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We evaluate the skill and sources of skill in initialized seasonal climate forecasts of monthly global mean temperature from the North American Multi‐Model Ensemble (NMME) during the period 1991–2024. The forecasts demonstrate skill in addition to that from the long‐term trend, and that skill is primarily attributable to ENSO. However, the skill varies seasonally, with skill being lowest for target periods during Northern Hemisphere summer. Single model ensembles show underdispersion at short leads, while the multi‐model ensemble is overdispersed, suggesting initial condition errors and highlighting the importance of model initialization for quantification of forecast uncertainty. Lead‐time dependent errors in global mean temperature trends appear related to Pacific trend errors. The multi‐model mean captured the overall trend but underestimated the record‐breaking temperatures of 2023. Forecasts for the remainder of 2024 indicate cooling by the end of the year. Plain Language Summary Our study looked at how well current climate forecast models can predict the global average temperature up to a year in advance. We found that these models can predict temperature changes better than just looking at long‐term trends, and that their ability to do so was related to the climate phenomenon called ENSO (El Niño‐Southern Oscillation). However, the accuracy of these predictions depends on the time of year, being less accurate when predicting temperatures during the Northern Hemisphere's summer months. We also found that individual models often have too narrow a range of predictions, while combining multiple models results in a range that is too wide. Despite these deficiencies, the combined model predictions generally followed the observed temperatures but failed to predict the extreme high temperatures of 2023. Looking ahead, the models suggest a cooling trend by the end of 2024. This research improves our understanding of what current climate forecast models can tell us about global mean temperature in the short term and highlights areas where improvement is needed. Key Points Initialized seasonal climate forecasts of global mean temperature show skill beyond the trend and that skill is largely related to ENSO Forecast skill is lowest for target months during northern hemisphere summer due to forecast amplitudes that are too large At short leads single model ensembles are underdispersed and the multi‐model ensemble is overdispersed, which suggests initialization errors

The El Niño of 2015/16 was among the strongest El Niño events observed since 1950 and took place almost two decades after the previous major event in 1997/98. Here, perspectives of the event are shared by scientists from three national meteorological or climate services that issue regular operational updates on the status and prediction of El Niño–Southern Oscillation (ENSO). Public advisories on the unfolding El Niño were issued in the first half of 2015. This was followed by significant growth in sea surface temperature (SST) anomalies, a peak during November 2015–January 2016, subsequent decay, and its demise during May 2016. The life cycle and magnitude of the 2015/16 El Niño was well predicted by most models used by national meteorological services, in contrast to the generally overexuberant model predictions made the previous year. The evolution of multiple atmospheric and oceanic measures demonstrates the rich complexity of ENSO, as a coupled ocean–atmosphere phenomenon with pronounced global impacts. While some aspects of the 2015/16 El Niño rivaled the events of 1982/83 and 1997/98, we show that it also differed in unique and important ways, with implications for the study and evaluation of past and future ENSO events. Unlike previous major El Niños, remarkably above-average SST anomalies occurred in the western and central equatorial Pacific but were milder near the coast of South America. While operational ENSO systems have progressed markedly over the past several decades, the 2015/16 El Niño highlights several challenges that will continue to test both the research and operational forecast communities.

Marine heatwaves (MHWs)—periods of exceptionally warm ocean temperature lasting weeks to years—are now widely recognized for their capacity to disrupt marine ecosystems 1 – 3 . The substantial ecological and socioeconomic impacts of these extreme events present significant challenges to marine resource managers 4 – 7 , who would benefit from forewarning of MHWs to facilitate proactive decision-making 8 – 11 . However, despite extensive research into the physical drivers of MHWs 11 , 12 , there has been no comprehensive global assessment of our ability to predict these events. Here we use a large multimodel ensemble of global climate forecasts 13 , 14 to develop and assess MHW forecasts that cover the world’s oceans with lead times of up to a year. Using 30 years of retrospective forecasts, we show that the onset, intensity and duration of MHWs are often predictable, with skilful forecasts possible from 1 to 12 months in advance depending on region, season and the state of large-scale climate modes, such as the El Niño/Southern Oscillation. We discuss considerations for setting decision thresholds based on the probability that a MHW will occur, empowering stakeholders to take appropriate actions based on their risk profile. These results highlight the potential for operational MHW forecasts, analogous to forecasts of extreme weather phenomena, to promote climate resilience in global marine ecosystems. Climate forecast systems are used to develop and evaluate global predictions of marine heatwaves (MHWs), highlighting the feasibility of predicting MHWs and providing a foundation for operational MHW forecasts to support climate adaptation and resilience.

Background Implementation theory suggests that first-level leaders, sometimes referred to as middle managers, can increase clinicians’ use of evidence-based practice (EBP) in healthcare settings by enacting specific leadership behaviors (i.e., proactive, knowledgeable, supportive, perseverant with regard to implementation) that develop an EBP implementation climate within the organization; however, longitudinal and quasi-experimental studies are needed to test this hypothesis. Methods Using data collected at three waves over a 5-year period from a panel of 30 outpatient children’s mental health clinics employing 496 clinicians, we conducted a quasi-experimental difference-in-differences study to test whether within-organization change in implementation leadership predicted within-organization change in EBP implementation climate, and whether change in EBP implementation climate predicted within-organization change in clinicians’ use of EBP. At each wave, clinicians reported on their first-level leaders’ implementation leadership, their organization’s EBP implementation climate, and their use of both EBP and non-EBP psychotherapy techniques for childhood psychiatric disorders. Hypotheses were tested using econometric two-way fixed effects regression models at the organization level which controlled for all stable organizational characteristics, population trends in the outcomes over time, and time-varying covariates. Results Organizations that improved from low to high levels of implementation leadership experienced significantly greater increases in their level of EBP implementation climate ( d  = .92, p  = .017) and within-organization increases in implementation leadership accounted for 11% of the variance in improvement in EBP implementation climate beyond all other covariates. In turn, organizations that improved from low to high levels of EBP implementation climate experienced significantly greater increases in their clinicians’ average EBP use ( d  = .55, p  = .007) and within-organization improvement in EBP implementation climate accounted for 14% of the variance in increased clinician EBP use. Mediation analyses indicated that improvement in implementation leadership had a significant indirect effect on clinicians’ EBP use via improvement in EBP implementation climate ( d  = .26, 95% CI [.02 to .59]). Conclusions When first-level leaders increase their frequency of implementation leadership behaviors, organizational EBP implementation climate improves, which in turn contributes to increased EBP use by clinicians. Trials are needed to test strategies that target this implementation leadership–EBP implementation climate mechanism.

Forecast skill and potential predictability of 2-m temperature, precipitation rate, and sea surface temperature are assessed using 29 yr of hindcast data from models included in phase 1 of the North American Multimodel Ensemble (NMME) project. Forecast skill is examined using the anomaly correlation (AC); skill of the bias-corrected ensemble means (EMs) of the individual models and of the NMME 7-model EM are verified against the observed value. Forecast skill is also assessed using the root-mean-square error. The models’ representation of the size of forecast anomalies is also studied. Predictability was considered from two angles: homogeneous, where one model is verified against a single member from its own ensemble, and heterogeneous, where a model’s EM is compared to a single member from another model. This study provides insight both into the physical predictability of the three fields and into the NMME and its contributing models. Most of the models in the NMME have fairly realistic spread, as represented by the interannual variability. The NMME 7-model forecast skill, verified against observations, is equal to or higher than the individual models’ forecast ACs. Two-meter temperature (T2m) skill matches the highest single-model skill, while precipitation rate and sea surface temperature NMME EM skill is higher than for any single model. Homogeneous predictability is higher than reported skill in all fields, suggesting there may be room for some improvement in model prediction, although there are many regional and seasonal variations. The estimate of potential predictability is not overly sensitive to the choice of model. In general, models with higher homogeneous predictability show higher forecast skill.

The researcher aimed to identify how rehabilitation professionals engage in their peer leadership network during the first year following leader development training for the purpose of understanding the networking experiences, development of the peer leadership network, and expansion of collective leadership in an organization. A sequential exploratory mixed method design including Q-Methodology and focus group interviews identified the experiences of 11 rehabilitation professionals in an urban rehabilitation hospital during the first year following leader development training. Three themes were identified. These include: (a) an opportunity to connect, (b) a community of leaders, and (c) a healthy peer leadership network emerged from the data analysis. These results indicated that shared experiences and opportunities to connect in a robust peer leadership network can influence the growth of all leaders independent of their current leadership or networking competency. The opportunity to connect for shared discussions in a healthy peer leadership network can accentuate the learning following leader development curriculum as individual leaders develop leadership and as collectives advance organizational outcomes. Healthcare organizations should facilitate connections in a healthy leadership network to develop individual and collective leadership in an organization.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

The number of biologicals for the therapy of immunologically mediated diseases is constantly growing. In contrast to other agents that were previously introduced in rheumatologic or dermatologic diseases and only later adopted for the treatment of inflammatory bowel diseases (IBDs), the field of IBD was ground breaking for the concept of anti-adhesion blockade. Anti-adhesion antibodies selectively target integrins controlling cell homing to the intestine, which leads to reduction of inflammatory infiltration to the gut in chronic intestinal inflammation. Currently, the anti-α4β7-antibody vedolizumab is successfully used for both Crohn's disease and ulcerative colitis worldwide. In this mini-review, we will summarize the fundamental basis of intestinal T cell homing and explain the molecular groundwork underlying current and potential future anti-adhesion therapies. Finally, we will comment on noteworthy clinical aspects of anti-adhesion therapy and give an outlook to the future of anti-integrin antibodies and inhibitors.

El Niño‐Southern Oscillation (ENSO) influences global climate variability, including Atlantic tropical cyclone activity. The Niño‐3.4 index has long been used to characterize ENSO. However, new ENSO indices have been proposed in recent years. Here, in the context of Atlantic tropical cyclone activity, we compared Niño‐3.4 to three modern ENSO indices: the relative Niño‐3.4 index, the ENSO Longitudinal Index (ELI), and a Pacific sea surface temperature zonal gradient index. We examined the association of their August–October values with central Pacific convection, tropical cyclone‐related variables in the Atlantic (e.g., vertical wind shear and potential intensity), and Atlantic tropical cyclone activity. We also assessed the skill of seasonal forecasts of the ENSO indices and the skill of index‐based forecasts of Atlantic tropical cyclone activity. We found that the modern ENSO indices outperform the traditional Niño‐3.4 index in nearly all aspects, with the relative Niño‐3.4 index showing statistically significant advantages in many cases.