Abstract

Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from General Circulation Models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in ETo forecasts. However, time-dependent errors, resulting from GCM’s misrepresentations of climate trends, have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian Joint Probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 % in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modelling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasts, and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

Highlights

  • 25 As a critical process in the terrestrial water cycle, evapotranspiration transfers a large amount of water from the land surface to the atmosphere

  • We calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for MediumRange Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian Joint Probability 15 trend-aware (BJP-ti) model

  • 60 improved seasonal ETo forecasts based on Climate Forecast System version 2 (CFSv2) outputs in Florida, the U.S In the calibration of seasonal ETo forecasts in Australia, Zhao et al (2019b) used the Bayesian Joint Probability (BJP) model to post-process ETo forecasts constructed with climate forecasts from the Australian Bureau of Meteorology’s Australian Community Climate and Earth-System SimulatorSeasonal prediction system version 1 (ACCESS-S1) model across three weather stations

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Summary

Introduction

25 As a critical process in the terrestrial water cycle, evapotranspiration transfers a large amount of water from the land surface to the atmosphere. 60 improved seasonal ETo forecasts based on CFSv2 outputs in Florida, the U.S In the calibration of seasonal ETo forecasts in Australia, Zhao et al (2019b) used the Bayesian Joint Probability (BJP) model to post-process ETo forecasts constructed with climate forecasts from the Australian Bureau of Meteorology’s Australian Community Climate and Earth-System SimulatorSeasonal prediction system version 1 (ACCESS-S1) model across three weather stations. To correct errors associated with the representation of temporal changes and variability, Pasternack et al (2020) adopted a time-varying mean to characterize the climate trend in the calibration of decadal temperature forecasts. We hypothesize that reconstructing trends in seasonal ETo forecasts through statistical calibration will help correct timedependent errors and thereby improve forecast skills To test this hypothesis, we adopt the BJP-ti model to calibrate seasonal 80 ETo forecasts constructed with climate forecasts from the ECMWF SEAS5 model across Australia. This investigation aims to 1) reconstruct climate trends in seasonal ETo forecasts through statistical calibration and 2) investigate how trend reconstruction affects the skill of calibrated ETo forecasts

Observations and forecasts
Forecast calibration with the BJP-ti model
Correlation coefficient
Forecast skills
Trends in observations and raw/calibrated forecasts
Correlation coefficients between forecasts and observations
Skills of raw and calibrated ETo forecasts
Discussion
Implications for improving statistical calibration models
Future work for seasonal ETo forecasting
Conclusions
Findings
415 References
Full Text
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