Mixed-layer ocean responses to anthropogenic aerosol dimming from 1870 to 2000

Abstract A study has been made of the dynamic interactions between the surface layer of the ocean and the atmosphere using a climate model that contains a new approach to predicting the sea surface temperature (SST). The atmospheric conditions are simulated numerically with the NCAR Community Climate Model (CCM3). The SST is determined by a modified Kraus–Turner-type one-dimensional mixed layer ocean model (MLOM) for the upper ocean that has been coupled to CCM3. The MLOM simulates vertical ocean dynamics and demonstrates the effects of the seasonal variation of mixed layer depth and convective instability on the SST. A purely thermodynamic slab ocean model (SOM) is currently available for use with CCM3 to predict the SST. A large-scale ocean general circulation model (OGCM) may also be coupled to CCM3; however, the OGCM is computationally intensive and is therefore not a good tool for conducting multiple sensitivity studies. The MLOM provides an alternative to the SOM that contains seasonally and spatially specified mixed layer depths. The SOM also contains a heat flux correction called Q-flux that crudely accounts for ocean heat transport by artificially specifying a heat flux that forces the SOM to replicate the observed SST. The results of the coupled MLOM–CCM3 reveal that the MLOM may be used on a global scale and can therefore replace the standard coupled SOM–CCM3 that contains no explicit ocean dynamics. Additionally, stand-alone experiments of the MLOM that are forced with realistic winds, heat, and moisture fluxes show that the MLOM closely approximates the observed seasonal cycle of SST.

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Seasonal Sea Ice Prediction with the CICE Model and Positive Impact of CryoSat-2 Ice Thickness Initialization

10.5194/tc-2021-353 ◽

2021 ◽

Author(s):

Shan Sun ◽

Amy Solomon

Keyword(s):

Sea Ice ◽

Mixed Layer ◽

Initial Conditions ◽

Warm Season ◽

Seasonal Prediction ◽

The Arctic ◽

Ice Thickness ◽

Positive Bias ◽

Mixed Layer Ocean ◽

Ice Edge

Abstract. The Los Alamos sea ice model (CICE) is being tested in standalone mode for its suitability for seasonal time scale prediction. The prescribed atmospheric forcings to drive the model are from the NCEP Climate Forecast System Reanalysis (CFSR). A built-in mixed layer ocean model in CICE is used. Initial conditions for the sea ice and the mixed layer ocean in the control experiments are also from CFSR. The simulated sea ice extent in the Arctic in control experiments is generally in good agreement with observations in the warm season at all lead times up to 12 months, suggesting that CICE is able to provide useful ice edge information for seasonal prediction. However, the ice thickness forecast has a positive bias stemming from the initial conditions and often persists for more than a season, limiting the model’s seasonal forecast skill. In addition, thicker ice has a lower melting rate in the warm season, both at the bottom and top, contributing to this positive bias. When this bias is removed by initializing the model using ice thickness data from satellite observations while keeping all other initial fields unchanged, both simulated ice edge and thickness improve. This indicates the important role of ice thickness initialization in sea ice seasonal prediction.

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Dissolution of cobalt and zinc from natural and anthropogenic dusts in seawater

Biogeosciences ◽

10.5194/bg-7-1927-2010 ◽

2010 ◽

Vol 7 (6) ◽

pp. 1927-1936 ◽

Cited By ~ 20

Author(s):

C.-E. Thuróczy ◽

M. Boye ◽

R. Losno

Keyword(s):

Mixed Layer ◽

Flow Reactor ◽

Anthropogenic Sources ◽

Anthropogenic Aerosol ◽

Surface Ocean ◽

Anthropogenic Inputs ◽

The Pacific ◽

The Pacific Ocean ◽

Voltammetric Methods

Abstract. Atmospheric dust inputs to the surface ocean are a major source of trace metals likely to be bio-available for phytoplankton after their dissolution in seawater. Among them, cobalt (Co) and zinc (Zn) are essential for phytoplankton growth and for the distribution of the major groups such as coccolithophorids, cyanobacteria and diatoms. The solubility in seawater of Co and Zn present in natural and anthropogenic dusts was studied using an open-flow reactor with and without light irradiation. Those dusts can be transported in the atmosphere by the wind before being deposited to the surface ocean. The analyses of cobalt and zinc were conducted using voltammetric methods and the global elemental composition of dust was determined by ICP-AES. This study highlighted the role of the dust origin in revealing the solubility characteristics. Much higher dust solubility was found for zinc as compared to cobalt; cobalt in anthropogenic particles was much more soluble (0.78%) in seawater after 2 h of dissolution than Co in natural particles (0.14%). Zinc showed opposite solubility, higher in natural particles (16%) than in anthropogenic particles (5.2%). A natural dust event to the surface ocean could account for up to 5% of the cobalt inventory and up to 50% of the Zn inventory in the mixed layer in the Pacific Ocean whereas the cobalt and zinc inventories in the mixed layer of the Atlantic Ocean might already include the effects of natural dust inputs and the subsequent metal dissolution. Anthropogenic sources to the surface ocean could be as important as the natural sources, but a better estimate of the flux of anthropogenic aerosol to the surface ocean is needed to further estimate the anthropogenic inputs. Variations in natural and anthropogenic inputs may induce large shifts in the Co/Zn ratio in the surface ocean; hence it could impact the phytoplankton community structure.

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Design and Critical Appraisal of an Accelerated Integration Procedure for Atmospheric GCM/Mixed-Layer Ocean Models

Journal of Climate ◽

10.1175/1520-0442(1989)002<0641:dacaoa>2.0.co;2 ◽

1989 ◽

Vol 2 (7) ◽

pp. 641-655

Author(s):

Michael E. Schlesinger ◽

Zong-Cl Zhao ◽

Dean Vickers

Keyword(s):

Mixed Layer ◽

Critical Appraisal ◽

Integration Procedure ◽

Mixed Layer Ocean ◽

Ocean Models

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Physical characteristics of the Antarctic sea-ice zone derived from modelling and observations

Annals of Glaciology ◽

10.3189/s0260305500013707 ◽

1997 ◽

Vol 25 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

W. F. Budd ◽

Xingren Wu ◽

P. A. Reid

Keyword(s):

Numerical Modelling ◽

Sea Ice ◽

Mixed Layer ◽

Annual Cycle ◽

Surface Mixed Layer ◽

Present Climate ◽

Antarctic Sea Ice ◽

Mixed Layer Ocean ◽

The Antarctic ◽

Ice Model

Antarctic sea ice plays a key role in the present climate system, providing a regulating balance between the atmosphere and ocean heat fluxes, as well as influencing the salt fluxes and deep water formation over the continental shelves. The severe winter environmental conditions of the Antarctic sea-ice zone make it difficult to observe many of the physical characteristics in a comprehensive way. The inter-relations between the variables mean that much can be learnt from the observations of some features along with detailed numerical modelling of the whole system and the interactions between the variables. This study therefore aims to use numerical modelling of the atmosphere, sea ice and surface mixed-layer ocean in the sea-ice zone, together with observations to simulate a comprehensive range of parameters and their variability through the annual cycle to provide a basis for further observations and model validation for the present climate.The model includes a coupled atmospheric general circulation model with an interactive dynamic and thermodynamic sea-ice model and surface mixed-layer ocean. The deep ocean and ocean surface conditions outside the sea-ice zone are constrained to the present mean climate conditions to ensure no climatic drift. The sca-ice model is similar to previous published versions, bill has refined schemes for partitioning of the freezing of frazil ice within the leads and under the ice floes, and for rafting. These perform well in both polar regions with the same physics. The model simulates the annual cycle of atmospheric and sea-ice features well in comparison with data from the global atmospheric analyses, the satellite sensing of sea ice, and the limited in situ surface observations.The output from the model also includes: all components of the heart fluxes, atmospheric profiles and surface temperatures for air, ice and ice-ocean mixtures, open-water fractions, surface snow and snow-ice depths, and the sea-ice convergence-divergence and drift. The comparison of these features with additional observations provides a means for further validating the model and representing the present climate more closely.

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