Reply to “Comments on ‘The North American Regional Climate Change Assessment Program: Overview of Phase I Results\u27”

The authors of Mearns et al. (2012) are aware of the role of driving RCMs with reanalyses and have written extensively on the roles of different types of regional climate models (RCMs) simulations (e.g., Giorgi and Mearns 1999; Leung et al. 2003). Thus, we agree that the skill of dynamical downscaling in which global reanalysis is used to provide boundary conditions in general indicates an upper bound of skill compared to dynamical downscaling in which the boundary conditions come from global climate model simulations. This finding has long been established, as global climate model simulations cannot outperform global reanalysis in providing boundary conditions since the latter is constrained by observations through data assimilation (that is, unless the reanalyses themselves have been shown to have serious deficiences; e.g., Cerezo-Mota et al 2011). The classification of different types of dynamical downscaling introduced by Castro et al. (2005) further adds clarity to this point

Regional Extreme Monthly Precipitation Simulated by NARCCAP RCMs

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations

Numerical fatigue damage analysis of a variable speed Francis pump-turbine during start-up in generating mode

Variable speed hydropower units offer a large spectrum of grid regulation services and may therefore contribute to the stability of future power supply systems. Full Size Frequency Converters (FSFC) already found real world application in Pumped Storage Hydropower Plants up to a rated power of 100 MW and are even considered scalable up to a few hundred MW. Apart from the extension of the power range and grid regulation capacities, the FSFC technology also provides new control possibilities during transient operations such as start-up in generating mode. Thus, harsh conditions with damaging impact on the hydromechanical components may be avoided by tuning the operating point trajectory in the start-up phase. In this paper, runner fatigue damage during start-up in generating mode of a 5 MW variable speed Francis pump-turbine prototype equipped with a FSFC is numerically analyzed. The fixed speed solution is compared to a variable speed solution following a BEP tracking control strategy. 1D hydraulic transient simulations provide boundary conditions for detailed 3-D CFD/FEA simulations. Full and reduced numerical domains are used and compared. The overall outcome of the present numerical study indicates an important reduction of partial damages using variable speed drives for turbine start-up manoeuvres

Optimization of turbine start-up sequence of a full size frequency converter variable speed pump-turbine

The stability of the electricity grid will be disrupted by the massive integration of new renewable energies. Hydropower plants have a major role to play in this transformation of the electricity market by increasing their operational flexibility and their ability to provide ancillary services. However, this flexibility may lead to an accelerated degradation of mechanical components. By changing the turbine operating point far from the best efficiency point or by increasing the number of transient manoeuvres such as start and stop sequences, unsteady flow phenomena, cavitation development and additional wear and tear stress the unit's components and impact its lifetime. The present work aims to provide preliminary insight on the optimization of the start-up sequence of a 5 MW reversible Francis pump-turbine equipped with a Full Size Frequency Converter (FSFC). The goal of the optimization approaches are to determine a start-up sequence which minimizes the runner damage, the penstock fatigue and the water losses. The objective functions are evaluated by 1D hydraulic transient simulations with the SIMSEN software and which it allow to compare the relative mitigation between the conventional fixed-speed start-up and the linear variable-speed start-up equipped with a Full Size Frequency Converter

ESPO-G6-E5L : Ensemble de Simulations Post-traitées d'Ouranos - modèles Globaux CMIP6 - ERA5-Land / Ouranos Ensemble of Bias-adjusted Simulations - Global Models CMIP6 - ERA5-Land

<p><br><strong>Context</strong><br>The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, <a href="http://www.ouranos.ca">Ouranos</a> is working to create a set of operational multipurpose climate scenarios at a high spatial resolution called "Ensemble de Simulations Post-traitées d'Ouranos" (ESPO).</p><p><strong>Dataset</strong><br>In ESPO-G6 v1.0.0, CMIP6 global climate model simulations are bias-adjusted using the ERA5-Land reference dataset. The simulation ensemble covers the period for years 1950-2100 and includes the daily minimum temperature (tasmin), the daily maximum temperature (tasmax) and the daily mean precipitation flux (pr). The dataset has a resolution of 0.1° over a North American domain from  179.9°W to 10.0°W and from 10.0°N to 83.3°N.  Data is only available on land, as the reference dataset (ERA5-Land) is only defined there. The experiments included are SSP2-4.5 and SSP3-7.0. To avoid the "hot model problem", only models with a Transient Climate Response in the likely range (1.4–2.2 °C) were kept in the official ensemble. Extra "hot models" and experiments will also be made available even if they are not in the official ensemble.</p><p><br><strong>Reference</strong><br>The ERA5-Land reanalysis is used as the reference dataset. ERA5-Land is a rerun of the land component of the ERA5 climate reanalysis, forced by meteorological fields from ERA5 and cover the period from 1950 to the present (with a 2-3 month lag from the present day for data quality assurance reasons). ERA5-Land benefits from numerous improvements, making it more accurate for all types of land applications than the original ERA5. Specifically, ERA5-Land runs at an enhanced resolution (~9 km vs. ~31 km in ERA5).</p><p><strong>Method</strong><br>The code attached to this DOI performs the bias-adjustment on the raw simulations to create the ESPO-G6 v1.0.0 ensemble. First, each simulation is regridded with a bilinear interpolation in cascades onto the ERA5-Land reference grid. Then, they are adjusted following the <a href="https://xclim.readthedocs.io/en/stable/sdba.html#adjustment-methods">Detrended Quantile Mapping</a> procedure. The adjustment is performed on tasmax, pr and dtr (daily temperature range). tasmin is reconstructed from tasmax and dtr. The three variables are then assembled  to create the official timeseries.</p><p>The code also contains extra tasks. It computes diagnostics, indicators, climatology, deltas and ensemble statistics.</p><p><strong>Code and data availability</strong><br>At the time of publication, the data is stored on Ouranos THREDDS, a part of the PAVICS project: <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip6/ouranos/ESPO-G/ESPO-G6v1.0.0/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip6/ouranos/ESPO-G/ESPO-G6v1.0.0/catalog.html</a></p><p>This version of the code: <a href="https://github.com/Ouranosinc/ESPO-G/releases/tag/v1.0.0">https://github.com/Ouranosinc/ESPO-G/releases/tag/v1.0.0</a><br>The github repository: <a href="https://github.com/Ouranosinc/ESPO-G">https://github.com/Ouranosinc/ESPO-G</a></p&gt

ESPO-G6-R2 : Ensemble de scénarios polyvalents d'Ouranos - Modèles Globaux CMIP6 - RDRS v2.1 / Ouranos Multipurpose Climate Scenarios - Global models CMIP6 - RDRS v2.1

Context The need to adapt to climate change is present in a growing number of fields, leading to an increase in the demand for climate scenarios for often interrelated sectors of activity. In order to meet this growing demand and to ensure the availability of climate scenarios responding to numerous vulnerability, impact, and adaptation (VIA) studies, Ouranos is working to create a set of operational multipurpose climate scenarios at a high spatial resolution called "Ensemble de Scénarios Polyvalents d'Ouranos" (ESPO). Dataset In ESPO-G6-R2 v1.0.0, CMIP6 global climate model simulations are bias-adjusted using the RDRS v2.1 reference dataset. The simulation ensemble covers the period for years 1950-2100 and includes the daily minimum temperature (tasmin), the daily maximum temperature (tasmax) and the daily mean precipitation flux (pr). The dataset has a resolution of 0.1° over a North American domain from 179.9°W to 10.0°W and from 10.0°N to 83.3°N. Though, we recommend caution close to the edge of the domain, especially in the south.The experiments included are SSP2-4.5 and SSP3-7.0. To avoid the "hot model problem", only models with a Transient Climate Response in the likely range (1.4–2.2 °C) were kept in the official ensemble. Extra "hot models" and experiments will also be made available even if they are not in the official ensemble. Reference The ESPO-G6-R2 v1.0.0 dataset uses the RDRS v2.1 (Gasset et al., 2021) as reference dataset. This is a product from Environment and Climate Change Canada (ECCC) created by using the Regional Deterministic Reforecast System (RDRS) to downscale the Global Deterministic Reforecast System (GDRS) initialized by ERA-Interim. The system is also coupled with the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA). Method The code attached to this DOI performs the bias-adjustment on the raw simulations to create the ESPO-G6-R2 v1.0.0 ensemble. First, each simulation is regridded with a bilinear interpolation in cascades onto the RDRS v2.1 reference grid. Then, they are adjusted following the Detrended Quantile Mapping procedure. The adjustment is performed on tasmax, pr and dtr (daily temperature range). The variable tasmin is reconstructed from tasmax and dtr. The three variables are then assembled to create the official timeseries. The code also contains extra tasks. It computes diagnostics, indicators, climatology, deltas and ensemble statistics. Code and data availability At the time of publication, the data is stored on Ouranos THREDDS, a part of the PAVICS project: https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/bias_adjusted/cmip6/ouranos/ESPO-G/ESPO-G6-R2v1.0.0/catalog.html This version of the code: https://github.com/Ouranosinc/ESPO-G/releases/tag/ESPO-G6-R2v1.0.0 The github repository: https://github.com/Ouranosinc/ESPO-

Reply to “Comments on ‘The North American Regional Climate Change Assessment Program: Overview of Phase I Results'”

The authors of Mearns et al. (2012) are aware of the role of driving RCMs with reanalyses and have written extensively on the roles of different types of regional climate models (RCMs) simulations (e.g., Giorgi and Mearns 1999; Leung et al. 2003). Thus, we agree that the skill of dynamical downscaling in which global reanalysis is used to provide boundary conditions in general indicates an upper bound of skill compared to dynamical downscaling in which the boundary conditions come from global climate model simulations. This finding has long been established, as global climate model simulations cannot outperform global reanalysis in providing boundary conditions since the latter is constrained by observations through data assimilation (that is, unless the reanalyses themselves have been shown to have serious deficiences; e.g., Cerezo-Mota et al 2011). The classification of different types of dynamical downscaling introduced by Castro et al. (2005) further adds clarity to this point.This article is from Bull. Amer. Meteor. Soc., 94, 1077–1078. doi: http://dx.doi.org/10.1175/BAMS-D-13-00013.1. Posted with permission.</p

Regional Extreme Monthly Precipitation Simulated by NARCCAP RCMs

This paper analyzes the ability of the North American Regional Climate Change Assessment Program (NARCCAP) ensemble of regional climate models to simulate extreme monthly precipitation and its supporting circulation for regions of North America, comparing 18 years of simulations driven by the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis with observations. The analysis focuses on the wettest 10% of months during the cold half of the year (October–March), when it is assumed that resolved synoptic circulation governs precipitation. For a coastal California region where the precipitation is largely topographic, the models individually and collectively replicate well the monthly frequency of extremes, the amount of extreme precipitation, and the 500-hPa circulation anomaly associated with the extremes. The models also replicate very well the statistics of the interannual variability of occurrences of extremes. For an interior region containing the upper Mississippi River basin, where precipitation is more dependent on internally generated storms, the models agree with observations in both monthly frequency and magnitude, although not as closely as for coastal California. In addition, simulated circulation anomalies for extreme months are similar to those in observations. Each region has important seasonally varying precipitation processes that govern the occurrence of extremes in the observations, and the models appear to replicate well those variations.This article is from J. Hydrometeor, 11, 1373–1379. doi: http://dx.doi.org/10.1175/2010JHM1297.1. Posted with permission.</p

The North American Regional Climate Change Assessment Program: Overview of Phase I Results

The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II. This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.This article is from the Bulletin of the American Meteorological Society 93 (2012): 1337-1362, doi:10.1175/BAMS-D-11-00223.1. Posted with permission.</p