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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

Ricardo García-Herrera and David Barriopedro

The Mediterranean is a semi-enclosed sea surrounded by Europe to the north, Asia to the east, and Africa to the south. It covers an area of approximately 2.5 million km2, between 30–46 °N latitude and 6 °W and 36 °E longitude. The term Mediterranean climate is applied beyond the Mediterranean region itself and has been used since the early 20th century to classify other regions of the world, such as California or South Africa, usually located in the 30º–40º latitudinal band. The Mediterranean climate can be broadly characterized by warm to hot dry summers and mild wet winters. However, this broad picture hides important variations, which can be explained through the existence of two geographical gradients: North/South, with a warmer and drier south, and West/East, more influenced by Atlantic/Asian circulation. The region is located at a crossroad between the mid-latitudes and the subtropical regimes. Thus, small changes in the Atlantic storm track may lead to dramatic changes in the precipitation of the northwestern area of the basin. The variability of the descending northern branch of the Hadley cell influences the climate of the southern margin, while the eastern border climate is conditioned by the Siberian High in winter and the Indian Summer Monsoon during summer. All these large-scale factors are modulated by the complex orography of the region, the contrasting albedo, and the moisture and heat supplied by the Mediterranean Sea. The interactions occurring among all these factors lead to a complex picture with some relevant phenomena characteristic of the Mediterranean region, such as heatwaves and droughts, Saharan dust intrusions, or specific types of cyclogenesis. Climate model projections generally agree in characterizing the region as a climate change hotspot, considering that it is one of the areas of the globe likely to suffer pronounced climate changes. Anthropogenic influences are not new, since the region is densely populated and is the home of some the oldest civilizations on Earth. This has produced multiple and continuous modifications in the land cover, with measurable impacts on climate that can be traced from the rich available documentary evidence and high-resolution natural proxies.

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

Martin Claussen, Anne Dallmeyer, and Jürgen Bader

There is ample evidence from palaeobotanic and palaeoclimatic reconstructions that during early and mid-Holocene between some 11,700 years (in some regions, a few thousand years earlier) and some 4200 years ago, subtropical North Africa was much more humid and greener than today. This African Humid Period (AHP) was triggered by changes in the orbital forcing, with the climatic precession as the dominant pacemaker. Climate system modeling in the 1990s revealed that orbital forcing alone cannot explain the large changes in the North African summer monsoon and subsequent ecosystem changes in the Sahara. Feedbacks between atmosphere, land surface, and ocean were shown to strongly amplify monsoon and vegetation changes. Forcing and feedbacks have caused changes far larger in amplitude and extent than experienced today in the Sahara and Sahel. Most, if not all, climate system models, however, tend to underestimate the amplitude of past African monsoon changes and the extent of the land-surface changes in the Sahara. Hence, it seems plausible that some feedback processes are not properly described, or are even missing, in the climate system models. Perhaps even more challenging than explaining the existence of the AHP and the Green Sahara is the interpretation of data that reveal an abrupt termination of the last AHP. Based on climate system modeling and theoretical considerations in the late 1990s, it was proposed that the AHP could have ended, and the Sahara could have expanded, within just a few centuries—that is, much faster than orbital forcing. In 2000, paleo records of terrestrial dust deposition off Mauritania seemingly corroborated the prediction of an abrupt termination. However, with the uncovering of more paleo data, considerable controversy has arisen over the geological evidence of abrupt climate and ecosystem changes. Some records clearly show abrupt changes in some climate and terrestrial parameters, while others do not. Also, climate system modeling provides an ambiguous picture. The prediction of abrupt climate and ecosystem changes at the end of the AHP is hampered by limitations implicit in the climate system. Because of the ubiquitous climate variability, it is extremely unlikely that individual paleo records and model simulations completely match. They could do so in a statistical sense, that is, if the statistics of a large ensemble of paleo data and of model simulations converge. Likewise, the interpretation regarding the strength of terrestrial feedback from individual records is elusive. Plant diversity, rarely captured in climate system models, can obliterate any abrupt shift between green and desert state. Hence, the strength of climate—vegetation feedback is probably not a universal property of a certain region but depends on the vegetation composition, which can change with time. Because of spatial heterogeneity of the African landscape and the African monsoon circulation, abrupt changes can occur in several, but not all, regions at different times during the transition from the humid mid-Holocene climate to the present-day more arid climate. Abrupt changes in one region can be induced by abrupt changes in other regions, a process sometimes referred to as “induced tipping.” The African monsoon system seems to be prone to fast and potentially abrupt changes, which to understand and to predict remains one of the grand challenges in African climate science.

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

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

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

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

Gabriele Gramelsberger

Climate and simulation have become interwoven concepts during the past decades because, on the one hand, climate scientists shouldn’t experiment with real climate and, on the other hand, societies want to know how climate will change in the next decades. Both in-silico experiments for a better understanding of climatic processes as well as forecasts of possible futures can be achieved only by using climate models. The article investigates possibilities and problems of model-mediated knowledge for science and societies. It explores historically how climate became a subject of science and of simulation, what kind of infrastructure is required to apply models and simulations properly, and how model-mediated knowledge can be evaluated. In addition to an overview of the diversity and variety of models in climate science, the article focuses on quasiheuristic climate models, with an emphasis on atmospheric models.

Article

Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in improving model projections is comparisons with observations. Exceeding uncertainties associated with climate model simulation are uncertainties due to projections of future emissions of CO2 and other greenhouse gases. Humanity’s choices in emissions pathways will have profound effects on climate, especially after the mid-century. The African Sahel is a transition zone characterized by strong meridional precipitation and temperature gradients. Over West Africa, the Sahel marks the northernmost extent of the West African monsoon system. The region’s climate is known to be sensitive to sea surface temperatures, both regional and global, as well as to land surface conditions. Increasing atmospheric greenhouse gases are already causing amplified warming over the Sahara Desert and, consequently, increased rainfall in parts of the Sahel. Climate model projections indicate that much of this increased rainfall will be delivered in the form of more intense storm systems. The complicated and highly regional precipitation regimes of East Africa present a challenge for climate modeling. Within roughly 5º of latitude of the equator, rainfall is delivered in two seasons—the long rains in the spring, and the short rains in the fall. Regional climate model projections suggest that the long rains will weaken under greenhouse gas forcing, and the short rains season will extend farther into the winter months. Observations indicate that the long rains are already weakening. Changes in seasonal rainfall over parts of subtropical southern Africa are observed, with repercussions and challenges for agriculture and water availability. Some elements of these observed changes are captured in model simulations of greenhouse gas-induced climate change, especially an early demise of the rainy season. The projected changes are quite regional, however, and more high-resolution study is needed. In addition, there has been very limited study of climate change in the Congo Basin and across northern Africa. Continued efforts to understand and predict climate using higher-resolution simulation must be sustained to better understand observed and projected changes in the physical processes that support African precipitation systems as well as the teleconnections that communicate remote forcings into the continent.

Article

Many publics remain divided about the existence and consequences of anthropogenic climate change despite scientific consensus. A popular approach to climate change communication, and science communication more generally, is the information deficit model. The deficit model assumes that gaps between scientists and the public are a result of a lack of information or knowledge. As a remedy for this gap, the deficit model is a one-way communication model where information flows from experts to publics in an effort to change individuals’ attitudes, beliefs, or behaviors. Approaches to climate change communication that reflect the deficit model include websites, social media, mobile applications, news media, documentaries and films, books, and scientific publications and technical reports. The deficit model has been highly criticized for being overly simplistic and inaccurately characterizing the relationship between knowledge, attitudes, beliefs, and behaviors, particularly for politically polarized issues like climate change. Even so, it continues to be an integral part of climate change communication research and practice. In an effort to address the inadequacies of the deficit model, scholars and practitioners often utilize alternative forms of public engagement, including the contextual model, the public engagement model, and the lay expertise model. Each approach to public engagement carries with it a unique set of opportunities and challenges. Future work in climate change communication should explore when and how to most effectively use the models of public engagement that are available.

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

C.J.C. Reason

Southern Africa extends from the equator to about 34°S and is essentially a narrow, peninsular land mass bordered to its south, west, and east by oceans. Its termination in the mid-ocean subtropics has important consequences for regional climate, since it allows the strongest western boundary current in the world ocean (warm Agulhas Current) to be in close proximity to an intense eastern boundary upwelling current (cold Benguela Current). Unlike other western boundary currents, the Agulhas retroflects south of the land mass and flows back into the South Indian Ocean, thereby leading to a large area of anomalously warm water south of South Africa which may influence storm development over the southern part of the land mass. Two other unique regional ocean features imprint on the climate of southern Africa—the Angola-Benguela Frontal Zone (ABFZ) and the Seychelles-Chagos thermocline ridge (SCTR). The former is important for the development of Benguela Niños and flood events over southwestern Africa, while the SCTR influences Madden-Julian Oscillation and tropical cyclone activity in the western Indian Ocean. In addition to South Atlantic and South Indian Ocean influences, there are climatic implications of the neighboring Southern Ocean. Along with Benguela Niños, the southern African climate is strongly impacted by ENSO and to lesser extent by the Southern Annular Mode (SAM) and sea-surface temperature (SST) dipole events in the Indian and South Atlantic Oceans. The regional land–sea distribution leads to a highly variable climate on a range of scales that is still not well understood due to its complexity and its sensitivity to a number of different drivers. Strong and variable gradients in surface characteristics exist not only in the neighboring oceans but also in several aspects of the land mass, and these all influence the regional climate and its interactions with climate modes of variability. Much of the interior of southern Africa consists of a plateau 1 to 1.5 km high and a narrow coastal belt that is particularly mountainous in South Africa, leading to sharp topographic gradients. The topography is able to influence the track and development of many weather systems, leading to marked gradients in rainfall and vegetation across southern Africa. The presence of the large island of Madagascar, itself a region of strong topographic and rainfall gradients, has consequences for the climate of the mainland by reducing the impact of the moist trade winds on the Mozambique coast and the likelihood of tropical cyclone landfall there. It is also likely that at least some of the relativity aridity of the Limpopo region in northern South Africa/southern Zimbabwe results from the location of Madagascar in the southwestern Indian Ocean. While leading to challenges in understanding its climate variability and change, the complex geography of southern Africa offers a very useful test bed for improving the global models used in many institutions for climate prediction. Thus, research into the relative shortcomings of the models in the southern African region may lead not only to better understanding of southern African climate but also to enhanced capability to predict climate globally.

Article

Mental models are the sets of causal beliefs we “run” in our minds to infer what will happen in a given event or situation. Mental models, like other models, are useful simplifications most of the time. They can, however, lead to mistaken or misleading inferences, for example, if the analogies that inform them are misleading in some regard. The coherence and consistency of mental models a person employs to solve a given problem are a function of that person’s expertise. The less familiar and central a problem is, the less coherent and consistent the mental models brought to bear on that problem are likely to be. For problems such as those posed by anthropogenic climate change, most people are likely to recruit multiple mental models to make judgments and decisions. Common types of mental models of climate change and global warming include: (a) a carbon emissions model, in which global warming is a result of burning fossil fuels thereby emitting CO2, and of deforestation, which both releases sequestered CO2 and decreases the possible sinks that might take CO2 out of the atmosphere; (b) a stratospheric ozone depletion mental model, which conflates stratospheric ozone depletion with global warming; (c) an air pollution mental model, in which global warming is viewed as air pollution; and (d) a weather change model, in which weather and climate are conflated. As social discourse around global warming and climate change has increased, mental models of climate change have become more complex, although not always more coherent. One such complexity is the belief that climate changes according to natural cycles and due to factors beyond human control, in addition to changes resulting from human activities such as burning fossil fuels and releasing other greenhouse gases. As our inference engines, mental models play a central role in problem solving and subjective projections and are hence at the heart of risk perceptions and risk decision-making. However, both perceiving and making decisions about climate change and the risks thereof are affective and social processes foremost.

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.

Article

Stefano Tibaldi and Franco Molteni

The atmospheric circulation in the mid-latitudes of both hemispheres is usually dominated by westerly winds and by planetary-scale and shorter-scale synoptic waves, moving mostly from west to east. A remarkable and frequent exception to this “usual” behavior is atmospheric blocking. Blocking occurs when the usual zonal flow is hindered by the establishment of a large-amplitude, quasi-stationary, high-pressure meridional circulation structure which “blocks” the flow of the westerlies and the progression of the atmospheric waves and disturbances embedded in them. Such blocking structures can have lifetimes varying from a few days to several weeks in the most extreme cases. Their presence can strongly affect the weather of large portions of the mid-latitudes, leading to the establishment of anomalous meteorological conditions. These can take the form of strong precipitation episodes or persistent anticyclonic regimes, leading in turn to floods, extreme cold spells, heat waves, or short-lived droughts. Even air quality can be strongly influenced by the establishment of atmospheric blocking, with episodes of high concentrations of low-level ozone in summer and of particulate matter and other air pollutants in winter, particularly in highly populated urban areas. Atmospheric blocking has the tendency to occur more often in winter and in certain longitudinal quadrants, notably the Euro-Atlantic and the Pacific sectors of the Northern Hemisphere. In the Southern Hemisphere, blocking episodes are generally less frequent, and the longitudinal localization is less pronounced than in the Northern Hemisphere. Blocking has aroused the interest of atmospheric scientists since the middle of the last century, with the pioneering observational works of Berggren, Bolin, Rossby, and Rex, and has become the subject of innumerable observational and theoretical studies. The purpose of such studies was originally to find a commonly accepted structural and phenomenological definition of atmospheric blocking. The investigations went on to study blocking climatology in terms of the geographical distribution of its frequency of occurrence and the associated seasonal and inter-annual variability. Well into the second half of the 20th century, a large number of theoretical dynamic works on blocking formation and maintenance started appearing in the literature. Such theoretical studies explored a wide range of possible dynamic mechanisms, including large-amplitude planetary-scale wave dynamics, including Rossby wave breaking, multiple equilibria circulation regimes, large-scale forcing of anticyclones by synoptic-scale eddies, finite-amplitude non-linear instability theory, and influence of sea surface temperature anomalies, to name but a few. However, to date no unique theoretical model of atmospheric blocking has been formulated that can account for all of its observational characteristics. When numerical, global short- and medium-range weather predictions started being produced operationally, and with the establishment, in the late 1970s and early 1980s, of the European Centre for Medium-Range Weather Forecasts, it quickly became of relevance to assess the capability of numerical models to predict blocking with the correct space-time characteristics (e.g., location, time of onset, life span, and decay). Early studies showed that models had difficulties in correctly representing blocking as well as in connection with their large systematic (mean) errors. Despite enormous improvements in the ability of numerical models to represent atmospheric dynamics, blocking remains a challenge for global weather prediction and climate simulation models. Such modeling deficiencies have negative consequences not only for our ability to represent the observed climate but also for the possibility of producing high-quality seasonal-to-decadal predictions. For such predictions, representing the correct space-time statistics of blocking occurrence is, especially for certain geographical areas, extremely important.

Article

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