ArcticNAP

Exploring the natural aerosol baseline for improved model predictions of Arctic climate change

Started
November 1, 2021
Status
In Progress
Share this project

Abstract

The Arctic is warming two to three times faster than the global average. Arctic amplification has repercussions for the global climate, northern hemispheric weather, and local livelihoods. Current models have difficulties simulating Arctic change making future scenarios uncertain.

To improve model skills, the factors contributing to Arctic amplification need to be better represented. We focus on the role of aerosols. Aerosols can interact directly with solar radiation, and change cloud radiative properties. Considerable effort has gone into describing the role of anthropogenic aerosols in Arctic climate change. Much less focus has been on natural aerosols, which are gaining importance. This is partly because they are emitted in complex processes involving several environmental compartments, which is difficult to represent in models. To complicate things further, the natural state of the Arctic is changing rapidly thereby changing aerosol processes.

We propose to use an unprecedented combination of in-situ measurements, satellite-based observations, and numerical weather prediction data to reveal natural Arctic processes that drive climate-relevant aerosol properties. Aerosol data are available from eight Arctic observatories covering up to 30 years, and from two high Arctic drift expeditions. By developing a latent variable model, which accommodates the different data types, time series with gaps, and domain knowledge for constraints, we will explore relationships between environmental variables, such as sea ice extent, chlorophyll-a concentrations, or meteorological conditions, and the aerosol target variables.

After quantifying these relationships, we will test the output from 5 Earth System models (ESM) against the data-driven model results to identify where ESMs can be improved. We expect the project to have a high impact because results would be produced while several large Horizon 2020 polar research projects are carried out where our output can be used directly.

People

Collaborators

SDSC Team:
Eliza Harris
Michele Volpi
William Aeberhard

PI | Partners:

EPFL:

  • Dr. Jakob Boyd Pernov
  • Prof. Julia Schmale

More info

University of Helsinki:

  • Dr. Tuija Jokinen

More info

University of Leeds:

  • Prof. Kenneth S. Carslaw

More info

University of Stockholm:

  • Prof. Paul Zieger

More info

description

Problem:

This project focuses on the natural production of aerosols in the Arctic which plays an important role, albeit not well understood so far, in climate change. By combining many data sources in an unprecedented way (including in situ measurements, remote sensing, and outputs from numerical weather prediction models), we aim to construct a large model that would identify key environmental drivers and potentially describe processes that may evolve with climate change.

Proposed approach:

We are developing a complex non-linear regression model for the in situ aerosol concentration measurements. This model accounts for the measurements of irregular sampling differences by integrating non-linear feature effects over successive time windows. The model also involves various regularization techniques to allow the identification of interpretable effects among the vast number of features we compiled.

Impact:

The processes behind the emission of natural aerosols in the Arctic are some of the few missing puzzle pieces for Earth System Models. Correctly identifying drivers for such emissions is crucial to improve climate predictions on a global scale.

Gallery

Annexe

Additionnal resources

Bibliography

  1. Pörtner, H.-O. et al. (2019). IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. Report Home
  2. Schmale, J., Zieger, P., and Ekman, A. M. L. (2021). Aerosols in current and future Arctic climate. Nature Climate Change 11, 95-105. https://doi.org/10.1038/s41558-020-00969-5

Publications

Related Pages

More projects

ML4FCC

In Progress
Machine Learning for the Future Circular Collider Design
Big Science Data

CLIMIS4AVAL

In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment

SEMIRAMIS

Completed
AI-augmented architectural design
Energy, Climate & Environment

4D-Brains

In Progress
Extracting activity from large 4D whole-brain image datasets
Biomedical Data Science

News

Latest news

PassGPT | Using language models to enhance password security
February 6, 2024

PassGPT | Using language models to enhance password security

PassGPT | Using language models to enhance password security

PassGPT is a Large Language Model for password generation trained on leaked passwords, which can outperform existing methods based on generative adversarial networks by guessing twice as many unseen passwords.
ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning
January 24, 2024

ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning

ADORE | A benchmark dataset in ecotoxicology to foster the adoption of machine learning

Applying machine learning to ecotoxicology could help reduce the number of animal tests, costs, and animals sacrificed while preserving the accuracy of the in vivo tests.
License Flowers | Art and AI at SDSC
February 21, 2024

License Flowers | Art and AI at SDSC

License Flowers | Art and AI at SDSC

An adventure to create art using AI to raise awareness on code licenses

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!