arXiv subjects

Thea Sukianto

Publications and source records attributed to Thea Sukianto.

3 recordsLinked to original sources

arXiv2026

Improved Global Ocean Heat Content Estimation by Modeling Vertical Spatio-Temporal Dependence

Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are available deeper in the water column, previous studies have partitioned the ocean into at least two pressure layers and mapped each separately, which complicates the estimation of uncertainties when the maps are summed to get the total OHC. In this work, we consider the case of two pressure layers and propose an improved mapping and uncertainty quantification method using bivariate locally stationary Gaussian processes and conditional simulations to map the two sections jointly while accounting for the correlation between them. We find that modeling this correlation results in improved OHC anomaly mapping and up to a 15 percent reduction of global OHC anomaly uncertainties in comparison to mapping the two layers separately without accounting for their dependence. These estimated uncertainties are essential to analyze the statistical significance of OHC anomalies on both regional and global scales, which we demonstrate using several climatological case studies.

stat.AP↗

arXiv2026

Locally stationary Argo ocean heat content estimates: Modeling, validation and uncertainty quantification

Argo profiling floats measure seawater temperature and salinity in the upper 2000 meters of the ocean. These floats are uniquely capable of measuring the global Ocean Heat Content (OHC), a quantity that is of central importance for understanding Earth Energy Imbalance. Yet, producing Argo-based OHC estimates with reliable uncertainties is statistically challenging due to the complex structure and large size of the Argo dataset. Here we present an end-to-end mapping and uncertainty quantification framework for Argo-based OHC estimation using state-of-the-art methods from spatio-temporal statistics. The framework is based on modeling vertically integrated Argo temperature profiles as a locally stationary Gaussian process defined over space and time. This enables us to produce computationally tractable OHC anomaly maps based on data-driven decorrelation scales estimated from the Argo observations. Our modeling choices are validated using statistical cross-validation, which demonstrates the importance of including a climatological time trend in the mean field and accounting for time in the covariance function. We quantify the uncertainty of these maps using local conditional simulation ensembles, a novel approach that leads to principled spatially and temporally correlated uncertainty quantification. A new paired cross-validation technique is presented to validate these uncertainties. The mapping framework is implemented in an open-source codebase that is designed to be modular, reproducible and extensible. To demonstrate the mapping and uncertainty quantification capabilities of this approach, we present new Argo OHC maps with uncertainties for 2004-2022 and report on various downstream climatological estimates and their uncertainties.

stat.AP↗

arXiv2025

Seasonal trend assessment of US extreme precipitation via changepoint segmentation

Most climate trend studies analyze long-term trends as a proxy for climate dynamics. However, when examining seasonal data, it is unrealistic to assume that long-term trends remain consistent across all seasons. Instead, each season likely experiences distinct trends. Additionally, seasonal climate time series, such as seasonal maximum precipitation, often exhibit nonstationarities, including periodicities and location shifts. Failure to rigorously account for these features in modeling may lead to inaccurate trend estimates. This study quantifies seasonal trends in the contiguous United States' seasonal maximum precipitation series while addressing these nonstationarities. To ensure accurate trend estimation, we identify changepoints where the seasonal maximum precipitation shifts due to factors like measurement device changes, observer differences, or location moves. We employ a penalized likelihood method to estimate multiple changepoints, incorporating a generalized extreme value distribution with periodic features. A genetic algorithm based search algorithm efficiently explores the vast space of potential changepoints in both number and timing. Additionally, we compute seasonal return levels for extreme precipitation. Our methods are illustrated using two selected stations, and the results for the US are summarized through maps. We find that seasonal trends vary more when changepoints are considered than in studies that ignore them. Our findings also reveal distinct regional and seasonal patterns, with increasing trends more prevalent during fall in the South and along the East Coast when changepoints are accounted for.

stat.AP↗