Understanding and predicting seasonal-to-interannual climate variability - the producer perspective

Seasonal prediction is based on changes in the probability of weather statistics due to changes in slowly varying forcings such as sea surface temperature anomalies, most notably those associated with El Niňo–Southern Oscillation (ENSO). However, seasonal weather can be perturbed by many factors, and is very much influenced by internal variability of the atmosphere, so comprehensive models are needed to identify what can be predicted. The predictability and probabilistic nature of seasonal forecasts is explained with suitable examples. Current capabilities for seasonal prediction that have grown out of work done in the research community at both national and international levels are described. Dynamical seasonal prediction systems are operational or quasi-operational at a number of forecasting centres around the world. Requirements for seasonal prediction include initial conditions, particularly for the upper ocean but also other parts of the climate system; high quality models of the ocean-atmosphere-land system; and data for verification and calibration. The wider context of seasonal prediction and seamless forecasting is explained. Recommendations for the future of seasonal prediction and climate services are given

The Climate-system Historical Forecast Project: providing open access to seasonal forecast ensembles from centers around the globe

Fil: Tompkins, Adrian M.. The Abdus Salam; ItaliaFil: Ortiz de Zarate, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Vera, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Saulo, Andrea Celeste. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; ArgentinaFil: Merryfield, William J.. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Sigmond, Michael. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Lee, Woo Sung. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Baehr, Johanna. Universitat Hamburg; AlemaniaFil: Braun, Alain. Météo-France; FranciaFil: Amy Butler. National Ocean And Atmospheric Administration; Estados UnidosFil: Déqué, Michel. Météo-France; FranciaFil: Doblas Reyes, Francisco J.. Institució Catalana de Recerca i Estudis Avancats; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; EspañaFil: Gordon, Margaret. Met Office; Reino UnidoFil: Scaife, Adam A.. University of Exeter; Reino UnidoFil: Yukiko Imada. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Masayoshi Ishii. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Tomoaki Ose. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Kirtman, Ben. University of Miami; Estados UnidosFil: Kumar, Arun. National Ocean And Atmospheric Administration; Estados UnidosFil: Müller, Wolfgang A.. Max-Planck-Institut für Meteorologie; AlemaniaFil: Pirani, Anna. Université Paris-Saclay; FranciaFil: Stockdale, Tim. European Centre for Medium-Range Weather; Reino UnidoFil: Rixen, Michel. World Meteorological Organization. World Climate Research Programme; SuizaFil: Yasuda, Tamaki. Japan Meteorological Agency. Climate Prediction Division; Japó

Hurricanes and Climate: The U.S. CLIVAR Working Group on Hurricanes

While a quantitative climate theory of tropical cyclone formation remains elusive, considerable progress has been made recently in our ability to simulate tropical cyclone climatologies and to understand the relationship between climate and tropical cyclone formation. Climate models are now able to simulate a realistic rate of global tropical cyclone formation, although simulation of the Atlantic tropical cyclone climatology remains challenging unless horizontal resolutions finer than 50 km are employed. This article summarizes published research from the idealized experiments of the Hurricane Working Group of U.S. Climate and Ocean: Variability, Predictability and Change (CLIVAR). This work, combined with results from other model simulations, has strengthened relationships between tropical cyclone formation rates and climate variables such as midtropospheric vertical velocity, with decreased climatological vertical velocities leading to decreased tropical cyclone formation. Systematic differences are shown between experiments in which only sea surface temperature is increased compared with experiments where only atmospheric carbon dioxide is increased. Experiments where only carbon dioxide is increased are more likely to demonstrate a decrease in tropical cyclone numbers, similar to the decreases simulated by many climate models for a future, warmer climate. Experiments where the two effects are combined also show decreases in numbers, but these tend to be less for models that demonstrate a strong tropical cyclone response to increased sea surface temperatures. Further experiments are proposed that may improve our understanding of the relationship between climate and tropical cyclone formation, including experiments with two-way interaction between the ocean and the atmosphere and variations in atmospheric aerosols

Direct Numerical Simulation and Visualization of Subcooled Pool Boiling

A direct numerical simulation of the boiling phenomena is one of the promising approaches in order to clarify their heat transfer characteristics and discuss the mechanism. During these decades, many DNS procedures have been developed according to the recent high performance computers and computational technologies. In this paper, the state of the art of direct numerical simulation of the pool boiling phenomena during mostly two decades is briefly summarized at first, and then the nonempirical boiling and condensation model proposed by the authors is introduced into the MARS (MultiInterface Advection and Reconstruction Solver developed by the authors). On the other hand, in order to clarify the boiling bubble behaviors under the subcooled conditions, the subcooled pool boiling experiments are also performed by using a high speed and high spatial resolution camera with a highly magnified telescope. Resulting from the numerical simulations of the subcooled pool boiling phenomena, the numerical results obtained by the MARS are validated by being compared to the experimental ones and the existing analytical solutions. The numerical results regarding the time evolution of the boiling bubble departure process under the subcooled conditions show a very good agreement with the experimental results. In conclusion, it can be said that the proposed nonempirical boiling and condensation model combined with the MARS has been validated