Abstract
Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales.
Highlights
As an essential part of the third working group report from the Intergovernmental Panel on Climate Change’s (IPCC, 2018) sixth assessment report, food security under climate change conditionsAtmosphere 2020, 11, 109; doi:10.3390/atmos11010109 www.mdpi.com/journal/atmosphereAtmosphere 2020, 11, 109 plays a vital role in the survival and development of human beings
regional climate model (RCM) usually perform better than Global climate models (GCMs) in simulating climate at the regional scale, the resolution is still too coarse and the simulation is still biased to be used for local and site-specific impact studies [10,17,18,19]
The results showed that all bias correction methods could reduce the bias of RCM simulations and the distribution-based methods generally performed better than the mean-based methods
Summary
As an essential part of the third working group report from the Intergovernmental Panel on Climate Change’s (IPCC, 2018) sixth assessment report, food security under climate change conditionsAtmosphere 2020, 11, 109; doi:10.3390/atmos11010109 www.mdpi.com/journal/atmosphereAtmosphere 2020, 11, 109 plays a vital role in the survival and development of human beings. Global climate models (GCMs) are primary tools for studying the impacts of climate change on agriculture and hydrology, as well as other fields [1,2,3,4]. The resolution of GCM outputs is too coarse to be used as direct inputs into agricultural and hydrological models for site-specific and watershed climate change impact studies [5,6,7,8,9]. RCMs usually perform better than GCMs in simulating climate at the regional scale, the resolution is still too coarse and the simulation is still biased to be used for local and site-specific impact studies [10,17,18,19]
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