Related Experiment Video
Updated: Jan 16, 2026
06:27
Simulating Impacts of Ice Storms on Forest Ecosystems
Published on: June 30, 2020
7.4K
Non-linear dynamical approaches for characterizing multi-sector climate impacts under irreducible uncertainty.
Rachindra Mawalagedara1,2,3, Arnob Ray1,2, Puja Das1,2
1Sustainability and Data Sciences Laboratory, Northeastern University, Boston, MA USA.
NPJ Climate and Atmospheric Science
|September 29, 2025
Summary
Internal climate variability (ICV) creates uncertainty in climate projections. Nonlinear dynamical (NLD) methods can better analyze large ensembles to improve climate predictions and adaptation strategies.
Area of Science:
- Climate Science
- Dynamical Systems Theory
- Data Analysis
Background:
- Internal climate variability (ICV) is a key source of uncertainty in climate projections.
- This uncertainty complicates impact assessments for critical sectors and stakeholder-relevant scales.
- ICV arises from complex, nonlinear interactions within the climate system.
Purpose of the Study:
- To explore the underutilization of nonlinear dynamical (NLD) approaches in analyzing initial condition large ensembles (LEs).
- To argue for the application of NLD methods to improve the characterization of ICV.
- To enhance climate projection insights and support adaptation strategies.
Main Methods:
- Application of diverse nonlinear dynamical (NLD) approaches.
- Systematic analysis of initial condition large ensembles (LEs).
- Focus on extracting physically interpretable insights from climate system interactions.
Main Results:
- NLD approaches offer a promising pathway for robustly extracting insights from LEs.
- Effective application of NLD methods can uncover underlying patterns and variability within ensemble members.
- Improved characterization of ICV through NLD analysis.
Conclusions:
- NLD methods can significantly enhance the analysis of climate LEs.
- Harnessing the potential of LEs with NLD approaches refines fundamental climate projection insights.
- Bridging complex climate dynamics with practical resilience strategies for better decision-making.
Related Concept Videos
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
286
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
286
01:16What is Climate?
20.5K
Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
20.5K
00:59Uncertainty: Overview
1.6K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.6K
01:11Variability: Analysis
448
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
448
01:10Propagation of Uncertainty from Systematic Error
1.4K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.4K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
249
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
249