Data assimilation (DA) has been used to reconstruct paleoclimate fields. DA integrates model simulations and climate proxies based on their error sizes. Consequently, error information is vital for DA to function optimally. This study estimated observation errors using "innovation statistics" and demonstrated that DA with estimated errors outperformed previous studies.

Artificial intelligence (AI) is playing a bigger role in weather forecasting, often competing with physical models. However, combining AI models with data assimilation, a process that improves weather forecasts by incorporating observation data, is still relatively unexplored. This study explored the coupling of ensemble data assimilation with an AI weather prediction model, ClimaX, which succeeded in employing weather forecasts stably by applying techniques conventionally used for physical models.

The late Pleistocene glacial cycles are dominated by a 100-kyr periodicity, rather than other major astronomical periods like 19, 23, 41, or 400 kyr. Various models propose distinct mechanisms to explain this, but their diversity may obscure the key factor behind the 100-kyr periodicity. We propose a timescale-matching hypothesis, suggesting that the ice-sheet climate system responds to astronomical forcing at ∼100 kyr because its intrinsic timescale is closer to 100 kyr than to other periods.

Can cloud seeding be a strategy to mitigate localized heavy rainfall disasters? Our numerical experiments showed that injecting large amounts of ice nuclei into convective clouds inhibited the growth of individual ice crystals to sizes sufficient for rainfall, thereby reducing rainfall in the worst-hit area and shifting it downstream. This shows promise for using cloud seeding to lessen rain-related disasters, although further studies are needed to confirm its broader effectiveness.

We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.