Alaska Daily Extreme Precipitation Processes in a Subset of CMIP5 Global Climate Models

Abstract Global climate change is expected to have major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various Global Climate Models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, Generalized Extreme Value (GEV) distribution and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian Model Averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

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Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

Journal of Climate ◽

10.1175/jcli-d-18-0104.1 ◽

2018 ◽

Vol 32 (1) ◽

pp. 195-212 ◽

Cited By ~ 9

Author(s):

Sicheng He ◽

Jing Yang ◽

Qing Bao ◽

Lei Wang ◽

Bin Wang

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Yangtze River Basin ◽

Global Climate Model ◽

Global Climate Models ◽

Global Ocean ◽

East China ◽

Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

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Mean and extreme precipitation over European river basins better simulated in a 25 km AGCM

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3933-2018 ◽

2018 ◽

Vol 22 (7) ◽

pp. 3933-3950 ◽

Cited By ~ 7

Author(s):

Reinhard Schiemann ◽

Pier Luigi Vidale ◽

Len C. Shaffrey ◽

Stephanie J. Johnson ◽

Malcolm J. Roberts ◽

...

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

River Basins ◽

Global Climate Models ◽

Sensitivity Experiment ◽

Scale Parameters ◽

The North ◽

Resolution Model ◽

High Resolution Model

Abstract. Limited spatial resolution is one of the factors that may hamper applications of global climate models (GCMs), in particular over Europe with its complex coastline and orography. In this study, the representation of European mean and extreme precipitation is evaluated in simulations with an atmospheric GCM (AGCM) at different resolutions between about 135 and 25 km grid spacing in the mid-latitudes. The continent-wide root-mean-square error in mean precipitation in the 25 km model is about 25 % smaller than in the 135 km model in winter. Clear improvements are also seen in autumn and spring, whereas the model's sensitivity to resolution is very small in summer. Extreme precipitation is evaluated by estimating generalised extreme value distributions (GEVs) of daily precipitation aggregated over river basins whose surface area is greater than 50 000 km2. GEV location and scale parameters are measures of the typical magnitude and of the interannual variability of extremes, respectively. Median model biases in both these parameters are around 10 % in summer and around 20 % in the other seasons. For some river basins, however, these biases can be much larger and take values between 50 % and 100 %. Extreme precipitation is better simulated in the 25 km model, especially during autumn when the median GEV parameter biases are more than halved, and in the North European Plains, from the Loire in the west to the Vistula in the east. A sensitivity experiment is conducted showing that these resolution sensitivities in both mean and extreme precipitation are in many areas primarily due to the increase in resolution of the model orography. The findings of this study illustrate the improved capability of a global high-resolution model in simulating European mean and extreme precipitation.

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Evaluation of global climate models in simulating extreme precipitation in China

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v65i0.19799 ◽

2013 ◽

Vol 65 (1) ◽

pp. 19799 ◽

Cited By ~ 30

Author(s):

Tinghai Ou ◽

Deliang Chen ◽

Hans W. Linderholm ◽

Jee-Hoon Jeong

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Global Climate Models

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Extreme precipitation events and the applicability of global climate models to the study of floods and droughts

Mathematics and Computers in Simulation ◽

10.1016/s0378-4754(97)00008-6 ◽

1997 ◽

Vol 43 (3-6) ◽

pp. 261-268 ◽

Cited By ~ 11

Author(s):

Zavareh Kothavala

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Extreme Precipitation Events ◽

Floods And Droughts ◽

Precipitation Events

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

10.21203/rs.3.rs-227967/v1 ◽

2021 ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Model Uncertainty ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Human Influence ◽

Anthropogenic Forcing ◽

Global Datasets

Abstract Global climate models produce large increases in extreme precipitation when subject to anthropogenic forcing, but detecting this human influence in observations is challenging. Large internal variability makes the signal difficult to characterize. Models produce diverse precipitation responses to anthropogenic forcing, mirroring a variety of parameterization choices for subgrid-scale processes. And observations are inhomogeneously sampled in space and time, leading to multiple global datasets, each produced with a different homogenization technique. Thus, previous attempts to detect human influence on extreme precipitation have not incorporated internal variability or model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods, we find a physically interpretable anthropogenic signal that is detectable in all global datasets. Detection occurs even when internal variability and model uncertainty are taken into account. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in extreme precipitation.

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Effects of the horizontal resolution of climate models on the simulation of extreme hourly precipitation in the Tibetan Plateau and surrounding areas

10.5194/egusphere-egu21-8470 ◽

2021 ◽

Author(s):

Qing Bao ◽

Lei Wang ◽

Yimin Liu ◽

Guoxiong Wu ◽

Jinxiao Li ◽

...

Keyword(s):

Tibetan Plateau ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Horizontal Resolution ◽

Global Climate Models ◽

Boreal Summer ◽

The Tibetan Plateau ◽

Standard Resolution ◽

Surrounding Areas

Extreme precipitation events, represented by the extreme hourly precipitation (EHP), often occur in the Tibetan Plateau and surrounding areas (TPS) as a result of the complex topography and unique geographical location of this region and can lead to large losses of human life. Previous studies have shown that the performance of extreme precipitation simulations can be improved by increasing the resolution of the model, although the mechanisms are not yet not clear. In this study, we firstly compared the most recent high-quality satellite precipitation product &#160;with station data from Nepal, which is located on the southern edge of the Tibetan Plateau. The results showed that the GPM dataset can reproduce extreme precipitation well and we therefore used these data as a benchmark for climate models of the TPS. We then evaluated the fidelity of global climate models in the representation of the boreal summer EHP in the TPS using datasets from the CMIP6 High-Resolution Model Intercomparison Project (HighResMIP). We used four global climate models with standard (about 100 km) and enhanced (up to 25 km) resolution configurations to simulate the EHP. The models with a standard resolution largely underestimated the intensity of EHP, especially over the southern edge of the Tibetan Plateau. The EHP can reach up to 50 mm h&#8722;1in the TPS, whereas the maximum simulated EHP was <35 mm h&#8722;1 for all the standard resolution models. The mean intensity of EHP is about 5.06 mm h&#8722;1 in the GPM satellite products, whereas it was <3 mm h&#8722;1 in standard resolution models. The skill of the simulation of EHP is significantly improved at increased horizontal resolutions. The high-resolution models with a horizontal resolution of 25 km can reproduce the geographical distribution of the intensity of EHP in the TPS. The intensity&#8211;frequency distribution of EHP also resembles that from GPM products, showing the same features up to 50 mm h&#8722;1, although it slightly overestimates heavy precipitation events. Finally, we propose possible physical linkages between the simulation of EHP and the impacts of the resolution of the model and physical processes. Phenomena over the Indian Ocean at different timescales and the diurnal variation of precipitation in the TPS are used to propose possible physical linkages as they may play an important part in the simulation of EHP in the TPS. Further analysis shows that an increase in the horizontal resolution helps to accurately reproduce the features of water vapor transport on days with extreme precipitation, the northward-propagating intraseasonal oscillation over the Indian and western Pacific Ocean monsoon regions in the boreal summer, the intensity and number of tropical cyclones over the southern Asian monsoon regions, and the peak time and amplitude of the diurnal cycle of precipitation. This increase in accuracy contributes to the improvements in the simulation of EHP in the TPS. This study suggests improvements to increase the horizontal resolution of the GCMs and lay a solid foundation for the accurate reproduction and prediction of EHP in the TPS.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

Nature Communications ◽

10.1038/s41467-021-24262-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Sampled Data ◽

Anthropogenic Forcing ◽

Precipitation Response ◽

Observational Record

Abstract The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

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Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

10.21203/rs.3.rs-227967/v2 ◽

2021 ◽

Author(s):

Gavin D. Madakumbura ◽

Chad W. Thackeray ◽

Jesse Norris ◽

Naomi Goldenson ◽

Alex Hall

Keyword(s):

Machine Learning ◽

Extreme Precipitation ◽

Climate Models ◽

Global Climate ◽

Internal Variability ◽

Global Climate Models ◽

Sampled Data ◽

Anthropogenic Forcing ◽

Precipitation Response ◽

Observational Record

Abstract The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

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Downscaling of Future Precipitation in China’s Beijing-Tianjin-Hebei Region Using a Weather Generator

Atmosphere ◽

10.3390/atmos13010022 ◽

2021 ◽

Vol 13 (1) ◽

pp. 22

Author(s):

Yaoming Liao ◽

Deliang Chen ◽

Zhenyu Han ◽

Dapeng Huang

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Daily Precipitation ◽

Global Climate ◽

Weather Generator ◽

Global Climate Models ◽

Annual Number ◽

Drought Risk ◽

The Future ◽

Rcp 8.5

To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of five global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identified for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reflected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥25 mm and ≥40 mm, the greatest one-day and five-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥40 mm was projected to increase most significantly out of the eight indices, indicating the need to consider increased flooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease.

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