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California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

Atmospheric rivers (ARs) are narrow corridors of intense water vapor transport, shaping precipitation, floods, and economies. Temporal clustering of ARs tripled losses compared to isolated events, yet the reasons behind this clustering remain unclear. AR orientation further modulates hydrological impacts through terrain interaction. Here we identify unique ARs over the North Pacific and Western U.S. and utilize Cox regression and composite analysis to examine how six major climate modes influence temporal clustering of unique ARs and orientation during extended boreal winter (November to March). Results show that climate modes condition temporal clustering of unique ARs. The Pacific-North American weather pattern strongly modulates the clustering over the Western U.S. from early to late winter. The quasi-biennial oscillation and Pacific decadal oscillation affect late winter clustering, while the Arctic oscillation dominates early winter. Climate modes also strongly influence AR orientation, with ENSO particularly affecting the orientation of temporally clustered ARs. The Pacific-North American weather pattern significantly influences unique atmospheric river temporal clustering in the Western U.S., with quasi-biennial oscillation and Pacific decadal oscillation affecting late winter clustering and Arctic oscillation dominating early winter, according to analysis of six climate modes’ influence on unique atmospheric river clustering.

The Madden–Julian oscillation (MJO) is a unique type of organized tropical convection varying primarily on subseasonal time scales and is recognized as an important source of subseasonal predictability for midlatitude weather phenomena. This study provides observational evidence of MJO impacts on precipitation extreme intensity, frequency, and duration over the western United States. The results suggest a robust increase in precipitation extremes, especially in frequency, relative to climatological conditions over most of the western United States when the MJO is in its western Pacific phases during the extended boreal winter (October–March). Opposite changes are observed when the MJO is located over the Indian Ocean and Maritime Continent. The above MJO influence is characterized by strong seasonality, with the increase in extreme frequency mainly found in late autumn/early winter (OND) over California and a weaker or opposite response found in late winter (JFM). Also, MJO impacts have stronger regional consistency and persist for a longer time in OND compared to JFM. The seasonality of MJO impacts largely originates from the different amplitudes and patterns of both the MJO and basic states that are weaker and located/retreated more northwestward in OND than in JFM. This leads to different responses in MJO teleconnections including moisture transport and AR activity that contribute to the different precipitation extreme changes. The strong seasonality of the relationship between the MJO and western U.S. extreme precipitation shown in this study has implications to the source of subseasonal-to-seasonal predictions, which has a potential value to stakeholders including water resource managers.

An approach for downscaling daily precipitation extremes using historical analogs is applied to simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The method employs a multistep procedure in which the occurrence of extreme precipitation on a given target day is determined on the basis of the probability of extreme precipitation on that day’s closest historical analogs. If extreme precipitation is expected, daily precipitation observations associated with the historical analogs are used to approximate precipitation amounts on the target day. By applying the analog method to historical simulations, the ability of the CMIP5 models to simulate synoptic weather patterns associated with extreme precipitation is assessed. Differences between downscaled and observed precipitation extremes are investigated by comparing the precipitation frequency distributions for a subset of rarely selected extreme analog days with those for all observed days with extreme precipitation. A supplemental composite analysis of the synoptic weather patterns on these rarely selected analog days is utilized to elucidate the meteorological factors that contribute to such discrepancies. Overall, the analog method as applied to CMIP5 simulations yields realistic estimates of historical precipitation extremes, with return-period precipitation biases that are comparable in magnitude to those obtained from dynamically downscaled simulations. The analysis of rarely selected analog days reveals that precipitation amounts on these days are generally larger than precipitation amounts on all days with extreme precipitation, leading to an underestimation of return-period precipitation amounts at many stations. Furthermore, the synoptic composite analysis reveals that tropical cyclones are a common feature on these rarely selected analog days.

Ice storms are among the most hazardous, disruptive, and costly meteorological phenomena in the northeastern United States. The accretion of freezing rain during ice storms endangers human safety, compromises public infrastructure, and causes economic losses on local and regional scales. Furthermore, ice storms present a major operational forecast challenge due to the combined influence of synoptic, mesoscale, and microphysical processes on precipitation type. In consideration of these socioeconomic impacts and forecast issues, we have identified three primary objectives for this thesis: 1) create long-term climatologies of freezing rain and ice storms in the northeastern U.S., 2) identify antecedent environments conducive to ice storms and dynamical mechanisms responsible for freezing rain, and 3) increase situational awareness of the synoptic and mesoscale processes that govern the evolution of ice storms. The climatology portion of this thesis examines the temporal and spatial variability of freezing rain and ice storms during the 1975–2010 and 1993–2010 periods, respectively. Individual ice storms are also partitioned by five characteristic synoptic-scale weather patterns. Synoptic composite maps for the two most common event types (Type G and Type BC) illustrate how large-scale circulation patterns and associated quasi-geostrophic (QG) forcing, thermal boundaries, and moisture transport establish environments favorable for freezing rain. Composite cross sections also offer critical insights into synoptic–mesoscale linkages that influence the duration and intensity of freezing rain. The composite analysis for both event types suggests that ice storms occur in association with QG ascent and low-level frontogenetical forcing beneath the equatorward entrance region of an upper-level jet. Moreover, low- to midlevel warm advection and moisture transport, in conjunction with near-surface ageostrophic cold advection on the poleward side of a surface warm front, helps prolong freezing rain events by maintaining a thermodynamic profile conducive to freezing rain. Case studies of the 3–4 Jan 1999 (Type G) and 11–12 Dec 2008 (Type BC) ice storms investigate the synoptic evolution of each event and describe the physical mechanisms that prolonged the duration of freezing rain and resulted in significant ice accretions. These case studies not only reinforce the results of the composite analyses, but also demonstrate how mesoscale processes such as terrain–flow interactions and frontogenesis modify synoptic-scale circulations and thermodynamic environments on regional and local scales. Based on our findings from the composite and case study analyses, we propose conceptual models illustrating the key synoptic ingredients and dynamical processes associated with Type G and Type BC ice storms.

Before 2020, mental disorders were leading causes of the global health-related burden, with depressive and anxiety disorders being leading contributors to this burden. The emergence of the COVID-19 pandemic has created an environment where many determinants of poor mental health are exacerbated. The need for up-to-date information on the mental health impacts of COVID-19 in a way that informs health system responses is imperative. In this study, we aimed to quantify the impact of the COVID-19 pandemic on the prevalence and burden of major depressive disorder and anxiety disorders globally in 2020. We conducted a systematic review of data reporting the prevalence of major depressive disorder and anxiety disorders during the COVID-19 pandemic and published between Jan 1, 2020, and Jan 29, 2021. We searched PubMed, Google Scholar, preprint servers, grey literature sources, and consulted experts. Eligible studies reported prevalence of depressive or anxiety disorders that were representative of the general population during the COVID-19 pandemic and had a pre-pandemic baseline. We used the assembled data in a meta-regression to estimate change in the prevalence of major depressive disorder and anxiety disorders between pre-pandemic and mid-pandemic (using periods as defined by each study) via COVID-19 impact indicators (human mobility, daily SARS-CoV-2 infection rate, and daily excess mortality rate). We then used this model to estimate the change from pre-pandemic prevalence (estimated using Disease Modelling Meta-Regression version 2.1 [known as DisMod-MR 2.1]) by age, sex, and location. We used final prevalence estimates and disability weights to estimate years lived with disability and disability-adjusted life-years (DALYs) for major depressive disorder and anxiety disorders. We identified 5683 unique data sources, of which 48 met inclusion criteria (46 studies met criteria for major depressive disorder and 27 for anxiety disorders). Two COVID-19 impact indicators, specifically daily SARS-CoV-2 infection rates and reductions in human mobility, were associated with increased prevalence of major depressive disorder (regression coefficient [B] 0·9 [95% uncertainty interval 0·1 to 1·8; p=0·029] for human mobility, 18·1 [7·9 to 28·3; p=0·0005] for daily SARS-CoV-2 infection) and anxiety disorders (0·9 [0·1 to 1·7; p=0·022] and 13·8 [10·7 to 17·0; p<0·0001]. Females were affected more by the pandemic than males (B 0·1 [0·1 to 0·2; p=0·0001] for major depressive disorder, 0·1 [0·1 to 0·2; p=0·0001] for anxiety disorders) and younger age groups were more affected than older age groups (−0·007 [–0·009 to −0·006; p=0·0001] for major depressive disorder, −0·003 [–0·005 to −0·002; p=0·0001] for anxiety disorders). We estimated that the locations hit hardest by the pandemic in 2020, as measured with decreased human mobility and daily SARS-CoV-2 infection rate, had the greatest increases in prevalence of major depressive disorder and anxiety disorders. We estimated an additional 53·2 million (44·8 to 62·9) cases of major depressive disorder globally (an increase of 27·6% [25·1 to 30·3]) due to the COVID-19 pandemic, such that the total prevalence was 3152·9 cases (2722·5 to 3654·5) per 100 000 population. We also estimated an additional 76·2 million (64·3 to 90·6) cases of anxiety disorders globally (an increase of 25·6% [23·2 to 28·0]), such that the total prevalence was 4802·4 cases (4108·2 to 5588·6) per 100 000 population. Altogether, major depressive disorder caused 49·4 million (33·6 to 68·7) DALYs and anxiety disorders caused 44·5 million (30·2 to 62·5) DALYs globally in 2020. This pandemic has created an increased urgency to strengthen mental health systems in most countries. Mitigation strategies could incorporate ways to promote mental wellbeing and target determinants of poor mental health and interventions to treat those with a mental disorder. Taking no action to address the burden of major depressive disorder and anxiety disorders should not be an option. Queensland Health, National Health and Medical Research Council, and the Bill and Melinda Gates Foundation.

Jessica Okosun, David Sabatini and colleagues identify recurrent RRAGC mutations in follicular lymphoma, resulting in activated mTORC1 signaling. The activating nature of the mutations, their existence within the dominant clone and their stability during disease progression support the potential of these mutations as promising candidates for targeted therapy. Follicular lymphoma is an incurable B cell malignancy 1 characterized by the t(14;18) translocation and mutations affecting the epigenome 2 , 3 . Although frequent gene mutations in key signaling pathways, including JAK-STAT, NOTCH and NF-κB, have also been defined 2 , 3 , 4 , 5 , 6 , 7 , the spectrum of these mutations typically overlaps with that in the closely related diffuse large B cell lymphoma (DLBCL) 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 . Using a combination of discovery exome and extended targeted sequencing, we identified recurrent somatic mutations in RRAGC uniquely enriched in patients with follicular lymphoma (17%). More than half of the mutations preferentially co-occurred with mutations in ATP6V1B2 and ATP6AP1 , which encode components of the vacuolar H + -ATP ATPase (V-ATPase) known to be necessary for amino acid−induced activation of mTORC1. The RagC variants increased raptor binding while rendering mTORC1 signaling resistant to amino acid deprivation. The activating nature of the RRAGC mutations, their existence in the dominant clone and their stability during disease progression support their potential as an excellent candidate for therapeutic targeting.

TGF-β/Activin induces epithelial-to-mesenchymal transition and stemness in pancreatic ductal adenocarcinoma (PDAC). However, the microRNAs (miRNAs) regulated during this response have remained yet undetermined. Here, we show that TGF-β transcriptionally induces MIR100HG lncRNA, containing miR-100, miR-125b and let-7a in its intron, via SMAD2/3. Interestingly, we find that although the pro-tumourigenic miR-100 and miR-125b accordingly increase, the amount of anti-tumourigenic let-7a is unchanged, as TGF-β also induces LIN28B inhibiting its maturation. Notably, we demonstrate that inactivation of miR-125b or miR-100 affects the TGF-β-mediated response indicating that these miRNAs are important TGF-β effectors. We integrate AGO2-RIP-seq with RNA-seq to identify the global regulation exerted by these miRNAs in PDAC cells. Transcripts targeted by miR-125b and miR-100 significantly overlap and mainly inhibit p53 and cell–cell junctions’ pathways. Together, we uncover that TGF-β induces an lncRNA, whose encoded miRNAs, miR-100, let-7a and miR-125b play opposing roles in controlling PDAC tumourigenesis. In pancreatic ductal adenocarcinoma, TGF-β/Activin induce epithelial-to-mesenchymal transition (EMT) and stemness. Here, the authors show that TGF-β induces pro-tumourigenic miR-100 and miR-125b, but blocks anti-tumourigenic let-7a maturation via LIN28B, regulating pathways to promote stemness, EMT and tumourigenesis.

National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% [4·0–7·9] of variation), GDP per capita (4·2% [1·8–6·6] of variation), and the proportion of infections attributable to seasonality (2·1% [95% uncertainty interval 1·7–2·7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% [18·4–67·6] of variation), GDP per capita (3·1% [0·3–8·6] of variation), and national mean BMI (1·1% [0·2–2·6] of variation). 44·4% (29·2–61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7–17·8) for government trust and 40·3% (24·3–51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%. Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario. Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

The JWST observations of high-redshift galaxies are used to measure their star formation histories—the buildup of stellar mass in the earliest galaxies. Here we use a novel analysis program, SEDz*, to compare near-IR spectral energy distributions for galaxies with redshifts 5 < z < 7 to combinations of stellar population templates evolved from z = 12. We exploit NIRCam imaging in seven wide bands covering 1–5 μm taken in the context of the GLASS-JWST-ERS program and use SEDz* to solve for well-constrained star formation histories for 24 exemplary galaxies. In this first look, we find a variety of histories, from long, continuous star formation over 5 < z < 12 to short but intense starbursts, sometimes repeating, and, most commonly, contiguous mass buildup lasting ∼0.5 Myr, possibly the seeds of today’s typical M* galaxies.