dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement The corresponding recipe that can be used to reproduce the figures of this paper will be included in the ESMVal-Tool  (Eyring et al. , 2020; Lauer et al. , 2020; Righi et al. , 2020; Weigel et al. , 2021)  as soon as the paper is published. The new extensions described in this paper are available since ESMValTool v2. 6. 0. ESMValTool v2 is released under the Apache License, VERSION 2. 0. The latest release of ESMValTool v2 is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3401363 (Andela et al. , 2022a). The source code of the ESMVal-Core package, which is installed as a dependency of ESMValTool v2, is also publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3387139  (Andela et al. , 2022b). ESMValTool and ESMValCore are developed on the GitHub repositories available at https://github.
We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Bias correction can improve model carbon outputs, but carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS.
Additionally, we found that both the weighted ensemble-averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble-averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i. e. , that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables.
It is therefore important to reduce the uncertainty in carbon cycle projections over Australia, first to improve estimates of future carbon sinks, second to help constrain future atmospheric growth rates, and third because the improved understanding will ultimately enable better predictions of vegetation responses and of fire to climate change over Australia. Here, we assess the impact of different Coupled Model Intercomparison Project 6 (CMIP6) GCM selection, bias correction, and ensemble-averaging methods on the simulated carbon cycle in a synthetic experiment. We use a single dynamic global vegetation model, LPJ-GUESS  (Smith et al. , 2014) , forced with different versions of CMIP6 climate forcing, as well as LPJ-GUESS forced with the CRUJRA reanalysis  (Harris, 2019) , as a target dataset for the carbon variables and focus on responses at seasonal to centennial timescales.
Reanalysis We chose the CRUJRA reanalysis product  (Harris, 2019)  as the reference dataset to compare with the unconstrained CMIP6 results, as well as to derive bias corrections and ensemble weights. In addition, we use LPJ-GUESS runs forced with the CRUJRA reanalysis as reference datasets for carbon variables. CRUJRA is derived from the Climatic Research Unit gridded Time Series (CRU TS) v4. 03 monthly data  (Harris et al. , 2014)  and from the Japanese 55-year Reanalysis data (JRA-55)  (Kobayashi et al. , 2015). Temperature, downward solar radiation flux, specific humidity, and precipitation in JRA-55 are aligned to temperature, cloud fraction, vapor pressure, and precipitation in CRU TS (v4. 03), respectively. The CRUJRA dataset spans the years 1901-2018 on a 6 h time step, which we aggregated to a daily temporal resolution at a 0.
Dataset sensitivity The CRUJRA reanalysis is not "observations" and, as with all reanalyses, is subject to uncertainty itself. To test the sensitivity to the choice of reference dataset, we compared the CRUJRA to the ERA5 reanalysis dataset. ERA5 is the fifth-generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF;  Hersbach et al. , 2020). It uses a linearized quadratic 4D-Var assimilation scheme that takes the timing of the observations and model evolution within the assimilation window into account. Compared to the predecessor ERA-Interim reanalysis, it has a higher spatiotemporal resolution and assimilates more observations. The reanalysis is produced at an hourly time step and covers the time period 1979-2020. Its horizontal resolution is 0. 1 •. As for the CRUJRA reanalysis, we aggregated the data to a daily time step and regridded the dataset to a 0.
Skilled GCMs An intuitive way to select CMIP GCMs is to define a set of performance metrics and select those GCMs with a predefined level of skill (e. g. ,  Rowell et al. , 2016; Gershunov et al. , 2019). We calculated the metrics suggested by  Haughton et al. (2018)  (see Table  2 ) using the CRUJRA reanalysis as the reference dataset for daily, monthly, and annual precipitation; ranked all GCMs for each metric; and finally chose the GCMs with the highest average rank for monthly and annual timescales. For the last method (overlap of histogram), we estimated the intervals ("bin size") using the Freedman-Diaconis estimator  (Freedman and Diaconis, 1981)  for the reference dataset (CRUJRA) and then used the same bin size for the simulated variable (i. e. , CMIP forcing).
Step 2: bias correction methods Once a selection of GCMs is made, the biases of a given GCM can be corrected (see Fig. 1  step 2). We explored six approaches using CRUJRA as our reference dataset. We corrected the three climate forcing variables, i. e. , temperature, precipitation, and incoming shortwave radiation, and derived the correction based on the calibration time period 1989-2010 given this is common to both reanalysis products used here. We applied each method per pixel so that the different grid points were corrected independently of each other and tested the correction on both daily and monthly timescales, and we note that none of the correction methods used here are designed to correct temporal properties of the climate forcing. We show the corrections based on daily timescales in the main figures and use the corrections based on monthly timescales to assess the sensitivity to the correction timescale in the Supplement.
Step 3: run LPJ-GUESS We ran LPJ-GUESS with a reference dataset (CRUJRA reanalysis), the full raw CMIP6 ensemble (which includes the skilled, independent, and bounding models) and additionally with the bounding models (see Sect. 3. 1. 3) after they were bias-corrected according to the methods described in Sects. 3. 2. 1-3. 2. 6. LPJ-GUESS  (Smith et al. , 2014 ; Lund-Potsdam-Jena General Ecosystem Simulator) is a widely used dynamic global vegetation model for climate-carbon studies  (Sitch et al. , 2003; Smith et al. , 2014). LPJ-GUESS simulates the exchange of water, carbon, and nitrogen through the soilplant-atmosphere continuum  (Smith et al. , 2014)  by accounting for resource competition for light and space between plants. We adopted the global configuration of the model that uses 12 plant functional types (PFTs), simulating differences in growth form (grasses, broadleaved trees, or deciduous trees), photosynthetic pathway (C 3 or C 4 ), phenology (evergreen, summer green, or rain green), tree allometry, life history strategy, fire sensitivity, and bioclimatic limits for establishment and survival (see  Smith et al.
Consequently, x j k is the value of the kth bias-corrected model (i. e. , after subtracting the mean error from the dataset) at the j th grid cell. The weights (w T ) provide an analytical solution to the minimization of J j =1 (C j w -x j obs ) 2 , (6) L. Teckentrup et al. : Opening Pandora's box: how to constrain regional projections of the carbon cycle when subject to the constraint that the sum of the weights (w k ) always adds up to 1. The solution can be expressed as follows: w = A -1 1 1 T A -1 1 , (7) where 1 T = k elements [1, 1,. , 1] and A is the K × K difference covariance matrix.
The LG MPI-ESM1-2-HR run shows especially large variation in simulated C 4 grass FPC depending on the correction method. For LG INM-CM4-8 , the three approaches based on quantile mapping (QM, CDF-t, and dOTC) lower the median closer to the LG CRUJRA median. For the wet model, all approaches lead to significant improvement compared to the target dataset. None of the arithmetic or weighted ensemble averages in FPC match the LG CRUJRA median and are mostly below the lower quartile of LG CRUJRA. Overall, the analysis of FPC highlights important implications for bias correction. The results show that LPJ-GUESS responds very differently to the various bias correction methods because the change in the GCM forcing alters the competitive interactions between vegetation types. Importantly, although the spatial maps show similar agreement in C Total between correction methods, the change in FPC implies that the resulting change in carbon is simulated by difference underlying vegetation compositions.
1 and 0. 4 PgC month -1 at the peak of the wet season and ∼ 0 and 0. 15 PgC month -1 at the peak of the dry season). Through December to March, the maximum GPP during the wet season is lower compared to the reanalysis results but is closer to the reanalysis simulations in the dry season. As a result, the bias correction methods achieve similar C Total values (see Fig. 3 ) predominantly through reducing biases during the dry season and introducing an underestimation bias in the wet season. Notably, the R2D2 method always achieves the lowest bias to the target dataset compared to the remaining bias correction methods. For LG MPI-ESM2-2-HR , the raw climate forcing does not generate the right magnitude and timing of peak GPP. When show the CV based on LPJ-GUESS runs using sub-selections of the CMIP6 ensemble (skilled, independent, and bounding GCMs, respectively).
7 S). This is an artifact resulting from assumed model bioclimatic limits. In LPJ-GUESS, vegetation growth of C 4 grasses and tropical trees is restricted by a lower temperature boundary such that these vegetation types cannot establish or survive when the 20-year-average minimum temperature falls below 15. 5 • C. Therefore, C 4 grasses and tropical trees only grow north of the Tropic of Capricorn, while south of it only temperate trees and C 3 grasses are simulated. The strong variation across GCMs in simulated temperature thus leads to very different simulated vegetation cover (and thus high CV) in LPJ-GUESS. In contrast to the average C Total results, bias-correcting the forcing CMIP models does not necessarily lead to better results for other variables simulated by LPJ-GUESS. The different bias correction approaches did not necessarily lead to improved simulations of the change in C Total compared to the target dataset.
Further, competitive processes amongst tree species and C 3 and C 4 grasses that are driven by temperature (either dynamically or prescribed) can drive vegetation competition and therefore FPC. We found that temperate trees and C 4 grasses in particular can vary strongly in dominance and relative cover depending on the GCM used as the input forcing and the bias correction applied. Only the two multivariate approaches adjust the distribution so that it is more comparable to the reference dataset and the other ensemble members. This implies that both the model selection and the bias correction method can lead to small but potentially important differences in composition of vegetation distributions across the landscape. Models that show large differences in the vegetation distribution are also sensitive to the bias correction for seasonal GPP.
For the two models with the strongest divergence in C 4 grass distribution, all bias correction methods improve the seasonal productivity compared to the target dataset. However, correcting the climate forcing also led to a lower skill in predicting seasonal GPP for one model (LG NorESM2-MM ). We also found the foliar projective cover, especially that of C 4 grasses, showed a strong sensitivity to the bias correction method chosen for some models (e. g. , MPI-ESM1-2-HR). However, the spatial patterns in average bias of C Total remain relatively consistent across all bias correction methods tested and show some similarity to that of the raw model forcing (LG EC-Earth3-Veg , LG KIOST-ESM , and LG NorESM2-MM ). This outcome may emerge as we corrected each grid cell independently. When  François et al.
(2020)  correct their climate variables taking into account spatial properties, both methods tested here improved the results for small regional scales. Given the heterogeneity of climate and large area of the Australian continent, we did not attempt to correct the spatial scales given limitations in computation time, but this would be worth exploring in future work. In summary, within a framework of testing bias correction methods on the five models spanning the CMIP6 model spread, we found that the bias correction methods successfully reduced the bias to the reference dataset for averages over time and space (C Total ). Overall, the two multivariate approaches achieved a stronger reduction in bias for both individual GCMs and the ensemble average while also presenting a lower uncertainty across the ensemble.
Conversely, explicitly bias-correcting trends based on historical data, when the spatiotemporal nature may not yet have clearly emerged, could equally be problematic for unbiased estimation of climate system properties like equilibrium climate sensitivity. Despite the demonstrated limited impact of bias correction on temporal and spatial scales, correcting the driving forcing is still preferable to using raw climate forcing. DGVMs largely rely on bioclimatic limits that define where specific types of vegetation can grow. Relying on a biased climate forcing dataset might therefore result in a misrepresentation of the vegetation. Indeed, we found strong differences in the foliar projective cover of different vegetation groups. This mismatch in vegetation composition that can result from threshold-defined boundaries is likely to lead to diverging carbon and water cycle responses to the climate, which might be even more pronounced in areas with higher vegetation carbon mass than Australia.
Future studies could further explore options to improve temporal features in climate variables. Robin and Vrac (2021) , for example, include time as an additional variable for their multivariate bias correction, which may be a promising avenue for future research. Climate change impact studies need to be aware of the limitations of bias correction methods. As we have shown, bias correction cannot solve fundamental deficiencies in GCMs  (Maraun et al. , 2017). A possible flaw in applying univariate bias correction methods to a set of climate variables needed to force a dynamic vegetation model is a resulting inconsistency within the climate forcing. While all bias correction methods improve the averages of C Total compared to the target dataset, importantly, based on our findings it is not clear that one method systematically outperforms any other.
Sensitivity to ensemble-averaging methods and model selection methods We also tested the commonly used arithmetic ensemble average, a weighted averaging approach following  Bishop and Abramowitz (2013) , and a random forest regression approach. We found that the weighted average and the random forest approach outperform the arithmetic ensemble average for average C Total , and seasonal GPP with results very similar to the reference dataset. The random forest approach produces a small error magnitude when spatial dimensions are explored (see Fig. 5 ) while for the arithmetic and weighted ensemble average, systematic biases persist. While the FPC of tropical trees and C 3 grasses seems to be broadly captured by all averaging methods, C 4 grasses shows a strong bias where only the random forest approach achieves a median value within the IQR of the LG CRUJRA run.
Lastly, five models for all selection methods may seem like a small subset. However, earlier studies (e. g. ,  Pierce et al. , 2009)  found that the multi-model ensemble mean tends to converge towards a similar value after including five models. We therefore conclude that five models was a sufficient number in our testing framework. We further chose a relatively short calibration time period  to allow sensitivity tests with multiple reanalysis datasets. While these 22 years may not cover decadal variability, we assume it is sufficient to account for interannual variability such as the El Niño-Southern Oscillation, the Indian Ocean Dipole, and Southern Annular Mode, which have been shown to be important influences on the Australian carbon cycle (e. g. ,  Cleverly et al. , 2016). Other areas of uncertainty may include the sensitivity of the methods to the reference dataset.
Several studies have discussed that both bias correction methods (e. g. ,  Iizumi et al. , 2017; Famien et al. , 2018; Casanueva et al. , 2020)  and weighted ensemble-averaging methods (e. g. ,  Merrifield et al. , 2020)  depend on the observation dataset they are calibrated on. Casanueva et al. (2020)  demonstrate that precipitation in particular is sensitive to the choice of reference dataset. We therefore repeated the bias correction and chose ERA5 as a second dataset. We found high correlation coefficients between LPJ-GUESS runs that are based on GCMs corrected to CRUJRA and LPJ-GUESS runs that were based on GCMs corrected to ERA5 for C Total (0. 96-0. 98; not shown here). We conclude that our results were robust to the choice of reference dataset. Another concern frequently discussed is impact of the mismatch in spatial resolution (highresolution reanalysis product vs.
low-resolution GCM output). A solution to reduce the mismatch in spatial resolution might be to use dynamically downscaled datasets such as CORDEX. However,  Casanueva et al. (2020)  find the impact of the horizontal resolution on the bias correction results to be small in comparison to the impact of the bias correction method. Given dynamically downscaled products were only available for older CMIP generations (CORDEX is based on CMIP5, while NarCLIM is based on CMIP3) or contained only a small subset of GCMs (ISIMIP), and we expected the uncertainty associated with the spatial mismatch to be small, we chose the state-of-the-art CMIP6 GCM output. Further, in this study, we chose just one realization from each GCM, and therefore the results presented in this study do not fully reflect the uncertainty in simulations of the terrestrial carbon cycle linked to the entire spectrum of possible GCM forcings.
Lastly, we chose to correct daily climate data for the main analysis. However, correcting monthly data may be statistically more robust, especially for highly variable climate variables with a large number of null values such as daily precipitation. Indeed, our analysis of the corrected input variables surprisingly showed an increase in bias in simulated precipitation for two GCMs after correction (see Fig. 3 ) that is likely linked to a mismatch in simulated days without rain in the target dataset and the GCM simulation. We additionally tested the importance of timescales; i. e. , we biascorrected the GCMs on both daily and monthly timescales before forcing LPJ-GUESS with them. C Total simulated by LPJ-GUESS driven by daily and monthly corrected GCM output was strongly correlated (0. 92-0.
Importantly, bias correction methods do not correct temporal (such as IAV or trend) and spatial properties unless the methods are specifically designed and set up to do so. We found that using corrected GCM output can even increase the distance in change compared to our reference dataset. Future studies of ecosystem and carbon cycle impacts based on GCM climate forcing should therefore carefully choose a subset of models that is representative of the ensemble uncertainty and not rely on using a single GCM. To conclude, when Pandora opened the lid on her box she released the evils of the world, and these could never be put back into the box. We fear that we have also made the challenge of constraining the future regional-scale carbon budgets more difficult. We have, for example, raised more questions than answers, identified limitations of existing approaches, and ultimately provided a challenge to the community to find more robust strategies to reduce the uncertainty in the projection of regional carbon stores.
We acknowledge we have not provided easy answers, but we hope that by highlighting the challenges, strategies may be developed that can robustly constrain regional estimates of carbon storage. Code and data availability. The CMIP6 output used in this study is available via the Earth System Grid Federation (ESGF, 2023, https://esgf-node. llnl. gov/search/cmip6/). The CRUJRA reanalysis dataset is accessible via https://catalogue. ceda. ac. uk/uuid/ 7f785c0e80aa4df2b39d068ce7351bbb  (Harris, 2019). The bias correction is applied based on the Python SBCK package https://github. com/yrobink/SBCK  (Robin, 2022). The analysis code can be found at https://doi. org/10. 5281/zenodo. 7882380 (Teckentrup, 2023). Author contributions. LT, MGDK, and AJP designed and conducted the experimental analysis. LT, MGDK, AJP, and GA wrote the first draft with contributions from AMU, SH, BS, and BF.
Financial support. support The research was funded by the ARC Centre of Excellence for Climate Extremes (CE170100023) and by the New South Wales Department of Planning, Industry and Environment. Martin G. De Kauwe and Andrew J. Pitman received support from the ARC Discovery Grant (DP190101823). Martin G. De Kauwe received support from the NSW Research Attraction and Acceleration Program (RAAP). Anna M. Ukkola received support from the Australian Research Council (DE200100086).
Fiber detection using Gabor filters and graph extraction Fiber detection for pattern quantification in FN networks was produced using a pipeline, briefly described in a previous study  (1) , where it was primarily utilized for the extraction of local topological fiber properties. In the present study, the workflow was extended to enable a statistical analysis of parametric maps, focusing on a global spatial statistical analysis, which is relevant for quantitative and qualitative fiber analysis across multiple scales. The computational pipeline can be tested with a graphical user interface (GUI) on single samples [e. g. , immunofluorescence (IF) coupled with confocal microscopy, immunohistochemistry (IHC)], or a graph/Gabor-derived features collected in. csv files. The MATLAB source code and sample images can be found on GitHub, at github.
Statistical analysis based on GRF Statistical parametric maps (SPMs) are used to evaluate the probability of change in every pixel  (18)  by using decision tests based on the magnitude of the SPM values (i. e. the peak intensity of a cluster in SPM) and on the spatial extent of these clusters formed at a certain threshold. In our experiments, we focused on two different parametric maps representing the length of fibers and the isotropy of pore angles. We embedded the SPM framework, initially developed to analyze single datasets independently, into a machine learning paradigm. To evaluate the differences between two given groups of maps (e. g. , FN variants in normal state vs corresponding FN variants tumor-like state), we considered one of the groups as the normal realization of the GRF which we divide into a learning set and a smaller test set.