Time Series Analysis of Downscaled Climate Projections to Local Microalgal Primary Production

In this project, I used Intergovernmental Panel on Climate Change (IPCC) climate projections downscaled to a local area of France to drive a mechanistic model of microalgal primary production across both historical and future periods. Representative Concentration Pathways (RCP) 4.5 is a stabilization scenario meaning that the radiative forcing level stabilizes at 4.5 W m⁻² before 2100. The RCP 8.5 scenario reflects greenhouse gas emissions that continue to rise throughout the 21ˢᵗ century, leading to a radiative forcing of 8.5 W m⁻²; known as a worst-case scenario. The goal was to assess how long‑term changes in climate drivers — such as irradiance, sea surface temperature, and other environmental variables — will affect the dynamics, productivity, and ecological role of microalgal communities in coastal habitats.

Model outputs for microalgal productivity and environmental conditions across historical and future climate scenarios
Figure: Model outputs for microalgal productivity and environmental conditions across historical and future climate scenarios. Panels show yearly and low‑tide irradiance (a), mud surface temperature (c), net primary production (e), and secondary production (g), with associated seasonal trends (b, d, f, h) for the historical period (black) and RCP 4.5 (blue) and RCP 8.5 (red) scenarios. Shaded areas and error bars denote 95% confidence intervals across the model ensemble built from 11 General Circulation Models and Regional Circulation Models (GCM-RCM), and filled circles mark significant trends (Mann-Kendall test). The absence of trend markers for the RCP 8.5 scenario with sea‑level rise reflects a sharp, step-like shift in the time series, making trend estimates and statistical significance unreliable.

To understand how these changes evolve over time, I applied extensive time series analyses that combined statistical trend detection with robust significance testing. Monotonic trends in irradiance, mud surface temperature (MST), net primary production (NPP), and associated secondary production were estimated using the non parametric Mann-Kendall test. To account for long term persistence (LTP) and autocorrelation common in climate driven ecological data, I implemented the Hurst-Kolmogorov (HK) approach. In practice, this involved first assessing trend significance using the Sen slope estimate, and when a trend was significant (p < 0.05), estimating the Hurst exponent (H) to quantify the degree of long term persistence. If H was significant (p < 0.05), trend statistics were corrected to remove the LTP induced bias, yielding more robust significance estimates. Through this combined approach, I was able to reliably distinguish genuine climate driven trends from statistical noise and internal variability. This work highlights the value of coupling global climate projections with site specific ecological modeling and advanced statistical trend detection methods, providing actionable insights into how climate variability and long term changes will reshape microalgal productivity and its role in coastal ecosystem dynamics.

Read the full story here: Article in Nature Communications Earth & Environment.