Search Results
Abstract
Extratropical cyclones are the primary cause of midlatitude winds, precipitation, and clouds. Surface latent and sensible heat fluxes from the ocean impact cyclone intensity, but their role in moisture transport leading to cloud and precipitation is still under investigation. While numerical simulations can help establish such links, evaluating and constraining these simulations require observations. To this end, satellite-based observations of cloud and precipitation in low-latitude extratropical cyclones are examined for four distinct classifications based on the Cyclone Global Navigation Satellite System (CYGNSS) latent and sensible heat fluxes, averaged in the post-cold-frontal or warm-sector areas of the cyclones. Using a cyclone compositing approach, contrasts in cloud and precipitation in strong- versus weak-surface-flux conditions are examined. In the post-cold-frontal region, stronger latent or sensible heat fluxes are associated with lower precipitation rates and higher cloud opacity, indicating more vigorous shallow convection. However, larger sensible heat flux cases display larger cloud fraction, while larger latent heat flux cases exhibit lower cloud fraction, which could indicate differing cloud morphologies. In the comma region of the cyclones, clouds and precipitation depend on both cyclone strength and moisture availability. Consistent with this, larger cloud amount and precipitation are found for strong fluxes in the post-cold-frontal region, and weak or negative sensible heat fluxes, indicative of poleward warm advection, in the warm sector. The strong regional differences in the surface heat flux–cloud and precipitation relationships highlight the need for further investigation into moisture supply and transport in cyclones, while providing guidance for future work.
Abstract
Extratropical cyclones are the primary cause of midlatitude winds, precipitation, and clouds. Surface latent and sensible heat fluxes from the ocean impact cyclone intensity, but their role in moisture transport leading to cloud and precipitation is still under investigation. While numerical simulations can help establish such links, evaluating and constraining these simulations require observations. To this end, satellite-based observations of cloud and precipitation in low-latitude extratropical cyclones are examined for four distinct classifications based on the Cyclone Global Navigation Satellite System (CYGNSS) latent and sensible heat fluxes, averaged in the post-cold-frontal or warm-sector areas of the cyclones. Using a cyclone compositing approach, contrasts in cloud and precipitation in strong- versus weak-surface-flux conditions are examined. In the post-cold-frontal region, stronger latent or sensible heat fluxes are associated with lower precipitation rates and higher cloud opacity, indicating more vigorous shallow convection. However, larger sensible heat flux cases display larger cloud fraction, while larger latent heat flux cases exhibit lower cloud fraction, which could indicate differing cloud morphologies. In the comma region of the cyclones, clouds and precipitation depend on both cyclone strength and moisture availability. Consistent with this, larger cloud amount and precipitation are found for strong fluxes in the post-cold-frontal region, and weak or negative sensible heat fluxes, indicative of poleward warm advection, in the warm sector. The strong regional differences in the surface heat flux–cloud and precipitation relationships highlight the need for further investigation into moisture supply and transport in cyclones, while providing guidance for future work.
ABSTRACT
Using a high-spatial- and high-temporal-resolution precipitation dataset, Integrated Multi-satellite Retrievals for GPM (IMERG), extratropical cyclone precipitation is evaluated in two reanalyses and two climate models. Based on cyclone-centered composites, all four models overestimate precipitation in the western subsiding and dry side of the cyclones, and underestimate the precipitation in the eastern ascending and moist side. By decomposing the composites into frequency of occurrence and intensity (mean precipitation rate when precipitating), the analysis reveals a tendency for all four models to overestimate frequency and underestimate intensity, with the former issue dominating in the western half and the latter in the eastern half of the cyclones. Differences in frequency are strongly dependent on cyclone environmental moisture, while the differences in intensity are strongly impacted by the strength of ascent within the cyclone. There are some uncertainties associated with the observations: IMERG might underreport frozen precipitation and possibly exaggerate rates in vigorously ascending regions. Nevertheless, the analysis suggests that all models produce extratropical cyclone precipitation too often and too lightly. These biases have consequences when evaluating the changes in precipitation characteristics with changes in cyclone properties: the models disagree on the magnitude of the change in precipitation intensity with a change in environmental moisture and in precipitation frequency with a change in cyclone strength. This complicates accurate predictions of precipitation changes in a changing climate.
ABSTRACT
Using a high-spatial- and high-temporal-resolution precipitation dataset, Integrated Multi-satellite Retrievals for GPM (IMERG), extratropical cyclone precipitation is evaluated in two reanalyses and two climate models. Based on cyclone-centered composites, all four models overestimate precipitation in the western subsiding and dry side of the cyclones, and underestimate the precipitation in the eastern ascending and moist side. By decomposing the composites into frequency of occurrence and intensity (mean precipitation rate when precipitating), the analysis reveals a tendency for all four models to overestimate frequency and underestimate intensity, with the former issue dominating in the western half and the latter in the eastern half of the cyclones. Differences in frequency are strongly dependent on cyclone environmental moisture, while the differences in intensity are strongly impacted by the strength of ascent within the cyclone. There are some uncertainties associated with the observations: IMERG might underreport frozen precipitation and possibly exaggerate rates in vigorously ascending regions. Nevertheless, the analysis suggests that all models produce extratropical cyclone precipitation too often and too lightly. These biases have consequences when evaluating the changes in precipitation characteristics with changes in cyclone properties: the models disagree on the magnitude of the change in precipitation intensity with a change in environmental moisture and in precipitation frequency with a change in cyclone strength. This complicates accurate predictions of precipitation changes in a changing climate.
Abstract
The observation and representation in general circulation models (GCMs) of cloud vertical overlap are the objects of active research due to their impacts on the earth’s radiative budget. Previous studies have found that vertically contiguous cloudy layers show a maximum overlap between layers up to several kilometers apart but tend toward a random overlap as separations increase. The decorrelation length scale that characterizes the progressive transition from maximum to random overlap changes from one location and season to another and thus may be influenced by large-scale vertical motion, wind shear, or convection. Observations from the U.S. Department of Energy Atmospheric Radiation Measurement program ground-based radars and lidars in midlatitude and tropical locations in combination with reanalysis meteorological fields are used to evaluate how dynamics and atmospheric state influence cloud overlap. For midlatitude winter months, strong synoptic-scale upward motion maintains conditions closer to maximum overlap at large separations. In the tropics, overlap becomes closer to maximum as convective stability decreases. In midlatitude subsidence and tropical convectively stable situations, where a smooth transition from maximum to random overlap is found on average, large wind shears sometimes favor minimum overlap. Precipitation periods are discarded from the analysis but, when included, maximum overlap occurs more often at large separations. The results suggest that a straightforward modification of the existing GCM mixed maximum–random overlap parameterization approach that accounts for environmental conditions can capture much of the important variability and is more realistic than approaches that are only based on an exponential decay transition from maximum to random overlap.
Abstract
The observation and representation in general circulation models (GCMs) of cloud vertical overlap are the objects of active research due to their impacts on the earth’s radiative budget. Previous studies have found that vertically contiguous cloudy layers show a maximum overlap between layers up to several kilometers apart but tend toward a random overlap as separations increase. The decorrelation length scale that characterizes the progressive transition from maximum to random overlap changes from one location and season to another and thus may be influenced by large-scale vertical motion, wind shear, or convection. Observations from the U.S. Department of Energy Atmospheric Radiation Measurement program ground-based radars and lidars in midlatitude and tropical locations in combination with reanalysis meteorological fields are used to evaluate how dynamics and atmospheric state influence cloud overlap. For midlatitude winter months, strong synoptic-scale upward motion maintains conditions closer to maximum overlap at large separations. In the tropics, overlap becomes closer to maximum as convective stability decreases. In midlatitude subsidence and tropical convectively stable situations, where a smooth transition from maximum to random overlap is found on average, large wind shears sometimes favor minimum overlap. Precipitation periods are discarded from the analysis but, when included, maximum overlap occurs more often at large separations. The results suggest that a straightforward modification of the existing GCM mixed maximum–random overlap parameterization approach that accounts for environmental conditions can capture much of the important variability and is more realistic than approaches that are only based on an exponential decay transition from maximum to random overlap.
Abstract
The clouds in Southern Hemisphere extratropical cyclones generated by the GFDL climate model are analyzed against MODIS, CloudSat, and CALIPSO cloud and precipitation observations. Two model versions are used: one is a developmental version of “AM4,” a model GFDL that will utilize for CMIP6, and the other is the same model with a different parameterization of moist convection. Both model versions predict a realistic top-of-atmosphere cloud cover in the southern oceans, within 5% of the observations. However, an examination of cloud cover transects in extratropical cyclones reveals a tendency in the models to overestimate high-level clouds (by differing amounts) and underestimate cloud cover at low levels (again by differing amounts), especially in the post–cold frontal (PCF) region, when compared with observations. In focusing only on the models, it is seen that their differences in high and midlevel clouds are consistent with their differences in convective activity and relative humidity (RH), but the same is not true for the PCF region. In this region, RH is higher in the model with less cloud fraction. These seemingly contradictory cloud and RH differences can be explained by differences in the cloud-parameterization tuning parameters that ensure radiative balance. In the PCF region, the model cloud differences are smaller than either of the model biases with respect to observations, suggesting that other physics changes are needed to address the bias. The process-oriented analysis used to assess these model differences will soon be automated and shared.
Abstract
The clouds in Southern Hemisphere extratropical cyclones generated by the GFDL climate model are analyzed against MODIS, CloudSat, and CALIPSO cloud and precipitation observations. Two model versions are used: one is a developmental version of “AM4,” a model GFDL that will utilize for CMIP6, and the other is the same model with a different parameterization of moist convection. Both model versions predict a realistic top-of-atmosphere cloud cover in the southern oceans, within 5% of the observations. However, an examination of cloud cover transects in extratropical cyclones reveals a tendency in the models to overestimate high-level clouds (by differing amounts) and underestimate cloud cover at low levels (again by differing amounts), especially in the post–cold frontal (PCF) region, when compared with observations. In focusing only on the models, it is seen that their differences in high and midlevel clouds are consistent with their differences in convective activity and relative humidity (RH), but the same is not true for the PCF region. In this region, RH is higher in the model with less cloud fraction. These seemingly contradictory cloud and RH differences can be explained by differences in the cloud-parameterization tuning parameters that ensure radiative balance. In the PCF region, the model cloud differences are smaller than either of the model biases with respect to observations, suggesting that other physics changes are needed to address the bias. The process-oriented analysis used to assess these model differences will soon be automated and shared.
