The Sun, a crucial energy source for life on Earth, occasionally experiences violent eruptions known as solar storms, which release energetic particles that can damage modern technology. Today, extensive monitoring from ground-based and satellite observations has provided valuable information about the Sun's variability. However, historical observations and indirect proxy data indicate the Sun can produce much larger solar storms than those observed during the space era. The recurrence rate of such large solar storms and their link to solar activity remain unknown. Cosmogenic radionuclides (e.g., Be-10 and Cl-36) in ice cores could serve as "natural detectors" of past solar activity. However, uncertainties persist in their use as reliable solar proxies due to the influence of atmospheric transport and deposition processes. Understanding these processes is crucial for accurately interpreting the solar signals in radionuclide measurements from ice cores. Using two state-of-the-art global climate models, I will perform and analyze numerical simulations to address this knowledge gap and disentangle the climate-related (transport and deposition processes) and production-related (solar) signals on radionuclides measured from ice cores. The results will provide new scientific insights, reducing uncertainties in the application of radionuclides as natural detectors of past solar activity. The medium storage (35 000 GB, 1 million files) I apply for will be used to store the input data necessary for the model simulation and model output for each simulation. Note that the main computation resources will be provided by Medium Compute project S-CMIP (SNIC 2022/1-3) which I am just applied for.