Pushing the boundary of seasonal prediction with the lever of varying annual cycles.

Predicting climate anomalies months in advance is of tremendous socioeconomic value. Facing both theoretical and practical constraints, this realm of "seasonal prediction" progressed slowly in recent decades. Here we devise an innovative scheme that pushes the boundary of seasonal prediction by recognizing and isolating distinct spatiotemporal footprints left by modes of climate variability that cause varying annual cycles in response to the solar forcing. The predictive power harnessed from these spatiotemporal footprints results in a prediction skill surpassing existing models for seasonal forecasts of eastern China rainfall, which is one of the most challenging seasonal prediction problems. By considering varying annual cycles explicitly, the new scheme is able to predict multi-provincial flood and/or drought occurrences seamlessly over an entire year. This novel scheme is generically applicable for improving seasonal forecasts over other monsoon regions and for critical climate variables such as surface temperature and Arctic sea-ice extent.

Acute, diel, and annual temperature variability and the thermal biology of ectotherms.

Global warming is increasing mean temperatures and altering temperature variability at multiple temporal scales. To better understand the consequences of changes in thermal variability for ectotherms it is necessary to consider thermal variation at different time scales (i.e., acute, diel, and annual) and the responses of organisms within and across generations. Thermodynamics constrain acute responses to temperature, but within these constraints and over longer time periods, organisms have the scope to adaptively acclimate or evolve. Yet, hypotheses and predictions about responses to future warming tend not to explicitly consider the temporal scale at which temperature varies. Here, focusing on multicellular ectothermic animals, we argue that consideration of multiple processes and constraints associated with various timescales is necessary to better understand how altered thermal variability because of climate change will affect ectotherms.

Interrelationship among annual cycles of sex steroids, corticosterone and body condition in Nyctibatrachus humayuni.

Synergism between extrinsic and intrinsic factors is crucial for the seasonality of reproduction. Environmental factors such as photoperiod and temperature activate the hypothalamus-pituitary-gonadal axis leading to the secretion of steroid hormones that are crucial for reproduction. Sex steroids are not only essential for the maturation of gonads, but also for development of secondary sexual characters in males and reproductive behaviour of both the sexes. In the present study, we quantified the urinary testosterone (UTM) and corticosterone (UCM) metabolites in males and urinary estradiol metabolites (UEM) and UCM in females of Nyctibatrachus humayuni for two consecutive years to determine annual and seasonal variation in the levels of sex steroids, corticosterone and body condition index (BCI). The results show that sex steroids were highest during the breeding season and lowest during the non-breeding season in both the sexes. An increase in UTM and UEM was observed in males and females respectively during the breeding season. Testicular histology showed the presence of all stages of spermatogenesis throughout the year indicating that spermatogenesis is potentially continuous. Ovarian histology showed the presence of vitellogenic follicles only during the breeding season indicating that oogenesis is strictly seasonal. In males, UCM levels were highest during the breeding season, while in females their levels were highest just prior to the breeding season. In males, BCI was highest during the pre-breeding season, declined during the breeding season to increase again during the post-breeding season. In females, BCI was comparable throughout the year. In males, UTM levels were positively correlated with UCM levels but negatively correlated with BCI. Interestingly, UEM, UCM and BCI were not correlated in females. These results indicate that N. humayuni exhibits an associated pattern of reproduction. Quantification of urinary progesterone metabolites (UPM) during the breeding season showed UPM levels were higher in post-spawning females, suggesting the significance of progesterone in ovulation. Further, non-invasive enzyme immunoassay has been successfully standardized in N. humayuni for the quantification of urinary metabolites of steroid hormones.

Student's tutorial on bloom hypotheses in the context of phytoplankton annual cycles.

Phytoplankton blooms are elements in repeating annual cycles of phytoplankton biomass and they have significant ecological and biogeochemical consequences. Temporal changes in phytoplankton biomass are governed by complex predator-prey interactions and physically driven variations in upper water column growth conditions (light, nutrient, and temperature). Understanding these dependencies is fundamental to assess future change in bloom frequency, duration, and magnitude and thus represents a quintessential challenge in global change biology. A variety of contrasting hypotheses have emerged in the literature to explain phytoplankton blooms, but over time the basic tenets of these hypotheses have become unclear. Here, we provide a "tutorial" on the development of these concepts and the fundamental elements distinguishing each hypothesis. The intent of this tutorial is to provide a useful background and set of tools for reading the bloom literature and to give some suggestions for future studies. Our tutorial is written for "students" at all stages of their career. We hope it is equally useful and interesting to those with only a cursory interest in blooms as those deeply immersed in the challenge of understanding the temporal dynamics of phytoplankton biomass and predicting its future change.

Insights Into the Morphology of the East Asia PM2.5 Annual Cycle Provided by Machine Learning.

The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less (PM2.5) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM2.5 abundance and meteorological variables, but some of the relationships are nonlinear, non-Gaussian, and even unknown. Machine learning provides a broad range of practical algorithms to help examine this issue. In this study, we use machine learning to classify the morphology of PM2.5 seasonal cycles in East Asia. Machine learning is able to objectively classify the seasonal cycles and, without a priori assumption, is able to clearly distinguish between urban and rural areas. We show an example of this in the Sichuan Basin of China. Furthermore, machine learning is also able to provide physical insights by identifying the key factors associated with each distinct shape of the seasonal cycle, such as highlighting the key role played by the topography and the built environment.

Identifying seasonal and temporal trends in the pressures experienced by hospitals related to unscheduled care.

BACKGROUND: As part of an electronic dashboard operated by Public Health Wales, senior managers at hospitals in Wales report daily "escalation" scores which reflect management opinion on the pressure a hospital is experiencing and ability to meet ongoing demand with respect to unscheduled care. An analysis was undertaken of escalation scores returned for 18 hospitals in Wales between the years 2006 and 2014 inclusive, with a view to identifying systematic temporal patterns in pressure experienced by hospitals in relation to unscheduled care. METHODS: Exploratory data analysis indicated the presence of within-year cyclicity in average daily scores over all hospitals. In order to quantify this cyclicity, a Generalised Linear Mixed Model was fitted which incorporated a trigonometric function (sine and cosine) to capture within-year change in escalation. In addition, a 7-level categorical day of the week effect was fitted as well as a 3-level categorical Christmas holiday variable based on patterns observed in exploration of the raw data. RESULTS: All of the main effects investigated were found to be statistically significant. Firstly, significant differences emerged in terms of overall pressure reported by individual hospitals. Furthermore, escalation scores were found to vary systematically within-year in a wave-like fashion for all hospitals (but not between hospitals) with the period of highest pressure consistently observed to occur in winter and lowest pressure in summer. In addition to this annual variation, pressure reported by hospitals was also found to be influenced by day of the week (low at weekends, high early in the working week) and especially low over the Christmas period but high immediately afterwards. CONCLUSIONS: Whilst unpredictable to a degree, quantifiable pressure experienced by hospitals can be anticipated according to models incorporating systematic temporal patterns. In the context of finite resources for healthcare services, these findings could optimise staffing schedules and inform resource utilisation.