Teleconnections that link climate processes at widely separated spatial locations form a key component of the climate system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always reveal the dynamical mechanisms between different climatological processes. More recently, causal discovery methods based either on time series at grid locations or on modes of variability, estimated through dimension-reduction methods, have been introduced. A major challenge in the development of such analysis methods is a lack of ground truth benchmark datasets that have facilitated improvements in many parts of machine learning. Here, we present a simplified stochastic climate model that outputs gridded data and represents climate modes and their teleconnections through a spatially aggregated vector-autoregressive model. The model is used to construct benchmarks and evaluate a range of analysis methods. The results highlight that the model can be successfully used to benchmark different causal discovery methods for spatiotemporal data and show their strengths and weaknesses. Furthermore, we introduce a novel causal discovery method at the grid level and demonstrate that it has orders of magnitude better performance than the current approaches. Improved causal analysis tools for spatiotemporal climate data are pivotal to advance process-based understanding and climate model evaluation.

The concept of exoplanetary habitability is evolving. The driving force is a desire to define the biological potential of planets and identify which can host complex and possibly intelligent life. To assess this in a meaningful manner, climate models need to be applied to realistic surfaces. However, the vast majority of climate models are developed using generic aquaplanet, or swamp planet, scenarios that provide uniform, surface frictional coefficients. However, aside from planets with largely uniform oceans, these models are not obviously useful when it comes to understanding the impact of climate on biodiversity. Here, we show that contrary to expectation, the aquaplanet models can be directly applied to planets with a variety of land areas, with little need for modification. Using this premise, this paper provides a simple mathematical framework that may be applied to more complex planetary surfaces and identifies the majority of the climate-model components that are needed to accurately determine the biological potential of habitable exoplanets. As a proof-of-concept, an available climate model for Proxima b is used to determine its biological potential, given a suitable atmosphere.

The dust cycle plays an important role in the long-term evolution of the climate and environment. In this paper, an improved climate model including aerosol processes was used to carry out a set of sensitivity experiments and comparative analyses of the effects of high-latitude ice-sheet extent and abnormal dust erosion, as well as Earth’s orbital parameters and atmospheric greenhouse gas content, on dust activities during the last glacial maximum. The comparative analysis found that incorporating the abnormal surface erosion factor alone could increase dust emissions by 2.77-fold and 3.77-fold of the present-day global and Asian dust emissions, respectively. The high-latitude ice-sheet factor caused global dust emissions to increase by 1.25-fold that of the present day. Sensitivity experiments showed that increased surface erosion in Asia during the last glacial maximum made the greatest contribution to the increased dust emissions in Asia, followed by the high-latitude ice-sheet factor, while the contributions of the greenhouse gas content and orbital parameters were relatively weak. Strong dust emissions during the glacial period were therefore not only dependent on the development of the high-latitude ice sheets but were strongly associated with the underlying surface characteristics of local dust source regions.

Patterns of climate and C4plant abundance in the southwestern United States during the last glaciation were evaluated from isotopic study of herbivore tooth enamel. Enamel δ13C values revealed a substantial eastward increase in C4plant consumption for Mammuthusspp., Bisonspp., Equusspp., and Camelopsspp. The δ13C values were greatest in Bisonspp. (−6.9 to +1.7‰) and Mammuthusspp. (−9.0 to +0.3‰), and in some locales indicated C4-dominated grazing. The δ13C values of Antilocaprids were lowest among taxa (−12.5 to −7.9‰) and indicated C3feeding at all sites. On the basis of modern correlations between climate and C4grass abundance, the enamel data imply significant summer rain in parts of southern Arizona and New Mexico throughout the last glaciation. Enamel δ18O values range from +19.0 to +31.0‰ and generally increase to the east. This pattern could point to a tropical or subtropical source of summer rainfall. At a synoptic scale, the isotope data indicate that interactions of seasonal moisture, temperature, and lowered atmospheric pCO2determined glacial-age C4abundance patterns.

Fluvial and eolian successions of oxygen isotope stage 3 are compared with global (GCM) and regional climate (RCM) modeling experiments of the stage 3 and last glacial maximum climate in Europe. Differences in precipitation between stage-3 stades and interstades were minor, which is confirmed by the fluvial successions. The fluvial response to climate variation is non-uniform, and in southern Europe more pronounced than in northern Europe. The model simulations indicate a strong western winter circulation over Europe during stage 3, which is supported by the eolian deposits data. Wind speeds in the last glacial maximum simulation appear modest compared with those of stage 3, which contrasts with the abundance of eolian deposits. This suggests that during glacial climates the stabilizing effect of vegetation determines eolian sedimentation rates, rather than wind speed. Stage 3 can be divided into an older part (>45,000 cal yr B.P.) with a relatively stable landscape and moist climate and a younger part with more frequent climate change and decreasing landscape stability.