Program on Mathematical and Statistical Methods for Climate and the Earth System Opening Workshop, Developing Stochastic Parameterizations of Subgrid Variability of Clouds and Turbulence using High-Resolution Simulations - Chris Bretherton, Aug 24, 2017
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This document discusses using machine learning to help develop subgrid parameterizations for climate models based on high-resolution simulations. It notes that while high-resolution models have progressed faster than parameterizations, humans still develop parameterizations, which is slow. The document explores using machine learning on comprehensive training datasets from high-resolution models to relate coarse-grid variables to subgrid-scale quantities needed by climate models. Challenges include making schemes stochastic, handling data outside the training range, and instability in global models. Past work applying neural networks to a cloud-resolving model dataset showed promise but has not been used prognostically. Overall, machine learning may help break the parameterization bottleneck if technical challenges can be overcome.
Program on Mathematical and Statistical Methods for Climate and the Earth System Opening Workshop, Developing Stochastic Parameterizations of Subgrid Variability of Clouds and Turbulence using High-Resolution Simulations - Chris Bretherton, Aug 24, 2017
- 1. A vision for applying machine learning to parameterization of unresolved subgrid variability in climate models using higher-resolution simulations Christopher S. Bretherton Departments of Atmospheric Science and Applied Mathematics, University of Washington
- 2. What your atmospheric scientists were doing Monday
- 4. A single grid column of a global climate model (100 x 100 km)
- 5. Representing such unresolved variability is challenging Park et al. 2014 Park and Bretherton 2009 …so ‘parameterizations’ of moist physics are difficult to develop, subjective and work imperfectly.
- 6. Feeds into weather/climate model biases & uncertainties CMIP5 (models) GPCP (observations) Example: Double-ITCZ rainfall bias in climate models Subgrid parameterization improvements have come more through ‘targeted tweaking’ than from fundamental advances.
- 7. Cloud processes are better simulated on fine grids Giga-LES (Khairoutdinov et al. 2009) 100x100 km with 100 m grid
- 8. 1 km global simulations are (briefly) possible NICAM 3.5-14 km (mo-yr) Tomita et al. 2005 0.9 km (for 24 hr): Miyamoto et al. 2013
- 9. Multiyear simulations with ‘ultraparameterization’ Ultrafine-grid 2D cloud-resolving model in each GCM grid column (Δx = 250 m, Δz = 20 m for z=0.5-2 km) Parishani et al. 2017 0 8 km
- 10. Can hi-res simulations help subgrid parameterizations? • High-resolution models have progressed faster than moist physics parameterizations in GCMs • They are conceptually simpler than GCMs, because cloud properties and air velocity don’t vary much within a grid cell, so a complicated model for their subgrid covariability is not needed. • They must still parameterize smaller-scale processes (e. g. cloud droplets a few microns wide, complex ice crystals, turbulence, aerosols). Like other models, they are works in progress needing constant testing vs. observations. • Still, high-resolution simulations provide realistic reference datasets for parameterizing subgrid cloud process variability
- 11. The human bottleneck • Since 1992, the GCSS program has used this approach • Improved parameterizations of cumulus convection, turbulence, and cloud microphysics have been implemented in leading weather and climate models • But progress has been slow, and the uptake of new insights from hi-res modelling and new observations is difficult because humans concoct parameterizations.
- 12. So, how about machine learning (ML)? • Increasingly large and comprehensive training datasets from high-resolution simulations, if we trust them. • A coarse-graining problem: With variables computed by the coarse grid model (e. g. temperature, moisture and wind profiles), use the fine-grid model to return needed quantities to the coarse-grid model (e. g. rainfall, vertical profiles of fractional cloud cover, turbulence, atmospheric heating and drying). • Ideally, the needed quantities should be stochastic - sampled from the hi-res derived pdf of internal fine-grid variability consistent with the coarse-grid variables. • Could machine learning techniques help?
- 13. I wish I knew… Some machine learning challenges • Supervised (structured) or unsupervised learning? • How to use training data to make a scheme stochastic? • Inputs may be highly multidimensional (e. g. whole multilevel profiles of temperature and humidity) • The training dataset only covers real atmospheric states, but a GCM will stray outside such states. • What to do in the likely event that a ‘black box’ ML- based scheme is numerically unstable n a GCM? • Can we make the scheme ‘modular’ so as to separate subgrid variability issues from changes in microphysics? • How can we tune a ML-based scheme to minimize errors of the GCM vs. observations? • What if we change the GCM grid resolution?
- 14. What we are not trying to do with machine learning • Pure dimension reduction, i. e. using ML to find a computationally simper approximation to a deterministic but compute-intensive parameterization, e. g. atmospheric radiative transfer. • Learn governing equations. Our high resolution model uses equations for cloud microphysics and fluid motions that we assume are correct for this purpose. Ideally, we want to learn the subgrid variability needed to apply these equations on the coarse grid scale by integrating over distributions (e. g. joint pdfs of vertical air velocity, cloud water and rain water) that might not depend on the microphysics parameterizations.
- 15. So this is where you help me • We have just started a small (3 person) group in UW Atm Sci, with connections to our eScience institute • I will describe selected past work and our baby steps. • I would appreciate the feedback of those of you who have more thoughts about how to do this.
- 16. Past work I: Subramanian and Palmer 2017 JAMES • ‘Superparameterized’ ECMWF IFS (Small 2D cloud resolving model represents moist physics in each grid column of the global model) • Ensemble of SP-IFS forecasts made from particular days, differing only in random initial temperature noise used to kick off small-scale motions in each CRM. • Is spread of the ensemble forecasts a realistic guide to the overall forecast uncertainty? That is, is this a useful strategy for stochastic parameterization of moist processes? 125 km
- 17. Ensemble of 10 day SP-IFS rainfall forecasts initialized 21 Oct. 2011 Subramanian and Palmer 2017 JAMES
- 18. Error and spread of rainfall forecasts vs. satellite obs Error is within the forecast spread, as desired for stochastic parameterization …a credible stochastic parameterization strategy based on hi-res CRMs Subramanian and Palmer 2017 JAMES
- 19. Past work II: Krasnopolsky et al. 2013 Adv. Artif. Neur. Syst. • Training/testing dataset: 120-day CRM simulating a 256x256 km region of the W Pacific Ocean driven by observed boundary and surface conditions for Nov 1992-Feb 1993. Lots of deep cumulonimbus cloud systems and rainfall. First 80% for training, last 20% for testing. • Neural net (NN) used to learn how atmospheric heating & moistening profiles due to all CRM-simulated cloud, radiation and turbulent processes depend upon the time-varying atmospheric temperature & moisture profiles (unsupervised learning of parameterization of all moist physics).
- 20. NN training r = 256 In training data, underlying relation {T(z),q(z)} → {Q1(z),Q2(z),cloud} is not deterministic; it also depends upon the detailed time evolution of the convection in the CRM domain. For each use, a NN should be stochastically drawn from an ensemble of NNs that fit the training data ‘well enough’ to be plausible. They use a 10-member ensemble. 5 layers, 594 parameters Krasnopolsky et al. 2013
- 21. Validation using CRM test data set NN ensemble predicts the CRM heating and cloud profiles encouragingly well Krasnopolsky et al. 2013
- 22. Diagnostic test of NN param over tropical Pacific NN moist physics parameterization tested diagnostically using inputs from CAM climate model over a large region of the tropical Pacific. NN maintains a reasonable cloud cover over the region, but other aspects of the simulation were not comprehensively validated. Diagnostic test doesn’t demonstrate NN would perform well if used to ‘prognostically’ drive CAM. Cloud cover Krasnopolsky et al. 2013
- 23. Improving on this approach • Can a version of this approach work prognostically? • Best to train with more comprehensive datasets, e. g. super/ultraparameterization or global cloud resolving model. Narenpitak et al. 2017 JAMES
- 24. CHOMP: Advancing CLUBB using machine learning A supervised learning strategy for improving the CLUBB moist turbulence parameterization used in CAM6 (with Jeremy McGibbon) CLUBB = Cloud Layers Unified By Binormals (Golaz et al. 2002) CHOMP = Closing Higher Orders with Machine-learning Parameterization Based on large-eddy simulations of cloudy atmospheric boundary layers sampled on 12 cruises of a container ship from LA to Hawaii during DOE’s MAGIC campaign in Oct. 2011-Sept. 2012 (McGibbon and Bretherton 2017).
- 25. The LES dataset • Hourly 3D samples from 12 4-day cruises, 400+ vertical levels • 11 cruises used for training, 1 for testing
- 26. CLUBB and CHOMP • Higher-order turbulence closure (HOC): Gridcell-mean tendencies depend on second-order moments • 2nd-order depends on 3rd-order, etc. • CLUBB: 11 moments of u, v, w, qt and θl are prognosed; others are diagnosed (‘closed’) in terms of these assuming joint double-Gaussian PDFs errors! • CHOMP: Use random forest regression trained on LES output for cloud-topped boundary layer cases to relate unknown moments (blue) to prognostic variables. • Each of the 400+ model levels at each LES output time gives a sample. Prognostic variables One typical CLUBB moment equation (closure terms revised in CHOMP)
- 27. Profile of input moments at one time in test data
- 28. Profiles of desired output moments from same time • Random forest (CHOMP) matches LES better than CLUBB.
- 29. Challenges for CHOMP • Random forest outputs are piecewise constant functions of inputs, but we need to take their vertical derivatives. Even after smoothing this leads to numerical instability in the prognostic variables. • A more efficient neural net is harder to train and less robust on this data set. • What to do when inputs go well outside the range of the training data? • The LES outputs are partly stochastic functions of their inputs. This would be nice to preserve in CHOMP, but the random forest regression mostly averages this out.
- 30. Outlook • Machine learning using comprehensive training datasets from realistic high-resolution models of clouds, storms,turbulence → could break the human parameterization bottleneck? • But has not yet been successfully used to develop a moist physics parameterization implemented in a global model. • Best approach is unclear, including how to implement stochasticity and how to tune using observational data. • Lots of potential and technical challenges - your help needed!