Exploring Sources of Multi-year Predictability of Climate and Ecosystem

June Yi Lee

seminar
Feb. 6, 2024

11:10 am – 12:00 pm MST

Webcast

Main content

The demand for decision-relevant and evidence-based climate information in the near term is increasing. This encompasses the understanding and explanation of variability and changes in statistics of climate extremes and ecosystems to support disaster management and adaptation choices. As climate prediction from seasonal to decadal (S2D) scale broadens to include Earth system dimensions, such as terrestrial and marine ecosystems, deepening our scientific understanding of predictability sources for ecosystem variability and change becomes crucial. Here we explore sources of multi-year predictability of climate and ecosystem variables using a new seasonal-to-multi-year Earth System Prediction System which is based on the Community Earth System Model version 2 (CESM2) in 1 o horizontal resolution. The system consists of 50-member uninitialized historical simulations, 20- member ocean assimilations, and 20-member hindcast initiated from every January 1 st integrating for 5 years from 1961 to 2021. The 3-D ocean temperature and salinity anomaly assimilation runs serve as initial conditions of hindcast. The key variables assessed include surface temperature, precipitation, sea level pressure, soil moisture, wildfire occurrence, and marine and terrestrial Gross Primary Productivity. Our results indicate that land surface processes and terrestrial ecosystem variables in many parts of the world may be predictable 1 to 3 years in advance. This predictability primarily originates from anthropogenic forced signals and modes of climate variability, particularly El Nino and Southern Oscillation and Atlantic Multi-decadal variability. These modes of climate variability shift regional temperature and precipitation patterns, leading to changes in soil moisture, wildfire occurrence, and terrestrial productivity. We also quantify the degree to which marine biogeochemical variables can be predictable and constrained by physical conditions.

June Yi Lee

Pusan National University