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Article

Erik Kjellström and Ole Bøssing Christensen

Regional climate models (RCMs) are commonly used to provide detailed regional to local information for climate change assessments, impact studies, and work on climate change adaptation. The Baltic Sea region is well suited for RCM evaluation due to its complexity and good availability of observations. Evaluation of RCM performance over the Baltic Sea region suggests that: • Given appropriate boundary conditions, RCMs can reproduce many aspects of the climate in the Baltic Sea region. • High resolution improves the ability of RCMs to simulate significant processes in a realistic way. • When forced by global climate models (GCMs) with errors in their representation of the large-scale atmospheric circulation and/or sea surface conditions, performance of RCMs deteriorates. • Compared to GCMs, RCMs can add value on the regional scale, related to both the atmosphere and other parts of the climate system, such as the Baltic Sea, if appropriate coupled regional model systems are used. Future directions for regional climate modeling in the Baltic Sea region would involve testing and applying even more high-resolution, convection permitting, models to generally better represent climate features like heavy precipitation extremes. Also, phenomena more specific to the Baltic Sea region are expected to benefit from higher resolution (these include, for example, convective snowbands over the sea in winter). Continued work on better describing the fully coupled regional climate system involving the atmosphere and its interaction with the sea surface and land areas is also foreseen as beneficial. In this respect, atmospheric aerosols are important components that deserve more attention.

Article

Regional models were originally developed to serve weather forecasting and regional process studies. Typical simulations encompass time periods in the order of days or weeks. Thereafter regional models were also used more and more as regional climate models for longer integrations and climate change downscaling. Regional climate modeling or regional dynamic downscaling, which are used interchangeably, developed as its own branch in climate research since the end of the 1990s out of the need to bridge the obvious inconsistencies at the interface of global climate research and climate impact research. The primary aim of regional downscaling is to provide consistent regional climate change scenarios with relevant spatial resolution to serve detailed climate impact assessments. Similar to global climate modeling, the early attempts at regional climate modeling were based on uncoupled atmospheric models or stand-alone ocean models, an approach that is still maintained as the most common on the regional scale. However, this approach has some fundamental limitations, since regional air-sea interaction remains unresolved and regional feedbacks are neglected. This is crucial when assessing climate change impacts in the coastal zone or the regional marine environment. To overcome these limitations, regional climate modeling is currently in a transition from uncoupled regional models into coupled atmosphere-ocean models, leading to fully integrated earth system models. Coupled ice-ocean-atmosphere models have been developed during the last decade and are currently robust and well established on the regional scale. Their added value has been demonstrated for regional climate modeling in marine regions, and the importance of regional air-sea interaction became obvious. Coupled atmosphere-ice-ocean models, but also coupled physical-biogeochemical modeling approaches are increasingly used for the marine realm. First attempts to couple these two approaches together with land surface models are underway. Physical coupled atmosphere-ocean modeling is also developing further and first model configurations resolving wave effects at the atmosphere-ocean interface are now available. These new developments now open up for improved regional assessment under broad consideration of local feedbacks and interactions between the regional atmosphere, cryosphere, hydrosphere, and biosphere.

Article

Filippo Giorgi

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.

Article

William Joseph Gutowski and Filippo Giorgi

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.

Article

Rasmus Benestad

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

Article

Andreas Gobiet and Sven Kotlarski

The analysis of state-of-the-art regional climate projections indicates a number of robust changes of the climate of the European Alps by the end of this century. Among these are a temperature increase in all seasons and at all elevations and a significant decrease in natural snow cover. Precipitation changes, however, are more subtle and subject to larger uncertainties. While annual precipitation sums are projected to remain rather constant until the end of the century, winter precipitation is projected to increase. Summer precipitation changes will most likely be negative, but increases are possible as well and are covered by the model uncertainty range. Precipitation extremes are expected to intensify in all seasons. The projected changes by the end of the century considerably depend on the greenhouse-gas scenario assumed, with the high-end RCP8.5 scenario being associated with the most prominent changes. Until the middle of the 21st century, however, it is projected that climate change in the Alpine area will only slightly depend on the specific emission scenario. These results indicate that harmful weather events in the Alpine area are likely to intensify in future. This particularly refers to extreme precipitation events, which can trigger flash floods and gravitational mass movements (debris flows, landslides) and lead to substantial damage. Convective precipitation extremes (thunderstorms) are additionally a threat to agriculture, forestry, and infrastructure, since they are often associated with strong wind gusts that cause windbreak in forests and with hail that causes damage in agriculture and infrastructure. Less spectacular but still very relevant is the effect of soil erosion on inclined arable land, caused by heavy precipitation. At the same time rising temperatures lead to longer vegetation periods, increased evapotranspiration, and subsequently to higher risk of droughts in the drier valleys of the Alps. Earlier snowmelt is expected to lead to a seasonal runoff shift in many catchments and the projected strong decrease of the natural snow cover will have an impact on tourism and, last but not least, on the cultural identity of many inhabitants of the Alpine area.

Article

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.

Article

The ecosystems and the societies of the Baltic Sea region are quite sensitive to fluctuations in climate, and therefore it is expected that anthropogenic climate change will affect the region considerably. With numerical climate models, a large amount of projections of meteorological variables affected by anthropogenic climate change have been performed in the Baltic Sea region for periods reaching the end of this century. Existing global and regional climate model studies suggest that: • The future Baltic climate will get warmer, mostly so in winter. Changes increase with time or increasing emissions of greenhouse gases. There is a large spread between different models, but they all project warming. In the northern part of the region, temperature change will be higher than the global average warming. • Daily minimum temperatures will increase more than average temperature, particularly in winter. • Future average precipitation amounts will be larger than today. The relative increase is largest in winter. In summer, increases in the far north and decreases in the south are seen in most simulations. In the intermediate region, the sign of change is uncertain. • Precipitation extremes are expected to increase, though with a higher degree of uncertainty in magnitude compared to projected changes in temperature extremes. • Future changes in wind speed are highly dependent on changes in the large-scale circulation simulated by global climate models (GCMs). The results do not all agree, and it is not possible to assess whether there will be a general increase or decrease in wind speed in the future. • Only very small high-altitude mountain areas in a few simulations are projected to experience a reduction in winter snow amount of less than 50%. The southern half of the Baltic Sea region is projected to experience significant reductions in snow amount, with median reductions of around 75%.

Article

The modeling of climate over the Tibetan Plateau (TP) started with the introduction of Global Climate Models (GCMs) in the 1950s. Since then, GCMs have been developed to simulate atmospheric dynamics and eventually the climate system. As the highest and widest international plateau, the strong orographic forcing caused by the TP and its impact on general circulation rather than regional climate was initially the focus. Later, with growing awareness of the incapability of GCMs to depict regional or local-scale atmospheric processes over the heterogeneous ground, coupled with the importance of this information for local decision-making, regional climate models (RCMs) were established in the 1970s. Dynamic and thermodynamic influences of the TP on the East and South Asia summer monsoon have since been widely investigated by model. Besides the heterogeneity in topography, impacts of land cover heterogeneity and change on regional climate were widely modeled through sensitivity experiments. In recent decades, the TP has experienced a greater warming than the global average and those for similar latitudes. GCMs project a global pattern where the wet gets wetter and the dry gets drier. The climate regime over the TP covers the extreme arid regions from the northwest to the semi-humid region in the southeast. The increased warming over the TP compared to the global average raises a number of questions. What are the regional dryness/wetness changes over the TP? What is the mechanism of the responses of regional changes to global warming? To answer these questions, several dynamical downscaling models (DDMs) using RCMs focusing on the TP have recently been conducted and high-resolution data sets generated. All DDM studies demonstrated that this process-based approach, despite its limitations, can improve understandings of the processes that lead to precipitation on the TP. Observation and global land data assimilation systems both present more wetting in the northwestern arid/semi-arid regions than the southeastern humid/semi-humid regions. The DDM was found to better capture the observed elevation dependent warming over the TP. In addition, the long-term high-resolution climate simulation was found to better capture the spatial pattern of precipitation and P-E (precipitation minus evapotranspiration) changes than the best available global reanalysis. This facilitates new and substantial findings regarding the role of dynamical, thermodynamics, and transient eddies in P-E changes reflected in observed changes in major river basins fed by runoff from the TP. The DDM was found to add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. Although these advantages in the DDM over the TP are evidenced, there are unavoidable facts to be aware of. Firstly, there are still many discrepancies that exist in the up-to-date models. Any uncertainty in the model’s physics or in the land information from remote sensing and the forcing could result in uncertainties in simulation results. Secondly, the question remains of what is the appropriate resolution for resolving the TP’s heterogeneity. Thirdly, it is a challenge to include human activities in the climate models, although this is deemed necessary for future earth science. All-embracing further efforts are expected to improve regional climate models over the TP.