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Objective: Calcium pyrophosphate deposition (CPPD) disease is prevalent and has diverse presentations, but there are no validated classification criteria for this symptomatic arthritis. The American College of Rheumatology (ACR) and EULAR have developed the first-ever validated classification criteria for symptomatic CPPD disease. Methods: Supported by the ACR and EULAR, a multinational group of investigators followed established methodology to develop these disease classification criteria. The group generated lists of candidate items and refined their definitions, collected de-identified patient profiles, evaluated strengths of associations between candidate items and CPPD disease, developed a classification criteria framework, and used multi-criterion decision analysis to define criteria weights and a classification threshold score. The criteria were validated in an independent cohort. Results: Among patients with joint pain, swelling, or tenderness (entry criterion) whose symptoms are not fully explained by an alternative disease (exclusion criterion), the presence of crowned dens syndrome or calcium pyrophosphate crystals in synovial fluid are sufficient to classify a patient as having CPPD disease. In the absence of these findings, a score >56 points using weighted criteria, comprising clinical features, associated metabolic disorders, and results of laboratory and imaging investigations, can be used to classify as CPPD disease. These criteria had a sensitivity of 92.2% and specificity of 87.9% in the derivation cohort (190 CPPD cases, 148 mimickers), whereas sensitivity was 99.2% and specificity was 92.5% in the validation cohort (251 CPPD cases, 162 mimickers). Conclusion: The 2023 ACR/EULAR CPPD disease classification criteria have excellent performance characteristics and will facilitate research in this field.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven, DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015., Peer Reviewed, Postprint (author's final draft)

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change., D.M.S., A.A.S., N.J.D., L.H. and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). L.P.C. was supported by the Spanish MINECO HIATUS (CGL2015-70353-R) project. F.J.D.R. was supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) projects. W.A. M. and H.P. were supported by the German Ministry of Education and Research (BMBF) under the project MiKlip (grant 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US National Science Foundation (NSF) Collaborative Research EaSM2 Grant OCE-1243015. The NCAR contribution is also based upon work supported by NCAR, which is a major facility sponsored by the US NSF under Cooperative Agreement No. 1852977. The Community Earth System Model Decadal Prediction Large Ensemble (CESM-DPLE) was generated using computational resources provided by the US National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne (https://doi.org/10.5065/D6RX99HX) that was awarded by NCAR’s Computational and Information System Laboratory., Peer Reviewed, Postprint (published version)

The Paris Agreement calls for efforts to limit anthropogenic global warming to less than 1.5 °C above preindustrial levels. However, natural internal variability may exacerbate anthropogenic warming to produce temporary excursions above 1.5 °C. Such excursions would not necessarily exceed the Paris Agreement, but would provide a warning that the threshold is being approached. Here we develop a new capability to predict the probability that global temperature will exceed 1.5 °C above preindustrial levels in the coming 5 years. For the period 2017 to 2021 we predict a 38% and 10% chance, respectively, of monthly or yearly temperatures exceeding 1.5 °C, with virtually no chance of the 5‐year mean being above the threshold. Our forecasts will be updated annually to provide policy makers with advanced warning of the evolving probability and duration of future warming events., D.M.S., A.A.S., N.J.D., L.H., and R.E. were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). R.B., L.P.C., F.J.D.R., and M. M. were supported by the H2020 EUCP (GA 776613) and the Spanish MINECO CLINSA (CGL2017-85791-R) and HIATUS (CGL2015-70353-R) projects. L.P.C.’s contract is cofinanced by the MINECO under Juan de la Cierva Incorporación postdoctoral fellowship number IJCI-2015-23367. W.A.M. and H.P. acknowledge funding from the German Federal Ministry for Education and Research (BMBF) project MiKlip (FKZ 01LP1519A). The NCAR contribution was supported by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program grant NA13OAR4310138, by the US National Science Foundation (NSF) Collaborative Research EaSM2 grant OCE-1243015, by the Regional and Global Climate Modeling Program (RGCM) of the US Department of Energy’s, Office of Science (BER), Cooperative Agreement DE-FC02 97ER62402, and by the NSF through its sponsorship of NCAR. The NCAR simulations were generated using computational resources provided by the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC02-05CH11231, as well as by an Accelerated Scientific Discovery grant for Cheyenne that was awarded by NCAR’s Computational and Information Systems Laboratory. The EC-EARTH simulations by SMHI were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at NSC. Data used to create the figures are available at 10.5281/zenodo.1434700., Peer Reviewed, Postprint (published version)

This chapter assesses the scientific literature describing expectations for near-term climate (present through mid-century). Unless otherwise stated, "near-term" change and the projected changes below are for the period 2016-2035 relative to the reference period 1986-2005. Atmospheric composition (apart from CO2; see Chapter 12) and air quality projections through to 2100 are also assessed.