Potential for small scale added value of RCM’s downscaled climate change signal

In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the “delta method”. Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the “potential” of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ～5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ～5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields– were not explored in this research.     

Nonparametric analysis of high wind speed data

In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework.     

A Full-Sun Magnetic Index from Helioseismology Inferences

Solar magnetic indices are used to model the solar irradiance and ultimately to forecast it. However, the observation of such indices is generally limited to the Earth-facing hemisphere of the Sun. Seismic maps of the far side of the Sun have proven their capability to locate and track medium–large active regions at the non-visible hemisphere. We present here the possibility of using the average signal from these seismic far-side maps, combined with similarly calculated near-side maps, as a proxy to the full-Sun magnetic activity.     

Solar cycle distribution of strong solar proton events and the related solar-terrestrial phenomena

We investigated the solar cycle distribution of strong solar proton events (SPEs, peak flux ≥1000 pfu) and the solar-terrestrial phenomena associated with the strong SPEs during solar cycles 21–23. The results show that 37 strong SPEs were registered over this period of time, where 20 strong SPEs were originated from the super active regions (SARs) and 28 strong SPEs were accompanied by the X-class flares. Most strong SPEs were not associated with the ground level enhancement (GLE) event. Most strong SPEs occurred in the descending phases of the solar cycles. The weaker the solar cycle, the higher the proportion of strong SPES occurred in the descending phase of the cycle. The number of the strong SPEs that occurred within a solar cycle is poorly associated with the solar cycle size. The intensity of the SPEs is highly dependent of the location of their source regions, with the super SPEs (≥20000 pfu) distributed around solar disk center. A super SPE was always accompanied by a fast shock driven by the associated coronal mass ejection and a great geomagnetic storm. The source location of strongest GLE event is distributed in the well-connected region. The SPEs associated with super GLE events (peak increase rate ≥100%) which have their peak flux much lower than 10000 pfu were not accompanied by an intense geomagnetic storm.