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Velocity measurements of wind blowing near the North Sea border of Northern Germany and velocity measurements under local isotropic conditions of a turbulent wake behind a cylinder are compared. It is shown that wind gusts - measured by means of velocity increments - do show similar statistics to the laboratory data, if they are conditioned on an averaged wind speed value. Clear differences between the laboratory data and the atmospheric wind velocity measurement are found for the waiting time statistics between successive gusts above a certain threshold of interest.
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Different definitions of links in climate networks may lead to considerably different network topologies. We construct a network from climate records of surface level atmospheric temperature in different geographical sites around the globe using two commonly used definitions of links. Utilizing detrended fluctuation analysis, shuffled surrogates and separation analysis of maritime and continental records, we find that one of the major influences on the structure of climate networks is due to the auto-correlation in the records, that may introduce spurious links. This may explain why different methods could lead to different climate network topologies.
We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18-19$^circ$C, corresponding to a daily maximum temperature of 24-25$^circ$C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of $2^circ$C, relative to 1981-2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.
The initiation of the Indian summer monsoon circulation during late May / early June arises through large-scale land-sea thermal contrast and setting up of negative pressure gradient between the Monsoon Trough over the Indo-Gangetic plains and the Mascarene High over the subtropical Indian Ocean. The meridional pressure gradient together with the Earths rotation (Coriolis force) creates the summer monsoon cross-equatorial flow, while feedbacks between moisture-laden winds and latent heat release from precipitating systems maintain the monsoon circulation during the June-September (JJAS) rainy season (Krishnamurti and Surgi, 1987). This simplified view of the Indian monsoon is a useful starting point to draw insights into the variability of the large-scale monsoon circulation.
We analyse the time series of solar irradiance measurements using chaos theory. The False Nearest Neighbour method (FNN), one of the most common methods of chaotic analysis is used for the analysis. One year data from the weather station located at Nanyang Technological University (NTU) Singapore with a temporal resolution of $1$ minute is employed for the study. The data is sampled at $60$ minutes interval and $30$ minutes interval for the analysis using the FNN method. Our experiments revealed that the optimum dimension required for solar irradiance is $4$ for both samplings. This indicates that a minimum of $4$ dimensions is required for embedding the data for the best representation of input. This study on obtaining the embedding dimension of solar irradiance measurement will greatly assist in fixing the number of previous data required for solar irradiance forecasting.
Errors in applying regression models and wavelet filters used to analyze geophysical signals are discussed: (1) multidecadal natural oscillations (e.g. the quasi 60-year Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO)) need to be taken into account for properly quantifying anomalous accelerations in tide gauge records such as in New York City; (2) uncertainties and multicollinearity among climate forcing functions prevent a proper evaluation of the solar contribution to the 20th century global surface temperature warming using overloaded linear regression models during the 1900-2000 period alone; (3) when periodic wavelet filters, which require that a record is pre-processed with a reflection methodology, are improperly applied to decompose non-stationary solar and climatic time series, Gibbs boundary artifacts emerge yielding misleading physical interpretations. By correcting these errors and using optimized regression models that reduce multicollinearity artifacts, I found the following results: (1) the sea level in New York City is not accelerating in an alarming way, and may increase by about 350 mm from 2000 to 2100 instead of the previously projected values varying from 1130 mm to 1550 mm estimated using the methods proposed by Sallenger et al. (2012) and Boon (2012), respectively; (2) the solar activity increase during the 20th century contributed about 50% of the 0.8 K global warming observed during the 20th century instead of only 7-10% (IPCC, 2007; Benestad and Schmidt, 2009; Lean and Rind, 2009). These findings stress the importance of natural oscillations and of the sun to properly interpret climatic changes.
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