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Necessity for post-processing dynamically downscaled climate projections for impact and adaptation studies

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Abstract

This work aims to answer if post-processing climate model outputs will improve the accuracy of climate change impact assessment and adaptation evaluation. To achieve this aim, the daily outputs of CSIRO Conformal Cubic Atmospheric Model for periods 1960–1979, 1980–1999 and 2046–2065, and observed daily climate data (1960–1979, 1980–1999) were used by a stochastic weather generator, the Long Ashton Research Station-Weather Generator to construct long time series of local climate scenarios (CSs). The direct outputs of climate models (DOCM) and CSs were then fed into the Agricultural Production System sIMulator—Wheat model to calculate seasonal climate variables and production components at three locations spanning northern, central and southern wheat production areas in New South Wales (NSW), Australia. This study firstly compared the differences in climate variables and production components derived from DOCM and CSs against those from observed climate for period 1960–1979. The impact difference arising from the use of DOCM and CSs for the future period 2046–2065 was then quantified. Simulation results show that (1) both the median/mean and distribution/variation of climate variables and production components associated with CSs were closer to those derived from observed climate when compared to those from DOCM in most of the cases (median/mean, distribution/variation, climate variables, production components and locations); (2) the difference in the mean and distribution of climate variables and production components derived from DOCM and observed climate was significant in most of the cases; (3) longer dry spells in both winter and spring were found from CSs across the three locations considered in comparison with those from DOCM; (4) the median growing season (GS) rainfall total, GS average maximum temperature, GS average solar radiation, GS length and final wheat yield were lower from DOCM than those from CSs and vice versa for GS rainfall frequency and GS average minimum temperature in 2055; (5) the mean and distribution of these climate variables and production components arising from the use of DOCM and CSs are significantly different in most of the cases. This implied that using the direct outputs of spatially downscaled general circulation model without implementing post-processing procedures may lead to significant errors in projected climate impact and the identified effort in tackling climate change risk. It is therefore highly recommended that post-processing procedures be used in developing robust CSs for climate change impact assessment and adaptation evaluation.

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Acknowledgments

The author would like to thank Dr. J.L. McGregor, CSIRO Marine and Atmospheric Research, for providing the outputs of the CCAM, Dr. M. A. Semenov, Rothamsted Research, UK, for providing the LARS-WG. Dr. Anne Colville, University of Technology, Sydney, proof read this manuscript.

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  1. Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW, 2007, Australia

    Qunying Luo

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Luo, Q. Necessity for post-processing dynamically downscaled climate projections for impact and adaptation studies. Stoch Environ Res Risk Assess 30, 1835–1850 (2016). https://doi.org/10.1007/s00477-016-1233-7

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