Perturbation experiments are a common technique used to study how differences between model simulations evolve within chaotic systems. Such perturbation experiments include modifications to initial conditions (including those involved with data assimilation), boundary conditions, and model parameterizations. We have discovered, however, that any difference between model simulations produces a rapid propagation of very small changes throughout all prognostic model variables at a rate many times the speed of sound. The rapid propagation seems to be due to the model’s higher-order spatial discretization schemes, allowing the communication of numerical error across many grid points with each time step. This phenomenon is found to be unavoidable within the Weather Research and Forecasting (WRF) Model even when using techniques such as digital filtering or numerical diffusion. These small differences quickly spread across the entire model domain. While these errors initially are on the order of a millionth of a degree with respect to temperature, for example, they can grow rapidly through nonlinear chaotic processes where moist processes are occurring. Subsequent evolution can produce within a day significant changes comparable in magnitude to high-impact weather events such as regions of heavy rainfall or the existence of rotating supercells. Most importantly, these unrealistic perturbations can contaminate experimental results, giving the false impression that realistic physical processes play a role. This study characterizes the propagation and growth of this type of noise through chaos, shows examples for various perturbation strategies, and discusses the important implications for past and future studies that are likely affected by this phenomenon.

Local effects of inadvertent weather changes within and near wind farms have been well documented by a number of modeling studies and observational campaigns; however, the broader nonlocal atmospheric effects of wind farms are much less clear. e goal of this study is to determine whether wind farm-induced perturbations are able to evolve over periods of days, and over areas of thousands of square kilometers, to modify specific atmospheric features that have large impacts on society and the environment, specifically midlatitude and tropical cyclones. Here, an ensemble modeling approach is utilized with a wind farm parameterization to quantify the sensitivity of meteorological variables to the presence of wind farms. e results show that perturbations to nonlocal midlatitude cyclones caused by a wind farm are statistically significant, with magnitudes of roughly 1 hPa for mean sealevel pressure, 4 m/s for surface wind speed, and 15 mm for maximum 30-minute accumulated precipitation. Cyclone perturbation magnitude is also found to be dependent on wind farm size and location relative to the midlatitude cyclone genesis region and track.