Machine Learning for Moist Physics Parameterization in Weather and Climate Models

1. Machine Learning for Moist Physics Parameterization in Weather and Climate Models Christopher S. Bretherton Jeremy McGibbon and Noah Brenowitz Departments of Atmospheric Science and Applied Mathematics, University of Washington, Seattle, USA

2. Cloud processes are multiscale

3. Simulating weather and climate: resolution and complexity 1990 1995 2001 2007 2x resolution = 16x computation

4. A single grid column of a global climate model (100 x 100 km)

5. Representing such unresolved variability is challenging Park and Bretherton 2009 Park et al. 2014 • …so ‘parameterizations’ of moist physics are subjective, difficult to develop, & work imperfectly, especially as part of a system.

6. Feeds into weather/climate model biases & uncertainties Example: Double-ITCZ rainfall bias in climate models CMIP5 (models) GPCP (observations) Subgrid parameterization improvements have come more through ‘targeted tweaking’ than from fundamental advances.

7. Big Science = Big Opportunities? Big Problems • Global climate models still use grids of 25 km or larger and need sophisticated parameterizations of subgrid variability of clouds and circulations (+ ocean eddies, etc.). • Intermodel spread in climate sensitivity and regional precipitation trends remains large. Big Data • Global satellite data on radiation, clouds, precipitation, surface characteristics, aerosols, ice… • Accurate global analyses from weather forecast/DA models. • Global simulations with 2-5 km grids for up to a year. ~1 km grid regional models routinely used for forecasting ~150 m grid models over Germany.

8. The human bottleneck • I. Glenn based on Khairoutdinov et al.2009 Machine learning to the rescue?

9. Applications of machine learning to weather and climate - Detect extreme weather events in model output [Liu et al. 2016] - Emulate parameterizations to speed them up [Chevalier 1998; Krasnopolsky et al. 2005; O’Gorman and Dwyer 2018] - Automatically optimize adjustable parameters [Ollinaho et al. 2013] - Automatically learn from data or reanalysis [Dueben and Bauer 2018, McGibbon and Bretherton 2019] - Make coarse-grid parameterizations from fine-grid models. [Krasnopolsky et al. 2013; Brenowitz and Bretherton 2018, 2019; Rasp et al. 2018] Issues: • Machines only beat human learning if trained with lots of data • Shouldn’t extrapolate out of training climate regime (ML using present observations not good for a warmer climate)

10. Machine-Assisted Reanalysis Boundary-Layer Emulation (MARBLE)[McGibbon and Bretherton 2019 GRL, submitted] • Reanalysis = forecast + observational update • Forecast = airflow + physical parameterizations • ERA5 = world’s most accurate reanalysis (thanks, ECMWF!) • MARBLE: Machine learning scheme to emulate ‘heating’ and ‘moistening’ tendencies from combined ERA5 physical parameterizations + observational update in the lower atmosphere, where climate models struggle to correctly simulate clouds and atmospheric structure. • Observational update provides natural bias correction. • Like climate model parameterizations, MARBLE treats each atmospheric column separately. It uses 50 km grid of ERA5.

11. Regional pilot demo: Summertime NE Pacific 25-40 N 130-145 W

12. Method: • Data - ERA5 hourly analyses of: • Temperature, humidity, wind profiles in lowest 4 km • Surface sensible and latent heat flux • Sea-surface temperature • Insolation • Low clouds and precipitation • for NE Pacific domain over summers of 2008-2015.

13. Goal: Learn profiles of heating and moistening rates, cloud cover and precipitation as functions of temperature and moisture profiles and other auxiliary variables. Inputs in each grid column: • Initial temperature, humidity profiles • 7-day time series of: • ‘advective’ (airflow) tendencies of temperature, humidity • surface fluxes, SST, insolation Outputs: heating and moistening rates that are combined with known advective tendencies to make a 7-day column forecast of temperature and humidity Loss function for 3-layer feed-forward neural net 7-day mean weighted RMSE of this forecast + diagnosed radiation, clouds and precipitation (Bren. and Breth. 2018)

14. MARBLE network design Train: 2008-2015 Test: 2016

15. Good performance for a random test location/forecast ’Baseline’ MARBLE ERA5 humidity potl. temp. diabatic heating clear-sky radiative heating cloud Baseline uses tendencies from 14 ECMWF 0-12 hr forecasts initialized from ERA5

16. Mean performance vs. ERA5 MARBLE skill vs.forecast lead 3.5-7 day R2 MARBLE Baseline Temperature 0.88 0.69 Humidity 0.89 0.70 MARBLE beats the fine ‘baseline’ from short ECMWF forecasts because it learns the observational correction.

17. Diagnosed cloud cover also agrees well with ERA5 Colleagues at Brookhaven NL testing MARBLE in regional WRF setup

18. Prospectus • As observational and model datasets get bigger, machine learning is becoming an important tool for making forecast models better. • Designing a machine learning scheme that works well in a within a ‘hybrid’ context of a larger numerical model still takes expert humans. • Several research groups are making rapid progress toward machine learning parameterizations for atmospheric convection, ocean eddy mixing, and other processes. • Prediction: A machine learning parameterization will be included within the operational version of a numerical weather prediction or climate model within three years.