Targeted model evaluations for climate services: a case study on heat waves in Bangladesh

Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model's climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly. The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability

Improved seasonal prediction skill of rainfall for the Priera season in Central America

This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low-level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated

Heavy rainfall in Paraguay during the 2015/16 austral summer: causes and subseasonal-to-seasonal predictive skill

During the austral summer 2015/16, severe flooding displaced over 170 000 people on the Paraguay River system in Paraguay, Argentina, and southern Brazil. These floods were driven by repeated heavy rainfall events in the lower Paraguay River basin. Alternating sequences of enhanced moisture inflow from the South American low-level jet and local convergence associated with baroclinic systems were conducive to mesoscale convective activity and enhanced precipitation. These circulation patterns were favored by cross-time-scale interactions of a very strong El Niño event, an unusually persistent Madden–Julian oscillation in phases 4 and 5, and the presence of a dipole SST anomaly in the central southern Atlantic Ocean. The simultaneous use of seasonal and subseasonal heavy rainfall predictions could have provided decision-makers with useful information about the start of these flooding events from two to four weeks in advance. Probabilistic seasonal forecasts available at the beginning of November successfully indicated heightened probability of heavy rainfall (90th percentile) over southern Paraguay and Brazil for December–February. Raw subseasonal forecasts of heavy rainfall exhibited limited skill at lead times beyond the first two predicted weeks, but a model output statistics approach involving principal component regression substantially improved the spatial distribution of skill for week 3 relative to other methods tested, including extended logistic regressions. A continuous monitoring of climate drivers impacting rainfall in the region, and the use of statistically corrected heavy precipitation seasonal and subseasonal forecasts, may help improve flood preparedness in this and other regions

Mechanical properties and corrosion behavior of Mg-HAP composites

Mg and Mg-HAP composites containing 5, 10 and 15 wt% of hydroxyapatite have been produced following a powder metallurgy route that consists of mixing raw powders and consolidation by extrusion. The microstructure, texture, mechanical behavior and resistance to corrosion under a PBS solution have been studied. Addition of HAP increases the microhardness of the composites, however the yield strength under compression slightly decreases. Texture analyses reveal a fiber texture for pure Mg that is weakened increasing the HAP fraction. This texture promotes twinning and softening of Mg and Mg-5HAP during the initial deformation stages. Mg-10HAP and Mg-15HAP present a strain-hardening dependence showing no softening. The volume fraction of HAP particles weakens the texture and favors the activation of secondary slip systems. Corrosion experiments in PBS solution have shown that Mg-5HAP exhibits the best resistance to corrosion. Texture and porosity appear to be the main material features controlling the corrosion rates of Mg-HAP composites under the present conditions.This work has been supported by the Regional Government of Madrid through the ESTRUMAT-CM Program (MAT-1585).Publicad

Analyzing climate variations at multiple timescales can guide Zika virus response measures

Background: The emergence of Zika virus (ZIKV) in Latin America and the Caribbean in 2014–2016 occurred during a period of severe drought and unusually high temperatures, conditions that have been associated with the 2015–2016 El Niño event, and/or climate change; however, no quantitative assessment has been made to date. Analysis of related flaviviruses transmitted by the same vectors suggests that ZIKV dynamics are sensitive to climate seasonality and longer-term variability and trends. A better understanding of the climate conditions conducive to the 2014–2016 epidemic may permit the development of climate-informed short and long-term strategies for ZIKV prevention and control. Results: Using a novel timescale-decomposition methodology, we demonstrate that the extreme climate anomalies observed in most parts of South America during the current epidemic are not caused exclusively by El Niño or climate change, but by a combination of climate signals acting at multiple timescales. In Brazil, the dry conditions present in 2013–2015 are primarily explained by year-to-year variability superimposed on decadal variability, but with little contribution of long-term trends. In contrast, the warm temperatures of 2014–2015 resulted from the compound effect of climate change, decadal and year-to-year climate variability. Conclusions: ZIKV response strategies made in Brazil during the drought concurrent with the 2015-2016 El Niño event, may require revision in light of the likely return of rainfall associated with the borderline La Niña event expected in 2016–2017. Temperatures are likely to remain warm given the importance of long term and decadal scale climate signals. Keywords: Zika virus Epidemic Climate Climate change Decadal Inter-annual El Niño Brazil Drought Vector contro

Cross-time scales interactions and rainfall extreme events in southeastern South America for the austral summer. Part II: predictive skill

Potential and real predictive skill of the frequency of extreme rainfall in southeastern South America for the December–February season are evaluated in this paper, finding evidence indicating that mechanisms of climate variability at one time scale contribute to the predictability at another scale; that is, taking into account the interference of different potential sources of predictability at different time scales increases the predictive skill. Part I of this study suggested that a set of daily atmospheric circulation regimes, or weather types, was sensitive to these cross–time scale interferences, conducive to the occurrence of extreme rainfall events in the region, and could be used as a potential predictor. At seasonal scale, a combination of those weather types indeed tends to outperform all the other candidate predictors explored (i.e., sea surface temperature patterns, phases of the Madden–Julian oscillation, and combinations of both). Spatially averaged Kendall’s t improvements of 43% for the potential predictability and 23% for real-time predictions are attained with respect to standard models considering sea surface temperature fields alone. A new subseasonal-to-seasonal predictive methodology for extreme rainfall events is proposed based on probability forecasts of seasonal sequences of these weather types. The cross-validated real-time skill of the new probabilistic approach, as measured by the hit score and the Heidke skill score, is on the order of twice that associated with climatological values. The approach is designed to offer useful subseasonal-to-seasonal climate information to decision-makers interested not only in how many extreme events will happen in the season but also in how, when, and where those events will probably occur

Climate information for adaptation: from years to decades

In writing this book, we have prioritized shorter timescales of weather and climate variability because these have most direct relevance to the types of operational and planning decisions made in the health sector. However, in this chapter, we focus on longer-term health decisions that require climate knowledge and information at timescales beyond a year to multiple decades (up to 50 years). This time span incorporates both natural decadal variability and the influences of anthropogenic climate change. It is an extremely challenging timeframe at which to work because of the very limited operational predictive skill at multiannual timescales, and limited confidence about how rainfall may change on longer timescales. Despite these challenges, the timescales under consideration, especially the next five to 20 years, are particularly important when considering how to adapt to climate change. Here we discuss what we do and do not know about the climate in the coming years. We then focus on the practical use of climate information in understanding observed climate impacts on health as well as the prediction of multi-annual to multi-decadal risks. The multi-annual timescale is often referred to as ‘decadal’, even though the forecasts are for periods less than a decade

AEMIX: semantic verification of weather forecasts on the web

Ponencia presentada en: 12th International Conference on Web Information Systems and Technologies celebrada en Roma del 23 al 25 de abril de 2016The main objectives of a meteorological service are the development, implementation and delivery of weather forecasts. Weather predictions are broadcasted to society through different channels, i.e. newspaper, television, radio, etc. Today, the use of theWeb through personal computers and mobile devices stands out. The forecasts, which can be presented in numerical format, in charts, or in written natural language, have a certain margin of error. Providing automatic tools able to assess the precision of predictions allows to improve these forecasts, quantify the degree of success depending on certain variables (geographic areas, weather conditions, time of year, etc.), and focus future work on areas for improvement that increase such accuracy. Despite technological advances, the task of verifying forecasts written in natural language is still performed manually by people in many cases, which is expensive, time-consuming, and subjected to human errors. On the other hand, weather forecasts usually follow several conventions in both structure and use of language, which, while not completely formal, can be exploited to increase the quality of the verification. In this paper, we describe a methodology to quantify the accuracy of weather forecasts posted on the Web and based on natural language. This work obtains relevant information from weather forecasts by using ontologies to capture and take advantage of the structure and language conventions. This approach is implemented in a framework that allows to address different types of predictions with minimal effort. Experimental results with real data are promising, and most importantly, they allow direct use in a real meteorological service.This research work has been supported by the CICYT project TIN2013-46238-C4-4-R, and DGAFS

Critical wetting of a class of nonequilibrium interfaces: A mean-field picture

A self-consistent mean-field method is used to study critical wetting transitions under nonequilibrium conditions by analyzing Kardar-Parisi-Zhang (KPZ) interfaces in the presence of a bounding substrate. In the case of positive KPZ nonlinearity a single (Gaussian) regime is found. On the contrary, interfaces corresponding to negative nonlinearities lead to three different regimes of critical behavior for the surface order-parameter: (i) a trivial Gaussian regime, (ii) a weak-fluctuation regime with a trivially located critical point and nontrivial exponents, and (iii) a highly non-trivial strong-fluctuation regime, for which we provide a full solution by finding the zeros of parabolic-cylinder functions. These analytical results are also verified by solving numerically the self-consistent equation in each case. Analogies with and differences from equilibrium critical wetting as well as nonequilibrium complete wetting are also discussed.Comment: 11 pages, 2 figure