Characteristics of precipitation extremes over the Nordic region: added value of convection-permitting modeling

Abstract. It is well established that using km scale grid resolution for simulations of weather systems in weather and climate models enhances their realism. This study explores heavy and extreme precipitation characteristics over the Nordic region generated by the regional climate model, HARMONIE-Climate (HCLIM). Two model setups of HCLIM are used: ERA-Interim driven HCLIM12 covering Europe at 12 km resolution with parameterized convection and HCLIM3 covering the Nordic region with 3 km resolution and explicit deep convection. The HCLIM simulations are evaluated against several gridded and in situ observation datasets for the warm season from April to September regarding their ability to reproduce sub-daily and daily heavy precipitation statistics across the Nordic region. Both model setups are able to capture the daily heavy precipitation characteristics in the analyzed region. At sub-daily scale, HCLIM3 clearly improves the statistics of occurrence of the most intense heavy precipitation events, as well as the timing and amplitude of the diurnal cycle of these events compared to its forcing HCLIM12. Extreme value analysis shows that HCLIM3 provides added value in capturing sub-daily return levels compared to HCLIM12, which fails to produce the most extreme events. The results indicate clear benefits of the convection-permitting model in simulating heavy and extreme precipitation in the present-day climate, therefore, offering a motivating way forward to investigate the climate change impacts in the region.

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Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

Journal of Climate ◽

10.1175/jcli-d-14-00754.1 ◽

2015 ◽

Vol 28 (17) ◽

pp. 6938-6959 ◽

Cited By ~ 261

Author(s):

Alex J. Cannon ◽

Stephen R. Sobie ◽

Trevor Q. Murdock

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Coupled Model ◽

Precipitation Extremes ◽

Extreme Value Analysis ◽

Quantile Mapping ◽

Value Analysis

Abstract Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

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Improved Estimates of the European Winter Windstorm Climate and the Risk of Reinsurance Loss Using Climate Model Data

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2133.1 ◽

2010 ◽

Vol 49 (10) ◽

pp. 2092-2120 ◽

Cited By ~ 25

Author(s):

Paul M. Della-Marta ◽

Mark A. Liniger ◽

Christof Appenzeller ◽

David N. Bresch ◽

Pamela Köllner-Heck ◽

...

Keyword(s):

Bias Correction ◽

Climate Model ◽

Generalized Pareto Distribution ◽

Sampling Technique ◽

Reanalysis Data ◽

Added Value ◽

Extreme Value Analysis ◽

High Threshold ◽

Ensemble Forecasts ◽

Value Analysis

Abstract Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310–396 yr in length. Extended winter (October–April) windstorm climatologies are created using scalar extreme wind indices considering only data above a high threshold. The method identifies up to 2363 windstorms in s2d data and up to 380 windstorms in the 40-yr ECMWF Re-Analysis (ERA-40). Classical extreme value analysis (EVA) techniques are used to determine the windstorm climatologies. Differences between the ERA-40 and s2d windstorm climatologies require the application of calibration techniques to result in meaningful comparisons. Using a combined dynamical–statistical sampling technique, the largest influence on ERA-40 return period (RP) uncertainties is the sampling variability associated with only 45 seasons of storms. However, both maximum likelihood (ML) and L-moments (LM) methods of fitting a generalized Pareto distribution result in biased parameters and biased RP at sample sizes typically obtained from 45 seasons of reanalysis data. The authors correct the bias in the ML and LM methods and find that the ML-based ERA-40 climatology overestimates the RP of windstorms with RPs between 10 and 300 yr and underestimates the RP of windstorms with RPs greater than 300 yr. A 50-yr event in ERA-40 is approximately a 40-yr event after bias correction. Biases in the LM method result in higher RPs after bias correction although they are small when compared with those of the ML method. The climatologies are linked to the Swiss Reinsurance Company (Swiss Re) European windstorm loss model. New estimates of the risk of loss are compared with those from historical and stochastically generated windstorm fields used by Swiss Re. The resulting loss-frequency relationship matches well with the two independently modeled estimates and clearly demonstrates the added value by using alternative data and methods, as proposed in this study, to estimate the RP of high RP losses.

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Observed and Projected Scaling of Daily Extreme Precipitation with Dew Point Temperature at Annual and Seasonal Scales across the Northeast United Sates

Journal of Hydrometeorology ◽

10.1175/jhm-d-21-0183.1 ◽

2022 ◽

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Warm Season ◽

Dew Point ◽

Extreme Value Analysis ◽

Climate Projections ◽

Pool Data ◽

Value Analysis ◽

Point Temperature ◽

Dew Point Temperature

Abstract This study investigates how extreme precipitation scales with dew point temperature across the Northeast U.S., both in the observational record (1948-2020) and in a set of downscaled climate projections in the state of Massachusetts (2006-2099). Spatiotemporal relationships between dew point temperature and extreme precipitation are assessed, and extreme precipitation – temperature scaling rates are evaluated on annual and seasonal scales using non-stationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% per °C, but this varies by season, with most non-zero scaling rates in summer and fall and the largest rates (∼7.3% per °C) in the summer. Dew point temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between -2.5 and 6.2% per °C at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dew point temperature over the 21st century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% per °C). Overall, the observations suggest that extreme daily precipitation in the Northeast U.S. only thermodynamic scales with dew point temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming.

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Present Climate Evaluation and Added Value Analysis of Dynamically Downscaled Simulations of CORDEX—East Asia

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-18-0008.1 ◽

2018 ◽

Vol 57 (10) ◽

pp. 2317-2341 ◽

Cited By ~ 7

Author(s):

Delei Li ◽

Baoshu Yin ◽

Jianlong Feng ◽

Alessandro Dosio ◽

Beate Geyer ◽

...

Keyword(s):

East Asia ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Global Climate Models ◽

Added Value ◽

Small Scale ◽

Value Analysis ◽

Considerable Uncertainty

AbstractIn this study, we investigate the skills of the regional climate model Consortium for Small-Scale Modeling in Climate Mode (CCLM) in reproducing historical climatic features and their added value to the driving global climate models (GCMs) of the Coordinated Regional Climate Downscaling Experiment—East Asia (CORDEX-EA) domain. An ensemble of climate simulations, with a resolution of 0.44°, was conducted by downscaling four GCMs: CNRM-CM5, EC-EARTH, HadGEM2, and MPI-ESM-LR. The CCLM outputs were compared with different observations and reanalysis datasets. Results showed strong seasonal variability of CCLM’s ability in reproducing climatological means, variability, and extremes. The bias of the simulated summer temperatures is generally smaller than that of the winter temperatures; in addition, areas where CCLM adds value to the driving GCMs in simulating temperature are larger in the winter than in the summer. CCLM outperforms GCMs in terms of generating climatological precipitation means and daily precipitation distributions for most regions in the winter, but this is not always the case for the summer. It was found that CCLM biases are partly inherited from GCMs and are significantly shaped by structural biases of CCLM. Furthermore, downscaled simulations show added value in capturing features of consecutive wet days for the tropics and of consecutive dry days for areas to the north of 30°N. We found considerable uncertainty from reanalysis and observation datasets in temperatures and precipitation climatological means for some regions that rival bias values of GCMs and CCLM simulations. We recommend carefully selecting reference datasets when evaluating modeled climate means.

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Global Radiative Flux and Cloudiness Variability for the Period 1959–2010 in Belgium: A Comparison between Reanalyses and the Regional Climate Model MAR

Atmosphere ◽

10.3390/atmos9070262 ◽

2018 ◽

Vol 9 (7) ◽

pp. 262 ◽

Cited By ~ 4

Author(s):

Coraline Wyard ◽

Sébastien Doutreloup ◽

Alexandre Belleflamme ◽

Martin Wild ◽

Xavier Fettweis

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Radiative Flux ◽

Added Value ◽

Historical Trends ◽

The Mean

The use of regional climate models (RCMs) can partly reduce the biases in global radiative flux (Eg↓) that are found in reanalysis products and global models, as they allow for a finer spatial resolution and a finer parametrisation of surface and atmospheric processes. In this study, we assess the ability of the MAR («Modèle Atmosphérique Régional») RCM to reproduce observed changes in Eg↓, and we investigate the added value of MAR with respect to reanalyses. Simulations were performed at a horizontal resolution of 5 km for the period 1959–2010 by forcing MAR with different reanalysis products: ERA40/ERA-interim, NCEP/NCAR-v1, ERA-20C, and 20CRV2C. Measurements of Eg↓ from the Global Energy Balance Archive (GEBA) and from the Royal Meteorological Institute of Belgium (RMIB), as well as cloud cover observations from Belgocontrol and RMIB, were used for the evaluation of the MAR model and the forcing reanalyses. Results show that MAR enables largely reducing the mean biases that are present in the reanalyses. The trend analysis shows that only MAR forced by ERA40/ERA-interim shows historical trends, which is probably because the ERA40/ERA-interim has a better horizontal resolution and assimilates more observations than the other reanalyses that are used in this study. The results suggest that the solar brightening observed since the 1980s in Belgium has mainly been due to decreasing cloud cover.

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At-site and regional frequency analysis of extreme precipitation from radar-based estimates

10.5194/hess-2017-150 ◽

2017 ◽

Author(s):

Edouard Goudenhoofdt ◽

Laurent Delobbe ◽

Patrick Willems

Keyword(s):

Confidence Interval ◽

Frequency Analysis ◽

Extreme Precipitation ◽

Rain Gauge ◽

Regional Frequency Analysis ◽

Extreme Value Analysis ◽

Value Analysis ◽

Rain Gauges ◽

Rainfall Extremes ◽

Regional Frequency

Abstract. In Belgium, only rain gauge time-series have been used so far to study extreme precipitation at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, independent sliding 1 h and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The extremes are fitted to the exponential distribution using regression in QQ-plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift of convective cells and strong radar signal attenuation. Differences between radar and gauge values are caused by spatial and temporal sampling, gauge rainfall underestimations and radar errors due to the relation between reflectivity and rain rate. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis is performed on radar data within 20 km of the locations of 4 rain gauges with records from 1965 to 2008. Assuming that the extremes are correlated within the region, the fit to the two closest rain gauge data is within the confidence interval of the radar fit, which is small due to the sample size. In Brussels, the extremes on the period 1965–2008 from a rain gauge are significantly lower than the extremes from an automatic gauge and the radar on the period 2005–2016. For 1 h duration, the location parameter varies slightly with topography and the scale parameter exhibits some variations from region to region. The radar-based extreme value analysis can be extended to other durations.

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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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Evaluation of extreme precipitation over the Nordic region using a convection-permitting regional climate model

10.5194/egusphere-egu2020-10506 ◽

2020 ◽

Author(s):

Erika Toivonen ◽

Danijel Belušić ◽

Emma Dybro Thomassen ◽

Peter Berg ◽

Ole Bøssing Christensen ◽

...

Keyword(s):

High Resolution ◽

Regional Climate Model ◽

Extreme Precipitation ◽

Climate Model ◽

Daily Precipitation ◽

Regional Climate ◽

Precipitation Extremes ◽

Nordic Region ◽

Mesh Resolution ◽

Grid Mesh

Extreme precipitation events have a major impact upon our society. Although many studies have indicated that it is likely that the frequency of such events will increase in a warmer climate, little has been done to assess changes in extreme precipitation at a sub-daily scale. Recently, there is more and more evidence that high-resolution convection-permitting models (CPMs) (grid-mesh typically < 4 km) can represent especially short-duration precipitation extremes more accurately when compared with coarser-resolution regional climate models (RCMs).This study investigates sub-daily and daily precipitation characteristics based on hourly output data from the HARMONIE-Climate model at 3-km and 12-km grid-mesh resolution over the Nordic region between 1998 and 2018. The RCM modelling chain uses the ERA-Interim reanalysis to drive a 12-km grid-mesh simulation which is further downscaled to 3-km grid-mesh resolution using a non-hydrostatic model set-up.The statistical properties of the modeled extreme precipitation are compared to several sub-daily and daily observational products, including gridded and in-situ gauge data, from April to September. We investigate the skill of the model to represent different aspects of the frequency and intensity of extreme precipitation as well as intensity&#8211;duration&#8211;frequency (IDF) curves that are commonly used to investigate short duration extremes from an urban planning perspective. The high grid resolution combined with the 20-year-long simulation period allows for a robust assessment at a climatological time scale and enables us to examine the added value of high-resolution CPM in reproducing precipitation extremes over the Nordic region. Based on the tentative results, the high-resolution CPM can realistically capture the characteristics of precipitation extremes, for instance, in terms of improved diurnal cycle and maximum intensities of sub-daily precipitation.

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

10.5194/egusphere-egu2020-5989 ◽

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

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.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.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&#8217;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.

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Future extreme precipitation frequency in the eastern Mediterranean: a new approach exploiting climate model projections

10.5194/egusphere-egu2020-592 ◽

2020 ◽

Author(s):

Francesco Marra ◽

Moshe Armon ◽

Davide Zoccatelli ◽

Osama Gazal ◽

Chaim Garfinkel ◽

...

Keyword(s):

Extreme Precipitation ◽

Climate Models ◽

Climate Model ◽

Eastern Mediterranean ◽

Regional Climate ◽

Coastal Region ◽

Climatic Conditions ◽

Synoptic Scale ◽

Precipitation Frequency ◽

Extreme Quantiles

Understanding extreme precipitation under changing climatic conditions is crucial to manage weather- and flood-related hazards. Global and regional climate models are able to provide coarse scale information on future conditions under different emission scenarios, but large uncertainties affect the projected precipitation amounts, extremes in particular, so that frequency analyses cannot be quantitatively trusted. This study uses, for the first time, the Simplified Metastatistical Extreme Value (SMEV) approach to directly exploit synoptic scale information, better represented by climate models, for obtaining projections of future extreme precipitation frequency.We use historical rainfall data from >400 stations in Israel and Jordan to (a) provide a climatology of extreme daily precipitation (e.g., the 100-year return period amounts) in the steep climatic gradients of the region and (b) improve understanding of the SMEV description under changing climate. We demonstrate that, using SMEV, it is possible to (c) present the sensitivity of extreme quantiles to occurrence and intensity of Mediterranean lows and other synoptic systems, and (d) project future extreme quantiles starting from synoptic scale information generated by earlier climate-model-based studies. Under our working hypotheses, we project a general decrease of extreme precipitation quantiles for the RCP8.5 scenario; an increase is detected in the coastal region and the Negev arid lands. We discuss the apparent contrast of these results with previous findings.

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