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- Author or Editor: Claudia Christine Stephan x
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Abstract
Satellite images frequently show mesoscale arc-shaped cloud lines with a spacing of several tens of kilometers. These clouds form in a shallow mixed boundary layer in locations where the near-surface horizontal wind speed exceeds ~7 m s−1. Unlike other mesoscale cloud line phenomena, such as horizontal convective rolls, these cloud lines do not align with the wind direction but form at large oblique angles to the near-surface wind. A particularly distinct event of this pattern developed on 31 January 2020 over the western tropical Atlantic Ocean. Radiosonde soundings are available for this time and location, allowing a detailed analysis. By comparing observations with theoretical predictions that are based on Jeffreys’s drag-instability mechanism, it is shown that drag-instability waves may contribute to the formation of this cloud pattern. The theory is formulated in only two dimensions and predicts that wavelike horizontal wind perturbations of this wavelength can grow, because they modulate the surface friction in a way that reinforces the perturbations. The theoretical horizontal wavelengths of 40–80 km agree with the observations. Streamlines from the ERA5 reanalysis show that the directional change of the near-surface wind is likely to contribute to the arc shape but that a radial propagation of an initial instability is also required to explain the strong curvature. Moreover, ERA5 winds suggest that other known explanations for the formation of cloud lines are unlikely to apply in the case studied here.
Abstract
Satellite images frequently show mesoscale arc-shaped cloud lines with a spacing of several tens of kilometers. These clouds form in a shallow mixed boundary layer in locations where the near-surface horizontal wind speed exceeds ~7 m s−1. Unlike other mesoscale cloud line phenomena, such as horizontal convective rolls, these cloud lines do not align with the wind direction but form at large oblique angles to the near-surface wind. A particularly distinct event of this pattern developed on 31 January 2020 over the western tropical Atlantic Ocean. Radiosonde soundings are available for this time and location, allowing a detailed analysis. By comparing observations with theoretical predictions that are based on Jeffreys’s drag-instability mechanism, it is shown that drag-instability waves may contribute to the formation of this cloud pattern. The theory is formulated in only two dimensions and predicts that wavelike horizontal wind perturbations of this wavelength can grow, because they modulate the surface friction in a way that reinforces the perturbations. The theoretical horizontal wavelengths of 40–80 km agree with the observations. Streamlines from the ERA5 reanalysis show that the directional change of the near-surface wind is likely to contribute to the arc shape but that a radial propagation of an initial instability is also required to explain the strong curvature. Moreover, ERA5 winds suggest that other known explanations for the formation of cloud lines are unlikely to apply in the case studied here.
Abstract
Shallow convection over the oceans is responsible for the largest uncertainties in climate projections. Idealized simulations have shown decades ago that shallow clouds generate internal gravity waves, which under certain atmospheric background conditions become trapped inside the troposphere and influence the development of clouds. These feedbacks, which occur at horizontal scales of up to several tens of kilometers. are neither resolved nor parameterized in traditional global climate models (GCMs), while the newest generation of GCMs (grid spacings < 5 km) is starting to resolve them. The interactions between the convective boundary layer and trapped waves have almost exclusively been studied in highly idealized frameworks and it remains unclear to what degree this coupling affects the organization of clouds in the real atmosphere or in the new generation of GCMs. Here, the coupling between clouds and trapped waves is examined in 2.5-km simulations that span the entirety of the tropical Atlantic and are initialized and forced with meteorological analyses. The coupling between clouds and trapped waves is sufficiently strong to be detected in these simulations of full complexity. Stronger upper-tropospheric westerly winds are associated with a stronger cloud–wave coupling. In the simulations this results in a highly organized scattered cloud field with cloud spacings of about 19 km, matching the dominant trapped wavelength. Based on the large-scale atmospheric state, wave theory can reliably predict the regions and times where cloud–wave feedbacks become relevant to convective organization. Theory, the simulations, and satellite imagery imply a seasonal cycle in the trapping of gravity waves.
Abstract
Shallow convection over the oceans is responsible for the largest uncertainties in climate projections. Idealized simulations have shown decades ago that shallow clouds generate internal gravity waves, which under certain atmospheric background conditions become trapped inside the troposphere and influence the development of clouds. These feedbacks, which occur at horizontal scales of up to several tens of kilometers. are neither resolved nor parameterized in traditional global climate models (GCMs), while the newest generation of GCMs (grid spacings < 5 km) is starting to resolve them. The interactions between the convective boundary layer and trapped waves have almost exclusively been studied in highly idealized frameworks and it remains unclear to what degree this coupling affects the organization of clouds in the real atmosphere or in the new generation of GCMs. Here, the coupling between clouds and trapped waves is examined in 2.5-km simulations that span the entirety of the tropical Atlantic and are initialized and forced with meteorological analyses. The coupling between clouds and trapped waves is sufficiently strong to be detected in these simulations of full complexity. Stronger upper-tropospheric westerly winds are associated with a stronger cloud–wave coupling. In the simulations this results in a highly organized scattered cloud field with cloud spacings of about 19 km, matching the dominant trapped wavelength. Based on the large-scale atmospheric state, wave theory can reliably predict the regions and times where cloud–wave feedbacks become relevant to convective organization. Theory, the simulations, and satellite imagery imply a seasonal cycle in the trapping of gravity waves.
Abstract
The Silk Road pattern (SRP) teleconnection manifests in summer over Eurasia, where it is associated with substantial temperature and precipitation anomalies. The SRP varies on interannual and decadal scales; reanalyses show an increase in its decadal variability around the mid-1970s. Understanding what drives this decadal variability is particularly important, because contemporary seasonal prediction models struggle to predict the phase of the SRP. Based on analysis of observations and multiple targeted numerical experiments, this study proposes a mechanism for decadal SRP variability. Causal effect network analysis confirms a positive feedback loop between the eastern portion of the SRP pattern and vertical motion over India on synoptic time scales. Anomalies over a larger region of subtropical South Asia can reinforce a background state that projects onto the positive or negative SRP through this mechanism. This effect is isolated and confirmed in targeted numerical simulations. The transition from weak to strong decadal variability in the mid-1970s is consistent with more spatially coherent interannual precipitation variability over subtropical South Asia. Furthermore, results suggest that oceanic variability does not directly force the SRP. Nevertheless, sea surface temperatures in the North Atlantic and the North Pacific may indirectly affect the SRP by modulating South Asian rainfall on decadal time scales.
Abstract
The Silk Road pattern (SRP) teleconnection manifests in summer over Eurasia, where it is associated with substantial temperature and precipitation anomalies. The SRP varies on interannual and decadal scales; reanalyses show an increase in its decadal variability around the mid-1970s. Understanding what drives this decadal variability is particularly important, because contemporary seasonal prediction models struggle to predict the phase of the SRP. Based on analysis of observations and multiple targeted numerical experiments, this study proposes a mechanism for decadal SRP variability. Causal effect network analysis confirms a positive feedback loop between the eastern portion of the SRP pattern and vertical motion over India on synoptic time scales. Anomalies over a larger region of subtropical South Asia can reinforce a background state that projects onto the positive or negative SRP through this mechanism. This effect is isolated and confirmed in targeted numerical simulations. The transition from weak to strong decadal variability in the mid-1970s is consistent with more spatially coherent interannual precipitation variability over subtropical South Asia. Furthermore, results suggest that oceanic variability does not directly force the SRP. Nevertheless, sea surface temperatures in the North Atlantic and the North Pacific may indirectly affect the SRP by modulating South Asian rainfall on decadal time scales.
Abstract
Large uncertainties remain with respect to the representation of atmospheric gravity waves (GWs) in general circulation models (GCMs) with coarse grids. Insufficient parameterizations result from a lack of observational constraints on the parameters used in GW parameterizations as well as from physical inconsistencies between parameterizations and reality. For instance, parameterizations make oversimplifying assumptions about the generation and propagation of GWs. Increasing computational capabilities now allow GCMs to run at grid spacings that are sufficiently fine to resolve a major fraction of the GW spectrum. This study presents the first intercomparison of resolved GW pseudomomentum fluxes (GWMFs) in global convection-permitting simulations and those derived from satellite observations. Six simulations of three different GCMs are analyzed over the period of one month of August to assess the sensitivity of GWMF to model formulation and horizontal grid spacing. The simulations reproduce detailed observed features of the global GWMF distribution, which can be attributed to realistic GWs from convection, orography, and storm tracks. Yet the GWMF magnitudes differ substantially between simulations. Differences in the strength of convection may help explain differences in the GWMF between simulations of the same model in the summer low latitudes where convection is the primary source. Across models, there is no evidence for a systematic change with resolution. Instead, GWMF is strongly affected by model formulation. The results imply that validating the realism of simulated GWs across the entire resolved spectrum will remain a difficult challenge not least because of a lack of appropriate observational data.
Abstract
Large uncertainties remain with respect to the representation of atmospheric gravity waves (GWs) in general circulation models (GCMs) with coarse grids. Insufficient parameterizations result from a lack of observational constraints on the parameters used in GW parameterizations as well as from physical inconsistencies between parameterizations and reality. For instance, parameterizations make oversimplifying assumptions about the generation and propagation of GWs. Increasing computational capabilities now allow GCMs to run at grid spacings that are sufficiently fine to resolve a major fraction of the GW spectrum. This study presents the first intercomparison of resolved GW pseudomomentum fluxes (GWMFs) in global convection-permitting simulations and those derived from satellite observations. Six simulations of three different GCMs are analyzed over the period of one month of August to assess the sensitivity of GWMF to model formulation and horizontal grid spacing. The simulations reproduce detailed observed features of the global GWMF distribution, which can be attributed to realistic GWs from convection, orography, and storm tracks. Yet the GWMF magnitudes differ substantially between simulations. Differences in the strength of convection may help explain differences in the GWMF between simulations of the same model in the summer low latitudes where convection is the primary source. Across models, there is no evidence for a systematic change with resolution. Instead, GWMF is strongly affected by model formulation. The results imply that validating the realism of simulated GWs across the entire resolved spectrum will remain a difficult challenge not least because of a lack of appropriate observational data.
