Articles | Volume 10, issue 4
https://doi.org/10.5194/esd-10-765-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-10-765-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Changes in statistical distributions of sub-daily surface temperatures and wind speed
Robert J. H. Dunn
CORRESPONDING AUTHOR
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Kate M. Willett
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
David E. Parker
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
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Cited
22 citations as recorded by crossref.
- Spatiotemporal changes of climatic parameters extreme quantiles and their role on evaporation in N. Iran (Golestan province) K. Solaimani & S. Bararkhanpour 10.1007/s12517-021-09300-8
- Reanalysis-based mesoscale wind maps for the design of structures and infrastructures with an application to Italy L. Raffaele et al. 10.1016/j.jweia.2024.105844
- Characterizing temporal trends of meteorological extremes in Southern and Central Ontario, Canada L. Shah et al. 10.1016/j.wace.2022.100411
- Temporal variations of the contribution of combustion-derived water vapor to urban humidity during winter in Xi'an, China S. Liu et al. 10.1016/j.scitotenv.2022.154711
- Comparison of Bayesian and frequentist quantile regressions in studying the trend of discharge changes in several hydrometric stations of the Gorganroud basin in Iran K. Ghorbani et al. 10.2166/wcc.2023.305
- On the effect of reference periods on trends in percentile-based extreme temperature indices R. Dunn & C. Morice 10.1088/1748-9326/ac52c8
- Increasing Global Terrestrial Diurnal Temperature Range for 1980–2021 X. Huang et al. 10.1029/2023GL103503
- Temporal Variations of the Contribution of Combustion-Derived Water Vapor to Urban Humidity During Winter in Xi’An, China S. Liu et al. 10.2139/ssrn.3994594
- Seasonal relationship between climatic variables and evaporation based on Bayesian quantile regression method in southern Caspian region K. Solaimani 10.1007/s12517-022-10263-7
- Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling M. Khan et al. 10.1007/s41748-024-00453-6
- Asynchronous changes in terrestrial near-surface wind speed among regions across China from 1973 to 2017 T. Chuan et al. 10.1016/j.atmosres.2024.107220
- Low-wind climatology (1979–2018) over Europe from ERA5 reanalysis C. Gutiérrez et al. 10.1007/s00382-024-07123-3
- Spatiotemporal changes of evaporation in Golestan province based on quantile regressions S. Ahmadi et al. 10.2166/wcc.2023.506
- Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas O. Wani et al. 10.1038/s41598-024-77687-x
- Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset M. Molina et al. 10.1002/joc.7103
- Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3 R. Dunn et al. 10.1029/2019JD032263
- The added value of high‐resolution EURO‐CORDEX simulations to describe daily wind speed over Europe M. Molina et al. 10.1002/joc.7877
- Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” A. Poppick 10.1007/s13253-023-00537-2
- Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China J. Wu & G. Li 10.1080/15324982.2024.2417746
- Reanalysis-based mesoscale wind maps for the design of structures and infrastructures with an application to Italy L. Raffaele et al. 10.2139/ssrn.4822434
- Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts N. Effenberger et al. 10.1088/1748-9326/ad0bd6
- The added value of simulated near-surface wind speed over the Alps from a km-scale multimodel ensemble M. Molina et al. 10.1007/s00382-024-07257-4
22 citations as recorded by crossref.
- Spatiotemporal changes of climatic parameters extreme quantiles and their role on evaporation in N. Iran (Golestan province) K. Solaimani & S. Bararkhanpour 10.1007/s12517-021-09300-8
- Reanalysis-based mesoscale wind maps for the design of structures and infrastructures with an application to Italy L. Raffaele et al. 10.1016/j.jweia.2024.105844
- Characterizing temporal trends of meteorological extremes in Southern and Central Ontario, Canada L. Shah et al. 10.1016/j.wace.2022.100411
- Temporal variations of the contribution of combustion-derived water vapor to urban humidity during winter in Xi'an, China S. Liu et al. 10.1016/j.scitotenv.2022.154711
- Comparison of Bayesian and frequentist quantile regressions in studying the trend of discharge changes in several hydrometric stations of the Gorganroud basin in Iran K. Ghorbani et al. 10.2166/wcc.2023.305
- On the effect of reference periods on trends in percentile-based extreme temperature indices R. Dunn & C. Morice 10.1088/1748-9326/ac52c8
- Increasing Global Terrestrial Diurnal Temperature Range for 1980–2021 X. Huang et al. 10.1029/2023GL103503
- Temporal Variations of the Contribution of Combustion-Derived Water Vapor to Urban Humidity During Winter in Xi’An, China S. Liu et al. 10.2139/ssrn.3994594
- Seasonal relationship between climatic variables and evaporation based on Bayesian quantile regression method in southern Caspian region K. Solaimani 10.1007/s12517-022-10263-7
- Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling M. Khan et al. 10.1007/s41748-024-00453-6
- Asynchronous changes in terrestrial near-surface wind speed among regions across China from 1973 to 2017 T. Chuan et al. 10.1016/j.atmosres.2024.107220
- Low-wind climatology (1979–2018) over Europe from ERA5 reanalysis C. Gutiérrez et al. 10.1007/s00382-024-07123-3
- Spatiotemporal changes of evaporation in Golestan province based on quantile regressions S. Ahmadi et al. 10.2166/wcc.2023.506
- Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas O. Wani et al. 10.1038/s41598-024-77687-x
- Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset M. Molina et al. 10.1002/joc.7103
- Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3 R. Dunn et al. 10.1029/2019JD032263
- The added value of high‐resolution EURO‐CORDEX simulations to describe daily wind speed over Europe M. Molina et al. 10.1002/joc.7877
- Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” A. Poppick 10.1007/s13253-023-00537-2
- Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China J. Wu & G. Li 10.1080/15324982.2024.2417746
- Reanalysis-based mesoscale wind maps for the design of structures and infrastructures with an application to Italy L. Raffaele et al. 10.2139/ssrn.4822434
- Mind the (spectral) gap: how the temporal resolution of wind data affects multi-decadal wind power forecasts N. Effenberger et al. 10.1088/1748-9326/ad0bd6
- The added value of simulated near-surface wind speed over the Alps from a km-scale multimodel ensemble M. Molina et al. 10.1007/s00382-024-07257-4
Latest update: 20 Nov 2024
Short summary
Using a sub-daily dataset of in situ observations, we have performed a study to see how the distributions of temperatures and wind speeds have changed over the last 45 years. Changes in the location or shape of these distributions show how extreme temperatures or wind speeds have changed. Our results show that cool extremes are warming more rapidly than warm ones in high latitudes but that in other parts of the world the opposite is true.
Using a sub-daily dataset of in situ observations, we have performed a study to see how the...
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