Mean and extreme precipitation changes over China under SSP scenarios: results from high-resolution dynamical downscaling for CORDEX East Asia

2021 ◽  
Author(s):  
Liwei Zou ◽  
Tianjun Zhou
Keyword(s):  
High Resolution ◽  
East Asia ◽  
Extreme Precipitation ◽  
Dynamical Downscaling ◽  
Precipitation Changes ◽  
Cordex East Asia

scholarly journals A classification algorithm for selective dynamical downscaling of precipitation extremes

2018 ◽  
Vol 22 (8) ◽  
pp. 4183-4200 ◽  
Author(s):  
Edmund P. Meredith ◽  
Henning W. Rust ◽  
Uwe Ulbrich
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Clustering Algorithm ◽  
Dynamical Downscaling ◽  
Classification Algorithm ◽  
Climate Data ◽  
Catchment Scale ◽  
Computational Expense ◽  
Circulation Patterns ◽  
Climate Simulations

Abstract. High-resolution climate data O(1 km) at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. While dynamical downscaling with convection-permitting models is a valuable approach for producing quality high-resolution O(1 km) data, its added value can often not be realized due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convection-permitting resolution can be selectively performed on high-risk days only, drastically reducing total computational expense compared to continuous simulations; the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological predictors in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10 % of all days, though it includes essentially all extreme days; applying the algorithm to reanalysis-driven regional climate simulations over Europe (12 km resolution) shows similar performance, and the subsequently dynamically downscaled simulations (2 km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12 km resolution historical and future (RCP8.5) climate simulations, downscaled in 2 km resolution time slices, show the algorithm again reducing the number of days to simulate by over 90 % and performing consistently across climate regimes. The downscaling framework we propose represents a computationally inexpensive means of producing high-resolution climate data, focused on extreme precipitation, at the catchment scale, while still retaining the advantages of convection-permitting dynamical downscaling.

scholarly journals A classification algorithm for selective dynamical downscaling of precipitation extremes

2017 ◽  
Author(s):  
Edmund P. Meredith ◽  
Henning W. Rust ◽  
Uwe Ulbrich
Keyword(s):  
High Resolution ◽  
Extreme Precipitation ◽  
Clustering Algorithm ◽  
Dynamical Downscaling ◽  
Classification Algorithm ◽  
Climate Data ◽  
Catchment Scale ◽  
Computational Expense ◽  
Circulation Patterns ◽  
Climate Simulations

Abstract. High-resolution climate data [O(1 km)] at the catchment scale can be of great value to both hydrological modellers and end users, in particular for the study of extreme precipitation. Despite the well-known advantages of dynamical downscaling for producing quality high-resolution data, the added value of dynamically downscaling to O(1 km) resolutions can often not be realised due to the prohibitive computational expense. Here we present a novel and flexible classification algorithm for discriminating between days with an elevated potential for extreme precipitation over a catchment and days without, so that dynamical downscaling to convection-permitting resolution can be selectively performed on high-risk days only, drastically reducing total computational expense compared to continuous simulations; the classification method can be applied to climate model data or reanalyses. Using observed precipitation and the corresponding synoptic-scale circulation patterns from reanalysis, characteristic extremal circulation patterns are identified for the catchment via a clustering algorithm. These extremal patterns serve as references against which days can be classified as potentially extreme, subject to additional tests of relevant meteorological variables in the vicinity of the catchment. Applying the classification algorithm to reanalysis, the set of potential extreme days (PEDs) contains well below 10 % of all days, though includes essentially all extreme days; applying the algorithm to reanalysis-driven regional climate simulations over Europe (12 km resolution) shows similar performance and the subsequently dynamically downscaled simulations (2 km resolution) well reproduce the observed precipitation statistics of the PEDs from the training period. Additional tests on continuous 12- and 2 km resolution historical and future (RCP8.5) climate simulations show the algorithm again reducing the number of days to simulate by over 90 % and performing consistently across climate regimes. The downscaling framework we propose represents a computationally inexpensive means of producing high-resolution climate data, focused on extreme precipitation, at the catchment scale, while still retaining the advantages of the physically-based dynamical downscaling approach.

Added Value of reproduced precipitation by high resolved regional climate model simulation over CORDEX-East Asia

2020 ◽  
Author(s):  
Taehyung Kim ◽  
Dong-Hyun Cha ◽  
Gayoung Kim ◽  
Seok-Woo Shin ◽  
Changyong Park ◽  
...  
Keyword(s):  
East Asia ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Phase 1 ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Added Value ◽  
Phase 2 ◽  
Fine Resolution ◽  
Cordex East Asia

<p>In the framework of the CORDEX-East Asia, evaluation simulations using high-resolution regional climate models (SNURCM and HadGEM3-RA) with ~25km (Phase2) grid scale have been conducted. In this study, we investigate whether the higher-resolution regional climate models (RCMs) can generate added values for summer mean precipitation, large-scale circulation, and extreme precipitation compared to those with lower-resolution (~50km, Phase 1). In addition, the added value index is used to quantitatively analyze the abilities of fine- and coarse-resolution RCMs. Hence, sets of phase 1 and phase 2 simulations of two RCMs are compared to observations in the East Asia region. In SNURCM simulations, positive (negative) added value of summer mean precipitation is reproduced over most ocean (land) region of East Asia in fine-resolution simulation. Extreme precipitation over Korea and Japan is well reproduced in Phase 2 simulations because the simulations of typhoons and East Asia summer monsoon are improved. In HadGEM3-RA simulations, the results of summer mean precipitation over most East Asian regions above 25°N are improved in Phase 2, while worse results are reproduced below 25°N. But, extreme precipitation in fine-resolution simulation is adequately reproduced in most regions of East Asia except China and the Yellow sea. As a result, the results of the simulations are different depending on the characteristics of the individual models, but more positive added values for the intensity and spatial distribution of precipitation over East Asia are generated as the horizontal resolution of RCMs increases.</p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-01211)</p><p> </p>

Changes in spring and Mei-yu extreme precipitation in the Western North Pacific and East Asia in the warmer climate in two high-resolution AGCMs

2020 ◽  
Author(s):  
Chao-An Chen ◽  
Huang-Hsiung Hsu
Keyword(s):  
High Resolution ◽  
Spatial Pattern ◽  
East Asia ◽  
Extreme Events ◽  
North Pacific ◽  
Extreme Precipitation ◽  
General Circulation ◽  
Western North Pacific ◽  
Future Climate Change ◽  
Sst Warming

<p>In this study, we estimate the changes in extreme precipitation indices over the western North Pacific and East Asia region (WNP-EA) during the spring and Mei-yu seasons in the warmer climate. Our analyses are based on two high-resolution atmospheric general circulation model simulations. The high-resolution atmospheric Model (HiRAM) was used in a series of simulations, which were forced by 4 sets of sea surface temperature (SST) changes under Representative Concentration Pathways 8.5 (RCP8.5) scenario. The Database for Policy Decision-Making for Future Climate Change (d4PDF) consists of global warming simulation outputs from MRI-AGCM3.2 with large ensemble members and multiple SST warming scenarios.</p><p>In the spring season, the changes in the spatial pattern of SDII, RX1day, and PR99 demonstrate greater enhancement over the northern flank of the climatological rainy region in both HiRAM and d4PDF, implying a northward extension of spring rain band. Besides, the changes in probability distribution display a shifting tendency that heavier extreme events occur more frequently in the warmer climate. The above changes are larger than the internal variability and uncertainty associated with SST warming patterns, indicating the robustness of the projected enhancement in precipitation intensity in the WNP-EA region. The spatial pattern for changes in CDD and total rainfall occurrence are less consistent between two datasets. In the Mei-yu season, the tendency toward more frequent extreme events in the probability distributions are consistently found in HiRAM and d4PDF. However, the changes in the spatial pattern of all indices are less consistent between HiRAM and d4PDF, implying larger uncertainty in the projection of extreme precipitation in the Mei-yu period in the warmer climate.</p>

scholarly journals Comparison and Evaluation of Statistical Rainfall Disaggregation and High-Resolution Dynamical Downscaling over Complex Terrain

2018 ◽  
Vol 19 (12) ◽  
pp. 1973-1982 ◽  
Author(s):  
B. Poschlod ◽  
Ø. Hodnebrog ◽  
R. R. Wood ◽  
K. Alterskjær ◽  
R. Ludwig ◽  
...  
Keyword(s):  
High Resolution ◽  
Statistical Method ◽  
Extreme Precipitation ◽  
Complex Terrain ◽  
Dynamical Downscaling ◽  
Precipitation Data ◽  
Maximum Rainfall ◽  
Method Of Fragments ◽  
Hourly Data ◽  
Wet Spells

Abstract Representative methods of statistical disaggregation and dynamical downscaling are compared in terms of their ability to disaggregate precipitation data into hourly resolution in an urban area with complex terrain. The nonparametric statistical Method of Fragments (MoF) uses hourly data from rain gauges to split the daily data at the location of interest into hourly fragments. The high-resolution, convection-permitting Weather Research and Forecasting (WRF) regional climate model is driven by reanalysis data. The MoF can reconstruct the variance, dry proportion, wet hours per month, number and length of wet spells per rainy day, timing of the maximum rainfall burst, and intensities of extreme precipitation with errors of less than 10%. However, the MoF cannot capture the spatial coherence and temporal interday connectivity of precipitation events due to the random elements involved in the algorithm. Otherwise, the statistical method is well suited for filling gaps in subdaily historical records. The WRF Model is able to reproduce dry proportion, lag-1 autocorrelation, wet hours per month, number and length of wet spells per rainy day, spatial correlation, and 6- and 12-h intensities of extreme precipitation with errors of 10% or less. The WRF approach tends to underestimate peak rainfall of 1- and 3-h aggregates but can be used where no observations are available or when areal precipitation data are needed.

scholarly journals High-Resolution Downscaled Simulations of Warm-Season Extreme Precipitation Events in the Colorado Front Range under Past and Future Climates*

2013 ◽  
Vol 26 (21) ◽  
pp. 8671-8689 ◽  
Author(s):  
Kelly Mahoney ◽  
Michael Alexander ◽  
James D. Scott ◽  
Joseph Barsugli
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Wrf Model ◽  
Model Errors ◽  
Grid Spacing ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract A high-resolution case-based approach for dynamically downscaling climate model data is presented. Extreme precipitation events are selected from regional climate model (RCM) simulations of past and future time periods. Each event is further downscaled using the Weather Research and Forecasting (WRF) Model to storm scale (1.3-km grid spacing). The high-resolution downscaled simulations are used to investigate changes in extreme precipitation projections from a past to a future climate period, as well as how projected precipitation intensity and distribution differ between the RCM scale (50-km grid spacing) and the local scale (1.3-km grid spacing). Three independent RCM projections are utilized as initial and boundary conditions to the downscaled simulations, and the results reveal considerable spread in projected changes not only among the RCMs but also in the downscaled high-resolution simulations. However, even when the RCM projections show an overall (i.e., spatially averaged) decrease in the intensity of extreme events, localized maxima in the high-resolution simulations of extreme events can remain as strong or even increase. An ingredients-based analysis of prestorm instability, moisture, and forcing for ascent illustrates that while instability and moisture tend to increase in the future simulations at both regional and local scales, local forcing, synoptic dynamics, and terrain-relative winds are quite variable. Nuanced differences in larger-scale and mesoscale dynamics are a key determinant in each event's resultant precipitation. Very high-resolution dynamical downscaling enables a more detailed representation of extreme precipitation events and their relationship to their surrounding environments with fewer parameterization-based uncertainties and provides a framework for diagnosing climate model errors.

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