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The Afro-Asian summer monsoon (AfroASM) sustains billions of people living in many developing countries covering West Africa and Asia, vulnerable to climate change. Future increase in AfroASM precipitation has been projected by current state-of-the-art climate models, but large inter-model spread exists. Here we show that the projection spread is related to present-day interhemispheric thermal contrast (ITC). Based on 30 models from the Coupled Model Intercomparison Project Phase 6, we find models with a larger ITC trend during 1981–2014 tend to project a greater precipitation increase. Since most models overestimate present-day ITC trends, emergent constraint indicates precipitation increase in constrained projection is reduced to 70% of the raw projection, with the largest reduction in West Africa (49%). The land area experiencing significant increases of precipitation (runoff) is 57% (66%) of the raw projection. Smaller increases of precipitation will likely reduce flooding risk, while posing a challenge to future water resources management. A new study shows the latest climate models tend to overestimate future Afro-Asian monsoon rainfall and runoff due to present-day biases of warming patterns. By constraining biases, the rainfall increase is 70% of the raw projection. [ABSTRACT FROM AUTHOR]

The observational records have shown a strengthening of the Pacific Walker circulation (PWC) since 1979. However, whether the observed change is forced by external forcing or internal variability remains inconclusive, a solid answer to more societal relevantly question of how the PWC will change in the near future is still a challenge. Here we perform a quantitative estimation on the contributions of external forcing and internal variability to the recent observed PWC strengthening using large ensemble simulations from six state-of-the-art Earth system models. We find the phase transition of the Interdecadal Pacific Oscillation (IPO), which is an internal variability mode related to the Pacific, accounts for approximately 63% (~51–72%) of the observed PWC strengthening. Models with sufficient ensemble members can reasonably capture the observed PWC and IPO changes. We further constrain the projection of PWC change by using climate models' credit in reproducing the historical phase of IPO. The result shows a high probability of a weakened PWC in the near future. How the Pacific Walker circulation (PWC) changes under climate change is not well understood. Here, the authors show that the strengthening of the PWC since 1979 is related to internal variability of the Pacific and use this as a constraint to show that it is likely to weaken in the next decades. [ABSTRACT FROM AUTHOR]

Representation-based methods have achieved exciting results in recent applications of face recognition. However, it is still challenging for the face recognition task due to noise and outliers in the data. Many existing methods avoid these problems by constructing an auxiliary dictionary from the extended data but fail to achieve good performances because they use the main dictionary only for classification. In this paper, to avoid the need to manually construct an auxiliary dictionary and the effects of noise, we propose a Joint Latent Low-Rank and Non-Negative Induced Sparse Representation (JLSRC) for face recognition. Specifically, JLSRC adaptively learns two clean low-rank reconstructed dictionaries jointly via an extended latent low-rank representation to reveal the potential relationships in the data and then embeds a non-negative constraint and an Elastic Net regularization in the coefficient vectors of the dictionaries to enhance the performance on classification. In this way, the learned low-rank dictionaries can be mutually boosted to extract discriminative features and handle the noise, and the obtained coefficient vectors are simultaneously both sparse and discriminative. Moreover, the proposed method seamlessly and elegantly integrates low-rank learning and sparse representation-based classification. Extensive experiments on three challenging face databases demonstrate the effectiveness and robustness of JLSRC in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]