The Southeastern Tropical Atlantic SST Bias Investigated with a Coupled Atmosphere–Ocean Single-Column Model at a PIRATA Mooring Site

Deppenmeier, Anna-Lena; Haarsma, Reindert J.; Heerwaarden, Chiel C. van; Hazeleger, Wilco

doi:10.1175/jcli-d-19-0608.1

Cited by 11 publications

(17 citation statements)

References 49 publications

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“…The warm bias, in turn, weakens the lower-tropospheric stability and thus hinders the formation of the stratocumulus deck, which contributes to sustaining the surface warm bias. Other mechanisms have been proposed to explain this bias, including too weak equatorial and alongshore winds weakening upwelling (e.g., Richter et al, 2012;Koseki et al, 2018;Goubanova et al, 2019;, biases in regional atmospheric moisture (Hourdin et al, 2015), too weak offshore transport by ocean mesoscale eddies, and the misrepresentation of the coastal current system (Xu et al, 2014b) or vertical mixing in the upper ocean (e.g., Hazeleger and Haarsma, 2005;Exarchou et al, 2018;Deppenmeier et al, 2020). Richer (2015) extensively reviewed all these mechanisms.…”

Section: Upwelling Regionsmentioning

confidence: 99%

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

et al. 2022

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Abstract. We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from ∼1∘ (eddy-parametrized) to ∼0.25∘ (eddy-present). For one model, ocean resolution also reaches 1/12∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary.

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Section: Upwelling Regionsmentioning

confidence: 99%

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

et al. 2022

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“…To assess the model simulations, we use local observation data from the R/V Revelle. SSTs taken are obtained from the ship's intake thermosalinograph at 5 m depth corrected to represent the skin temperature based on the Sea Snake floating thermistor at 5 cm depth (Edson et al, 2016;de Szoeke et al, 2015). In the following the SST used as a reference will be the SST skin temperature.…”

Section: Reference Datasetsmentioning

confidence: 99%

“…In addition, prescribed surface fluxes do not account for the thermal adjustment of the atmospheric boundary layer to the sea surface conditions (e.g., Barnier et al, 1995). To overcome these caveats and properly study the interplay between the atmosphere and ocean boundary layers, only a few studies have so far developed coupled ocean-atmosphere SCMs (Clayson and Chen, 2002;Deppenmeier et al, 2020;Hartung et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration

et al. 2022

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Abstract. A single-column version of the CNRM-CM6-1 global climate model has been developed to ease development and validation of the boundary layer physics and air–sea coupling in a simplified environment. This framework is then used to assess the ability of the coupled model to represent the sea surface temperature (SST) diurnal cycle. To this aim, the atmospheric–ocean single-column model (AOSCM), called CNRM-CM6-1D, is implemented in a case study derived from the CINDY2011/DYNAMO campaign over the Indian Ocean, where large diurnal SST variabilities have been well documented. Comparing the AOSCM and its uncoupled components (atmospheric SCM and oceanic SCM, called OSCM) highlights the fact that the impact of coupling in the atmosphere results from both the possibility to take into account the diurnal variability of SST, which is not usually available in forcing products, and the change in mean state SST as simulated by the OSCM, with the ocean mean state not being heavily impacted by the coupling. This suggests that coupling feedbacks in the 3D model do not arise from the coupling of ocean and atmosphere vertical column physics but are more due to the large-scale dynamics resolved by the 3D model. Additionally, a sub-daily coupling frequency is needed to represent the SST diurnal variability, but the choice of the coupling time step between 15 min and 3 h does not impact the diurnal temperature range simulated much. The main drawback of a 3 h coupling is delaying the SST diurnal cycle by 5 h in asynchronous coupled models. Overall, the diurnal SST variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m. This framework is shown to be a very valuable tool to develop and validate the boundary layer physics and the coupling interface. It highlights the interest to develop other atmosphere–ocean coupling case studies.

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“…The communication between the model components is handled by the OASIS3-MCT coupler. A detailed description of the AOSCM and how to design an experiment with it can be found in Hartung et al (2018) and a tropical application is presented in Deppenmeier et al (2020). The corresponding host model to the AOSCM is the EC-Earth global climate model used in CMIP5 (Hazeleger et al, 2010(Hazeleger et al, , 2012 and CMIP6 (Döscher et al, 2021).…”

Section: The Coupled Atmosphere-ocean Single-column Modelmentioning

confidence: 99%

“…(2018) and a tropical application is presented in Deppenmeier et al. (2020). The corresponding host model to the AOSCM is the EC‐Earth global climate model used in CMIP5 (Hazeleger et al., 2010, 2012) and CMIP6 (Döscher et al., 2021).…”

Section: Model Description and Data Sourcesmentioning

confidence: 99%

Exploring the Dynamics of an Arctic Sea Ice Melt Event Using a Coupled Atmosphere‐Ocean Single‐Column Model (AOSCM)

Hartung

Svensson

Holt

et al. 2022

J Adv Model Earth Syst

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Improving model performance through parameterization development is a difficult, time-consuming but potentially very rewarding task. It usually involves disentangling the model behavior, considering existing compensating errors (Dommenget & Rezny, 2018), unbalanced physics representations and numerical issues, before benefits of new, more advanced and physically based schemes are realized. As an intermediate step in understanding the model behavior, and to focus on specific parts of the model, there is a long tradition of using single-column models (SCM) for atmospheric processes (e.g., Holtslag et al., 2013;Neggers et al., 2012), and ocean vertical mixing (e.g., Li et al., 2019). More recently, Bogenschutz et al. (2020) seamlessly integrated an SCM in a general circulation model and Hourdin et al. ( 2021) applied a SCM to find optimal parameterization parameters as part of model tuning.Improving the representation of the polar regions in weather and climate models has been a challenging task (Jung et al., 2016). Additional small scale processes and properties of the climate system become relevant at high

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The Southeastern Tropical Atlantic SST Bias Investigated with a Coupled Atmosphere–Ocean Single-Column Model at a PIRATA Mooring Site

Cited by 11 publications

References 49 publications

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration

Exploring the Dynamics of an Arctic Sea Ice Melt Event Using a Coupled Atmosphere‐Ocean Single‐Column Model (AOSCM)

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