Evaluation and Projection of Extreme Precipitation over Northern China in CMIP5 Models

2020 ◽  
Author(s):  
Xi Lu
Keyword(s):  
Extreme Precipitation ◽  
Northeast China ◽  
Climate Models ◽  
Extreme Rainfall ◽  
Northern China ◽  
Northwest China ◽  
Cmip5 Models ◽  
Historical Period ◽  
Extreme Precipitation Indices ◽  
Group 1

<p>This study evaluates 32 climate models from CMIP5 compared with a daily gridded<br>observation dataset of extreme precipitation indices including total extreme precipitation (R95p),<br>maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of<br>precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show<br>the majority models have good performance on spatial distribution but overestimate the amplitude of<br>precipitation over Northern China. Most models can also capture interannual variation of R95p and<br>RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best<br>multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p,<br>RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising<br>region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty<br>analysis shows the increase of extreme rainfall over Northwest China has a low confidence level.<br>The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer<br>extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian<br>Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which<br>result in more extreme precipitation.</p>

scholarly journals Evaluation and Projection of Extreme Precipitation over Northern China in CMIP5 Models

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 691 ◽  
Author(s):  
Xiaoqiang Rao ◽  
Xi Lu ◽  
Wenjie Dong
Keyword(s):  
Extreme Precipitation ◽  
Northeast China ◽  
Climate Models ◽  
Extreme Rainfall ◽  
Northern China ◽  
Northwest China ◽  
Cmip5 Models ◽  
Historical Period ◽  
Extreme Precipitation Indices ◽  
Group 1

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation.

scholarly journals Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

2015 ◽  
Vol 28 (21) ◽  
pp. 8603-8619 ◽  
Author(s):  
Zhihong Jiang ◽  
Wei Li ◽  
Jianjun Xu ◽  
Laurent Li
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Eastern China ◽  
Northern China ◽  
Western China ◽  
Cmip5 Models ◽  
The West ◽  
Southeastern China ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices

Abstract Compared to precipitation extremes calculated from a high-resolution daily observational dataset in China during 1960–2005, simulations in 31 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have been quantitatively assessed using skill-score metrics. Four extreme precipitation indices, including the total precipitation (PRCPTOT), maximum consecutive dry days (CDD), precipitation intensity (SDII), and fraction of total rainfall from heavy events (R95T) are analyzed. Results show that CMIP5 models still have wet biases in western and northern China. Especially in western China, the models’ median relative error is about 120% for PRCPTOT; the 25th and 75th percentile errors are of 70% and 220%, respectively. However, there are dry biases in southeastern China, where the underestimation of PRCPTOT reach 200 mm. The performance of CMIP5 models is quite different between western and eastern China. The simulations are more reliable in the east than in the west in terms of spatial pattern and interannual variability. In the east, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble constructed from a selection of the most skillful models shows improved behavior compared to the all-model ensemble. The wet bias in western and northern China and dry bias over southeastern China are all decreased. The median of errors for PRCPTOT has a decrease of 69% and 17% in the west and east, respectively. The good reproduction of the southwesterlies along the east coast of the Arabian Peninsula is revealed to be the main factor explaining the improvement of precipitation patterns and extreme events.

Investigating the seasonal response of precipitation extremes to global warming using observations and large-ensembles of coupled climate models

2020 ◽  
Author(s):  
Andrew Williams ◽  
Paul O'Gorman
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Precipitation Extremes ◽  
Cmip5 Models ◽  
Historical Period ◽  
Societal Relevance ◽  
Coupled Climate Models

<p>Changes in extreme precipitation are amongst the most impactful consequences of global warming, with potential effects ranging from increased flood risk and landslides to crop failures and impacts on ecosystems. Thus, understanding historical and future changes in extreme precipitation is not only important from a scientific perspective, but also has direct societal relevance.</p><p>However, while most current research has focused on annual precipitation extremes and their response to warming, it has recently been noted that climate model projections show a distinct seasonality to future changes in extreme precipitation. In particular, CMIP5 models suggest that over Northern Hemisphere (NH) land the summer response is weaker than the winter response in terms of percentage changes.</p><p>Here we investigate changes in seasonal precipitation extremes using observations and simulations with coupled climate models. First, we analyse observed trends from the Hadley Centre’s global climate extremes dataset (HadEX2) to investigate to what extent there is already a difference between summer and winter trends over NH land. Second, we use 40 ensemble members from the CESM Large Ensemble to characterize the role played by internal variability in trends over the historical period. Lastly, we use CMIP5 simulations to explore the possibility of a link between the seasonality of changes in precipitation extremes and decreases in surface relative humidity over land.</p>

scholarly journals Evaluation of uncertainties in mean and extreme precipitation under climate change for northwestern Mediterranean watersheds from high-resolution Med and Euro-CORDEX ensembles

2018 ◽  
Vol 22 (1) ◽  
pp. 673-687 ◽  
Author(s):  
Antoine Colmet-Daage ◽  
Emilia Sanchez-Gomez ◽  
Sophie Ricci ◽  
Cécile Llovel ◽  
Valérie Borrell Estupina ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Historical Period ◽  
Mountainous Regions ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract. The climate change impact on mean and extreme precipitation events in the northern Mediterranean region is assessed using high-resolution EuroCORDEX and MedCORDEX simulations. The focus is made on three regions, Lez and Aude located in France, and Muga located in northeastern Spain, and eight pairs of global and regional climate models are analyzed with respect to the SAFRAN product. First the model skills are evaluated in terms of bias for the precipitation annual cycle over historical period. Then future changes in extreme precipitation, under two emission scenarios, are estimated through the computation of past/future change coefficients of quantile-ranked model precipitation outputs. Over the 1981–2010 period, the cumulative precipitation is overestimated for most models over the mountainous regions and underestimated over the coastal regions in autumn and higher-order quantile. The ensemble mean and the spread for future period remain unchanged under RCP4.5 scenario and decrease under RCP8.5 scenario. Extreme precipitation events are intensified over the three catchments with a smaller ensemble spread under RCP8.5 revealing more evident changes, especially in the later part of the 21st century.

scholarly journals Reduced global warming from CMIP6 projections when weighting models by performance and independence

2020 ◽  
Vol 11 (4) ◽  
pp. 995-1012
Author(s):  
Lukas Brunner ◽  
Angeline G. Pendergrass ◽  
Flavio Lehner ◽  
Anna L. Merrifield ◽  
Ruth Lorenz ◽  
...  
Keyword(s):  
Climate Change ◽  
Retrospective Analysis ◽  
Climate Models ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Historical Period ◽  
Climate Change Scenarios ◽  
Uncertainty Range ◽  
Future Warming ◽  
The Mean

Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 % decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 % in the likely range compared with the unweighted case.

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 Extreme rainfall drives early onset cyanobacterial bloom

2019 ◽  
Author(s):  
Megan L. Larsen ◽  
Helen M. Baulch ◽  
Sherry L. Schiff ◽  
Dana F. Simon ◽  
Sébastien Sauvé ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Algal Blooms ◽  
Nutrient Loading ◽  
Climate Models ◽  
Cyanobacterial Bloom ◽  
Extreme Rainfall ◽  
Precipitation Event ◽  
List Type ◽  
Precipitation Frequency ◽  
Extreme Precipitation Events

AbstractThe increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production.We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom, successional progression of the phytoplankton community, toxins, and geochemistry.An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks post flood and ~5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks.Synthesis and applications: These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure. In contrast to the effects of early season extreme rainfall, a mid-summer runoff event appeared to help mitigate the bloom in some areas of the reservoir by increasing flushing.

scholarly journals Global trends in extreme precipitation: climate models versus observations

2015 ◽  
Vol 19 (2) ◽  
pp. 877-891 ◽  
Author(s):  
B. Asadieh ◽  
N. Y. Krakauer
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Daily Precipitation ◽  
Average Rate ◽  
Global Scale ◽  
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 Intercomparison 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 sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5% in relative terms. This corresponds to an increase of 10% K−1 in 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 both 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.

Precipitation extremes over southern South America and their synoptic environment in a set of CORDEX regional climate models

2021 ◽  
Author(s):  
Matias Ezequiel Olmo ◽  
Maria Laura Bettolli
Keyword(s):  
South America ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Extreme Rainfall ◽  
Regional Climate Models ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Circulation Types ◽  
Observational Uncertainty

<p>Southern South America (SSA) is a wide populated region exposed to extreme rainfall events, which are recognised as some of the major threats in a warming climate. These events produce large impacts on socio-economic activities, energy demand and health systems. Hence, studying this phenomena requires high-quality and high-resolution observational data and model simulations. In this work, the main features of daily extreme precipitation and circulation types over SSA were evaluated using a 4-model set of CORDEX regional climate models (RCMs) driven by ERA-Interim during 1980-2010: RCA4 and WRF from CORDEX Phase 1 and RegCM4v7 and REMO2015 from the brand-new CORDEX-CORE simulations. Observational uncertainty was assessed by comparing model outputs with multiple observational datasets (rain gauges, CHIRPS, CPC and MSWEP). </p><p>The inter-comparison of extreme events, characterized in terms of their intensity, frequency and spatial coverage, varied across SSA exhibiting large differences among observational datasets and RCMs, pointing out the current observational uncertainty when evaluating precipitation extremes, particularly at a daily scale. The spread between observational datasets was smaller than for the RCMs. Most of the RCMs successfully captured the spatial pattern of extreme rainfall across SSA, reproducing the maximum intensities in southeastern South America (SESA) and central and southern Chile during the austral warm (October to March) and cold (April to September) seasons, respectively. However, they often presented overestimations over central and southern Chile, and more variable results in SESA. RegCM4 and WRF seemed to well represent the maximum precipitation amounts over SESA, while REMO showed strong overestimations and RCA4 had more difficulties in representing the spatial distribution of heavy rainfall intensities. Focusing over SESA, differences were detected in the timing and location of extremes (including the areal coverage) among both observational datasets and RCMs, which poses a particular challenge when performing impact studies in the region. Thus, stressing that the use of multiple datasets is of key importance when carrying out regional climate studies and model evaluations, particularly for extremes. </p><p>The synoptic environment was described by a classification of circulation types (CTs) using Self-Organizing Maps (SOM) considering geopotential height anomalies at 500 hPa (Z500). Specific CTs were identified as they significantly enhanced the occurrence of extreme rainfall events in sectorized areas of SESA. In particular, a dipolar structure of Z500 anomalies that produced a marked trough at the mid-level atmosphere, usually located east of the Andes, significantly favoured the occurrence of extreme precipitation events in the warm season. The RCMs were able to adequately reproduce the SOM frequencies, although simplifying the predominant CTs into a reduced number of configurations. They appropriately reproduced the observed extreme precipitation frequencies conditioned by the CTs and their atmospheric configurations, but exhibiting some limitations in the location and intensity of the resulting precipitation systems.</p><p>In this sense, continuous evaluations of observational datasets and model simulations become necessary for a better understanding of the physical mechanisms behind extreme precipitation over the region, as well as for its past and future changes in a climate change scenario.</p>

scholarly journals Spatiotemporal characteristics of extreme precipitation at multiple timescales over Northeast China during 1961–2014

2017 ◽  
Vol 8 (3) ◽  
pp. 535-556 ◽  
Author(s):  
X. J. Yang ◽  
Z. X. Xu ◽  
W. F. Liu ◽  
Lin Liu
Keyword(s):  
Extreme Precipitation ◽  
Northeast China ◽  
Agricultural Drought ◽  
Crop Failure ◽  
Growing Period ◽  
Daily Precipitation Data ◽  
Extreme Precipitation Indices ◽  
Precipitation Anomalies ◽  
Precipitation Indices ◽  
Increasing Trends

Few studies of extreme precipitation have been conducted in Northeast China, particularly at multi-timescales. We aim to enhance the understanding of changes and variability in extreme precipitation over the past 54 years (1961–2014) in this region. We have investigated the potential relationship among extreme precipitation, climate and agricultural drought focusing on several timescales in this region. Thirteen extreme precipitation indices at seasonal, annual, and growing-period scales were estimated on the basis of daily precipitation data from 70 meteorological stations. The results indicate that all extreme precipitation indices that reflect the features of extreme wet events showed increasing trends in spring, and the trends of these indices were almost negative for the other timescales. Spatially, the frequency, duration and intensity of extreme wet events decreased gradually from south to north. The range of influence and the duration of extreme dry events increased continuously in Northeast China. In Northeast China, extreme precipitation was more easily influenced by the polar climate than the monsoon. Furthermore, correlation between the extreme precipitation indices and comprehensive crop failure ratios of agricultural drought disasters (C index) confirmed that agricultural drought was heavily influenced by precipitation anomalies in this area.

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