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date: 05 April 2020

Coordination of Regional Downscaling

Summary and Keywords

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.

Keywords: climate information for regions, CORDEX, coordinated regional modeling, downscaling, Flagship Pilot Studies, high-resolution climate simulation, RCM, regional climate modeling

Introduction

Regional climate downscaling has been motivated by the objective to understand how climate forcings and processes not resolved by global models can influence the evolution of a region’s climate, and by the need to provide climate change information on the space and time scales where impacts are most felt. These are often the scales of processes governing application sectors such as water management, agriculture and food production, ecosystem services, health, infrastructure, and others.

There are four primary approaches to regional downscaling: regional climate models (RCMs), empirical statistical downscaling (ESD), variable resolution global climate models (VARGCMs), and “time-slice” simulations with high-resolution global climate models (HIRGCMs). Downscaling using RCMs is often referred to as dynamical downscaling to contrast it with statistical downscaling. The predominant effort to coordinate regional downscaling activities has involved RCMs. There have been efforts to coordinate the other approaches, either on their own (e.g., the Stretched-Grid Model Intercomparison Project, SGMIP (Fox-Rabinovitz, Côté, Dugas, Déqué, & McGregor, 2006) for VARGCMs) or in combination with dynamical downscaling (e.g., the Statistical and Regional dynamical Downscaling of Extremes for European regions program, STARDEX (STARDEX, 2005), and the European Cooperation in Science and Technology (COST) Action VALUE project (Maraun et al., 2010)), but such efforts have occurred less frequently. Therefore, the coordination of dynamical downscaling using RCMs is the primary focus of this article.

Regional climate modeling as performed by RCMs consists of using lateral meteorological boundary conditions and sea surface temperatures obtained from global climate models (GCMs) or analyses of observations to drive the “nested,” limited-area RCMs over selected regions of interest (Giorgi, 1990; Giorgi & Gutowski, 2015). The strategy underlying this downscaling methodology is that the driving GCM can simulate the response of the global circulation to large scale forcings (e.g., increasing greenhouse gas concentrations) while the nested RCM enhances the regional information produced by the GCMs by accounting for high-resolution processes and forcings of relevance for the region (e.g., topography, coastlines, and land use).

The Value of Coordination

By design, regional climate downscaling is affected by multiple sources of uncertainty, which can arise from the regional modeling itself, sources of GCM boundary conditions, assumptions about climate change scenarios, and the inherent uncertainty associated with the nonlinear and chaotic behavior of the climate system. Uncertainty from the regional modeling includes inherent structural uncertainty in the regional model and potential effects from structural differences between the RCM and the driving GCM. The first three reflect primarily error that arises from shortcomings in our understanding and modeling of climate processes, including the role of humans, but the inherent uncertainty in the climate system produces unforced, unpredictable variability that will always yield an inherent uncertainty in regional climate projections, and that tends to grow relatively large versus change signals when considering smaller and smaller scales (e.g., Giorgi, 2002). Accounting for the uncertainty in regional climate downscaling is thus a complex multidimensional problem, and coordinated programs involving large ensembles of multiple models, scenarios, and driving GCM conditions provide an excellent opportunity for characterizing and discriminating the different sources of uncertainty (Hawkins & Sutton, 2009).

A further important aim of coordinated efforts is to provide directions for improving systematic errors in RCMs, which is a key requisite for enhancing the quality of downscaled climate change information. For example, early RCM intercomparison efforts have identified a number of common biases across ensembles of RCMs, which have led to more deep investigations of the underlying deficiencies in the models (Boberg & Christensen, 2012; Jacob et al., 2007; Kim et al., 2013; Solman et al., 2013). Similarly, multi-model programs sharing intercomparable simulation protocols can help to identify the added value of downscaled information compared with that produced by the driving GCMs (Rummukainen, 2016). A number of studies have shown, for example, that high-resolution RCMs can improve the representation of the effect of topography on precipitation patterns and their changes, along with precipitation intensity distributions and extremes (Di Luca, de Elía, & Laprise, 2012; Giorgi et al., 2016; Torma, Giorgi, & Coppola, 2015). The consistency of this finding across different models and regional domains has provided a strong element of robustness to this result.

Because different communities are often interested in different climatic processes or climate impacts, they may have varied needs and technical requirements. However, regional downscaling programs that use common experimental protocols can facilitate the analysis and intercomparison of results and give greater opportunity to understand models, processes, and projections. At the same time, inter-model agreement through large ensembles and the identification of underlying processes common to the models can increase the confidence in projected regional climate changes (e.g., Christensen et al., 2007b, 2013).

To achieve the goal of accessing dimensions of uncertainty space as thoroughly as possible, the design of a coordinated downscaling experiment requires an appropriate, internally consistent common simulation protocol in order to maximize the value of results. This is especially important when dealing with different downscaling techniques. For example, RCMs are sensitive to model resolution, physics parameterizations, and domain specifications (Giorgi et al., 2012; Laprise et al., 2008; Larsen, Thejll, Christensen, Refsgaard, & Jensen, 2013). If working with ESD techniques, their outcomes can be highly dependent on choices of input variables and statistical assumptions, and the observations necessary to calibrate them vary in quality and density across regions (Hewitson & Crane, 1996; Maraun et al., 2010; Wilby & Wigley, 1997). Therefore it is very important to carefully design consistent and intercomparable simulation protocols.

Model validation and assessment is also a complex issue. Because of the complexity of the climate system and the wide range of user needs for the output, assessment of regional climate downscaling should use multiple criteria and, ideally, multiple observation sources, since observations themselves can be characterized by substantial uncertainty due to the density of observing sites, the use of different calibration methods for satellite-based products, and the use of different interpolation procedures for gridded products. On the one hand, systematic model errors can be assessed through common performance metrics across different domains, and on the other hand, metrics for regionally specific processes can provide additional elements of model assessment for different regional contexts. Key to such analyses is the assessment of multiple climate characteristics and thus the availability of multiple observation-based variables, possibly with known error characteristics. In many parts of the world, this can be a challenge, as the density of observations may be coarse compared with the process scales resolved by the downscaling (Lake, Gutowski, Giorgi, & Lee, 2017). Reanalyses can partially fill this gap, and several global or regional reanalysis products exist that work on grid spacings of a few tens of kilometers (e.g., Gelaro et al., 2017; Mesinger et al., 2006). An important consideration to remember, however, is that the reanalyses are ultimately products of a model, and are thus also dependent not only on the input observations and assimilation techniques, but also on the underlying forecast model used by the reanalysis system. Satellite-based observations offer potential for data at resolutions of a few kilometers, and efforts are ongoing to more systematically exploit this resource (e.g., Lee et al., 2017).

Another critical issue in the design of a downscaling simulation protocol is the matrix of GCM–RCM combinations, especially because this matrix is often necessarily sparse due to limitations in computational infrastructures. The selection of sub-ensembles of driving GCMs has often been based on the availability of archived GCM information necessary for downscaling (a so-called “ensemble of opportunity”). More well-considered criteria should, however, be used in the selection of sub-ensembles of GCMs for downscaling. Among these, the GCM performance in representing key climate processes and phenomena, over the target region as well as globally (e.g., the location of storm tracks or simulation of El Niño–Southern Oscillation (ENSO)), and the capability of the selected GCMs to cover the range of regional changes found in the full GCM ensembles (Elguindi, Giorgi, & Turuncoglu, 2014; McSweeney, Jones, Lee, & Rowell, 2015), governed in part by their global climate sensitivity. In addition, careful experiment design based on statistical principles is needed to optimize the usefulness of the downscaling matrix (Benestad et al., 2017) and to use statistical methods such as pattern scaling to substantially fill the empty cells in a partially filled matrix (Mearns et al., 2012, 2013). It is thus clear that a careful design of common simulation matrices and protocols is paramount to the generation of an optimal set of downscaled projections for use in impact and adaptation studies.

Early Regional Climate Downscaling Intercomparison Projects

Initially, downscaling activities were directed toward specific, individual projects, such as the foundational efforts by several groups for various time periods and regions of the world (e.g., Dickinson, Errico, Giorgi, & Bates, 1989; Giorgi, 1990; Giorgi & Bates, 1989; Giorgi, Marinucci, & Visconti, 1990; Hostetler, Giorgi, Bates, & Bartlein, 1994; Kida, Koide, Sasaki, & Chiba, 1991; Liu, Giorgi, & Washington, 1994; McGregor & Walsh, 1994; McGregor, Walsh, & Katzfey, 1993), which demonstrated the credibility of using RCMs. For example, Giorgi, Shields Brodeur, and Bates (1994) showed that the topographic barrier represented by the Sierra Nevada and Coastal ranges can substantially affect the precipitation change signal over the western United States due to the orographic precipitation shadowing effect. However, an RCM’s focus on climatic processes relevant to a specific region meant that early RCM programs around the world could have distinctly different simulation needs and technical requirements. Thus, there was typically little similarity across early projects in terms of focus region, resolution, time period, boundary conditions, and phenomena of interest. As a consequence, it was difficult to export the conclusions of one study to another regional and modeling setting. This lack of coordination thus hindered a systematic evaluation of downscaling methods, because sources of success or problems could be specific to model formulation, phenomena studied, or the method itself. In other words, there were multiple dimensions to the uncertainty space covered by regional downscaling that required more systematic and coordinated participation by the downscaling community.

The need for more thorough, coordinated evaluation of downscaling prompted the organization of the first dynamical-downscaling intercomparison projects. By engaging multiple RCMs and, in some cases, multiple sources of boundary conditions, these programs could explore the dimensions of the uncertainty space in regional-climate downscaling. Some of the earliest efforts included the Project to Intercompare Regional Climate Simulations (PIRCS; Anderson et al., 2003; Takle et al., 1999) in the central United States, the Modelling European Regional Climate: Understanding and Reducing Errors program (MERCURE; Christensen et al., 1997; Hagemann et al., 2004), the Arctic Model Intercomparison Project (ARCMIP; Curry & Lynch, 2002), and the Regional Model Intercomparison Project (RMIP) for East Asia (Fu et al., 2005). These programs used atmospheric reanalyses for initial and lateral boundary conditions to produce simulations by an ensemble of different RCMs. Their primary goals were to understand sources of differences between participating downscaling models while also identifying possible common systematic errors in RCM simulations.

Early RCM intercomparison projects not only identified a number of areas where improvements in modeling would be most beneficial, but also provided indications of the potential capability of regional simulations. For example, PIRCS identified common behaviors among participating models despite differences in their parameterizations for surface processes, convection, and methods for ingesting surface boundary conditions (Takle et al., 1999). All models reproduced well the observed synoptic variability of daily maximum and minimum temperatures, with differences across models being associated with Bowen-ratio differences, thereby indicating the importance of local surface processes.

Some early intercomparison programs also used initial and lateral boundary conditions from GCMs, which yielded projections of climate change and evaluation of regional processes contributing to the change. In this regard, a landmark project was the Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects (PRUDENCE) project (Christensen, Carter, Rummukainen, & Amanatidis, 2007a; Christensen & Christensen, 2007), in which for the first time a partially populated matrix of four GCM x eight RCM x two Scenarios was completed over a European domain for a reference (1961–1990) and a future (2071–2100) time slice. This matrix allowed for the identification of the contributions of different sources of uncertainty in the regional projections (Déqué et al., 2007), showing that for variables such as temperature, the GCM and scenario uncertainty sources were dominating, while for phenomena more dependent on local processes, such as winter and especially summer precipitation, the uncertainty related to the use of different RCMs was also important. Another pioneering aspect of PRUDENCE was that the outputs from the RCM simulations were used in a range of impact studies, showing the value of downscaled information for such types of applications.

Other intercomparison projects followed and expanded on the PRUDENCE example, such as ENSEMBLES for EUROPE (e.g., Boberg, Berg, Thejll, Gutowski, & Christensen, 2010; Kjellström et al., 2010; Sanchez-Gomez, Somot, & Déqué, 2009), the North American Regional Climate Change Assessment Program (NARCCAP) for the continental United States (Mearns et al., 2012, 2013), CLARIS for South America (Sánchez et al., 2015; Solman et al., 2013), the African Monsoon Multidisciplinary Analysis (AMMA) project for West Africa (Redelsperger et al., 2006), and further extensions of RMIP (Li et al., 2016; Niu et al., 2015). However, although there was greater uniformity within a program, coordination across programs was limited. Thus, some of the same differences in experiment protocols cited earlier remained (e.g., resolution, time period, etc.). This made transferring knowledge across these programs difficult, so that downscaling experience remained to some extent regionally fragmented. Further reviews of these earlier programs appear in a number of sources (e.g., Arritt & Rummukainen, 2011; Giorgi, 2006; Laprise, 2008; Rockel, 2015; Wang et al., 2004).

The Coordinated Regional Downscaling Experiment

Recognition of the need for further coordination has led to the implementation of the Coordinated Regional Downscaling Experiment (CORDEX) under the auspices of the World Climate Research Programme (WCRP). Building on experience gained in the earlier regional programs, the initial CORDEX effort focused on establishing and performing a common framework for carrying out large and homogeneous ensembles of dynamically downscaled simulations over multiple regions around the world (Giorgi, Jones, & Asrar, 2009; Jones, Giorgi, & Asrar, 2011). A special focus was on regions, such as Africa, where impacts of climate change could be substantial within a context of high vulnerability, but where there has been relatively little organized downscaling activity.

Further goals of CORDEX through the common framework were to foster coordination internationally (i.e., across regions), providing in particular a forum to enhance participation of scientists from developing countries, and to foster greater interaction between the global and regional climate modeling communities and the communities of users of climate information for vulnerability-impact-adaptation (VIA) studies.

A Task Force on Regional Climate Downscaling (TFRCD) established by the WCRP created the initial structure and guidance for CORDEX activities. The TFRCD developed a prototype framework for the activities of the first phase of the CORDEX activities. It defined the specifications of a set of domains covering nearly all continental land masses of the globe plus the Arctic (Figure 1), and identified simulation requirements in terms of time periods to cover, sources of boundary conditions, and protocols for the output variables and metadata to store in CORDEX archives (Giorgi et al., 2009; Jones et al., 2011). The framework included “perfect boundary condition experiments” using forcing data from the ERA-interim reanalysis of observations (Dee et al., 2011) to assess and possibly improve the models and a simulation matrix composed of multiple RCMs downscaling multiple Coupled Model Intercomparison Project, Phase 5 (CMIP5) GCM projections over the different domains. For the RCMs, a relatively large model grid spacing of 50 km (approx. 0.44˚ latitude) was adopted as baseline in order to foster participation by a wide community. This framework has rapidly become an organizing structure for downscaling activities around the world. For example, it spawned new regional efforts in Southeast Asia (e.g., Ngo-Duc et al., 2017) and in a region covering the Middle East/North Africa domain (Almazroui et al., 2016; Bucchignani, Cattaneo, Panitz, & Mercogliano, 2016). It has also laid a foundation for an effort to coordinate empirical statistical downscaling for assessment in and of itself and jointly with dynamical downscaling.

Coordination of Regional Downscaling

Figure 1. Coordinated Regional Downscaling Experiment (CORDEX) domains.

Credit: G. Nikulin and E. O’Rourke, personal communication; adapted from Giorgi and Gutowski (2015).

The initial CORDEX activities have produced ensembles of contemporary and projected climates for most domains, including Africa (e.g. Nikulin et al., 2012), Europe (e.g., Jacob et al., 2014; Kotlarski et al., 2014), the Mediterranean (e.g., Ruti et al., 2016), the Arctic (e.g., Koenigk, Berg, & Döscher, 2015), South Asia (e.g., Ghimire, Choudhary, & Dimri, 2018), East Asia (e.g., Park et al., 2016), Southeast Asia (e.g., Ngo-Duc et al., 2017), South America (e.g., Solman et al., 2013), North America (e.g., Cerezo-Mota et al., 2016; Diaconescu, Gachon, Laprise, & Scinocca, 2016), Central America (e.g., Diro et al., 2014), the Middle East/North Africa (e.g., Almazroui et al., 2016; Bucchignani et al., 2016), and Central Asia (e.g., Ozturk, Altinsoy, Türkeș, & Kurnaz, 2012). The case of Africa is particularly illustrative. A large set of projections was completed for the Africa domain (Figure 2), and a CORDEX analysis team, composed primarily by African scientists, was created to analyze them in depth. As part of this effort, a series of workshops on model analysis and scientific paper writing were also organized, thus representing an example of scientific capacity-building for other regions in the world. The team’s activities resulted in the publication of three papers in high-quality peer-reviewed scientific journals that analyzed an ensemble of simulations by 10 different RCMs for their ability to simulation climate processes in eastern Africa (Endris et al., 2013), southern Africa (Kalognomou et al., 2013) and West Africa (Gbobaniyi et al., 2014). A common, key finding in all regions was that the multi-model ensemble average generally outperformed the individual models in comparison with observations of temperature and precipitation climatology.

Coordination of Regional Downscaling

Figure 2. July–August–September precipitation for 1998–2008 over central Africa from (top row) different observational datasets and from (rows 2–4) the ERA-Interim reanalysis, the ensemble mean of all participating RCMs and the individual RCMs.

Credit: Adapted from Nikulin et al. (2012).

In Europe, RCM activities have traditionally been very advanced, mostly as part of EU-sponsored programs such as PRUDENCE and ENSEMBLES. CORDEX has built on these efforts to develop the EURO-CORDEX (Jacob et al., 2014) and MED-CORDEX (Ruti et al., 2016) programs, which have in fact represented fundamental advances in RCM research. For example, an ensemble of unprecedented size and resolution (0.11°, or ~12 km) was produced by EURO-CORDEX, representing an invaluable resource extensively used for VIA applications. MED-CORDEX focused on the development and use of coupled Regional Earth System Models for the Mediterranean basin that link atmosphere, ocean, river, and aerosol sub-models, which also resulted in an unprecedented set of coupled regional simulations. In both cases the CORDEX framework was instrumental in enhancing the involvement of the scientific community and leveraging national and European funding.

As CORDEX became established as a major project in the WCRP and outcomes supporting CORDEX goals were produced (e.g., Giorgi et al., 2012; Nikulin et al., 2012; Ozturk et al., 2012), the TFRCD was consolidated into a CORDEX Science Advisory Team (SAT) in 2012, reporting to the Joint Scientific Committee (JSC), the board that oversees all WCRP activities. An International Project Office for CORDEX (IPOC) was also established at the Swedish Hydrometeorological Institute in Norrköping, Sweden, to support and coordinate the different CORDEX efforts. The SAT first refined the CORDEX overarching goals to:

  1. 1. better understand relevant regional/local climate phenomena, and their variability and changes, through downscaling;

  2. 2. evaluate and improve regional-climate downscaling models and techniques;

  3. 3. produce coordinated sets of regional downscaled projections worldwide, with data access via CORDEX Data (2019); and

  4. 4. foster communication and knowledge exchange with users of regional climate information.

The SAT further identified five CORDEX Regional Scientific Challenges of priority for CORDEX research:

  1. 1. Added value. The added value of downscaling is a common concern. Downscaling techniques are useful to the extent that they add useful information to that provided by the GCM towards application to VIA studies. In general, this is not always the case, and the added value is a function of the variables, statistics, and the temporal and spatial scales considered (e.g., Di Luca et al., 2012; Giorgi & Gutowski, 2015; Rummukainen, 2016; Torma et al., 2015). On the one hand, the downscaling procedure is typically unable to correct a driving GCM’s biases in large-scale circulations, which therefore substantially influence the RCM performance. On the other hand, the added value can be expected mostly for regional settings where high-resolution forcings are important, for example mountainous regions, or for processes that occur at small spatial and temporal scales, such as extreme events. Therefore, the added value should be carefully assessed to ensure that there is sufficient additional information gained from downscaling, because regional-climate downscaling methods can engage substantial use of computing and analysis resources. Many studies have demonstrated the added value of downscaling in specific contexts, both for current climate (Castro, Pielke Sr., & Leoncini, 2005; De Sales & Xue, 2011; Diaconescu & Laprise, 2013; Feser, 2006; Feser, Rockel, von Storch, Winterfeldt, & Zahn, 2011; Prömmel, Geyer, Jones, & Widmann, 2009; Winterfeldt & Weisse, 2009) and for climate projections (Di Luca, de Elía, & Laprise, 2013a, 2013b; Mariotti, Coppola, Sylla, Giorgi, & Piani, 2011; Mariotti, Diallo, Coppola, & Giorgi, 2014); however, a more systematic assessment of the added value of downscaling over a comprehensive set of regional settings is necessary (e.g., Laprise, 2014; Racherla, Shindell, & Faluvegi, 2012), and CORDEX offers an excellent framework for carrying this out. This issue is particularly relevant in view of the increasing capability of running very high-resolution (1–5 km), and highly expensive, convection-permitting models for climate applications. Thus, whether or not climate downscaling results in more credible fine-scale climate change signals requires continuous assessment (Di Luca et al., 2013b; Giorgi et al., 2016).

Within this context, the value of post-processing techniques used for VIA application (e.g., bias correction (Berg, Bosshard, & Yang, 2015; Berg, Feldmann, & Panitz, 2012) or pattern scaling (Tebaldi & Arblaster, 2014)) needs to be better explored. ESD methods side by side with RCM downscaling can help delineate factors that determine relevant regional patterns or lead to downscaling biases, and such evaluation is vital, because extracting credible information on regional climate change contexts is important for using RCM‐based information in VIA applications. This generally requires an analysis of large amounts of data from different sources (ensembles of GCM and RCM simulations, ESD information, etc.) that aims at “distilling” the most robust and credible information.

  1. 2. Human element. High-resolution models allow for studying a range of human activities and their effects on regional and local climates. Example activities include land-use change and urban development (and, in particular, the growth and spread of coastal megacities) or the impacts of aerosols of anthropogenic origin. The literature contains a number of individual RCM-based studies of land-use and aerosol effects (e.g., Giorgi & Gutowski, 2015). However, a coordinated approach to this issue is required to fully evaluate the importance of local forcings on regional climates. In fact, the explicit inclusion of the human component in models is one of the next challenges in climate modeling, and RCMs offer a tremendous opportunity to explore this issue.

  2. 3. Coordination of regional coupled modeling activities. One of the frontiers in regional climate modeling is the coupling at the regional scales of different climate system components (e.g., atmosphere, ocean, land and ocean biosphere, sea ice, chemistry/aerosol), leading toward the goal of developing Regional Earth System Models (RESMs). The Med-CORDEX (Ruti et al., 2016) and Arctic-CORDEX (Dethloff et al., 2012) programs are already moving in that direction, but model coupling activities would benefit from greater integration and coordination across all the CORDEX regional settings, thus allowing greater understanding of the opportunities and challenges associated with RESMs.

  3. 4. Precipitation. Multiple societal sectors are impacted by changes in precipitation patterns, regimes, and extreme events. As a result, for VIA studies, precipitation is one of the most frequently used variables from downscaling archives. Precipitation is also one of the most difficult to simulate in current climate models, being an integrator of many dynamical and thermodynamical processes. However, precipitation is a variable where the high resolution of RCMs can provide a substantial added value, especially in the simulation of extremes, mesoscale convective systems, and coastal storms (Giorgi & Gutowski, 2015). Projected precipitation changes can have significant uncertainty because of model systematic errors and large natural variability, and a characterization of this uncertainty requires the completion of large ensembles. CORDEX can thus provide a framework for making the projection of regional hydroclimatic change more robust.

  4. 5. Local wind systems. Devastating impacts can arise from strong regional and local winds, such as the mistral and bora in the Mediterranean or Chinook winds in western North America, as well as intense tropical and extratropical storms, particularly in coastal environments where wind-driven storm surge can have severe consequences. The typical resolution of current global models is insufficient to simulate accurately local, intense wind systems, because these are often tied to fine-scale topography or surface–atmosphere exchanges. There has also been relatively little analysis of surface winds in both global and regional models, and it can be expected that the move to very high-resolution convection-permitting modeling will in fact provide strong opportunities to improve the simulation of dangerous wind systems.

In addition to the impacts of strong winds, analysis of evolving wind patterns could be important for economic sectors such as wind energy production, including the impact of wind farms themselves on the regional wind patterns. However, inconsistencies can occur between different observation-based wind datasets (Pryor et al., 2009). Other potentially useful observations may be inaccessible: those for wind energy by the private sector are often proprietary and not openly released. CORDEX can provide an essential contribution to research in this area.

In addition to the CORDEX Regional Challenges, an important issue indicated by them is the “distillation” of robust and credible climate information for use in VIA studies. While CORDEX goals can involve working with users of regional climate information, CORDEX is not itself a climate service. Rather, it seeks to provide the datasets that are the starting point for distillation, with production of climate information a further effort beyond the remit of CORDEX. However, users of downscaled data often have a perception that these data have a high level of credibility, a perception that can be misleading due to the many uncertainties affecting the data, and lead to inappropriate use of them (Hewitson et al., 2014b; Hewitson, Daron, Crane, Zermoglio, & Jack, 2014a). The distillation of credible information from multiple sources is an emerging area of research of particular interest within the context of climate service activities, whose progress requires increasing interactions between the climate, VIA, and stakeholder communities (Daron, Sutherland, Jack, & Hewitson, 2015; Hewitson, Waagsaether, Wohland, Kloppers, & Kara, 2017; Steynor, Padgham, Jack, Hewitson, & Lennard, 2016).

Coordinating Advances in Regional Climate Downscaling: Flagship Pilot Studies

One advantage of global coordination is that it can allow for efforts to address the CORDEX- Regional Scientific Challenges in a cross-domain approach. CORDEX has highlighted such efforts through the concept of Flagship Pilot Studies (FPS), that is, studies that can explore specific science questions through more targeted and in-depth investigations that cannot be carried out within the standard CORDEX framework. A typical example would be the exploration of the added value of using very high-resolution, convection-permitting models, which cannot be run over the continental-scale CORDEX domains, but can be used over smaller ones. Another example may be the detailed intercomparison of different downscaling techniques over sub-regions where sufficient calibration data are available.

The FPS concept of addressing specific issues through targeted experimental set-ups first emerged during the first International Conference on Regional Climate–CORDEX 2013 (Boscolo & O’Rourke, 2013), held in November 2013 in Brussels, and the procedure for developing FPS programs was then finalized at the second International Conference on Regional Climate–CORDEX 2016 (Lake et al., 2017), held in Stockholm in May 2016. The FPS are intended to focus primarily, but not exclusively, on sub-continental-scale regions, so as to facilitate the use of a variety of downscaling approaches and models. The FPS offer several possibilities to advance the state of regional climate modeling:

  • run RCMs at a broad range of resolutions, down to convection-permitting;

  • promote side-by-side experimental design and evaluations of both statistical and dynamical downscaling techniques at scales more typical of VIA applications;

  • design targeted experiments aimed at investigating specific regional processes and circulations;

  • investigate the importance of regional-scale forcings (aerosols, land-use change, vegetation, etc.);

  • compile and use high-quality, high-resolution (both spatial and temporal), multi-variable observation datasets for model validation and analysis of processes;

  • coordinate with specific activities in other WCRP projects, most notably the GEWEX (Global Energy and Water Exchanges) regional hydroclimate projects;

  • design end-to-end, climate-to-end-user projects demonstrating the actionable value of downscaled climate change projections; and

  • increase the potential for funding by focusing on specific issues of interest for a certain region.

The CORDEX SAT also recognized that, due to their very nature, FPS cannot be conceived in general terms but should be driven by the regional CORDEX communities, albeit by sharing protocols consistent with the CORDEX approach so as to allow for easier exchange of know-how. The SAT has developed a mechanism of solicitation of FPS proposals to be submitted by the regional communities to the IPOC director and assessed by the SAT, with the aid of external experts, for formal CORDEX endorsement. Factors guiding the development of an FPS proposal appear in Figure 3, and specific requirements for the FPS appear in Table 1 and are provided on the CORDEX website with a six-month deadline application cycle. Several FPS have been endorsed by the CORDEX SAT, including those addressing, among others, issues such as convection-permitting modeling, regional land-use change effects, and aerosol effects.

Coordination of Regional Downscaling

Figure 3. Factors guiding the development of a CORDEX Flagship Pilot Studies proposal.

Credit: The authors.

Table 1. Criteria Satisfied by CORDEX Flagship Pilot Studies, with Distinction between Those Essential to FPS and Those That Are Not Essential but Highly Recommended

Criterion

Essential

Highly recommended

1.

Targeting fine-scale processes and clear scientific questions of interest (e.g., relevant to CORDEX Regional Scientific Challenges)

  • Not addressed by GCMs or coarser-resolution downscaling

  • Have potential to demonstrate the added value of downscaling

  • Not addressed within the existing standard CORDEX framework

  • Can be usefully approached by both dynamical and statistical downscaling methods, so as to allow an intercomparison of the approaches

  • Investigate regional processes, circulations, and forcings of interest

2.

Use of observational data including not only meteorological but also derived data (e.g., soil moisture, streamflow, etc.)

  • Studies should be based upon data of sufficient quality to support the objectives

The observation data should enable the capability to:

  • Investigate regional processes

  • Validate dynamical models down to convection-permitting resolutions and sub-daily scales

  • Provide information suitable to calibrate and validate statistical downscaling tools

  • Enable cross-analysis and validation of multiple variables, processes, feedbacks, and interactions across climate system components

3.

End-to-end perspective and potential to support demonstrated local/regional needs

  • Impact of the study from the physical science and/or VIA viewpoints

  • Stakeholder needs determined by the interactions with VIA community or existing literature on the topic

4. Applicant group

  • Multiple participants must be involved in the study

  • Transnational and multidisciplinary applicant groups are encouraged

Regional Climate Downscaling for Climate Assessment: The Coordinated Output for Regional Evaluations (CORE) Program

One of the main problems emerging at the CORDEX 2013 and CORDEX 2016 conferences was the large inhomogeneity in the availability of simulations across different domains. While some domains, for example Africa, Europe, and the Mediterranean, have large ensembles of model simulations, others, such as Central Asia, Central America, and Australia, have only a few simulations available. This heterogeneity creates difficulty for CORDEX to provide consistent information to international programs such as the Intergovernmental Panel on Climate Change (IPCC) and to transfer the scientific understanding gained on physical processes and downscaling procedures to different regions of the world. This heterogeneity has motivated a program within CORDEX aimed at producing a succinct set of homogeneous simulations across domains, the CORDEX Coordinated Output for Regional Evaluations (CORE). As such, the CORDEX CORE serves as a prototype for organizing a more rigorously structured global program that could support needs for climate information with a uniform, global foundation.

The CORDEX CORE framework (Figure 4) was presented and discussed at the CORDEX 2016 conference, with further input and discussion generated from a questionnaire sent to the CORDEX community through the Points of Contact in each CORDEX domain. CORDEX CORE has a primary goal of providing a core set of comprehensive and homogeneous projections via the engagement of a core set of RCMs downscaling a core set of GCM projections over all, or most, CORDEX domains. Through the involvement of community-based RCMs (e.g., the RegCM4 (Giorgi et al., 2012), COSMO-CLM (Rockel, Will, & Hense, 2008), WRF (Powers et al., 2017), REMO (Jacob et al., 2012) systems), which allow for coordinated sharing of the simulation efforts, the CORE framework is intended to be ambitious but sufficiently cost-effective to be able to attract broad participation and to provide a foundation in each region for further downscaling activities. It is also intended to provide an unprecedented downscaling-based database for use in VIA studies.

Coordination of Regional Downscaling

Figure 4. The simulation framework for the CORDEX Coordinated Output for Regional Evaluations (CORE) program.

Credit: The authors.

To facilitate CORDEX CORE, the CORDEX program has become a diagnostic model intercomparison project (MIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6; Gutowski et al., 2016), and as such it will use CMIP DECK, CMIP6 Historical Simulation, and ScenarioMIP output to provide input conditions for both statistical and dynamical downscaling. The CORE framework will use some specifications from the previous CORDEX program, such as historical and future simulations periods and scenarios, but aims at increasing the horizontal resolution to values of ~20 km to improve applicability to VIA studies. A critical issue is the choice of a core set of GCMs to be downscaled, whose selection will be based on the following criteria:

  1. 1. cover as well as possible the range of GCM climate model sensitivity;

  2. 2. provide acceptable quality of historical climate simulations in the regions where they supply boundary conditions;

  3. 3. provide acceptable quality of historical climate simulations for important large-scale features affecting regional climates, such as ENSO and the North American Oscillation; and

  4. 4. have a distinctive model development history.

In addition, simulations should be available for at least a high-end Representative Concentration Pathway future-climate scenario (RCP8.5) and low-end scenario (RCP2.6) (see Moss et al., 2010). Such a set of GCMs promotes the comparison and transfer of results and lessons learned across different regions. Projects for individual CORDEX regions may also choose additional GCMs, and individual RCM groups may complete simulations for a small set of domains, which will complement the CORE model information.

As for the standard CORDEX protocol, CORDEX CORE focuses on historical climate simulations for the second half of the 20th century and projections for the 21st century. Driving GCMs will be initially selected from the CMIP5 archive, but then the ensemble will be augmented by CMIP6-driven simulations as these become available. ESD methods will also produce gridded output at the same RCM grid spacing, which will allow for a more rigorous intercomparison across these methodologies, and the CORDEX CORE is also expected to enable direct comparison with the highest-resolution GCM experiments planned for the HighResMIP program (Haarsma et al., 2016) in CMIP6.

Directions Forward for CORDEX

CORDEX has by now become the reference program for worldwide coordination of downscaling research. The CORDEX framework enables the implementation of several additional efforts that can benefit from the coordination. Perhaps first and foremost is the potential for inclusion of other primary approaches to regional downscaling: ESD, VARGCMs, and “time-slice” simulations with HIRGCMs. Each of these approaches offers distinctive strengths and challenges. ESD can make efficient use of computing resources through statistical models that are numerically simpler than in other methods and by focusing on specific locales. However, ESD methods require the availability of high-quality observations to develop and calibrate the statistical modes, which may pose a problem for a number of regions in the world. The VARGCMs and HIRGCMs offer global coverage; but especially for HIRGCMs, the computational demands may limit the time periods covered. Although not formally part of CORDEX, some VARGCMs have performed simulations targeting CORDEX regions (McGregor, 2015). In addition to specific challenges, statistical and dynamic modeling also include assumptions of stationarity in statistical relationships, which may not be realized in changing climate. For ESD, the statistical relationships used are typically based on present climate. For dynamical models, the parameterization of sub-grid behavior also rests on statistical relationships. Evaluation of all methods side by side is needed to compare the properties and outcomes of each. This understanding is needed for developing the best possible climate-projection information for use by researchers concerned with understanding climate change and its impacts. Developing protocols to encompass a broader range of downscaling methods will thus benefit not only the scientific community, but also those using the information produced by regional downscaling.

In particular, ESD poses some distinctive challenges because a wide variety of statistical approaches are used, in contrast to the dynamical methods, which rely on fundamental conservation laws of energy, mass, and momentum. Indeed, there are numerous practitioners worldwide who seek to provide climate information to a wide range of users (Maraun & Widmann, 2018). However, many operate independently with ad hoc applications of their work, and as a consequence, assessing the general capability of ESD to address the needs of its users is difficult. One result of this diversity of methods and applications has been a lack of systematic evaluation applied to regions around the world, which may be a reason why statistical downscaling outcomes have been little used in climate assessments such as the IPCC reports. Despite these challenges, and perhaps because of them, efforts have arisen to develop an ESD component to CORDEX that seeks to evaluate ESD methods with respect to each other and in comparison with dynamical approaches. A challenge in such evaluations is to demonstrate the distinctive advantages of ESD methods compared with dynamical ones. For example, ESD methods can provide information at local scales or for quantities not directly simulated in dynamical models, such as the annual blossoming date of fruit trees or the date of springtime ice breakup on rivers.

Regional modeling appears to have two primary pathways for future development (Giorgi & Gutowski, 2015). First, RCMs can grow into Regional Earth System Models, which would entail coupling the major components of the climate system, such as atmosphere, ocean, land, and cryosphere, with biogeochemical and ecosystem processes included. This can allow for the study of interactions across the system’s components at more relevant scales than currently captured by GCMs. RESMs also offer the potential for including as an interactive component human decision-making, such as for regional agriculture and land management. The inclusion of humans as interactive elements in the climate systems is indeed one of the main challenges in the development of the next-generation Earth System Models. Several RESM systems are already available, and RESM development will certainly continue in the future, therefore it is important that these research efforts are better coordinated and integrated to foster the exchange of relevant know-how across activities.

The second primary pathway for RCM research is the development of very high-resolution, convection-permitting models with grid spacings of 1–5 km. On the one hand, non-hydrostatic dynamics become very important at the scales resolved by models with such resolution, and therefore must be part of the model formulation. On the other hand, some parameterizations of physical processes may lose their validity at these grid spacing and may not even be needed. For example, the atmospheric dynamics of deep cumulus convection start to be resolved so that parameterizations may not be necessary. Finer resolution also offers the potential for simulating more accurately the climatology of short-term extreme events, such as strong downpours that can lead to flash flooding and land erosion. Clearly, substantial model development is necessary to ensure processes at these scales are simulated well, especially within a climate simulation context. The numerical weather prediction community already runs models at these resolutions, and the experiences of that community could benefit the RCM community.

Several researchers have performed and analyzed very high-resolution RCM simulations (e.g., Ban, Schmidli, & Schär, 2014; Fosser, Khodayar, & Berg, 2015; Kanada, Wada, & Sugi, 2013; Kendon et al., 2014; Prein et al., 2013; Wakazuki, Nakamura, Kanada, & Muroi, 2008). They show that in some regions, the climate change signal may be substantially modified, especially for statistics of precipitation frequency, intensity, and extremes and for the behavior of intense tropical and extratropical storms. Very high resolution also appears to give an explicit description of organized convection and cloud processes, leading to better simulation of precipitation’s diurnal cycle (Ban et al., 2014; Fosser et al., 2015; Kendon, Roberts, Senior, & Roberts, 2012). Advances in understanding climatic processes at these scales can contribute not only to the improvement of RCMs, but also to the development of higher-resolution global models, just as the development of higher-resolution global models can aid RCM simulation by providing better boundary conditions (Roberts et al., 2018).

A challenge for assessing output at very high resolution, and also for both CORDEX CORE and the FPS, is having observation-based data at scales comparable to those simulated (10–20 km or less). Within individual CORDEX regions, participating modeling groups continually seek observations that can support evaluation of CORDEX simulations. In addition, ongoing efforts to secure fine-resolution datasets are occurring in the obs4MIPS (Teixeira et al., 2014) and ana4MIPS (ana4MIPs, 2017) programs. Process-based regional analyses provide important insight into physical functioning of a model, but these require the acquisition of observations for variables beyond the standard ones (e.g., temperature and precipitation), such as fluxes and regional circulations. Deploying new observation stations that could resolve such scales on a global basis is highly unlikely, due to the expense involved for maintaining instrumentation and support personnel. Remote sensing will be a necessary resource for meeting this need, perhaps in conjunction with reanalyses produced with comparable, high-resolution grid spacings.

More broadly, the coordination established by CORDEX has been useful not only for establishing a framework for scientific progress in understanding regional-scale climate processes, but for providing climate information to practitioners who address wide-ranging societal needs. To this end, a further level of coordination involves partnerships with other programs, in particular within the context of climate service activities (e.g., WCRP Report No. 23/2016, 2016). CORDEX, as a scientific program focused on regional climate, must retain its focus on key scientific issues, such as the CORDEX Regional Scientific Challenges. However, interfacing with other, complementary programs can provide opportunities to share knowledge that would be relevant to these programs. In addition, understanding the needs of climate applications and the people they serve can yield new perspectives on the performance of regional downscaling and inspire new research directions. The interface must, therefore, promote dialogue and not simply a one-way flow of data from regional downscaling to practitioners in other disciplines.

An example of such an interface program is Future Earth (Future Earth, 2013), which engages networks of both social and natural scientists on promoting the United Nations’ Sustainable Development Goals and climate and biodiversity agreements, such as the United Nations Framework Convention on Climate Change and the Convention on Biological Diversity. The information provided by CORDEX can contribute to the understanding of the evolution of climate and its impacts on human and natural systems. The information needs identified by these UN conventions can also prompt further CORDEX simulation protocols and model development and thus inspire further fundamental climate research.

Another activity that can have strong interactions with CORDEX is the Global Framework for Climate Services (GFCS), a program of the World Meteorological Organization that seeks to provide climate information at temporal scales from seasonal to multi-decadal and centennial in support of user needs and decision-making. More specifically, the Copernicus Climate Change Services promotes CORDEX results for the European region. Climate services combine climate data and model output with other types of information, such as farming practices, health networks, road systems, and so forth, in order to support climate-impacts planning. Thus, climate services are outside the immediate realm of CORDEX activities, but climate datasets and, especially, their understanding generated by CORDEX-related research would be an important input to climate services.

In addition, climate services and sustainability efforts provide necessary ingredients for economic development around the world. Thus, development organizations such as the World Bank can be important partners in establishing policies that are responsive to local and regional needs while accounting for climate change information. In fact, interaction with policymakers can provide yet another avenue of research for CORDEX. Policy decisions can affect agriculture, water management, and other human activities that affect the evolution of land use and properties. Regional downscaling can explore the feedbacks on the climate system produced by such management choices and thus play a role in determining the effectiveness of policies.

CORDEX also offers substantial opportunity for growing scientific capacity in developing regions. Performing and analyzing regional downscaling simulations demands relatively modest computing power compared with developing global models and simulating several ensemble members, each spanning many decades if not centuries. This allows scientists in developing regions the opportunity to perform leading-edge climate research that is part of an international program and that also is based on their local knowledge of their region’s weather and climate systems and addresses locally relevant climate issues. The workshops and publications of the African analysis team presented earlier is an example that has been repeated in other regions of the world. CORDEX conferences in 2013 and 2016 included special side events directed toward professional development of early career scientists attending the meetings. The growth in this capacity allows these scientists to become informed, contributing partners in international efforts to develop policy responses to looming human-induced climate change.

In conclusion, regional downscaling is continuously growing as an important tool to provide climate information in support of VIA assessment studies and related decision-making. It is also an increasingly useful tool to understand regional processes and how they affect climate changes at local to regional scales. Finally, CORDEX can help to enable the scientific community from developing countries to be directly involved in climate change research and can provide a framework for stronger interactions between the climate science and the VIA and policymaking communities, which will in fact benefit all these communities. However, much work remains to be done in order to fully explore its potentials and limitations, an aspect particularly important in view of the wide range of uncertainties characterizing regional climate change. Coordinated efforts, such as CORDEX, can provide a framework for exploring the potential of regional modeling that is broadly accessible, thus allowing a wide range of analysis perspectives and downscaling applications.

Further Reading

Brunke, M. A., Cassano, J. J., Dawson, N., DuVivier, A. K., Gutowski Jr., W. J., Hamman, J., … Zeng, X. (2018). Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations. Geoscientific Model Development, 11, 4817–4841.Find this resource:

Cassano, J. J., DuVivier, A., Roberts, A., Hughes, M., Seefeldt, M., Brunke, M., … Zeng, X. (2017). Development of the Regional Arctic System Model (RASM): Near-Surface Atmospheric Climate Sensitivity. Journal of Climate, 30, 5729–5753.Find this resource:

Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., & Zahn, M. (2011). Regional climate models add value to global model data: A review and selected examples. Bulletin of the American Meteorological Society, 92, 1181–1192.Find this resource:

Giorgi, F., & Gutowski, W. J. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40, 467–490.Find this resource:

Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., … Tangang, F. (2016). WCRP COordinated Regional Downscaling EXperiment (CORDEX): A diagnostic MIP for CMIP6. Geoscientific Model Development, 9, 4087–4095.Find this resource:

Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F., & Jack, C. (2014). Interrogating empirical-statistical downscaling. Climatic Change, 122, 539–554.Find this resource:

Hewitson, B., Waagsaether, K., Wohland, J., Kloppers, K., & Kara, T. (2017). Climate information websites: An evolving landscape. Wiley Interdisciplinary Reviews: Climate Change, 8, e470.Find this resource:

Lake, I., Gutowski, W., Giorgi, F., & Lee, B. (2017). CORDEX: Climate research and information for regions. Bulletin of the American Meteorological Society, 98, ES189–ES192.Find this resource:

Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., … Leung, R. (2015). A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Reviews of Geophysics, 53, 323–361.Find this resource:

Prein, A. F., Rasmussen, R., & Stephens, G. (2017). Challenges and advances in convection-permitting climate modeling. Bulletin of the American Meteorological Society, 98, 1027–1030.Find this resource:

Rummukainen, M. (2016). Added value in regional climate modeling. Wiley Interdisciplinary Reviews: Climate Change, 7, 145–159.Find this resource:

Solman, S. A. (2013). Regional climate modeling over South America: A review. Advances in Meteorology, 2013(504357), 1–13.Find this resource:

References

Almazroui, M., Islam, M. N., Alkhalaf, A. K., Saeed, F., Dambul, R., & Rahman, M. A. (2016). Simulation of temperature and precipitation climatology for the CORDEX-MENA/Arab domain using RegCM4. Arabian Journal of Geosciences, 9, 13.Find this resource:

ana4MIPs (2017). ana4MIPa.

Anderson, C. J., Arritt, R. W., Pan, Z., Takle, E. S., Gutowski, W. J., Otieno, F. O., … Roads, J. (2003). Hydrological processes in regional climate model simulations of the central United States flood of June–July 1993. Journal of Hydrometeorology, 4, 584–598.Find this resource:

Arritt, R. W., & Rummukainen, M. (2011). Challenges in regional-scale climate modeling. Bulletin of the American Meteorological Society, 92, 365–368.Find this resource:

Ban, N., Schmidli, J., & Schär, C. (2014). Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. Journal of Geophysical Research: Atmospheres, 119, 7889–7907.Find this resource:

Benestad, R., Sillmann, J., Thorarinsdottir, T. L., Guttorp, P., Mesquita, M. d. S., Tye, M. R., … Parding, K. M. (2017). New vigour involving statisticians to overcome ensemble fatigue. Nature Climate Change, 7, 697–703.Find this resource:

Berg, P., Bosshard, T., & Yang, W. (2015). Model consistent pseudo-observations of precipitation and their use for bias correcting regional climate models. Climate, 3, 118–132.Find this resource:

Berg, P., Feldmann, H., & Panitz, H.-J. (2012). Bias correction of high resolution regional climate model data. Journal of Hydrology, 448–449, 80–92.Find this resource:

Boberg, F., Berg, P., Thejll, P., Gutowski, W. J., & Christensen, J. H. (2010). Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models. Climate Dynamics, 35, 1509–1520.Find this resource:

Boberg, F., & Christensen, J. H. (2012). Overestimation of Mediterranean summer temperature projections due to model deficiencies. Nature Climate Change, 2, 433–436.Find this resource:

Boscolo, R., & O’Rourke, E. (2013). A brief account of the International Conference on Regional Climate—CORDEX 2013. WCRP E-zine, No. 27.Find this resource:

Bucchignani, E., Cattaneo, L., Panitz, H.-J., & Mercogliano, P. (2016). Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain. Meteorology and Atmospheric Physics, 128, 73–95.Find this resource:

Castro, C. L., Pielke Sr., R. A., & Leoncini, G. (2005). Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). Journal of Geophysical Research, 110, D05108.Find this resource:

Cerezo-Mota, R., Cavazos, T., Arritt, R., Torres-Alavez, A., Sieck, K., Nikulin, G., Moufouma-Okia, W., & Salinas-Prieto, J. A. (2016). CORDEX-NA: Factors inducing dry/wet years on the North American Monsoon region. International Journal of Climatology, 36, 824–836.Find this resource:

Christensen, J. H., Carter, T. R., Rummukainen, M., & Amanatidis, G. (2007a). Evaluating the performance and utility of regional climate models: The PRUDENCE project. Climatic Change, 81, 1–6.Find this resource:

Christensen, J. H., & Christensen, O. B. (2007). A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Climatic Change, 81, 7–30.Find this resource:

Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I.,… Whetton, P. (2007b). Regional climate projections. In S. Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M. Tignor, et al. (Eds.), Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 847–940). Cambridge, UK: Cambridge University Press).Find this resource:

Christensen, J. H., Krishna Kumar, K., Aldrian, E., An, S.-I., Cavalcanti, I. F. A., Castro, M. de, … Zhou, T. (2013). Climate phenomena and their relevance for future regional climate change. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1217–1308). Cambridge, UK: Cambridge University Press.Find this resource:

Christensen, J. H., Machenhauer, B., Jones, R. G., Schär, C., Ruti, P. M., Castro, M., & Visconti, G. (1997). Validation of present-day regional climate simulations over Europe: LAM simulations with observed boundary conditions. Climate Dynamics, 13, 489–506.Find this resource:

CORDEX Data, cited as 2019.

Curry, J. A., & Lynch, A. H. (2002). Comparing Arctic regional climate model. Eos, Transactions American Geophysical Union, 83, 87.Find this resource:

Daron, J. D., Sutherland, K., Jack, C., & Hewitson, B. C. (2015). The role of regional climate projections in managing complex socio-ecological systems. Regional Environmental Change, 15, 1–12.Find this resource:

De Sales, F., & Xue, Y. (2011). Assessing the dynamic-downscaling ability over South America using the intensity-scale verification technique. International Journal of Climatology, 31, 1205–1221.Find this resource:

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Vitart, F. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553–597.Find this resource:

Déqué, M., Rowell, D. P., Lüthi, D., Giorgi, F., Christensen, J. H., Rockel, B., … van den Hurk, B. (2007). An intercomparison of regional climate simulations for Europe: Assessing uncertainties in model projections. Climatic Change, 81, 53–70.Find this resource:

Dethloff, K., Rinke, A., Lynch, A., Dorn, W., Saha, S., & Handorf, D. (2012). Arctic regional climate models. In P. Lemke and H. W. Jacobi (Eds.), Arctic climate change (pp. 325–356). Dordrecht: Springer.Find this resource:

Di Luca, A., de Elía, R., & Laprise, R. (2012). Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Climate Dynamics, 38, 1229–1247.Find this resource:

Di Luca, A., de Elía, R., & Laprise, R. (2013a). Potential for added value in temperature simulated by high-resolution nested RCMs in present climate and in the climate change signal. Climate Dynamics, 40, 443–464.Find this resource:

Di Luca, A., de Elía, R., & Laprise, R. (2013b). Potential for small scale added value of RCM’s downscaled climate change signal. Climate Dynamics, 40, 601–618.Find this resource:

Diaconescu, E. P., Gachon, P., Laprise, R., & Scinocca, J. F. (2016). Evaluation of precipitation indices over North America from various configurations of regional climate models. Atmosphere–Ocean, 54, 418–439.Find this resource:

Diaconescu, E. P., & Laprise, R. (2013). Can added value be expected in RCM-simulated large scales? Climate Dynamics, 41, 1769–1800.Find this resource:

Dickinson, R., Errico, R., Giorgi, F., & Bates, G. (1989). A regional climate model for the western United States. Climatic Change, 15, 383–422.Find this resource:

Diro, G. T., Giorgi, F., Fuentes-Franco, R., Walsh, K. J. E., Giuliani, G., & Coppola, E. (2014). Tropical cyclones in a regional climate change projection with RegCM4 over the CORDEX Central America domain. Climatic Change, 125, 79–94.Find this resource:

Elguindi, N., Giorgi, F., & Turuncoglu, U. (2014). Assessment of CMIP5 global model simulations over the subset of CORDEX domains used in the Phase I CREMA. Climatic Change, 125, 7–21.Find this resource:

Endris, H. S., Omondi, P., Jain, S., Lennard, C., Hewitson, B., Chang’a, L., … Tazalika, L. (2013). Assessment of the performance of CORDEX regional climate models in simulating East African rainfall. Journal of Climate, 26, 8453–8475.Find this resource:

Feser, F. (2006). Enhanced detectability of added value in limited-area model results separated into different spatial scales. Monthly Weather Review, 134, 2180–2190.Find this resource:

Feser, F., Rockel, B., von Storch, H., Winterfeldt, J., & Zahn, M. (2011). Regional climate models add value to global model data: a review and selected examples. Bulletin of the American Meteorological Society, 92, 1181–1192.Find this resource:

Fosser, G., Khodayar, S., & Berg, P. (2015). Benefit of convection permitting climate model simulations in the representation of convective precipitation. Climate Dynamics, 44, 45–60.Find this resource:

Fox-Rabinovitz, M., Côté, J., Dugas, B., Déqué, M., & McGregor, J. L. (2006). Variable resolution general circulation models: Stretched-grid model intercomparison project (SGMIP). Journal of Geophysical Research, 111, D16104.Find this resource:

Fu, C., Wang, S., Xiong, Z., Gutowski, W. J., Lee, D.-K., McGregor, J. L., … Suh, M.-S. (2005). Regional climate model intercomparison project for Asia. Bulletin of the American Meteorological Society, 86, 257–266.Find this resource:

Future Earth (2013). Future Earth initial design: Report of the transition team. Paris.Find this resource:

Gbobaniyi, E., Sarr, A., Sylla, M. B., Diallo, I., Lennard, C., Dosio, A., … Lamptey, B. (2014). Climatology, annual cycle and interannual variability of precipitation and temperature in CORDEX simulations over West Africa. International Journal of Climatology, 34, 2241–2257.Find this resource:

Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., … Zhao, B. (2017). The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30, 5419–5454.Find this resource:

Ghimire, S., Choudhary, A., & Dimri, A. P. (2018). Assessment of the performance of CORDEX-South Asia experiments for monsoonal precipitation over the Himalayan region during present climate: Part I. Climate Dynamics, 50, 2311–2334.Find this resource:

Giorgi, F. (1990). Simulation of regional climate using a limited area model nested in a general circulation model. Journal of Climate, 3, 941–963.Find this resource:

Giorgi, F. (2002). Dependence of the surface climate interannual variability on spatial scale. Geophysical Research Letters, 29, 16-1–16-4.Find this resource:

Giorgi, F. (2006). Regional climate modeling: Status and perspectives. Journal de Physique IV, 139, 101–118.Find this resource:

Giorgi, F., & Bates, G. T. (1989). The climatological skill of a regional model over complex terrain. Monthly Weather Review, 117, 2325–2347.Find this resource:

Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M., Bi, X., … Brankovic, C. (2012). RegCM4: Model description and preliminary tests over multiple CORDEX domains. Climate Research, 52, 7–29.Find this resource:

Giorgi, F., & Gutowski, W. J. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40, 467–490.Find this resource:

Giorgi, F., Jones, C., & Asrar, G. R. (2009). Addressing climate information needs at the regional level: The CORDEX framework. Bulletin–World Meteorological Organization, 58, 175–183.Find this resource:

Giorgi, F., Marinucci, M. R., & Visconti, G. (1990). Use of a limited-area model nested in a general circulation model for regional climate simulation over Europe. Journal of Geophysical Research, 95, 18413.Find this resource:

Giorgi, F., Shields Brodeur, C., & Bates, G. T. (1994). Regional climate change scenarios over the United States produced with a nested regional climate model. Journal of Climate, 7, 375–399.Find this resource:

Giorgi, F., Torma, C., Coppola, E., Ban, N., Schär, C., & Somot, S. (2016). Enhanced summer convective rainfall at Alpine high elevations in response to climate warming. Nature Geoscience, 9, 584–589.Find this resource:

Gutowski, W. J., Jr., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., … Tangang, F. (2016). WCRP Coordinated Regional Downscaling Experiment (CORDEX): A diagnostic MIP for CMIP6. Geoscientific Model Development, 9, 4087–4095.Find this resource:

Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., … von Storch, J.-S. (2016). High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geoscientific Model Development, 9, 4185–4208.Find this resource:

Hagemann, S., Machenhauer, B., Jones, R., Christensen, O. B., Déqué, M., Jacob, D., & Vidale, P. L. (2004). Evaluation of water and energy budgets in regional climate models applied over Europe. Climate Dynamics, 23, 547–567.Find this resource:

Hawkins, E., & Sutton, R. (2009). The potential to narrow uncertainty in regional climate predictions. Bulletin of the American Meteorological Society, 90, 1095–1108.Find this resource:

Hewitson, B., & Crane, R. (1996). Climate downscaling: Techniques and application. Climate Research, 7, 85–95.Find this resource:

Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F., & Jack, C. (2014a). Interrogating empirical-statistical downscaling. Climatic Change, 122, 539–554.Find this resource:

Hewitson, B., Janetos, A. C., Carter, T. R., Giorgi, F., Jones, R. G., Kwon, W.-T., & van Aalst, M. K. (2014b). Regional context. In V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, … White, L. L. (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1133–1197). Cambridge, UK: Cambridge University Press.Find this resource:

Hewitson, B., Waagsaether, K., Wohland, J., Kloppers, K., & Kara, T. (2017). Climate information websites: An evolving landscape. Wiley Interdisciplinary Reviews: Climate Change, 8, e470.Find this resource:

Hostetler, S. W., Giorgi, F., Bates, G. T., & Bartlein, P. J. (1994). Lake–atmosphere feedbacks associated with paleolakes Bonneville and Lahontan. Science (80-.), 263, 665–668.Find this resource:

Jacob, D., Bärring, L., Christensen, O. B., Christensen, J. H., de Castro, M., Déqué, M., & van den Hurk, B. (2007). An inter-comparison of regional climate models for Europe: Model performance in present-day climate. Climatic Change, 81, 31–52.Find this resource:

Jacob, D., Elizalde, A., Haensler, A., Hagemann, S., Kumar, P., Podzun, R., … Wilhelm, C. (2012). Assessing the transferability of the regional climate model REMO to different Coordinated Regional Climate Downscaling Experiment (CORDEX) regions. Atmosphere (Basel), 3, 181–199.Find this resource:

Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., …Yiou, P. (2014). EURO-CORDEX: New high-resolution climate change projections for European impact research. Regional Environmental Change, 14, 563–578.Find this resource:

Jones, C., Giorgi, F., & Asrar, G. R. (2011). The Coordinated Regional Downscaling Experiment: CORDEX; An international downscaling link to CMIP5. CLIVAR Exchanges, 16, 34–40.Find this resource:

Kalognomou, E.-A., Lennard, C., Shongwe, M., Pinto, I., Favre, A., Kent, M., … Büchner, M. (2013). A diagnostic evaluation of precipitation in CORDEX models over Southern Africa. Journal of Climate, 26, 9477–9506.Find this resource:

Kanada, S., Wada, A., & Sugi, M. (2013). Future changes in structures of extremely intense tropical cyclones using a 2-km mesh nonhydrostatic model. Journal of Climate, 26, 9986–10005.Find this resource:

Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., & Senior, C. A. (2014). Heavier summer downpours with climate change revealed by weather forecast resolution model. Nature Climate Change, 4, 570–576.Find this resource:

Kendon, E. J., Roberts, N. M., Senior, C. A., & Roberts, M. J. (2012). Realism of rainfall in a very high-resolution regional climate model. Journal of Climate, 25, 5791–5806.Find this resource:

Kida, H., Koide, T., Sasaki, H., & Chiba, M. (1991). A new approach for coupling a limited area model to a GCM for regional climate simulations. Journal of the Meteorological Society of Japan. Series II, 69, 723–728.Find this resource:

Kim, J., Waliser, D. E., Mattmann, C. A., Mearns, L. O., Goodale, C. E., Hart, A. F., … Boustani, M. (2013). Evaluation of the surface climatology over the conterminous United States in the North American Regional Climate Change Assessment Program hindcast experiment using a regional climate model evaluation system. Journal of Climate, 26, 5698–5715.Find this resource:

Kjellström, E., Boberg, F., Castro, M., Christensen, J., Nikulin, G., & Sánchez, E. (2010). Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Climate Research, 44, 135–150.Find this resource:

Koenigk, T., Berg, P., & Döscher, R. (2015). Arctic climate change in an ensemble of regional CORDEX simulations. Polar Research, 34, 24603.Find this resource:

Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., … Wulfmeyer, V. (2014). Regional climate modeling on European scales: A joint standard evaluation of the EURO-CORDEX RCM ensemble. Geoscientific Model Development, 7, 1297–1333.Find this resource:

Lake, I., Gutowski, W., Giorgi, F., & Lee, B. (2017). CORDEX: Climate research and information for regions. Bulletin of the American Meteorological Society, 98, ES189–ES192.Find this resource:

Laprise, R. (2008). Regional climate modelling. Journal of Computational Physics, 227, 3641–3666.Find this resource:

Laprise, R. (2014). Comment on “The added value to global model projections of climate change by dynamical downscaling: A case study over the continental U.S. using the GISS-ModelE2 and WRF models” by Racherla et al. Journal of Geophysical Research: Atmospheres, 119, 3877–3881.Find this resource:

Laprise, R., de Elía, R., Caya, D., Biner, S., Lucas-Picher, P., Diaconescu, E., … Separovic, L. (2008). Challenging some tenets of regional climate modelling. Meteorology and Atmospheric Physics, 100, 3–22.Find this resource:

Larsen, M. A. D., Thejll, P., Christensen, J. H., Refsgaard, J. C., & Jensen, K. H. (2013). On the role of domain size and resolution in the simulations with the HIRHAM region climate model. Climate Dynamics, 40, 2903–2918.Find this resource:

Lee, H., Waliser, D. E., Ferraro, R., Iguchi, T., Peters‐Lidard, C. D., Tian, B., … Wright, D. B. (2017). Evaluating hourly rainfall characteristics over the U.S. Great Plains in dynamically downscaled climate model simulations using NASA‐Unified WRF. Journal of Geophysical Research: Atmospheres, 122, 7371–7384.Find this resource:

Li, Q., Wang, S., Lee, D.-K., Tang, J., Niu, X., Hui, P., … Sasaki, H. (2016). Building Asian climate change scenario by multi-regional climate models ensemble. Part II: Mean precipitation. International Journal of Climatology, 36, 4253–4264.Find this resource:

Liu, Y., Giorgi, F., & Washington, W. M. (1994). Simulation of summer monsoon climate over East Asia with an NCAR regional climate model. Monthly Weather Review, 122, 2331–2348.Find this resource:

McGregor, J. L. (2015). Recent developments in variable-resolution global climate modelling. Climatic Change, 129, 369–380.Find this resource:

McGregor, J. L., & Walsh, K. (1994). Climate change simulations of Tasmanian precipitation using multiple nesting. Journal of Geophysical Research, 99, 20889.Find this resource:

McGregor, J. L., Walsh, K. J., & Katzfey, J. J. (1993). Nested modelling for regional climate studies. In A. J. Jakeman, M. B. Beck, & M. J. McAleer (Eds.), Modelling change in environmental systems (pp. 367–386). Chichester, UK: Wiley.Find this resource:

McSweeney, C. F., Jones, R. G., Lee, R. W., & Rowell, D. P. (2015). Selecting CMIP5 GCMs for downscaling over multiple regions. Climate Dynamics, 44, 3237–3260.Find this resource:

Maraun, D., Huth, R., Gutiérrez, J. M., Martín, D. S., Dubrovsky, M., Fischer, A., … Bedia, A. (2017). The VALUE perfect predictor experiment: evaluation of temporal variability. International Journal of Climatology, 39(9).Find this resource:

Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., … Thiele-Eich, I. (2010). Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48, RG3003.Find this resource:

Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research. 1st ed. Cambridge, UK: Cambridge University Press.Find this resource:

Mariotti, L., Coppola, E., Sylla, M. B., Giorgi, F., & Piani, C. (2011). Regional climate model simulation of projected 21st century climate change over an all-Africa domain: Comparison analysis of nested and driving model results. Journal of Geophysical Research, 116, D15111.Find this resource:

Mariotti, L., Diallo, I., Coppola, E., & Giorgi, F. (2014). Seasonal and intraseasonal changes of African monsoon climates in 21st century CORDEX projections. Climatic Change, 125, 53–65.Find this resource:

Mearns, L. O., Arritt, R., Biner, S., Bukovsky, M. S., McGinnis, S., Sain, S., … Snyder, M. (2012). The North American Regional Climate Change Assessment Program: Overview of Phase I results. Bulletin of the American Meteorological Society, 93, 1337–1362.Find this resource:

Mearns, L. O., Sain, S., Leung, L. R., Bukovsky, M. S., McGinnis, S., Biner, S., … Sloan, L. (2013). Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP). Climatic Change, 120, 965–975.Find this resource:

Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., … Shi, W. (2006). North American regional reanalysis. Bulletin of the American Meteorological Society, 87, 343–360.Find this resource:

Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., … Wilbanks, T. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756.Find this resource:

Ngo-Duc, T., Tangang, F. T., Santisirisomboon, J., Cruz, F., Trinh-Tuan, L., Nguyen-Xuan, T., … Aldrian, E. (2017). Performance evaluation of RegCM4 in simulating extreme rainfall and temperature indices over the CORDEX-Southeast Asia region. International Journal of Climatology, 37, 1634–1647.Find this resource:

Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota, R., … Sushama, L. (2012). Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. Journal of Climate, 25, 6057–6078.Find this resource:

Niu, X., Wang, S., Tang, J., Lee, D.-K., Gao, X., Wu, J., … McGregor, J. (2015). Multimodel ensemble projection of precipitation in eastern China under A1B emission scenario. Journal of Geophysical Research: Atmospheres, 120, 9965–9980.Find this resource:

Ozturk, T., Altinsoy, H., Türkeș, M., & Kurnaz, M. (2012). Simulation of temperature and precipitation climatology for the Central Asia CORDEX domain using RegCM 4.0. Climate Research, 52, 63–76.Find this resource:

Park, C., Min, S.-K., Lee, D., Cha, D.-H., Suh, M.-S., Kang, H.-S., … Kwon, W.-T. (2016). Evaluation of multiple regional climate models for summer climate extremes over East Asia. Climate Dynamics, 46, 2469–2486.Find this resource:

Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., & Gochis, D. J. (2017). The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society, 98, 1717–1737.Find this resource:

Prein, A. F., Gobiet, A., Suklitsch, M., Truhetz, H., Awan, N. K., Keuler, K., & Georgievski, G. (2013). Added value of convection permitting seasonal simulations. Climate Dynamics, 41, 2655–2677.Find this resource:

Prömmel, K., Geyer, B., Jones, J. M., & Widmann, M. (2009). Evaluation of the skill and added value of a reanalysis-driven regional simulation for Alpine temperature. International Journal of Climatology, 30(5).Find this resource:

Pryor, S. C., Barthelmie, R. J., Young, D. T., Takle, E. S., Arritt, R. W., Flory, D., … Roads, J. (2009). Wind speed trends over the contiguous United States. Journal of Geophysical Research, 114, D14105.Find this resource:

Racherla, P. N., Shindell, D. T., & Faluvegi, G. S. (2012). The added value to global model projections of climate change by dynamical downscaling: A case study over the continental U.S. using the GISS-ModelE2 and WRF models. Journal of Geophysical Research: Atmospheres, 117.Find this resource:

Redelsperger, J.-L., Thorncroft, C. D., Diedhiou, A., Lebel, T., Parker, D. J., & Polcher, J. (2006). African Monsoon multidisciplinary analysis: An international research project and field campaign. Bulletin of the American Meteorological Society, 87, 1739–1746.Find this resource:

Roberts, M. J., Vidale, P. L., Senior, C., Hewitt, H. T., Bates, C., Berthou, S., … Wehner, M. F. (2018). The benefits of global high resolution for climate simulation: Process understanding and the enabling of stakeholder decisions at the regional scale. Bulletin of the American Meteorological Society, 99, 2341–2359.Find this resource:

Rockel, B. (2015). The regional downscaling approach: A brief history and recent advances. Current Climate Change Reports, 1, 22–29.Find this resource:

Rockel, B., Will, A., & Hense, A. (2008). The regional climate model COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17, 347–348.Find this resource:

Rummukainen, M. (2016). Added value in regional climate modeling. Wiley Interdisciplinary Reviews: Climate Change, 7, 145–159.Find this resource:

Ruti, P. M., Somot, S., Giorgi, F., Dubois, C., Flaounas, E., Obermann, A., … Vervatis, V. (2016). Med-CORDEX initiative for Mediterranean climate studies. Bulletin of the American Meteorological Society, 97, 1187–1208.Find this resource:

Sanchez-Gomez, E., Somot, S., & Déqué, M. (2009). Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961–2000. Climate Dynamics, 33, 723–736.Find this resource:

Sánchez, E., Solman, S., Remedio, A. R. C., Berbery, H., Samuelsson, P., Da Rocha, R. P., … Jacob, D. (2015). Regional climate modelling in CLARIS-LPB: A concerted approach towards twenty-first century projections of regional temperature and precipitation over South America. Climate Dynamics, 45, 2193–2212.Find this resource:

Solman, S. A., Sanchez, E., Samuelsson, P., da Rocha, R. P., Li, L., Marengo, J., … Jacob, D. (2013). Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: Model performance and uncertainties. Climate Dynamics, 41, 1139–1157.Find this resource:

STARDEX (2005). STARDEX: Statistical and Regional dynamical Downscaling of Extremes for European regions.

Steynor, A., Padgham, J., Jack, C., Hewitson, B., & Lennard, C. (2016). Co-exploratory climate risk workshops: Experiences from urban Africa. Climate Risk Management, 13, 95–102.Find this resource:

Takle, E. S., Gutowski, W. J., Arritt, R. W., Pan, Z., Anderson, C. J., da Silva, R. R., … Roads, J. O. (1999). Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results. Journal of Geophysical Research: Atmospheres, 104, 19443–19461.Find this resource:

Tebaldi, C., & Arblaster, J. M. (2014). Pattern scaling: Its strengths and limitations, and an update on the latest model simulations. Climatic Change, 122, 459–471.Find this resource:

Teixeira, J., Waliser, D., Ferraro, R., Gleckler, P., Lee, T., & Potter, G. (2014). Satellite observations for CMIP5: The genesis of Obs4MIPs. Bulletin of the American Meteorological Society, 95, 1329–1334.Find this resource:

Torma, C., Giorgi, F., & Coppola, E. (2015). Added value of regional climate modeling over areas characterized by complex terrain: Precipitation over the Alps. Journal of Geophysical Research: Atmospheres, 120, 3957–3972.Find this resource:

Wakazuki, Y., Nakamura, M., Kanada, S., & Muroi, C. (2008). Climatological reproducibility evaluation and future climate projection of extreme precipitation events in the Baiu season using a high-resolution non-hydrostatic RCM in comparison with an AGCM. Journal of the Meteorological Society of Japan, 86, 951–967.Find this resource:

Wang, Y., Leung, L. R., McGregor, J. L., Lee, D.-K., Wang, W.-C., Ding, Y., & Kimura, F. (2004). Regional climate modeling: Progress, challenges, and prospects. Journal of the Meteorological Society of Japan. Series II, 82, 1599–1628.Find this resource:

WCRP Report No. 23/2016. (2016). Scoping a framework for WCRP regional activities. Geneva, Switzerland.Find this resource:

Wilby, R. L., & Wigley, T. M. L. (1997). Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography: Earth and Environment, 21, 530–548.Find this resource:

Winterfeldt, J., & Weisse, R. (2009). Assessment of value added for surface marine wind speed obtained from two regional climate models. Monthly Weather Review, 137, 2955–2965.Find this resource: