Search

This paper evaluates stock return predictability with inflation and output gap, which typically enter the Federal Reserve Bank's interest rate setting rule. We introduce Taylor rule fundamentals into the Fed model that relates stock returns to earnings and long‐term yields. Using real‐time data from 1970 to 2008, we find evidence that the Fed model with Taylor rule fundamentals performs better in‐sample and out‐of‐sample than the constant return and original Fed models. Economic significance tests indicate that the models with Taylor rule fundamentals consistently produce higher utility gains than the benchmark models. Though the performance of the Taylor rule model weakens when we extend the sample to include the post‐2008 period characterized by prolonged zero lower bound episodes, it still outperforms the benchmark models. [ABSTRACT FROM AUTHOR]

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species–environment associations. In this study, we evaluated how two components of temporal environmental variation—variability and predictability—impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long‐Term Ecological Research site, we used in situ, high‐resolution temporal measurements of soil moisture and temperature from 13 locations ("nodes") distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within‐day) and seasonal (2‐ to 4‐month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes. [ABSTRACT FROM AUTHOR]

Understanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food‐web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator–prey interactions, and a generic, modular model is needed to describe all the possible variation in predator–prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food‐web impacts of well‐studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world. [ABSTRACT FROM AUTHOR]

Our paper has two main objectives. First, we aim to investigate the relationship between global economic contraction (GECON) and the return volatility of gold. Second, we examine the role of climate change as a mediator in this connection. To achieve this, we use the GARCH‐MIDAS model, which accommodates data in different frequencies in the same model. This prevents the loss of important information when aggregating high‐frequency data to lower‐frequency data. We also use alternative measures of GECON from Kilian and Zhou (Journal of International Money and Finance, 2018; 88, 54–78) and Baumeister et al. (Review of Economics and Statistics, 2020;104(4), 828–844) to ensure the robustness of our findings. Our results show that global economic contraction positively impacts the return volatility of gold. Additionally, our findings confirm that the increased uncertainty caused by climate change makes gold a safe haven for investors. This means that gold's return volatility is not negatively impacted by the rising level of uncertainty caused by climate‐induced contraction. Moreover, we note that the index of GECON that accommodates more dynamics can produce more accurate predictability for gold market volatility. Our analysis of stock market volatility further confirms that gold has a safe haven potential relative to the stock market. [ABSTRACT FROM AUTHOR]

The mesoscale spectrum describes the distribution of kinetic energy in the Earth's atmosphere between length scales of 10 and 400 km. Since the first observations, the origins of this spectrum have been controversial. At synoptic scales, the spectrum follows a −3 spectral slope, consistent with two‐dimensional turbulence theory, but a shallower −5/3 slope was observed at the shorter mesoscales. The cause of the shallower slope remains obscure, illustrating our lack of understanding. Through a novel coarse‐graining methodology, we are able to present a spatio‐temporal climatology of the spectral slope. We find convection and orography have a shallowing effect and can quantify this using "conditioned spectra." These are typical spectra for a meteorological condition, obtained by aggregating spectra where the condition holds. This allows the investigation of new relationships, such as that between energy flux and spectral slope. Potential future applications of our methodology include predictability research and model validation. Plain Language Summary: The kinetic energy spectrum describes how much energy is at different spatial scales in the atmosphere, from km‐scale atmospheric waves to large‐scale weather systems 1,000 km across. This distribution may influence predictability. Edward Lorenz argued that the spectrum can determine whether a fluid can be forecast arbitrarily far into the future or not. In this paper, we employ a novel method to reveal how the spectrum varies in different locations on Earth. In addition, we generate the first "conditioned spectra," which are the aggregated spectra for different levels of orography, convection and energy transfer. We are able to demonstrate the tendency of convection and orography to increase small‐scale energy and show their effect on the classic global spectrum. Spectra are vital for model validation and predictability research; therefore, these results and the methods used to obtain them are of interest to meteorology practitioners, theorists and those in neighboring fields. Key Points: Global maps of spectral slope are produced through a novel coarse‐graining methodOrography and precipitation shallow the spectral slope in the troposphere significantlyConditioned spectra quantify the relationship between slope, orography, precipitation and energy flux [ABSTRACT FROM AUTHOR]

Restoration outcomes are notoriously unpredictable and this challenges the capacity to reliably meet goals. To harness ecological restoration's full potential, significant advances to predictive capacity must be made in restoration ecology. We outline a process for predicting restoration outcomes, based on the model of iterative forecasting. We then describe six challenges that impede predictive capabilities in restoration and, for each, an agenda for overcoming the challenge. Key challenges include the lack of clear goals, insufficient knowledge of why restoration outcomes vary, difficulty quantifying known drivers of variation prior to initiation of restoration projects, model uncertainty, the need to scale up local understanding to guide large‐scale restoration efforts, and temporally variable conditions that hinder long‐term forecast accuracy. Meeting these challenges will require research to resolve key drivers of variation in restoration outcomes; however, there is also a critical need to begin forecasting efforts in restoration ecology immediately. Although early efforts may be of limited practical utility, iterating between model development and evaluation will resolve data needs, minimize uncertainty, and lead to predictions that practitioners can confidently embrace. In turn, a robust predictive capacity will help to reliably meet goals, enhance cost‐effectiveness, and guide policy decisions to help see out the promise of the Decade on Ecosystem Restoration. [ABSTRACT FROM AUTHOR]

Skill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales. Plain Language Summary: Early warning of severe weather is particularly important in Africa, where resilience to storm hazards such as flash flooding is weak. Given large‐scale atmospheric conditions favorable for convective activity, understanding where storms will occur is challenging for conventional weather prediction models. In semi‐arid regions such as the Sahel, the spatial distribution of SM provides additional predictability of convective rain, via its impact on heating and moistening of the atmosphere. Given that convection is favored over drier soils and that storms create new SM patterns every few days during the wet season, the extent to which knowledge of today's SM aids rainfall prediction in future days is unclear. Here we use 17 years of satellite observations to document how surface properties evolve over 20 days after a storm, and how the surface influences subsequent rainfall patterns. We find that even in regions of West Africa where storms are frequent, the suppression of rain over recently‐wetted soils is evident out to 2 days. In climatologically drier regions, this predictability extends out to 8 days. Overall, the feedback between SM and rainfall enhances rainfall predictability in the short‐term (days), but effectively degrades the skill of longer‐term (weeks) forecasts. Key Points: Satellite observations over the Sahel reveal how the land surface evolves in the 20 days after a Mesoscale Convective System (MCS)After an MCS, rainfall is suppressed over wet soils for 2 days in humid regions and up to 8 days in drier areasInitially soil moisture enhances rainfall predictability, but the strong land feedback degrades skill at longer lead times [ABSTRACT FROM AUTHOR]

This study investigates the medium‐range predictability of warm and cold extremes in the Northern Hemisphere and the role that upper‐tropospheric circulation biases play in this regard. Deterministic ERA5 reforecasts for the period 1979–2019 are evaluated based on the ERA5 reanalysis of the respective period, thus providing a large sample for verification and bias identification. The predictability of temperature extremes at 850 hPa is assessed based on the Gilbert Skill Score and other metrics and is shown to exhibit regional and seasonal variations. Summer is generally characterized by lower forecast skill scores than winter for both warm and cold extremes. Moreover, cold extremes in summer have slightly lower skill scores than warm extremes, while the opposite is true in winter. Biases in the frequency of temperature extremes are, to some extent, consistent with biases in mean temperature and indicate an underestimation in the total amount of extremes for much of the hemisphere in summer. Associated with the latter, biases also emerge in the standard deviation of the daily temperature distribution, with the summer values being largely underestimated over most of the hemisphere. The role of upper‐tropospheric circulation in these biases is then assessed by verifying the representation of Rossby‐wave packet (RWP) properties. It is found that the amplitude of RWPs is systematically underestimated in most of the hemisphere in summer, while it is overestimated in many parts of the midlatitudes in winter. Overall, the results suggest that the underestimation of RWP amplitude in summer hinders the medium‐range predictability of temperature extremes in the explored retrospective and operational forecasts. Although operational European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts gradually improve between 2013 and 2022 in terms of the 850‐hPa temperature and 300‐hPa RWP amplitude absolute errors, the aforementioned summer biases remain qualitatively similar. [ABSTRACT FROM AUTHOR]

Aims: We intended to characterize the superiority of triglyceride glucose‐body mass index (TyG‐BMI) in predicting type 2 diabetes mellitus (T2DM) compared with triglyceride glucose (TyG) and homeostatic model assessment for insulin resistance (HOMA‐IR). Methods: A total of 699 nondiabetic participants in the Da Qing IGT and Diabetes Study were involved in the present analysis and classified according to the median of baseline TyG‐BMI, namely the G1 (low TyG‐BMI) and G2 (high TyG‐BMI) groups. Information on developing diabetes was assessed from 1986 to 2020. Results: During the 34‐year follow‐up, after adjustment for confounders, the G2 group had a higher risk of developing type 2 diabetes than the G1 group (hazard ratio [HR]: 1.92, 95% confidence interval [CI]: 1.51–2.45, p < 0.0001). Restricted cubic spline analyses showed that increased TyG‐BMI was linearly related to higher risks of type 2 diabetes (p for non‐linearity>0.05). Time‐dependent receiver operator characteristics curves suggested that TyG‐BMI exhibited higher predictive ability than TyG (6‐year: area under the curve [AUC]TyG‐BMI vs. AUCTyG, 0.78 vs. 0.70, p = 0.03; 34‐year: AUCTyG‐BMI vs. AUCTyG, 0.79 vs. 0.73, p = 0.04) and HOMA‐IR (6‐year: AUCTyG‐BMI vs. AUCHOMA‐IR, 0.78 vs. 0.70, p = 0.07; 34‐year: AUCTyG‐BMI vs. AUCHOMA‐IR, 0.79 vs. 0.71, p = 0.04) in both short and long terms, and the thresholds of TyG‐BMI to predict type 2 diabetes were relatively stable (195.24–208.41) over the 34‐year follow‐up. Conclusions: In this post hoc study, higher TyG‐BMI was associated with an increased risk of type 2 diabetes and demonstrated better predictability than TyG and HOMA‐IR, favoring the application of TyG‐BMI as a potential tool for evaluating the risk of type 2 diabetes in clinical practice. [ABSTRACT FROM AUTHOR]

Assemblages in seasonal ecosystems undergo striking changes in species composition and diversity across the annual cycle. Despite a long‐standing recognition that seasonality structures biogeographic gradients in taxonomic diversity (e.g., species richness), our understanding of how seasonality structures other aspects of biodiversity (e.g., functional diversity) has lagged. Integrating seasonal species distributions with comprehensive data on key morphological traits for bird assemblages across North America, we find that seasonal turnover in functional diversity increases with the magnitude and predictability of seasonality. Furthermore, seasonal increases in bird species richness led to a denser packing of functional trait space, but functional expansion was important, especially in regions with higher seasonality. Our results suggest that the magnitude and predictability of seasonality and total productivity can explain the geography of changes in functional diversity with broader implications for understanding species redistribution, community assembly and ecosystem functioning. [ABSTRACT FROM AUTHOR]

The ocean surface mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum, and nutrients from the surface to the deep ocean. In this study, we introduce a framework to assess the evolution of the mixed layer depth (MLD) for realistic forcings and preconditioning conditions. Our approach involves a physically‐based parameter space defined by three dimensionless numbers: λs representing the relative contribution of the buoyancy flux and the wind stress at the air‐sea interface, Rh the Richardson number which characterizes the stability of the water column relative to the wind shear, and f/Nh which characterizes the importance of the Earth's rotation (ratio of the Coriolis frequency f and the pycnocline stratification Nh). Four MLD evolution regimes ("restratification," "stable," "deepening," and "strong deepening") are defined based on the values of the normalized temporal evolution of the MLD. We evaluate the 3D parameter space in the context of 1D simulations and we find that considering only the two dimensions (λs, Rh) is the best choice of 2D projection of this 3D parameter space. We then demonstrate the utility of this two‐dimensional λs − Rh parameter space to compare 3D realistic ocean simulations: we discuss the impact of the horizontal resolution (1°, 1/12°, or 1/60°) and the Gent‐McWilliams parameterization on MLD evolution regimes. Finally, a proof of concept of using observational data as a truth indicates how the parameter space could be used for model calibration. Plain Language Summary: Vertical mixing of water near the ocean surface occurs when cold air temperatures create dense cold water at the surface that tends to sink in the ocean or when a strong wind induces turbulence at the ocean surface. These processes mix heat and salt and create a layer at the top of the ocean that has a uniform temperature and salinity and that is called the "mixed layer." This mixed layer plays a fundamental role in the Earth climate system, and the representation of its evolution in ocean models hence needs to be assessed. For this purpose, we propose to map the mixed layer evolution in a three‐dimensional space where the first axis is related to the wind and the surface heat flux, the second axis to the stability of the water column, and the third axis to the Earth's rotation. We show that this tool performs statistically well and we present how to use it in the context of realistic ocean models. Key Points: A parameter space is proposed to assess the evolution of the mixed layer depth for realistic forcings and preconditioning conditionsAn evaluation of a collection of 1D simulations shows a statistically good performance of the parameter spaceTwo applications demonstrate the utility of the parameter space for assessing and comparing realistic 3D simulations [ABSTRACT FROM AUTHOR]

Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The 'intrinsic predictability' of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise.We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie (Pygoscelis adeliae) and emperor penguin (Aptenodytes forsteri) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species.We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%).rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance. [ABSTRACT FROM AUTHOR]

Summary The present study aims at identifying sleep patterns in insomnia in a clinical sample using three strategies to define poor nights. Sleep diaries and self‐reported questionnaires were collected from 77 clinical patients with insomnia. The conditional probabilities of observing a poor night after 1, 2, or 3 consecutive poor nights were computed according to three strategies with same criteria for sleep onset latency, wake after sleep onset, and sleep efficiency, but varying criterion for total sleep time. Latent profile analyses were conducted to derive sleep patterns. Uni‐ and multivariate analyses were conducted to characterise the sleep patterns identified. A total of 1586 nights were analysed. The strategy used significantly influenced the average percentage of reported poor nights. Two to three sleep patterns were derived per strategy. Within each strategy, sleep patterns differed from each other on sleep variables and night‐to‐night variability. Results suggest the existence of sleep patterns in insomnia among individuals consulting in psychological clinics. Adding a total sleep time of 6‐h cut‐off as a criterion to define poor nights increases the accuracy of the strategy to define poor night and allows to identify sleep patterns of poor nights in insomnia. [ABSTRACT FROM AUTHOR]

Introduction: The attentional boost effect, characterized by better memory for background scenes coinciding with a detection target than a nontarget, is believed to stem from a temporary increase in attentional capacity at the time of an acute behavior‐related event occurring. Sisk and Jiang's study found that the attentional boost effect also occurs when the target's appearance was predictable. Unfortunately, the duration of the predictive interval in Sisk and Jiang's study was fixed. Since different predictive intervals had different weakening degrees to the acuteness of the target, this fixed duration hindered further investigation into the impact of different levels of predictability on the attentional boost effect. Method: Using the encoding‐recognition paradigm and the remembering/knowing paradigm, and setting target stimuli with different predictive interval in target detection tasks, the current study aimed to explore the influence of varying the duration of the predictive interval on the attentional boost effect. Results: The attentional boost effect was observed only in the short and medium predictive duration conditions, but not in the long predictive duration condition. Moreover, as the duration of the predictive interval increased, participants' memory performance on target‐paired words gradually declined, while their memory performance on distractor‐paired and baseline‐paired words gradually improved. Conclusions: Predictability may alter the task demands, allowing participants to more effectively allocate attentional resources to the two tasks at hand. [ABSTRACT FROM AUTHOR]

We investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies. Plain Language Summary: Observations show that ocean anomalies propagate across the North Atlantic and further north along the Norwegian coast. Anomalous ocean temperatures can modify heat exchange between ocean and atmosphere and influence the circulation of the atmosphere. Since such anomalies can persist for months, it potentially opens a door to seasonal prediction of the atmosphere. However, climate models used for seasonal and decadal prediction fail to predict these propagating anomalies. In our study we aim to find causes for this lack of predictability. We use the record low temperatures observed in the North Atlantic in 2015 as a test case. We employ two climate prediction systems and make two types of predictions: one type is a standard prediction initialized with winter 2014/2015 ocean conditions; the other type is initialized in the same way and in addition, the model is kept close to observations in the subpolar gyre during the whole prediction, in the region where the cold blob was formed. Comparing the two types of predictions, we see that keeping the model close to observations in the subpolar gyre increases the predictability along the Norwegian coast. This points to the subpolar gyre as a key area where models need to be improved. Key Points: Knowing temperature and salinity in the subpolar gyre increases skill downstream along the Atlantic Water pathwaySalinity anomalies propagate northward, while temperature anomalies are more difficult to traceA pacemaker experiment has been used for decadal predictions for the first time [ABSTRACT FROM AUTHOR]

In July and August 2022, the Yangtze River basin (YRB) experienced its hottest summer since 1961. The SINTEX‐F2 seasonal prediction system initialized in early May predicted the hotter‐than‐normal summer due to its successful prediction of central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the tropical West Pacific‐East Indian Ocean (TWP_EIO). The common SST forcing explains only about 26% to the heatwave strength, while the internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction account for approximately 65%, based on the analysis of 108 ensemble members. These factors have collectively increased the maximum temperature over the YRB through the enhancement and westward expansion of western North Pacific subtropical high. Our findings quantify the relative contributions of external forcing and internal variations to the unprecedented hot event, offering insights into its forming mechanism and potential predictability. Plain Language Summary: A record‐breaking heatwave event occurred in the YRB in July and August 2022, posing significant risks on human health, power supply, and social economic activities. Recognizing the importance of such an event, our study aims to identify key factors influencing its prediction using the SINTEX‐F2 system. The central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the TWP_EIO provide the dominant predictability, but accounts only about 26% of the heatwave strength. However, internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction collectively explain about 65%. Our results suggest the necessity of large‐ensemble prediction in capturing this kind of unprecedented extreme event. Key Points: The SINTEX‐F2 initialized on 2022 early May captures the warmer‐than‐normal air temperatures in Yangtze River basinThe model successfully predicts co‐occurrences of CP La Niña and negative IOD in 2022However, the direct SST forcing contributes only 26% to the 2022 extreme event in Yangtze River basin [ABSTRACT FROM AUTHOR]

The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective. Plain Language Summary: The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are two important indicators of atmospheric variability in the North Atlantic. They can have a substantial impact on European winter weather. The ability of seasonal forecast models to forecast the NAO and EA varies from year to year. This intermittency of forecast skill is investigated in seven different seasonal forecast systems from the Copernicus C3S database, by focusing on the most well‐forecast and poorly‐forecast years. Years where the NAO is well‐forecast are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Similar but weaker results hold for the EA. These results are valuable for increasing the usability of seasonal forecasts by identifying conditions under which forecasts are more likely to be skillful. Key Points: Seasonal forecast skill of the wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) is intermittentWell‐forecast NAO/EA winters generally occur when there is substantial tropical forcing [ABSTRACT FROM AUTHOR]

Atmospheric prediction at 2–6 weeks lead time (so‐called subseasonal‐to‐seasonal timescales) entails large forecast uncertainty. Here we investigate the flow‐dependence of this uncertainty during Boreal winter. We categorize the large‐scale flow using North Atlantic‐European weather regimes. First, we show that forecast uncertainty of near‐surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy‐driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime‐uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe. Plain Language Summary: Weather is chaotic, and forecasts several weeks ahead are quite uncertain. Nevertheless, the degree of uncertainty varies, which can be relevant for long‐term planning in various sectors, including agriculture, energy supply, and public health. Here we show that the degree of uncertainty depends on the weather at the time of forecast start, which we categorize using eight characteristic weather patterns for the North Atlantic and Europe. We analyze a large set of forecasts during winter, with lead times up to 6 weeks. For example, persistent high‐pressure systems over Greenland are known to favor low temperatures over northern Europe. Our results indicate that, in addition, temperatures are highly variable in these cases, leading to unusually high forecast uncertainty. In contrast, uncertainty of near‐surface air pressure tends to decrease due to less frequent storms over the North Atlantic. Furthermore, we show that forecast uncertainty of the weather patterns themselves varies, which is useful when large‐scale flow conditions are more critical than local weather. We analyze forecasts under different circulation conditions in the Arctic stratosphere, as these can have long‐lasting impacts on surface weather. We find forecasts of weather patterns become less uncertain when the circumpolar winds in the Arctic stratosphere are weak. Key Points: Ensemble spread of near‐surface weather in subseasonal forecasts is sensitive to the prevailing North Atlantic‐European weather regimeGreenland blocking is linked to the smallest ensemble spread of geopotential but to the largest spread of temperature over northern EuropeWeak polar vortex states are followed by reduced forecast uncertainty of weather regimes but increased uncertainty of European temperature [ABSTRACT FROM AUTHOR]

The characteristics of the Madden–Julian oscillation (MJO) have changed and are projected to continue changing with the expansion of the Indo–Pacific warm pool, which is the Earth's largest region of warm sea surface temperatures (SSTs). However, the likelihood of a change in MJO predictability following warm pool expansion remains unaddressed. Therefore, this study investigated the effect of warm pool expansion on MJO variability and predictability using the highly idealized aquaplanet configuration of Community Earth System Model 2 (CESM2). By expanding the warm pool in the Indo–Pacific, MJO‐like waves become more regionally confined, short‐lived convective events with weaker magnitude and less robust eastward propagating signals, possibly due to stronger zonal SST gradients and wider meridional widths of the warm pool. Perfect‐model ensemble forecast experiments revealed that the MJO predictability decreased by approximately 5 days, the forecast error proliferated, and the signal rapidly reduced following warm pool expansion. Plain Language Summary: The Madden–Julian oscillation (MJO) is a prominent mode of tropical intra‐seasonal variability in which its convective anomalies drive anomalous circulation within the tropics and remotely in the extratropics, thus providing a "forecast of opportunity" for global weather and climate events. Over the decades, MJO characteristics have changed and are projected to continue changing with the expansion of the Indo–Pacific warm pool—the largest pool with the warmest ocean temperature on Earth. If warm pool expansion changes the MJO characteristics, it is likely that its predictability has changed and will continue to do so in the future. In this study, the change in MJO predictability by expanding the Indo–Pacific warm pool was explored using CESM2 aquaplanet model experiments. The results show that, with warm pool expansion, MJO predictability decreased by approximately 5 days. This decrease is primarily attributed to the change of the MJO‐like waves into more regionally confined, short‐lived convective events with weaker magnitude and less robust eastward propagating signals, owing to the change in SST and circulations. Key Points: A sensitivity experiment was performed using Community Earth System Model 2 aquaplanet with different prescribed sea surface temperaturesExpanding the Indo–Pacific warm pool makes the Madden–Julian oscillation‐like waves less organizedThe warm pool expansion quickens the forecast error increase and predictability decrease [ABSTRACT FROM AUTHOR]

The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme. [ABSTRACT FROM AUTHOR]

Cold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase. [ABSTRACT FROM AUTHOR]

This paper investigates the extent to which commodity prices can predict economic growth, the growth exposure of the countries to specific commodity group, and whether the country stage of development matters for growth dependency on commodities. We use a novel time‐varying mixed‐frequency vector autoregressive model to jointly estimate causality between monthly commodity prices and quarterly economic growth for the period from January 1980 to March 2020. Our findings suggest that growth dependency on commodities varies over time and across different synthetic measures, but in general solid evidence of predictability is determined. The overall commodity index shows better performance in predicting economic growth than the segregated proxies of either fuel or non‐fuel commodities in developed economies. However, the overall commodity index is as effective as the fuel index in predicting economic growth for their developing counterparts. We develop a new index of global commodity growth connectivity (GCGCI) to determine the economic growth dependency on commodities over time. Our results show that economic growth dependency on commodities has increased at least three times with financialization of commodity markets. The GCGCI for all commodities reveals a higher growth dependency on commodities in developing countries than developed ones in the post‐financialization period. Interestingly, we find that growth decreases its reliance on fuel commodities in both developed and developing countries after financialization of commodity markets. Our findings bear implications for policymakers to design stabilization policies in an attempt to protect economies vulnerable to commodity price shocks and lay the foundation for sustainable economic growth. [ABSTRACT FROM AUTHOR]

Seasonal hindcasts have previously been demonstrated to show multi‐decadal variability in skill across the twentieth century in indices describing El‐Niño Southern Oscillation (ENSO), which drives global seasonal predictability. Here, we analyze the skill of predicting ENSO events' magnitude and spatial pattern, in the CSF‐20C coupled seasonal hindcasts in 1901–2010. We find minima in the skill of predicting the first (in 1930–1950) and second (in 1940–1960) principal components of sea‐surface temperature (SST) in the tropical Pacific. This minimum is also present in the spatial correlation of SSTs, in 1930–1960. The skill reduction is explained by lower ENSO magnitude and variance in 1930–1960, as well as decreased SST persistence. The SST skill minima project onto surface winds, leading to worse predictions in coupled hindcasts compared to hindcasts using prescribed SSTs. Questions remain about the offset between the first and second principal components' skill minima, and how the skill minima impact the extra‐tropics. Plain Language Summary: Seasonal forecasts use slowly changing elements of the climate system to predict average weather and climate several months ahead, and have applications across the energy, agriculture, and insurance industries. The most important signal is El‐Niño Southern Oscillation (ENSO), an ocean temperature pattern in the tropical Pacific which varies between warm and cold phases every 2–7 years. ENSO's specific pattern, including how warm/cold it is compared to normal, and where heating/cooling is concentrated, influences global weather. Seasonal forecasting models are tested by running 'reforecasts': comparing a 'forecast' of the past with observations. In this study, we investigate how skilfully reforecasts from a state‐of‐the‐art seasonal forecasting model reproduce observations, for sea‐surface temperatures in the tropical Pacific (where ENSO occurs). We analyze reforecast skill in every Northern Hemisphere winter from 1901 to 2010 and find periods of high skill at the start and end of the century, with reduced skill in 1930–1960. This was likely caused by a reduction in the size of ENSO events, and decreased sea‐surface temperature memory. This reduction in ocean skill leads to lower skill for surface‐level winds. These results are important for understanding whether season‐ahead predictability changes over decades/centuries, and how climate change might impact this predictability. Key Points: The skill of predicting the spatial pattern of ENSO events in coupled seasonal hindcasts is low in 1930–1960, compared to before and afterThe skill minimum is attributable to lower ENSO variability and decreased SST persistence in the mid‐century periodDecreased skill in the SST spatial pattern is linked to decreased surface wind skill in coupled hindcasts (vs. atmosphere‐only hindcasts) [ABSTRACT FROM AUTHOR]

Ensemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal‐to‐noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal‐to‐noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal‐to‐decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi‐model ensemble of uninitialized atmosphere‐only simulations, focusing on boreal winter. To assess signal‐to‐noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi‐model ensemble‐mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher‐resolution models produce greater observation‐model correlations and greater RPC values than lower‐resolution models in all four climate indices. We find that signal‐to‐noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free‐running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal‐to‐noise errors originate in the atmosphere or in ocean–atmosphere coupling. [ABSTRACT FROM AUTHOR]

Coastal El Niño is an extreme situation of El Niño‐Southern Oscillation (ENSO) with sea surface temperature warming confined in the far‐eastern equatorial Pacific. Some coastal El Niños evolve into a basin scale El Niño, and some don't, implying a diversity in ENSO evolutions after a coastal El Niño event. In this study, the coastal El Niños in 2017 and 2023 are selected to examine their subsequent evolution. Both coastal El Niños developed after a La Niña, with the former followed by a La Niña and the latter by a basin‐scale El Niño. The cold (warm) subsurface temperatures in 2017 (2023) were key factors leading to the divergent ENSO evolution. Convection over the western tropical Pacific and the atmospheric circulation anomalies across the equatorial Pacific also contributed to the differences. Model predictions suggest that differences in ENSO evolution after a coastal El Niño are associated with differences in ENSO predictability. Plain Language Summary: Compared with the global impact of basin‐scale El Niño–Southern Oscillation (ENSO) events, coastal El Niño impacts are mainly focused along the South American coast. They are less studied, especially, in terms of temporal evolution and longer‐term development. Here, we examine the divergent evolution of ENSO conditions in the tropical Pacific after the coastal El Niños in 2017 and 2023. This subsequent divergent evolution of these events was associated with both preceding subsurface ocean heat content levels, convection over the western tropical Pacific, and concurrent atmospheric circulation. Specifically, preceding subsurface ocean cooling combined with low‐level easterly wind anomalies led to the growth of La Niña after the coastal El Niño in 2017, while strong preceding subsurface ocean warming led to the growth of El Niño after the coastal El Niño in 2023. These differences in the evolution of tropical Pacific Ocean conditions after a coastal El Niño were associated with different levels of ENSO predictability. Key Points: The coastal El Niño in 2017 was followed by a La Niña, while the coastal El Niño in 2023 evolved into a basin‐scale El NiñoSubsurface ocean heat content levels, western Pacific convection, and off‐equatorial circulation differences affect El Niño‐Southern Oscillation (ENSO) evolutionDivergent evolution of conditions in the tropical Pacific after a coastal El Niño is associated with differences in ENSO predictability [ABSTRACT FROM AUTHOR]

Risk rates for and predisposing factors to fractures occurring in Thoroughbred racing that have been published in peer reviewed journals are documented. The potential for currently available techniques to identify horses at increased risk for fracture is discussed on the bases of principles, practicalities, advantages, disadvantages and current data. All are reviewed in light of justifiable decision making and importance of fractures to horseracing's social license. [ABSTRACT FROM AUTHOR]

We investigate the impact of seasonal forecast biases in the Tropical Atlantic on the North Atlantic. The analysis uses a novel ensemble-based method to estimate the impact of tropical rainfall bias on forecasts of the Extratropical North Atlantic. The inter-ensemble spread of the forecast model is used to estimate the impact of the bias in Tropical Atlantic rainfall on the North Atlantic by selecting model members that happen to produce forecast anomalies that most closely resemble the tropical rainfall bias and using these as a proxy for the model error. The Tropical Atlantic rainfall bias impacts Rossby wave sources over the Subtropical Atlantic and there is a clear Rossby wave pattern originating from this area which is comparable to the mean bias in hindcasts. We argue that Tropical Atlantic rainfall errors explain a significant amount of the bias in seasonal forecasts over the Extratropical North Atlantic. [ABSTRACT FROM AUTHOR]

The openness of the future is rightly considered one of the qualifying aspects of the temporality of modern society. The open future, which does not yet exist in the present, implies radical unpredictability. This article discusses how, in the last few centuries, the resulting uncertainty has been managed with probabilistic tools that compute present information about the future in a controlled way. The probabilistic approach has always been plagued by three fundamental problems: performativity, the need for individualization, and the opacity of predictions. We contrast this approach with recent forms of algorithmic forecasting, which seem to turn these problems into resources and produce an innovative form of prediction. But can a predicted future still be an open future? We explore this specific contemporary modality of historical futures by examining the recent debate about the notion of actionability in precision medicine, which focuses on a form of individualized prediction that enables direct intervention in the future it predicts. [ABSTRACT FROM AUTHOR]

Animals under human care often experience predictable daily husbandry events, which can promote the development of anticipatory behavior. Previous research suggests even short delays in the arrival of a predictable, desired outcome can lead to negative welfare outcomes for animals. As such, providing reliable information to animals regarding the onset of important events may be a simple but useful method to support positive welfare. Here we evaluated the quantitative characteristics of anticipatory behavior of a California sea lion at the San Francisco Zoo in three situations (1) when the animal had accurate information about the occurrence of the event (temporally predictable training sessions), (2) when the information was semi‐reliable (unscheduled training session), and (3) when a reliable signal was offered before unscheduled sessions. Results showed that providing a reliable cue resulted in a stronger temporal correlation of anticipation before the beginning of the unscheduled session, similar to the temporally predictable training session. However, providing a reliable cue did not reduce the intensity of the anticipatory behavior. We propose to take into account two aspects of the quantitative characteristics of anticipatory behavior: correlation with time until a desired event (correlation) and intensity, where the correlation indicates the ability of an individual animal to predict the occurrence of an event, and the intensity indicates the degree of sensitivity of the animal to reward. We discuss the implications for animal welfare and husbandry. [ABSTRACT FROM AUTHOR]

The openness of the future is rightly considered one of the qualifying aspects of the temporality of modern society. The open future, which does not yet exist in the present, implies radical unpredictability. This article discusses how, in the last few centuries, the resulting uncertainty has been managed with probabilistic tools that compute present information about the future in a controlled way. The probabilistic approach has always been plagued by three fundamental problems: performativity, the need for individualization, and the opacity of predictions. We contrast this approach with recent forms of algorithmic forecasting, which seem to turn these problems into resources and produce an innovative form of prediction. But can a predicted future still be an open future? We explore this specific contemporary modality of historical futures by examining the recent debate about the notion of actionability in precision medicine, which focuses on a form of individualized prediction that enables direct intervention in the future it predicts. [ABSTRACT FROM AUTHOR]

The unprecedented sea ice loss in the Arctic is due to its stronger surface warming than other parts across the globe, resulting in far‐flung effects on weather and climate at different spatial and temporal scales. We investigate how Arctic Sea ice and Indian summer monsoon rainfall (ISMR) are associated at seasonal scale and when preconditioned by Arctic Oscillation (AO) phases. This study also examines the potential predictability using correlation and composite analysis. The results indicate a significant decline in sea ice extent (SIE) at a rate of 0.055 Mkm2/year (p < 0.05). The strong association between SIE and precipitation in the Indian region is confirmed by correlation values ranging from −0.6 to 0.6 (p < 0.05). The spatial patterns of seasonal SIE and precipitation association remain consistent for 1979–2021. We found that the prevailing AO phases influence the association of sea ice with ISMR precipitation. The high correlation between SIE and ISMR anomalies suggests that Arctic Sea ice could be a reliable predictor for ISMR. [ABSTRACT FROM AUTHOR]

Making predictions about when and where a given mechanism of invasion will be weak or strong is crucial for the effective management of non‐native species. Despite the importance of disturbance on invasion, our understanding of how variation in abiotic and/or biotic conditions may modify the disturbance‐invasion relationship is scarce. Here, we aimed to evaluate how abiotic (soil type) and biotic (tree and shrub cover) contexts affect the disturbance–invasion relationship in disturbed and nearby non‐disturbed communities in the semi‐arid open forest of central Argentina (ca 36°S) using field sampling. We found that abiotic context modulated non‐native species success in disturbed communities, whereas both abiotic and biotic context modulated success in nearby non‐disturbed communities. These findings suggest that the plant invasion–disturbance relationship is context‐dependent. Our results hint at the possibility that the significance of disturbance in predicting invasion might diminish as the importance of abiotic filters increases. [ABSTRACT FROM AUTHOR]

A number of studies have investigated the large‐scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental‐scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions. [ABSTRACT FROM AUTHOR]

Sudden stratospheric warming (SSW) describes a disruption of the stratospheric polar vortex in the winter hemisphere. It affects not only the stratospheric circulation but also the surface climate for up to 2 months, serving as an important source of subseasonal‐to‐seasonal (S2S) predictability in midlatitudes. This study evaluates the predictability of the 2021 SSW and investigates the crucial factors that determine its predictability in the ECMWF and JMA S2S real‐time forecasts. In both models, only a subset of the ensemble members predicted the SSW at the lead time of about 2 weeks before the onset. By comparing the 10 ensembles with successful SSW predictions and those with failed predictions, we found that the ensembles predicting the SSW have relatively stronger wave fluxes from the upper troposphere to the stratosphere than the others. Stronger wave fluxes, particularly those of zonal wavenumber one, are not the result of the tropospheric precursors such as the Ural blocking and Aleutian cyclones but they result from the modulation of the wave propagation by the background state. In particular, the ensembles with failed SSW predictions tend to have a negative potential vorticity gradient in the upper troposphere and lower stratosphere, which limits the upward wave propagation into the stratosphere and provides an unfavorable condition for the SSW. This result suggests that not only the wave sources in the troposphere but also the background state in the upper troposphere and lower stratosphere can modulate the predictability of SSW in S2S prediction models. Plain Language Summary: Sudden stratospheric warming (SSW) refers to the rapid increase in temperature in the polar stratosphere during winter. It plays a crucial role in predicting weather patterns within a timeframe ranging from 2 weeks to 2 months (subseasonal‐to‐seasonal; S2S). This study investigates the factors that contribute to the predictability of the 2021 SSW. By comparing the S2S forecasts with and without the SSW, it is found that the predictability of SSW is not determined by the tropospheric precursors, but by the zonal‐mean flow in the upper troposphere and lower stratosphere that modulates the wave propagation from the troposphere to the stratosphere. This result suggests that a better prediction of the wave propagation condition is a critical factor for a successful SSW prediction on the S2S time scale. Key Points: The crucial factors that determine the predictability of the 2021 SSW in S2S prediction models are examinedThe k = 1 wave propagating into the stratosphere plays an essential role in the SSW predictionThe PV gradient in the upper troposphere and lower stratosphere can control the SSW predictability by modulating the upward wave propagation [ABSTRACT FROM AUTHOR]

The Madden‐Julian Oscillation (MJO) is a promising target for improving sub‐seasonal weather forecasts. Current forecast models struggle to simulate the MJO due to imperfect convective parameterizations and mean state biases, degrading their forecast skill. Previous studies have estimated a potential MJO predictability 5–15 days higher than current forecast skill, but these estimates also use models with parameterized convection. We perform a perfect‐model predictability experiment using a superparameterized global model in which the convective parameterization is replaced by a cloud resolving model. We add a second "silent" cloud resolving component to the control simulation that independently calculates convective‐scale processes using the same large‐scale forcings. The second set of convective states are used to initialize forecasts, representing uncertainty on the convective scale. We find a potential predictability of the MJO of 35–40 days in boreal winter using a single‐member ensemble forecast. Plain Language Summary: The Madden‐Julian Oscillation is a convective signal in the tropics that has the potential to improve 10‐40‐day weather forecasts. Current weather forecast models struggle to simulate the MJO, leading to a lower forecast skill than many studies estimate could be possible. We use a model with a comparatively good representation of the MJO that calculates convection information with a cloud permitting model. We modify this multiscale model's structure to generate MJO forecasts for its own MJO. Results from these forecasts suggest that the MJO in this model could be predictable up to 35–40 days using a single‐member ensemble forecast, which is 5–10 days longer than current state‐of‐the‐art forecasts. Key Points: A novel approach is used to conduct perfect‐model predictability experiments using a superparameterized global modelA single ensemble member model with superparameterized convection finds a potential Madden‐Julian Oscillation predictability of 35–40 daysResulting predictability estimates are comparable to those from current state‐of‐the‐art multiple ensemble member forecasting models [ABSTRACT FROM AUTHOR]

Parallel and convergent evolution offer some of the most compelling evidence for the significance of natural selection in evolution, as the emergence of similar adaptive solutions is unlikely to occur by random chance alone. However, these terms are often employed inconsistently, leading to misinterpretation and confusion, and recently proposed definitions have unintentionally diminished the emphasis on the evolution of similar adaptive solutions. Here, I examine various conceptual frameworks and definitions related to parallel and convergent evolution and propose a consolidated framework that enhances our comprehension of these evolutionary patterns. The primary aim of this framework is to harmonize the concepts of parallel and convergent evolution together with natural selection and the idea of similarity. Both concepts involve the evolution of similar adaptive solutions as a result of environmental challenges. The distinction lies in ancestral phenotypes. Parallel evolution takes place when the ancestral phenotypes (before selection) of the lineages are similar. Convergent evolution happens when the lineages have distinct ancestral phenotypes (before selection). Because an ancestral‐based distinction will inevitably lead to cases where uncertainty in the distinction may arise, the framework includes a general term, repeated evolution, which can be used as a term applying to the evolution of similar phenotypes and genotypes as well as similar responses to environmental pressures. Based on the argument that genetic similarity may frequently arise without selection, the framework posits that the similarity of genetic sequences is not of great interest unless linked to the actions of natural selection or to the origins (mutation, standing genetic variation, gene flow) and locations of the similar sequences. [ABSTRACT FROM AUTHOR]

The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Niño-Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air-sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air-sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific. [ABSTRACT FROM AUTHOR]

The key factors that are needed for bone regeneration to take place include cells (osteoprogenitor and immune‐inflammatory cells), a scaffold (blood clot) that facilitates the deposition of the bone matrix, signaling molecules, blood supply, and mechanical stability. However, even when these principles are met, the overall amount of regenerated bone, its stability over time and the incidence of complications may significantly vary. This manuscript provides a critical review on the main local and systemic factors that may have an impact on bone regeneration, trying to focus, whenever possible, on bone regeneration simultaneous to implant placement to treat bone dehiscence/fenestration defects or for bone contouring. In the future, it is likely that bone tissue engineering will change our approach to bone regeneration in implant dentistry by replacing the current biomaterials with osteoinductive scaffolds combined with cells and mechanical/soluble factors and by employing immunomodulatory materials that can both modulate the immune response and control other bone regeneration processes such as osteogenesis, osteoclastogenesis, or inflammation. However, there are currently important knowledge gaps on the biology of osseous formation and on the factors that can influence it that require further investigation. It is recommended that future studies should combine traditional clinical and radiographic assessments with non‐invasive imaging and with patient‐reported outcome measures. We also envisage that the integration of multi‐omics approaches will help uncover the mechanisms responsible for the variability in regenerative outcomes observed in clinical practice. [ABSTRACT FROM AUTHOR]

This study shows that the geographic network of public firms facilitates the propagation of local economic conditions across the United States. We identify economic connections among U.S. states based on the 10‐K listings of public firms and show that the returns and liquidity of firms headquartered in connected states exhibit excess comovement. The economic connections also generate spillover effects where the economy of a state affects its connected states and amplifies the impact of local economic conditions on the U.S. economy. In particular, a 1% production increase in a large state like California is associated with a 6.71% change in annual U.S. GDP growth, relative to average GDP growth. [ABSTRACT FROM AUTHOR]

Sudden stratospheric warming (SSW) events can form a window of forecast opportunity for polar vortex predictions on subseasonal‐to‐seasonal time scales. Analyzing numerical ensemble simulations, we quantify the associated enhanced predictability due to reduced upward planetary wave fluxes during the mostly radiatively driven recovery phase following SSWs. Ensembles that predict an SSW show reduced ensemble spread in terms of polar vortex strength for several weeks to follow, as well as a corresponding reduction in forecast errors. This increased predictability is particularly pronounced for strong SSWs and even occurs if not all ensemble members predict a major SSW. Furthermore, we found a direct impact of the occurrence of SSWs on the date of the final warming (FW): the decrease in upward wave fluxes delays the FW significantly. The reduced spread after SSWs and the delay in FW date have potentially further implications for (subseasonal) predictions of the tropospheric and mesospheric circulations. Plain Language Summary: The polar vortex is a large scale circulation active during winter in the higher levels of the polar atmosphere. Changes in the strength of the polar vortex can have an impact on the weather over mid‐latitude regions like Europe. This is the case especially for the period after so‐called sudden stratospheric warming (SSW) events, where the polar vortex breaks down very abruptly and then slowly recovers over several weeks. Such a break‐down of the polar vortex tends to suppress wave activity and hence reduces the dynamical variability in the polar stratosphere, leading to a more predictable evolution of the circulation. We quantify the strength and timescale of this increase in predictability of the polar vortex after an SSW using a large set of winter time model forecasts. Key Points: Sudden stratospheric warmings (SSWs) lead to reduced forecast spread in the polar stratosphere for several weeks after the eventReduced forecast spread after SSWs is driven by suppressed vertical planetary wave propagation due to persistent negative wind anomaliesFinal warmings are delayed for winters with SSW, consistent with reduced upward wave fluxes following the SSW [ABSTRACT FROM AUTHOR]

SNOwpack TELemetry observations and model simulations both demonstrate that the fractional contribution of October through December (early season) snowfall to peak snow accumulation in North America increases with latitude due to both colder temperatures and Pacific storm tracks focusing further north earlier in the season. Early season snowfall also makes up greater than 60 percent of peak accumulation in interior low‐precipitation locations leeward of mountains, particularly those that are subject to strong, warm winds and midwinter snow loss. Early season snow observations show promise in predicting peak snow water equivalent in locations where large‐scale ocean‐atmosphere patterns similarly influence fall and winter conditions, and in northern maritime locations where winter temperatures are warm enough that rain on snow and midwinter melt occur. Because climate change is likely to increase the extent of midwinter melt, the latter relationship is expected to become important over more locations in the future. Plain Language Summary: Across North America, snow accumulates from October until the following spring. Early season snow, defined as the snow that accumulates by the end of December, is a greater fraction of total snow accumulation at higher latitudes and at colder locations. Early season snow accumulation can be used to predict peak snow accumulation. Predictions are more skillful at locations where fall precipitation is correlated with winter precipitation because more snow at the end of December indicates that weather patterns are favorable for greater than average snowfall to continue. Predictions also have skill at warmer northern locations where more snow on the ground early season increases the likelihood that mixed rain‐snow events add water to the snowpack rather than melting the snow. Predictions at warm southern locations do not have skill because winter and spring snowfall is highly variable, and frequently little to no snow has accumulated by the end of December. Key Points: Fall snow contributes more to peak snow at colder and more northerly locationsFall snow can be used to predict peak snow where large scale ocean and atmosphere patterns influence annual snowfall variationsFall snow regulates cold content, which in warm regions may tip a snowpack between lasting or ablating midwinter, influencing peak snow [ABSTRACT FROM AUTHOR]

The mesoscale orography over one of the wettest regions of the world makes the Northeast India (NEI) vulnerable to hydrological disasters while sustaining a biodiversity "hotspot." The monsoon rainy season over the NEI is known to be longer than June–September (JJAS), but an objective definition has been lacking. Understanding the drivers and predictability of rainfall variability over the region is key for sustainable development planning and adaptation to increasing disasters in the backdrop of a warming climate but remained poor due to lack of a working definition of "summer monsoon season" over NEI. Here, using the relationship between rainfall over the region and winds at 850 hPa over North Bay of Bengal (BoB), we provide an objective definition of the monsoon season over NEI and argue that the "summer monsoon rainy season" over the NEI (NEIR) is from May to September (MJJAS). In contrast to a significant negative relationship between JJAS Central India (CI) rainfall and a JJAS El Niño–Southern Oscillation (ENSO) index, the MJJAS rainfall over the NEI has no relationship with MJJAS or JJAS ENSO. Instead, we unravel that (a) the tropical Northwest‐Pacific (TNWP) sea surface temperature (SST), (b) the South Equatorial Indian Ocean Dipole (SEIOD) and (c) the Atlantic Zonal mode (AZM) are potential drivers of the NEIR variability. Using a causal inference algorithm, namely the Peter and Clark Momentary Conditional Independence (PCMCI) method, we show that SEIOD directly influences NEIR while TNWP SST has a two‐way connection with the NEIR. While "internal variability" may be higher over the NEI, significant modulation of the variances of the subseasonal fluctuations by predictable drivers like the Atlantic Niño and North Atlantic water temperature provides optimism for seasonal prediction of the NEIR. [ABSTRACT FROM AUTHOR]

The growing number of cross‐listed stocks has significantly contributed to the integration of national economies over the last few decades, and has consequently strengthened the interdependence between international stock markets. This phenomenon has sparked a fervent debate in academia and in decision‐making circles dealing with volatility spillovers between international stock prices. Accordingly, isolated examinations of single foreign cross‐listing markets appear inadequate. On this basis, the aim of this article is to recognize volatility transmission patterns within international equity markets using a unique and comprehensive sample of highly traded companies simultaneously cross‐listed in the United States and the UK. An important fact to be noted here is that long‐range dependence is statistically proved in the studied cases, which means that the efficient market hypothesis is utterly rejected. Therefore, a multivariate fractionally co‐integrated model is preliminarily used to fit various samples of high‐frequency time series. Resulting residuals are then subject to a deeper analysis, allowing us to reveal significant volatility transmissions between markets of multiple cross‐listed stocks. Causality experiments are finally conducted on processed data. The results show strong bi‐directional volatility causations between various marketplaces. The overall deduction of this work is that price discovery takes place essentially in local and British markets, thus implying a satellite role in the price discovery process for American exchanges. [ABSTRACT FROM AUTHOR]

Recent studies suggest seasonal forecasts for European winters are now skilful, but they also identify a "signal‐to‐noise paradox", wherein models predict the real world more skilfully (higher correlation) than the evolution of their ensemble members. Here, we analyse seasonal hindcasts from the Met Office GloSea5 seasonal forecast system to identify sources of predictability and seek insight into the signal‐to‐noise problem. For the first time, we use an optimal detection method to identify predictable signals over the North Atlantic region within the forecast system on subseasonal time‐scales. We find two primary predictable modes: a Pacific North America (PNA)‐like mode and a North Atlantic oscillation (NAO)‐like mode. The latter is the leading predictable mode in December–January, and its spatial pattern closely resembles the NAO. The PNA‐like mode dominates in January–February. Whereas the PNA‐like mode is driven by Pacific Ocean sea‐surface temperatures, the NAO‐like mode is driven at least partly by Indian Ocean sea‐surface temperatures, not solely due to the common trend. We develop a novel method of comparing the magnitude of these modes in the forecast system and observations that complements previous approaches. This suggests that the signal‐to‐noise problem in GloSea5 is primarily a feature of the December–January NAO‐like mode, with the observed mode being three times larger than in the model. The magnitude of the PNA‐like mode is better captured by the forecasts, although there is still evidence of a weaker signal‐to‐noise problem. This suggests particular mechanisms may lead to the lower signal to noise seen in NAO hindcasts, rather than a global weakness of the forecast system in responding to initialization and external forcing. Our results, though specific to GloSea5, provide insights into the causes of the signal‐to‐noise problem in seasonal forecasts of European winters. They also imply there is significant potential for improving such forecasts and suggest how such improvements may be achieved. [ABSTRACT FROM AUTHOR]

In 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China. [ABSTRACT FROM AUTHOR]

Renewable power generation from wind and solar energy is strongly dependent on the weather. To plan future sustainable energy systems that are robust to weather variability, a better understanding of why and when periods of low wind and solar power output occur is valuable. We call such periods of low wind speed and insolation "Dunkelflauten", the German word for "dark wind lulls". In this article, we analyse the meteorological conditions during Dunkelflauten in Germany by applying the concept of weather regimes. Weather regimes are quasi‐stationary, recurrent and persistent large‐scale circulation patterns that explain multi‐day atmospheric variability (5–15 days). We use a regime definition that allows us to distinguish four different types of blocked regimes, characterized by high‐pressure situations in the North Atlantic‐European region. We find that Dunkelflauten in Germany occur mainly in winter when the solar power output is low due to the seasonal cycle of solar irradiance and wind power output drops for several consecutive days. A high‐pressure system over Germany, associated with the European Blocking regime, is responsible for most of the Dunkelflauten. Dunkelflauten during the Greenland Blocking regime are associated with colder temperatures than usual, causing higher electricity demand, and would present a particular challenge as space heating becomes electrified in the future. Furthermore, we show that Dunkelflauten occur predominantly when a weather regime is well established and persists longer than usual. Our study provides novel insight into the occurrence and meteorological characteristics of Dunkelflauten, which is essential for planning resilient energy systems and supporting grid operators to prepare for potential shortages in supply. [ABSTRACT FROM AUTHOR]

For small ectotherm species, dealing with microclimatic variation during sensitive stages of their development is a critical component of their ecological interactions within and among different landscape settings. However, it is often unknown whether the use of habitats in different landscape settings may affect microclimatic conditions at the level of microhabitats (e.g., at host plants for herbivorous insects). As opposed to standard ecological monitoring experiments, we aim to identify the microclimatic variables that accurately represent the environment in which a small arthropod spends most of its life. As a case study, we selected grassy sites that corresponded to the microhabitat of grass‐feeding insects in general, and larvae of the butterfly Pararge aegeria in particular, as this insect recently expanded its habitat use from forest (edges) to agricultural and urban environments. We tested the extent to which local microclimates and microclimatic buffering capacity differed between tufts of grass in forest and in two anthropogenic (i.e., agricultural and urban) landscape settings by measuring microclimatic variables with sensors at the level of the grasses. We compared temperature, relative humidity, and vapor pressure deficit (VPD) during an exceptionally warm and dry summer period with parallel data from weather stations and tested for differences between microclimatic profiles among the three landscape settings. Microclimatic conditions correspond only partly to variables based on weather station data. Differences are particularly significant for relative humidity and VPD, but not for day and night temperatures. Effects of canopy openness on the variability of microclimatic variables are landscape‐specific. Host plants in agricultural settings experience strong microclimatic fluctuations and low short‐term variability in VPD. In urban settings, urban heat island effects increase night temperature but also show microclimatic buffering effects similar to the ones in forest settings. Our findings stress the functional implications of landscape‐specific microclimatic profiles at the appropriate organism‐centered scale. We discuss the significance of such microclimatic profiles for ecological studies dealing with ectotherms under climate and land use change. [ABSTRACT FROM AUTHOR]

The Once‐in‐a‐Century extreme rainstorm event caused severe floods over Henan province during July 18–21, 2021, which resulted in large casualty and property losses. Although the rainstorm event occurred in Henan after July 18, the excessive rainfall had occurred to the east of Henan before July 18, with the 4‐day accumulated rainfall exceeding +130 mm during July 14–17, 2021. How the rainfall evolving westward and intensifying after July 18 remained a puzzle, which is the focus of this study. The prerainstorm stage (July 14–17) was related to the South Asian High (SAH) extending eastward and the western Pacific subtropical high (WPSH) extending northwestward, and a low vortex between the SAH and WPSH caused above‐normal rainfall to the east of Henan. The rainstorm stage (July 18–21) was associated with an inverted trough and excessive southerly and southeasterly water vapor transportation above Henan, which resulted from the combined effects of a deep trough in the upper troposphere and typhoon activities. Additionally, three subseasonal forecasting systems predicted this rainstorm event 3 days in advance, with the European Center for Medium Range Weather Forecasts (ECMWF) performing the best, which was related to a better prediction of the inverted trough and the water vapor transportation in the middle‐lower troposphere. These results advance our understanding of the extreme rainstorm event in July 2021 in Henan. [ABSTRACT FROM AUTHOR]

The impacts of weather forecast uncertainties have not been quantified in current air quality forecasting systems. To address this, we developed an efficient 2‐D convolutional neural network‐surface ozone ensemble forecast (2DCNN‐SOEF) system using 2‐D convolutional neural network and weather ensemble forecasts, and we applied the system to 216‐hr ozone forecasts in Shenzhen, China. The 2DCNN‐SOEF demonstrated comparable performance to current operating forecast systems and met the air quality level forecast accuracies required by the Chinese authorities up to 144‐hr lead time. Uncertainties in weather forecasts contributed 38%–54% of the ozone forecast errors at 24‐hr lead time and beyond. The 2DCNN‐SOEF enabled an "ozone exceedance probability" metric, which better represented the risks of air pollution given the range of possible weather outcomes. Our ensemble forecast framework can be extended to operationally forecast other meteorology‐dependent environmental risks globally, making it a valuable tool for environmental management. Plain Language Summary: Weather forecasts are intrinsically uncertain, but the impacts of that uncertainty on air quality forecasts are not explicitly quantified in current air quality forecast systems. We proposed here a surface ozone ensemble forecast system, analogous to modern weather ensemble forecast systems, to represent the probability distribution of forecasted surface ozone concentrations given 30–50 possible future weather outcomes. The computation costs of this surface ozone ensemble forecast system were greatly reduced using deep learning techniques that emphasized the spatial patterns of weather. We showed that the surface ozone ensemble forecast system's accuracy met the Chinese operational requirements. However, half of the ozone forecast error was due to weather forecast uncertainties, which cannot be completely eliminated even with perfect pollutant emission estimates and chemistry models. This weather‐induced innate uncertainty in air quality forecasts should be considered for effective air quality management. Key Points: We built a deep‐learning surface ozone ensemble forecast system to quantify pollution risks given the range of possible weather outcomesDeep‐learning models accentuating the spatial patterns of weather effectively represented the ozone‐meteorology relationshipWeather forecast uncertainties contributed 38%–54% of the ozone forecast errors at 24‐hr lead time in Shenzhen [ABSTRACT FROM AUTHOR]

This paper expands on work showing that the winter North Atlantic Oscillation (NAO) is predictable on decadal timescales to quantify the skill in capturing the North Atlantic eddy‐driven jet's location and speed. By focusing on decadal predictions made for years 2–9 from the sixth Coupled Model Intercomparison Project over 1960–2005 we find that there is significant skill in jet latitude and, especially, jet speed associated with the skill in the NAO. However, the skill in the NAO, jet latitude and speed indices appears to be sensitive to the period over which it is assessed. In particular, skill drops considerably when evaluating hindcasts up to the present day as models fail to capture the recent observed northern shift and strengthening of the winter eddy‐driven jet, and more thus positive NAO. We suggest that the drop in atmospheric circulation skill is related to reduced skill in North Atlantic sea surface temperatures. Plain Language Summary: Climate models have the capability to predict the evolution of mean atmospheric circulation over long timescales, from annual to decadal and longer. However, models are overestimating the chaotic, unpredictable component of the climate's variability and, although model predictions follow the observed oscillations of the climate, the strength of these oscillations is critically underestimated. While climate models have demonstrated skill in predicting the North Atlantic Oscillation (NAO), this paper assesses model skill in predicting the evolution of the wintertime North Atlantic eddy‐driven jet, with the aim to highlight how much of the predictive skill of the NAO is derived from accurate predictions of the jet's state. We find levels of skill similar to that for the NAO, with slightly higher skill for the jet's strength (speed) over its location (latitude of maximum speed). We also notice a drop in skill over the last decade, as models fail to capture the latest trends in the NAO and jet's evolution, and suggest that it might be related to degradation in skill at predicting surface temperature variability. Key Points: The winter North Atlantic eddy‐driven jet is predictable on decadal time‐scales with skill in jet speed comparable to that for the North Atlantic Oscillation (NAO)Anomalies in the jet are substantially smaller than expected from skill alone and so suffer from noise overestimationSkill drops significantly in the last decade, as hindcasts do not capture the return to positive NAO conditions post‐2010 [ABSTRACT FROM AUTHOR]