Using Radar Data to Calibrate a Stochastic Parametrization of Organized Convection

Stochastic parameterizations are increasingly becoming skillful in representing unresolved atmospheric processes for global climate models. The stochastic multicloud model, used to simulate the life cycle of the three most common cloud types (cumulus congestus, deep convective, and stratiform) in tropical convective systems, is one example. In this model, these clouds interact with each other and with their environment according to intuitive-probabilistic rules determined by a set of predictors, depending on the large-scale atmospheric state and a set of transition time scale parameters. Here we use a Bayesian statistical method to infer these parameters from radar data. The Bayesian approach is applied to precipitation data collected by the Shared Mobile Atmospheric Research and Teaching Radar truck-mounted C-band radar located in the Maldives archipelago, while the corresponding large-scale predictors were derived from meteorological soundings taken during the Dynamics of the Madden-Julian Oscillation field campaign. The transition time scales were inferred from three different phases of the Madden-Julian Oscillation (suppressed, initiation, and active) and compared with previous studies. The performance of the stochastic multicloud model is also assessed, in a stand-alone mode, where the cloud model is forced directly by the observed predictors without feedback into the environmental variables. The results showed a wide spread in the inferred parameter values due in part to the lack of the desired sensitivity of the model to the predictors and the shortness of the training periods that did not include both active and suppressed convection phases simultaneously. Nonetheless, the resemblance of the stand-alone simulated cloud fraction time series to the radar data is encouraging.

[1]  A. Majda,et al.  Implementation and calibration of a stochastic convective 1 parameterization in the NCEP Climate Forecast System , 2017 .

[2]  Matthias Steiner,et al.  Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data , 1995 .

[3]  Harry H. Hendon,et al.  The Relationship Between Tropical Cyclones of the Western Pacific and Indian Oceans and the Madden-J , 1994 .

[4]  Y. Takayabu,et al.  The development of organized convection associated with the MJO during TOGA COARE IOP: Trimodal characteristics , 2004 .

[5]  C. Jakob,et al.  How important is tropospheric humidity for coastal rainfall in the tropics? , 2016, 1603.02392.

[6]  A. Majda,et al.  Models for Stratiform Instability and Convectively Coupled Waves , 2001 .

[7]  Mark D. Zelinka,et al.  Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth , 2012 .

[8]  A. Majda,et al.  Implementation and calibration of a stochastic multicloud convective parameterization in the NCEP Climate Forecast System (CFSv2) , 2017 .

[9]  Andrew J. Majda,et al.  A Simple Multicloud Parameterization for Convectively Coupled Tropical Waves. Part I: Linear Analysis , 2006 .

[10]  Andrew J. Majda,et al.  Stochastic Behavior of Tropical Convection in Observations and a Multicloud Model , 2012 .

[11]  H. Hendon,et al.  The diurnal cycle of tropical convection , 1993 .

[12]  Stefano Schiavon,et al.  Climate Change 2007: The Physical Science Basis. , 2007 .

[13]  V. Ramaswamy,et al.  Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics , 2016 .

[14]  John F. B. Mitchell,et al.  Carbon Dioxide and Climate. The Impact of Cloud Parameterization , 1993 .

[15]  A. Majda,et al.  The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization , 2014 .

[16]  Brian E. Mapes,et al.  An integrated view of the 1987 Australian monsoon and its mesoscale convective systems. I: horizontal structure , 1993 .

[17]  Philip Heidelberger,et al.  A spectral method for confidence interval generation and run length control in simulations , 1981, CACM.

[18]  D. Randall,et al.  Observed Characteristics of the MJO Relative to Maximum Rainfall , 2007 .

[19]  D. Randall,et al.  Large‐Eddy Simulation of Maritime Deep Tropical Convection , 2009 .

[20]  S. Mullen,et al.  Short-Range Ensemble Forecasts of Precipitation during the Southwest Monsoon , 2002 .

[21]  Harry H. Hendon,et al.  Seasonal Dependence of the MJO-ENSO Relationship , 2006 .

[22]  Richard H. Johnson,et al.  Evaluation of Budget Analyses during MISMO , 2010 .

[23]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[24]  C. Jakob,et al.  The four cumulus cloud modes and their progression during rainfall events: A C‐band polarimetric radar perspective , 2013 .

[25]  Andrew Stuart,et al.  Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations , 2017, 1709.00037.

[26]  Carl J. Schreck,et al.  Large-Scale Atmospheric and Oceanic Conditions during the 2011-12 DYNAMO Field Campaign , 2013 .

[27]  P. Webster,et al.  TOGA COARE: The Coupled Ocean-Atmosphere Response Experiment. , 1992 .

[28]  M. Zelinka,et al.  Mixed‐phase cloud physics and Southern Ocean cloud feedback in climate models , 2015 .

[29]  Chris Kidd,et al.  Comparison of Precipitation Derived from the ECMWF Operational Forecast Model and Satellite Precipitation Datasets , 2013 .

[30]  Johnny Wei-Bing Lin,et al.  Considerations for Stochastic Convective Parameterization , 2002 .

[31]  Wendell A. Nuss,et al.  Use of Multiquadric Interpolation for Meteorological Objective Analysis , 1994 .

[32]  Steven C. Sherwood,et al.  Convective Precursors and Predictability in the Tropical Western Pacific , 1999 .

[33]  A. Pier Siebesma,et al.  The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. , 2016 .

[34]  A. Majda,et al.  Effect of Stratiform Heating on the Planetary-Scale Organization of Tropical Convection , 2016 .

[35]  Richard H. Johnson,et al.  Episodic Trade Wind Regimes over the Western Pacific Warm Pool , 1997 .

[36]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[37]  A. Majda,et al.  Using the Stochastic Multicloud Model to Improve Tropical Convective Parameterization: A Paradigm Example , 2012 .

[38]  A. P. Siebesma,et al.  The Cloud Feedback Model Intercomparison Project (CFMIP) , 2016 .

[39]  S. Bony,et al.  Interpretation of the positive low-cloud feedback predicted by a climate model under global warming , 2013, Climate Dynamics.

[40]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[41]  A. Majda,et al.  Stochasticity of convection in Giga-LES data , 2016, Climate Dynamics.

[42]  A. P. Siebesma,et al.  Cloud representation in general‐circulation models over the northern Pacific Ocean: A EUROCS intercomparison study , 2004 .

[43]  R. Plant,et al.  A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics , 2008 .

[44]  A. Majda,et al.  Equatorial Convectively Coupled Waves in a Simple Multicloud Model , 2008 .

[45]  S. Hagos,et al.  A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds , 2018 .

[46]  Boualem Khouider,et al.  A coarse grained stochastic multi-type particle interacting model for tropical convection: Nearest neighbour interactions , 2014 .

[47]  S. Klein,et al.  Evaluation of tropical cloud and precipitation statistics of Community Atmosphere Model version 3 using CloudSat and CALIPSO data , 2010 .

[48]  E. Maloney,et al.  Madden–Julian Oscillation Pacific Teleconnections: The Impact of the Basic State and MJO Representation in General Circulation Models , 2017 .

[49]  Richard H. Johnson,et al.  Diurnally Modulated Cumulus Moistening in the Preonset Stage of the Madden–Julian Oscillation during DYNAMO* , 2015 .

[50]  R. Wood,et al.  The Change in Low Cloud Cover in a Warmed Climate Inferred from AIRS, MODIS, and ERA-Interim , 2017 .

[51]  A. Majda,et al.  Improving synoptic and intraseasonal variability in CFSv2 via stochastic representation of organized convection , 2017 .

[52]  Andrew J. Majda,et al.  Climate science in the tropics: waves, vortices and PDEs , 2012 .

[53]  Peter J. Webster,et al.  The Boreal Summer Intraseasonal Oscillation: Relationship between Northward and Eastward Movement of Convection , 2002 .

[54]  J. Neelin,et al.  Toward stochastic deep convective parameterization in general circulation models , 2003 .

[55]  H. Hendon,et al.  The Intraseasonal (30–50 day) Oscillation of the Australian Summer Monsoon , 1990 .

[56]  S. Klein,et al.  Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review , 2017, Surveys in Geophysics.

[57]  C. Bretherton,et al.  Mechanisms of Low Cloud–Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3) , 2008 .

[58]  C. Jakob.  The representation of cloud cover in atmospheric general circulation models , 2001 .

[59]  Andrew J. Majda,et al.  Calibration of the Stochastic Multicloud Model Using Bayesian Inference , 2014, SIAM J. Sci. Comput..

[60]  S. McFarlane,et al.  Constructing a Merged Cloud–Precipitation Radar Dataset for Tropical Convective Clouds during the DYNAMO/AMIE Experiment at Addu Atoll , 2014 .

[61]  Andrew J. Majda,et al.  Multicloud Models for Organized Tropical Convection: Enhanced Congestus Heating , 2008 .

[62]  C. Jakob,et al.  Improved MJO‐simulation in ECHAM6.3 by coupling a Stochastic Multicloud Model to the convection scheme , 2017 .

[63]  P. Zuidema,et al.  The mesoscale convection life cycle: building block or prototype for large-scale tropical waves? , 2006 .

[64]  Heikki Haario,et al.  Ensemble prediction and parameter estimation system: the concept , 2012 .

[65]  S. Sherwood,et al.  Processes Responsible for Cloud Feedback , 2016, Current Climate Change Reports.

[66]  A. Majda,et al.  Role of stratiform heating on the organization of convection over the monsoon trough , 2016, Climate Dynamics.

[67]  S. Bony,et al.  Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models , 2005 .

[68]  Brian E. Mapes,et al.  Convective Inhibition, Subgrid-Scale Triggering Energy, and Stratiform Instability in a Toy Tropical Wave Model , 2000 .

[69]  Shoichi Shige,et al.  Analysis of rainfall characteristics of the Madden–Julian oscillation using TRMM satellite data , 2006 .

[70]  Philip J. Klotzbach,et al.  The Madden-Julian Oscillation's Impacts on Worldwide Tropical Cyclone Activity , 2014 .

[71]  K. Emanuel,et al.  The Representation of Cumulus Convection in Numerical Models , 1993 .

[72]  M. Zelinka,et al.  Cloud feedback mechanisms and their representation in global climate models , 2017 .

[73]  G. Kiladis,et al.  Vertical-Mode Decompositions of 2-Day Waves and the Madden–Julian Oscillation , 2008 .

[74]  Daniel T. Gillespie,et al.  An Exact Method for Numerically Simulating the Stochastic Coalescence Process in a Cloud , 1975 .

[75]  D. Hartmann.  Tropical anvil clouds and climate sensitivity , 2016, Proceedings of the National Academy of Sciences.

[76]  Pierre Gentine,et al.  Deep learning to represent subgrid processes in climate models , 2018, Proceedings of the National Academy of Sciences.

[77]  Leo J. Donner,et al.  A Cumulus Parameterization Including Mass Fluxes, Vertical Momentum Dynamics, and Mesoscale Effects , 1993 .

[78]  Richard H. Johnson,et al.  Structure and Properties of Madden–Julian Oscillations Deduced from DYNAMO Sounding Arrays , 2013 .

[79]  Sarah M. Kang,et al.  The impact of parametrized convection on cloud feedback , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[80]  Noah D. Brenowitz,et al.  Prognostic Validation of a Neural Network Unified Physics Parameterization , 2018, Geophysical Research Letters.

[81]  Bjorn Stevens,et al.  Preconditioning Deep Convection with Cumulus Congestus , 2013 .

[82]  A. Majda,et al.  Improved Tropical Modes of Variability in the NCEP Climate Forecast System (Version 2) via a Stochastic Multicloud Model , 2017 .

[83]  D. Crommelin,et al.  A data-driven multi-cloud model for stochastic parametrization of deep convection , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[84]  R. Plant,et al.  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) , 2016 .

[85]  S. Klein,et al.  The Cloud Feedback Model , 2022 .

[86]  W. Landman.  Climate change 2007: the physical science basis , 2010 .

[87]  Pierre Gentine,et al.  Could Machine Learning Break the Convection Parameterization Deadlock? , 2018, Geophysical Research Letters.

[88]  Daehyun Kim,et al.  MJO and Convectively Coupled Equatorial Waves Simulated by CMIP5 Climate Models , 2013 .

[89]  Chidong Zhang.  Madden–Julian Oscillation: Bridging Weather and Climate , 2013 .

[90]  B. Khouider,et al.  Coastal Tropical Convection in a Stochastic Modeling Framework , 2017, 1708.06868.

[91]  Philip J. Rasch,et al.  Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models. Part I: Convective Signals , 2006 .

[92]  Andrew J. Majda,et al.  A stochastic multicloud model for tropical convection , 2010 .

[93]  J. Holton.  An introduction to dynamic meteorology , 2004 .

[94]  Andrew J Majda,et al.  Coarse-grained stochastic models for tropical convection and climate , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[95]  S. Bony,et al.  Thermodynamic control of anvil cloud amount , 2016, Proceedings of the National Academy of Sciences.

[96]  B. Khouider,et al.  The Deepening of Tropical Convection by Congestus Preconditioning , 2010 .

[97]  Duane E. Waliser,et al.  Cracking the MJO nut , 2013 .

[98]  G. Stephens.  Cloud Feedbacks in the Climate System: A Critical Review , 2005 .

[99]  T. Palmer.  A nonlinear dynamical perspective on model error: A proposal for non‐local stochastic‐dynamic parametrization in weather and climate prediction models , 2001 .

[100]  Chidong Zhang,et al.  Madden‐Julian Oscillation , 2005 .

[101]  B. Stevens,et al.  The effect of atmospheric radiative heating by clouds on the Madden‐Julian Oscillation , 2015 .

[102]  Charles N. Long,et al.  Tracking Pulses of the Madden–Julian Oscillation , 2013 .

[103]  Richard H. Johnson,et al.  Trimodal Characteristics of Tropical Convection , 1999 .

[104]  Richard H. Johnson,et al.  Corrected TOGA COARE Sounding Humidity Data: Impact on Diagnosed Properties of Convection and Climate over the Warm Pool , 2003 .

[105]  A. Majda,et al.  Model multi-cloud parameterizations for convectively coupled waves: Detailed nonlinear wave evolution , 2006 .