Downscaling Global Climate Model Forecasts by Using Neural Networks Mark Bailey, Becca Latto, Dr. Nabin Malakar, Dr. Barry Gross, Pedro Placido The City College of New York, NY As a result of global climate change, it is necessary to provide high resolution regional climate data in order to assist in resource management and policy planning There is a greater demand to better forecast climate on a regional scale at less than 5 km resolution Global Climate Models (GCMs) are mathematical based models that are capable of forecasting long term climate, but are limited in their spatial resolution (generally 0.5 degrees) so they cannot be effective on a local scale. It is essential to adapt the low resolution GCM data to be able to apply it to regional models such as terrestrial ecosystems or hydrologic models. Statistical downscaling with neural networks can be utilized to effectively provide high resolution regional climate data Background and Goals Possible Solutions Results Data Daymet (NASA funded), uses algorithms and computer software to make meteorological predictions along with land observations. (Considered to be the ground truth) 5 ISIMIP Global Circulation Models that are compiled by a collaboration of about 30 global climate impact modelling groups MODIS Land Classification, Land Surface Temperature, Cloud Fraction (NASA) Approaches to Neural Networks Monthly neural network- Trained on Target: Daymet Inputs: Digital elevation and IPSL variables. Yearly neural network- Trained on complete years of data ( ). Target: Daymet Inputs: Digital elevation, IPSL variable, and month label (i.e. January = 1) Applied both methods to maximum temperature, minimum temperature and shortwave solar radiation. Yearly method was tested with monthly data to provide grounds to compare both methods. Materials and Methods Based on an analysis of correlation coefficients, IPSL was found to be the best matched to Daymet. Therefore, IPSL was used for further study. The NN’s obtained were an effort to remove the bias of the GCM and produce a higher resolution result. Based on our findings, the monthly NN approach offered a better ability to predict than yearly NN for temperature data. This result may not be universal but can be attributed to the fact that more data can confuse the NN in its effort to draw connections to it’s target. As shown in the statistical analysis, the NN was able to reduce the root-mean-square error, increase the correlation coefficient as well as make the regression line nearly 1:1. The example provided is a based on a year the NN had never seen before, thus it showcases the NN’s ability to predict future temperatures. Using MODIS Land Classification data, the ability of the NN to more accurately represent mesoscale effects such as those found in urban areas are highlighted. Similar studies were also performed on Tmin, which produced similar conclusions. It was assumed that the good temperature correlation of IPSL would translate to shortwave solar radiation, but this was not the case. Downwelling Shortwave solar radiation resulted in months with anti-correlation between Daymet and the NN. Cloud fraction data represents a possible solution to this problem but more data is required. These high resolution models can be used in the future to be applied to hydrologic or ecosystem simulation models, for example, to quantify impacts based on our predictions to give decision makers a the framework to plan for climate change impacts. Downscaling Methods Downscaling is the process of utilizing large scale global climate data to make predictions about small scale regional climates. Statistical Downscaling- Computationally inexpensive and can easily be applied to other regions. Neural networks are one way of doing this type of downscaling. A large sample of historical data is crucial. Dynamical Downscaling- A regional climate model is driven by a global model to make predictions on a smaller scale. While this method is more robust than statistical methods, it is often too demanding computationally to be in use. Digital Elevation IPSL GCM Model NN Result Tmax (10/2011)Daymet Tmax (10/2011) Performance of NN in Different Land Classes showing Improvements in R R values between Daymet, 5 GCMs, and MODIS The non-linear dependence of temperature on elevation IPSL GCM Tmax (10/2011) NN Result Tmax (6/2012)Daymet Tmax (6/2012)IPSL GCM Tmax (6/2012) Discussion/Conclusions We would like to thank the National Oceanic and Atmospheric Administration Cooperative Remote Sensing Science and Technology Center (NOAA CREST), the New York City Research Initiative (NYCRI) and the National Science Foundation (NSF) for their support of the project. The NASA NYCRI students would also like to thank Dr. Barry Gross for accepting them into the program, as well as Dr. Nabin Malakar and Pedro Placido for their tremendous amounts of help and education they provided. Acknowledgements The histogram matching shows that the NN Tmax is well correlated with the Daymet data Shortwave Solar Radiation Correlation vs. Anti-Correlation (Order: Daymet-IPSL-MODIS Cloud Fraction) Artificial Neural Networks (Statistical Downscaling) Mathematical models that take multiple input data streams, multiply it by specified weights and biases, perform linear superposition as well as non linear stretching to produce an output that should match a given input as closely as possible. A given neural network (NN) has three stages: Training- inputs are presented and the network is adjusted according to biases Validation-used to measure network generalization and stops training when generalization stops improving Testing- an independent measure of network performance during and after training Consistent improvements in R January 2008 June 2008