- Yeasmin, Alea, Chand, Savin, Turville, Christopher, Sultanova, Nargiz
- Authors: Yeasmin, Alea , Chand, Savin , Turville, Christopher , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 41, no. 11 (2021), p. 5318-5330
- Full Text: false
- Reviewed:
- Description: Tropical cyclones (TCs) are one of the most destructive synoptic systems and can cause enormous loss of life and property damages in the South Pacific island nations. The impact of tropical depressions (TDs, i.e. weaker systems that do not develop into TCs) can also be staggering in the region in terms of heavy flooding and landslides, but a lack of complete records often hinders research involving TD impacts. A methodology has been developed here to detect TDs in the ERA-5 reanalysis dataset (the fifth generation ECMWF atmospheric reanalysis of the global climate) using the Okubo–Weiss–Zeta parameter (OWZP) detection scheme. The new South Pacific Enhanced Archive for Tropical Cyclones dataset (SPEArTC), the Dvorak analysis of satellite-based cloud patterns over the South Pacific Ocean basin, and a rainfall dataset for various stations and historical archives have been utilized to validate ERA5-derived TCs and TDs for the period between 1979 and 2019. Results indicate that the OWZP method shows substantial skill in capturing the realistic climatological distribution of TDs (as well as TCs) for the South Pacific Ocean in the ERA5 reanalysis, paving a way forward for future climatological studies involving the impacts of TCs and TDs over the island nations using longer-term reanalyses products such as the 20th-century reanalysis dataset that extends back to the 1850s. © 2021 Royal Meteorological Society
North Indian ocean tropical cyclone activity in CMIP5 experiments : future projections using a model-independent detection and tracking scheme
- Bell, Samuel, Chand, Savin, Tory, Kevin, Ye, Hua, Turville, Christopher
- Authors: Bell, Samuel , Chand, Savin , Tory, Kevin , Ye, Hua , Turville, Christopher
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 40, no. 15 (2020), p. 6492-6505
- Full Text:
- Reviewed:
- Description: The sensitivity of tropical cyclone (TC) projection results to different models and the detection and tracking scheme used is well established. In this study, future climate projections of TC activity in the North Indian Ocean (NIO) are assessed with a model- and basin-independent detection and tracking scheme. The scheme is applied to selected models from the coupled model intercomparison project phase 5 (CMIP5) experiments forced under the historical and representative concentration pathway 8.5 (RCP8.5) conditions. Most models underestimated the frequency of early season (April–June) TCs and contained genesis biases equatorward of ~7.5°N in comparison to the historical records. TC tracks detected in reanalysis and model data were input to a clustering algorithm simultaneously, with two clusters in the Arabian Sea and two in the Bay of Bengal (k = 4). Projection results indicated a slight decrease of overall TC genesis frequency in the NIO, with an increase of TC genesis frequency in the Arabian Sea (30–64%) and a decrease in the Bay of Bengal (22–43%), consistent between clusters in each of these sub-regions. These changes were largely due to changes in the pre-monsoon season (April–June) where Bay of Bengal TCs significantly decreased, consistent with changes in vertical ascent. Northern Arabian Sea TCs significantly increased during the pre-monsoon season, consistent with changes in vertical wind shear and relative humidity. There was a projected increase of TC frequency in the post-monsoon season (October–December), consistent with changes in relative humidity and vertical ascent, although not all clusters followed this trend; noting a different response in the southern Bay of Bengal. In turn, these projections caused changes to the climate averaged TC track density, including a decrease (up to 2 TCs per decade) affecting the eastern coast of India and a small increase (up to 0.5 TCs per decade) affecting eastern Africa, Oman and Yemen. © 2020 Royal Meteorological Society
- Authors: Bell, Samuel , Chand, Savin , Tory, Kevin , Ye, Hua , Turville, Christopher
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 40, no. 15 (2020), p. 6492-6505
- Full Text:
- Reviewed:
- Description: The sensitivity of tropical cyclone (TC) projection results to different models and the detection and tracking scheme used is well established. In this study, future climate projections of TC activity in the North Indian Ocean (NIO) are assessed with a model- and basin-independent detection and tracking scheme. The scheme is applied to selected models from the coupled model intercomparison project phase 5 (CMIP5) experiments forced under the historical and representative concentration pathway 8.5 (RCP8.5) conditions. Most models underestimated the frequency of early season (April–June) TCs and contained genesis biases equatorward of ~7.5°N in comparison to the historical records. TC tracks detected in reanalysis and model data were input to a clustering algorithm simultaneously, with two clusters in the Arabian Sea and two in the Bay of Bengal (k = 4). Projection results indicated a slight decrease of overall TC genesis frequency in the NIO, with an increase of TC genesis frequency in the Arabian Sea (30–64%) and a decrease in the Bay of Bengal (22–43%), consistent between clusters in each of these sub-regions. These changes were largely due to changes in the pre-monsoon season (April–June) where Bay of Bengal TCs significantly decreased, consistent with changes in vertical ascent. Northern Arabian Sea TCs significantly increased during the pre-monsoon season, consistent with changes in vertical wind shear and relative humidity. There was a projected increase of TC frequency in the post-monsoon season (October–December), consistent with changes in relative humidity and vertical ascent, although not all clusters followed this trend; noting a different response in the southern Bay of Bengal. In turn, these projections caused changes to the climate averaged TC track density, including a decrease (up to 2 TCs per decade) affecting the eastern coast of India and a small increase (up to 0.5 TCs per decade) affecting eastern Africa, Oman and Yemen. © 2020 Royal Meteorological Society
Statistical downscaling of general circulation model outputs to precipitation-part 2 : Bias-correction and future projections
- Sachindra, Dhanapala, Huang, Fuchun, Barton, Andrew, Perera, Bimalka
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3282-3303
- Full Text:
- Reviewed:
- Description: This article is the second of a series of two articles. In the first article, two models were developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and HadCM3 outputs, for statistically downscaling these outputs to monthly precipitation at a site in north-western Victoria, Australia. In that study, it was seen that the downscaling model developed with NCEP/NCAR reanalysis outputs performs much better than the model developed with HadCM3 outputs. Furthermore, it was found that there is large bias in HadCM3 outputs which needs to be corrected. In this article, the downscaling model developed with NCEP/NCAR reanalysis outputs was used to downscale HadCM3 20th century climate experiment outputs to monthly precipitation over the period 1950-1999. In all four seasons, the precipitation downscaled with HadCM3 20th century outputs, displayed a large scatter and the majority of precipitation was overestimated. The precipitation downscaled with HadCM3 outputs was bias-corrected against the observed precipitation pertaining to the period 1950-1999, using three techniques: (1) equidistant quantile mapping (EDQM), (2) monthly bias-correction (MBC) and (3) nested bias-correction (NBC). Although all these bias-correction techniques were able to adequately correct the statistics of downscaled precipitation, the magnitude of the scatter of precipitation remained almost the same. Considering the performances and its ability to correct the cumulative distribution of precipitation, EDQM was selected for the bias-correction of future precipitation projections. HadCM3 outputs for the A2 and B1 greenhouse gas scenarios were introduced to the downscaling model and the downscaled precipitation for the period 2000-2099 was bias-corrected with the EDQM technique. Both A2 and B1 scenarios indicated a rise in the average of future precipitation in winter and a drop in it in summer and spring. These scenarios showed an increase in the maximum monthly precipitation in all seasons and an increase in percentage of months with zero precipitation in summer, autumn and spring. © 2014 Royal Meteorological Society
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3282-3303
- Full Text:
- Reviewed:
- Description: This article is the second of a series of two articles. In the first article, two models were developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and HadCM3 outputs, for statistically downscaling these outputs to monthly precipitation at a site in north-western Victoria, Australia. In that study, it was seen that the downscaling model developed with NCEP/NCAR reanalysis outputs performs much better than the model developed with HadCM3 outputs. Furthermore, it was found that there is large bias in HadCM3 outputs which needs to be corrected. In this article, the downscaling model developed with NCEP/NCAR reanalysis outputs was used to downscale HadCM3 20th century climate experiment outputs to monthly precipitation over the period 1950-1999. In all four seasons, the precipitation downscaled with HadCM3 20th century outputs, displayed a large scatter and the majority of precipitation was overestimated. The precipitation downscaled with HadCM3 outputs was bias-corrected against the observed precipitation pertaining to the period 1950-1999, using three techniques: (1) equidistant quantile mapping (EDQM), (2) monthly bias-correction (MBC) and (3) nested bias-correction (NBC). Although all these bias-correction techniques were able to adequately correct the statistics of downscaled precipitation, the magnitude of the scatter of precipitation remained almost the same. Considering the performances and its ability to correct the cumulative distribution of precipitation, EDQM was selected for the bias-correction of future precipitation projections. HadCM3 outputs for the A2 and B1 greenhouse gas scenarios were introduced to the downscaling model and the downscaled precipitation for the period 2000-2099 was bias-corrected with the EDQM technique. Both A2 and B1 scenarios indicated a rise in the average of future precipitation in winter and a drop in it in summer and spring. These scenarios showed an increase in the maximum monthly precipitation in all seasons and an increase in percentage of months with zero precipitation in summer, autumn and spring. © 2014 Royal Meteorological Society
Statistical downscaling of general circulation model outputs to precipitation—part 1: calibration and validation
- Sachindra, Dhanapala, Huang, Fuchun, Barton, Andrew, Perera, Bimalka
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3264-3281
- Full Text:
- Reviewed:
- Description: This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
- Authors: Sachindra, Dhanapala , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 34, no. 11 (2014), p. 3264-3281
- Full Text:
- Reviewed:
- Description: This article is the first of two companion articles providing details of the development of two separate models for statistically downscaling monthly precipitation. The first model was developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (/) reanalysis outputs and the second model was built using the outputs of Hadley Centre Coupled Model version 3 (). Both models were based on the multi‐linear regression () technique and were built for a precipitation station located in Victoria, Australia. Probable predictors were selected based on the past literature and hydrology. Potential predictors were selected for each calendar month separately from the / reanalysis data, considering the correlations that they maintained with observed precipitation. Based on the strength of the correlations, these potential predictors were introduced to the downscaling model until its performance in validation, in terms of Nash–Sutcliffe Efficiency (), was maximized. In this manner, for each calendar month, the final sets of potential predictors and the best downscaling models with / reanalysis data were identified. The 20th century climate experiment data corresponding to these final sets of potential predictors were used to calibrate and validate the second model. In calibration and validation, the model developed with / reanalysis data displayed of 0.74 and 0.70, respectively. The model built with outputs showed of 0.44 and 0.17 during the calibration and validation periods, respectively. Both models tended to under‐predict high precipitation values and over‐predict near‐zero precipitation values, during both calibration and validation. However, this prediction characteristic was more pronounced by the model developed with outputs. A graphical comparison of observed precipitation, the precipitation reproduced by the two downscaling models and the raw precipitation output of , showed that there is large bias in the precipitation output of . This indicated the need of a bias‐correction, which is detailed in the second companion article.
- Sachindra, D. A., Huang, Fuchun, Barton, Andrew, Perera, Bimalka
- Authors: Sachindra, D. A. , Huang, Fuchun , Barton, Andrew , Perera, Bimalka
- Date: 2013
- Type: Text , Journal article
- Relation: International Journal of Climatology Vol. 33, no. 5 (2013), p. 1087-1106
- Full Text: false
- Description: This study employed least square support vector machine regression (LS-SVM-R) and multi-linear regression (MLR) for statistically downscaling monthly general circulation model (GCM) outputs directly to monthly catchment streamflows. The scope of the study was limited to calibration and validation of the downscaling models. The methodology was demonstrated by its application to a streamflow site in the Grampian water supply system in northwestern Victoria, Australia. Probable predictors for the study were selected from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set based on the past literature and hydrology. Probable variables that displayed the best significant correlations, consistently with the streamflows over the entire period of the study (1950-2010) and under three 20-year time slices (1950-1969, 1970-1989 and 1990-2010) were selected as potential predictors. To better capture seasonal variations of streamflows, downscaling models were developed for each calendar month. The standardized potential predictors were introduced to the LS-SVM-R and MLR models, starting from the best correlated three and then, others one by one, based on their correlations with the streamflows, until the model performance in validation was maximized. This stepwise model development enabled the identification of the optimum number of potential variables for each month. The model calibration was performed over the period 1950-1989 and validation was done for 1990-2010. LS-SVM-R model parameter optimization was achieved using simplex algorithm and leave-one-out cross-validation. The MLR models were optimized by minimizing the sum of squared errors. In both modelling techniques, validation was performed as an independent simulation. In calibration, LS-SVM-R and MLR models displayed equally good performances with a trend of under-predicting high flows. During validation, LS-SVM-R outperformed MLR, though both techniques over-predicted most of the streamflows. It was concluded that LS-SVM-R is a better technique for statistically downscaling GCM outputs to streamflows than MLR, but still MLR is a potential technique for the same task. Copyright © 2012 Royal Meteorological Society.
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