Preprints
https://doi.org/10.5194/egusphere-2024-1124
https://doi.org/10.5194/egusphere-2024-1124
17 Apr 2024
 | 17 Apr 2024

A global dust emission dataset for estimating dust radiative forcings in climate models

Danny M. Leung, Jasper F. Kok, Longlei Li, David M. Lawrence, Natalie M. Mahowald, Simone Tilmes, and Erik Kluzek

Abstract. Sedimentary records indicate that atmospheric dust has increased substantially since preindustrial times. However, state-of-the-art global Earth system models (ESMs) are unable to capture this historical increase, posing challenges in assessing the impacts of desert dust on Earth’s climate. To address this issue, we construct a globally gridded dust emission dataset (DustCOMMv1) spanning 1841–2000. We do so by combining 19 sedimentary records of dust deposition with observational and modeling constraints on the modern-day dust cycle. The derived emission dataset contains interdecadal variability of dust emissions as forced by the deposition flux records, which increased by approximately 50 % from the 1850s to the 1990s. We further provide future dust emission datasets for 2000–2100 by assuming three possible scenarios for how future dust emissions will evolve. We evaluate the dust emission dataset and illustrate its effectiveness in enforcing a historical dust increase in ESMs by implementing conducting a long-term (1851–2000) dust cycle simulation with the Community Earth System Model (CESM2). The simulated dust deposition is in reasonable agreement with the long-term increase in most sedimentary dust deposition records and with measured long-term trends in dust concentration at sites in Miami and Barbados. This contrasts with the CESM2 simulations using a process-based dust emission scheme and with simulations from the Coupled Model Intercomparison Project (CMIP6), which show little to no secular trends in dust deposition, concentration, and optical depth. The DustCOMM emissions thus enables ESMs to account for the historical radiative forcings (RFs), including due to dust direct interactions with radiation (direct RF). Our CESM2 simulations estimate a 1981–2000 minus 1851–1870 direct RF of –0.10 W m-2 from dust particles up to 10 μm in diameter (PM10).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Danny M. Leung, Jasper F. Kok, Longlei Li, David M. Lawrence, Natalie M. Mahowald, Simone Tilmes, and Erik Kluzek

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1124', Anonymous Referee #1, 08 Jun 2024
  • RC2: 'Comment on egusphere-2024-1124', I. Pérez, 23 Jul 2024
  • AC1: 'Comment on egusphere-2024-1124', Danny Leung, 03 Sep 2024
Danny M. Leung, Jasper F. Kok, Longlei Li, David M. Lawrence, Natalie M. Mahowald, Simone Tilmes, and Erik Kluzek
Danny M. Leung, Jasper F. Kok, Longlei Li, David M. Lawrence, Natalie M. Mahowald, Simone Tilmes, and Erik Kluzek

Viewed

Total article views: 646 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
458 153 35 646 51 23 20
  • HTML: 458
  • PDF: 153
  • XML: 35
  • Total: 646
  • Supplement: 51
  • BibTeX: 23
  • EndNote: 20
Views and downloads (calculated since 17 Apr 2024)
Cumulative views and downloads (calculated since 17 Apr 2024)

Viewed (geographical distribution)

Total article views: 672 (including HTML, PDF, and XML) Thereof 672 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Oct 2024
Download
Short summary
This study derives a desert dust emission dataset for 1841–2000, by employing a combination of observed dust records from sedimentary cores as well as reanalyzed global dust cycle constraints. We evaluate the ability of global models to replicate the observed historical dust variability by using the emission dataset to force a historical simulation in an Earth system model. We show that prescribing our emissions forces the model to match better against observations than other mechanistic models.