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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

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

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

Fedor Mesinger, Miodrag Rančić, and R. James Purser

The astonishing development of computer technology since the mid-20th century has been accompanied by a corresponding proliferation in the numerical methods that have been developed to improve the simulation of atmospheric flows. This article reviews some of the numerical developments concern the ongoing improvements of weather forecasting and climate simulation models. Early computers were single-processor machines with severely limited memory capacity and computational speed, requiring simplified representations of the atmospheric equations and low resolution. As the hardware evolved and memory and speed increased, it became feasible to accommodate more complete representations of the dynamic and physical atmospheric processes. These more faithful representations of the so-called primitive equations included dynamic modes that are not necessarily of meteorological significance, which in turn led to additional computational challenges. Understanding which problems required attention and how they should be addressed was not a straightforward and unique process, and it resulted in the variety of approaches that are summarized in this article. At about the turn of the century, the most dramatic developments in hardware were the inauguration of the era of massively parallel computers, together with the vast increase in the amount of rapidly accessible memory that the new architectures provided. These advances and opportunities have demanded a thorough reassessment of the numerical methods that are most successfully adapted to this new computational environment. This article combines a survey of the important historical landmarks together with a somewhat speculative review of methods that, at the time of writing, seem to hold out the promise of further advancing the art and science of atmospheric numerical modeling.

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

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

Julian Brimelow

Hail has been identified as the largest contributor to insured losses from thunderstorms globally, with losses costing the insurance industry billions of dollars each year. Yet, of all precipitation types, hail is probably subject to the largest uncertainties. Some might go so far as to argue that observing and forecasting hail is as difficult, if not more difficult, than is forecasting tornadoes. The reasons why hail is challenging are many and varied and reflected by the fact that hailstones display a wide variety of shapes, sizes and internal structures. There is also an important clue in this diversity—nature is telling us that hail can grow by following a wide variety of trajectories within thunderstorms, each having a unique set of conditions. It is because of this complexity that modeling hail growth and forecasting size is so challenging. Consequently, it is understandable that predicting the occurrence and size of hail seems an impossible task. Through persistence, ingenuity and technology, scientists have made progress in understanding the key ingredients and processes at play. Technological advances mean that we can now, with some confidence, identify those storms that very likely contain hail and even estimate the maximum expected hail size on the ground hours in advance. Even so, there is still much we need to learn about the many intriguing aspects of hail growth.

Article

This article retrospects the studies of the predictability of El Niño-Southern Oscillation (ENSO) events within the framework of error growth dynamics and reviews the results of previous studies. It mainly covers (a) the advances in methods for studying ENSO predictability, especially those of optimal methods associated with initial errors and model errors; and (b) the applications of these optimal methods in the studies of “spring predictability barrier” (SPB), optimal precursors for ENSO events (or the source of ENSO predictability) and target observations for ENSO predictions. In this context, some of major frontiers and challenges remaining in ENSO predictability are addressed.

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

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

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

Charles A. Doswell III

Convective storms are the result of a disequilibrium created by solar heating in the presence of abundant low-level moisture, resulting in the development of buoyancy in ascending air. Buoyancy typically is measured by the Convective Available Potential Energy (CAPE) associated with air parcels. When CAPE is present in an environment with strong vertical wind shear (winds changing speed and/or direction with height), convective storms become increasingly organized and more likely to produce hazardous weather: strong winds, large hail, heavy precipitation, and tornadoes. Because of their associated hazards and their impact on society, in some nations (notably, the United States), there arose a need to have forecasts of convective storms. Pre-20th-century efforts to forecast the weather were hampered by a lack of timely weather observations and by the mathematical impossibility of direct solution of the equations governing the weather. The first severe convective storm forecaster was J. P. Finley, who was an Army officer, and he was ordered to cease his efforts at forecasting in 1887. Some Europeans like Alfred Wegener studied tornadoes as a research topic, but there was no effort to develop convective storm forecasting. World War II aircraft observations led to the recognition of limited storm science in the topic of convective storms, leading to a research program called the Thunderstorm Product that concentrated diverse observing systems to learn more about the structure and evolution of convective storms. Two Air Force officers, E. J. Fawbush and R. C. Miller, issued the first tornado forecasts in the modern era, and by 1953 the U.S. Weather Bureau formed a Severe Local Storms forecasting unit (SELS, now designated the Storm Prediction Center of the National Weather Service). From the outset of the forecasting efforts, it was evident that more convective storm research was needed. SELS had an affiliated research unit called the National Severe Storms Project, which became the National Severe Storms Laboratory in 1963. Thus, research and operational forecasting have been partners from the outset of the forecasting efforts in the United States—with major scientific contributions from the late T. T. Fujita (originally from Japan), K. A. Browning (from the United Kingdom), R. A. Maddox, J. M. Fritsch, C. F. Chappell, J. B. Klemp, L. R. Lemon, R. B. Wilhelmson, R. Rotunno, M. Weisman, and numerous others. This has resulted in the growth of considerable scientific understanding about convective storms, feeding back into the improvement in convective storm forecasting since it began in the modern era. In Europe, interest in both convective storm forecasting and research has produced a European Severe Storms Laboratory and an experimental severe convective storm forecasting group. The development of computers in World War II created the ability to make numerical simulations of convective storms and numerical weather forecast models. These have been major elements in the growth of both understanding and forecast accuracy. This will continue indefinitely.

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

Scientists who study issues such as climate change are often called on by both their colleagues and broader society to share what they know and why it matters. Many are willing to do so—and do it well—but others are either unwilling or may communicate without clear goals or in ways that may fail to achieve their goals. There are several central topics involved in the study of scientists as communicators. First, it is important to understand the evolving arguments behind why scientists are being called on to get involved in public engagement about contentious issues such as climate change. Second, it is also useful to consider the factors that social science suggests actually lead scientists to communicate about scientific issues. Last, it is important to consider what scientists are trying to achieve through their communication activities, and to consider to what extent we have evidence about whether scientists are achieving their desired goals.

Article

Storms are characterized by high wind speeds; often large precipitation amounts in the form of rain, freezing rain, or snow; and thunder and lightning (for thunderstorms). Many different types exist, ranging from tropical cyclones and large storms of the midlatitudes to small polar lows, Medicanes, thunderstorms, or tornadoes. They can lead to extreme weather events like storm surges, flooding, high snow quantities, or bush fires. Storms often pose a threat to human lives and property, agriculture, forestry, wildlife, ships, and offshore and onshore industries. Thus, it is vital to gain knowledge about changes in storm frequency and intensity. Future storm predictions are important, and they depend to a great extent on the evaluation of changes in wind statistics of the past. To obtain reliable statistics, long and homogeneous time series over at least some decades are needed. However, wind measurements are frequently influenced by changes in the synoptic station, its location or surroundings, instruments, and measurement practices. These factors deteriorate the homogeneity of wind records. Storm indexes derived from measurements of sea-level pressure are less prone to such changes, as pressure does not show very much spatial variability as wind speed does. Long-term historical pressure measurements exist that enable us to deduce changes in storminess for more than the last 140 years. But storm records are not just compiled from measurement data; they also may be inferred from climate model data. The first numerical weather forecasts were performed in the 1950s. These served as a basis for the development of atmospheric circulation models, which were the first generation of climate models or general-circulation models. Soon afterward, model data was analyzed for storm events and cyclone-tracking algorithms were programmed. Climate models nowadays have reached high resolution and reliability and can be run not just for the past, but also for future emission scenarios which return possible future storm activity.

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

Aitor Anduaga

A typhoon is a highly organized storm system that develops from initial cyclone eddies and matures by sucking up from the warm tropical oceans large quantities of water vapor that condense at higher altitudes. This latent heat of condensation is the prime source of energy supply that strengthens the typhoon as it progresses across the Pacific Ocean. A typhoon differs from other tropical cyclones only on the basis of location. While hurricanes form in the Atlantic Ocean and eastern North Pacific Ocean, typhoons develop in the western North Pacific around the Philippines, Japan, and China. Because of their violent histories with strong winds and torrential rains and their impact on society, the countries that ring the North Pacific basin—China, Japan, Korea, the Philippines, and Taiwan—all often felt the need for producing typhoon forecasts and establishing storm warning services. Typhoon accounts in the pre-instrumental era were normally limited to descriptions of damage and incidences, and subsequent studies were hampered by the impossibility of solving the equations governing the weather, as they are distinctly nonlinear. The world’s first typhoon forecast was made in 1879 by Fr. Federico Faura, who was a Jesuit scientist from the Manila Observatory. His brethren from the Zikawei Jesuit Observatory, Fr. Marc Dechevrens, first reconstructed the trajectory of a typhoon in 1879, a study that marked the beginning of an era. The Jesuits and other Europeans like William Doberck studied typhoons as a research topic, and their achievements are regarded as products of colonial meteorology. Between the First and Second World Wars, there were important contributions to typhoon science by meteorologists in the Philippines (Ch. Deppermann, M. Selga, and J. Coronas), China (E. Gherzi), and Japan (T. Okada, and Y. Horiguti). The polar front theory developed by the Bergen School in Norway played an important role in creating the large-scale setting for tropical cyclones. Deppermann became the greatest exponent of the polar front theory and air-masses analysis in the Far East and Southeast Asia. From the end of WWII, it became evident that more effective typhoon forecasts were needed to meet military demands. In Hawaii, a joint Navy and Air Force center for typhoon analysis and forecasting was established in 1959—the Joint Typhoon Warning Center (JTWC). Its goals were to publish annual typhoon summaries and conduct research into tropical cyclone forecasting and detection. Other centers had previously specialized in issuing typhoon warnings and analysis. Thus, research and operational forecasting went hand in hand not only in the American JTWC but also in China (the Hong Kong Observatory, the Macao Meteorological and Geophysical Bureau), Japan (the Regional Specialized Meteorological Center), and the Philippines (Atmospheric, Geophysical and Astronomical Service Administration [PAGASA]). These efforts produced more precise scientific knowledge about the formation, structure, and movement of typhoons. In the 1970s and the 1980s, three new tools for research—three-dimensional numerical cloud models, Doppler radar, and geosynchronous satellite imagery—provided a new observational and dynamical perspective on tropical cyclones. The development of modern computing systems has offered the possibility of making numerical weather forecast models and simulations of tropical cyclones. However, typhoons are not mechanical artifacts, and forecasting their track and intensity remains an uncertain science.

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%.