Performance of the general circulation models in simulating temperature and precipitation over Iran

Abbasian, Mohammadsadegh; Moghim, Sanaz; Ali, Ahmad

doi:10.1007/s00704-018-2456-y

Cited by 77 publications

(32 citation statements)

References 42 publications

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“…Figures and show training pixels in the delineated regions (D1, D2, D3, D4, D5) to calibrate bias correction models for temperature and precipitation in all months (1970–2008) and seasons (1901–2013), respectively. The performance of the CCSM to simulate varied climate variables like temperature and precipitation is different (Gleckler et al , ; Sillmann et al , ; Abbasian et al , ). Also, systematic errors in climate models are space and time dependent quantities (van Oldenborgh et al , ; de Szoeke and Xie, ; Moghim et al , ).…”

Section: Resultsmentioning

confidence: 99%

Regression‐based regionalization for bias correction of temperature and precipitation

Moghim

Bras

2019

Intl Journal of Climatology

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Statistical bias correction methods are inferred relationships between inputs and outputs. The constructed functions are based on available observations, which are limited in time and space. This study investigates the ability of regression models (linear and nonlinear) to regionalize a domain by defining a minimum number of training pixels necessary to achieve a good level of bias correction performance. Linear regression is used to divide northern South America into five regions. To correct the biases of temperature and precipitation, an artificial neural network (ANN) model was trained with selected pixels within each region and then used to reproduce bias‐corrected temperature and precipitation at all pixels within the delineated regions. The Community Climate System Model (CCSM) provided the climate model data. Results confirm that it is possible to identify regions in terms of physical features such as land cover, topography, and climatology over which models trained with a few pixels can correct the biases of climate variables with good accuracy over the entire domain. This approach saves computational time and reduces memory usage of using ANNs for correcting biases in climate model outputs.

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Section: Resultsmentioning

confidence: 99%

Regression‐based regionalization for bias correction of temperature and precipitation

Moghim

Bras

2019

Intl Journal of Climatology

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“…Moreover, Hawkins and Sutton [16] suggested that model uncertainty was more important than internal variability for decadal time scales and regional spatial scales (~2000 km). Therefore, a GCM that can simulate observed temperature reasonably well should be selected before a climate change projection is made [5,17,18].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

et al. 2019

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In this study, we assessed the performance of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general climate models (GCMs) for simulating the observed temperature over the Lower Mekong Basin (LMB) in 1961–2004. An improved score-based method was used to rank the performance of the GCMs over the LMB. Two methods of multi-model ensemble (MME), sub-ensemble from the top 25% ranked GCMs and full ensemble from the entire GCMs, were calculated using arithmetic mean (AM) method and downscaled using the Delta method to project future temperature change during two future time periods, the near future (2006–2049) and the far future (2050–2093), under representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5 scenarios) over the LMB. The improved score-based method combining multiple criteria showed a robust assessment of the GCMs performance over the LMB, which can provide good information for projecting future temperature change. The results showed a significant increase in temperature over the LMB under the two ensembles. However, there were differences in the magnitudes of the future temperature increase between the two ensemble methods, with a higher mean annual temperature increase from full ensemble and sub-ensemble at 1.26 °C (1.09 °C), 1.90 °C (1.70 °C), and 2.97 °C (2.78 °C) during 2050–2093 under the RCP2.6, RCP4.5, and RCP8.5 scenarios compared to the values at 0.93 °C (0.87 °C), 0.99 °C (0.95 °C), and 1.09 °C (1.06 °C) during 2006–2049, respectively, relative to the reference time period of 1961–2004. In the future (2006–2093), the temperature is likely to increase at 0.04 °C, 0.16 °C, and 0.37 °C decade-1 under the RCP2.6, RCP4.5, and RCP8.5 scenarios by the sub-ensemble, while a higher temperature increase at 0.05 °C, 0.17 °C, and 0.39 °C was found by the full ensemble over the LMB, relative to the reference time period of 1961–2004. On the whole, the higher warming mainly occurred in the northern and central areas over the LMB, while the lower warming mainly occurred in the southeast and the southwest, especially under the RCP4.5 and RCP8.5 scenarios, with the warming increased with increasing RCP for both ensembles. Moreover, in order to reduce the uncertainty of temperature projection in further studies in the LMB, multiple methods of GCMs ensemble should be considered and compared.

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“…General circulation models (GCMs) are one of the most important and feasible methods for predicting future large-scale climate change and have become a major research tool in the field of global change [3][4][5]. However, it is difficult for GCMs to understand and adequately model climate systems due to their complexity and topography, and large uncertainties thus exist in their projections, especially at the regional scale.…”

Section: Introductionmentioning

confidence: 99%

“…This study first attempted to combine the SVR methods for ensemble simulation and the QM method for bias correction on the basis of 21 NEX-GDDP precipitation models in the Han River basin to improve the reliability of future projections under the Representative Concentration Pathway (RCP)4.5 and RCP8. 5.…”

Section: Introductionmentioning

confidence: 99%

Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

Chen

2019

Atmosphere

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After the release of the high-resolution downscaled National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, it is worth exploiting this dataset to improve the simulation and projection of local precipitation. This study developed support vector regression (SVR) and quantile mapping (SVR_QM) ensemble and correction models on the basis of historic precipitation in the Han River basin and the 21 NEX-GDDP models. The generated SVR_QM models were applied to project changes of precipitation during the 21st century for the region. Several statistical metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. The results demonstrated the superior performance of SVR_QM compared with multi-layer perceptron (MLP), SVR, and random forest (RF), as well as simple model average (MME) ensemble methods and single NEX-GDDP models. PCC was up to 0.84 from 0.61–0.71 for the single NEX-GDDP models, RMSE was up to 34.02 mm from 48–51 mm, and Rbias values were almost removed. Additionally, the projected precipitation changes during the 21st century in most stations had an increasing trend under both Representative Concentration Pathway RCP4.5 and RCP8.5 emissions scenarios; the regional average precipitation during the middle (2040–2059) and late (2070–2089) 21st century increased by 3.54% and 5.12% under RCP4.5 and by 7.44% and 9.52% under RCP8.5, respectively.

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Performance of the general circulation models in simulating temperature and precipitation over Iran

Cited by 77 publications

References 42 publications

Regression‐based regionalization for bias correction of temperature and precipitation

Regression‐based regionalization for bias correction of temperature and precipitation

Assessing the Performance of CMIP5 GCMs for Projection of Future Temperature Change over the Lower Mekong Basin

Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

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