How hard is the euro area core? A wavelet analysis of growth cycles in Germany, France and Italy

Using recent advances in time-varying spectral methods, this research analyses the growth cycles of the core of the euro area in terms of frequency content and phasing of cycles. The methodology uses the continuous wavelet transform (CWT) and also Hilbert wavelet pairs in the setting of a non-decimated discrete wavelet transform in order to analyse bivariate time series in terms of conventional frequency domain measures from spectral analysis. The findings are that coherence and phasing between the three core members of the euro area (France, Germany and Italy) have increased since the launch of the euro

How hard is the euro are core? An evaluation of growth cycles using wavelet analysis

Using recent advances in time-varying spectral methods, this research analyses the growth cycles of the core of the euro area in terms of frequency content and phasing of cycles. The methodology uses the con-tinuous wavelet transform (CWT) and also Hilbert wavelet pairs in the setting of a non-decimated discrete wavelet transform in order to analyse bivariate time series in terms of conventional frequency domain measures from spectral analysis. The findings are that coherence and phasing between the three core members of the euro area (France, Germany and Italy) have increased since the launch of the euro.time-varying spectral analysis; coherence; phase; business cycles; EMU; growth cycles; Hilbert trans-form; wavelet analysis

Reply to “Comment on ‘Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue’”

In his comment, G. Bürger criticizes the conclusion that inflation of trends by quantile mapping is an adverse effect.He assumes that the argument would be ‘‘based on the belief that long-term trends and along with them future climate signals are to be large scale.’’ His line of argument reverts to the so-called inflated regression. Here it is shown, by referring to previous critiques of inflation and standard literature in statistical modeling as well as weather forecasting, that inflation is built upon a wrong understanding of explained versus unexplained variability and prediction versus simulation. It is argued that a sound regressionbased downscaling can in principle introduce systematic local variability in long-term trends, but inflation systematically deteriorates the representation of trends. Furthermore, it is demonstrated that inflation by construction deteriorates weather forecasts and is not able to correctly simulate small-scale spatiotemporal structure

MAXIMIZING LIGNIN YIELD USING EXPERIMENTAL DESIGN ANALYZING THE IMPACT OF SOLVENT COMPOSITION AND FEEDSTOCK PARTICLE SIZE ON THE ORGANOSOLV PROCESS IN THE PRESENCE OF FEEDSTOCK CONTAMINATION

The depletion of fossil feedstock and the unfavorable environmental effects accompanying by its exploitation are the driving forces in the process of transitioning to renewable feedstock as the primary resource. Similar to petrorefineries, a new modern biorefinery would use biomass to produce a variety of different chemical products and transportation fuels. Lignin, a potential low-cost, high volume output process stream derived from lignocellulosic biomass is currently being researched to better support the economics of the future biorefinery. In this study, experimental design was used to determine the optimal level for each process factor in an organosolv fractionation process that targets maximum attainable lignin yield, even in the presence of feedstock contaminants. The process factors studied were two different fractionation times (56, 90 min), two different fractionation temperatures (140°C, 160°C), three mixed feedstock loadings containing mixtures of switchgrass (Panicum virgatum) and tulip poplar (Liriodendron tulipifera) in three different weight ratios ([10/90], [50/50], [90/10]), three different poplar chip sizes (coarse, medium, fine), three different solvent compositions containing different ratios of the fractionation solvents methyl isobutylketone (MIBK), ethanol (EtOH) and water (H2O) ([07/30/63], [16/34/50], [62/27/11]), and three different acid concentrations (0.025, 0.05, 0.1 M). Based on the results found it is predicted that, even in the presence of switchgrass contaminants an estimated mean lignin yield of ~ 90 wt % is attainable if the levels of the organosolv process are set to a fractionation time of 90 minutes at a fractionation temperature of 160°C, use of a feedstock mixture containing 10% switchgrass and 90% medium poplar particles, and the use of the 16/34/50 solvent mixture with an added acid concentration of 0.1 M. The practical implications of these results on biorefinery operation will also be discussed

Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums

Bias correcting climate models implicitly assumes stationarity of the correction function. This assumption is assessed for regional climate models in a pseudo reality for seasonal mean temperature and precipitation sums. An ensemble of regional climate models for Europe is used, all driven with the same transient boundary conditions. Although this model-dependent approach does not assess all possible bias non-stationarities, conclusions can be drawn for the real world. Generally, biases are relatively stable, and bias correction on average improves climate scenarios. For winter temperature, bias changes occur in the Alps and ice covered oceans caused by a biased forcing sensitivity of surface albedo; for summer temperature, bias changes occur due to a biased sensitivity of cloud cover and soil moisture. Precipitation correction is generally successful, but affected by internal variability in arid climates. As model sensitivities vary considerably in some regions, multi model ensembles are needed even after bias correction. Key Points: - Bias correction in general improves future climate simulations - Cloud cover, soil moisture and albedo changes may cause temperature bias changes - Precipitation biases in arid regions are affected by internal variabilit

Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping-based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis ("prog") downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required

Cross wavelet analysis: significance testing and pitfalls

In this paper, we present a detailed evaluation of cross wavelet analysis of bivariate time series. We develop a statistical test for zero wavelet coherency based on Monte Carlo simulations. If at least one of the two processes considered is Gaussian white noise, an approximative formula for the critical value can be utilized. In a second part, typical pitfalls of wavelet cross spectra and wavelet coherency are discussed. The wavelet cross spectrum appears to be not suitable for significance testing the interrelation between two processes. Instead, one should rather apply wavelet coherency. Furthermore we investigate problems due to multiple testing. Based on these results, we show that coherency between ENSO and NAO is an artefact for most of the time from 1900 to 1995. However, during a distinct period from around 1920 to 1940, significant coherency between the two phenomena occurs

The representation of location by a regional climate model in complex terrain

To assess potential impacts of climate change for a specific location, one typically employs climate model simulations at the grid box corresponding to the same geographical location. But based on regional climate model simulations, we show that simulated climate might be systematically displaced compared to observations. In particular in the rain shadow of moutain ranges, a local grid box is therefore often not representative of observed climate: the simulated windward weather does not flow far enough across the mountains; local grid boxes experience the wrong airmasses and atmospheric circulation. In some cases, also the local climate change signal is deteriorated. Classical bias correction methods fail to correct these location errors. Often, however, a distant simulated time series is representative of the considered observed precipitation, such that a non-local bias correction is possible. These findings also clarify limitations of bias correcting global model errors, and of bias correction against station data