dataesr/scientific-paragraphs-categorization huggingface dataset:
The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise-to-noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training dataset is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere-Ocean General Circulation Model (AOGCM) simulations. The neural network utilized for training is a U-Net, a widely adopted convolutional network primarily designed for image segmentation.
Abstract The internal variability pertains to fluctuations originating from processes inherent to the climate component and their mutual interactions. On the other hand, forced variability delineates the influence of external boundary conditions on the physical climate system. A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise-to-noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training dataset is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere-Ocean General Circulation Model (AOGCM) simulations. The neural network utilized for training is a U-Net, a widely adopted convolutional network primarily designed for image segmentation.
, 2006) , CCSM4  (Gent et al. , 2011) , CESM  (Kay et al. , 2015) , MPI-ESM  (Maher et al. , 2019) , FGOALS-g3  (Li et al. , 2020) , CanESM2  (Chylek et al. , 2011) , and IPSL-CM6A-LR  (Bonnet et al. , 2021) , among others. This offers a valuable dataset for crafting methodologies dedicated to the disentanglement of forced and internal variability. Notably, employing members of a large ensemble model as surrogate observations allows for a comparison of results with the ensemble mean. Differences primarily mirror residual internal variability or limitations inherent in the method. Nevertheless, the forced variability estimated through an ensemble mean remains contingent upon the specific climate model employed. These climate models carry substantial uncertainties, particularly in terms of their climate sensitivity  (Sherwood et al.
Neural network We design a neural network to remove the internal variability from the SAT. The input data is structured with dimensions  (120, 90, 180) , corresponding to time spanning from 1901 to 2020, latitude, and longitude, respectively. On the other hand, the output holds dimensions of  (112, 90, 180) , encompassing the years 1905 to 2016, while maintaining the latitude and longitude dimensions intact. Notably, the output's temporal span is truncated compared to the input, by excluding the initial and final four years. This reduction addresses the substantial uncertainty typically observed at the dataset's endpoints, an aspect that will be elaborated upon later. A neural network's characteristics are shaped by its hyperparameters, which dictate both its architecture and training process.
Constitution of the database To construct the training dataset, we adapt a noise-to-noise methodology originally introduced in  Lehtinen et al. (2018). This approach was initially designed to train a neural network in denoising images. In this method, the network is exclusively trained on noisy images depicting various objects. Each object has more than one noised image depicting it. In the noise to noise method, we create an input/output training database that comprises pairs of noisy image combinations for identical objects. It's essential to note that the network cannot effectively learn to transform a random noise realization into another. Instead, the configuration is designed to approximate the mathematical expectation of all noisy images associated with the same object, culminating in an estimate that closely resembles the noise-free image.
For our application, we consider the forced spatio-temporal SAT anomalies from each climate model as distinct objects. These anomalies, inherent to each member, can be likened to noisy images, where the internal variability introduces the noise compo-nent. The ensemble members' mathematical expectation equates to the forced variability, which can be approximated through the ensemble mean. To create the training dataset, we follow a procedure wherein we compute pairs of members for each climate model, except for MPI-ESM, FGOALS-g3, and MIROC6, which are reserved for testing and validation purposes. Adopting an approach similar to  Lehtinen et al. (2018) , we augment the dataset by introducing the ensemble mean of the climate model's members as an additional member. This inclusion serves to expedite the training process without introducing any other influences.
In this process, each pair of members becomes an input/output pair. If we denote the number of ensemble members obtained from a specific climate model as n, this approach yields n(n + 1) input/output pairs per model. By accumulating such pairs from all models, the resulting training dataset primarily comprises simulations characterized by the most extensive ensemble sizes (namely IPSL-CM6A-LR, CanESM5, CNRM-CM6-1, and ACCESS-ESM1-5). To create the validation set, we employ the ensemble simulation data from the MIROC6 model, which ranks as the third-largest ensemble in terms of size (with n = 50 members). For this purpose, we designate the ensemble members as inputs, while the ensemble mean spanning the period from 1905 to 2016 serves as the desired output. To form the test dataset, we draw upon data derived from the FGOALS-g3 and MPI-ESM models, leveraging their extensive ensemble sizes of n = 110 and n = 100 respectively.
The chosen cost function is the root mean squared error (RSME), calculated using an area-weighted mean of the gridded data. The validation dataset is utilized to determine the optimal values for two key hyperparameters: the number of epochs and the number of filters used in the convolutional layers. The term "number of filters" pertains to the thickness of the convolutional layers. The number of epochs refers to how many times the training dataset is processed during the training phase. These hyperparameters are selected to minimize the root mean squared error (RMSE) using the validation dataset. Examination of the validation RMSE for different values of epochs and layer thickness reveals a consistent pattern (see Fig. S1 ): a significant reduction in RMSE occurs in the initial epochs, followed by a gradual increase.
10c ). In addition the SAT in the Niño 3. 4 region are not affected by the forcing from the main volcanic eruptions. Therefore, no evidence of a Niño-like response to volcanic eruption (as in  Khodri et al. (2017) ) is found. A novel approach is introduced in this study to effectively eliminate internal variability from a time-evolving two-dimensional dataset, specifically focusing on surface air temperature. The method employs a U-Net neural network and draws inspiration from the noise-to-noise technique. This framework treats internal variability as an analogous noise superimposed on the underlying forced variability. The U-Net model is trained using outputs from a diverse ensemble of climate models obtained from the CMIP simulations. Subsequently, this trained network is applied to observational data to unveil the forced variability signal by attenuating internal variability.
Variables such as sea level surface pressure and precipitation could be explored, capitalizing on potential correlations among these variables to provide more comprehensive insights. Lastly, the proposed method holds the potential for wider applications, including its deployment on simulations from projects like the Detection and Attribution Model Intercomparison Project  (Gillett et al. , 2016)  or the Large Ensemble Single Forcing Model Intercomparaison Project (D. M. Smith et al. , 2022). By leveraging transfer learning, the U-Net trained on historical simulations could be adapted to these datasets. This adaptation could facilitate the evaluation of specific forcing effects in individual climate models, offering a valuable tool for studying the impact of different external factors on the climate system.
Neural network We design a neural network to remove the internal variability from the SAT. The input data is structured with dimensions (120, 90, 180), corresponding to time spanning from 1901 to 2020, latitude, and longitude, respectively. On the other hand, the output holds dimensions of  (112, 90, 180) , encompassing the years 1905 to 2016, while maintaining the latitude and longitude dimensions intact. Notably, the output's temporal span is truncated compared to the input, by excluding the initial and final four years. This reduction addresses the substantial uncertainty typically observed at the dataset's endpoints, an aspect that will be elaborated upon later. A neural network's characteristics are shaped by its hyperparameters, which dictate both its architecture and training process.
Acknowledgments We acknowledge the support of the  SCAI doctoral program  managed by the  ANR  with the reference  ANR-20-THIA-0003 , the support of the  EUR IPSL Climate Graduate School  project managed by the  ANR  under the " Investissements d'avenir" programme  with the reference  ANR-11-IDEX-0004-17-EURE-0006. This work was performed using HPC resources from  GENCI-TGCC A0090107403  and  A0110107403 , and  GENCI-IDRIS AD011013295. Guillaume Gastineau  was funded by the  JPI climate/JPI ocean ROADMAP project  (grant number  ANR-19-JPOC-003 ). outputs obtained from each ensemble member, and the forced variability obtained with ensemble averages subsampling ensemble of size 1 to 40; for (red) MPI-ESM and (blue) FGOALS-g3. The RMSE calculated from the U-Net and each ensemble member is given by (color shade) the interval including 90% of the distribution, assuming a gaussian distribution, and (horizontal dashed line) the mean RMSE.
Acknowledgments We acknowledge the support of the  SCAI doctoral program  managed by the  ANR  with the reference  ANR-20-THIA-0003 , the support of the  EUR IPSL Climate Graduate School  project managed by the  ANR  under the " Investissements d'avenir" programme  with the reference  ANR-11-IDEX-0004-17-EURE-0006. This work was performed using HPC resources from  GENCI-TGCC A0090107403  and  A0110107403 , and  GENCI-IDRIS AD011013295. Guillaume Gastineau  was funded by the  JPI climate/JPI ocean ROADMAP project  (grant number  ANR-19-JPOC-003 ).
ability. In the interval from 1996 to 2016, the U-Net-derived forced SAT (depicted in Fig-ure 8a) illustrates a fairly uniform warming, with amplified warming evident over the Arctic region, consistent with Arctic amplification. Furthermore, this warming effect is slightly more pronounced over land compared to oceans. Conversely, the Southern Ocean experiences less warming in comparison to other global regions. The spatial distribution of standard deviations (Figure8b), computed from 1905 to 2016 using U-Net output, mirrors the anomalies observed in the 1996-2016 period. This agreement indicates the prevailing influence of increasing anthropogenic forcing. Notably, this pattern closely resembles the changes observed in the multi-model mean (MMM) (as depicted in Fig. 2). This underscore the significant contribution of the training dataset in determining the identified forced changes.
A methodology is formulated to distinguish between internal and forced variability within the surface air temperature. The noise-to-noise approach is employed for training a neural network, drawing an analogy between internal variability and image noise. A large training dataset is compiled using surface air temperature data spanning from 1901 to 2020, obtained from an ensemble of Atmosphere-Ocean General Circulation Model (AOGCM) simulations. The neural network utilized for training is a U-Net, a widely adopted convolutional network primarily designed for image segmentation. To assess performance, comparisons are made between outputs from two single-model initialcondition large ensembles (SMILEs), the ensemble mean, and the U-Net's predictions. The U-Net reduces internal variability by a factor of four, although notable discrepancies are observed at the regional scale.
This experiment is expected to reveal common model deficiencies which are not clearly visible in volcanic experiments, providing valuable information for guiding improvements in stratospheric aerosol models. Here, we follow the proposed BG setup and compare simulations from nine atmospheric models participating in ISA-MIP, as listed in Table  1. The aim is to quantify the range of simulated burdens and distributions of stratospheric aerosols and to evaluate the model results against satellite-derived observations. We quantify the atmospheric sulfur budget and determine the model spread for each compound. We provide model estimates for sulfur fluxes, where available, and compare the stratospheric burdens of aerosol and SO 2 with satellite-derived datasets. We, therefore, identify existing uncertainties in state-of-the-art stratospheric aerosol models by examining various aspects of the spatial and temporal distribution of the most abundant sulfur species, and examine the effective radius and surface area density (SAD).
The experimental setup and the models involved are described in Sect. 2. 1 and 2. 2, respectively. Sect. 2. 3 describes all observational datasets used for model evaluation. In Sect. 3. 1, we discuss the full sulfur budget in the participating models, including some of the main chemical processes influencing the aerosol layer, emissions of aerosol precursors, cross-tropopause fluxes, stratospheric burdens, reactions, and deposition. We also present the total sulfur budget, summed over all sulfur species. Seasonal cycles and meridional distributions are presented in Sect. 3. 2. The distribution of three major sulfur species is shown in  Sect. 3. 3. Sect. 3. 4  discusses effective radius and surface area density. Finally, the conclusions of this work are presented in Sect. 4.
Experimental setup We follow the setup described by  Timmreck et al. (2018)  in the BG experiment BG_QBO for all models unless otherwise specified in the model description (hereafter called REF). The simulations are set up as 20-year time-slice simulations using repeating boundary conditions of the year 2000. A second simulation, termed BG_NAT (hereafter called NAT), has the same setup, except all anthropogenic sulfur emissions were excluded  (Timmreck et al. , 2018). The difference in SO 2 emissions between these two experiments is shown in Fig. A1. All aerosol and sources of aerosol precursors, except explosive volcanic eruptions, are included in this study. In the models without an internally generated QBO (see Table  1 ), the QBO is nudged to the 1981-2000 period. All models have been recommended to use prescribed sea surface temperatures (SSTs) and sea ice coverage (SIC) from the MetOffice Hadley Center Observational Dataset  (Rayner et al.
, 2003). Sulfur emissions from anthropogenic sources as well as biomass burning are taken from the Monitoring Atmospheric Composition and Climate (MACC-City) inventory  (Granier et al. , 2011). Emissions from continuously degassing volcanoes are given by  Dentener et al. (2006) , based on  Andres and Kasgnoc (1998). OCS surface concentrations are prescribed and constant at 510 pptv  (Montzka et al. , 2007; SPARC, 2006). If allowed by the model setup, DMS emissions are calculated online using concentrations in the global oceans given by  Lana et al. (2011). Some models include similar online calculations for dust and sea salt, in which case, the oceanic concentrations for these compounds are also taken from  Lana et al. (2011). Otherwise, models use their usual database. Stratospheric burdens, if not directly provided in the output (in CAM5-CARMA, ECHAM5-HAM, WACCM6-CARMA, WACCM6-MAM4, and UM-UKCA) were calculated from monthly mean mixing ratios, using standard air in CAM5-CARMA and UM-UKCA, and the provided air mass in all other models.
Observational and reanalysis data We use several satellite-derived datasets for the stratospheric aerosol layer (NASA/LARC/SD/ASDC, 2012;  Damadeo et al. , 2013; Revell et al. , 2017)  and stratospheric SO 2  (Günther et al. , 2018)  and also in-situ measurements from high altitude balloon soundings  (Deshler et al. , 2019). An overview of these datasets is provided in Table  2. For each of these datasets, we use the period 1999-2004 (unless otherwise specified) that is relatively unaffected by volcanic eruptions. Comparing transient observations with time-slice simulations comes with certain caveats, including the influence of the QBO. The influence of the QBO is not limited to the tropical stratosphere, but also affects export from the tropics to mid-latitudes and modulates the strength of the winter polar vortex  (Baldwin et al.
, 2001). Therefore, we do not try to validate models against observational data. Rather, we attempt to qualitatively assess the differences and refer the reader to previous papers in which quantitative model validations have been conducted. We use an aerosol dataset derived using the "3-λ mechanism" as described by  Revell et al. (2017) , which is based on the Global Space-based Stratospheric Aerosol Climatology version 2. 2 (GloSSACv2. 2) dataset (see below). These data are derived from extinction measurements at three wavelengths by the Stratospheric Aerosol and Gas Experiment II (SAGE II)  (Thomason et al. , 2018). The dataset, hereafter called "SAGE-3λ", includes the zonal mean distribution of aqueous H provides data at altitudes every 500 m between 5 km and 40 km. We apply this dataset above the lapse rate tropopause derived from ERA-Interim temperature data  (Dee et al.
, 2011). We evaluate the model-derived extinctions at 525 nm (the wavelength corresponding to SAGE II data with the smallest uncertainty) and compare them with the GloSSACv2. 2 data, although optical properties are not the main focus of this analysis. The GloSSACv2. 2 dataset includes the solar occultation SAGE II measurements at four wavelengths out of the seven SAGE II wavelengths available  (Kovilakam et al. , 2020 ; NASA/LARC/SD/ASDC, 2022). The dataset is composed of monthly zonal mean values at 32 latitudes ranging from 80°S to 80°N and vertical levels from 0. 5 km to 40 km in 500-m intervals. A level-3 dataset for SO 2 derived from MIPAS is also used in the present study  (Günther et al. , 2018). To minimize the volcanic influence, several months in the dataset that appeared to be affected by eruptions were excluded from the analysis, as described by  Höpfner et al.
(2015). As this leaves significant gaps in the dataset, we use an average over the whole period from 2002 to 2012 for our analysis. Furthermore, we use the temperature distribution provided by this dataset, to derive the lapse rate tropopause and calculate stratospheric burdens. We also use the effective radius and aerosol surface area density dataset derived from SAGE II with the SAGE version 7. 0 algorithm  (Damadeo et al. , 2013 ; NASA/LARC/SD/ASDC, 2012). Aerosol was assumed to be composed of aqueous sulfuric acid solution at 75 wt% H 2 SO 4. We also compare the simulated size distributions to balloon measurements from Laramie, Wyoming at 41°N and 104°W  (Deshler and Kalnajs, 2022). The data have been collected using optical particle counters (OPC), which measure the particle concentration in 12 size bins from 0.
The global atmospheric sulfur budget In Fig. 1  we present the global atmospheric sulfur budget represented by burdens and fluxes of major sulfur species, based on the input by the nine global atmospheric aerosol models involved in this study (see Table  1 ). The major sulfur species are specified in Gg(S) for the burdens and Gg(S) yr -1 for fluxes. For the four models CAM5-CARMA, WACCM6-CARMA, WACCM6-MAM4 and UM-UKCA, stratospheric burdens are calculated from monthly mean volume or mass mixing ratios, integrated from the model tropopause upward, as these burdens were not directly provided by some of the modeling groups. Mean emission and deposition fluxes are calculated by averaging the corresponding output from those models that provided them. Observational data (in brackets) are from the SAGE-3λ and MIPAS satellite datasets described in Sect.
, 2019). Tropospheric aqueous and gas-phase SO 2 oxidation fluxes are presented in Fig. 1  for SOCOL-AERv2 and ECHAM5-HAM, however ECHAM5-HAM shows lower values for both pathways. In the lower stratosphere, SO 2 lifetime with respect to oxidation by OH is 3-4 weeks, making it the source of H 2 SO 4 and subsequently the Junge aerosol. From these considerations, it appears that most of the models underestimate the turnover in the chemical reaction of SO 2 + OH. The stratospheric aerosol burden is about 156 ± 51 Gg sulfur in the multi-model mean (with standard deviation, see Table  3 ). This value differs only slightly from the value derived from the SAGE-3λ observational dataset described by  Revell et al. (2017, and personal communication with B. Luo). The scatter between the models is rather small, however still with a coefficient of variation of 33% with a factor three difference between model outliers.
Note that these fluxes are not fully balanced, although they are expected to be somewhat in equilibrium. The reason for ULAQ-CCM is that only the SO 2 flux is available, and the contribution of other species (mainly OCS and DMS, as seen from SOCOL-AERv2 data in Fig. 1 ) is missing. SOCOL-AERv2 takes into account all the species, therefore the imbalance could be a result of deficient mass conservation in its transport scheme, notably where the tracer gradients are steep  (Stenke et al. , 2013). As for emissions and deposition fluxes, the values provided in the figure are quite scattered, because not all variables calculated by models were present in the output provided in the model database and could therefore not be included in our budget calculations. This also explains the inconsistency between the total deposition and the emission fluxes for individual models.
Further, the effective radius varies, as discussed in Sect. 3. 4 with potential impacts on SAOD. The relationship between the SAOD and stratospheric aerosol burden is illustrated in Fig. 3 (a-e ). (Note that we use the pure sulfate SAOD for SOCOL-AERv2 and ULAQ-CCM, whereas in WACCM6-CARMA, it is the SAOD of both pure and mixed aerosol and in all other models, it is the total SAOD). In general, a higher stratospheric aerosol burden also leads to higher SAOD (Fig. 3  a-e). This relationship is less clear in the extratropics. The relationship remains highly linear for SAGE-3λ, which is connected to the assumptions on size distributions in the construction of this dataset. As the relationship between burdens and SAOD is very similar among models in the tropics, the conversion schemes in all models are likely to be very similar.
This then results in the inter-hemispheric asymmetry between the models in terms of the lower stratospheric SO 2. Although MIPAS (k) has a distribution more similar to e. g. ECHAM6-SALSA than the models with lower stratospheric SO 2 burdens, the values in Fig. 1  are closer to e. g. SOCOL-AERv2. However, as the MIPAS dataset only extends to 23km, not considering large parts of the stratosphere, we cannot make a definitive statement about the stratospheric mass of SO 2 in the MIPAS dataset. In the stratosphere, most models have a secondary maximum of SO 2 in the tropical region between about 10-20 hPa. This can be attributed to the photolysis of OCS, discussed in the previous section, as well as to the evaporation of aerosol and subsequent photolysis of H 2 SO 4  (Turco et al. , 1979). As ECHAM6-SALSA does not include OCS, SO 2 only resides in the lower stratosphere.