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date: 06 June 2020

Regional Dynamical Downscaling

Summary and Keywords

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.

Keywords: downscaling, regional climate modeling, high resolution modeling, stretched grid modeling, convection permitting modeling, climate change, regional climate projection, extremes, precipitation, added value

Why Dynamical Downscaling?

The climate of a given region or location is characterized by processes and phenomena interacting on a wide range of spatial and temporal scales. At the large scale, features of the planetary general circulation determine the sequence of weather events that affect the region. For example, at mid-latitudes the location of the storm track and jet stream drives the direction of storm systems, while in the tropics regional climates are influenced by phenomena such as the north–south migration of the Intertropical Convergence Zone or natural modes of variability (e.g., the El Nino Southern Oscillation). Embedded in these large and synoptic scale systems, regional and local forcings (e.g., due to complex topography, land use and coastline patterns or tropospheric aerosols) may spatially modulate the local climate signal. In addition, mesoscale systems show substantial fine-scale structure. For example, extreme precipitation events are often highly localized in space and time, while mesoscale convective systems may organize in spatially complex structures (e.g., Houze, 2004).

As a result of these processes, the production of regional to local fine-scale climate information usable in climate service activities requires the simulation of processes progressing from the planetary to the local scale, along with their interactions. The primary tools available today to produce climate variability and change information are coupled atmosphere-ocean global climate models (AOGCMs). AOGCMs are numerical representations of the equations that describe the behavior of the different components of the climate system after discretization on equivalent three-dimensional grids covering the entire globe. They include a number of interactive components, such as the atmosphere, oceans, cryosphere, biosphere (both continental and marine), and chemosphere. As such, they are extremely complex model systems that require the use of a very large infrastructure. The resolution of AOGCMs is determined by the grid spacing of their three-dimensional global grids and is mostly constrained by the availability of computing resources. With the rapid increase of computing power over the past few decades, the atmospheric resolution of fully coupled AOGCMs has increased from the order of several hundred kilometers in the 80s to ~1o or even higher today.

Even at this relatively high resolution, however, the climate information produced by AOGCMs does not have sufficiently adequate spatial and temporal detail to represent and investigate local-scale processes and phenomena or to be directly used in impact applications. Therefore, during the past three decades a range of so-called downscaling techniques have been developed to spatially and temporally enhance the AOGCM information. The common principle behind these techniques is to take as input the large-scale climate fields produced by AOGCMs and generate as output finer, sub-AOGCM grid scale climate information usable for process studies or for climate service application (Figure 1). Thus, conceptually, the AOGCMs are used to simulate the effect of global and large-scale forcings (e.g., due to greenhouse gases or changes in solar radiation) on the Earth’s general circulation features, while downscaling accounts for the local modulation of large-scale climate patterns by regional and local scale forcings and processes (e.g., topography, land use, coastlines).

Regional Dynamical Downscaling

Figure 1. Schematic depiction of downscaling and application to impact studies and climate services. ESD = empirical-statistical downscaling.

Broadly speaking, downscaling can be divided in two categories: empirical-statistical downscaling and dynamical downscaling. In empirical-statistical methods, the downscaling is essentially carried out by developing statistical relationships between large-scale predictors and local predictands (e.g., temperature and precipitation) and applying these relationships to the output of GCMs. Conversely, in dynamical downscaling methods the downscaling is conducted using physically based models. A plethora of empirical-statistical methods are available in the literature, and they are reviewed in Benestad (2016) and in previous review papers (Benestad, 2004; Giorgi & Hewitson, 2001; Hewitson & Crane, 1996; Hewitson, Daron, Crane, Zermoglio, & Jack, 2014; Wilby & Wigley, 1997; Wilby et al., 1998).

Dynamical downscaling techniques can be divided into three categories (e.g., Giorgi & Hewitson, 2001): high-resolution atmospheric GCMs (HIRGCMs), variable resolution atmospheric GCMs (VARGCMS), and regional climate models (RCMs). Of these techniques, RCMs have been by far the most used in the past three decades, although a significant scientific literature also exists for HIRGCMs and VARGCMs. A comprehensive review of the thousands of studies on dynamical downscaling available in the literature can be found in a series of review papers that have been produced in the last three decades (e.g., Arritt & Rummukainen, 2011; Foley, 2010; Giorgi, 2006, 2019; Giorgi & Gao, 2018; Giorgi & Gutowski, 2015; Giorgi & Hewitson, 2001; Giorgi & Mearns, 1991, 1999; Hall, 2014; Laprise, 2008; Laprise et al., 2008; Leung, Mearns, Giorgi, & Wilby, 2003; McGregor, 1997, 2015; Mearns, Bogardi, Giorgi, Matyakovszky, & Pilecki, 1999; Rockel, 2015; Rummukainen, 2010; Rummukainen, Rockel, Barring, Christensen, & Reckermann, 2015; Solman, 2013; Wang et al., 2004).

Dynamical Downscaling: The Basic Techniques, Underlying Assumptions and Limitations


HIRGCMs and VARGCMs essentially share the same basic technique. HIRGCMs are global atmospheric models that can be run at higher resolution than coupled AOGCMs because they do not include other components of the climate system (e.g., ocean, sea ice, chemistry, etc.). They use standard three-dimensional grids, relatively homogeneous in the horizontal. VARGCMs, often called “stretched grid models,” are also atmospheric-only global models, but they have high resolution over one or more regions of interest, with the mesh size increasing (resolution decreasing), or stretching, moving away from these focus regions. Thus, typically, the model resolution in a VARGCM is maximum over the region of interest and minimum at the antipodes. Different types of stretched grids have been adopted using various grid transformations (McGregor, 2015).

When run in a stand-alone mode with the aim of producing climate projections, HIRGCMs and VARGCMs need as input the external forcings (greenhouse gases, aerosols, land use, topography, etc.) along with time-evolving sea surface temperature and sea ice distribution and temperature. The forcings are typically provided by the scenario being simulated, while the sea surface temperature and sea ice variables are provided by a corresponding coupled AOGCM simulation. Both the HIRGCMs and VARGCMs have been mostly used in the so-called time-slice mode (e.g., Cubasch, Waskewitz, Hegerl, & Perlwitz, 1995; McGregor, 2015), consisting of simulating a present day, or reference, time slice of 20 to 30 years in length (e.g., 1981–2000) and a corresponding future time slice of the same length (e.g., 2081–2100).

VARGCMs have also been run in the so-called nudged mode, especially in configurations of strongly stretched grids (McGregor, 2015). In this case an intermediate resolution AGCM is first run and then the output from this simulation (or the output of the original AOGCM if an intermediate resolution simulation is not carried out) is employed to directly force the solution of the VARGCM. This is done, for example, by applying a Newtonian relaxation term on some of the prognostic variables (e.g., wind and temperature) either in the areas away from the fine-scale region of interest or throughout the entire globe but only for the long-wave component of the solution. This approach insures a consistency between the large-scale climatology of the driving AOGCM and the VARGCM. As discussed later, when run in nudged mode, the use of VARGCMs is conceptually similar to that of nested RCMs.

An alternative way to run VARGCMs and HIRGCMs in climate change simulations has been to first run the models for a present-day time slice using observed sea surface temperatures and then carrying out an analogous simulation by adding a sea surface temperature perturbation (along with corresponding variations of greenhouse gases) calculated from a AOGCM scenario run (McGregor, 2015). Alternatively, a bias correction to the driving sea surface temperatures can be adopted. Both these techniques, which can be used with RCM simulations as well, are intended to minimize the effects of possible systematic biases in the driving AOGCM sea surface temperature fields.

The primary advantage of both the HIRGCM and VARGCM approaches is that they can simulate the two-way interactions of the high-resolution climate of the region of interest with the global climate, at least in terms of atmospheric-only processes. The main conceptual issue associated with these techniques when run in stand-alone mode is that the climatology of the high-resolution atmospheric component may not be consistent with that of the underlying driving sea surface temperatures, which are obtained from a coarser resolution, and possibly different, coupled AOGCM. A further problem related to the use of VARGCMs is that the coarse resolution away from the area of interest might lead to a degradation of the model climatology, which might in turn affect also the high-resolution interest area. In addition, the model physics parameterizations may be resolution-dependent and thus may have different behaviors in the remote low-resolution and focus high-resolution regions. Finally, although HIRGCMs and VARGCMs are atmospheric-only models, they are still global and thus are computationally expensive to run, which limits the resolution increase and/or the length of simulations.


What is commonly referred to as the “nested” one-way regional climate modeling technique consists of running a limited area RCM over a selected domain of interest with initial and time-dependent meteorological lateral boundary conditions (LBC) and sea surface temperature, provided either by global reanalyses of observations (“perfect boundary conditions” experiments) or by AOGCM simulations (e.g., Giorgi & Gutowski, 2015). The initial conditions and LBC are needed for the model prognostic variables, typically wind components, temperature, water vapor, and surface pressure, or some variants of these variables. In this approach, after a spin-up period of a few days (depending on the model domain size), a dynamical equilibrium establishes between the LBC information pervading the model domain and the internal model dynamics and physics. This equilibrium prevents the model solution from deviating too much from the LBC, which thus exert a strong forcing on the RCM large-scale climatology.

Since there is no feedback between the RCM fields and the driving AOGCM ones, the RCM is not expected to strongly modify the large-scale climatology produced by the AOGCM but only to add realistic small-scale detail as driven by mesoscale forcings and processes. This is illustrated by Figure 2, which shows improved realism in the simulation of the spatial structure of synoptic systems by an RCM compared to the “driving” AOGCM.

Regional Dynamical Downscaling

Figure 2. Instantanueous 850 hPa specific humidity field at simulation time January 12, 2005, 12:00:00 UTC of a long-term continuous simulation started on January 1, 1970, (model time). (a) MPI-ESM-LR AOGCM at ~1.875o resolution, or ~150 km at mid-latitudes, interpolated onto the RegCM4 grid; (b) RegCM4 at ~12.5 km grid spacing. Units are g/kg. Data are from a EURO-CORDEX experiment.

The most commonly used method to provide the LBC is the so-called relaxation technique (Davies & Turner, 1977), which essentially consists of adding a Newtonian relaxation term to each prognostic equation driving the model variables toward the imposed LBC in a lateral buffer area (e.g., Marbaix, Gallee, Brasseur, & Van Ypersele, 2003). It has been pointed out that this procedure, in general, constitutes an ill-posed problem, being overspecified at the outflow boundary points (e.g., McDonald, 2003; Mesinger & Veljovic, 2013; Oliger & Sundstrom, 1978; Staniforth, 1997; Warner, Peterson, & Treadon, 1997), where mismatches between the model-generated solution and the LBC can generate noise and wave reflection (e.g., Mesinger & Veljovic; 2013). This problem has indeed called into question the applicability of the LBC relaxation method (e.g., McDonald, 2003; Staniforth, 1997), and alternate approaches have been proposed, such as the use of interior point values at the outflow points (Mesinger & Veljovic, 2013) or the use of extended lateral buffer zones along with diffusion terms and smoothed relaxation functions (e.g., Giorgi, Marinucci, Bates, & DeCanio, 1993).

A method used to provide an additional constraint to the RCMs in the interior domain is the so-called spectral nudging, by which the relaxation term is applied throughout the entire model domain but only to the long-wave component of the solution, while the model is left free to simulate the short-wave component (Kida, Koide, Sasaki, & Chiba, 1991; von Storch, Langenberg, & Feser, 2000). This technique insures a stronger consistency between the large-scale forcing and model-simulated fields, but it constraints the model physics and is of difficult application for moisture variables (Omrani et al., 2012; Radu et al., 2008; Da Silva et al., 2018; Tang et al., 2017; and Liu et al., 2012). On the other hand, there is the opinion that spectral nudging, by increasing the large-scale constraint, ameliorates the problem of diverging internal dynamics within the domain. (von Storch et al. 2000). Despite the problems associated with the provision of LBCs, RCMs have proven successful in producing realistic weather systems even after long simulation times, as illustrated by the example in Figure 2, and rigorous analyses have demonstrated the suitability of using RCMs for long-term climate simulations (e.g., Laprise et al., 2008).

The main advantage of RCMs is that, since they are physically based models, they can, and have been, applied for a wide variety of problems, from studies of regional processes and phenomena to paleoclimate and future climate simulations. The possibility of running RCMs only over limited domains of interest also allows higher resolutions and longer simulation lengths than achievable by HIRGCMs and VARGCMs. For example, large ensembles of centennial-scale projections over continental-scale domains down to grid spacing of ~10 km are available today (e.g., Jacob et al., 2013). In addition, multidecadal simulations at convection-permitting resolutions (~1.5–3 km) are being carried out (e.g., Coppola et al., 2019), with the potential of producing climate information down to the local scales.

On the other hand, nested RCMs have a number of limitations, which need to be well understood. First, RCMs have been mostly used in one-way mode, meaning that the RCM information does not feedback to the AOGCM. Some two-way nested experiments have been carried out (e.g., Inatsu & Kimoto, 2009; Lorenz & Jacob, 2005), but the difficulty of running such experiments has strongly limited their use. Second, the LBC exert a strong forcing on the RCM climatology, and therefore systematic errors in the driving AOGCMs are essentially inherited by the nested RCM. This issue has been referred to as the “garbage in, garbage out” issue (Giorgi & Gutowski, 2015; Giorgi & Mearns, 1999; Hall, 2014; Rummukainen, 2010) and has prevented the widespread use of RCMs in the early years of model development because of substantial errors in the synoptic climatologies of AOGCMs (e.g., displacement of mid-latitude storm tracks). One approach used to circumvent this problem has been to add climatological corrections to the LBC of the driving GCMs to remove systematic circulation biases (Bruyère, Done, Holland, & Fredrick, 2014), although this approach can potentially produce an imbalance across variables at the level of instantaneous fields.

Fortunately, the performance of AOGCMs in reproducing observed climatologies and processes at synoptic scales has considerably improved with the enhancement in model resolution and physics representations (e.g., Flato et al., 2013), and this has allowed a great improvement also in RCM performance. However, a mandatory step prior to the completion of a nested RCM experiment is the careful analysis of the driving AOGCM, and if this shows large systematic errors, the RCM experiment becomes essentially useless.

A third issue with RCMs, often underestimated by the modeling community, is the configuration of the model simulations. In the early years of RCM development, the notion was that the RCM climatology would be nearly uniquely defined by the LBC. However, many experiments have shown that an RCM simulation is strongly affected by multiple factors, such as choice of physics parameterizations, domain size and location, resolution, LBC method, and so on (e.g., Colin, Déqué, Radu, & Somot, 2010; Denis, Laprise, & Caya, 2003; Laprise et al., 2008; Rauscher, Seth, Qian, & Camargo, 2006; Seth & Giorgi, 1998). In addition, as any nonlinear model, RCMs are characterized by substantial internal variability, whose magnitude depends on domain size, season, and climate regime (e.g., tropical vs. mid-latitude; Caya & Biner, 2004; Christensen, Gaertner, Prego, & Polcher, 2001; Cretat, Macron, Pohl, & Richard, 2011; Giorgi & Bi, 2000; Lucas-Picher, Caya, de Elia, & Laprise, 2008; Nikiema & Laprise, 2011; Rinke, Marbaix, & Dethloff, 2004). This internal variability can in fact be so large as to mask forced signals and requires the completion of large ensembles or long simulations to be filtered out. Because of all of these dependencies, the configuration of an RCM simulation needs to be fully tested and possibly optimized through extensive preliminary experiments, since precise rules on how to choose a configuration are not available. Although this can be a time-consuming exercise, it is a paramount step before proceeding to production runs. In this regard, a formal method to test the downscaling ability of a model configuration over a given domain is the “big brother–little brother” experiment originally proposed by Denis, Laprise, Caya, and Côté(2002).

Finally, the one-way nested technique can also be used in the so-called multiple nesting mode (e.g., Gao, Pal, & Giorgi, 2006; Im, Park, Kwon, & Giorgi, 2008) to achieve high local resolutions. In this mode, the RCM is first run at intermediate resolutions, and the output from these runs is used to drive higher resolution simulations with the same or another RCM. In addition, some models include the capability of running inner subdomains in two-way nested mode (e.g., WRF; Powers et al., 2017), and this approach can also be used to locally increase the model resolution.

Historical Perspective and Current Status of Dynamical Downscaling Techniques


The first VARGCMs were developed already in the late 1970s and 1980s for application to numerical weather prediction (Courtier & Geleyn, 1988; Staniforth & Mitchell, 1978); however, the first application of VARGCMs to climate simulations is found in Déqué and Piedelievre (1995), who ran the ARPEGE global stretched model at a resolution of about 50 km over the European region. At about the same time, the concept of running HIRGCMs for time-slice climate simulations was proposed by Cubasch et al., (1995), who ran an HIRGCM for 30-year present-day, 2 × CO2 and 3 × CO2 time slices at about 1.5o resolution with sea surface temperatures from a corresponding ~300 km resolution run. In the 1990s ARPEGE was run again for climate simulations over Europe (Déqué et al., 2007) and the Antarctic region (Krinner & Genthon, 1997; Krinner, Genthon, Li, & Le Van, 1997).

After these early studies, in the 2000s and 2010s, VARGCMs were increasingly used to complete climate and climate change simulations focusing on a wide range of regions: Europe (e.g., Gibelin & Déqué,2003; Jaedicke et al., 2008), Australia and Tasmania (e.g., Nunez & McGregor, 2007; Watterson, McGregor, & Nguyen, 2008), southern Africa (e.g., Engelbrecht, McGregro, & Engelbrecht, 2009), North America (Berbery & Fox-Rabinowitz, 2003; Markovich, Lin, & Winger, 2010), East Asia (e.g., Nguyen & McGregor, 2009; Zhou & Li, 2002), South Asia (Sabin et al., 2013), and Fiji (e.g., Lal, McGregor, & Nguyen, 2008). Fox-Rabinowitz et al. (2002) also ran a VARGCM over multiple stretched grid areas of interest.

A major step in VARGCM research was the inception of the Stretched Grid Model Intercomparison Project (Rabinowitz et al., 2008; Fox-Rabinowits, Côté, Dugas, Déqué, & McGregor, 2006). Four models participated to this initiative, which focused on the simulation of a 12-year period (1987–1998) with sea surface temperatures from observations and a mesh size of about 50 km centered over the continental United States. The models generally showed a good performance compared to analogous uniform grid GCM simulations. VARGCMs also participated in downscaling intercomparison projects such as RMIP over East Asia (Fu et al., 2005) and PRUDENCE over Europe (Christensen Carter, Rummukainen, & Amanatidis, 2007; see later discussion). Today, five major VARGCMs systems are available that are used by different communities and can reach stretched horizontal resolutions up to 10 km and less over any region of the globe: ARPEGE (Déqué, Dreveton, Braun, & Cariolle, 1994), CCAM (McGregor & Dix (2001, 2008), GEOS-SG (Fox-Rabinowitz, Takacs, & Govindaraju, 2002), GEM (Côté & Gravel, 1998), and LMDZ (Hourdin et al., 2006).

HIRGCMs have also been run extensively in climate and climate change time-slice mode, with horizontal resolutions of up to 20 km. Among studies utilizing HIRGCMs examples are Beersma, Rider, Komen, Kaas, and Kharin (1997), Timbal, Mahfouf, Royer, Cubasch, and Murphy (1997), May and Roeckner (2001), Voss, May, and Roeckner (2002), Gowindasamy, Duffy, and Coquard (2003), Bueh, Cubch, and Hagemann (2003), May (2002), Coppola and Giorgi (2005), Mizuta et al. (2006), Ghan and Shippert (2006), Zhao, Held, Lin, and Vecchi (2009), Feng, Zhou, Wu, Li, and Luo (2011), and Kito and Endo (2016). HIRGCMs essentially follow the development of the atmospheric components of corresponding coupled AOGCMs, and a major intercomparison project is underway under the CMIP6 program (the High resolution Atmospheric Model Intercomparison Project, HighResMIP; Haarsma et al., 2016), aimed at exploring in a systematic way the effect of increased resolution on climate model simulations.


The first RCMs were developed in the late 1980s with the purpose of downscaling climate simulations in the mountainous regions of the western United States. The papers by Dickinson, Errico, Giorgi, and Bates (1989) and Giorgi and Bates (1989) were the first publications in which an RCM was driven by analysis of observations. However, while Dickinson et al. proposed the use of large ensembles of short (three- to five-day) simulation to characterize the climatology of the model (i.e., a direct extension of numerical weather prediction simulations), Giorgi and Bates’ was the first paper in which an RCM was run in the so-called climate mode (i.e., for simulations exceeding few days in length, in this case one month). This was a key conceptual step in the use of limited area models that paved the way to the field of regional climate modeling as conceived today. The first month-long RCM runs driven by a AOGCM, still over the continental United States, were then completed by Giorgi (1990).

The early to mid-1990s saw a substantial expansion of RCM activities. The first multiyear RCM simulations driven either by analyses of observations (Giorgi, Bates, & Nieman, 1993) or by AOGCMs (Giorgi, Brodeur, & Bates, 1994) still covered the continental United States. However, multiple parallel activities in other regions were also conducted in Europe (Christensen et al., 1997; Christensen, Christensen, Machenhauer, & Botzet, 1998; Giorgi et al., 1990; Jones, Murphy, & Noguer, 1995; Jones, Murphy, Noguer, & Keen, 1997; Machenhauer, 1998), East Asia (Hirakuchi & Giorgi, 1995; Kida et al., 1991), the Arctic (Lynch, Chapman, Walsh, & Weller, 1995), Australia (McGregor & Katzfey, 1997), and North America (Caya & Laprise, 1999). These studies consisted of regional climate change simulations based on ensembles of month-long runs or multiyear time slices (5- to 10-year length) of climate under reference and increased (e.g., doubled) CO2 concentrations. The model grid spacing was mostly around ~50 km.

Starting from the mid- to late 1990s, the European research community provided the strongest impetus to RCM research through a series of projects sponsored by the European Union (EU): MERCURE (Machenhauer, 1998), PRUDENCE (Christensen, Drews, & Christensen, 2007), ENSEMBLES (van der Linden & Mitchell, 2009), PRINCIPLES (coordinated by the Swedish Meteorological and Hydrological Institute), and EUCP. In particular, PRUDENCE was a true landmark project that led to the production of the first coordinated ensemble of multidecadal (30 years) simulations for present and future climates over the European region (~50 km grid spacing) with multiple RCMs driven by multiple AOGCMs for different green house gas concentration scenarios (Christensen et al., 2007). This allowed for the first time an assessment of different sources of uncertainty in RCM-based projections (Déqué et al., 2007). ENSEMBLES then expanded the PRUDENCE framework to complete the first set of full centennial transient runs for the 21st century at 25 km grid spacing (Déqué et al., 2012). Currently, an unprecedented set of 21st-century RCM projections at ~12 km grid spacing is being completed as part of the EURO-CORDEX program (Jacob et al., 2013).

One of the main problems of early RCM activities has been its fragmented nature. Different groups were typically interested in different scientific problems and regional settings and thus adopted different simulation protocols. This made the transfer of know-how across the community difficult. This realization led to the development of the first coordinated intercomparison projects in which multiple models adopted the same simulation protocol. The first RCM intercomparison project was the Project to Intercompare Regional Climate Simulations, which focused on the simulation of a drought and a flood season over the continental United States (Takle et al., 1999). This was followed, in addition to the European projects mentioned earlier, by a series of intercomparison projects for different domains, such as East Asia (RMIP; Fu et al., 2005), South America (CLARIS; Boulanger, Carrill, & Sanchez, 2016), North America (NARCCAP; Mearns et al., 2012), West Africa (AMMA; Paeth et al., 2011), the Baltic Sea (within BALTEX; Jacob et al., 2001), and the Arctic (ARCMIP; Curry & Lynch, 2002).

These projects enabled the identification of model systematic behaviors characteristic of the different regions, for example a systematic underestimation of precipitation over the La Plata basin. However, since different simulation protocols and strategies were used over the different domains, it was difficult to extend the findings from one region to another. Thus a more general framework was needed that would allow the assessment of the transferability of results across domains (Takle et al., 2007). This realization led to the inception of the Coordinated Regional Climate Downscaling EXperiment (CORDEX; Giorgi, Jones, & Asrar, 2009; Gutowski et al., 2016; Jones, Giorgi, & Asrar, 2011). CORDEX likely represents the most important development in RCM research since the late 2000s. Initiated under the auspices and sponsorship of the World Climate Research Program (WCRP) with the primary mission to advance climate downscaling science through a global effort, it has rapidly grown to become the main reference framework for the regional downscaling community, with a role analogous to that of the Climate Model Intercomparison Project (CMIP) for the global modeling community.

The goals of CORDEX are to generate large multimodel ensembles of high-resolution climate projections for regions worldwide, to understand mechanisms underlying regional climate change signals and systematic model errors, and to produce fine-scale climate information usable for impact assessment and climate service activities. All the CORDEX experiments share the same simulation protocols in terms of resolution, scenarios, and simulation periods, and this facilitates the exchange of information across regions. CORDEX is expected to provide also a test-bed for the intercomparison of different downscaling techniques, both dynamical and empirical-statistical, although to date essentially only RCMs have participated in the program. Tens of modeling groups are involved in CORDEX, and the Phase I activities have resulted in the production of ensembles of projections for all continental domains, which are increasingly being used for a wide variety of applications.

Another important area of RCM research has been the development of coupled regional Earth system models including different interacting components, such as atmosphere, ocean, biosphere, cryosphere, and aerosol/chemistry (Giorgi & Gao, 2018). Interactive aerosols were first introduced in RCMs in the late 1990s and early 2000s (Giorgi, Bi, & Qian, 2003; Qian & Giorgi, 1999; Qian, Leung, Ghan, & Giorgi, 2003) to study the aerosol effects on the climate of East Asia, and later coupled RCM/aerosol models were used for a variety of regional studies (Das, Dey, Dash, Giuliani, & Solmon, 2015; Konare et al., 2008; Nabat, Solmon, Mallet, Kok, & Somot, 2012; Nabat, Somot, Mallet, Sanchez-Lorenzo, & Wild, 2014; Solmon et al., 2008; Zanis, Ntogras, Zakey, Pytharoulis, & Karacostas, 2012). Coupled RCM-lake models have also been developed since the early years of regional modeling (Hostetler, Bates, & Giorgi, 1993) and have since been used for different lakes such as the Great Lakes (e.g., Notaro et al., 2013), Lake Victoria (e.g., Thiery et al., 2015), Lake Malawi (Diallo, Giorgi, & Stordal, 2018), the Aral Sea (Small, Sloan, Hostetler, & Giorgi, 1999), and the Caspian Sea (Turuncoglu, Elguindi, Giorgi, Fournier, & Giuliani, 2013).

More recent was the coupling of three-dimensional regional ocean models to RCMs, but today coupled models exist for a number of ocean basins, and in particular the Mediterranean (e.g., Ruti et al., 2016), the Baltic Sea (e.g., Doscher et al., 2002; Jacob et al., 2001; Kjellström, Doscher, & Meier, 2009; Schrum, Hubner, Jacob, & Podzun, 2003), the Indian Ocean (e.g., Di Sante, Coppola, Farneti, & Giorgi, 2019; Ratnam, Giorgi, Kaginalker, & Cozzini, 2009; Samala, Nagaraju, Banerjee, Kaginalkar, & Dalvi, 2013), the Arctic (e.g., Doscher, Wyser, Meyer, Qian, & Redler, 2009; Roberts et al., 2009), Antarctic (e.g., Bailey & Lynch, 2000), the Caspian Sea (e.g., Turuncoglu, Giuliani, Elguindi, & Giorgi, 2013), the maritime continent (e.g., Wei, Malanotte-Rizzoli, Eltahir, Xue, & Xu, 2014), and China Sea (e.g., Zou & Zhou, 2016). These coupled model experiments have indeed shown that the coupling can improve the model performance over ocean as well as contiguous land areas and can affect the regional climate change signal (e.g., Somot, Sevault, Déqué, & Crepon, 2008). Experiments with coupled RCM-dynamical vegetation models have been more limited, but some examples can be found in the literature (e.g., Lu et al., 2001; Smith, Samuelsson, Wramneby, & Rummukainen, 2011; Zhang, Jansson, Miller, Smith, & Samuelsson, 2014). In fact, today a few fully coupled regional Earth system models integrating multiple interconnected components have been developed (e.g., Sitz et al., 2017; Smith et al., 2011; Will et al., 2017).

Clearly, during the past three decades regional climate modeling has seen tremendous growth. Today, a number of flexible and portable RCMs are available, with large user communities across the world: RegCM (Giorgi et al., 2012), WRF (Powers et al., 2017), PRECIS (Massey et al., 2015), COSMO-CLM (Rockel, Will, & Hense, 2008), RCA (Strandberg, Bärring, et al., 2014), RSM (Roads et al., 2003), REMO (Jacob & Podzun, 1997), CRCM (Music & Caya, 2007), ETA (Mesinger et al., 2012), ALADIN/AROME (Termonia et al., 2018), HIRHAM (Christensen et al., 2007), RACMO (Lenderink, van Ulden, van den Hurk, & van Meijgard, 2007), MAR (Gallee & Schayes, 1994), and PROMES (Castro, Fernandez, & Gaertner, 1993). Virtually all land regions of the world have been simulated with RCMs. Simulation length and resolution for continental scale domains has increased from ~50 km and several years, respectively, in the 1990s to ~ 10 km and centennial scale today. In fact, the RCM community is rapidly moving to convection permitting resolutions of a few kilometers. In addition, large multimodel ensembles of projections have been produced as part of international programs such as CORDEX, which are allowing a better understanding of the uncertainties in regional climate projections and an increasing application to impact assessment studies such as hydrology (e.g., Graham, Andreasson, & Carlsson, 2007; Jha, Pan, Takle, & Gu, 2004; Teutschbein & Seibert, 2010), agriculture (e.g., Liu et al., 2018; Mearns et al., 2003; Olesen et al., 2007), runoff (e.g., Rauscher, Pal, Diffenbaugh, & Benedetti, 2008), storm surges and extreme winds (e.g., Coppola et al., 2019; Rockel & Woth, 2007; Woth, Weisse, & von Storch, 2006), human health (e.g., Ermert, Fink, Morse, & Paeth, 2012; Huang et al., 2011; Im, Kang, & Eltahir, 2018; Im, Pal, & Eltahir, 2017; Lake et al., 2018; Pal & Eltahir, 2016; Rodo et al., 2013), air quality (e.g., Knowlton et al., 2004; Meleux, Solmon, & Giorgi, 2007; Weaver et al., 2009), fire risk (e.g., Bedia, Herrera, Camia, Moreno, & Gutierrez, 2014; Flannigan et al., 2001; Moriondo et al, 2006), natural ecosystem responses (e.g., Koca, Smith, & Sykes, 2006), energy production (e.g., Jerez et al., 2015; Pryor & Barthelmie, 2011; Tobin et al., 2015, 2016), and tourism (e.g., Endler & Matzarakis, 2011). RCM-based projections are also providing data for national and regional climate change assessment reports and climate service activities.

Some Key Issues With Climate Downscaling

Added Value

Any downscaling technique, dynamical or empirical/statistical, is justified to the extent that it adds useful and robust fine-scale information to that provided by the driving AOGCMs. This issue is usually referred to as the “added value” of a downscaling method, and it has been sharply debated since the early days of climate downscaling. The added value question is not trivial and needs to be properly posed. The conceptual framework of the added value problem is thoroughly discussed, for example, in the reviews of Di Luca, de Elia, and Laprise (2015) and Rummukainen (2016).

Although it could be argued that a better representation of meso-scale scale processes by downscaling tools should lead to improvements at all scales, in general it should not be expected that a downscaling experiment improves (“adds value” to) all aspects of an AOGCM simulation. For example, if one is interested in large-scale averages over a flat region, downscaling may not add value compared to the driving AOGCM, since the AOGCM itself may be sufficiently accurate at the scales resolved through its subgrid scale physics parameterizations.

Rather, one should certainly expect added value in those instances in which fine-scale processes and forcings are important, for example over complex topography or for localized extreme events and mesoscale convective systems. In these cases, the added value should be seen on the fine-scale component of the climate signal, and not necessarily at the AOGCM resolution scale. If an RCM produces added value not only at fine scales but also when the fields are upscaled at the AOGCM resolution, the results are more robust because it is an indication that the added value is obtained for the correct reasons (e.g., better representation of forcings and processes), but it is not a strict requirement. In other words, added value at the AOGCM scales is an important “bonus” but not a necessary requirement of the downscaling exercise.

A second aspect of the added value question is that it may be strongly dependent on the models being used. For example, an RCM could be characterized by systematic errors related to the model physics packages, which could actually amplify errors found in the driving AOGCM. This behavior has sometimes been erroneously interpreted as a general failure of the downscaling technique rather than a specific behavior of a given model simulation. It can also occur that added value is found for some RCM variables, or over some subregions of the domain, while an error amplification is found for other variables and subregions. Again, this may be tied to the specific model and simulation configuration being used.

A third important issue is whether, when carrying out climate change experiments, the added value found in a simulation of present-day climate conditions carries over to the climate change signal. For example, an RCM typically produces a better simulation of fine-scale temperature patterns in topographically complex regions than a coarse scale AOGCM simply because of the elevation forcing on temperature. However, if this is a linear effect, it cancels out when taking differences between a future and a present-day temperature pattern. Thus, the added value obtained by downscaling carries over to the climate change signal only if it is related to nonlinear processes.

These considerations have important consequences for the assessment of added value. First, the added value should be searched for those variables and scales where it is indeed expected using appropriate quantitative metrics. Typical examples are regions characterized by complex topography, land use, and coastlines or for processes and phenomena relevant at sub-AOGCM resolution mesoscales (e.g., extreme events, local wind systems, or organized mesoscale convective systems). A typical metric of added value could be the correlation between observed and simulated spatial patterns or the distance between observed and simulated precipitation intensity distributions.

In addition, in order to lead to robust conclusions, the assessment of added value should not be based on a single model or simulation but preferably on an ensemble of different models and regional settings so as to minimize the effects of systematic biases characterizing a specific model configuration or domain. Finally, as mentioned, the added value found for present-day climate conditions does not necessarily carry over to the corresponding climate change signals. Therefore, since in principle a climate change simulation cannot be verified against observations, the assessment of added value in climate projections needs to be based on a robust understanding of underlying physical processes.

These considerations also point to the importance of the availability of high-quality observation data sets at proper high spatial and temporal scales. These data sets are not available for many regions of the world, especially when going to sub-10 km spatial scales and sub-daily temporal scales. Moreover, observational data sets may be characterized themselves by a substantial uncertainty, and this may lead to misleading interpretations of model errors (e.g., Prein & Gobiet, 2017; Sylla, Giorgi, Coppola, & Mariotti, 2013). This observational uncertainty makes a rigorous assessment of added value quite difficult.

To date, a large number of studies have addressed the issue of added value mostly within the context of RCM simulations (see, e.g, the reviews of Di Luca et al., 2015; Giorgi & Gutowski, 2015; Rummukainen, 2016); however, they have mostly assessed individual models, experiments, and regions (e.g., Diaconescu & Laprise, 2013; Di Luca, Argüeso, Evans, De Elía, & Laprise, 2016; Kanamitsu & deHaan, 2011). As an example of added value, Figure 3 shows daily precipitation intensity distributions over a West Africa region in a simulation with the MPI-ESM-LR AOGCM (sameas for Figure 2) and in a corresponding nested RCM run with the RegCM4 at 25 km grid spacing. These are compared with low resolution (GPCP at 1o; Huffmann, Adler, et al., 2001) and high resolution (TRMM at 25 km; Huffmann, Bolvin, Nelkin, & Wolff, 2001) observations. While the AOGCM reproduces the low-resolution distribution, the RCM is much closer to the high-resolution one, in particular to the high-intensity tail of the distribution missed by the AOGCM.

Regional Dynamical Downscaling

Figure 3. Simulated (RegCM4, 25 km grid spacing; MPI-ESM-LR, 1.875o; CORDEX-Africa domain) and observed (TRMM, 25 km grid; GPCP, 1o grid) frequency of occurrence of daily precipitation intensity for events over a region covering Western Africa (5o–20oN, 18oW–8oE) in a 30-year present-day (1971–2000) simulation with RegCM4 nested in MPI-ESM-LR.

A general conceptual framework for identifying and quantifying the added value was proposed by Di Luca, de Elia, and Laprise (2012, 2013). However, they use as an added value metric the root mean square error between models and observations calculated over a certain area within the model domain. This may be misleading because this metric may be strongly affected by systematic region-wide biases, which may actually mask the contribution of fine-scale processes. A more appropriate metric may thus be the Taylor diagram (Taylor, 2001) because it filters out region-wide biases. Particularly illustrative examples of added value assessment studies are the publications by Torma, Giorgi, and Coppola (2015) and Giorgi et al. (2016). They focused on precipitation over an Alpine region (i.e., a mountainous area) in an ensemble of high resolution (~12 km) RCM simulations of present-day and future climate conditions. For this region, a high-quality daily precipitation data set is available on a 5 km grid obtained from a high-density network of thousands of stations (Isotta et al., 2014).

Torma et al. (2015) first focused on present climate conditions and used the following added value metrics: the Taylor diagram to assess spatial precipitation patterns; the Kolmogorov distance to assess the simulation of daily precipitation intensity distributions; and correlation patterns for the 95th percentile of the distribution as a metric for extreme events. All these are quantities and metrics for which we do expect to obtain added value in RCM simulations. They compared these metrics for an ensemble of RCM experiments completed as part of the EURO-CORDEX (Jacob et al., 2013) and MED-CORDEX (Ruti et al., 2016) programs with the corresponding ensemble of driving AOGCM experiments. In addition, they calculated these metrics both at the resolution of the scale of the RCM resolution (~12 km) and at a scale typical of the AOGCM resolution (~150 km). The comparison demonstrated added value in all metrics and at all scales considered.

The work of Torma et al. (2015) was then extended by Giorgi et al. (2016), who focused on the change signal for summer precipitation. They found that, for late 21st century conditions under the high-end RCP8.5 scenario (Moss et al., 2010), while the driving AOGCM ensemble projected a ubiquitous decrease of summer precipitation over the entire Alpine region, the high-resolution RCM ensemble (as well as each of its model members) projected an increase over the highest mountain peaks. This result was attributed to increased convection occurring at high elevations in response to strong surface warming and moistening. In addition, this RCM-based signal was also found to be more in line with current observed trends. Because of these multiple lines of evidence (cross-model agreement, better performance in present-day conditions, identification of an underlying physical process, consistency with observed trends), Giorgi et al. concluded that the RCM-based signal was more plausible than the AOGCM-based one. The work of Giorgi et al. thus represents one of the few examples in the literature of added value in a climate change context.

Concerning the HIRGCM and VARGCM downscaling methods, specific studies of added value have been sparse. Although the increase in resolution for AOGCMs tends to improve the simulation of different characteristics of the global circulation (Intergovernmental Panel on Climate Change [IPCC]), this improvement is not always found for all variables and can be model and region dependent (Flato et al., 2013). For example, higher resolution in an atmospheric model improved the simulation of blocking events over the Euro-Atlantic sector but degraded it over the Asia-Pacific sector (Matsueda, Mizuta, & Kusunoki, 2009).

Clearly, the issue of added value is complex. Many studies have demonstrated added value in different simulations and regional contexts; however, a fully encompassing cross-regional study is still not available in the literature. As we will see, the next phase of the CORDEX program will provide the opportunity to address this issue more comprehensively.

Uncertainties in RCM-Based Regional Climate Projections

A key issue in downscaling is the uncertainty in downscaling-based regional projections. Over the years, a number of studies have addressed the issue of uncertainties in climate projections (e.g., Giorgi, 2005; Giorgi et al., 2008; Hawkins & Sutton, 2009). Global climate projections are generally based on ensembles of 21st-century simulations with different AOGCMs for different greenhouse gas emission/concentration scenarios. The uncertainty associated with these projection ensembles is essentially due to three main sources: uncertainty in greenhouse gas emission/concentration scenarios (or “scenario uncertainty”), response of different AOGCMs to the same scenario (or “AOGCM structural uncertainty”), and internal variability of the climate system (or “variability uncertainty”).

The scenario uncertainty depends on the different emission scenarios being considered and can be approximated by the range in ensemble mean projected values for a given variable. For example, in the latest generation of IPCC projections, the ensemble average global temperature change by 2100 varies from about ~1oC in the low end RCP2.6 scenario to ~4oC in the high end RCP8.5 (i.e., the scenario uncertainty is of the order of 3oC). The GCM structural uncertainty is due to the fact that different AOGCMs respond differently to the same greenhouse gas forcing scenario because of their different representations of dynamical and physical processes. It can be measured by the range in AOGCM projected response for a given scenario, and for the latest set of global temperature change projections it is of the order 2o to 3oC (Stocker et al., 2013). The variability uncertainty is usually estimated by performing different realizations with the same GCM using different ocean initial conditions. The relative importance of these three sources of uncertainty depends on the time horizon being considered (Hawkins & Sutton, 2009). For near future projections (e.g., 10–20 years), the scenario uncertainty is negligible compared to the AOGCM structural and variability uncertainties because different scenarios start to diverge significantly only after about the mid-21st century. On the other end, for end-of-century projections the variability uncertainty becomes relatively less important; that is, the end-of-century conditions are less sensitive to the initial ocean state, although some studies suggest that at local to regional scales the internal variability remains a significant factor (Benestad, Parding, Isacsen, & Mezghani, 2016). In general, the relative role of the uncertainty due to internal variability increases going from the global to the regional scale (Benestad et al., 2016).

The use of downscaling adds further sources of uncertainty (Figure 4), namely those due to different downscaling approaches (e.g., dynamical vs. empirical-statistical or “downscaling approach uncertainty”) or to different models within the same method (or “downscaling model structural uncertainty”). Rigorous comparative assessments of projections using different downscaling methods have been relatively limited (e.g., Mearns et al., 1999; Mezghani, Dobler, Benestad, Haugen, & Parding, 2019); however, intercomparison projects such as PRUDENCE (Déqué et al., 2007) and AMMA (Paeth et al., 2011) have shown that the downscaling model structural uncertainty can be comparable, if not greater, than the AOGCM structural uncertainty for variables that depend mostly on local conditions, such as summer, or tropical, convective precipitation. In other words, for these variables different RCM projections can vary substantially even when driven by the same AOGCM. In addition, the AOGCM structural and variability uncertainties (e.g., climate sensitivity) are also inherited by the downscaling methods.

Regional Dynamical Downscaling

Figure 4. Schematic depiction of different sources of uncertainty in regional climate projections.

These considerations imply that the use of downscaling may actually increase the overall uncertainty in regional projections, and thus, in order to fully characterize the uncertainty in regional projections, it is necessary to complete large and optimally designed scenario-AOGCM-downscaling matrices of simulations. This has traditionally been a limitation with downscaling-based projections, which the CORDEX program is attempting to address.

Future Directions in Dynamical Downscaling Research and Application

Transition to Convection Permitting RCMs

The main ongoing development in RCM research is the transition to convection permitting RCMs (Prein et al., 2015), that is, models that can be used to produce climate information down to local scales of a few kilometers. This transition is not just an increase of model resolution but requires the upgrade and/or development of models to nonhydrostatic dynamical cores and to more detailed representations of physical processes such as boundary layer and cloud microphysics. Most RCM systems are being upgraded in this direction, and a number of pilot studies employing convection-permitting RCMs in climate mode have already been carried out (e.g., Ban, Schmidli, & Schar, 2014; Coppola et al., 2019; Fosser, Khodayar, & Berg, 2014; Kanada, Wada, & Sugi, 2013; Kendon, Roberts, Senior, & Roberts, 2012; Kendon et al., 2014; Liu et al., 2017; Prein et al., 2013; Prein et al., 2015; Rasmussen et al., 2011). In this regard, it is important to emphasize that the extension of convection-permitting RCMs from weather prediction, a context where they have been extensively used, to climate applications is not immediate and requires substantial experimentation (e.g., Coppola et al., 2019).

In addition, as the spatial scale is refined, the natural variability of many climate variables (e.g., precipitation) tends to substantially increase (e.g., Giorgi, 2002), which makes the detection of forced signals more difficult. This implies that large ensembles may be necessary to identify local-scale forced signals, and this poses an issue of computing and data storage requirements of climate simulations at convection-permitting scales. A careful design of ensemble matrices is thus an important step for the application of convection-permitting RCMs to climate change studies. The CORDEX program is addressing this challenge through a dedicated study (Coppola et al., 2019), run in coordination with more focused projects such as the EU-sponsored EUCP.

A third issue concerns the added value gained by convection permitting RCMs, given that their use requires an increase in computational requirements of two to three orders of magnitude compared to coarser resolution RCMs. Early studies have shown, for example, that convection-permitting RCMs improve the simulation of precipitation intensity and extremes and that the use of explicit rather than parameterized representation of convection better captures the observed diurnal cycle of convection (Prein et al., 2015). Convection-permitting RCMs also allow a better description of local-scale circulations, such as the sea breeze, and the representation of organized mesoscale convective systems and tropical storms (Prein et al., 2015). In addition, they can improve the description of local feedbacks, such as the soil moisture–precipitation relation (Hohenegger, Brockhaus, Bretherton, & Schar, 2009; Taylor et al., 2013). The improvement produced by convection-permitting RCMs can also upscale from small to large areas, thereby ameliorating some model systematic biases, for example related to tropical convection (Hart, Washington, & Stratton, 2018) or the simulation of the Doldrums in the tropical Atlantic (Klocke, Brueck, Hohenegger, & Stevens, 2017).

A key issue related to the use of convection-permitting RCMs is the availability of high-quality, high-resolution observations to validate the models, which is a problem for many regions of the world. High-resolution data are also needed to represent the physiographic information needed as input to the models, such as land use, land cover, and corresponding changes over time. This can also be a problem for many regions.

In summary, the transition to convection-permitting RCMs will require strong efforts by the scientific community, both from the modeling and infrastructure points of view. This effort will likely be one of the main foci of RCM research for the decades to come.

Further Development and Use of Coupled Regional Earth System Models

The development of coupled regional models will also certainly be another area of focus in the future. The enhancement in model resolution will make this coupling increasingly consistent with the scales of systems being considered, such as, for example, those typical of hydrologic river basins, land ecosystems, urban environments, lakes, aerosol plumes, or glaciers. Running fully coupled regional models also requires a considerable increase in computing resources compared to uncoupled runs, and therefore the added value of using such models will need to be carefully considered and might be strongly region-dependent.

An especially challenging development in coupled regional modeling research will be the inclusion of interactive human components. To date, human activities, such as land use change, urbanization, and pollution emissions, have been considered external forcings in climate models. However, human societies do respond to environmental stresses, and therefore there might be important society–environment feedbacks to be considered. A regional modeling context can constitute an especially suitable framework to include an interactive human component in climate models, possibly paving the way for the future inclusion of humans in global models. The development of coupled regional Earth system models also provide the advantage of focusing on particular regions of interest for climate change impacts, such as the “Food Baskets of the World” regions identified by the WCRP or fast-growing coastal megacity environments in developing countries.

This coupled model research will clearly entail a strong cross-disciplinary effort across the physical and social sciences, which the downscaling community is already undertaking within the context of climate service activities.

Future Directions in the CORDEX Program, Interactions With HIRGCM Projects, and Application to Climate Service Activities

The first phase of the CORDEX initiative was remarkably successful in involving a large segment of the downscaling community, in particular that involved in RCM research. Ensembles of 21st-century projections were completed for all CORDEX continental domains at the nominal 50 km grid spacing (higher for Europe). However, these ensembles were quite heterogeneous in terms of number of simulations; they were largest for Europe and smallest for Australasia and Central Asia. In addition, a number of scientific issues emerged from the first analysis of these simulations:

  1. 1. Better quantitative and comprehensive assessment of the added value of downscaling.

  2. 2. Better assessment of the role of anthropogenic forcing such as due to aerososls and land use change.

  3. 3. Improved coordination of regional coupled modeling activities.

  4. 4. More comprehensive analysis of changes in precipitation characteristics, including extremes, convective systems, coastal systems, regimes, etc.

  5. 5. More comprehensive analysis of changes in intense wind systems and storms.

These considerations have guided the design of the second phase of CORDEX, which is essentially composed of two initiatives. The first consists of the CORDEX-CORE initiative (Figure 5), whose plan is to downscale a common set of CGM projections over all continental-wide CORDEX domains with a core set of RCMs at 25 km grid spacing (~12 km over Europe) for two greenhouse gas emission scenarios, RCP8.5 and RCP2.6 (Gutowski et al., 2016). Currently, two RCM systems are participating to this initial endeavor, downscaling three common AOGCMs. This initial ensemble will thus constitute a homogeneous basis of simulations to be augmented by the addition of other RCM experiments. The CORDEX-CORE framework will also allow a more comprehensive exploration of the added value issue.

Regional Dynamical Downscaling

Figure 5. Schematic depiction of the CORDEX-CORE simulation protocol.

The second new CORDEX initiative is represented by the so-called flagship pilot studies. These are projects targeted at specific scientific questions identified within the CORDEX program. To date, seven pilot studies are underway on topics such as the development and testing of convection-permitting RCMs for mountainous regions, assessment of the effects of anthropogenic and natural aerosols along with land use changes, assessment of the importance of air-sea coupling in different ocean basins (Mediterranean and Western Africa), and the effect of global warming on extremes (with a focus on Africa and South America). A procedure for proposing a flagship pilot study can be found on the CORDEX website.

A final aspect of future CORDEX development is a better integration of empirical-statistical, VARGCM, and HIRGCM experiments. Originally, the CORDEX program was meant to provide a framework encompassing all downscaling methodologies, while to date it has been mostly an RCM endeavor. It is auspicial that the other dynamical and empirical-statistical downscaling tools will be incorporated in the program not only to better understand relative advantages and limitations but also to assess how the information from different techniques can be compounded to improve understandings of regional climate change.

Much of the interaction of CORDEX with the HIRGCM community will be through the HighResMip (Haarsma et al., 2016) and HAPPI (Mitchell et al., 2017) projects. HighResMip will produce ensembles of atmospheric-only simulations at different grid spacings down to 25 to 50 km (i.e., comparable to those of the CORDEX-CORE program). It will provide information not only on the resolution dependency of model behaviors and systematic errors in the present-day simulation and future projection contexts, but, through a comparison with the CORDEX-CORE results, on the relative importance of large-scale teleconnections/interactions and local high-resolution processes and forcings. Conversely, the HAPPI project will allow the study of the relative role of external forcings and internal variability through the completion of large ensembles of atmosphere-only global models.

As part of these new CORDEX initiatives, larger and more comprehensive ensembles of high-resolution projections will be produced and made available for impact applications and climate service activities, which will pose the problem of identifying the most credible and actionable information from these multimethod, multimodel sources of data. This process will require not only a more careful and process-based analysis of the climate change signals but also a stronger interaction with the stakeholder community to extract the most impact-relevant information and guide users in the proper interpretation of the available climate information.

Final Considerations

The field of dynamical downscaling has grown tremendously in the past three decades, becoming an established area of research. A fundamental aspect of this growth has been the increasing self-organization of the downscaling scientific community, which, starting from isolated research efforts, has developed into widely encompassing international collaborative efforts such as CORDEX. The resolution of downscaling modeling systems has increased over the years, and in particular for RCMs it has now reached the convection-permitting scale. It can be envisioned that over the next years RCMs running at convection-permitting resolutions will become the state of the art, and this will pose a formidable challenge from the infrastructure and data management points of view due to the computational and storing requirements of these models. This is especially the case in view of the need to produce large ensembles to properly characterize uncertainties and produce robust projections at regional to local scales (Mezghani et al., 2019). The dynamical downscaling community thus needs to develop appropriate technological strategies to address this challenge, which can probably be best addressed at the international level through programs such as CORDEX.

Information derived from dynamical downscaling will be increasingly used for impact and climate service applications, possibly with the use of dedicated websites and software to facilitate access to this information. It is thus important that the users of dynamically downscaled information understand its value, limitations, and uncertainties. User guidelines can be very useful tools for a proper assessment and use of the data (e.g., Mearns et al., 2003; Wilby et al., 2004). In addition, communication across the modeling and end-user communities needs to be enhanced, and this may require the formation of a new generation of interdisciplinary scientists lying at the interface between the climate modeling and stakeholder communities.

To date, dynamical downscaling has been mostly viewed as a tool for producing climate change information for impact applications; however, other areas of application of dynamical downscaling should be more extensively explored. Among them the application to seasonal prediction (e.g., Cocke & LaRow, 2000; Druyan, Fulakeza, & Lonergan, 2002; Fennessy & Shukla, 2000; Xue, Janjic, Dudhia, Vasic, & De Sales, 2014), paleoclimate simulation (e.g., Diffenbaugh & Sloan, 2004; Hostetler & Giorgi, 1992; Hostetler, Bartlein, Clark, Small, & Solomon, 2000; Hostetler, Giorgi, Bates, & Bartlein, 1994; Schimanke, Meyer, Kjellstrom, Strandberg, & Hordoir, 2012; Sloan, 2006; Strandberg et al., 2011, Standberg, Kjellström, et al., 2014), or detection and attribution of trends and extreme events (e.g., Massey et al., 2015; Mote et al., 2016; Otto et al., 2015; Schaller et al., 2018; Sparrow, Huntingford, Massey, & Allen, 2013).

As a final consideration, traditionally different dynamical downscaling techniques have been seen in competition with each other and in competition with empirical-statistical and global models. This has somewhat hampered the full exploitation of downscaling. In fact, much can be learned from the use of multiple techniques. For example, empirical-statistical tools can be used to validate the downscaling ability of dynamical models, to study connections between large and small scales, or to emulate RCM results. RCMs can provide information on the behavior of models at resolutions that global models would eventually reach in the future. This competition paradigm is clearly obsolete, and all the information available from different models and approaches can be combined toward a better understanding of climate processes and climate change at regional to local scales.


I would like to thank J. Ciarlo, and F. Raffaele for help in producing some of the figures in this article. The data used in this work can be found at the websites for EURO-CORDEX and CMIP5.

Further Reading

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

Di Luca, A., de Elia, R., & Laprise, R. (2015). Challenges in the quest for added value of regional climate dynamical downscaling. Current Climate Change Reports, 1, 10–21.Find this resource:

Foley, A. M. (2010). Uncertainty in regional climate modeling: A review. Progress in Physical Geography, 34, 647–670.Find this resource:

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

Giorgi, F. (2019). Thirty years of regional climate modeling: Where are we and where are we going? Journal of Geophysical Research: Atmospheres, 124(11), 5696–5723.Find this resource:

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