An Analogue Approach to Identify Heavy Precipitation Events: Evaluation and Application to CMIP5 Climate Models in the United States

An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979–2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency.United States. National Aeronautics and Space Administration. Energy and Water Cycle Study Research Announcement (NNH07ZDA001N)National Science Foundation (U.S.). MacroSystems Biology Program (NSF-AES EF 1137306

WMO Space-Based Weather and Climate Extremes Monitoring Demonstration Project (SEMDP): First Outcomes of Regional Cooperation on Drought and Heavy Precipitation Monitoring for Australia and Southeast Asia

To improve monitoring of extreme weather and climate events from space, the World Meteorological Organization (WMO) initiated the space-based weather and climate extremes monitoring demonstration project (SEMDP). Presently, SEMDP is focused on drought and heavy precipitation monitoring over Southeast Asia and the Pacific. Space-based data and derived products form critical part of meteorological services’ operations for weather monitoring; however, satellite products are still not fully utilized for climate applications. Using SEMDP satellite-derived precipitation products, it would be possible to monitor extreme precipitation events with uniform spatial coverage and over various time periods – pentad, weekly, 10 days, monthly and longer time-scales. In this chapter, SEMDP satellite-derived precipitation products over the Asia-Pacific region produced by the Earth Observation Research Center/Japan Aerospace Exploration Agency (EORC/JAXA) and the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA) are introduced. Case studies for monitoring (i) drought in Australia in July-October 2007 and September 2018 and (ii) heavy precipitation over Australia in December 2010 and Thailand and the Peninsular Malaysia in November-December 2014 which caused widespread flooding are also presented. Satellite observations are compared with in situ data to demonstrate value of satellite-derived estimates of precipitation for drought and heavy rainfall monitoring

The Emerging of Hydrovoltaic Materials as a Future Technology: A Case Study for China

Water contains tremendous energy in various forms, but very little of this energy has yet been harvested. Nanostructured materials can generate electricity by water-nanomaterial interaction, a phenomenon referred to as hydrovoltaic effect, which potentially extends the technical capability of water energy harvesting. In this chapter, starting by describing the fundamental principle of hydrovoltaic effect, including water-carbon interactions and fundamental mechanisms of harvesting water energy with nanostructured materials, experimental advances in generating electricity from water flows, waves, natural evaporation, and moisture are then reviewed. We further discuss potential applications of hydrovoltaic technologies, analyze main challenges in improving the energy conversion efficiency and scaling up the output power, and suggest prospects for developments of the emerging technology, especially in China

Relatively slow stochastic gene-state switching in the presence of positive feedback significantly broadens the region of bimodality through stabilizing the uninduced phenotypic state

Within an isogenic population, even in the same extracellular environment, individual cells can exhibit various phenotypic states. The exact role of stochastic gene-state switching regulating the transition among these phenotypic states in a single cell is not fully understood, especially in the presence of positive feedback. Recent high-precision single-cell measurements showed that, at least in bacteria, switching in gene states is slow relative to the typical rates of active transcription and translation. Hence using the lac operon as an archetype, in such a region of operon-state switching, we present a fluctuating-rate model for this classical gene regulation module, incorporating the more realistic operon-state switching mechanism that was recently elucidated. We found that the positive feedback mechanism induces bistability (referred to as deterministic bistability), and that the parameter range for its occurrence is significantly broadened by stochastic operon-state switching. We further show that in the absence of positive feedback, operon-state switching must be extremely slow to trigger bistability by itself. However, in the presence of positive feedback, which stabilizes the induced state, the relatively slow operon-state switching kinetics within the physiological region are sufficient to stabilize the uninduced state, together generating a broadened parameter region of bistability (referred to as stochastic bistability). We illustrate the opposite phenotype-transition rate dependence upon the operon-state switching rates in the two types of bistability, with the aid of a recently proposed rate formula for fluctuating-rate models. The rate formula also predicts a maximal transition rate in the intermediate region of operon-state switching, which is validated by numerical simulations in our model. Overall, our findings suggest a biological function of transcriptional “variations” among genetically identical cells, for the emergence of bistability and transition between phenotypic states

Efficacy and Safety of Agents in IgA Nephropathy: An Update Network Meta-Analysis

Background/Aims: The present network meta-analysis of randomized controlled trials (RCTs) was to explore the efficacy and safety of different pharmacologic interventions in IgA nephropathy with proteinuria more than 0.75 g/d. Methods: We systematically searched the Cochrane Library, Embase, and PubMed database for studies compared the rate of clinical remission and/or serious adverse events in IgA nephropathy patients with proteinuria (> 0.75 g/d) up to August 1, 2018. We ranked the comparative effects of all drugs against placebo on the surface under the cumulative ranking area (SUCRA) probabilities. Results: There were 29 RCTs comprising 2517 participants included for the comparisons of 9 interventions. The rank of the most effective treatments for inducing clinical remission was renin-angiotensin system inhibitors (RASi) in combination with steroid, tonsillectomy combined with steroid pulse therapy, and azathioprine plus RASi with SUCRA of 82.9%, 80.5%, and 67.6%, respectively. RASi in combination with steroid (SUCRA 3.9%) was the most effective in prevention of end-stage renal disease or doubling serum creatinine, followed by RASi monotherapy (SUCRA 38.4%) and azathioprine combined with steroid (SUCRA 49.0%). As for the occurrence of serious adverse events, azathioprine combined with RASi (SUCRA 88.0%) and steroid plus RASi (SUCRA 74.6%) showed the first and second highest incidence of adverse events, respectively. Conclusion: RASi combined with steroid demonstrated the most effective therapeutic approach for IgA nephropathy patients in terms of reducing proteinuria and stabilizing renal function

An insight into the structural evolution of waxy maize starch chains during growth based on nonlinear rheology

This work investigated the chain structure and large amplitude oscillatory shear (LAOS) rheological properties of waxy maize starch (WMS) at different growth periods and established a motion model to clarify the evolution of starch chain structure. During the growth period of 20–25 days, the apparent viscosity and dynamic moduli for WMS paste decreased, along with a greater degree of nonlinearity. This could be correlated with an increasing branching degree of starch due to more short-branched chains generated during growth. With the growth period reaching 25 days, the apparent viscosity, dynamic moduli and zero-strain nonlinearity (Q0) for WMS increased and the b value deviated from 2 significantly, indicating obvious nonlinearity accompanied by a weak stress overshoot and shear-thickening. This could be explained by the growing length of branched chains and the rising content of B3- and B4-chains. Thus, this work demonstrate LAOS rheology can be used as a new and effective method to characterize the chain structural evolution of starch through monitoring the motion pattern of different starch chains under shear conditions

Climate Risk and Early Warning Systems (CREWS) for Papua New Guinea

Developing and least developed countries are particularly vulnerable to the impact of climate change and climate extremes, including drought. In Papua New Guinea (PNG), severe drought caused by the strong El Niño in 2015–2016 affected about 40% of the population, with almost half a million people impacted by food shortages. Recognizing the urgency of enhancing early warning systems to assist vulnerable countries with climate change adaptation, the Climate Risk and Early Warning Systems (CREWS) international initiative has been established. In this chapter, the CREWS-PNG project is described. The CREWS-PNG project aims to develop an improved drought monitoring and early warning system, running operationally through a collaboration between PNG National Weather Services (NWS), the Australian Bureau of Meteorology and the World Meteorological Organization that will enable better strategic decision-making for agriculture, water management, health and other climate-sensitive sectors. It is shown that current dynamical climate models can provide skillful predictions of regional rainfall at least 3 months in advance. Dynamical climate model-based forecast products are disseminated through a range of Web-based information tools. It is demonstrated that seasonal climate prediction is an effective solution to assist governments and local communities with informed decision-making in adaptation to climate variability and change