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Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.

We assessed different coupled data assimilation strategies with a hierarchy of coupled models, ranging from the simple coupled Lorenz model to the state-of-the-art coupled general circulation model CFSv2. With the coupled Lorenz model, we assessed the analysis accuracy by strongly-coupled Ensemble Kalman Filter (EnKF) and 4D-Variational (4D-Var) methods with varying assimilation window lengths. The analysis accuracy of the strongly-coupled EnKF with a short assimilation window is comparable to that of 4D-Var with a long assimilation window. For 4D-Var, the strongly-coupled approach with the coupled model produces more accurate ocean analysis than the ECCO-like approach using the uncoupled ocean model. Experiments with the coupled quasi-geostrophic model conclude that the strongly-coupled approach outperforms the weakly-coupled and uncoupled approaches for both the full-rank EnKF and 4D-Var, with the strongly-coupled EnKF and 4D-Var showing a similar level of accuracy higher than other coupled data assimilation approaches such as the outer loop coupling. A strongly-coupled EnKF software framework is developed and applied to the intermediatecomplexity coupled model SPEEDY-NEMO and the state-of-the-art operational coupled model CFSv2. Experiments assimilating synthetic or real atmospheric observations into the ocean through strongly-coupled EnKF show that the strongly-coupled approach improves the analysis of the atmosphere and upper oceans, but degrades observation fits in the deep ocean, probably due to the unreliable error correlation estimated by a small ensemble. The correlation-cutoff method is developed to reduce the unreliable error correlations between physically irrelevant model states and observations. Experiments with the coupled Lorenz model demonstrate that strongly-coupled EnKF informed by the correlation-cutoff method produces more accurate coupled analyses than the weakly-coupled and plain strongly-coupled EnKF regardless of the ensemble size. To extend the correlation-cutoff method to operational coupled models, a neural network approach is proposed to systematically acquire the observation localization functions for all pairs between the model state and observation types. The following strongly-coupled EnKF experiments with an intermediate-complexity coupled model show promising results with this method. [ABSTRACT FROM AUTHOR]

The Human System is within the Earth System. They should be modeled bidirectionally coupled, as they are in reality. The Human System is rapidly expanding, mostly due to consumption of fossil fuels (approximately one million times faster than Nature accumulated them) and fossil water. This threatens not only other planetary subsystems but also the Human System itself. Carrying Capacity is an important tool to measure sustainability, but there is a widespread view that Carrying Capacity is not applicable to humans. Carrying Capacity has generally been prescribed a priori, mostly using the logistic equation. However, the real dynamics of human population and consumption are not represented by this equation or its variants. We argue that Carrying Capacity should not be prescribed but should insteadbe dynamically derived a posteriori from the bidirectional coupling of Earth System submodels with the Human System model. We demonstrate this approach with a minimal model of Human–Nature interaction (HANDY). ▪ The Human System is a subsystem of the Earth System, with inputs (resources) from Earth System sources and outputs (waste, emissions) to Earth System sinks. ▪ The Human System is growing rapidly due to nonrenewable stocks of fossil fuels and water and threatens the sustainability of the Human System and to overwhelm the Earth System. ▪ Carrying Capacity has been prescribed a priori and using the logistic equation, which does not represent the dynamics of the Human System. ▪ Our new approach to human Carrying Capacity is derived from dynamically coupled Earth System–Human System models and can be used to estimate the sustainability of the Human System. [ABSTRACT FROM AUTHOR]