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A machine learning based deep convective trigger for climate models

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Abstract

The present study focuses on addressing the issue of too frequent triggers of deep convection in climate models, which are primarily based on physics-based classical trigger functions such as convective available potential energy (CAPE) or cloud work function (CWF). To overcome this problem, the study proposes using machine learning (ML) based deep convective triggers as an alternative. The deep convective trigger is formulated as a binary classification problem, where the goal is to predict whether deep convection will occur or not. Two elementary classification algorithms, namely support vector machines and neural networks, are adopted in this study. Additionally, a novel method is proposed to rank the importance of input variables for the classification problem, which may aid in understanding the underlying mechanisms and factors influencing deep convection. The accuracy of the ML-based methods is compared with the widely used convective available potential energy (CAPE)-based and dynamic generation of CAPE (dCAPE) trigger function found in many convective parameterization schemes. Results demonstrate that the elementary machine learning-based algorithms can outperform the classical CAPE-based triggers, indicating the potential effectiveness of ML-based approaches in dealing with this issue. Furthermore, a method based on the Mahalanobis distance is presented for binary classification, which is easy to interpret and implement. The Mahalanobis distance-based approach shows accuracy comparable to other ML-based methods, suggesting its viability as an alternative method for deep convective triggers. By correcting for deep convective triggers using ML-based approaches, the study proposes a possible solution to improve the probability density of rain in the climate model. This improvement may help overcome the issue of excessive drizzle often observed in many climate models.

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Data Availability

Data used in the present study is available from the following sources: The dCAPE-based trigger functions (Ukkonen and Mäkelä 2019) is based on code made available on GitHub at the following link: https://github.com/peterukk/ConvectiveIndices.jl ERA5 data single level data: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form ERA5 pressure level data: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form Brightness temperature data : https://disc.gsfc.nasa.gov/datasets/GPM_MERGIR_1/summary?keywords=Merged%20IR$ Rainfall data: https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHHL_06/summary?keywords=imerg

References

  • Anthes RA (1977) A cumulus parameterization scheme utilizing a one-dimensional cloud model. Monthly Weather Rev 105(3):270–286

    Article  Google Scholar 

  • Arakawa A, Schubert WH (1974) Interaction of a cumulus cloud ensemble with the large-scale environment. Part I. J Atmospheric Sci 31(3):674–701

    Article  Google Scholar 

  • Bechtold P, Chaboureau JP, Beljaars A et al (2004) The simulation of the diurnal cycle of convective precipitation over land in a global model. Q J Royal Meteorological S 130(604):3119–3137

    Article  Google Scholar 

  • Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Machine Learn Res 13(2)

  • Betts A, Miller M (1986) A new convective adjustment scheme. part ii: Single column tests using gate wave, bomex, atex and arctic air-mass data sets. Q J Royal Meteorological Soc 112(473):693–709

  • Betts AK (1986) A new convective adjustment scheme. part i: Observational and theoretical basis. Q J Royal Meteorological Soc 112(473):677–691

  • Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. Springer

    Google Scholar 

  • Chandra MP et al (1936) On the generalised distance in statistics. In: Proceedings of the national institute of sciences of India, pp 49–55

  • Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):1–27

    Article  Google Scholar 

  • Chung ES, Sohn BJ, Schmetz J (2006) Diurnal variation of upper tropospheric humidity over the tropics and its relations to convective activities. In: 27th Conference on hurricanes and tropical meteorology

  • Dai A (2006) Precipitation characteristics in eighteen coupled climate models. J Climate 19:4605–4630. https://doi.org/10.1175/JCLI3884.1

    Article  Google Scholar 

  • Fiedler S, Crueger T, D’Agostino R et al (2020) Simulated tropical precipitation assessed across three major phases of the coupled model intercomparison project (cmip). Monthly Weather Rev 148(9):3653–3680

    Article  Google Scholar 

  • Ganai M, Krishna RPM, Mukhopadhyay P et al (2016) The impact of revised simplified arakawa-schubert scheme on the simulation of mean and diurnal variability associated with active and break phases of indian summer monsoon using cfsv2. J Geophysical Res: Atmospheres 121(16):9301–9323

    Google Scholar 

  • Goswami BN, Xavier PK (2009) Diurnal cycle of convection, rainfall, and the surface heat budget over tropical indian ocean during the winter monsoon. J Climate 22(13):3751–3768

    Google Scholar 

  • Han J, Bretherton CS (2019) TKE-based moist eddy-diffusivity mass-flux (EDMF) parameterization for vertical turbulent mixing. Weather Forecasting 34(4):869–886

    Article  Google Scholar 

  • Harrison DE, Henderson-Sellers A (1994) Diurnal patterns of rainfall in northwestern australia. J Climate 7(11):1830–1844

    Google Scholar 

  • Hernandez-Deckers D (2022) Features of atmospheric deep convection in Northwestern South America obtained from infrared satellite data. Q J Royal Meteorological Soc 148(742):338–350

    Article  Google Scholar 

  • Hersbach H, Bell B, Berrisford P et al (2020) The ERA5 global reanalysis. Q J Royal Meteorological Soc 146:1999–2049. https://doi.org/10.1002/qj.3803

    Article  Google Scholar 

  • Holloway CE, Neelin JD (2009) Moisture vertical structure, column water vapor, and tropical deep convection. J Atmospheric Sci 66(6):1665–1683

    Article  Google Scholar 

  • Huffman GJ, Stocker EF, Bolvin DT, et al (2019) GPM IMERG final precipitation L3 half hourly 0.1 degree x 0.1 degree V06. Goddard Earth Sciences Data and Information Services Center (GES DISC): Greenbelt, MD, USA

  • Ivanov A, Riccardi G (2012) Kolmogorov-Smirnov test for feature selection in emotion recognition from speech. 2012 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, Kyoto, Japan, pp 5125–5128

    Chapter  Google Scholar 

  • Janowiak J, Joyce B, Xie P (2017) NCEP/CPC L3 half hourly 4km global (60S-60N) merged IR V1. Greenbelt, MD, Goddard Earth Sciences Data and Information Services Central (GES DISC), Accessed [10-Dec-2020] 10:P4HZB9N27EKU

  • Jones S (2001) Intertropical convergence zone: A spatial analysis. J Climate 15(6):789–804

    Google Scholar 

  • Khouider B, Majda AJ, Katsoulakis MA (2003) Coarse-grained stochastic models for tropical convection and climate. Proceedings of the national academy of sciences of the United States of America 100:11941–11946. https://doi.org/10.1073/pnas.1634951100

    Article  CAS  Google Scholar 

  • Kolmogorov A, Smirnov N (1934) Kolmogorov-smirnov test. Biometrika 26(4):291–302. https://doi.org/10.2307/2333639

    Article  Google Scholar 

  • Konduru RT, Takahashi HG (2020) Effects of convection representation and model resolution on diurnal precipitation cycle over the indian monsoon region: Toward a convection-permitting regional climate simulation. J Geophysical Res: Atmospheres 125(16):e2019JD032150

  • Kruskal JB (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1):1–27

    Article  Google Scholar 

  • Kruskal JB (1964) Nonmetric multidimensional scaling: a numerical method. Psychometrika 29(2):115–129

    Article  Google Scholar 

  • Kuo YH, Anthes RA (1984) Semiprognostic tests of Kuo-type cumulus parameterization schemes in an extratropical convective system. Monthly Weather Rev 112(8):1498–1509

    Article  Google Scholar 

  • Liess S, Geller MA (2012) On the relationship between QBO and distribution of tropical deep convection. J Geophysical Res: Atmospheres 117(D3)

  • Lin JWB, Neelin JD (2003) Toward stochastic deep convective parameterization in general circulation models. Geophysical Res Lett 30:1–4. https://doi.org/10.1029/2002GL016203

    Article  Google Scholar 

  • Mahalanobis PC (2018) On the generalized distance in statistics. Sankhy\(\bar{\rm a}\): Indian J Statistics, Series A (2008-) 80:S1–S7

  • Majda AJ, Khouider B (2002) Stochastic and mesoscopic models for tropical convection. Proceedings of the national academy of sciences of the united states of America 99:1123–1128. https://doi.org/10.1073/pnas.032663199

    Article  CAS  Google Scholar 

  • Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert. A parameterization of moist convection for general circulation models. Monthly Weather Rev 120(6):978–1002

  • Pan HLHL, Wu WSWS (1995) Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC office note

  • Pfister L, Ueyama R, Jensen E et al (2022) Deep convective cloud top altitudes at high temporal and spatial resolution. Earth Space Sci 9(11):e2022EA002475

  • Platt J et al (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances Large Margin Classifiers 10(3):61–74

    Google Scholar 

  • Rajeevan M, Bhate J, Kale J (2012) Variability of convection and convective rainfall over the indian subcontinent and its association with the enso. Climate Dynamics 39(3–4):863–879

    Google Scholar 

  • Rasp S, Pritchard MS, Gentine P (2018) Deep learning to represent subgrid processes in climate models. In: Proceedings of the national academy of sciences 115(39):9684–9689

  • Russell AE, Young R, Manins PC et al (2008) Diurnal cycles of precipitation, clouds, and lightning in the vicinity of sao paulo city. Monthly Weather Rev 136(5):1920–1938

    Google Scholar 

  • Sen Roy S, Balling RC Jr (2007) Diurnal variations in summer season precipitation in india. Int J Climatol: A J Royal Meteorological Soc 27(7):969–976

    Article  Google Scholar 

  • Siems ST, Houze RA Jr, Manton MJ (2000) Diurnal variations of rainfall frequency and intensity over north queensland. J Climate 13(14):2061–2075

    Google Scholar 

  • Smith D (2010) Impact of local topography on deep convection. J Geophysical Res: Atmospheres 115(D14)

  • Smith J (2005) Spatial variability of deep convection. J Atmospheric Sci 30(2):123–135

    Google Scholar 

  • Song F, Zhang GJ (2018) Understanding and improving the scale dependence of trigger functions for convective parameterization using cloud-resolving model data. J Climate 31(18):7385–7399

    Article  Google Scholar 

  • Song FF, Zhang GJ (2017) Improving trigger functions for convective parameterization schemes using goamazon observations. J Climate 30:8711–8726. https://doi.org/10.1175/JCLI-D-17-0042.1

    Article  Google Scholar 

  • Suhas E, Zhang GJ (2014) Evaluation of trigger functions for convective parameterization schemes using observations. J Climate 27:7647–7666. https://doi.org/10.1175/JCLI-D-13-00718.1

    Article  Google Scholar 

  • Tawfik AB, Dirmeyer PA (2014) A process-based framework for quantifying the atmospheric preconditioning of surface-triggered convection. Geophysical Res Lett 41(1):173–178

    Article  Google Scholar 

  • Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Monthly Weather Rev 117(8):1779–1800

    Article  Google Scholar 

  • Ukkonen P, Mäkelä A (2019) Evaluation of machine learning classifiers for predicting deep convection. J Adv Model Earth Syst 11(6):1784–1802

    Article  Google Scholar 

  • Van Der Donckt J, Van Der Donckt J, Deprost E et al (2023) Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring. Biomed Signal Process Control 81:104429

    Article  Google Scholar 

  • Villalba-Pradas A, Tapiador FJ (2022) Empirical values and assumptions in the convection schemes of numerical models. Geoscientific Model Development 15(9):3447–3518

    Article  Google Scholar 

  • Xie S, Zhang M (2000) Impact of the convection triggering function on single-column model simulations. J Geophys Res: Atmospheres 105(D11):14983–14996

    Article  Google Scholar 

  • Yano JI, Bister M, Fuchs Ž et al (2013) Phenomenology of convection-parameterization closure. Atmospheric Chemistry Physics 13(8):4111–4131

    Article  CAS  Google Scholar 

  • Zhang C, Xie S, Klein SA et al (2018) CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site. J Geophysical Res: Atmospheres 123:2968–2992. https://doi.org/10.1002/2017JD027200

    Article  Google Scholar 

  • Zhang GJ (2002) Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization. J Geophysical Res: Atmospheres 107(D14):ACL–12

  • Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmosphere-ocean 33(3):407–446

    Article  Google Scholar 

  • Zhang T, Lin W, Vogelmann AM, et al (2021) Improving convection trigger functions in deep convective parameterization schemes using machine learning. J Adv Model Earth Syst 13(5):e2020MS002365

  • Zhou W, Leung LR, Lu J (2022) Linking large-scale double-itcz bias to local-scale drizzling bias in climate models. J Climate 35(24):7965–7979

    Article  Google Scholar 

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Acknowledgements

Indian Institute of Tropical Meteorology, Pune is a fully funded autonomous institute under Ministry of Earth Sciences, Government of India. Director, IITM is duly acknowledged for support and encouragement.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Indian Institute of Tropical Meteorology, Pune, Ministry of Earth Sciences, Government of India, Pune, India

    Siddharth Kumar & P Mukhopadhyay

  2. Indian Institute of Technology, Madras, India

    C Balaji

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  1. Siddharth Kumar
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  2. P Mukhopadhyay
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  3. C Balaji
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Contributions

All authors contributed to the study conception and design. Manuscript preparation and analysis were performed by Siddharth Kumar with valuable feedbacks from P. Mukhopadhyay and C. Balaji. All authors have read and approved the final manuscript.

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Correspondence to Siddharth Kumar.

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Kumar, S., Mukhopadhyay, P. & Balaji, C. A machine learning based deep convective trigger for climate models. Clim Dyn 62, 8183–8200 (2024). https://doi.org/10.1007/s00382-024-07332-w

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