scholarly journals Ensemble Projection of Extreme Precipitation Over China Based on Three Dynamical Downscaling Simulations

2021 ◽  
Vol 9 ◽  
Author(s):  
Guangtao Dong ◽  
Ye Xie ◽  
Ya Wang ◽  
Dongli Fan ◽  
Zhan Tian
Keyword(s):  
21St Century ◽  
Extreme Precipitation ◽  
North China ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate Models ◽  
Vapor Flux ◽  
The Future ◽  
Late 21St Century

Based on the outputs of the global climate models (GCMs) HadGEM2-ES, NorESM1-M and MPI-ESM-LR from Coupled Model Intercomparison Project Phase 5 (CMIP5) and the downscaling results with the regional climate model (RCM) REMO, the ability of the climate models to reproduce the extreme precipitation in China during the current period (1986–2005) is evaluated. Then, the future extreme precipitation in the mid (2036–2065) and the late 21st century (2066–2095) is projected under the RCP8.5 scenario. The results show that the RCM simulations have great improvements compared with the GCMs, and the ensemble mean of the RCM results (ensR) outperforms each single RCM simulation. The annual precipitation of the RCM simulations is more consistent with the observation than that of the GCMs, with the overestimation of the peak precipitation reduced, and the ensR further reduces the bias. For the extreme precipitation, the RCM simulations significantly decrease the underestimation of intensity in the GCMs. The RCM simulations and the ensR can greatly improve the simulations of Rx5day and CWD compared with the GCMs, decreasing the wet bias in North China and Northwest China. In the future, the consecutive dry days (CDD) will decrease in the northern arid regions, especially in North China and Northeast China. However, the southern regions will experience longer dry period. Both the amount and the intensity of precipitation will increase in various regions of China. The number of wet days will decrease in the south and increase in the north area. The significantly greater Rx5day and R95t indicate more intensive extreme precipitation in the future, and the intensity in the late 21st century will be stronger than that in the middle. Attribution analysis indicates that the extreme precipitation indices especially the R95t have significant positive temporal and spatial correlations with the water vapor flux.

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

2018 ◽  
Vol 32 (1) ◽  
pp. 195-212 ◽  
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.

scholarly journals Spatio-temporal trends of hydrological components: the case of the Tafna basin (northwestern Algeria)

2021 ◽  
Author(s):  
Amina Mami ◽  
Djilali Yebdri ◽  
Sabine Sauvage ◽  
Mélanie Raimonet ◽  
José Miguel
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Assessment Tool ◽  
Global Climate ◽  
Swat Model ◽  
Regional Climate ◽  
Temporal Trends ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
The Future

Abstract Climate change is expected to increase in the future in the Mediterranean region, including Algeria. The Tafna basin, vulnerable to drought, is one of the most important catchments ensuring water self-sufficiency in northwestern Algeria. The objective of this study is to estimate the evolution of hydrological components of the Tafna basin, throughout 2020–2099, comparing to the period 1981–2000. The SWAT model (Soil and Water Assessment Tool), calibrated and validated on the Tafna basin with good Nash at the outlet 0.82, is applied to analyze the spatial and temporal evolution of hydrological components, over the basin throughout 2020–2099. The application is produced using a precipitation and temperature minimum/maximum of an ensemble of climate model outputs obtained from a combination of eight global climate models and two regional climate models of Coordinated Regional Climate Downscaling Experiment project. The results of this study show that the decrease of precipitation in January, on average −25%, ranged between −5% and −44% in the future. This diminution affects all of the water components and fluxes of a watershed, namely, in descending order of impact: the river discharge causing a decrease −36%, the soil water available −31%, the evapotranspiration −30%, and the lateral flow −29%.

Subsampling Impact on the Climate Change Signal over Poland Based on Simulations from Statistical and Dynamical Downscaling

2019 ◽  
Vol 58 (5) ◽  
pp. 1061-1078 ◽  
Author(s):  
Abdelkader Mezghani ◽  
Andreas Dobler ◽  
Rasmus Benestad ◽  
Jan Erik Haugen ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Emission Scenario ◽  
Dynamical Downscaling ◽  
Full Range ◽  
Global Climate Models ◽  
Climate Change Signal ◽  
High Emission ◽  
Selection Of

ABSTRACTMost impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical–statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071–2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071–2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal.

A regional atmospheric warming threshold for irreversible Greenland ice sheet mass loss

2020 ◽  
Author(s):  
Michiel van den Broeke ◽  
Brice Noël ◽  
Leo van Kampenhout ◽  
Willem-Jan van de Berg
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Sea Level Rise ◽  
Sea Level ◽  
Climate Models ◽  
Climate Model ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Global Climate Models ◽  
The Future

<p>The mass balance of the Greenland ice sheet (GrIS, units Gt per year) equals the surface mass balance (SMB) minus solid ice discharge across the grounding line. As the latter is definite positive, an important threshold for irreversible GrIS mass loss occurs when long-term average SMB becomes negative. For this to happen, runoff (mainly meltwater, some rain) must exceed mass accumulation (mainly snowfall minus sublimation). Even for a single year, this threshold has not been passed since at least 1958, the first year with reliable estimates of SMB components, although recent years with warm summers (e.g. 2012 and 2019) came close. Simply extrapolating the recent (1991-present) negative SMB trend into the future suggests that the SMB = 0 threshold could be reached before ~2040, but such predictions are extremely uncertain given the very large interannual SMB variability, the relative brevity of the time series and the uncertainty in future warming. In this study we use a cascade of models, extensively evaluated with in-situ and remotely sensed (GRACE) SMB observations, to better constrain the future regional warming threshold for the 5-year average GrIS SMB to become negative. To this end, a 1950-2100 climate change run with the global model CESM2 (app. 100 km resolution) was dynamically downscaled using the regional climate model RACMO2 (app. 11 km), which in turn was statistically downscaled to 1 km resolution. The result is a threshold regional Greenland warming of close to 4 degrees. We then use a range of CMIP5 and CMIP6 global climate models to translate the regional value into a global warming threshold for various warming scenarios, including its timing this century. We find substantial differences, ranging from stabilization before the threshold is reached in the RCP/SSP2.6 scenarios with a limited but still significant sea-level rise contribution (< 5 cm by 2100) to an imminent crossing of the warming threshold for the RCP/SSP8.5 scenarios with substantial and ever-growing contributions to sea level rise (> 10 cm by 2100). These results stress the need for strong mitigation to avoid irreversible GrIS mass loss. We finish by discussing the caveats and uncertainties of our approach.</p>

scholarly journals The Impacts of Climate Change on Maximum Daily Discharge in the Payab Jamash Watershed, Iran

2019 ◽  
Vol 11 (1) ◽  
pp. 1035-1045
Author(s):  
Farzad Parandin ◽  
Asadollah Khoorani ◽  
Ommolbanin Bazrafshan
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Climate Models ◽  
River Flow ◽  
Flow Simulation ◽  
Global Climate Models ◽  
Historical Period ◽  
Future Period ◽  
The Future ◽  
Daily Discharge

Abstract One of the most crucial consequences of climate change involves the alteration of the hydrologic cycle and river flow regime of watersheds. This study was an endeavor to investigate the contributions of climate change to maximum daily discharge (MDD). To this end, the MDD simulation was carried out through implementing the IHACRES precipitation-runoff model in the Payyab Jamash watershed for the 21st century (2016-2100). Subsequently, the observed precipitation and temperature data of the weather stations (1980-2011) as well as 4 multi-model outputs of Global Climate Models (GCMs) under the maximum and minimum Representative Concentration Pathways (RCPs) (2016-2100) were utilized. In order to downscale the output of GCMs, Bias Correction (BC) statistical method was applied. The projections for the 21st century indicated a reduction in Maximum Daily Precipitation (MDP) in comparison with the historic period in the study area. The average projected MDP for the future period was 9 mm/day and 5 mm/ day under 2.6 and 8.5 RCPs (4.6% and 2.6% decrease compared with the historical period), respectively. Moreover, the temperature increased in Jamash Watershed based on 2.6 and 8.5 RCPs by 1∘C and 2∘C(3.7% and 7.4% increase compared with the historical period), respectively. The findings of flow simulation for the future period indicated a decrease in MDD due to the diminished MDP in the study area. The amount of this decrease under RCP8.5 was not remarkable (0.75 m3/s), whereas its value for RCP2.6 was calculated as 40m3/s (respectively, 0.11% and 5.88% decrease compared with the historical period).

scholarly journals Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques

2017 ◽  
Vol 56 (12) ◽  
pp. 3245-3262 ◽  
Author(s):  
A. Wootten ◽  
A. Terando ◽  
B. J. Reich ◽  
R. P. Boyles ◽  
F. Semazzi
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Model Experiments ◽  
Complete Set ◽  
Regional Adaptation ◽  
Model Ensembles

AbstractIn recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Temperature scaling of convective cells in present and future conditions

2020 ◽  
Author(s):  
Christopher Purr ◽  
Erwan Brisson ◽  
Bodo Ahrens
Keyword(s):  
High Temperatures ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Maximum Intensity ◽  
Convective Precipitation ◽  
Slight Reduction ◽  
The Future ◽  
Temperature Scaling ◽  
Convective Cells

<p>Convection permitting climate models (CPMs) agree on an increase in short-term, extreme precipitation in the future. However, different studies using CPM simulations found regionally varying temperature scaling rates of hourly extreme precipitation either close to or above the Clausius-Clapeyron-rate (CC-rate) of 7%/K. These variations suggest that the dynamics of convective events strongly regulate the local scaling rates. In order to understand how the characteristics of convective events change in the future, we apply a tracking algorithm to precipitation data with 5-min temporal resolution from a regional climate model (COSMO-CLM) simulation. The model is run over central Europe at a grid size of 0.025° for an evaluation period (1981-2015) driven by ERA-Interim reanalysis data, as well as a present-day (1976-2005) and a future (2071-2100) period driven by the EC-Earth global model. We investigate the temperature scaling of convective cell characteristics like total precipitation per cell, mean area, lifetime and maximum intensity, as well as changes in the diurnal cycle of convective cells which might explain the overall scaling rates. The cell characteristics precipitation sum, mean area and maximum intensity show an exponential increase with temperature across most of the temperature range with a drop-off at high temperatures very similar to fixed location scaling curves. While the maximum intensity and area scale at rates close to the CC-rate, the precipitation sum scales at a rate close to twice the CC-rate. In contrast to this, the lifetime of convective cells does not increase with temperature but stays constant with a drop-off at high temperatures. The future simulation shows a shift of the scaling curves towards higher peak values at higher temperatures. Convective activity is projected to decrease during daytime and increase during nighttime. While the mean intensity of convective cells increases throughout the whole day, the number of cells is reduced during the afternoon peak and increased during nighttime. This leads to a slight reduction of convective precipitation during daytime and almost a doubling of convective precipitation during nighttime.</p>

scholarly journals Projected Changes in Precipitation Extremes over Shaanxi Province, China, in the 21st Century

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ling Li ◽  
Ziniu Xiao ◽  
Shuxiang Luo ◽  
Aili Yang
Keyword(s):  
Global Warming ◽  
21St Century ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Global Climate Model ◽  
Precipitation Extremes ◽  
Shaanxi Province ◽  
Extreme Precipitation Events ◽  
The Future ◽  
Precipitation Events

Extreme precipitation events, which have intensified with global warming, will have a pernicious influence on society. It would be desirable to understand how they will evolve in the future as global warming becomes more serious with time. Thus, the primary objective of this study is to provide a comprehensive understanding of the changing characteristics of the precipitation extremes in the 21st century over Shaanxi Province, a climate-sensitive and environmentally fragile area located in the east of northwestern China, based on a consecutive simulation of the 21st century conducted by the regional climate model RegCM4 forced by the global climate model HadGEM2-ES at high resolution under middle emission scenario of the Representative Concentration Pathway 4.5 (RCP4.5). Basic validation of the model performance was carried out, and six extreme precipitation indices (EPIs) were used to assess the intensity and frequency of the extreme precipitation events over Shaanxi Province. The results show that RegCM4 reproduces the observed characteristics of extreme precipitation events over Shaanxi Province well. Overall for the domain, the EPIs excluding consecutive dry days (CDD) have a growing tendency during 1980–2098 although they exhibit spatial variability over Shaanxi Province. Some areas in the arid northern Shaanxi may have more heavy rainfalls by the middle of the 21st century but less wet extreme events by the end of the 21st century. And the humid central and southern regions would suffer more precipitation-related natural hazards in the future.

scholarly journals Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California

2020 ◽  
Vol 6 (29) ◽  
pp. eaba1323 ◽  
Author(s):  
Xingying Huang ◽  
Daniel L. Swain ◽  
Alex D. Hall
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Flood Management ◽  
Global Climate Models ◽  
Precipitation Extremes ◽  
Atmospheric River ◽  
Extreme Precipitation Events

Precipitation extremes will likely intensify under climate change. However, much uncertainty surrounds intensification of high-magnitude events that are often inadequately resolved by global climate models. In this analysis, we develop a framework involving targeted dynamical downscaling of historical and future extreme precipitation events produced by a large ensemble of a global climate model. This framework is applied to extreme “atmospheric river” storms in California. We find a substantial (10 to 40%) increase in total accumulated precipitation, with the largest relative increases in valleys and mountain lee-side areas. We also report even higher and more spatially uniform increases in hourly maximum precipitation intensity, which exceed Clausius-Clapeyron expectations. Up to 85% of this increase arises from thermodynamically driven increases in water vapor, with a smaller contribution by increased zonal wind strength. These findings imply substantial challenges for water and flood management in California, given future increases in intense atmospheric river-induced precipitation extremes.

scholarly journals Global trends in extreme precipitation: climate models vs. observations

2014 ◽  
Vol 11 (10) ◽  
pp. 11369-11393 ◽  
Author(s):  
B. Asadieh ◽  
N. Y. Krakauer
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Average Rate ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Annual Maximum ◽  
Rate Of Increase

Abstract. Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Inter-comparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to spatially and temporally sample them in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901 with largest changes in deep tropics, although annual-maximum daily precipitation has increased faster in the observations than in most of the CMIP5 models. Global average of observational annual-maximum daily precipitation has increased about 5.73 mm over the last 110 years or 8.5% in relative terms and has increased by approximately 10% per K of global warming since 1901, which is larger than the average of climate models with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms.

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