Bias Correction of RCM Precipitation by TIN-Copula Method: A Case Study for Historical and Future Simulations in Cyprus

Abstract

<jats:p>Numerical models are being used for the simulation of recent climate conditions as well as future projections. Due to the complexity of the Earth’s climate system and processes occurring at sub-grid scales, model results often diverge from the observed values. Different methods have been developed to minimize such biases. In the present study, the recently introduced “triangular irregular networks (TIN)-Copula” method was used for the bias correction of modelled monthly total and extreme precipitation in Cyprus. The method was applied to a 15-year historical period and two future periods of the same duration. Precipitation time-series were derived from a 12-km resolution EURO-CORDEX regional climate simulation. The results show that the TIN-Copula method significantly reduces the positive biases between the model results and observations during the historical period of 1986–2000, for both total and extreme precipitation (&gt;80%). However, the level of improvement differs temporally and spatially. For future periods, the model tends to project significantly higher total precipitation rates prior to bias correction, while for extremes the differences are smaller. The adjustments slightly affect the overall climate change signal, which tends to be enhanced after bias correction, especially for total precipitation and for the autumn period.</jats:p>

TIN‐Copula bias‐correction method for model‐derived maximum temperature in the MENA region

“…In the present study, we did not investigate the potential impact of the proposed methodology on the projected climate change signal since we only considered simulated data of the recent past. However, a previous study, based on a similar approach (Lazoglou et al ., 2020b), did not report any significant alterations in the climate change signal after the application of the bias correction.…”

“…Lazoglou et al . (2019, 2020a, 2020b) combined Copulas with Triangular Irregular Networks (TIN), developing a new method, the TIN‐Copula, to simulate monthly extreme temperatures in several stations in Greece and the Mediterranean region. In both studies, the bias correction was accomplished on a monthly basis using reference data from station observations.…”

“…These studies have been based on Laux et al (2011) and Vogl et al (2012), who applied precipitation data in bias correction using copulas on individual stations. Lazoglou et al ( , 2020aLazoglou et al ( , 2020b combined Copulas with Triangular Irregular Networks (TIN), developing a new method, the TIN-Copula, to simulate monthly extreme temperatures in several stations in Greece and the Mediterranean region. In both studies, the bias correction was accomplished on a monthly basis using reference data from station observations.…”

“…This is of particular relevance when the impacts of extreme heatwave events are assessed. In a warmer world, such events are expected to increase in duration, frequency and severity in several regions, including for the Middle East and North Africa (MENA) climate change hot-spot (Lelieveld et al, 2016;Driouech et al, 2020;Saeed et al, 2021;Zittis et al, 2021). Heatwave events tend to overall increasing trend, both in terms of their total number as well as number of their duration (Ali et al, 2018).…”

Using a new local high resolution daily gridded dataset for Attica to statistically downscale climate projections

“…The aforementioned process can be achieved with various methods ranging from simple scaling of the mean and/or variance to more complex methods that scale the whole distribution (e.g. Casanuneva et al 2020;Lazoglou et al 2020Lazoglou et al , 2021. Furthermore, bias adjustment techniques can be categorized as univariate and multivariate.…”