Regional downscaling of climate data using deep learning and applications for drought and rainfall forecasting

ABSTRACT / INTRODUCTIONDynamical downscaling of global climate model (GCM) data from ~150 km to 12 km resolution or smaller requires running a computationally expensive regional climate model (RCM) using GCM forcing data at the lateral and surface boundaries. In addition, RCM output data (temperature, precipitation, etc.) are biased with respect to in-situ observations due to various physical processes that are not adequately represented in the model, sometimes due to sub-grid scale effects. Methods in machine learning have the potential to extract more abstract relationships between in-situ observations and GCMs in addition to using RCM simulation reference data, and thus could improve the representation and accuracy of downscaled variables. In particular, precipitation is notoriously difficult to predict due to complex sub-grid scale processes and local features such as orography. In this study, we explore and test different machine learning approaches with the aim of improving the accuracy of regionally downscaled GCM output.To test the effectiveness of methods in machine learning, we downscaled a variety of regional circulation indices to monthly rainfall anomalies (mm/day) for a single location (Whenuapai, Auckland). The circulation indices used were the M1 and Z1 Trenberth indices - which describe both zonal and meridional flow across New Zealand, and the Southern Oscillation Index (SOI) - which describes the atmospheric phase of the El Ni𝑛 o Southern Oscillation. These results were tested against a baseline multivariate linear regression. With the help of NeSI’s consultancy service, we developed a scalable pipeline to automatically run a variety of experiments including varying the number of lagged circulation indices and training a large selection of models. For all our linear models (e.g., OLS, Gradient Boosting) the minimum root mean square error (rms) was achieved using approximately 96 lagged months of the circulation indices, which in turn explained approximately 10 -15 % of the variance in the rainfall anomalies. However, through using a deep neural network, we can explain approximately 50% of the variance in rainfall. The significant improvement in accuracy is a strong indication that deep neutral networks can extract more abstract relationships from the lagged history of circulation indices.Since our initial results are promising, we have applied the trained model to data from past and future climate model projections and compared the estimates of climate change signal at the study site from machine learning with dynamical regional climate models output. Future work will include downscaling circulation and synoptic flow patterns, that is two-dimensional synoptic fields to both daily and monthly gridded rainfall anomalies. ABOUT THE AUTHOR