The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of …
Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five …
Detecting abnormal events within time series is crucial for analyzing and understanding the dynamics of the system in many research areas. In this paper, we propose a methodology to detect these anomalies in multivariate environmental data. Five …
Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of …
Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of …
Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of …
Understanding the impacts of climate extremes on the carbon cycle is important for quantifying the carbon-cycle climate feedback and highly relevant to climate change assessments. Climate extremes and fires can have severe regional effects, but a …
Spatiotemporal observations in Earth System sciences are often affected by numerous and/or systematically distributed gaps. This data fragmentation is inherited from instrument failures, sparse measurement protocols, or unfavourable conditions (e.g. …
Climate extremes such as droughts and heat waves affect terrestrial ecosystems and may alter local carbon budgets. However, it still remains uncertain to what degree extreme impacts in the carbon cycle influence the carbon cycle-climate feedback both …
Quantifying the interannual variability (IAV) of the terrestrial ecosystem productivity and its sensitivity to climate is crucial for improving carbon budget predictions. In this context it is necessary to disentangle the influence of climate from …