Chun Kit Ho
University of Reading
6 Papers
Chun Kit Ho is an academic researcher from University of Reading. The author has contributed to research in topics: Climate change & Climate model. The author has an hindex of 6, co-authored 6 publications.
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Papers
Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe
TL;DR: It is concluded that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections.
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Calibration Strategies: A Source of Additional Uncertainty in Climate Change Projections
TL;DR: Ho et al. as mentioned in this paper proposed a model for predicting the future of the UK's weather system based on the NCAS climate and the Department of Meteorology at the University of Reading.
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Increasing influence of heat stress on French maize yields from the 1960s to the 2030s
Ed Hawkins,Thomas E. Fricker,Andrew J. Challinor,Christopher A. T. Ferro,Chun Kit Ho,Tom M. Osborne +5 more
TL;DR: It is concluded that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.
Real-time multi-model decadal climate predictions
Doug Smith,Adam A. Scaife,George J. Boer,Mihaela Caian,Francisco J. Doblas-Reyes,Virginie Guemas,Ed Hawkins,Wilco Hazeleger,Wilco Hazeleger,Leon Hermanson,Chun Kit Ho,Masayoshi Ishii,Viatcheslav Kharin,Masahide Kimoto,Ben P. Kirtman,Judith Lean,Daniela Matei,William J. Merryfield,Wolfgang A. Müller,Holger Pohlmann,Anthony Rosati,Bert Wouters,Klaus Wyser +22 more
TL;DR: In this article, the first climate prediction of the coming decade made with multiple models, initialized with prior observations, is presented, and the forecast is experimental, since the skill of the multi-model system is as yet unknown, but the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill.
Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions
TL;DR: In this paper, the authors presented two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST.