In this work we take advantage of eleven different sunspot group, sunspot, and active region databases to characterize the area and flux distributions of photospheric magnetic structures. We find that, when taken separately, different databases are better fitted by different distributions (as has been reported previously in the literature). However, we find that all our databases can be reconciled by the simple application of a proportionality constant, and that, in reality, different databases are sampling different parts of a composite distribution. This composite distribution is made up by linear combination of Weibull and log-normal distributions -where a pure Weibull (log-normal) characterizes the distribution of structures with fluxes below (above) 10 21 Mx (10 22 Mx). We propose that this is evidence of two separate mechanisms giving rise to visible structures on the photosphere: one directly connected to the global component of the dynamo (and the generation of bipolar active regions), and the other with the small-scale component of the dynamo (and the fragmentation of magnetic structures due to their interaction with turbulent convection). Additionally, we demonstrate that the Weibull distribution shows the expected linear behaviour of a power-law distribution (when extended into smaller fluxes), making our results compatible with the results of Parnell et al. (2009).

Context. The long term study of the Sun is necessary if we are to determine the evolution of sunspot properties and thereby inform modeling of the solar dynamo, particularly on scales of a solar cycle. Aims. We aim to determine a number of sunspot properties over cycle 23 using the uniform database provided by the SOHO Michelson Doppler Imager data. We focus in particular on their distribution on the solar disk, maximum magnetic field and umbral/penumbral areas. We investigate whether the secular decrease in sunspot maximum magnetic field reported in Kitt Peak data is present also in MDI data. Methods. We have used the Sunspot Tracking And Recognition Algorithm (STARA) to detect all sunspots present in the SOHO Michelson Doppler Imager continuum data giving us 30 084 separate detections. We record information on the sunspot locations, area and magnetic field properties as well as corresponding information for the umbral areas detected within the sunspots, and track them through their evolution. Results. We find that the total visible umbral area is 20−40% of the total visible sunspot area regardless of the stage of the solar cycle. We also find that the number of sunspots observed follows the Solar Influences Data Centre international sunspot number with some interesting deviations. Finally, we use the magnetic information in our catalogue to study the long term variation of magnetic field strength within sunspot umbrae and find that it increases and decreases along with the sunspot number. However, if we were to assume a secular decrease as was reported in the Kitt Peak data and take into account sunspots throughout the whole solar cycle we would find the maximum umbral magnetic fields to be decreasing by 23.6 ± 3.9 Gauss per year, which is far less than has previously been observed by other studies (although measurements are only available for solar cycle 23). If we only look at the declining phase of cycle 23 we find the decrease in sunspot magnetic fields to be 70 Gauss per year.

The distributions of sunspot longitude at first appearance and at disappearance display an east-west asymmetry, that results from a reduction in visibility as one moves from disk centre to the limb. To first order, this is explicable in terms of simple geometrical foreshortening. However, the centre to limb visibility variation is much larger than that predicted by foreshortening. Sunspot visibility is known also to be affected by the Wilson effect -the apparent 'dish' shape of the sunspot photosphere caused by the temperature-dependent variation of the geometrical position of the τ = 1 layer. In this paper we investigate the role of the Wilson effect on the sunspot appearance distributions, deducing a mean depth for the umbral τ = 1 layer of 500-1500 km. This is based on the comparison of observations of sunspot longitude distribution and MonteCarlo simulations of sunspot appearance using different models for spot growth rate, growth time and depth of Wilson depression.

The algorithms involved in this study are as follows:1. The Solar Monitor Active Region Tracker (SMART) extracts, characterises, and tracks the evolution of active regions across the solar disk using line-of-sight magnetograms and a combination of image processing techniques. 2. The Automated Solar Activity Prediction code (ASAP) converts continuum images from heliocentric coordinates to Carrington heliographic coordinates, detects and tracks sunspots using thresholding and morphological methods. 3. The Sunspot Tracking And Recognition Algorithm (STARA) is used to detect and track sunspots from continuum images using a technique known as the top-hat transform. 4. The Spatial Possibilistic Clustering Algorithm (SPoCA) is a multi-channel unsupervised spatiallyconstrained fuzzy clustering method that automatically segments solar EUV images into active regions, coronal holes and quiet Sun. In the present paper, it is used to detect, characterise and track coronal active regions.We describe the fundamental properties of each algorithm along with a detailed comparison of outputs obtained from the analysis of about one month of data from the SOHO-MDI and SOHO-EIT instruments during 12 May -23 June, 2003. We track two active regions over time to study their properties in detail, and exploit the entire dataset to investigate correlations between physical properties determined by the algorithms. This study allows us to prepare the algorithms in the best possible way for robust analysis of the large SDO data-stream.The detection rates of the algorithms are compared with findings of the National Oceanic and Atmospheric Administration (NOAA) and the Solar Influences Data Analysis Centre (SIDC). By performing an inter-comparison of the algorithms, the physical properties of the solar features detected are measured at different heights of the solar atmosphere. Solar Physics DOI: 10.1007/•••••-•••-•••-••••-•A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithmsThe launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere.The algorithms involved in this study are as follows:1. The Solar Monitor Active Region Tracker (SMART) extracts, characterises, and tracks the evolution of active regions across the solar disk using line-ofsight magnetograms and a combination of image processing techniques. 2. The Automated Solar Activity Prediction code (ASAP) converts continuum images from heliocentric coordin...

The recent solar minimum and rise phase of solar cycle 24 have been unlike any period since the early 1900s. This article examines some of the properties of sunspot umbrae over the last 17 years with three different instruments on the ground and in space: MDI, HMI and BABO. The distribution of magnetic fields and their evolution over time is shown and reveals that the field distribution in cycle 24 is fundamentally different from that in cycle 23. The annual average umbral magnetic field is then examined for the 17 year observation period and shows a small decrease of 375 Gauss in sunspot magnetic fields over the period 1996-2013, but the mean intensity of sunspot umbrae does not vary significantly over this time. A possible issue with sample sizes in a previous study is then explored to explain disagreements in data from two of the source instruments. All three instruments show that the relationship between umbral magnetic fields and umbral intensity agrees with past studies in that the umbral intensity decreases as the field strength increases. This apparent contradiction can be explained by the range of magnetic field values measured for a given umbral intensity being larger than the measured 375 G change in umbral field strength over time.