scholarly journals Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales

2016 ◽  
Vol 121 (4) ◽  
pp. 1575-1590 ◽  
Author(s):  
Alejandro Di Luca ◽  
Daniel Argüeso ◽  
Jason P. Evans ◽  
Ramón de Elía ◽  
René Laprise
Keyword(s):  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Added Value

scholarly journals A Comparison between One-Step and Two-Step Nesting Strategy in the Dynamical Downscaling of Regional Climate Model COSMO-CLM at 2.2 km Driven by ERA5 Reanalysis

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 260
Author(s):  
Mario Raffa ◽  
Alfredo Reder ◽  
Marianna Adinolfi ◽  
Paola Mercogliano
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Weather Forecast ◽  
Medium Range Weather Forecast ◽  
Spatial And Temporal Scales ◽  
Starting Point ◽  
One Step

Recently, the European Centre for Medium Range Weather Forecast (ECMWF) has released a new generation of reanalysis, acknowledged as ERA5, representing at the present the most plausible picture for the current climate. Although ERA5 enhancements, in some cases, its coarse spatial resolution (~31 km) could still discourage a direct use of precipitation fields. Such a gap could be faced dynamically downscaling ERA5 at convection permitting scale (resolution < 4 km). On this regard, the selection of the most appropriate nesting strategy (direct one-step against nested two-step) represents a pivotal issue for saving time and computational resources. Two questions may be raised within this context: (i) may the dynamical downscaling of ERA5 accurately represents past precipitation patterns? and (ii) at what extent may the direct nesting strategy performances be adequately for this scope? This work addresses these questions evaluating two ERA5-driven experiments at ~2.2 km grid spacing over part of the central Europe, run using the regional climate model COSMO-CLM with different nesting strategies, for the period 2007–2011. Precipitation data are analysed at different temporal and spatial scales with respect to gridded observational datasets (i.e., E-OBS and RADKLIM-RW) and existing reanalysis products (i.e., ERA5-Land and UERRA). The present work demonstrates that the one-step experiment tendentially outperforms the two-step one when there is no spectral nudging, providing results at different spatial and temporal scales in line with the other existing reanalysis products. However, the results can be highly model and event dependent as some different aspects might need to be considered (i.e., the nesting strategies) during the configuration phase of the climate experiments. For this reason, a clear and consolidated recommendation on this topic cannot be stated. Such a level of confidence could be achieved in future works by increasing the number of cities and events analysed. Nevertheless, these promising results represent a starting point for the optimal experimental configuration assessment, in the frame of future climate studies.

Process‐Based Analysis of the Added Value of Dynamical Downscaling Over Central Africa

2020 ◽  
Vol 47 (17) ◽  
Author(s):  
Alain T. Tamoffo ◽  
Alessandro Dosio ◽  
Derbetini A. Vondou ◽  
Denis Sonkoué
Keyword(s):  
Dynamical Downscaling ◽  
Central Africa ◽  
Added Value

scholarly journals Assessment of Bias Assumptions for Climate Models

2014 ◽  
Vol 27 (17) ◽  
pp. 6799-6818 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Lower Troposphere ◽  
Regional Climate Models ◽  
Added Value ◽  
Model Biases ◽  
Interaction Component

Abstract Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods. Such assumptions are inevitable because of the lack of future observations. This manuscript reviews different bias assumptions found in the literature and provides measures to assess their validity. The authors explicitly separate climate change from multidecadal variability to systematically analyze climate model biases in seasonal and regional surface temperature averages, using global and regional climate models (GCMs and RCMs) from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project over Europe. For centennial time scales, it is found that a linear bias extrapolation for GCMs is best supported by the analysis: that is, it is generally not correct to assume that model biases are independent of the climate state. Results also show that RCMs behave markedly differently when forced with different drivers. RCM and GCM biases are not additive, and there is a significant interaction component in the bias of the RCM–GCM model chain that depends on both the RCM and GCM considered. This result questions previous studies that deduce biases (and ultimately projections) in RCM–GCM combinations from reanalysis-driven simulations. The authors suggest that the aforementioned interaction component derives from the refined RCM representation of dynamical and physical processes in the lower troposphere, which may nonlinearly depend upon the larger-scale circulation stemming from the driving GCM. The authors’ analyses also show that RCMs provide added value and that the combined RCM–GCM approach yields, in general, smaller biases in seasonal surface temperature and interannual variability, particularly in summer and even for spatial scales that are, in principle, well resolved by the GCMs.

scholarly journals Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

2011 ◽  
Vol 92 (9) ◽  
pp. 1181-1192 ◽  
Author(s):  
Frauke Feser ◽  
Burkhardt Rockel ◽  
Hans von Storch ◽  
Jörg Winterfeldt ◽  
Matthias Zahn
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Spatial Scales ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Small Scale ◽  
Model Data ◽  
Polar Lows

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical downscaling leads to output fields that are superior to the driving global data, but little work has been carried out to substantiate these expectations. Here a series of articles is reviewed that evaluate the benefit of dynamical downscaling by explicitly comparing results of global and regional climate model data to the observations. These studies show that the regional climate model generally performs better for the medium spatial scales, but not always for the larger spatial scales. Regional models can add value, but only for certain variables and locations—particularly those influenced by regional specifics, such as coasts, or mesoscale dynamics, such as polar lows. Therefore, the decision of whether a regional climate model simulation is required depends crucially on the scientific question being addressed.

scholarly journals Impacts of Dynamical Downscaling on Circulation type Statistics: Potential added Value in the Euro-CORDEX Ensemble

2021 ◽  
Author(s):  
Julie Røste ◽  
Oskar A Landgren
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Circulation Type ◽  
Global Model ◽  
Added Value ◽  
Classification Methods ◽  
Circulation Types ◽  
Climate Model Simulations

Abstract Atmospheric circulation type classification methods were applied to an ensemble of 57 regional climate model simulations from Euro-CORDEX, their 11 boundary models from CMIP5 and the ERA5 reanalysis. We compared frequencies of the different circulation types in the simulations with ERA5 and found that the regional models add value especially in the summer season. We applied three different classification methods (the subjective Grosswettertypes and the two optimisation algorithms SANDRA and distributed k-means clustering) from the cost733class software and found that the results are not particularly sensitive to choice of circulation classification method. There are large differences between models. Simulations based on MIROC-MIROC5 and CNRM-CERFACS-CNRM-CM5 show an over-representation of easterly flow and an under-representation of westerly. The downscaled results retain the large-scale circulation from the global model most days, but especially the regional model IPSL-WRF381P changes the circulation more often, which increases the error relative to ERA5. Simulations based on ICHEC-EC-EARTH and MPI-M-MPI-ESM-LR show consistently smaller errors relative to ERA5 in all seasons. The ensemble spread is largest in the summer and smallest in the winter. Under the future RCP8.5 scenario, more than half of the ensemble shows an increase in frequency of north-easterly flow and decrease in the Central-Eastern European high and south-easterly flow. There is in general a strong agreement in the sign of the change between the regional simulations and the data from the corresponding global model.

scholarly journals An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

2021 ◽  
Vol 9 ◽  
Author(s):  
Eun-Soon Im ◽  
Subin Ha ◽  
Liying Qiu ◽  
Jina Hur ◽  
Sera Jo ◽  
...  
Keyword(s):  
Heat Stress ◽  
High Resolution ◽  
Dynamical Downscaling ◽  
Initial Conditions ◽  
Added Value ◽  
Stress Index ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Hourly Temperature

This study evaluates the performance of dynamical downscaling of global prediction generated from the NOAA Climate Forecast System (CFSv2) at subseasonal time-scale against dense in-situ observational data in Korea. The Weather Research and Forecasting (WRF) double-nested modeling system customized over Korea is adopted to produce very high resolution simulation that presumably better resolves geographically diverse climate features. Two ensemble members of CFSv2 starting with different initial conditions are downscaled for the summer season (June-July-August) during past 10-year (2011–2020). The comparison of simulations from the nested domain (5 km resolution) of WRF and driving CFSv2 (0.5°) clearly demonstrates the manner in which dynamical downscaling can drastically improve daily mean temperature (Tmean) and daily maximum temperature (Tmax) in both quantitative and qualitative aspects. The downscaled temperature not only better resolves the regional variability strongly tied with topographical elevation, but also substantially lowers the systematic cold bias seen in CFSv2. The added value from the nested domain over CFSv2 is far more evident in Tmax than in Tmean, which indicates a skillful performance in capturing the extreme events. Accordingly, downscaled results show a reasonable performance in simulating the plant heat stress index that counts the number of days with Tmax above 30°C and extreme degree days that accumulate temperature exceeding 30°C using hourly temperature. The WRF simulations also show the potential to capture the variation of Tmean-based index that represents the accumulation of heat stress in reproductive growth for the mid-late maturing rice cultivars in Korea. As the likelihood of extreme hot temperatures is projected to increase in Korea, the modeling skill to predict the ago-meteorological indices measuring the effect of extreme heat on crop could have significant implications for agriculture management practice.

Regional Dynamical Downscaling

2020 ◽  
Author(s):  
Filippo Giorgi
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Added Value ◽  
Climate Information ◽  
Atmospheric Models ◽  
Future Directions ◽  
Climate Change Research ◽  
Climate Downscaling

Dynamical downscaling has been used for about 30 years to produce high-resolution climate information for studies of regional climate processes and for the production of climate information usable for vulnerability, impact assessment and adaptation studies. Three dynamical downscaling tools are available in the literature: high-resolution global atmospheric models (HIRGCMs), variable resolution global atmospheric models (VARGCMs), and regional climate models (RCMs). These techniques share their basic principles, but have different underlying assumptions, advantages and limitations. They have undergone a tremendous growth in the last decades, especially RCMs, to the point that they are considered fundamental tools in climate change research. Major intercomparison programs have been implemented over the years, culminating in the Coordinated Regional climate Downscaling EXperiment (CORDEX), an international program aimed at producing fine scale regional climate information based on multi-model and multi-technique approaches. These intercomparison projects have lead to an increasing understanding of fundamental issues in climate downscaling and in the potential of downscaling techniques to provide actionable climate change information. Yet some open issues remain, most notably that of the added value of downscaling, which are the focus of substantial current research. One of the primary future directions in dynamical downscaling is the development of fully coupled regional earth system models including multiple components, such as the atmosphere, the oceans, the biosphere and the chemosphere. Within this context, dynamical downscaling models offer optimal testbeds to incorporate the human component in a fully interactive way. Another main future research direction is the transition to models running at convection-permitting scales, order of 1–3 km, for climate applications. This is a major modeling step which will require substantial development in research and infrastructure, and will allow the description of local scale processes and phenomena within the climate change context. Especially in view of these future directions, climate downscaling will increasingly constitute a fundamental interface between the climate modeling and end-user communities in support of climate service activities.

Coordination of Regional Downscaling

2020 ◽  
Author(s):  
William Joseph Gutowski ◽  
Filippo Giorgi
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Added Value ◽  
Climate Services ◽  
Global Models ◽  
Scientific Goal ◽  
The World ◽  
Regional Downscaling

Regional climate downscaling has been motivated by the objective to understand how climate processes not resolved by global models can influence the evolution of a region’s climate and by the need to provide climate change information to other sectors, such as water resources, agriculture, and human health, on scales poorly resolved by global models but where impacts are felt. There are four primary approaches to regional downscaling: regional climate models (RCMs), empirical statistical downscaling (ESD), variable resolution global models (VARGCM), and “time-slice” simulations with high-resolution global atmospheric models (HIRGCM). Downscaling using RCMs is often referred to as dynamical downscaling to contrast it with statistical downscaling. Although there have been efforts to coordinate each of these approaches, the predominant effort to coordinate regional downscaling activities has involved RCMs. Initially, downscaling activities were directed toward specific, individual projects. Typically, there was little similarity between these projects in terms of focus region, resolution, time period, boundary conditions, and phenomena of interest. The lack of coordination hindered evaluation of downscaling methods, because sources of success or problems in downscaling could be specific to model formulation, phenomena studied, or the method itself. This prompted the organization of the first dynamical-downscaling intercomparison projects in the 1990s and early 2000s. These programs and several others following provided coordination focused on an individual region and an opportunity to understand sources of differences between downscaling models while overall illustrating the capabilities of dynamical downscaling for representing climatologically important regional phenomena. However, coordination between programs was limited. Recognition of the need for further coordination led to the formation of the Coordinated Regional Downscaling Experiment (CORDEX) under the auspices of the World Climate Research Programme (WCRP). Initial CORDEX efforts focused on establishing and performing a common framework for carrying out dynamically downscaled simulations over multiple regions around the world. This framework has now become an organizing structure for downscaling activities around the world. Further efforts under the CORDEX program have strengthened the program’s scientific motivations, such as assessing added value in downscaling, regional human influences on climate, coupled ocean­–land–atmosphere modeling, precipitation systems, extreme events, and local wind systems. In addition, CORDEX is promoting expanded efforts to compare capabilities of all downscaling methods for producing regional information. The efforts are motivated in part by the scientific goal to understand thoroughly regional climate and its change and by the growing need for climate information to assist climate services for a multitude of climate-impacted sectors.

scholarly journals Dynamical Downscaling–Based Projections of Great Lakes Water Levels*+

2015 ◽  
Vol 28 (24) ◽  
pp. 9721-9745 ◽  
Author(s):  
Michael Notaro ◽  
Val Bennington ◽  
Brent Lofgren
Keyword(s):  
Great Lakes ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Surface Fluxes ◽  
Global Climate Models ◽  
Water Levels ◽  
Added Value ◽  
Lake Model ◽  
Projected Change ◽  
Lake Evaporation

Abstract Projections of regional climate, net basin supply (NBS), and water levels are developed for the mid- and late twenty-first century across the Laurentian Great Lakes basin. Two state-of-the-art global climate models (GCMs) are dynamically downscaled using a regional climate model (RCM) interactively coupled to a one-dimensional lake model, and then a hydrologic routing model is forced with time series of perturbed NBS. The dynamical downscaling and coupling with a lake model to represent the Great Lakes create added value beyond the parent GCM in terms of simulated seasonal cycles of temperature, precipitation, and surface fluxes. However, limitations related to this rudimentary treatment of the Great Lakes result in warm summer biases in lake temperatures, excessive ice cover, and an abnormally early peak in lake evaporation. While the downscaling of both GCMs led to consistent projections of increases in annual air temperature, precipitation, and all NBS components (overlake precipitation, basinwide runoff, and lake evaporation), the resulting projected water level trends are opposite in sign. Clearly, it is not sufficient to correctly simulate the signs of the projected change in each NBS component; one must also account for their relative magnitudes. The potential risk of more frequent episodes of lake levels below the low water datum, a critical shipping threshold, is explored.

scholarly journals Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

2018 ◽  
Vol 33 (2) ◽  
pp. 369-388 ◽  
Author(s):  
Peter Vogel ◽  
Peter Knippertz ◽  
Andreas H. Fink ◽  
Andreas Schlueter ◽  
Tilmann Gneiting
Keyword(s):  
Bayesian Model Averaging ◽  
Spatial Scales ◽  
Monsoon Season ◽  
Model Averaging ◽  
Added Value ◽  
Ensemble Prediction ◽  
Full Potential ◽  
Tropical Africa ◽  
Ensemble Forecasts ◽  
Ensemble Model Output Statistics

AbstractAccumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated precipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical postprocessing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007–14, but overall they have little added value compared to climatology. The suspicion is that parameterization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

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