2021
DOI: 10.5194/bg-18-3823-2021
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Evaluation of ocean dimethylsulfide concentration and emission in CMIP6 models

Abstract: Abstract. Characteristics and trends of surface ocean dimethylsulfide (DMS) concentrations and fluxes into the atmosphere of four Earth system models (ESMs: CNRM-ESM2-1, MIROC-ES2L, NorESM2-LM, and UKESM1-0-LL) are analysed over the recent past (1980–2009) and into the future, using Coupled Model Intercomparison Project 6 (CMIP6) simulations. The DMS concentrations in historical simulations systematically underestimate the most widely used observed climatology but compare more favourably against two recent obs… Show more

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Cited by 31 publications
(38 citation statements)
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“…The work shows that the variability lengthscale for DMS is typically small (< 30 km) and that a substantial proportion of the campaign average variance can be explained by the VLS of key biological (Chl) and physical (density, SSHA) observations (Model 7, Table 1). The results improve confidence in the validity of the biological and physical parameters used to currently parameterise seawater DMS at large scales and used in many global climate models (e.g., Bock et al, 2021;Galí et al, 2018;Mulcahy et al, 2020;Simó & Dachs, 2002). However, there is substantial variability in VLSDMS when assessing individual transects, which suggests that unaccounted-for variables are also important (e.g., light, wind speed, microbial activity).…”
Section: Discussionmentioning
confidence: 59%
“…The work shows that the variability lengthscale for DMS is typically small (< 30 km) and that a substantial proportion of the campaign average variance can be explained by the VLS of key biological (Chl) and physical (density, SSHA) observations (Model 7, Table 1). The results improve confidence in the validity of the biological and physical parameters used to currently parameterise seawater DMS at large scales and used in many global climate models (e.g., Bock et al, 2021;Galí et al, 2018;Mulcahy et al, 2020;Simó & Dachs, 2002). However, there is substantial variability in VLSDMS when assessing individual transects, which suggests that unaccounted-for variables are also important (e.g., light, wind speed, microbial activity).…”
Section: Discussionmentioning
confidence: 59%
“…Additionally, strong broadscale correlations between DMS concentration and solar radiation have also been reported [31] and between solar radiation and DMS synthesis [32]. Overall, these studies lend support to parts of the CLAW hypothesis, however modeling attempts to assess the direction and magnitude of the DMS-climate feedback [33][34][35] have often led to contradictory results [36][37][38][39][40]. Other regional modeling studies indicate significant meridional variability in future DMS emissions under warming, with the strongest response simulated at high latitudes in both hemispheres [41][42][43][44].…”
Section: Introductionmentioning
confidence: 73%
“…Bock et al [39] examine trends of surface ocean DMS concentration and flux of four Earth system models (ESMs: CNRM-ESM2-1, MIROC-ES2L, NorESM2-LM, and UKESM1-0-LL) over the recent past and into the future, using Coupled Model Intercomparison Project 6 (CMIP6) simulations. The four ESMs disagree on the sign of the trend of the global DMS flux from 1980 onwards, with two models showing an increase and two models a decrease.…”
Section: Modelingmentioning
confidence: 99%
“…Sea surface DMS distribution and its change under global warming have been simulated by characterizing main processes of DMS cycle and/or using empirical parameterization of influencing factors like Chl a, MLD, radiation and nutrients (Bock et al, 2021;Cameron-Smith et al, 2011;Gabric et al, 2004;Kloster et al, 2007;Six et al, 2013;Vallina et al, 2007b;Wang et al, 2018). However, distinct ocean environments and complexity of DMS (Hoffmann et al, 2016;Novak et al, 2021;Veres et al, 2020).…”
Section: Discussionmentioning
confidence: 99%