Working Groups

The following working groups (WGs) were defined at SOLARIS-HEPPA meetings in 2022 (SPARC newsletter 59, 2022, p. 36).

  • WG1: Solar and geomagnetic forcing datasets

  • WG2: Solar influence on climate (solar signals, possible mechanisms and processes) and near-term prediction

  • WG3: Statistical analysis and methodological aspects

The WG leaders will coordinate the analyses within their WG. If you are interested in participating in one of the WGs, please get in touch with the respective WG leaders or contact Bernd Funke <> or Wenjuan Huo <>

WG1: Solar and geomagnetic forcing datasets

WG1 aims to create updated solar and geomagnetic forcing input datasets to be used in the next round of model intercomparison projects (i.e., CMIP7 and HeppaV ). The new dataset will incorporate knowledge gained from prior model intercomparisons and recent observations of the solar spectral irradiance and energetic particle fluxes and impacts. The datasets will be suitable for centennial length climate simulations and include potential future scenarios for climate model projections up to 2300. WP1 will also provide recommendations for implementation of these new solar and geomagnetic forcing into climate models. In the case of geomagnetic forcing from energetic particles precipitating into the atmosphere, the new dataset will include updated atmospheric ionization rates for magnetospheric mid/medium energy electrons, solar energetic, and Galactic Cosmic Rays. For models that do not include interactive chemistry or with limited vertical extent, WG1 will provide recommendations on methods to include solar and geomagnetic variability in key atmospheric constituents such as ozone and nitric oxide.

The datasets produced by WG1 will be validated against the historical record (including paleoclimate reconstructions) and uncertainty estimates provided.  Working with modeling centers, the working group will assess how changes from the prior forcing impact climate model radiative balance and modeled Earth System components.

The working group aims to embrace 'Open Science' principles with respect to participation, methodology, source code development, data access and peer review.

Co-leads: Bernd Funke <>, Daniel Marsh <> and Natalie Krivova <>

Scientific questions of WG1:

  • What are the sources of uncertainties in long term solar radiance reconstructions and how can they be incorporated into uncertainty estimates on forcing datasets that combine multiple solar irradiance models?
  • Why do current estimates of energetic particle fluxes fail to reproduce the observed atmospheric response?
  • How does the modeled response of the Earth’s atmosphere to solar and geomagnetic variation with the revised dataset differ from that used for CMIP6?
  • How does stratospheric ozone and temperature vary with changes in solar and geomagnetic forcing?

WG2: Solar influence on climate (solar signals, possible mechanisms and processes) and near-term prediction

WG2 aims to assess the robustness and significance of the solar signals, review the proposed physical mechanisms for solar influence on climate, and assess the solar contribution to climate variability and predictability. Although several mechanisms were proposed in the past (e.g. the particle-driven direct and indirect effect, the TSI-driven “bottom-up” mechanism, and the UV-driven “top-down” mechanism), the stratosphere-troposphere-ocean coupling processes involved in these mechanisms are still unclear. For climate variability at a certain timescale (like decadal variability), complex interactions of the solar-forced component with internal components and other external forced components limit the robustness of the “detected” solar signals in the climate system. Large ensemble climate simulations based on state-of-the-art climate models allow us to do so, but the results are very diverse and “model-dependent”. The large discrepancy calls into question whether the solar effects as well as the proposed mechanism really exist and how much we can trust the simulated solar signals in climate models.

Therefore, using observational/reanalysis data and outputs from multiple models, WG2 will try to address the following scientific questions. The analysis methodology will be in accordance to the recommendations of the “WG3 Statistical analysis and methodological aspects”.

Co-leads: Wenjuan Huo <>, Tobias Spiegl <>, and Timo Asikainen <> 

Scientific questions of WG2:

  • How does the upper and middle atmosphere respond to solar forcings (spectral solar irradiance and high energetic particles) and how robust are the detected signals across different ensemble members and models?  (Solar signals)
  • What determines if potential middle atmosphere solar signals can penetrate downward to the troposphere? How important are the in-situ middle atmosphere (e.g. the polar vortex) and troposphere (e.g. large-scale climate modes) background states? (Mechanisms)
  • How do solar signals on different spatial and temporal time scales compare to anthropogenic and other natural induced (e.g. volcanic eruptions) climate signals? (Interactions with other forcings)
  • What are the potential contributions of the solar cycle to the near-term ((sub-)seasonal and decadal) prediction? (predictability)

WG3: Statistical analysis and methodological aspects

WG3 is dedicated to conducting a comprehensive evaluation of existing statistical approaches employed in the analysis of solar signals within model and observational data. The primary objectives of this group are to improve the accuracy and reliability of solar signal attribution by addressing the limitations of current methods and exploring alternative statistical techniques that account for non-linearity and other relevant factors. By accomplishing these goals, the group aims to support climate scientists in their endeavors to design and apply robust statistical methods to investigate Sun-climate connections.

By addressing the following research questions, WG3 seeks to advance the field of solar signal analysis, providing climate scientists with valuable tools and insights to enhance their research on Sun-climate connections. The ultimate goal is to foster the development of more robust and suitable statistical methods that can be effectively employed to solve a wide range of problems in this domain.

Co-leads: Aleš Kuchař <>

Scientific questions of WG3:

  • How can the attribution of solar signals using Multiple Linear Regression (MLR) or Superposed Epoch Analysis (SEA) benefit from more robust techniques that capture non-linearity, aliasing, and other important considerations? The working group will delve into enhancing the MLR and SEA methodologies by incorporating more robust techniques that account for non-linear relationships, aliasing effects, and other factors that can impact the attribution of solar signals. By addressing these aspects, the group aims to improve the accuracy and reliability of the statistical analyses, reducing the risk of misattributing signals and drawing erroneous conclusions.
  • Can we explain the interactions between solar forcings and other climate agents (such as ENSO, QBO) by employing new techniques in eXplainable Artificial Intelligence (XAI)? The working group intends to explore the application of innovative XAI techniques to elucidate the intricate interactions between solar forcings and other climate agents, such as El Niño-Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). By leveraging XAI methods, which offer interpretability and transparency, the group aims to gain insights into the underlying mechanisms and contribute to a better understanding of these complex relationships.
  • How can we mitigate the impact of internal variability and increase the signal-to-noise ratio in SOLARIS-HEPPA (SoHe) attribution activities? The working group will investigate strategies to overcome the challenges posed by internal variability and enhance the signal-to-noise ratio in SoHe attribution activities. By developing techniques that effectively separate the solar signals from background noise and internal variability, the group aims to strengthen the robustness and reliability of the attribution analyses, improving our ability to discern and understand solar influences on climate.