For base year 2010, anthropogenic activities created ~210 (190 to 230) TgN of reactive nitrogen Nr from N2. This human-caused creation of reactive nitrogen in 2010 is at least 2 times larger than the rate of natural terrestrial creation of ~58 TgN (50 to 100 TgN yr−1) (Table 6.9, Section 1a). Note that the estimate of natural terrestrial biological fixation (58 TgN yr−1) is lower than former estimates (100 TgN yr−1, Galloway et al., 2004), but the ranges overlap, 50 to 100 TgN yr−1 vs. 90 to 120 TgN yr−1, respectively). Of this created reactive nitrogen, NOx and NH3 emissions from anthropogenic sources are about fourfold greater than natural emissions (Table 6.9, Section 1b). A greater portion of the NH3 emissions is deposited to the continents rather than to the oceans, relative to the deposition of NOy, due to the longer atmospheric residence time of the latter. These deposition estimates are lower limits, as they do not include organic nitrogen species. New model and measurement information (Kanakidou et al., 2012) suggests that incomplete inclusion of emissions and atmospheric chemistry of reduced and oxidized organic nitrogen components in current models may lead to systematic underestimates of total global reactive nitrogen deposition by up to 35% (Table 6.9, Section 1c). Discharge of reactive nitrogen to the coastal oceans is ~45 TgN yr−1 (Table 6.9, Section 1d). Denitrification converts Nr back to atmospheric N2. The current estimate for the production of atmospheric N2 is 110 TgN yr−1 (Bouwman et al., 2013).

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Carbon and Other Biogeochemical Cycles

There is large uncertainty about the magnitude of warming and how rainfall patterns will change in response to any given scenario of future changes in atmospheric composition and land use. The models used for future climate projections were developed and calibrated using climate observations from the past 40 years. The geologic record of environmental responses to climate changes provides a unique opportunity to test model performance outside this limited climate range. Evaluation of model simulations against palaeodata shows that models reproduce the direction and large-scale patterns of past changes in climate, but tend to underestimate the magnitude of regional changes. As part of the effort to reduce model-related uncertainty and produce more reliable estimates of twenty-first century climate, the Palaeoclimate Modelling Intercomparison Project is systematically applying palaeoevaluation techniques to simulations of the past run with the models used to make future projections. This evaluation will provide assessments of model performance, including whether a model is sufficiently sensitive to changes in atmospheric composition, as well as providing estimates of the strength of biosphere and other feedbacks that could amplify the model response to these changes and modify the characteristics of climate variability.

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Evaluation of climate models using palaeoclimatic data

Ocean acidification may have severe consequences for marine ecosystems; however, assessing its future impact is difficult because laboratory experiments and field observations are limited by their reduced ecologic complexity and sample period, respectively. In contrast, the geological record contains long-term evidence for a variety of global environmental perturbations, including ocean acidification plus their associated biotic responses. We review events exhibiting evidence for elevated atmospheric CO2, global warming, and ocean acidification over the past ~300 million years of Earth’s history, some with contemporaneous extinction or evolutionary turnover among marine calcifiers. Although similarities exist, no past event perfectly parallels future projections in terms of disrupting the balance of ocean carbonate chemistry—a consequence of the unprecedented rapidity of CO2 release currently taking place.

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The Geological Record of Ocean Acidification

New indices and calibrations derived from the distribution of crenarchaeal isoprenoid tetraether lipids: Implications for past sea surface temperature reconstructions

Subfossil pollen and plant macrofossil data derived from 14 C-dated sediment profiles can provide quantitative information on glacial and interglacial cli- mates. The data allow climate variables related to growing- season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate recon- structions and their uncertainties, obtained using modern- analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on

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Pollen-based continental climate reconstructions at 6 and 21 ka: A global synthesis

Observation-based reconstructions of sea surface temperature from relatively stable periods in the past, such as the Last Glacial Maximum, represent an important means of constraining climate sensitivity and evaluating model simulations1. The first quantitative global reconstruction of sea surface temperatures during the Last Glacial Maximum was developed by the Climate Long-Range Investigation, Mapping and Prediction (CLIMAP) project in the 1970s and 1980s (refs 2, 3). Since that time, several shortcomings of that earlier effort have become apparent4. Here we present an updated synthesis of sea surface temperatures during the Last Glacial Maximum, rigorously defined as the period between 23 and 19 thousand years before present, from the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO) project5. We integrate microfossil and geochemical reconstructions of surface temperatures and include assessments of the reliability of individual records. Our reconstruction reveals the presence of large longitudinal gradients in sea surface temperature in all of the ocean basins, in contrast to the simulations of the Last Glacial Maximum climate available at present6, 7.

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Constraints on the magnitude and patterns of ocean cooling at the Last Glacial Maximum

The OMERACT-EULAR Synovitis Scoring (OESS) system is worldwide used to evaluate arthritis severity on ultrasound (US) images. Because of inter-observer and intra-observer variability, deep learning (DL) has been applied in high-quality image interpretation and analysis. Previous studies mostly focused on Doppler US (DUS) classification by convolutional neural network (CNN), which could provide objective assessment. However, the reports of DL intervention in grey scale (GS) US image automatic measurements are limited.The aim of this study was to develop an integrated multiple CNN model in precise scoring GS US images from rheumatoid arthritis (RA) patients.The standard US images from patients of RA were retrospectively selected by three 10-years US experienced rheumatologist together and were graded according to the OESS system. Six different joints data were taken, including proximal interphalangeal, metacarpophalangeal, wrist, elbow, knee and ankle joints. We conducted the DL model integrating three binary CNNs to predict four-class GS US scoring (Figure 1). The accuracy of the trained model was tested by an independent test data.Total 678 images from 447 patients of RA were used in this study. These images were divided into training (n=611) and testing (n=67) sets. The integrated multiple CNNs model could achieve a four-class accuracy of 77.6%. The individual accuracy of grades 0, 1, 2 and 3 were 68.4%, 77.3%, 73.3% and 100%, respectively (Table 1). Furthermore, we found that adding on anatomic site parameters or labeling areas of interest would establish a better average area under curve (AUC) with 92.6% and 89.0%.Our study suggests the possibility of using the integrated multiple CNNs model in grading synovial hypertrophy of RA, which is critical in RA healthcare. External validation would be required to confirm the predictive ability of this model.[1]D’Agostino MA et al. RMD Open. 2017 Jul 11;3(1):e000428.[2]Andersen JKH et al. RMD Open. 2019 Mar 30;5(1):e000891.[3]Christensen ABH et al. Ann Rheum Dis. 2020 Sep;79(9):1189-1193.[4]Shin Y et al. Ultrasonography. 2021 Jan;40(1):30-44.[5]Zhou Z et al. Patterns (N Y). 2022 Sep 29;3(10):100592.Figure 1.The proposed integrated multiple CNNs model to predict grade 3 synovium hypertrophy in rheumatoid arthritis. CNN: convolutional neural network.Table 1.Prediction results of the integrated model for grey scale ultrasound scoresIntegrated modelAccuracyRheumatologist0123Total(%)0134201968.41417102277.32121111573.330001111100Total182314126777.6NIL.None Declared.

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C.-C. Huang

Papers

Constraints on the magnitude and patterns of ocean cooling at the Last Glacial Maximum

Pos0925 automatic scoring of ultrasound synovial hypertrophy in rheumatoid arthritis through integrating multiple convolutional neural network models