scholarly journals Return levels of sub-daily extreme precipitation over Europe

2020 ◽  
Author(s):  
Benjamin Poschlod ◽  
Ralf Ludwig ◽  
Jana Sillmann
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Internal Variability ◽  
Return Level ◽  
Data Set ◽  
Model Simulations ◽  
Return Levels ◽  
Climate Model Simulations ◽  
Precipitation Events

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolution, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability of the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24-hourly duration with a spatial resolution of 0.11° (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with centred Pearson product-moment coefficients of linear correlation > 0.7 for the area covered with observations. Also, the rainfall intensity of the observational data set is in the range of the climate model generated intensities in 52 % (77 %, 79 %, 84 %, 78 %) of the area for hourly (3-hourly, 6-hourly, 12-hourly, 24-hourly) durations. This results in biases between −19.3 % (hourly) to +8.0 % (24-hourly) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3-hourly to 24-hourly durations in large parts of the study area. Though, for hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

scholarly journals Ten-year return levels of sub-daily extreme precipitation over Europe

2021 ◽  
Vol 13 (3) ◽  
pp. 983-1003
Author(s):  
Benjamin Poschlod ◽  
Ralf Ludwig ◽  
Jana Sillmann
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Internal Variability ◽  
Return Level ◽  
Data Set ◽  
Model Simulations ◽  
Return Levels ◽  
Climate Model Simulations ◽  
Precipitation Events

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments, and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolutions, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe, and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy-precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme-rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability in the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24 h durations with a spatial resolution of 0.11∘ (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with Spearman's rank correlation coefficients >0.76 for the area covered by observations. Also, the rainfall intensity of the observational data set is in the range of the climate-model-generated intensities in 60 % (77 %, 78 %, 83 %, 78 %) of the area for hourly (3, 6, 12, 24 h) durations. This results in biases between −16.3 % (hourly) to +8.2 % (24 h) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3 to 24 h durations in large parts of the study area. However, for an hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

scholarly journals Chemistry-climate model simulations of the Mt. Pinatubo eruption using CCMI and CMIP6 stratospheric aerosol data

2017 ◽  
Author(s):  
Laura Revell ◽  
Andrea Stenke ◽  
Beiping Luo ◽  
Stefanie Kremser ◽  
Eugene Rozanov ◽  
...  
Keyword(s):  
Lower Stratosphere ◽  
Climate Model ◽  
Stratospheric Aerosol ◽  
Radiative Properties ◽  
Gap Filling ◽  
Data Set ◽  
Model Simulations ◽  
Ozone Loss ◽  
Pinatubo Eruption ◽  
Climate Model Simulations

Abstract. To simulate the impacts of volcanic eruptions on the stratosphere, chemistry-climate models that do not include an online aerosol module require temporally and spatially resolved aerosol size parameters for heterogeneous chemistry and aerosol radiative properties as a function of wavelength. For phase 1 of the Chemistry-Climate Model Initiative (CCMI-1) and, later, for phase 6 of the Coupled Model Intercomparison Project (CMIP6) two such stratospheric aerosol data sets were compiled, whose functional capability and representativeness are compared here. For CCMI-1, the SAGE-4λ data set was compiled, which hinges on the measurements at four wavelengths of the SAGE (Stratospheric Aerosol and Gas Experiment) II satellite instrument and uses ground-based Lidar measurements for gap-filling immediately after the Mt. Pinatubo eruption, when the stratosphere was optically opaque for SAGE II. For CMIP6, the new SAGE-3λ data set was compiled, which excludes the least reliable SAGE II wavelength and uses CLAES (Cryogenic Limb Array Etalon Spectrometer) measurements on UARS, the Upper Atmosphere Research Satellite, for gap-filling following the Mt. Pinatubo eruption instead of ground-based Lidars. Here, we performed SOCOLv3 (Solar Climate Ozone Links version 3) chemistry-climate model simulations of the recent past (1986–2005) to investigate the impact of the Mt. Pinatubo eruption in 1991 on stratospheric temperature and ozone and how this response differs depending on which aerosol data set is applied. The use of SAGE-4λ results in heating and ozone loss being overestimated in the lower stratosphere compared to observations in the post-eruption period by approximately 3 K and 0.2 ppmv, respectively. However, less heating occurs in the model simulations based on SAGE-3λ, because the improved gap-filling procedures after the eruption lead to less aerosol loading in the tropical lower stratosphere. As a result, simulated temperature anomalies in the model simulations based on SAGE-3λ for CMIP6 are in excellent agreement with MERRA and ERA-Interim reanalyses in the post-eruption period. Less heating in the simulations with SAGE-3λ means that the rate of tropical upwelling does not strengthen as much as it does in the simulations with SAGE-4λ, which limits dynamical uplift of ozone and therefore provides more time for ozone to accumulate in tropical mid-stratospheric air. Ozone loss following the Mt. Pinatubo eruption is overestimated by 0.1 ppmv in the model simulations based on SAGE-3λ, which is a better agreement with observations than in the simulations based on SAGE-4λ. Overall, the CMIP6 stratospheric aerosol data set, SAGE-3λ, allows SOCOLv3 to more accurately simulate the post-Pinatubo eruption period.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

Adaptation and Application of the large LAERTES-EU RCM Ensemble for Hydrological Modeling

2021 ◽  
Author(s):  
Florian Ehmele ◽  
Lisa-Ann Kautz ◽  
Hendrik Feldmann ◽  
Yi He ◽  
Martin Kadlec ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Central Europe ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Regional Climate ◽  
Added Value ◽  
Data Set ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
Rhine Catchment

&lt;p&gt;Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs). To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing 12.000 simulated years. LAERTES-EU is adapted and applied for the use in an HM to calculate discharges for large river catchments in Central Europe, where the Rhine catchment serves as the pilot area for calibration and validation. Quantile mapping with a fixed density function is used to correct the bias in model precipitation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values of precipitation and discharges. While for the Rhine catchment a clear added value is identified, the results are more mixed for other catchments (e.g., the Upper Danube).&lt;/p&gt;

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

scholarly journals Adaptation and Application of the large LAERTES-EU RCM Ensemble for Modeling Hydrological Extremes: A pilot study for the Rhine basin

2021 ◽  
Author(s):  
Florian Ehmele ◽  
Lisa-Ann Kautz ◽  
Hendrik Feldmann ◽  
Yi He ◽  
Martin Kadlec ◽  
...  
Keyword(s):  
Pilot Study ◽  
Central Europe ◽  
Climate Model ◽  
Regional Climate ◽  
River Basins ◽  
Large River ◽  
Data Set ◽  
Model Simulations ◽  
Climate Model Simulations ◽  
Large River Basins

Abstract. Enduring and extensive heavy precipitation associated with widespread river floods are among the main natural hazards affecting Central Europe. Since such events are characterized by long return periods, it is difficult to adequately quantify their frequency and intensity solely based on the available observations of precipitation. Furthermore, long-term observations are rare, not homogeneous in space and time, and thus not suitable to run hydrological models (HMs) with respect to extremes. To overcome this issue, we make use of the recently introduced LAERTES-EU (LArge Ensemble of Regional climaTe modEl Simulations for EUrope) data set, which is an ensemble of regional climate model simulations providing over 12.000 simulated years. LAERTES-EU is adapted for the use in an HM to calculate discharges for large river basins by applying a quantile mapping with a fixed density function to correct the mainly positive bias in model precipitation. The Rhine basin serves as a pilot area for calibration and validation. The results show clear improvements in the representation of both precipitation (e.g., annual cycle and intensity distributions) and simulated discharges by the HM after the bias correction. Furthermore, the large size of LAERTES-EU improves the statistical representativeness also for high return values above 100 years of discharges. We conclude that the bias-corrected LAERTES-EU data set is generally suitable for hydrological applications and posterior risk analyses. The results of this pilot study will soon be applied to several large river basins in Central Europe.

scholarly journals Future changes in extreme precipitation in the Rhine basin based on global and regional climate model simulations

2012 ◽  
Vol 16 (12) ◽  
pp. 4517-4530 ◽  
Author(s):  
S. C. van Pelt ◽  
J. J. Beersma ◽  
T. A. Buishand ◽  
B. J. J. M. van den Hurk ◽  
P. Kabat
Keyword(s):  
Time Series ◽  
Regional Climate Model ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Regional Climate ◽  
Future Change ◽  
Model Simulations ◽  
Rhine Basin ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract. Probability estimates of the future change of extreme precipitation events are usually based on a limited number of available global climate model (GCM) or regional climate model (RCM) simulations. Since floods are related to heavy precipitation events, this restricts the assessment of flood risks. In this study a relatively simple method has been developed to get a better description of the range of changes in extreme precipitation events. Five bias-corrected RCM simulations of the 1961–2100 climate for a single greenhouse gas emission scenario (A1B SRES) were available for the Rhine basin. To increase the size of this five-member RCM ensemble, 13 additional GCM simulations were analysed. The climate responses of the GCMs are used to modify an observed (1961–1995) precipitation time series with an advanced delta change approach. Changes in the temporal means and variability are taken into account. It is found that the range of future change of extreme precipitation across the five-member RCM ensemble is similar to results from the 13-member GCM ensemble. For the RCM ensemble, the time series modification procedure also results in a similar climate response compared to the signal deduced from the direct model simulations. The changes from the individual RCM simulations, however, systematically differ from those of the driving GCMs, especially for long return periods.

scholarly journals Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

2021 ◽  
Vol 12 (2) ◽  
pp. 401-418
Author(s):  
Nicola Maher ◽  
Sebastian Milinski ◽  
Ralf Ludwig
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Internal Variability ◽  
Climate System ◽  
Single Type ◽  
Special Issue ◽  
Large Ensemble ◽  
Model Simulations ◽  
Different Types ◽  
Climate Model Simulations

Abstract. Single model initial-condition large ensembles (SMILEs) are valuable tools that can be used to investigate the climate system. SMILEs allow scientists to quantify and separate the internal variability of the climate system and its response to external forcing, with different types of SMILEs appropriate to answer different scientific questions. In this editorial we first provide an introduction to SMILEs and an overview of the studies in the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. These studies analyse a range of different types of SMILEs including global climate models (GCMs), regionally downscaled climate models (RCMs), a hydrological model with input from a RCM SMILE, a SMILE with prescribed sea surface temperature (SST) built for event attribution, a SMILE that assimilates observed data, and an initialised regional model. These studies provide novel methods, that can be used with SMILEs. The methods published in this issue include a snapshot empirical orthogonal function analysis used to investigate El Niño–Southern Oscillation teleconnections; the partitioning of future uncertainty into model differences, internal variability, and scenario choices; a weighting scheme for multi-model ensembles that can incorporate SMILEs; and a method to identify the required ensemble size for any given problem. Studies in this special issue also focus on RCM SMILEs, with projections of the North Atlantic Oscillation and its regional impacts assessed over Europe, and an RCM SMILE intercomparison. Finally a subset of studies investigate projected impacts of global warming, with increased water flows projected for future hydrometeorological events in southern Ontario; precipitation projections over central Europe are investigated and found to be inconsistent across models in the Alps, with a continuation of past tendencies in Mid-Europe; and equatorial Asia is found to have an increase in the probability of large fire and drought events under higher levels of warming. These studies demonstrate the utility of different types of SMILEs. In the second part of this editorial we provide a perspective on how three types of SMILEs could be combined to exploit the advantages of each. To do so we use a GCM SMILE and an RCM SMILE with all forcings, as well as a naturally forced GCM SMILE (nat-GCM) over the European domain. We utilise one of the key advantages of SMILEs, precisely separating the forced response and internal variability within an individual model to investigate a variety of simple questions. Broadly we show that the GCM can be used to investigate broad-scale patterns and can be directly compared to the nat-GCM to attribute forced changes to either anthropogenic emissions or volcanoes. The RCM provides high-resolution spatial information of both the forced change and the internal variability around this change at different warming levels. By combining all three ensembles we can gain information that would not be available using a single type of SMILE alone, providing a perspective on future research that could be undertaken using these tools.

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