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Canonical understanding based on general circulation models (GCMs) is that the atmospheric circulation response to midlatitude sea‐surface temperature (SST) anomalies is weak compared to the larger influence of tropical SST anomalies. However, the ∼100‐km horizontal resolution of modern GCMs is too coarse to resolve strong updrafts within weather fronts, which could provide a pathway for surface anomalies to be communicated aloft. Here, we investigate the large‐scale atmospheric circulation response to idealized Gulf Stream SST anomalies in Community Atmosphere Model (CAM6) simulations with 14‐km regional grid refinement over the North Atlantic, and compare it to the responses in simulations with 28‐km regional refinement and uniform 111‐km resolution. The highest resolution simulations show a large positive response of the wintertime North Atlantic Oscillation (NAO) to positive SST anomalies in the Gulf Stream, a 0.4‐standard‐deviation anomaly in the seasonal‐mean NAO for 2°C SST anomalies. The lower‐resolution simulations show a weaker response with a different spatial structure. The enhanced large‐scale circulation response results from an increase in resolved vertical motions with resolution and an associated increase in the influence of SST anomalies on transient‐eddy heat and momentum fluxes in the free troposphere. In response to positive SST anomalies, these processes lead to a stronger and less variable North Atlantic jet, as is characteristic of positive NAO anomalies. Our results suggest that the atmosphere responds differently to midlatitude SST anomalies in higher‐resolution models and that regional refinement in key regions offers a potential pathway to improve multi‐year regional climate predictions based on midlatitude SSTs. Plain Language Summary: Variations in the ocean surface temperature (SST) influence the atmospheric circulation and thus climate over land. Canonical understanding is that tropical SSTs are more important than SSTs in midlatitudes. However, this understanding is based on climate models that don't resolve processes at scales less than 100 km. Here, we show that by increasing the atmospheric model resolution to resolve features on smaller scales, such as weather fronts, we find a larger atmospheric circulation response to midlatitude SST anomalies in the North Atlantic. North Atlantic SST anomalies can be predicted multiple years in advance, and a larger atmospheric circulation response to these predictable SST anomalies therefore implies increased predictability of climate over the surrounding land regions. Key Points: There is a large circulation response to idealized Gulf Stream sea‐surface temperature (SST) anomalies in an atmospheric model with 14‐km regional grid refinementThis response is weaker or absent in simulations with 28‐km or coarser resolution, which do not fully resolve mesoscale frontal processesTransient‐eddy fluxes of heat and momentum are modified as fronts pass over warm SSTs, leading to a large‐scale circulation response [ABSTRACT FROM AUTHOR]

BackgroundInfluenza causes substantial morbidity and mortality despite available treatments. Anecdotal reports suggest that plasma with high antibody titres to influenza might be of benefit in the treatment of severe influenza.MethodsIn this randomised, open-label, multicentre, phase 2 trial, 29 academic medical centres in the USA assessed the safety and efficacy of anti-influenza plasma with haemagglutination inhibition antibody titres of 1:80 or more to the infecting strain. Hospitalised children and adults (including pregnant women) with severe influenza A or B (defined as the presence of hypoxia or tachypnoea) were randomly assigned to receive either two units (or paediatric equivalent) of anti-influenza plasma plus standard care, versus standard care alone, and were followed up for 28 days. The primary endpoint was time to normalisation of patients' respiratory status (respiratory rate of ≤20 breaths per min for adults or age-defined thresholds of 20-38 breaths per min for children) and a room air oxygen saturation of 93% or more. This study is registered with ClinicalTrials.gov, number NCT01052480.FindingsBetween Jan 13, 2011, and March 2, 2015, 113 participants were screened for eligibility and 98 were randomly assigned from 20 out of 29 participating sites. Of the participants with confirmed influenza (by PCR), 28 (67%) of 42 in the plasma plus standard care group normalised their respiratory status by day 28 compared with 24 (53%) of 45 participants on standard care alone (p=0·069). The hazard ratio (HR) comparing plasma plus standard care with standard care alone was 1·71 (95% CI 0·96-3·06). Six participants died, one (2%) from the plasma plus standard care group and five (10%) from the standard care group (HR 0·19 [95% CI 0·02-1·65], p=0·093). Participants in the plasma plus standard care group had non-significant reductions in days in hospital (median 6 days [IQR 4-16] vs 11 days [5-25], p=0·13) and days on mechanical ventilation (median 0 days [IQR 0-6] vs 3 days [0-14], p=0·14). Fewer plasma plus standard care participants had serious adverse events compared with standard care alone recipients (nine [20%] of 46 vs 20 [38%] of 52, p=0·041), the most frequent of which were acute respiratory distress syndrome (one [2%] vs two [4%] patients) and stroke (one [2%] vs two [4%] patients).InterpretationAlthough there was no significant effect of plasma treatment on the primary endpoint, the treatment seemed safe and well tolerated. A phase 3 randomised trial is now underway to further assess this intervention.FundingNational Institute of Allergy and Infectious Diseases, US National Institutes of Health.

Accelerated progress in climate modeling is urgently needed for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet climatically important, such as turbulence and cloud formation. These processes will not be explicitly resolvable for the foreseeable future, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary artificial intelligence (AI)-based methods to model subgrid-scale processes. This strategy employs AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. In addition, increasing the resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distribution. However, currently feasible horizontal resolutions are limited to O(10km) because higher resolutions would impede the creation of the ensembles that are needed for model calibration and uncertainty quantification, for sampling atmospheric and oceanic internal variability, and for broadly exploring and quantifying climate risks. By synergizing decades of scientific development with advanced AI techniques, our approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. [ABSTRACT FROM AUTHOR]

Antarctic sea ice has exhibited significant variability over the satellite record, including a period of prolonged and gradual expansion, as well as a period of sudden decline. A number of mechanisms have been proposed to explain this variability, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatiotemporal structure of observed Antarctic sea ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low and accounts for most of the pan-Antarctic sea ice variability and almost all of the sea ice variability in the Weddell Sea. The third mode is also related to periods of abrupt Antarctic sea ice decline that are associated with a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat transport. Broadly, these results suggest that climate model biases in long-term Antarctic sea ice and large-scale sea surface temperature trends are related to each other and that eastern Pacific ENSO variability is a key ingredient for abrupt Antarctic sea ice changes. [ABSTRACT FROM AUTHOR]

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)--key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections. [ABSTRACT FROM AUTHOR]

Accelerating progress in climate modeling is urgent for proactive and effective climate change adaptation. The central challenge lies in accurately representing processes that are small in scale yet are climatically important, such as turbulence and cloud formation. These processes are not explicitly resolvable, necessitating the use of parameterizations. We propose a balanced approach that leverages the strengths of traditional process-based parameterizations and contemporary AI-based data-driven methods to model subgrid-scale processes. This strategy focuses on employing AI to derive data-driven closure functions from both observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing resolution to resolve a larger fraction of small-scale processes can aid progress toward improved and interpretable climate predictions outside the observed climate distributions, but it must still allow the generation of large ensembles for model calibration and the broad exploration of possible climate outcomes – currently O (10 km) horizontal resolutions are feasible. By synergizing decades of scientific development with advanced AI techniques, this approach aims to significantly boost the accuracy, interpretability, and trustworthiness of climate predictions. [ABSTRACT FROM AUTHOR]

We conducted a comprehensive economic analysis of rodent control in swine production facilities. An interdisciplinary working group was assembled to identify all necessary input variables and values associated with rodent damage and control. We incorporated data from production models, scientific literature, product literature, producers and personal experience into an interactive STELLA systems model. The model generates cost-benefit analyses and predicts outcomes of various levels of control of house mice for site-specific swine confinement facilities. We developed a website on rodent control (http://rodent.swine.unl.edu/) to promote use of the model, increase producer awareness of the costs associated with house mouse damage, and provide information on integrated strategies for managing rodents. Although the model is relatively robust and comprehensive, we noted important gaps in research-based information, particularly associated with the economic impacts of rodents in disease transmission, feed contamination, food safety, quality assurance, and human dimensions. We will continue to improve the model and website as new information becomes available.