dataesr/scientific-paragraphs-categorization huggingface dataset:
Reference Data Sets Our assessment is fully placed in the CMIP6 context, with the objective to provide information to relate our results with those obtained with other models using "official" CMIP6 prescribed ozone and/or aerosol fields (see CMIP6 input4MIPs page, https://esgf-node. llnl. gov/projects/input4mips/). So our initial reference data sets are these official CMIP6 data sets, and then we enlarged slightly our analysis to closely related data sets. More specifically, two aerosol data sets are used as references. The first one provides aerosol optical properties and an estimate of the Twomey effect computed with an off-line version of the MACv2-SP (Simple Plume) code  Stevens et al. (2017) , run on a T63 grid (courtesy of S. Fiedler). It is thereafter referred to as the MACv2-SP aerosol data set.
The Simple Plume code has been developed to be implemented in climate models to investigate some of the aerosol effects on the climate system (e. g. , within RFMIP; see  Pincus et al. , 2016). It has already been used in several publications  (Fiedler et al. , 2017 (Fiedler et al. , , 2019;; Nordling et al. , 2019). and used in a number of climate studies  (Michou et al. , 2015; Toll et al. , 2016)  and which is the baseline MACv2-SP attempts to approximate. This data set provides monthly aerosol optical properties on a 1 degree grid, derived from a combination of observations and model outputs. Observations include the AERONET ground-based sun photometers network  (Holben et al. , 1998 ; http://aeronet. gsfc. nasa. gov), as well as measurements over ocean provided by the Maritime Aerosol Network  (Smirnov et al.
, 2009). Model outputs are derived from the AeroCom global modeling initiative (e. g. ,  Kinne et al. , 2006; Koffi et al. , 2016 ; https:// aerocom. met. no/references. html, for the list of AeroCom related references). MACv2 covers the 1850-2100 period and includes interannual variability for the anthropogenic aerosols, while natural aerosols consider only monthly variations. Optical properties for the main aerosol types (i. e. , DD,SS, BC, OM, and SO 4 ) are provided. One could argue that as MACv2-SP is a fit to MACv2, there is not much to learn by comparing to MACv2-SP in addition to comparing to MACv2. However, as MACv2-SP is an official CMIP6 forcing, the comparison of CNRM results against this data set is a basic pillar of the present study. Second, MACv2 appears as a rather obvious data set to enlarge to other aerosol properties an analysis using MACv2-SP which describes only anthropogenic aerosol properties.
Aerosol-Cloud Interactions As the only indirect effect represented in CNRM-CM6-1 is the Twomey effect, the change in CDNC between PI and PD can be seen as a relevant proxy of the anthropogenic aerosol-cloud interactions in CNRM-CM6-1. This change in CDNC has been calculated from a pair of dedicated 10 year fixed SST CNRM-CM6-1 simulations with interactive aerosols, one with 1850 static anthropogenic aerosol emissions (PI) and one with 2000 emissions (PD). Ratios of CDNC between PD and PI simulations are shown in Figure  9  for the MACv2-SP dataset and CNRM-CM6-1 at the cloud top, as well as at various pressure levels (950, 800, and 700 hPa) for CNRM-CM6-1 only. Although the magnitude of the change depends on the altitude to a certain extent, at all levels, the largest changes in CNRM-CM6-1 appear over Asia in agreement with changes in MACv2-SP (see equation 15 of  Stevens et al.
, 2015). However, more recently, Checa-Garcia et al. (2018) provide information of specific interest for this study as they detail both the stratospheric and tropospheric ozone forcing of the CMIP6-OZ data set, here again computed with an off-line radiative transfer model. Circa 2010, the Checa-Garcia et al. (  2018 ) ERF of the stratospheric ozone is -0. 029 W m -2 , with LW ERF of -0. 109 W m -2 and SW ERF of 0. 08 W m -2. In CNRM-CM6-1, total ERF is -0. 036 W m -2 , with LW ERF of 0. 140 W m -2 and SW ERF of -0. 18W m -2 (see also Figure  14 ). Although the total (LW + SW) forcing in CNRM-CM6-1 is quite similar to that of  Checa-Garcia et al. (2018) , the individual LW and SW contributions are of opposite signs. The negative SW ERF in CNRM-CM6-1 can be explained by radiative transfer theory and reveals that the SW impact of the reduction of ozone in the troposphere is larger than that of the reduction of ozone in the stratosphere.
Conclusions This paper documents characteristics of the aerosol and ozone fields of the CNRM-CM6-1 and CNRM-ESM2-1 climate models used in the course of the CMIP6 initiative. PD and historical (1850-2014) features of these fields are analyzed against reference data sets, and their respective PD and transient ERFs are discussed. These ERFs result from diagnostics of a number of CMIP6 or CMIP6-like simulations designed in the RFMIP and AerChemMIP projects. It is important to note first that CNRM-CM6-1 and CNRM-ESM2-1 share a common physical-dynamical core and have been run with the same vertical and horizontal resolutions. Even the calibration achieved for CNRM-CM6-1 (see  Voldoire et al. , 2019)  has been kept unchanged in CNRM-ESM2-1. CNRM-ESM2-1 adds an interactive representation of the land and ocean carbon cycle, in addition to a more complex description of the atmospheric chemistry and of the aerosols, of specific interest to this article.
For the ERF of ozone, the linear ozone scheme of CNRM-CM6-1 allows simulations with ozone coefficients for a specific year, all other forcings remaining unchanged, and this offers a quite unique way to compute this ERF term. Such a methodology cannot be applied in a model with full chemistry like CNRM-ESM2-1. Compared to reference data sets, MACv2 and MACv2-SP, and to MERRA-2, the PD CNRM-CM6-1 total AOD (0. 10) is biased low, although in the range of aerosol models, with a too small transport away from source regions. Most of this bias comes from the anthropogenic aerosol AOD (0. 018) over land. The CNRM-CM6-1 AAOD (4. 510 -3 ) appears also weak, with the largest biases over biomass burning regions. The change in CDNC at cloud top from 1850 to 2014, used to characterize the Twomey effect, is larger (1.
Over the historical period, CNRM-CM6-1 and CNRM-ESM2-1 have similar trends of anthropogenic aerosols, and these trends are lower than the MACv2 and MACv2-SP ones, possibly because of differences in emissions but also in overall aerosol representation (see  Flato et al. , 2013). Transient ERFs computed from a pair of AMIP-type simulations are confirmed when evaluated from pairs of 30 year fixed SSTs simulations. Zooming over Asia, the large negative trend that started in 1950 is very similar in both ERFaci and ERFari. PD ozone mixing ratios of CNRM-CM6-1, CNRM-ESM2-1, and the CMIP6-OZ reference data set are within 10% throughout most of the stratosphere, with exceptions around the tropopause and in the mesosphere. The TCO is overall well simulated by the CNRM climate models, with compensating biases depending on the latitude though.
Its historical evolution over 1960-2014 is, globally averaged, very close to that of the NIWA3. 3 data set. Biases over this period reflect those of the PD, and latitude-dependent year-to-year variability of the model outputs is relatively realistic. The PD CNRM-CM6-1 ozone ERF that represents a stratospheric ozone ERF, computed with a 70 year fixed SST simulation, is -0. 040 ± 0. 032 W m -2 , in coherence with literature. The transient ERFs, computed with two different methods, produce very similar results for the global mean total ERF: no trend over the whole period and opposite trends for the LW (positive trend) and SW (negative trend) ERF components. Finally, interesting results in relation to ECS estimates include the satisfactory behavior of the CNRM-CM6-1 linear ozone scheme under 4xCO2 conditions, while the ECS estimate of CNRM-CM6-1 with TACTIC_v2 differs noticeably from that of CNRM-CM6-1 with prescribed aerosols.
Aerosol-and cloud-induced changes in diffuse light have important impacts on the global land carbon cycle, as they alter light distribution and photosynthesis in vegetation canopies. However, this effect remains poorly represented or evaluated in current land surface models. Here, we add a light partitioning module and a new canopy light transmission module to the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) land surface model (trunk version, v5453)  and use the revised model, OR-CHIDEE_DF, to estimate the fraction of diffuse light and its effect on gross primary production (GPP) in a multilayer canopy. We evaluate the new parameterizations using flux observations from 159 eddy covariance sites over the globe. Our results show that, compared with the original model, ORCHIDEE_DF improves the GPP simulation under sunny conditions and captures the observed higher photosynthesis under cloudier conditions in most plant functional types (PFTs).
We evaluated the GPP simulated by ORCHIDEE_DF and the same version of the ORCHIDEE code without diffuse light (trunk version, v5453) using observations collected from 159 eddy covariance flux sites over 11 plant functional types (PFTs;  Baldocchi et al. , 2001). Using both model simulations and observations at the flux sites, we also investigated the interactions between diffuse light fraction and biotic and abiotic factors on GPP, with the objective of understanding when and how much the light quality affects photosynthesis. Because diffuse light is expected to enhance the photosynthesis of shaded leaves in deep canopy, we tested whether the enhancement of GPP due to diffuse radiation is larger in canopies with a larger LAI. We also tested whether environmental factors such as temperature or VPD affect this enhancement from diffuse light.
Light partitioning in ORCHIDEE_DF The lack of light quality (diffuse light fraction) information in most datasets to drive LSMs is one of the main difficulties when simulating the diffuse light effect. This field can be calculated in atmospheric light transmission models when aerosol and cloud information is available  (Yue and Unger, 2017; Malavelle et al. , 2019). However, the aerosol and cloud information is not always available. Here, we use the empirical equations following  Weiss and Norman (1985)  to partition the 30 min downward PAR, which can be derived from the shortwave radiation, into diffuse and direct components. Compared with another empirical method  (Spitters et al. , 1986) , we found that this method more successfully reproduces the observed diffuse light fraction at the flux sites used in this study (Figs.
Flux data and site-level simulations To evaluate ORCHIDEE_DF, we collected flux site measurements from the La Thuile dataset, which includes 965 site-year observations from 252 sites in total (https://fluxnet. fluxdata. org/data/la-thuile-dataset/, last access 1 November 2020). Because our ORCHIDEE simulations assume that the ecosystems are in equilibrium and do not experience disturbances (e. g. , logging, fire), we selected flux sites without strong disturbances over the last 10 years. For sites that also provided growing season LAI information, we also removed forests site with LAI < 2, which may be considered as sparse forests with understory vegetation. In the end, observations of 655 site-years from 159 sites were retained (Table  S2 ). The annual mean temperature of the sites spans from -9 to 27 • C, and the annual precipitation spans from 67 mm yr -1 to over 3000 mm yr -1 (Fig.
S2 ), which is representative of most of the climate conditions over the globe. The dataset provides in situ meteorology, net ecosystem exchange (NEE), gross primary productivity (GPP) and data quality information at 30 min time steps. The GPP provided by this dataset is partitioned from NEE and gap filled using the method of  Reichstein et al. (2005). Specifically, 64 of the 159 sites provided measurements of both total and diffuse PPFD, which allows us to evaluate the light partitioning parametrization (Eqs. [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]. The gaps and missing variables in meteorology are filled using the approach from  Vuichard and Papale (2015)  to meet the model input requirements. Because ORCHIDEE has different photosynthesis parameters for different PFTs, we classified the vegetation at each site into the 13 ORCHIDEE PFTs (Table  S1 ) according to the International Geosphere-Biosphere Programme (IGBP) land cover types specified on the FLUXNET website (https: //fluxnet.
Analyses When evaluating the modeled GPP response to diffuse light, we did not use all the 30 min data points due to several concerns. First, all nighttime data points were excluded from the analyses given that GPP is zero at night. Second, all data points flagged with poor quality in the FLUXNET archive were removed. Third, ORCHIDEE might not be perfect at capturing the seasonality of leaf flushing and shedding. In order to minimize the uncertainty from phenology, we used only data from the growing season at each site, which is dehttps://doi. org/10. 5194/gmd-13-5401-2020 Geosci. Model Dev. , 13, 5401-5423, 2020 fined as months when GPP m > GPP m,min + GPP m,max -GPP m,min 4 , (39) where GPP m is the observed monthly GPP, and GPP m,min and GPP m,max are the observed minimum and maximum monthly GPP at the corresponding sites, respectively.
In contrast, ORCHIDEE_DF reproduces this GPP difference in most PFTs except tropical deciduous broad-leaved forests (TrDBF), Boreal deciduous broadleaved forests (BoDBF) and C 4 grasslands (C4Gra). However, there is only one TrDBF site and very few C4Gra sites in our dataset. Furthermore, at most C4Gra sites, we are not able to find PPFD levels where sunny and cloudy conditions coexist. Therefore, we are not able to carry out further evaluation of cloudy-minus-sunny GPP differences for TrDBF and C4Gra. At three of the four BoDBF sites, the modeled GPP difference under cloudy and sunny conditions is relatively small (not shown). This might be because the model overestimated the deleterious effect of low temperature on photosynthesis at the BoDBF sites (mean annual temperature < 3 • C). In total, observations from about 70 % of the sites show a remarkably higher GPP under cloudy conditions compared with sunny conditions.
Improvement of ORCHIDEE_DF The role of diffuse light on photosynthesis has been found and modeled in different vegetation types  (Gu et al. , 2003; Niyogi et al. , 2004; Misson et al. , 2005; Alton et al. , 2007b ;   Knohl and Baldocchi, 2008; Mercado et al. 2009; Oliphant et al. , 2011; Kanniah et al. , 2013; Williams et al. , 2014; Cheng et al. , 2015; Wang et al. , 2018). However, very few studies have attempted to account for the diffuse light effect in a global land surface model, and fewer studies have used large FLUXNET datasets for evaluation. Here, by using flux observations from 159 sites over the globe, we show that -by separating the direct and diffuse light -ORCHIDEE_DF im-proves the simulation of GPP under sunny conditions and, more importantly, reproduces the observed impacts of diffuse light on GPP and LUE for most of the PFTs (Figs.
4, , 5, 6 ). Under cloudy conditions, ORCHIDEE_DF seems to perform slightly worse than ORCHIDEE trunk (Fig. 4 ). However, it should be noted that ORCHIDEE_DF has not been recalibrated, and all parameters are those from ORCHIDEE trunk despite the substantial changes in the code with respect https://doi. org/10. 5194/gmd-13-5401-2020 Geosci. Model Dev. , 13, 5401-5423, 2020 to light partitioning and canopy light transmission. Furthermore, the GPP simulated by ORCHIDEE trunk shows different GPP SD biases under sunny and cloudy conditions, whereas ORCHIDEE_DF gives a more systematically underestimated GPP SD, which should be more easily corrected in a future calibration. The site-level comparison (Fig. 5 ) also explains how ORCHIDEE_DF reproduces the GPP increase compared with ORCHIDEE trunk. At most sites, the GPP simulated by the two models shows a similar magnitude under cloudy conditions, but the GPP simulated by ORCHIDEE_DF is significantly smaller under sunny conditions.
Conclusion In this study, we added a module to partition the downward surface solar radiation into diffuse and direct components as well as a new canopy radiative transfer model, which separates the existing multilayer canopy into sunlit and shaded leaves, to the ORCHIDEE trunk model. The resulting new land surface model, ORCHIDEE_DF, is evaluated using the La Thuile flux dataset over 159 sites comprising 11 PFTs. Compared with ORCHIDEE trunk, OR-CHIDEE_DF improves the GPP simulation under sunny conditions. This improvement successfully reproduces the observed enhancement of GPP under cloudier conditions at most of the sites. Diffuse downward photosynthetic photon flux density at the top of the canopy (µmol m -2 s -1 ) I df,i Net photosynthetic photon flux density derived from I df,0 at the top of canopy layer i (µmol m -2 s -1 ) I dr,0 Direct downward photosynthetic photon flux density at the top of the canopy (µmol m -2 s -1 ) I dr,df,i Net diffuse photosynthetic photon flux density derived from the scattering of I dr,0 at the top of canopy layer i (µmol m -2 s -1 ) I dr,dr,i Downward direct photosynthetic photon flux density at the top of canopy layer i (µmol m -2 s -1 ) I dr,i Net PPFD derived from I dr,0 at the top of canopy layer i, the sum of I dr,dr,i and I dr,df,i (µmol m -2 s -1 ) I i Downward shortwave radiation arriving at canopy layer i (W m -2 ) J Rate of electron transport (µmol e -m -2 s -1 ) Jmax Maximum value of J under saturated light, depending on temperature (µmol e -m -2 s -1 ) Jmax 0 Jmax at the top of the canopy (µmol e -m -2 s -1 ) Jmax i Jmax at the canopy layer i (µmol e -m -2 s Average photosynthetic photon flux density absorption per unit leaf area in canopy layer i (µmol m -2 s -1 ) PPFD df , I df,0 Diffuse downward photosynthetic photon flux density above canopy (µmol m -2 s -1 ) PPFD t Total downward photosynthetic photon flux density above canopy (µmol m -2 s -1 ) https://doi.
Y. Zhang et al. :  Modeling the impacts of diffuse light fraction Code and data availability. The ORCHIDEE_DF model code is available at https://doi. org/10. 14768/20200407003. 1  (Zhang, 2020). The flux data (La Thuile) used in this study are available at https://fluxnet. fluxdata. org/data/la-thuile-dataset/ (Fluxdata, 2017). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-5401-2020-supplement. Author contributions. PC, OB and LL designed the project. YZ developed the model code with help from AB, FM, DG and AC. NV provided the code for data gap filling. YZ prepared the paper with contributions from all co-authors.
Acknowledgements. The authors are very grateful to the FLUXNET communities for their efforts with respect to making the sites and collecting data. We would especially like to thank the flux site PIs who are not in the author list but have given constructive suggestions on this paper. The authors also acknowledge Yves Balkanski and Nicolas Viovy for their suggestions during this work. Financial support. This research has been supported by the European Research Council (grant no. SyG-2013-610028), the ANR CLAND Convergence Institute (grant no. 16-CONV-0003), the ANR China Trend Stream and the new 4C project. Bogdan Chojnicki was supported by the National Science Foundation, Poland (grant no. UMO-2017/27/B/ST10/02228), within the framework of the "Carbon dioxide uptake potential of sphagnum peatlands in the context of atmospheric optical parameters and climate changes" (KUSCO2) project.
Furthermore, in two empirical applications using annual data from the M3-competition dataset  (Makridakis and Hibon, 2000)  and quarterly credit-to-GDP data published by the Bank for International Settlements (BIS), we provide further evidence that the damped trend model in the MSOE form competes well with its SSOE counterpart, confirming the Monte Carlo results. These findings are remarkable since it has been shown that the MSOE approach is more restrictive than the SSOE approach (see Chapter 13 in  Hyndman et al. , 2008). This conceivably could have a negative impact on the MSOE's predictive performance. However, our empirical results provide evidence that both MSOE and SSOE approaches have a comparable forecasting performance. Therefore, our findings might have far-reaching implications given that the damped trend model in SSOE form is considered a benchmark model difficult to beat in terms of predictive accuracy  (Gardner and McKenzie, 2011).
227-230). The rest of this work is organized as follows. Section 2 introduces the damped trend model in MSOE form and presents the new estimation method. Section 3 explains how these new results can also be used in order to simplify the estimation of the smoothed state vector, conditional on the information available in the whole sample. Section 4 discusses the results of a Monte Carlo experiment comparing the forecasting performance of the damped trend model estimated, respectively, within the MSOE and SSOE frameworks. Section 5 presents two empirical applications using real data aimed at testing the forecasting ability of the damped trend model in MSOE form in comparison with the damped trend model in SSOE form: the first application deals with the annual time series from the M3-competition  (Makridakis and Hibon, 2000) , while the second one focuses on quarterly credit-to-GDP series as published in the BIS database of total credit to the non-financial sector.
Annual M3-competition data In this section, we present the results of an empirical application undertaken on the 645 yearly time series from the M3-competition  (Makridakis and Hibon, 2000). The aim of this exercise is to compare, working with a real dataset, the out-of-sample forecasting performance of the damped trend model in MSOE form with that of the damped trend model in SSOE form. The focus is on yearly data given that both damped trend models, either in MSOE or in SSOE form, are not meant to capture any seasonality in the data. 7 he yearly dataset includes several types of time series data (micro, industry, macro, finance, demographic, other). The series have different length. We refer to  Makridakis and Hibon (2000)  for further details and explanation on the M3-Competition database.
Quarterly BIS credit data In this section, we focus on a different dataset of quarterly data, which includes time series on total credit (i. e. , loans and debt securities) to the non-financial sector relative to GDP (credit-to-GDP ratio). 8 On the lending side, total credit comprises financing from all sources, including banks and other financial corporations. On the borrowing side, total credit to the non-financial sector is broken down into credit to the government sector and to the private non-financial sector (i. e. , non-financial corporations and households). 9 The database is published by the Bank for International Settlements. It covers almost 50 economies with, on average, more than 45 years of data, reaching back to the 1940s and the 1950s in some cases. 10   Figure  1  shows private credit-to-GDP ratios for the United States, the United Kingdom, Japan and the Euro area.
This is an important aspect given that the damped trend is not suitable to model seasonal time series. 9 Further details are available at the webpage https://www. bis. org/statistics/about_ credit_stats. htm 10 "Long series on credit to the non-financial sector" database, accessed on October 31, 2019. The series considered refer to the credit to non-financial sector (adjusted for breaks), from all sectors, at market value, expressed as a percentage of GDP. The following economies/countries are included: All reporting countries (aggregate), emerging markets (aggregate), advanced economies (aggregate), Argentina, Austria, Australia, Belgium, Brazil, Canada, Switzerland, Chile, China, Colombia, Czech Republic, Germany, Denmark, Spain, Finland, France, G20 (aggregate), United Kingdom, Greece, Hong Kong SAR, Hungary, Indonesia, Ireland, Israel, India, Italy, Japan, Korea, Luxembourg, Mexico, Malaysia, Netherlands, Norway, New Zealand, Poland, Portugal, Russia, Saudi Arabia, Sweden, Singapore, Thailand, Turkey, United States, Euro area, South Africa.