Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

A possible linkage of Eurasian heat wave and East Asian heavy rainfall in Relation to the Rapid Arctic warming.

In June-July 2020 two remarkable weather events occurred in northern Eurasia. One is a severe heat wave that produced a record-breaking temperature of 38 °C at Verkhoyansk, eastern Siberia on 20 June. The other one is heavy rainfall events observed in East Asia (southern China and southwestern Japan) in early July, causing severe floods that brought about considerable damage to infrastructure and the economy, as well as the loss of human lives. Despite the accumulated evidence of stronger and more extreme heat waves and heavy rainfall as a result of global warming, little is known about the linkage between these phenomena. Given that the Arctic is warming twice as fast as the global mean, Arctic warming might be enhancing the increase of heavy rainfall events in East Asia. Here, we investigated the relationship between the Siberian heat wave and the East Asian heavy rainfall that occurred summer in 2020. An empirical orthogonal function (EOF) analysis applied to atmospheric reanalysis data of 1958-2020 period captures dominant summer circulation patterns reflecting atmospheric internal variability and externally forced anomalies. On the basis of these EOF patterns, operational forecasts of summer 2020 using the global model from the Japan Meteorological Agency (JMA) and a global climate model experiment based on 2-K warming future projection are utilized to examine roles of the internal variability and external forcing, respectively. Consistent results between them reveal that development of the blocking high over eastern Siberia has certain impacts on rainfall anomalies over East Asia. By a statistical technique applied to the ensemble forecast data, prediction of the East Asian precipitation is improved by 10-20% of its amplitude. Our research demonstrates possibility that East Asian rainfall is being enhanced by high-latitude atmospheric circulations due to the Arctic warming even in the current climate in which the tropical warming is not yet severe. Suggestions are given that continued Arctic warming and a future increase of tropical warming will lead to increases of the frequency and severity of heavy rainfall events in East Asia.

Conservation biogeography of the Cerrado's wild edible plants under climate change: Linking biotic stability with agricultural expansion.

UNLABELLED: REMISE OF THE STUDY: Wild edible plants (WEPs) have an important cultural and economic role in human population worldwide. Human impacts are quickly converting natural habitats in agricultural, cattle ranch, and urbanized lands, putting native species on peril of risk of extinction, including some WEPs. Moreover, global climate changes also can pose another threat to species persistency. Here, we established conservation priorities for the Cerrado, a neotropical region in South America with high levels of plant endemism and vulnerability, aiming to assure long-term persistency of 16 most important WEPs. We evaluated these conservation priorities using a conservation biogeography framework using ecological patterns and process at a biogeographical scale to deal with species conservation features. METHODS: We built ecological niche models for 16 WEPs from Cerrado in the neotropics using climate models for preindustrial, past (Last Glacial Maximum) and future (year 2080) time periods to establish climatically stable areas through time, finding refugias for these WEPs. We used a spatial prioritization algorithm based on the spatial pattern of irreplaceability across the neotropics, aiming to ensure the persistence of at least 25% of range size in climatically stable areas for each WEP, using agricultural models as constraints. KEY RESULTS: The Southeast Cerrado was the most biotically stable and irreplaceable region for the WEPs compared with other areas across the neotropics. CONCLUSIONS: Our findings strongly suggest that the Southeast Cerrado should be considered a conservation priority, with new protected areas to be sustainably managed and restored, to guarantee the supply of cultural and ecosystem services provided from the Cerrado's WEPs.

Application progress of ensemble forecast technology in influenza forecast based on infectious disease model.

To comprehensively understand the application progress of ensemble forecast technology in influenza forecast based on infectious disease model, so as to provide scientific references for further research. In this study, two keywords of "influenza" and "ensemble forecast" are selected to search and select the relevant literatures, which are then outlined and summarized. It is found that: In recent years, some studies about ensemble forecast technology for influenza have been reported in the literature, and some well-performed influenza ensemble forecast systems have already been operationally implemented and provide references for scientific prevention and control. In general, ensemble forecast can well represent various uncertainties in forecasting influenza cases based on infectious disease models, and can achieve more accurate forecasts and more valuable information than single deterministic forecast. However, there are still some shortcomings in the current studies, it is suggested that scientists engaged in influenza forecast based on infectious disease models strengthen cooperation with scholars in the field of numerical weather forecast, which is expected to further improve the skills and application level of ensemble forecast for influenza.

Real-time forecasting of epidemic trajectories using computational dynamic ensembles.

Forecasting the trajectory of social dynamic processes, such as the spread of infectious diseases, poses significant challenges that call for methods that account for data and model uncertainty. Here we introduce an ensemble model for sequential forecasting that weights a set of plausible models and use a frequentist computational bootstrap approach to evaluate its uncertainty. We demonstrate the feasibility of our approach using simple dynamic differential-equation models and the trajectory of outbreak scenarios of the Ebola Forecasting Challenge. Specifically, we generate sequential short-term forecasts of epidemic outbreaks by combining phenomenological models that incorporate flexible epidemic growth scaling, namely the Generalized-Growth Model (GGM) and the Generalized Logistic Model (GLM). We rely on the root-mean-square error (RMSE) to quantify the quality of the models' fits during the calibration periods for weighting their contribution to the ensemble model while forecasting performance was evaluated using the RMSE of the forecasts. For a given forecasting horizon (1-4 weeks), we report the performance for each model as the percentage of the number of times each model outperforms the other models. The overall mean RMSE performance of the GLM and the GGM-GLM ensemble models outcompeted that of participant models of the Ebola Forecasting Challenge. We also found that the ensemble model provided more accurate forecasts with higher frequency than the GGM and GLM models, but its performance varied across forecasting horizons. For instance, across all of the Ebola Challenge Scenarios, the ensemble model outperformed the other models at horizons of 2 and 3 weeks while the GLM outperformed other models at horizons of 1 and 4 weeks.

Coupling GIS spatial analysis and Ensemble Niche Modelling to investigate climate change-related threats to the Sicilian pond turtle Emys trinacris, an endangered species from the Mediterranean.

The pond turtle Emys trinacris is an endangered endemic species of Sicily showing a fragmented distribution throughout the main island. In this study, we applied "Ensemble Niche Modelling", combining more classical statistical techniques as Generalized Linear Models and Multivariate Adaptive Regression Splines with machine-learning approaches as Boosted Regression Trees and Maxent, to model the potential distribution of the species under current and future climatic conditions. Moreover, a "gap analysis" performed on both the species' presence sites and the predictions from the Ensemble Models is proposed to integrate outputs from these models, in order to assess the conservation status of this threatened species in the context of biodiversity management. For this aim, four "Representative Concentration Pathways", corresponding to different greenhouse gases emissions trajectories were considered to project the obtained models to both 2050 and 2070. Areas lost, gained or remaining stable for the target species in the projected models were calculated. E. trinacris' potential distribution resulted to be significantly dependent upon precipitation-linked variables, mainly precipitation of wettest and coldest quarter. Future negative effects for the conservation of this species, because of more unstable precipitation patterns and extreme meteorological events, emerged from our analyses. Further, the sites currently inhabited by E. trinacris are, for more than a half, out of the Protected Areas network, highlighting an inadequate management of the species by the authorities responsible for its protection. Our results, therefore, suggest that in the next future the Sicilian pond turtle will need the utmost attention by the scientific community to avoid the imminent risk of extinction. Finally, the gap analysis performed in GIS environment resulted to be a very informative post-modeling technique, potentially applicable to the management of species at risk and to Protected Areas' planning in many contexts.

Increasing horizontal resolution in numerical weather prediction and climate simulations: illusion or panacea?

The steady path of doubling the global horizontal resolution approximately every 8 years in numerical weather prediction (NWP) at the European Centre for Medium Range Weather Forecasts may be substantially altered with emerging novel computing architectures. It coincides with the need to appropriately address and determine forecast uncertainty with increasing resolution, in particular, when convective-scale motions start to be resolved. Blunt increases in the model resolution will quickly become unaffordable and may not lead to improved NWP forecasts. Consequently, there is a need to accordingly adjust proven numerical techniques. An informed decision on the modelling strategy for harnessing exascale, massively parallel computing power thus also requires a deeper understanding of the sensitivity to uncertainty--for each part of the model--and ultimately a deeper understanding of multi-scale interactions in the atmosphere and their numerical realization in ultra-high-resolution NWP and climate simulations. This paper explores opportunities for substantial increases in the forecast efficiency by judicious adjustment of the formal accuracy or relative resolution in the spectral and physical space. One path is to reduce the formal accuracy by which the spectral transforms are computed. The other pathway explores the importance of the ratio used for the horizontal resolution in gridpoint space versus wavenumbers in spectral space. This is relevant for both high-resolution simulations as well as ensemble-based uncertainty estimation.