dataesr/scientific-paragraphs-categorization huggingface dataset:
Section 5. 3 builds on SROCC covering the change in ocean acidification due to oceanic CO2 uptake across the paleo, historical periods and future projections using CMIP6, with consequences for marine life (assessed in sixth assessment report (AR6) working group II (WGII)) and biogeochemical cycles. The section also assesses changes in deoxygenation of the oceans due to warming, increased stratification of the surface ocean and slowing of the meridional overturning circulation. Section 5. 4 covers the future projections of biogeochemical cycles and their feedbacks to the climate system fully utilising the database of the concentration-driven coupled model intercomparison project phase 6 (CMIP6). Since AR5, Earth system models (ESMs) have made progress towards including more complex carbon cycle and associated biogeochemical processes that enable exploring a range of possible future carbon-climate feedbacks and their influences on the climate system.
g. , IPCC Guidelines for National Greenhouse Gas Inventories). Several organisations or groups provide estimates of fossil CO2 emissions, with each dataset having slightly different system boundaries, methods, activity data, and emission factors  (Andrew, 2020). Datasets cover different time periods, which can dictate the datasets and methods that are used for a particular application. The data reported here is from an annually updated data source that combines multiple sources to maximise temporal coverage  (Friedlingstein et al. , 2020). The uncertainty in global fossil CO2 emissions is estimated to be ±5% (1 standard deviation). Fossil CO2 emissions have grown continuously since the beginning of the industrial era (Figure  5. 5) with short intermissions due to global economic crises or social instability  (Peters et al.
1% yr -1 in both the 2000s and 2010s. CO2 emissions from gas use grew at 2. 5% yr -1 in the 2000s and 2. 4% yr -1 in 2010s, but is showing signs of accelerated growth of 3% yr -1 since 2015  (Peters et al. , 2020a). Direct CO2 emissions from carbonates in cement production are around 4% of total fossil CO2 emissions, and grew at 5. 8% yr -1 in the 2000s but a slower 2. 4% yr -1 in the 2010s. The uptake of CO2 in cement infrastructure (carbonation) offsets about one half of the carbonate emissions from current cement production  (Friedlingstein et al. , 2020). These results are robust across the different fossil CO2 emission datasets, despite minor differences in levels and rates as expected given the reported uncertainties  (Andrew, 2020). During 2020, the COVID-19 pandemic led to a rapid, temporary decline in fossil CO2 emissions, estimated to be around 7% based on a synthesis of four estimates.
, 2018). LULUCF fluxes following national GHG inventories or FAO datasets, including recent estimates (Tubiello   , 2021) , are thus excluded from our global assessment, but their comparison against the academic approach is available elsewhere (at the global scale  (Jia et al. , 2019)  and European level  (Petrescu et al. , 2020). Land-use-related component fluxes can be verified by the growing databases of global satellite-based biomass observations in combination with information on remotely-sensed land cover change, but differ from bookkeeping and modelling with Dynamic Global Vegetation Models (DGVMs) in excluding legacy emissions from pre-satellite-era land use change and land management, and neglecting soil carbon changes, and often focus on gross deforestation, not regrowth  (Jia et al.
2). Despite recent progress in separating wetlands from inland waters, there is double-counting in the bottom-up estimates of their emissions  (Thornton et al. , 2016). Although there is evidence that regional human activities and warming both increase inland water CH4 emissions  (Beaulieu et al. , 2019) , the increase in the decadal emissions since AR5  (Ciais et al. , 2013)  rather reflect improvements in the estimate (medium confidence), due to updates in the datasets and new upscaling approaches  (Saunois et al. , 2020). A C C E P T E D V E R S I O N S U B J E C T T O F I N A L E D I T S.
Atmospheric inversions indicate that changes in surface emissions rather than in the atmospheric transport or sink of N2O are the cause for the increased atmospheric growth rate of N2O (robust evidence, high agreement)  (Thompson et al. , 2019). However, the increase of  1. 6 (1. 4-1. 7 ) TgN yr -1 in global emissions between 2000-2005 and 2010-2015 based on atmospheric inversions is somewhat larger than bottom-up estimates over the same period, primarily because of differences in the estimates of land-based emissions. (Tian et al. , 2020)  based on a compilation of inventories, bottom-up 6 models, as well as atmospheric inversions. For detailed information on datasets, see Data Fossil Fuel combustion and Industry 0. 9 (0. 8-1. 1) 0. 9 (0. 9-1. 0) 1,0 (0. 8-1. 0) A C C E P T E D V E R S I O N S U B J E C T T O F I N A L E D I T S 1.
Evaluation of the Contemporary Carbon Cycle in Concentration-Driven Runs Models need to be compared to as wide an array of observational benchmarks as possible in order to have confidence in their projections. This is particularly the case for highly-uncertain land carbon cycle feedbacks  ( Arora et al. , 2013; Friedlingstein et al. , 2014b). Land models within ESMs can be compared to multiple different datasets of processes such as gross carbon uptake, physical predictions such as leaf area and carbon stocks which influence carbon fluxes and are diagnostic of carbon turnover times, as well as linkages between carbon and water cycles and other aspects of the terrestrial carbon cycle. To provide these multiple orthogonal constraints, a model benchmarking system, the international land model benchmarking (ILAMB), has been developed  (Collier et al.
, 2018b)  (Smith et al. , 2018a)  1. 0-2. 2 5 to 95% range; blended global mean SAT and SSTs  (Cowtan and Way, 2014) ; observationally constrained probabilistic setup of simple climate model  (Matthews et al. , 2021)  1. 0-2. 2; median: 1. 5 5 to 95% range; blended global mean SAT and SSTs; human-induced warming  (Haustein et al. , 2017)  based on an average of three full coverage datasets; observationally constrained estimate using the current non-CO2 fraction of total anthropogenic forcing  (Arora et al. , 2020) 1. 3-2. 4; mean: 1. 8; median: 1. 65  Model range; GSAT, diagnosed CO2 emissions in CMIP6 ESMs  (Williams et al. , 2020)  1. 2-2. 1; mean: 1. 6 1-sigma range; GSAT, diagnosed CO2 emissions in 9 CMIP6 ESMs  (Jones and Friedlingstein, 2020)  1. 2-2. 7; median: 1. 8 5 to 95% range; GSAT; Estimate based on decomposition presented in  (Jones and Friedlingstein, 2020)  with ranges of carbon cycle feedback parameters from CMIP6  (Arora et al.
, 2018; Tokarska et al. , 2018)  and explicitly  (Rogelj et al. , 2015a (Rogelj et al. , , 2018b;; Collins et al. , 2018; Matthews et al. , 2021)  by varying the assumptions of non-CO2 emissions and associated warming. Internally consistent evolutions of future CO2 and non-CO2 emissions allow to derive non-CO2 warming contributions consistent with global CO2 emissions reaching net zero levels and therewith capping maximum future CO2 emissions  (Smith and Mizrahi, 2013; Clarke et al. , 2014; Huppmann et al. , 2018; Rogelj et al. , 2018b; Matthews et al. , 2021). Pathways that reflect such development typically show a stabilisation or decline in non-CO2 radiative forcing and warming at and after the time of global CO2 emissions reaching net zero levels, as illustrated in the scenario database underlying the IPCC SR1.
, 2011a; Rogelj et al. , 2016; Friedlingstein et al. , 2014; Matthews et al. , 2017). Solutions to these two limitations have been published since AR5 and represent the second and third methodological improvement compared to AR5. The reliance on an ensemble of opportunity (i. e. a serendipitous collection of scenario data from a variety of sources and studies) is avoided by methodologically separating the assessment of future warming contributions of non-CO2 emissions from the spread in TCRE  (Rogelj et al. , 2018b (Rogelj et al. , , 2019) )  (see Section 5. 5. 2). This facilitates the explicit representation of TCRE uncertainty by a formal distribution, in this case a normal distribution with a 1. 0-2. 3°C PgC -1 1-sigma range (Section 5. 5. 1. 4). The effect of this methodological advance can be estimated from a direct comparison of the frequency distribution of TCRE in CMIP5 models that were used in AR5 and the formal TCRE distribution used in AR6, but is limited in precision.
For estimates of the remaining carbon budget in line with limiting warming to 1. 5°C or 2°C relative to pre-industrial levels, this improvement is estimated to lead to a reduction of budgets of the order of about 100 GtCO2 between AR5 and AR6. The third methodological improvement is a more direct estimation of the warming contribution of non-CO2 emissions, consistent with pathways that bring global CO2 emissions down to net zero. Instead of deriving this contribution implicitly from the CMIP5 ensemble, climate emulators  (Meinshausen et al. , 2011c; Schwarber et al. , 2018; Smith et al. , 2018b)  that are calibrated to the combined AR6 assessment  are used to estimate the non-CO2 contribution across a wide variety of stringent mitigation scenarios  (Huppmann et al. , 2018). The specific relative effect of this advance compared to AR5 is not calculable because CMIP5 data does not isolate non-CO2 from CO2-induced warming.
These results corroborate the high confidence placed by WGI AR5 Chapter 6 on the partial compensation of CO2 removal from the atmosphere by CO2 outgassing from the land and ocean. Due to disagreement between models, the magnitude of this outgassing and in the relative contribution of land and ocean fluxes remains low confidence. atmosphere. The 'pulse' removal is applied from a model state in equilibrium with a pre-industrial atmospheric CO2 concentration (CDRMIP experiment CDR-pi-pulse;  Keller et al. , 2018b). Changes in land and ocean carbon reservoirs are calculated relative to a pre-industrial control simulation. Data for the UVic ESCM is from  Zickfeld et al. (2021). Further details on data sources and processing are available in the chapter data table  (Table 5. SM. 6). [END FIGURE 5.
Due to limited agreement between models there is low confidence in the timing of the sink-tosource transition and the magnitude of the CO2 source in scenarios with net-negative CO2 emissions. [START FIGURE  5. 33 HERE] IPSL-CM6a-LR) and one EMIC (UVic ESCM;  (Mengis et al. , 2020) ). Net CO2 emissions are calculated from concentration-driven Earth system model simulations as the residual from the rate of increase in atmospheric CO2 and land and ocean CO2 fluxes. Fluxes are accumulated over each 50-year period and converted to concentration units (ppm). Further details on data sources and processing are available in the chapter data table  (Table 5. SM. 6). [END FIGURE 5. 33 HERE] 5. 6. 2. 1. 3  Removal Effectiveness of CDR It is well understood that land and ocean carbon fluxes are sensitive to the level of atmospheric CO2 and climate change and differ under different future scenarios (Section 5.
, 2013; Shindell et al. , 2013; Stocker et al. , 2013; Zhang et al. , 2017) ; (f) Section 5. 4. 7  (Xu-Ri et al. , 2012; Zaehle, 2013; Stocker et al. , 2013; Tian et al. , 2019) ; (g) Section 5. 4. 7  (Martinez-Rey et al. , 2015; Landolfi et al. , 2017; Battaglia and Joos, 2018). (h) Section 6. 3, Table  6. 9 mean and the 5-95 th percentile range the assessed feedback parameter. Further details on data sources and processing are available in the chapter data table  (Table 5. SM. 6). simulations for the five scenarios of the AR6 core set (SSP1-1. 9, green; SSP1-2. 6, blue; SSP2-4. 5, yellow; SSP3-7. 0, red; SSP5-8. 5, maroon), starting from 2015. Diagnosed carbon emissions  (Arora et al. , 2020)  are complemented with estimated land-use change emissions for each respective scenario  (Gidden et al.
AVHRR data are accessible during 1982-2016 and MODIS data are accessible during 2000-2018. (c) Near-infrared reflectance of vegetation (NIRv) and contiguous solarinduced chlorophyll fluorescence (CSIF). The standardised anomaly of area-weighted NIRv during 2001-2018 (Badgley et al. , 2017 ) and CSIF during 2000-2018 (Zhang et al. , 2018). (d) Gross primary production (GPP). The GPP from Cheng et al. (2017) , DGVMs and MODIS GPP product (MOD17A3). GPP from Cheng et al. (2017) is based on an analytical model driven by climate change, rising atmospheric CO2, AVHRR leaf area index datasets and evapotranspiration datasets. GPP from DGVMs is the ensemble mean global GPP estimated by the same 17 DGVMs that provide the net land CO2 sink estimates. Shaded area indicates 1-σ inter-model spread except for atmospheric inversions, whose ranges were used due to limited number of models.
1 :Figure 1 : 1 : 111 The Arctic permafrost is a big pool of carbon that is sensitive to climate change. (left) Quantity of carbon stored in the permafrost, to 3 m depth (NCSCDv2 dataset) and (right) area of permafrost vulnerable to abrupt thaw (Circumpolar Thermokarst Landscapes dataset). Changes in aspects of climate change in response to a peak and decline in the atmospheric CO2 concentration (top panel). The vertical grey dashed line indicates the time of peak CO2 concentration in all panels. It is shown that the reversal of global surface warming lags the decrease in the atmospheric CO2 concentration by a few years, the reversal of permafrost area decline lags the decrease in atmospheric CO2 by decades, and ocean thermal expansion continuesfor several centuries. Note that the quantitative information in the figure (i.
END FIGURE 5. 10 HERE] 5. 2. 1. 4. 2  Interannual variability in land-atmosphere CO2 exchange AR5 stated that the interannual variability of the atmospheric CO2 growth rate is dominated by tropical land ecosystems. A set of new satellite measurements applied to assess the variability of the tropical land carbon balance since AR5 Final Government Distribution Chapter 5 IPCC AR6 WGI [. (d) Gross primary production (GPP). The GPP from Cheng et al. (2017), DGVMs and MODIS GPP product (MOD17A3). A C C S U B J E C T T O E P T E D F I N A L V E R S I O N E D I T S GPP from Cheng et al. (2017) is based on an analytical model driven by climate change, rising atmospheric CO2, AVHRR leaf area index datasets and evapotranspiration datasets. GPP from DGVMs is the ensemble mean global GPP estimated by the same 17 DGVMs that provide the net land CO2 sink estimates.
pH reconstructions is currently limited by the uncertainty on the calibration of the δ 11 B proxy in different tropical coral species. 5. 3. 2. 2 Observations of Ocean Acidification over the Recent DecadesSROCC (Section 5. 2. 2. 3) indicated it is virtually certain that the ocean has undergone acidification globally in response to ocean CO2 uptake and concluded that pH in open ocean surface water has changed by a virtually certain range of -0. 017 to -0. 027 pH units per decade since the late 1980s. Since SROCC, continued observations of seawater carbonate chemistry at ocean time series stations and compiled shipboard studies providing temporally resolved and methodologically consistent datasets have further strengthened the evidence of the progress of acidification across all regions of the oceans (Jiang et al.
The model reproduces the observed patterns with higher resolution (Fig. 4c ) and, when resampled similarly (Fig. 4b ), shows good spatial agreement with the PREFCLIM climatology (r = 0. 85). The seasonal variations of the NEMO and PREFCLIM heat fluxes are also very well correlated except along the Equator (Fig. 4d ). However, the NEMO model flux is biased low compared to PREFCLIM. It shows a net flux towards the atmosphere along zonal bands around 6 • N and 12 • S. These differences can be explained by the different data sources used for surface heat flux in the PREFCLIM climatology (TropFlux) and as forcing for the NEMO model (DFS5. 2), but also due to the feedback of simulated oceanic conditions on the heat fluxes through bulk formulae in the model. As expected, there are important differences between the maps of offline heat advection, calculated based on the coarsened model currents and hydrography (Fig.
However, the NEMO freshwater flux shows a negative bias along the 6-12 • N band and in the Gulf of Guinea and a high positive bias in the rest of the eastern tropical Atlantic basin. These differences are likely due to the different data sources used for surface freshwater flux in the PREFCLIM climatology (TropFlux for evaporation and GPCP for precipitation) and as forcing for the NEMO model (DFS5. 2). However, the seasonal variations of freshwater flux are generally well correlated between NEMO and PREFCLIM except for a few regions (Fig. 7d ). As expected, there are important differences between the maps of offline salt advection (Fig. 7f ) and online salt advection (Fig. 7g , resampled at 2. 5 • ). In the online version, advection strongly acts to decrease mixed-layer salinity almost everywhere in the eastern tropical Atlantic.
8d ). Poor data density can also be associated with a seasonal bias that may prevent capturing the full seasonal cycle. Second, hydrographic profiles, notably those from Argo floats, do not sample the salinity minimum found in the upper few meters of the ocean in regions highly stratified by rain and river plumes, which induces SSS esti-mations higher than those observed from satellites  (Boutin et al. , 2016; Houndegnonto et al. , 2021). This leads to overestimation of mixed-layer salinity too. Third, while the NEMO model configuration has high vertical resolution in the upper few meters and homogeneous spatial coverage, the way it reproduces mixed-layer salinity highly depends on its freshwater forcing, including river runoff, and its own dynamics that are of course not perfect. Despite these differences between observations and the model, the comparison is instructive.
One problem is that globally available in situ data can only explicitly resolve near-surface horizontal processes, particularly advection, not vertical processes that have to be estimated as a residual. A second problem is that in situ observation density does not allow estimating horizontal advection at the high resolution available from mod-els. Therefore, to be properly compared with those available from observations, model horizontal advection terms must be computed offline at the spatiotemporal resolution of observations. Our results indeed show that the time-averaged spatial distribution of NEMO offline heat and salt advection terms compares much better to PREFCLIM horizontal advection terms than the online heat and salt advection terms. However, when examining the seasonal cycle of horizontal advection in selected boxes, NEMO offline terms do not always compare well with PREFCLIM, sometimes less than online terms.
Acknowledgements. This work is part of the PhD thesis of  Roy Dorgeless Ngakala , funded by the  DAAD  ( Deutscher Akademischer Austauschdientst/German Academic Exchange Service)  in the framework of the " In-Country/In-Region Scholarship Programme " for  Sub-Saharan Africa. The  PREFCLIM climatology was produced in the framework of the European Union FP7 PREFACE  project. This study was supported by the  TRIATLAS  project, which has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  817578. This study is also supported by the  TOSCA SMOS  and  SWOT-GG  projects funded by  CNES. Financial support. This research has been supported by the  H2020 TRIATLAS  project (grant no. 817578 ). Review statement. This paper was edited by  Karen J.
2 Wm-2 in a simulation run without SRM (Fig. 1a, c ). Although a phase-out of CDR at the end would be more likely, this would not fundamentally change the main message of our study. After termination of CDR deployment, fossil-CO2 emissions remain at 0 GtCO2yr-1. Emission values prior to 2000 are from MAGICC's historical database. In our scenarios, SRM initialization is determined by the year where GMT increase exceeds  1. 5°C (2034). The termination criterion of SRM equals that of CDR (total radiative forcing = 2. 2 Wm-2). Perturbation of solar irradiance is used to mimic the effects of SRM on temperature. The unmodified solar irradiance in the MAGICC6 setup consists of observed solar irradiance until 2009 and is held constant from then onwards at 0. 12 Wm-2. All global climate model simulations are conducted with the simple climate emulator MAGICC6, commonly used in several leading IAMs and in the IPCC.
Acknowledgments Funding was provided by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant nos. 820829  ( CONSTRAIN ) and the  German Federal Ministry of Education and Research  ( 01LN1711A ). https://doi. org/10. 5194/esd-2022-17Preprint. Discussion started: 29 April 2022 c Author(s)  2022. CC BY 4. 0 License.
Biodiversity measures For measures of taxonomic diversity, we obtained species richness (rarefied by the minimum rowSums) and Shannon diversity for each sampling point with the 'specnumber' and 'diversity' functions of the R vegan package, respectively  (Oksanen et al. , 2022). We also made log (1 þ x) transformation for bird abundance of each sampling point. For measures of functional diversity (FD), we calculated Functional richness (FRic) and Rao's quadratic entropy (RaoQ) of each sampling point. FRic measures the volume of multidimensional functional space occupied by a community, while RaoQ reflects changes in the abundanceweighted sum of pairwise functional distance between functional entities  (Mouillot et al. , 2013). We determined the functional diversity metrics by combining the relative bird abundance data with eight biological traits: beak width, tarsus length, hand wing index (HWI), mass, range size, migration, habitat type and trophic level, which are obtained from the AVONET database, a compilation of individual-level trait measurements for all the world's bird species  (Tobias et al.
NMDS was run using the first two dimensions and random starting iterations to obtain the lowest stress value. NMDS was complemented by permutational multivariate analysis of variance (PERMANOVA) with 999 permutations to test the significance of observed differences between identified bird associations. The 'metaMDS' and 'adonis 2' functions in the vegan package  (Oksanen et al. , 2022)  of R version 4. 1. 2 (R Core Team, 2021) were applied for NMDS and PERMANOVA, respectively. The relationship between Mongolian Lark occurrence and six biodiversity measures (species richness, Shannon diversity, abundance, sesF-Ric, sesRaoQ and sesPD) at each sampling point were examined using linear models. We built six individual models and used occurrence of Mongolian Lark as covariate and biodiversity measures as response variables, respectively.
Discussion The identification of biodiversity indicator is one of the most important and challenging tasks in conservation ecology, especially in the face of limited data and available resources  (Pearman and Weber, 2007). In the present study, we confirmed the hypothesis that Mongolian Lark can serve as an effective indicator for different components of avian biodiversity in Mongolian steppes. The occurrence of Mongolian Lark was closely associated with several other grassland specialists, and species richness was significantly higher at sites with Mongolian Larks. At the same time, the presence of Mongolian Lark also indicated sites with lower species abundance, functional richness, Rao's quadratic entropy and phylogenetic diversity. To our knowledge, this is the first study that Here we included species with indicator value ! 0.