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Recent studies have shown that potential predictability and actual forecast skill have varied throughout the historical record, primarily due to natural decadal variability. In this study, we explore whether and how potential predictability is projected to change in the future as a distinct response to anthropogenic climate change. We estimate the potential predictability of El Niño–Southern Oscillation (ENSO) as well as global surface temperature, precipitation, and upper-atmospheric circulation anomalies from 1921 to 2100, within a perfect model framework, using five coupled model large ensembles. We find that historical and projected ENSO amplitude changes generate global-scale shifts in climate predictability via ENSO-driven changes in the signal-to-noise ratio of seasonal forecasts, with a 10% change in Niño-3.4 standard deviation leading to a 14% change in globally averaged forecast skill at 12-month lead. This relationship suggests that potential predictability changes across much of the globe in the coming decades could be linked to anthropogenic climate change of ENSO. However, since current models substantially disagree on the sign and intensity of projected ENSO change, the trajectory of future global predictability changes cannot yet be determined. This problem is demonstrated by widely varying predictability changes seen across the five large ensembles, with models exhibiting a robust increase, robust decrease, or no significant change in predictability, depending upon their respective projected ENSO amplitude trends. Our results highlight the need for climate model development aimed at better capturing past forced and unforced changes to ENSO variability, which is necessary (if not sufficient) to constrain projected changes to climate predictability worldwide. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 placentitis represents a readily recognizable pattern of placental injury occurring in some pregnant women who develop COVID-19. (1) Pregnant women are at increased risk of severe COVID-19-associated illness compared [...], Context.--A severe third wave of COVID-19 disease affected Ireland in the first 3 months of 2021. In this wave, 1 second-trimester miscarriage and 6 stillbirths were observed in the Irish population because of placental insufficiency as a result of SARS-CoV-2 placentitis. This observation was at odds with the country's previous experience with COVID-19 disease in pregnant mothers. Objective.--To describe the clinical and pathologic features of these pregnancy losses. Design.--Retrospective review of clinical and pathologic data of cases of second-trimester miscarriage, stillbirth, or neonatal death identified by perinatal pathologists as being due to SARS-CoV-2 placentitis during the third wave of COVID-19 in Ireland. Results.--Clinical and pathologic data were available for review in 6 pregnancies. Sequencing or genotyping of the virus identified SARS-CoV-2 alpha (B.1.1.7) in all cases. Three of the 6 cases had maternal thrombocytopenia, and fetal growth restriction was not prominent, suggesting a rapidly progressive placental disease. Conclusions.--The identification of SARS-CoV-2 alpha in all these cases suggests that the emergence of the variant was associated with an increased risk of fetal death due to SARS-CoV-2 placentitis when compared with the original virus. Maternal thrombocytopenia may have potential as a clinical marker of placentitis, but other inflammatory markers need investigation. Three of the 6 women had been assessed for reduced fetal movements in hospital some days before the fetal deaths actually occurred; this could suggest that there may be a window for intervention in some cases. (Arch Pathol Lab Med. 2022;146:529-537; doi: 10.5858/ arpa.2021-0586-SA)

Under the Paris Agreement, signatory nations aim to keep global warming well below 2 °C above pre-industrial levels and preferably below 1.5 °C. This implicitly requires achieving net-zero or net-negative greenhouse gas emissions to ensure long-term global temperature stabilisation or reduction. Despite this requirement, there have been few analyses of stabilised climates, and there is a lack of model experiments to address our need for understanding the implications of the Paris Agreement. Here, we describe a new set of experiments using the Australian Community Climate and Earth System Simulator Earth system model (ACCESS-ESM-1.5) that enables the analysis of climate evolution under net-zero emissions, and we present initial results. Seven 1000-year-long simulations were run with global temperatures stabilising at levels in line with the Paris Agreement and at a range of higher global warming levels (GWLs). We provide an overview of the experimental design and use these simulations to demonstrate the consequences of delayed attainment of global net-zero carbon dioxide emissions. We show that there are substantial differences between transient and stabilising climate states and differences in stabilisation between GWLs. As the climate stabilises under net-zero emissions, we identify significant and robust changes in temperature and precipitation patterns including continued Southern Ocean warming and changes in regional precipitation trends. Changes under net-zero emissions differ greatly between regions, including contrasting trajectories of sea ice extent between the Arctic and Antarctic. We also examine the El Niño–Southern Oscillation (ENSO) and find evidence of reduced amplitude and frequency of ENSO events under climate stabilisation relative to projections under transient warming. An analysis at specific GWLs shows that significant regional changes continue for centuries after emission cessation and that these changes are stronger at higher GWLs. Our findings suggest substantial long-term climate changes are possible even under net-zero emission pathways. These simulations are available for use in the community and will hopefully motivate further experiments and analyses based on other Earth system models. [ABSTRACT FROM AUTHOR]

Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century. [ABSTRACT FROM AUTHOR]

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of statistical model error. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on empirical orthogonal functions (EOFs), namely signal-to-noise maximizing EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. We use the Max Planck Institute Grand Ensemble (MPI-GE) as a test bed for both methods, as it is a type of initial-condition large ensemble designed for an optimal characterization of the externally forced response. We show that the two methods tested here more efficiently reduce errors than conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterizing their common response to external forcing reduces the random error by almost 60 %, a reduction that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single-realization modeling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern filtering leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root mean squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or only a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model, leading to substantial differences in emulated dynamic sea level compared to unfiltered simulations. [ABSTRACT FROM AUTHOR]

Regional emulation tools based on statistical relationships, such as pattern scaling, provide a computationally inexpensive way of projecting ocean dynamic sea-level change for a broad range of climate change scenarios. Such approaches usually require a careful selection of one or more predictor variables of climate change so that the statistical model is properly optimized. Even when appropriate predictors have been selected, spatiotemporal oscillations driven by internal climate variability can be a large source of model disagreement. Using pattern recognition techniques that exploit spatial covariance information can effectively reduce internal variability in simulations of ocean dynamic sea level, significantly reducing random errors in regional emulation tools. Here, we test two pattern recognition methods based on Empirical Orthogonal Functions (EOF), namely signal-to-noise maximising EOF pattern filtering and low-frequency component analysis, for their ability to reduce errors in pattern scaling of ocean dynamic sea-level change. These two methods are applied to the initial-condition large ensemble MPI-GE, so that internal variability is optimally characterized while avoiding model biases. We show that pattern filtering provides an efficient way of reducing errors compared to other conventional approaches such as a simple ensemble average. For instance, filtering only two realizations by characterising their common response to external forcing reduces the random error by almost 60 %, a reduction level that is only achieved by averaging at least 12 realizations. We further investigate the applicability of both methods to single realization modelling experiments, including four CMIP5 simulations for comparison with previous regional emulation analyses. Pattern scaling leads to a varying degree of error reduction depending on the model and scenario, ranging from more than 20 % to about 70 % reduction in global-mean root-mean-squared error compared with unfiltered simulations. Our results highlight the relevance of pattern recognition methods as a tool to reduce errors in regional emulation tools of ocean dynamic sea-level change, especially when one or a few realizations are available. Removing internal variability prior to tuning regional emulation tools can optimize the performance of the statistical model and simplify the choice of suitable predictors. [ABSTRACT FROM AUTHOR]

Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find non-linear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The non-linear changes in ENSO SST variability found in many models demonstrate the importance of characterising this time-dependent behaviour, as it implies that ENSO impacts may vary dramatically throughout the 21st century. [ABSTRACT FROM AUTHOR]