This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.
Dynamical downscaling (DD) consists of the use of physical models to downscale the large-scale climate information produced by coupled Atmosphere-Ocean Global Climate Models (AOGCMs). This can be achieved with global high-resolution atmospheric GCMs (HIRGCMs), variable resolution GCMs (VARGCMs) and limited area Regional Climate Models (RCMs). Borrowing from numerical weather prediction, DD techniques originated in the late 1980s from the need to produce high-resolution regional climate information for application to impact studies. The philosophy behind DD is that the AOGCM can simulate the response of the global circulation to large-scale forcings (e.g., due to greenhouse gases) and the DD tools can regionally enhance this response to account for the contribution of fine-scale processes and forcings, for example, due to aerosols and complex topography, coastlines, and vegetation cover.
Since the 1990s the use of DD for climate studies, and principally RCMs, has grown tremendously, to the point that DD techniques, along with Empirical-Statistical Downscaling (ESD), are considered key elements in the production of climate information for regions. In fact, the use of DD is justified to the extent that it adds useful and robust high-resolution information to that produced by AOGCMs, and considerable research has gone into investigating this central issue, often referred to as “added value,” which is still often debated. Today a number of flexible and portable RCM systems are available, which can be routinely run for up to centennial-scale experiments over domains distributed worldwide for a wide range of applications, from process studies to paleo and future climate simulations. The model resolution has steadily increased up to grid spacings of ~10–25 km, and a new generation of non-hydrostatic RCMs is being developed and tested for use in very-high-resolution (~ few km) convection-permitting simulations. In addition, the development of coupled regional earth system models is a new frontier area of research aimed at exploring the importance of air-sea-land interactions at regional scales.
A fundamental step toward a better understanding of DD techniques has been the inception of multimodel intercomparison studies. These were originally regional in nature, which prevented the application of common protocols and thus hindered the transfer of know-how across projects. However, this problem was addressed through the creation in the late 2000s of the Coordinated Regional Climate Downscaling Experiment (CORDEX), which provided a common simulation protocol across regions worldwide, representing a fundamental growth step for the DD community.
Often different DD and ESD techniques have been seen in competition with each other, and with AOGCMs. However the realization is growing that they all represent complementary pieces to compose the puzzle of generating robust and credible climate services to address the needs and concerns of different regions, countries, and societal sectors. DD will continue to be increasingly used in the generation of actionable climate information, but a solid understanding of the advantages and limitations of DD is paramount to its use in this process.
H.E. Markus Meier and Sofia Saraiva
In this article, the concepts and background of regional climate modeling of the future Baltic Sea are summarized and state-of-the-art projections, climate change impact studies, and challenges are discussed. The focus is on projected oceanographic changes in future climate. However, as these changes may have a significant impact on biogeochemical cycling, nutrient load scenario simulations in future climates are briefly discussed as well. The Baltic Sea is special compared to other coastal seas as it is a tideless, semi-enclosed sea with large freshwater and nutrient supply from a partly heavily populated catchment area and a long response time of about 30 years, and as it is, in the early 21st century, warming faster than any other coastal sea in the world. Hence, policymakers request the development of nutrient load abatement strategies in future climate. For this purpose, large ensembles of coupled climate–environmental scenario simulations based upon high-resolution circulation models were developed to estimate changes in water temperature, salinity, sea-ice cover, sea level, oxygen, nutrient, and phytoplankton concentrations, and water transparency, together with uncertainty ranges. Uncertainties in scenario simulations of the Baltic Sea are considerable. Sources of uncertainties are global and regional climate model biases, natural variability, and unknown greenhouse gas emission and nutrient load scenarios. Unknown early 21st-century and future bioavailable nutrient loads from land and atmosphere and the experimental setup of the dynamical downscaling technique are perhaps the largest sources of uncertainties for marine biogeochemistry projections. The high uncertainties might potentially be reducible through investments in new multi-model ensemble simulations that are built on better experimental setups, improved models, and more plausible nutrient loads. The development of community models for the Baltic Sea region with improved performance and common coordinated experiments of scenario simulations is recommended.