Considerations of Domain Size and Large-Scale Driving for Nested Regional Climate Models: Impact on Internal Variability and Ability at Developing Small-Scale Details

2012 ◽  
pp. 181-199 ◽  
Author(s):  
René Laprise ◽  
Dragana Kornic ◽  
Maja Rapaić ◽  
Leo Šeparović ◽  
Martin Leduc ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Domain Size ◽  
Regional Climate Models ◽  
Internal Variability ◽  
Small Scale

Sensitivity Study of Regional Climate Model Simulations to Large-Scale Nudging Parameters

2009 ◽  
Vol 137 (5) ◽  
pp. 1666-1686 ◽  
Author(s):  
Adelina Alexandru ◽  
Ramon de Elia ◽  
René Laprise ◽  
Leo Separovic ◽  
Sébastien Biner
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Power Spectra ◽  
Domain Size ◽  
Regional Climate Models ◽  
Internal Variability ◽  
Sensitivity Study ◽  
Simulated Climate

Abstract Previous studies with nested regional climate models (RCMs) have shown that large-scale spectral nudging (SN) seems to be a powerful method to correct RCMs’ weaknesses such as internal variability, intermittent divergence in phase space (IDPS), and simulated climate dependence on domain size and geometry. Despite its initial success, SN is not yet in widespread use because of disagreement regarding the main premises—the unconfirmed advantages of removing freedom from RCMs’ large scales—and lingering doubts regarding its potentially negative side effects. This research addresses the latter issue. Five experiments have been carried out with the Canadian RCM (CRCM) over North America. Each experiment, performed under a given SN configuration, consists of four ensembles of simulations integrated on four different domain sizes for a summer season. In each experiment, the effects of SN on internal variability, time means, extremes, and power spectra are discussed. As anticipated from previous investigations, the present study confirms that internal variability, as well as simulated-climate dependence on domain size, decreases with increased SN strength. Our results further indicate a noticeable reduction of precipitation extremes as well as low-level vorticity amplitude in almost all length scales, as a side effect of SN; these effects are mostly perceived when SN is the most intense. Overall results indicate that the use of a weak to mild SN may constitute a reasonable compromise between the risk of decoupling of the RCM internal solution from the lateral boundary conditions (when using large domains without SN) and an excessive control of the large scales (with strong SN).

scholarly journals Does Spectral Nudging Have an Effect on Dynamical Downscaling Applied in Small Regional Model Domains?

2017 ◽  
Vol 145 (10) ◽  
pp. 4303-4311 ◽  
Author(s):  
Benjamin Schaaf ◽  
Hans von Storch ◽  
Frauke Feser
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Regional Scale ◽  
Regional Climate Models ◽  
Small Scale ◽  
Spectral Nudging ◽  
Atmospheric State ◽  
Model Region

Spectral nudging is a method that was developed to constrain regional climate models so that they reproduce the development of the large-scale atmospheric state, while permitting the formation of regional-scale details as conditioned by the large scales. Besides keeping the large-scale development in the interior close to a given state, the method also suppresses the emergence of ensemble variability. The method is mostly applied to reconstructions of past weather developments in regions with an extension of typically 1000–8000 km. In this article, the authors examine if spectral nudging is having an effect on simulations with model regions of the size of about 700 km × 500 km at midlatitudes located mainly over flat terrain. First two pairs of simulations are compared, two runs each with and without spectral nudging, and it is found that the four simulations are very similar, without systematic or intermittent phases of divergence. Smooth fields, which are mainly determined by spatial patterns, such as air pressure, show hardly any differences, while small-scale and heterogeneous fields such as precipitation vary strongly, mostly on the gridpoint scale, irrespective if spectral nudging is employed or not. It is concluded that the application of spectral nudging has little effect on the simulation when the model region is relatively small.

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Surface accumulation in Northern Central Greenland during the last 300 years

2020 ◽  
Vol 61 (81) ◽  
pp. 214-224 ◽  
Author(s):  
Nanna B. Karlsson ◽  
Sebastian Razik ◽  
Maria Hörhold ◽  
Anna Winter ◽  
Daniel Steinhage ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Core ◽  
Regional Climate Models ◽  
Radar Data ◽  
Accumulation Rates ◽  
Ground Penetrating ◽  
Core Project ◽  
North Greenland

AbstractThe internal stratigraphy of snow and ice as imaged by ground-penetrating radar may serve as a source of information on past accumulation. This study presents results from two ground-based radar surveys conducted in Greenland in 2007 and 2015, respectively. The first survey was conducted during the traverse from the ice-core station NGRIP (North Greenland Ice Core Project) to the ice-core station NEEM (North Greenland Eemian Ice Drilling). The second survey was carried out during the traverse from NEEM to the ice-core station EGRIP (East Greenland Ice Core Project) and then onwards to Summit Station. The total length of the radar profiles is 1427 km. From the radar data, we retrieve the large-scale spatial variation of the accumulation rates in the interior of the ice sheet. The accumulation rates range from 0.11 to 0.26 m a−1 ice equivalent with the lowest values found in the northeastern sector towards EGRIP. We find no evidence of temporal or spatial changes in accumulation rates when comparing the 150-year average accumulation rates with the 321-year average accumulation rates. Comparisons with regional climate models reveal that the models underestimate accumulation rates by up to 35% in northeastern Greenland. Our results serve as a robust baseline to detect present changes in either surface accumulation rates or patterns.

Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain

2008 ◽  
Vol 31 (7-8) ◽  
pp. 927-940 ◽  
Author(s):  
Philippe Lucas-Picher ◽  
Daniel Caya ◽  
Ramón de Elía ◽  
René Laprise
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Internal Variability ◽  
Large Domain

Integrating Model Evaluation and Observations into a Production-Release Pipeline

2020 ◽  
Author(s):  
Philipp S. Sommer ◽  
Ronny Petrik ◽  
Beate Geyer ◽  
Ulrike Kleeberg ◽  
Dietmar Sauer ◽  
...  
Keyword(s):  
Evaluation System ◽  
Large Scale ◽  
Climate Models ◽  
Model Simulation ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Active Communication ◽  
The Individual ◽  
Scientific Questions

<p>The complexity of Earth System and Regional Climate Models represents a considerable challenge for developers. Tuning but also improving one aspect of a model can unexpectedly decrease the performance of others and introduces hidden errors. Reasons are in particular the multitude of output parameters and the shortage of reliable and complete observational datasets. One possibility to overcome these issues is a rigorous and continuous scientific evaluation of the model. This requires standardized model output and, most notably, standardized observational datasets. Additionally, in order to reduce the extra burden for the single scientist, this evaluation has to be as close as possible to the standard workflow of the researcher, and it needs to be flexible enough to adapt it to new scientific questions.</p><p>We present the Free Evaluation System Framework (Freva) implementation within the Helmholtz Coastal Data Center (HCDC) at the Institute of Coastal Research in the Helmholtz-Zentrum Geesthacht (HZG). Various plugins into the Freva software, namely the HZG-EvaSuite, use observational data to perform a standardized evaluation of the model simulation. We present a comprehensive data management infrastructure that copes with the heterogeneity of observations and simulations. This web framework comprises a FAIR and standardized database of both, large-scale and in-situ observations exported to a format suitable for data-model intercomparisons (particularly netCDF following the CF-conventions). Our pipeline links the raw data of the individual model simulations (i.e. the production of the results) to the finally published results (i.e. the released data). </p><p>Another benefit of the Freva-based evaluation is the enhanced exchange between the different compartments of the institute, particularly between the model developers and the data collectors, as Freva contains built-in functionalities to share and discuss results with colleagues. We will furthermore use the tool to strengthen the active communication with the data and software managers of the institute to generate or adapt the evaluation plugins.</p>

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

scholarly journals Stochastic Rainfall Downscaling of Climate Models

2014 ◽  
Vol 15 (2) ◽  
pp. 830-843 ◽  
Author(s):  
D. D’Onofrio ◽  
E. Palazzi ◽  
J. von Hardenberg ◽  
A. Provenzale ◽  
S. Calmanti
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Amplitude Distribution ◽  
Global Climate Models ◽  
Added Value ◽  
Small Scale ◽  
Physically Based ◽  
Stochastic Downscaling

Abstract Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model.

Sign in / Sign up

Export Citation Format

Share Document