Northeast Yucatan hurricane activity during the Maya Classic and Postclassic periods.

The collapse of the Maya civilization in the late 1st/early 2nd millennium CE has been attributed to multiple internal and external causes including overpopulation, increased warfare, and environmental deterioration. Yet the role hurricanes may have played in the fracturing of Maya socio-political networks, site abandonment, and cultural reconfiguration remains unexplored. Here we present a 2200 yearlong hurricane record developed from sediment recovered from a flooded cenote on the northeastern Yucatan peninsula. The sediment archive contains fine grain autogenic carbonate interspersed with anomalous deposits of coarse carbonate material that we interpret as evidence of local hurricane activity. This interpretation is supported by the correlation between the multi-decadal distribution of recent coarse beds and the temporal distribution of modern regional landfalling storms. In total, this record allows us to reconstruct the variable hurricane conditions impacting the northern lowland Maya during the Late Preclassic, Classic, and Postclassic Periods. Strikingly, persistent above-average hurricane frequency between ~ 700 and 1450 CE encompasses the Maya Terminal Classic Phase, the declines of Chichén Itza, Cobá, and subsequent rise and fall of the Mayapán Confederacy. This suggests that hurricanes may have posed an additional environmental stressor necessary of consideration when examining the Postclassic transformation of northern Maya polities.

Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types.

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.

Global multivariate point pattern models for rain type occurrence.

We seek statistical methods to study the occurrence of multiple rain types observed by satellite on a global scale. The main scientific interests are to relate rainfall occurrence with various atmospheric state variables and to study the dependence between the occurrences of multiple types of rainfall (e.g. short-lived and intense versus long-lived and weak; the heights of the rain clouds are also considered). Commonly in point process model literature, the spatial domain is assumed to be a small, and thus planar domain. We consider the log-Gaussian Cox Process (LGCP) models on the surface of a sphere and take advantage of cross-covariance models for spatial processes on a global scale to model the stochastic intensity function of the LGCP models. We present analysis results for rainfall observations from the TRMM satellite and atmospheric state variables from MERRA-2 reanalysis data over the tropical Eastern and Western Pacific Ocean, as well as over the entire tropical and subtropical ocean regions. Statistical inference is done through Monte Carlo likelihood approximation for LGCP models. We employ covariance approximation to deal with massive data.

Predictive Statistical Representations of Observed and Simulated Rainfall Using Generalized Linear Models.

This study explores the feasibility of predicting subdaily variations and the climatological spatial patterns of rain in the tropical Pacific from atmospheric profiles using a set of generalized linear models: logistic regression for rain occurrence and gamma regression for rain amount. The prediction is separated into different rain types from TRMM satellite radar observations (stratiform, deep convective, and shallow convective) and CAM5 simulations (large-scale and convective). Environmental variables from MERRA-2 and CAM5 are used as predictors for TRMM and CAM5 rainfall, respectively. The statistical models are trained using environmental fields at 0000 UTC and rainfall from 0000 to 0600 UTC during 2003. The results are used to predict 2004 rain occurrence and rate for MERRA-2/TRMM and CAM5 separately. The first EOF profile of humidity and the second EOF profile of temperature contribute most to the prediction for both statistical models in each case. The logistic regression generally performs well for all rain types, but does better in the east Pacific compared to the west Pacific. The gamma regression produces reasonable geographical rain amount distributions but rain rate probability distributions are not predicted as well, suggesting the need for a different, higher-order model to predict rain rates. The results of this study suggest that statistical models applied to TRMM radar observations and MERRA-2 environmental parameters can predict the spatial patterns and amplitudes of tropical rainfall in the time-averaged sense. Comparing the observationally trained models to models that are trained using CAM5 simulations points to possible deficiencies in the convection parameterization used in CAM5.