dataesr/scientific-paragraphs-categorization huggingface dataset:
4 The Biscay-Pyrenean system: a natural laboratory to study inheritance The Biscay-Pyrenean domain, located between the southern North Atlantic and Alpine Tethys, includes one of the best-investigated, partly reactivated magma-poor extensional systems worldwide. Moreover, it shows a transition from a slightly to completely reactivated pair of rifted margins/ rift system that is well imaged by industry and academic surveys and that has been studied by several academic consortiums, including the recent OROGEN project. In the OROGEN project, the Biscay-Pyrenean domain was chosen as a natural laboratory to investigate the role of inheritance within an incomplete Wilson Cycle. This choice was justified by: (1) the position of the Biscay-Pyrenean system between the southern N-Atlantic and Alpine Tethys systems, both belonging to the best-investigated magma-poor rift systems worldwide; (2) the access to a unique dataset that includes geophysical and geological data from both academia and industry, with a scientific community that has been investigating these sites over generations; (3) the possibility to work on one and the same tectonic system onshore and offshore; and (4) the access to different genetic stages of an incomplete Wilson Cycle that initiates after a Paleozoic orogenic cycle (Variscan), goes through a Mesozoic extensional cycle, before undergoing an Alpine reactivation during Late Cretaceous to Cenozoic.
Restorations can be done using software such as ImageJ (freely available on the Internet: https://imagej. nih. gov/ij/) or 3DMove. Area conservation, i. e. , neither addition nor loss of crust out of the section can be formulated as: Ac initial ¼ Ac final ; ð1Þ where Ac initial is the area of the crust at the pre-rift stage and Ac final the area at the end of shortening. Such a reconstruction must be done at a crustal scale and needs to be based on the definition of top and base crust, which requires access to geophysical and geological data. More detailed restorations in which both the brittle and ductile crusts are restored bear the problem that the thermal evolution and/or hydration reactions may change the position of the brittle ductile transition (e. g. , embrittlement concept of the crust in extensional systems;  Pérez-Gussinyé et al.
Heat flow data is usually used to assess the thermal state of a (equilibrated) margin. However, these data do not allow to retrieve the thermal state during the (transient) rifting process. Moreover, there is only little public data where heat flow is calibrated by drill holes. Studies from young, not yet thermally equilibrated rifted margins such as the Gulf of Aden  (Lucazeau et al. , 2010)  show generally high heat flow values that are, however, highly variable across the ocean continent transition. Similar observations were reported from the Red Sea  (Agulles et al. , 2020) , the Gulf of California  (Neumann et al. , 2017)  and the South China Sea. For the latter example,  Nirrengarten et al. (2020)  showed based on IODP drill hole data that pre-rift sediments were heated to temperatures as high as 200 C during the early stages of continental/lithospheric breakup, which requires a high heat flow during rifting given the relatively small sedimentary burial of the margin.
Therefore, mapping and correlating thrust structures from one rift segment towards the adjacent segment is challenging. It may explain why the Pyrenean-Cantabrian junction remains one of the most debated parts in the Pyrenean system. Similar situations as that described from the Pyrenean-Cantabrian junction, may occur elsewhere in the Biscay-Pyrenean system as discussed in  Cadenas et al. (2021). 12 The lessons learned from the OROGEN project 12. 1 What's new and what remains debated? The OROGEN project provided access to new field and seismic reflection data, better and higher resolution tomographic imaging, and new thermochronological and thermal data that enabled to propose, test and calibrate new interpretations for the tectonic evolution of the peri-Iberian system. Here we focus on the role of inheritance within the incomplete Wilson cycle recorded by the geology of the Biscay-Pyrenean system.
Advantages of this system include excellent datasets, the long history of investigation on the stacking of three main tectonic events, namely a Variscan orogenic system that has been overprinted by a multistage Mesozoic rifting, which was in turn reactivated in its easternmost parts, leading to the development of the Pyrenees. That only few Variscan structures have been used or reactivated by Mesozoic rift structures in the Pyrenean domain suggests that the former orogenic inheritance did not systematically control later rifting, contrary to common belief. While prominent first-order structures such as the Pamplona or Leiza faults have been regarded as inherited and/or reactivating Variscan and/or Permian structures in the past, new studies question the existence of the Pamplona fault and reinterpret the Leiza fault as a second-order structure  (Lescoutre et al.
It appears that the thermal state of a margin is less important than previously thought for the localization of subduction but may control the flexural behaviour of the lithosphere during collision  (Mouthereau et al. , 2013). In contrast, it seems that serpentinized mantle is a key in controlling onset of subduction. This may be explained by the low friction at its top contrasting with the strength of the underlying non-serpentinized mantle, and its high density comparing to the surrounding crustal rocks. Last but not least, trying to integrate the role of rift inheritance in the understanding of orogens may also imply to change the approach used in the field and in interpreting datasets. While detailed structural analysis assuming layer cake geology may work for post-rift sequences/at external parts of the orogen, such methods are more difficult to use in distal margins/internal parts of orogens, where the inherited geological template is more complex.
In the following, we apply the Fourier analysis-based approach to observed time series of C E , C A , and T A to estimate β k and γ k for the industrial period (1850-2017). By ignoring the timescale dependence of β, and using the value of β for the industrial period, we estimated γ k at different timescales using large ensembles based on combinations of ice-core atmospheric CO 2 records (C A ) and reconstructed surface air temperature (T A ) datasets ("Methods") over the period 1000-1850. Finally, we compare the observation-based estimates of β and γ, and corresponding feedback gain factor (g) with those estimated from the simulations from multiple Earth system models. Estimates of β and γ* for the industrial period (1850-2017). Figure  1  shows the observed increase in atmospheric CO 2 (ΔC A ) from ~285 ppm in 1850 to 405 ppm in 2017 in response to the increase in cumulative CO 2 emissions (ΔC E ), including fossil fuel combustion  22  and land-use change  23, 24  , which amounted to ~630 ± 42 GtC by 2017 since 1850, with respect to a concurrent increase in global-mean annual temperature (ΔT A ) of ~1.
1 K over the same period based on the averages of the four global temperature datasets. Using Fourier analysis, we found that the amplitudes of C E , C A , and T A for the industrial period increased nonlinearly with timescale (Fig. 2a  and b ), but the variations in (TCRE -1 ) k and (α -1 ) k with timescale were linearly related to each other (R 2 = 0. 99, P < 0. 001) with a slope (mean ± 1 standard deviation) of 2. 52 ± 0. 15 (see Fig. 2c  and Supplementary Table  1  for individual estimates). Across different timescales from interannual to multi-decadal (e. g. , k from 2 to 90 years) for the 1880-2017 period, (α -1 ) k varied from 50 to 250 GtC K -1 and (TCRE -1 ) k from 125 to 600 GtC K -1 (Fig. 2c ). The slope of the linear regression between (TCRE -1 ) k and (α -1 ) k across different timescales can be used to estimate (1 + β k ) based on Eq.
(  2 ). The result in Fig. 2c  shows that the value of β k is approximately constant across different timescales, with an average value of 3. 22 ± 0. 32 GtC ppm -1 (or 1. 52 ± 0. 15 GtC GtC -1 ) throughout the industrial period in 1880-2017 (Supplementary Table  1 ). The uncertainty in β results from the uncertainties in C E from fossil fuel combustion and land-use change estimates and errors in the temperature datasets and the regression (see "Methods"). Using the estimated β for the industrial period, we estimated the corresponding γ* to be -10. 9 ± 3. 6 GtC K -1 for the period 1880-2017 based on Eq. (6). To diagnose the constancy of β estimated from the industrial period and the reliability of our Fourier analysis-based approach, we used a box model to predict changes in temperature and CO 2 concentration with CO 2 emissions as forcing input (see "Methods", Eqs.
Our results confirmed that η k systematically increased with timescale, varying from 6 ± 6 ppm K -1 on timescales of 10-70 years to 20 ± 8 ppm K -1 over timescales of 200-800 years due to the increased γ k on magnitude with timescale (Supplementary Fig. 6 ). The range of the EnOBSbased η 100yr for 1000-1850 was 7-23 ppm K -1 with a median of α (=ΔC A /ΔT A ) across timescales against 1 TCRE (=ΔC E /ΔT E ) for four observational global-mean temperature datasets with atmospheric CO 2 records (or anthropogenic emissions) for 1880-2017. The solid line with the shaded area between the dashed lines is the linear regression of all datasets with a slope (p) of 2. 52 ± 0. 15 ppm ppm -1 , which indicates that the carbonconcentration feedback parameter β is 3. 22 ± 0. 32 GtC ppm -1. Fig. 3  Ensemble estimates of climate-carbon cycle feedback parameters from observations and models.
a Histogram of the β estimates (error bars for mean ± 1σ) derived from four instrumental temperature and CO 2 datasets for 1880-2017 (in gray), β BGC derived from 11 C 4 MIP models  7  for 1880-2017 (in dodger blue) and for 2018-2100 (in navy blue), and β BGC derived from nine CMIP5 models  11  for the 140-year 1pctCO 2 experiment (in deep pink). b Boxplot of the γ* derived from four instrumental temperature and CO 2 for 1880-2017 (in gray), and γ 100yr estimates derived from an ensemble of 15 members (3 ice-core CO 2 records × 5 reconstructed temperature, in black) and from the EnOBS for 1000-1850 (in green), and γ COU-BGC derived from 11 C 4 MIP models for 1880-2017 (in dodger blue) and for 2018-2100 (in navy blue), and γ COU-BGC derived from nine CMIP5 models (in deep pink). In b, the large ensemble estimates of the γ 100yr from the EnOBS also provides probability distributions (right panel) of the cooler period of Little Ice Age (LIA, 1400-1700) and the warmer period of Medieval Climate Anomaly (MCA, 1000-1300), compared to those of the full preindustrial last millennium (PILM, 1000-1850).
82 ± 60. 16 GtC K -1 from the 3 ice-core CO 2 records × 5 temperature reconstructions and of -81. 91 ± 41. 90 GtC K -1 from EnOBS for 1000-1850. Uncertainties related to the estimated γ 100yr from the two datasets (15 members versus >1500 members of EnOBS) did not overlap, which may suggest the uncertainty in γ 100yr was from -180 to -40 GtC K -1. We then compared our observation-based estimates of β and γ using Fourier analysis-based approach with those from Earth system models. Estimates of β and γ from Earth system models were calculated from three sets of model simulations: the biogeochemically-coupled (BGC), radiatively coupled (RAD), and fully coupled (COU) simulations based on the FEA approach  7, 14  (see "Methods"). Eleven first-generation coupled climate-carbon cycle models (C 4 MIP) were driven with the prescribed CO 2 emissions from the historical period (1860-2005) and the future period (2006-2100) under the IPCC SRES A2 scenario without land-use change  7.
Units were converted from GtC to ppm by dividing m (=2. 12 GtC ppm -1 ) for comparison with atmospheric CO 2 concentration  52. The conversion factor m was also used for unit conversions of β, γ, α, and η parameters. Instrumental and ice-core reconstructed atmospheric CO 2 records. Global annual atmospheric CO 2 concentration over 1850-2017 was reconstructed from a combination of ice-core CO 2 records and instrumental CO 2 measurements since 1950s  53, 54. Information about the inter-annual variability in the CO 2 data covering 1850-1940s from ice-core CO 2 would be lost, as ice cores smooth atmospheric CO 2 records by firn diffusion, which may lead to some biases in the timescale analysis. The highly resolved CO 2 covering the preindustrial period of 1000-1850 was compiled from ice-core records in Antarctica including three datasets at Law Dome  30, 55  , WAIS Divide 28 , and Dronning Maud Land  31  , respectively.
As the three records have different resolutions, CO 2 values were resampled to the nearest calendar year and then smoothed using 30-year splines (Fig. 1a ). However, large variations remain among the three records of CO 2 from ice-core (see Fig. 1a  and Supplementary Fig. 3a ). Instrumental and reconstructed temperature datasets. All available instrumental temperature datasets including HadCRUT4  56  and Berkeley Earth 57 over 1850-2017, and GISTEMP  58  and NOAA GlobalTemp 59 over 1880-2017 were used. Global annual temperature anomalies were calculated by area-weighted averaging over both land (2 m air temperature) and ocean (sea surface temperature) minus the mean surface air temperature of land and ocean for the period from 1961 to 1990. Five reconstructions of Northern Hemispheric temperature were obtained from four groups which were abbreviated as PAGES2k 32 , Frank2010 16 , Mann2009 34 , MannEIV  33  , and Moberg2005 35 in this study (Fig.
1b ). All temperature reconstructions were smoothed with 5-year splines, and adjusted to removedmeans with respect to 1961-1990. Northern Hemispheric-mean temperature had been shown to be highly representative for global-mean temperature variations  16. The ensemble estimates of 521 calibrated temperature reconstructions were obtained from the Frank2010  16  , in which all amplitude and variability on >30-year timescales are well preserved for uncertainty analysis. The ensemble estimates were recalibrated by nine available datasets of Northern Hemispheric-mean temperature reconstructions using a reconstructing technique based on a state-space time series and Kalman filter algorithm  60. The ensemble means (Fig. 1b ) and individual temperature reconstructions (Supplementary Fig. 4a ) by Frank2010 are used for a large ensemble of >1500 estimates (EnOBS) of the η and γ over the preindustrial period.
The uncertainty in F LUC (σ F LUC ) was ±0. 7 GtC year -1 at each year 1. To estimate the β and its uncertainty (σ β ), we first estimated the β i and σ β;i (i ¼ 1; Á Á Á ; 4) for each combination of 1 CO 2 × 1 CO 2 emission × 4 temperature datasets (HadCRUT4, GISTEMP, Berkeley Earth, and NOAA GlobalTemp) that included uncertainty on C E and the regression. We then estimated the mean value of β by β ¼ 1 4 ∑β i and the σ β by σ β ¼ 1 4 ffiffiffiffiffiffiffiffiffiffi ∑σ 2 β i q (Supplementary Table  1 ). Similarly, we estimated uncertainty in γ (σ γ ) for the industrial period at a given timescale by σ γ ¼ 1 4 ffiffiffiffiffiffiffiffiffi ffi ∑σ 2 γ i q. As the uncertainty in γ (σ γ ) for the preindustrial period arises from uncertainties in ice-core CO 2 records and temperature reconstructions, we estimated the σ γ over 1000-1850 or 1400-1700, or 1000-1300 periods at a given timescale by calculating the standard deviation of the ensemble of γ from 3 ice-core CO 2 records × 5 temperature reconstructions (or the EnOBS): σ γ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 nÀ1 ∑ nÀ1 i¼1 ðγ i À γÞ 2 q (Supplementary Table  2 , Fig.
Acknowledgements This study was funded by the  National Key Research and Development Program of China  ( 2017YFA0604603 ), the  CAS Pioneer Talent Program , and the  National Key Research and Development Program of China  ( 2016YFA0602501 ). X. Z. Z. was also sponsored by the  National Natural Science Foundation of China  ( 42001019 ) and the  Shanghai Sailing Program  ( 19YF1413100 ). Y. P. W. was supported by the  National Environmental Science Program  (climate change and earth system science). P. C. acknowledges support from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. We gratefully thank  Dr. Pierre Friedlingstein  for providing constructive comments. We acknowledge the following data providers and model developers: the  Global Carbon Project , the  University of East Anglia Climatic Research Unit (CRU) , the  NASA Goddard Institute for Space Studies , the  NOAA , and  Berkeley Earth.
Data availability Resulting data that support the findings in this study are available in the Supplementary Tables, and more related datasets can be downloaded at https://github. com/ xuanzezhang/climate-carbon-cycle-feedback. Global Carbon Project report 2018 dataset (including anthropogenic CO 2 emission): https://www. icos-cp. eu/GCP/2018. Observed atmospheric CO 2 is available at http://wwww. esrl. noaa. gov/gmd/ccgg/trends/global. html. Ice-core CO 2 and temperature reconstruction datasets are retrieved from their published papers listed in References. The HadCRUT4 is available at https://www. metoffice. gov. uk/ hadobs/hadcrut4/. The GISTEMP is retrieved from https://data. giss. nasa. gov/gistemp/. NOAA GlobalTemp is retrieved from https://climatedataguide. ucar. edu/climate-data/ global-surface-temperature-data-mlost-noaa-merged-land-ocean-surface-temperature/.
Precipitation We use the SPAZM (SPAtialisation en Zone de Montagne) precipitation data set in the northern French Alps over the period 1950-2017  (Gottardi et al. , 2012). SPAZM is a 1 km × 1 km gridded interpolation of daily rainfall accumulations measured by more than 1800 daily rain gauges over the French Alps, the Pyrenees, the Massif Central, Switzerland, Spain and Italy. SPAZM decomposes the rainfall field into a guess field incorporating orography and residuals to this field. A distinctive feature of SPAZM is that the guess field is conditional on the weather pattern of the target day, using the weather pattern classification of  Garavaglia et al. (2010). This allows the altitudinal gradients to be adjusted according to the interaction between air flow and mountainous areas. Following the works of  Blanc et al.
HRDPS-CHM was applied to simulate snow con-ditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼ 1000 km 2 ) in the Canadian Rockies. Simulations were evaluated using highresolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases.
Airborne lidar snow depth data and snow persistence indexes derived from Sentinel-2 images were used to evaluate the ability of the different CHM configurations to capture the elevation-snow depth relationship as well as snow redistribution around wind-exposed ridges. The paper is organized as follows: Sect. 2 presents the study area and the different observation datasets used in this study, and it also describes the CHM modelling platform, the winddownscaling strategy and the configurations of the CHM experiments. Section 3 evaluates the impact of the wind field downscaling and the quality of the snowpack simulations using airborne lidar snow depth data and snow persistence indexes; Sect. 4 discusses the main challenges associated with snowdrift-permitting modelling of mountain snowpack and associated limitations.
The characteristics of the generated mesh are given in Table  1. For the Kananaskis domain, 383 200 raster grid cells with a 50 m resolution are required to represent the terrain, whereas 101 700 triangles are used in M 15 50 (Fig. 2 ). Large triangles are found in valley bottoms of low topographic variability, whereas small triangles dominate in alpine terrain, close to ridges where windinduced snow redistribution is common. A dataset of tall vegetation (> 5 m) coverage, with a resolution of 30 m (Fig. 1a ), was obtained from  Hansen et al. (2013). These fractional values were applied to the triangular mesh via mesher by averaging the raster cells that correspond to each triangle and assigning this average to the triangle. Triangles with an average fraction of high vegetation larger than 0.
Thus, f acts as a speedup/slowdown factor that accounts for topographic impacts on wind speed. Only one value for the initial wind speed was used to build the wind library due to the insensitivity of the transfer function to the initial wind speed found with WindNinja. To account for impacts on direction, the following approach was taken. The rasters of the wind map library containing the horizontal u and v wind components and the transfer function f for each initial wind direction were applied to the triangles of the unstructured mesh using the mesher code  (Marsh et al. , 2018). At each CHM time step, the HRDPS u HRDPS and v HRDPS wind components were spatially interpolated to the triangles' centres with an inversedistance interpolant using the four closest HRDPS grid points. For each triangle, the interpolated HRDPS wind direction, θ HRDPS , was then reconstructed from the interpolated HRDPS wind components, u HRDPS_int and v HRDPS_int.
25  (Winstral et al. , 2009). Note finally that a mass-and momentum-conserving version of WindNinja is also available  (Wagenbrenner et al. , 2019). Wagenbrenner et al. (2019)  have shown that momentum conservation improved flow simulation at windward and leeward locations compared to the mass-conserving version, but numerical instabilities made this version of the code unusable in the complex topography of the Canadian Rockies.
Airborne lidar snow depth data Airborne laser scanning (ALS) surveys were performed over the Kananaskis region on 5 October 2017 (late summer scan) and on 27 April 2018 (winter scan) using a Riegl Q-780 infrared (1024 nm) laser scanner with a dedicated Applanix POS AV Global Navigation Satellite System (GNSS) inertial measurement unit (IMU). The Q-780 scanner was flown at heights of approximately 2500 m above the terrain that yielded swath widths of 2000 to 3000 m. Postprocessing of the ALS survey flight trajectory yielded vertical and horizontal positional uncertainties of ±15 cm (1σ ). Post-processed point cloud data were exported into LAS files, and LAStools (https://rapidlasso. com/lastools/, last access: 29 January 2021) was used to generate 5 m resolution digital elevation models (DEMs).
, 2014)  and can arise from steep terrain that was effectively in the shadow of the laser scanner. For model evaluation, the 5 m snow depth map was then resampled over the same 50 m raster as the CHM output, taking for each cell of the 50 m raster the average of all non-masked cells in the 5 m snow depth map. Cells of the 50 m raster that contained more than 75 % of masked cells in the 5 m snow depth map were masked out. In addition, grid points covered by glaciers identified in the Randolph Glacier Inventory  (Pfeffer et al. , 2014)  were removed from the analysis since elevation change over these surfaces is also influenced by ice dynamics  (Pelto et al. , 2019). Finally, forested pixels identified using the global database of  Hansen et al. (2013)  at 30 m grid spacing were masked out as well since this study focuses on snow redistribution processes in open terrain.