DAOD climatology is discussed in detail in Sect. 3. 2. The CALIOP-based dust climatology dataset contains monthly mean DAOD and dust vertical extinction profiles on a 5 • (longitude) ×2 • (latitude) spatial resolution grid for the period 2007-2019. The CALIOP-based DAOD and dust vertical distribution climatology from 2007 to 2010 are used to derive monthly mean size-resolved dust DREE datasets in this study. The selection of 4 years  (2007) (2008) (2009) (2010)  for DREE calculations is based on several considerations. Firstly, the multiyear DREE calculations allow us to investigate the effect of interannual variations of atmospheric and surface properties on dust DRE.
The 10 size bins represent a wide range of dust geometric diameters (i. e. , diameter of a sphere with the same volume) ranging from 0. 1 to 100 µm. The geometric diameter (hereafter diameter or D) range of each size bin is listed in Fig. 3. For each size bin k, the spectral scattering properties (Q e λ k , ω λ k and g λ k ) are calculated for each dust model shown in Table  1  and each spectral band. In the calculations of scattering properties (Q e λ k , ω λ k and g λ k ), dust particle number (dN/dD) is assumed to be uniformly distributed within each size bin. We use the Lorenz-Mie theory code of  Wiscombe (1980)  to compute the spectral optical properties of dust particles in the assumption of sphericity. The spectral optical properties of spheroidal dust particles are derived from the database of  Meng et al.
DREE dataset Based on the dust scattering properties shown in Fig. 3  and the procedures summarized in Fig. 4 , we compute the size-resolved dust DREE for the MeanSWRI-MeanLWRI-Spheroid dust model in SW and LW. In this section, we focus on demonstrating the method of deriving size-resolved dust DREE for one dust model, but this method is applicable to all six dust models listed in Table  1. First, we use RRTM to simulate monthly mean dust DRE from 2007 to 2010 for each 5 • (longitude) ×2 • (latitude) grid with CALIOP-based DAOD 532 nm exceeding 0. 01. The DAOD 532 nm ≥ 0. 01 threshold ensures most dusty regions over the globe are covered (see Figs. S1 and S2 in the Supplement) and in the meanwhile balances the computational cost. Dust DREs are calculated for each size bin using the extinction properties of the corresponding size bin shown in Fig.