Modulation of precipitation by conditional symmetric instability release

Although many theoretical and observational studies have investigated the mechanism of conditional symmetric instability (CSI) release and associated it with mesoscale atmospheric phenomena such as frontal precipitation bands, cloud heads in rapidly developing extratropical cyclones and sting jets, its climatology and contribution to precipitation have not been extensively documented. The aim of this paper is to quantify the contribution of CSI release, yielding slantwise convection, to climatological precipitation accumulations for the North Atlantic and western Europe. Case studies reveal that CSI release could be common along cold fronts of mature extratropical cyclones and the North Atlantic storm track is found to be a region with large CSI according to two independent CSI metrics. Correlations of CSI with accumulated precipitation are also large in this region and CSI release is inferred to be occurring about 20% of the total time over depths of over 1km. We conclude that the inability of current global weather forecast and climate prediction models to represent CSI release (due to insufficient resolution yet lack of subgrid parametrization schemes) may lead to errors in precipitation distributions, particularly in the region of the North Atlantic storm track

Evaluating statistical cloud schemes: what can we gain from ground-based remote sensing?

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the “perfect model approach.” This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme

A pan-African convection-permitting regional climate simulation with the Met Office Unified Model: CP4-Africa

A convection-permitting multi-year regional climate simulation using the Met Office Unified Model has been run for the first time on an Africa-wide domain. The model has been run as part of the Future Climate for Africa (FCFA) IMPALA (Improving Model Processes for African cLimAte) project and its configuration, domain and forcing data are described here in detail. The model (CP4-Africa) uses a 4.5km horizontal grid spacing at the equator and is run without a convection parametrization, nested within a global atmospheric model driven by observations at the sea-surface which does include a convection scheme. An additional regional simulation, with identical resolution and physical parametrizations to the global model, but with the domain, land surface and aerosol climatologies of the CP4-Africa model, has been run to aid understanding of the differences between the CP4-Africa and global model, in particular to isolate the impact of the convection parametrization and resolution. The effect of enforcing moisture conservation in the CP4-Africa model is described and its impact on reducing extreme precipitation values is assessed. Preliminary results from the first 5 years of the CP4-Africa simulation show substantial improvements in JJA average rainfall compared to the parameterized convection models, with most notably a reduction in the persistent dry bias in West Africa - giving an indication of the benefits to be gained from running a convection-permitting simulation over the whole African continent

Sensitivity of cloud-radiative effects to cloud fraction parametrizations in tropical, midlatitude, and arctic kilometre-scale simulations

The regional atmosphere (RA) configuration of the Met Office Unified Model currently requires different cloud fraction parametrizations (CFPs) for tropical and midlatitude simulations. To explore the scope for unification of these two RA configurations, this article presents a detailed evaluation of simulations over tropical, midlatitude, and arctic domains, with two different diagnostic CFPs: a prognostic CFP, and no CFP at all. Furthermore, a novel, hybrid approach was used that treats liquid cloud diagnostically and ice cloud prognostically. Using observations from three US Department of Energy Atmospheric Radiation Measurement supersites, it is shown that none of these CFPs stands out as superior over all domains. Over the frequently overcast Arctic, the all-or-nothing approach best captures the cloud radiative properties. Conversely, CFPs are of benefit in regions with frequent partial cloudiness, such as the midlatitudes and the Tropics. However, their improved cloud radiative properties often hide an error compensation. All models underestimate overcast, low-base cloud with small water paths in convective environments. In addition, midlatitude overcast, low-base, optically thick clouds in the morning, possibly associated with overnight convection, are frequently too broken. Diagnostic schemes compensate for these errors by producing spurious, scattered afternoon cloud, which could be due to a correct cloud response to too eager convective initiation. Winter clouds over the midlatitudes are improved when liquid cloud is represented diagnostically with a bimodal saturation-departure probability density function, without error compensation. Although it is difficult to unify the RA across the globe around a single CFP scheme, the newly proposed hybrid scheme performs reasonably well for cloud cover across all regions. It also exhibits short-wave biases that are smaller than most other configurations and is less affected by excessive liquid water paths and compensating errors than fully diagnostic schemes are. Surface precipitation is fairly insensitive to the CFP in the simulations shown here

A bimodal diagnostic cloud fraction parameterization : part II : evaluation and resolution sensitivity

A wide range of approaches exists to account for subgrid cloud variability in regional simulations of the atmosphere. This paper addresses the following questions: 1) Is there still benefit in representing subgrid variability of cloud in convection-permitting simulations? 2) What is the sensitivity to the cloud fraction parameterization complexity? 3) Are current cloud fraction parameterizations scale- aware across convection-permitting resolutions? These questions are addressed for regional simulations of a 6-week observation campaign in the U. S. southern Great Plains. Particular attention is given to a new diagnostic cloud fraction scheme with a bimodal subgrid saturation-departure PDF, described in Part I. The model evaluation is performed using ground- based remote sensing synergies, satellitebased retrievals, and surface observations. It is shown that not using a cloud fraction parameterization results in underestimated cloud frequency and water content, even for stratocumulus. The use of a cloud fraction parameterization does not guarantee improved cloud property simulations, however. Diagnostic and prognostic cloud schemes with a symmetric subgrid saturation-departure PDF underestimate cloud fraction and cloud optical thickness, and hence overestimate surface shortwave radiation. These schemes require empirical bias-correction techniques to improve the cloud cover. The new cloud fraction parameterization, introduced in Part I, improves cloud cover, liquid water content, cloud-base height, optical thickness, and surface radiation compared to schemes reliant on a symmetric PDF. Furthermore, cloud parameterizations using turbulence-based, rather than prescribed constant subgrid variances, are shown to be more scale-aware across convection-permitting resolutions

Evaluating statistical cloud schemes: what can we gain from ground-based remote sensing?

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the “perfect model approach.” This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme

Evaluating statistical cloud schemes: what can we gain from ground-based remote sensing?

Statistical cloud schemes with prognostic probability distribution functions have become more important in atmospheric modeling, especially since they are in principle scale adaptive and capture cloud physics in more detail. While in theory the schemes have a great potential, their accuracy is still questionable. High-resolution three-dimensional observational data of water vapor and cloud water, which could be used for testing them, are missing. We explore the potential of ground-based remote sensing such as lidar, microwave, and radar to evaluate prognostic distribution moments using the “perfect model approach.” This means that we employ a high-resolution weather model as virtual reality and retrieve full three-dimensional atmospheric quantities and virtual ground-based observations. We then use statistics from the virtual observation to validate the modeled 3-D statistics. Since the data are entirely consistent, any discrepancy occurring is due to the method. Focusing on total water mixing ratio, we find that the mean ratio can be evaluated decently but that it strongly depends on the meteorological conditions as to whether the variance and skewness are reliable. Using some simple schematic description of different synoptic conditions, we show how statistics obtained from point or line measurements can be poor at representing the full three-dimensional distribution of water in the atmosphere. We argue that a careful analysis of measurement data and detailed knowledge of the meteorological situation is necessary to judge whether we can use the data for an evaluation of higher moments of the humidity distribution used by a statistical cloud scheme

Towards retrieving critical relative humidity from ground-based remote-sensing observations

Nearly all large-scale cloud parametrizations require the specification of the critical relative humidity (RHcrit). This is the grid-box mean relative humidity at which the subgrid fluctuations in temperature and water vapour are assumed to become so large that part of a subsaturated grid box becomes saturated and cloud starts to form. Until recently, the lack of high-resolution observations of temperature and moisture variability has hindered achievement of a reasonable estimate of RHcrit. However, the advent of ground-based Raman lidar now allows the acquisition of long records of temperature and moisture with subminute sample rates. Lidar observations are inherently noisy and any analysis of higher-order moments will be dependent on the ability to quantify and remove this noise. We present an exploratory study aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapour are sufficiently low to obtain a reasonable estimate of RHcrit. We show that vertical profiles of RHcrit can be derived with an uncertainty of a few per cent. RHcrit tends to be smallest near the boundary-layer top and seems to be insensitive to the horizontal grid spacing at the scales investigated here (30-120 km). However, larger sensitivity was found to the vertical grid spacing. RHcrit is observed to decrease by 10% as the vertical grid spacing quadruples. By way of example, the lidar-retrieved RHcrit profiles were used to evaluate a parametrization that estimates RHcrit from variances diagnosed from the boundary-layer parametrization. It is shown that this parametrization overestimates RHcrit by up to 10%, but captures the diurnal variability of RHcrit well, with lower values of RHcrit near the boundary-layer top. While we show that the uncertainties associated with the retrievals are large, lidar observations seem promising to diagnose and evaluate a very important parameter to predict cloud fraction in climate and numerical weather prediction models

A bimodal diagnostic cloud fraction parameterization : part I : motivating analysis and scheme description

Cloud fraction parameterizations are beneficial to regional, convection-permitting numerical weather prediction. For its operational regional midlatitude forecasts, the Met Office uses a diagnostic cloud fraction scheme that relies on a unimodal, symmetric subgrid saturation-departure distribution. This scheme has been shown before to underestimate cloud cover and hence an empirically based bias correction is used operationally to improve performance. This first of a series of two papers proposes a new diagnostic cloud scheme as a more physically based alternative to the operational bias correction. The new cloud scheme identifies entrainment zones associated with strong temperature inversions. For model grid boxes located in this entrainment zone, collocated moist and dry Gaussian modes are used to represent the subgrid conditions. The mean and width of the Gaussian modes, inferred from the turbulent characteristics, are then used to diagnose cloud water content and cloud fraction. It is shown that the new scheme diagnoses enhanced cloud cover for a given gridbox mean humidity, similar to the current operational approach. It does so, however, in a physically meaningful way. Using observed aircraft data and ground-based retrievals over the southern Great Plains in the United States, it is shown that the new scheme improves the relation between cloud fraction, relative humidity, and liquid water content. An emergent property of the scheme is its ability to infer skewed and bimodal distributions from the large-scale state that qualitatively compare well against observations. A detailed evaluation and resolution sensitivity study will follow in Part II