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Techniques to preprocess the climate projections—a review

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Abstract

Model-derived climate projections have been used by decision-makers for climate change impact assessment, adaptation, and vulnerability studies at large scale. However, they are reported to have significant bias against observed data. The accuracy of dynamically downscaled data depends on the large-scale forcings; however, they still have some systematic errors, so it requires further bias correction. Before using these data for further studies, they need to be processed for performance evaluation. This review article provides current understanding in the field of analyzing global climate projections. It includes studies from the multi-criteria decision-making approaches along with its pros/cons to the performance evaluation of climate models. Moreover, this article discusses several bias correction approaches, multi-model ensemble approaches, and their applications for climate change studies.

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The authors would like to express their gratitude for supporting the research by providing grants under National Innovations in Climate Resilient Agriculture (NICRA)-ICAR and partial funding under the Department of Science and Technology (DST)-MRDP project, Ministry of Science and Technology, Government of India, to the corresponding author for creating research facilities.

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  1. Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi, 110012, India

    Shweta Panjwani & S. Naresh Kumar

  2. International Water Management Institute (IWMI-CGIAR), IWMI Headquarters, Colombo, Sri Lanka

    Shweta Panjwani

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Panjwani, S., Kumar, S.N. Techniques to preprocess the climate projections—a review. Theor Appl Climatol 152, 521–533 (2023). https://doi.org/10.1007/s00704-023-04431-2

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