Scale‐Aware Space‐Time Stochastic Parameterization of Subgrid‐Scale Velocity Enhancement of Sea Surface Fluxes

Bessac, Julie; Christensen, Hilary; Endo, Kazuhiko; Monahan, Adam H.; Weitzel, Nils

doi:10.1029/2020ms002367

Cited by 3 publications

(5 citation statements)

References 44 publications

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“…However, as, in contrast to other studies, Bessac et al . (2021) parametrize

\delta U

directly, we also briefly discuss in Section 5.3 how the change in the parametrized field may impact our results.…”

Section: Surface Fluxes and Mesoscale Enhancementmentioning

confidence: 99%

“…We therefore only present the results for the gustiness approach hereafter. However, as, in contrast to other studies, Bessac et al (2021) parametrize 𝛿U directly, we also briefly discuss in Section 5.3 how the change in the parametrized field may impact our results.…”

Section: 3mentioning

confidence: 99%

“…(2002) argued for the relevance of the cloud fraction. More recently, Bessac et al . (2019; 2021) chose the GCM‐resolved wind speed and rainfall rate as two complementary

{U}_{\mathrm{g}}

predictors. The authors also addressed the stochastic component of mesoscale flux enhancement.…”

Section: Introductionmentioning

confidence: 99%

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Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

Blein

Roehrig

Voldoire

2022

Quart J Royal Meteoro Soc

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The mesoscale enhancement of surface turbulent fluxes at the air–sea interface is driven by the mesoscale surface wind‐speed variability, especially the gustiness velocity and the mesoscale wind‐speed standard variation. This study proposes a parametrization of these two variables. A large dataset based on the operational 2.5‐km AROME convection‐permitting model is used in a coarse‐graining framework, to quantify various quantities that are subgrid at the scale of a 100‐km resolution global circulation model grid cell. This provides a learning dataset to help build the parametrization. The analysis of two case studies of intense wind‐speed mesoscale variability, combined with a literature review, provides a physically based set of 12 potential predictors, accounting for the convection activity and the large‐scale dynamics. The least absolute shrinkage and selection operator then frames a penalized multivariate linear regression approach to identify the most relevant predictors objectively. Five predictors are selected for predicting the gustiness velocity: the updraft mass flux at the lifting condensation level, the density‐current spreading velocity, the large‐scale horizontal shear and divergence, and the large‐scale wind speed. The parametrization of the mesoscale wind‐speed standard deviation requires an additional predictor, namely the cold‐pool object aggregation index. The proposed parametrization performs significantly better than the previously published parametrizations and is able to capture 80, 99, and 93%$$ \% $$ of the mesoscale enhancement of the momentum, sensible heat, and latent heat fluxes, respectively. From the perspective of a global circulation model implementation, in which some predictors may be unavailable, simpler versions of the parametrization, that is, involving fewer predictors, are also discussed.

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“…However, as, in contrast to other studies, Bessac et al . (2021) parametrize

\delta U

directly, we also briefly discuss in Section 5.3 how the change in the parametrized field may impact our results.…”

Section: Surface Fluxes and Mesoscale Enhancementmentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

See 1 more Smart Citation

Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

Blein

Roehrig

Voldoire

2022

Quart J Royal Meteoro Soc

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show abstract

“…In addition to the aforementioned bivariate and circular nature, wind exhibits spatiotemporal features that vary across different spatial and temporal scales e.g., spatial variation due to topography, distance to coast, temporal variation due to diurnal cycle, weather regimes, seasonality, and interannual variability. In particular some of these features and dependencies vary across scales, such as observed spatio-temporal correlation varying across resolutions (e.g., Bessac et al, 2021). In addition, wind speeds over large water bodies are generally stronger than over the land.…”

Section: Introductionmentioning

confidence: 99%

“…High-resolution (less than 20 km) RCMs resolve spatial and temporal dependencies much better than do GCMs Di Luca et al, 2012) and therefore can better resolve wind conditions. Meanwhile, some research have incorporated small-scale features of wind speed into the parameterizations in numerical models (Zeng et al, 2002;Zhang et al, 2016;Bessac et al, 2019Bessac et al, , 2021.…”

Section: Introductionmentioning

confidence: 99%

Station-wise statistical joint assessment of wind speed and direction under future climates across the United States

Wu¹,

Bessac²,

Huang³

et al. 2022

Preprint

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This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore locations across the Continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Directional wind speed distributions are estimated using two statistical methods: a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resulting estimates of directional distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. In particular, this work extends the concept of internal variability to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model capture both historical wind speed, wind direction, and their dependencies reasonably well over both inland and offshore locations. In the future, most of the locations show no significant changes in mean wind speeds in both winter and summer, although the changes in standard deviation and 95thquantile show some robust changes over certain locations in winter. However, in winter, high directional wind speeds are projected to decrease in Northwest, Colorado and Northern Great Plains. In summer, directional high wind speeds over southern Great Plains slightly increase while high directional wind speeds over offshore locations do not change in future. The proposed conditional approach enables the characterization of the directional wind speed distributions, which offers additional insights for the joint assessment of speed and direction.

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A conditional approach for joint estimation of wind speed and direction under future climates

Bessac

Huang

et al. 2022

Adv. Stat. Clim. Meteorol. Oceanogr.

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Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.

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Scale‐Aware Space‐Time Stochastic Parameterization of Subgrid‐Scale Velocity Enhancement of Sea Surface Fluxes

Cited by 3 publications

References 44 publications

Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

Station-wise statistical joint assessment of wind speed and direction under future climates across the United States

A conditional approach for joint estimation of wind speed and direction under future climates

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