Are climate model simulations useful for forecasting precipitation trends? Hindcast and synthetic-data experiments

Abstract

Water scientists and managers currently face the question of whether trends in climate variables that affect water supplies and hazards can be anticipated. We investigate to what extent climate model simulations may provide accurate forecasts of future hydrologic nonstationarity in the form of changes in precipitation amount. We compare gridded station observations (GPCC Full Data Product, 1901–2010) and climate model outputs (CMIP5 Historical and RCP8.5 simulations, 1901–2100) in real and synthetic-data hindcast experiments. The hindcast experiments show that imputing precipitation trends based on the climate model mean reduced the root mean square error of precipitation trend estimates for 1961–2010 by 9% compared to making the assumption (implied by hydrologic stationarity) of no trend in precipitation. Given the accelerating pace of climate change, the benefits of incorporating climate model assessments of precipitation trends in water resource planning are projected to increase for future decades. The distribution of climate models’ simulated precipitation trends shows substantial spatially coherent biases, suggesting that there may be room for further improvement in how climate models are parametrized and used for precipitation estimation. Linear extrapolation of observed trends in long precipitation records may also be useful, particularly for lead times shorter than about 25 years. Overall, our findings suggest that simulations by current global climate models, combined with the continued maintenance of in situ hydrologic observations, can provide useful information on future changes in the hydrologic cycle.

Keywords

• hydrologic prediction
• nonstationarity
• climate change
• precipitation
• climate model
• trend estimation