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Abstract

Surface flux transport (SFT) simulations are commonly used to describe and forecast the evolution of solar surface magnetic fields. These simulations are driven by active regions (AR) emerging on the solar surface. Here we use SFT simulations to predict the photospheric magnetic fields several years up to one cycle into the future by predicting the level of sunspot activity and using a statistical relation between sunspots and active regions.Prediction of sunspot activity uses our recently developed statistical method, which employs past sunspot numbers and geomagnetic activity to predict the next cycle. We study how the properties of active regions depend on the sunspot numbers (SSN) and phase of the sunspot cycle. We then construct probability distributions of AR properties (magnetic flux, latitude, tilt angle) that are used to derive ARs for any SSN level and cycle phase. We use two types of ARs to drive SFT simulations: bipolar active regions, where the magnetic field is Gaussian-distributed around the bipole centers, and active regions obtained from observed magnetograms by bootstrap sampling.We describe these SFT simulations and compare the results of photospheric magnetic field evolution from the two types of ARs with each other and with the results of SFT simulations using observed active regions. We will also use the PFSS model and the simulated photospheric magnetic field to study the evolution of the open solar flux.