Stochastic Convective Parameterization Research Articles

Effects of Improved Simulation of Precipitation on Evapotranspiration and Its Partitioning Over Land

AbstractEvapotranspiration (ET) is a key component of the global hydrological cycle, which is strongly modulated by the occurrence of different rainfall intensities. Global climate models (GCMs) commonly suffer from “too much light rain” and a negative bias in the ratio of transpiration (T) to ET (T/ET). It is unclear whether these biases are related. Here we show that with the improved simulation of probability density functions of rainfall intensity by suppressing light‐rain occurrence using a stochastic convection parameterization in the NCAR CESM1.2, the canopy T increases in tropical forests while evaporation from canopy interception and bare soil decreases. The simulated T/ET is increased by 2.5% globally and up to 8% regionally, primarily attributable to reduced fraction of wet leaves due to less frequent light rain despite its weak intensity. These results imply that excessive light rain is an important cause of the negative T/ET bias in GCMs.

Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model

Abstract. A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

Linking Stochasticity of Convection to Large-Scale Vertical Velocity to Improve Indian Summer Monsoon Simulation in the NCAR CAM5

The Plant–Craig (PC) stochastic convective parameterization scheme is modified by linking the stochastic generation of convective clouds to the change of large-scale vertical pressure velocity at 500 hPa with time so as to better account for the relationship between convection and the large-scale environment. Three experiments using the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 5 (CAM5), are conducted: one with the default Zhang–McFarlane deterministic convective scheme, another with the original PC stochastic scheme, and a third with the modified PC stochastic scheme. Evaluation is focused on the simulation of the Indian summer monsoon (ISM), which is a long-standing challenge for all current global circulation models. Results show that the modified stochastic scheme better represents the annual cycle of the climatological mean rainfall over central India and the mean onset date of ISM compared to other simulations. Also, for the simulations of ISM intraseasonal variability for quasi-biweekly and 30–60-day modes, the modified stochastic parameterization produces more realistic propagation and magnitude, especially for the observed northeastward movement of the 30–60-day mode, for which the other two simulations show the propagation in the opposite direction. Causes are investigated through a moisture budget analysis. Compared to the other two simulations, the modified stochastic scheme with an appropriate representation of convection better represents the patterns and amplitudes of large-scale dynamical convergence and moisture advection and thus corrects the monsoon cycle associated with their covariation during the peaks and troughs of intraseasonal oscillation.

Simulation of Precipitation Extremes Using a Stochastic Convective Parameterization in the NCAR CAM5 Under Different Resolutions

AbstractWith the incorporation of the Plant‐Craig stochastic deep convection scheme into the Zhang‐McFarlane deterministic parameterization in the Community Atmospheric Model version 5 (CAM5), its impact on extreme precipitation at different resolutions (2°, 1°, and 0.5°) is investigated. CAM5 with the stochastic deep convection scheme (experiment (EXP)) simulates the precipitation extreme indices better than the standard version (control). At 2° and 1° resolutions, EXP increases high percentile (>99th) daily precipitation over the United States, Europe, and China, resulting in a better agreement with observations. However, at 0.5° resolution, due to enhanced grid‐scale precipitation with increasing resolution, EXP overestimates extreme precipitation over southeastern U.S. and eastern Europe. The reduced biases in EXP at each resolution benefit from a broader probability distribution function of convective precipitation intensity simulated. Among EXP simulations at different resolutions, if the spatial averaging area over which input quantities used in convective closure are spatially averaged in the stochastic convection scheme is comparable, the modeled convective precipitation intensity decreases with increasing resolution, when gridded to the same resolution, while the total precipitation is not sensitive to model resolution, exhibiting some degree of scale‐awareness. Sensitivity tests show that for the same resolution, increasing the size of spatial averaging area decreases convective precipitation but increases the grid‐scale precipitation.

Stochastic convective parameterization improving the simulation of tropical precipitation variability in the NCAR CAM5

AbstractThe Plant‐Craig (PC) stochastic convective parameterization scheme is implemented into the National Center for Atmospheric Research Community Atmosphere Model version 5 (CAM5) to couple with the Zhang‐McFarlane deterministic convection scheme. To evaluate its impact on tropical precipitation simulation, two experiments are conducted: one with the standard CAM5 and the other with the stochastic scheme incorporated. Results show that the PC stochastic parameterization decreases the frequency of weak precipitation and increases the frequency of strong precipitation, resulting in better agreement with observations. The most striking improvement is in the probability distribution function (PDF) of precipitation intensity, with the well‐known too‐much‐drizzle problem in CAM5 largely eliminated. In the global tropical belt, the precipitation intensity PDF from the simulation agrees remarkably well with that of Tropical Rainfall Measuring Mission observations. The stochastic scheme also yields a similar magnitude of intraseasonal variability of precipitation to observations and improves the simulation of the eastward propagating intraseasonal signals of precipitation and zonal wind.

Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

Abstract. The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Stochastic Convection Parameterization with Markov Chains in an Intermediate-Complexity GCM

Abstract Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic convection parameterizations. In this paper, it is demonstrated how two different CMC models can be used as mass flux closures in convection parameterizations. More specifically, the CMC models provide a stochastic estimate of the convective area fraction that is directly proportional to the cloud-base mass flux. Since, in one of the models, the number of CMCs decreases with increasing resolution, this approach makes convection parameterizations scale aware and introduces stochastic fluctuations that increase with resolution in a realistic way. Both CMC models are implemented in a GCM of intermediate complexity. It is shown that with the CMC models, trained with observational data, it is possible to improve both the subgrid-scale variability and the autocorrelation function of the cloud-base mass flux as well as the distribution of the daily accumulated precipitation in the tropics. Hovmöller diagrams and wavenumber–frequency diagrams of the equatorial precipitation indicate that, in this specific GCM, convectively coupled equatorial waves are more sensitive to the mean cloud-base mass flux than to stochastic fluctuations. A smaller mean mass flux tends to increase the power of the simulated MJO and to diminish equatorial Kelvin waves.

Examination of a Stochastic and Deterministic Convection Parameterization in the COSMO Model

Abstract Stochastic parameterizations allow the representation of the small-scale variability of parameterized physical processes. This study investigates whether additional variability introduced by a stochastic convection parameterization leads to improvements in the precipitation forecasts. Forecasts are calculated with two different ensembles: one considering large-scale and convective variability with the stochastic Plant–Craig convection parameterization and one considering only large-scale variability with the standard Tiedtke convection parameterization. The forecast quality of both ensembles is evaluated in comparison with radar observations for two case studies with weak and strong synoptic forcing of convection and measured with neighborhood and probabilistic verification methods. The skill of the ensemble based on the Plant–Craig convection parameterization relative to the ensemble with the Tiedtke parameterization strongly depends on the synoptic situation in which convection occurs. In the weak forcing case, where the convective precipitation is highly intermittent, the ensemble based on the stochastic parameterization is superior, but the scheme produces too much small-scale variability in the strong forcing case. In the future, the degree of stochastic variability could be tuned, and these results show that parameters should be chosen in a regime-dependent manner.

A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling

Abstract. A convective parameterization is described and evaluated that may be used in high resolution non-hydrostatic mesoscale models as well as in modeling system with unstructured varying grid resolutions and for convection aware simulations. This scheme is based on a stochastic approach originally implemented by Grell and Devenyi (2002). Two approaches are tested on resolutions ranging from 20 km to 5 km. One approach is based on spreading subsidence to neighboring grid points, the other one on a recently introduced method by Arakawa et al. (2011). Results from model intercomparisons, as well as verification with observations indicate that both the spreading of the subsidence and Arakawa's approach work well for the highest resolution runs. Because of its simplicity and its capability for an automatic smooth transition as the resolution is increased, Arakawa's approach may be preferred. Additionally, interactions with aerosols have been implemented through a cloud condensation nuclei (CCN) dependent autoconversion of cloud water to rain as well as an aerosol dependent evaporation of cloud drops. Initial tests with this newly implemented aerosol approach show plausible results with a decrease in predicted precipitation in some areas, caused by the changed autoconversion mechanism. This change also causes a significant increase of cloud water and ice detrainment near the cloud tops. Some areas also experience an increase of precipitation, most likely caused by strengthened downdrafts.

Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

A novel approach based on the neural network (NN) ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP) models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM) initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR) Community Atmospheric Model (CAM). It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.

A Stochastic Model for the Transition to Strong Convection

Abstract A simple stochastic model is designed and analyzed in order to further understand the transition to strong convection. The transition has been characterized recently in observational data by an array of statistical measures, including (i) a sharp transition in mean precipitation, and a peak in precipitation variance, at a critical value of column water vapor (CWV), (ii) an approximate power law in the probability density of precipitation event size, (iii) exponential tails in the probability density of CWV values, when conditioned on either precipitating or nonprecipitating locations, and (iv) long and short autocorrelation times of CWV and precipitation, respectively, with approximately exponential and power-law decays in their autocorrelation functions, respectively. The stochastic model presented here captures these four statistical features in time series of CWV and precipitation at a single location. In addition, analytic solutions are given for the exponential tails, which directly relates the tails to model parameters. The model parameterization includes three stochastic components: a stochastic trigger turns the convection on and off (a two-state Markov jump process), and stochastic closures represent variability in precipitation and in “external” forcing (Gaussian white noise). This stochastic external forcing is seen to be crucial for obtaining extreme precipitation events with high CWV and long lifetimes, because it can occasionally compensate for the heavy precipitation and encourage more of it. This stochastic model can also be seen as a simplified stochastic convective parameterization, and it demonstrates simple ways to turn a deterministic parameterization—the trigger and/or closure—into a stochastic one.

A stochastic model for the transition to stong convection

A simple stochastic model is designed and analyzed in order to further understand the transition to strong convection. The transition has been characterized recently in observational data by an array of statistical measures, including (i) a sharp transition in mean precipitation, and a peak in precipitation variance, at a critical value of column water vapor (CWV), (ii) an approximate power law in the probability density of precipitation event size, (iii) exponential tails in the probability density of CWV values, when conditioned on either precipitating or nonprecipitating locations, and (iv) long and short autocorrelation times of CWV and precipitation, respectively, with approximately exponential and power-law decays in their autocorrelation functions, respectively. The stochastic model presented here captures these four statistical features in time series of CWV and precipitation at a single location. In addition, analytic solutions are given for the exponential tails, which directly relates the tails to model parameters. The model parameterization includes three stochastic components: a stochastic trigger turns the convection on and off (a two-state Markov jump process), and stochastic closures represent variability in precipitation and in “external” forcing (Gaussian white noise). This stochastic external forcing is seen to be crucial for obtaining extreme precipitation events with high CWV and long lifetimes, because it can occasionally compensate for the heavy precipitation and encourage more of it. This stochastic model can also be seen as a simplified stochastic convective parameterization, and it demonstrates simple ways to turn a deterministic parameterization—the trigger and/or closure—into a stochastic one.

Considerations for Stochastic Convective Parameterization

Convective parameterizations in general circulation models (GCMs) generally only aim to simulate the mean or first-order moment of convection; higher moments associated with subgrid variability are not explicitly considered. In this study, an empirically based stochastic convective parameterization is developed that uses an assumed mixed lognormal distribution of rainfall, tuned with parameter values derived from observations, to control selected nonmean statistical properties of convection. Testing of this stochastic convective parameterization reveals that large-scale model dynamics interacts heavily with the convective parameterization, in ways such that the resulting output is fundamentally different from the input. This suggests stochastic parameterizations cannot be calibrated outside of a model’s dynamical framework. Implications are discussed for the relative merits of the empirical approach versus another approach that introduces the stochastic process within the framework of the convective parameterization. Inclusion of the variance arising from unresolved scales by stochastic parameterization of convection is found to have a substantial impact upon atmospheric variability in the Tropics, including intraseasonal and longer timescales.

Influence of a stochastic moist convective parameterization on tropical climate variability

Convective parameterizations used in general circulation models (GCMs) generally only simulate the mean or first‐order moment of convective ensembles and do not explicitly include higher‐order moments. The influence of including unresolved higher‐order moments is investigated using a simple stochastic convective parameterization that includes a random contribution to the convective available potential energy (CAPE) in the deep convective scheme. Impacts are tested in an tropical atmospheric model of intermediate complexity. Adding convective noise noticeably affects tropical intraseasonal variability, suggesting inclusion of such noise in GCMs might be beneficial. Model response to the noise is sensitive not only to the noise amplitude, but also to such particulars of the stochastic parameterization as autocorrelation time.