Found 9976 publications. Showing page 210 of 400:
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2013
Influence of solar wind energy flux on the interannual variability of ENSO in the subsequent year
Previous studies have tended to adopt the quasi-decadal variability of the solar cycle (e.g. sunspot number (SSN) or solar radio flux at 10.7 cm (F10.7) to investigate the effect of solar activity on El Niño–Southern Oscillation (ENSO). As one of the major terrestrial energy sources, the effect of solar wind energy flux in Earth’s magnetosphere (Ein) on the climate has not drawn much attention, due to the big challenge associated with its quantitative estimation. Based on a new Ein index estimated by three-dimensional magnetohydrodynamic simulations from a previous study, this study reveals that Ein exhibits both quasi-decadal variability (periodic 11-year) and interannual (2–4 years) variability, which has rarely before been detected by SSN and F10.7. A significant interannual relationship between the annual mean Ein and subsequent early-winter ENSO is further revealed. Following high Ein, the sea level pressure in the subsequent early winter shows significant positive anomalies from Asia southward to the Maritime Continent, and significant negative anomalies over the Southeast and Northeast Pacific, resembling the Southern Oscillation. Meanwhile, significant upper-level anomalous convergence and divergence winds appear over the western and eastern Pacific, which is configured with significant lower-level anomalous divergence and convergence, indicating a weakening of the Walker circulation. Consequently, notable surface easterly wind anomalies prevail over the eastern tropical Pacific, leading to El Niño-like sea surface temperature anomalies. It is suggested that better describing the processes in the solar wind–magnetosphere–ionosphere coupled system is essential to understand the solar influence on climate change.
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Inferring surface energy fluxes using drone data assimilation in large eddy simulations
Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.
2022