The energy partition study of Emslie et al. (2012) was restricted to 38 large solar eruptive events. In a more comprehensive study of the global flare energetics we choose a data set that contains the 400 largest (GOES M- and X-class) flare events observed during the first 3.5 years of the SDO era. Previously, we determined the dissipated magnetic energies Emag in these flares based on fitting the vertical-current approximation of a nonlinear force-free field (NLFFF) solution to the loop geometries detected in EUV images from SDO/AIA, a new method that could be applied to 177 events with a heliographic longitude of 





 (Paper I). We also determined the thermal energy Eth in the soft X-ray and EUV-emitting plasma during the flare peak times based on a multi-temperature DEM forward-fitting method to SDO/AIA image pixels with spatial synthesis, which was applicable to 391 events (Paper II). In the present study, we determined the nonthermal energy Ent contained in accelerated electrons based on spectral fits to RHESSI data using the OSPEX software, which was applicable to 191 events. The major conclusions of the new results emerging from this study are as follows.

1.
The (logarithmic) mean energy ratio of the nonthermal energy to the total magnetically dissipated flare energy is found to be 





, with a logarithmic standard deviation corresponding to a factor of 





, which yields an uncertainty of 





 for the mean, i.e., 





. The majority (





) of the flare events fulfill the inequality 





, which suggests that magnetic energy dissipation (most likely by a magnetic reconnection process) provides sufficient energy to accelerate the nonthermal electrons detected by bremsstrahlung in hard X-rays. Our results yield an order of magnitude higher electron acceleration efficiency than previous estimates, i.e., 





 (with N = 37, Emslie et al. 2012).


2.
The (logarithmic) mean of the thermal energy Eth to the nonthermal energy Ent is found to be 





, with a logarithmic standard deviation corresponding to a factor of 





. The fraction of flares with thermal energy smaller than the nonthermal energy, as expected in the thick-target model, is found to be the case for 





 only. Therefore, the thick-target model is sufficient to explain the full amount of thermal energy in most flares, in the framework of the warm-target model. The cross-over method shows the opposite tendency, but we suspect that the cross-over method overestimates the low-energy cutoff and underestimates the nonthermal energies. Previous estimates yielded a similar ratio, i.e., 





 (Emslie et al. 2012).


3.
A corollary of the two previous conclusions is that the thermal to magnetic energy ratio is 





. A total of 95% of flares fulfills the inequality 





, indicating that all thermal energy in flares is supplied by magnetic energy. Previous estimates were a factor of 17 lower, i.e., 





 (Emslie et al. 2012), which would imply a very inefficient magnetic to thermal energy conversion process.


4.
The largest uncertainty in the calculation of nonthermal energies, the low-energy cutoff, is found to yield different values for two used methods, i.e., 





 keV for the warm thick-target model, versus 





 for the thermal/nonthermal cross-over method. The calculation of the nonthermal energies is highly sensitive to the value of the low-energy cutoff, which strongly depends on the assumed (warm-target) temperature.


5.
The flare temperature can be characterized with three different definitions, for which we found the following (67%-standard deviation) ranges: 





 MK for the AIA DEM peak temperature, 





 MK for the emission measure-weighted temperatures, and 





 MK for the RHESSI high-temperature DEM tails. The median ratios are found to be 





 and 





. The mean active region temperature evaluated from DEMs with AIA, Te = 8.6 MK, is used to estimate the low-energy cutoff ec of the nonthermal component according to the warm-target model, i.e., 





. The low-energy cutoff ec of the nonthermal spectrum has a strong functional dependence on the temperature TR.
Citation: Emslie et al. (2012)
Citation function: Compare/Contrast; Compare/Contrast
Citation: Emslie et al. 2012
Citation function: Differences
Citation: Emslie et al. 2012
Citation function: Similarities
Citation: Emslie et al. 2012
Citation function: Differences; Differences