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Ole Bøssing Christensen and Erik Kjellström
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%.
Florian Sévellec and Bablu Sinha
The Atlantic meridional overturning circulation (AMOC) is a large, basin-scale circulation located in the Atlantic Ocean that transports climatically important quantities of heat northward. It can be described schematically as a northward flow in the warm upper ocean and a southward return flow at depth in much colder water. The heat capacity of a layer of 2 m of seawater is equivalent to that of the entire atmosphere; therefore, ocean heat content dominates Earth’s energy storage. For this reason and because of the AMOC’s typically slow decadal variations, the AMOC regulates North Atlantic climate and contributes to the relatively mild climate of Europe. Hence, predicting AMOC variations is crucial for predicting climate variations in regions bordering the North Atlantic. Similar to weather predictions, climate predictions are based on numerical simulations of the climate system. However, providing accurate predictions on such long timescales is far from straightforward. Even in a perfect model approach, where biases between numerical models and reality are ignored, the chaotic nature of AMOC variability (i.e., high sensitivity to initial conditions) is a significant source of uncertainty, limiting its accurate prediction.
Predictability studies focus on factors determining our ability to predict the AMOC rather than actual predictions. To this end, processes affecting AMOC predictability can be separated into two categories: processes acting as a source of predictability (periodic harmonic oscillations, for instance) and processes acting as a source of uncertainty (small errors that grow and significantly modify the outcome of numerical simulations). To understand the former category, harmonic modes of variability or precursors of AMOC variations are identified. On the other hand, in a perfect model approach, the sources of uncertainty are characterized by the spread of numerical simulations differentiated by the application of small differences to their initial conditions. Two alternative and complementary frameworks have arisen to investigate this spread. The pragmatic framework corresponds to performing an ensemble of simulations, by imposing a randomly chosen small error on the initial conditions of individual simulations. This allows a probabilistic approach and to statistically characterize the importance of the initial condition by evaluating the spread of the ensemble. The theoretical framework uses stability analysis to identify small perturbations to the initial conditions, which are conducive to significant disruption of the AMOC.
Beyond these difficulties in assessing the predictability, decadal prediction systems have been developed and tested through a range of hindcasts. The inherent difficulties of operational forecasts span from developing efficient initialization methods to setting accurate radiative forcing to correcting for model drift and bias, all these improvements being estimated and validated through a range of specifically designed skill metrics.
Sharon E. Nicholson
This article provides an in-depth look at all aspects of the climate of the Sahel, including the pervasive dust in the Sahelian atmosphere. Emphasis is on two aspects: West African monsoon and the region’s rainfall regime. This includes an overview of the prevailing atmospheric circulation at the surface and aloft and the relationship between this and the rainfall regime. Aspects of the rainfall regime that are considered include its unique characteristics, its changes over time, the storm systems that produce rainfall, and factors governing its variability on interannual and decadal time scales. Variability is examined on three time scales: millennial (as seen is the paleo records of the last 20,000 years), multi-decadal (as seen over the last few centuries as seen from proxy data and, more recently, in observations), and interannual to decadal (quantified by observations from the late 19th century and onward). A unique feature of Sahel climate is that is rainfall regime is perhaps the most sensitive in the world and this sensitivity is apparent on all of these time scales.
Christopher K. Wikle
The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.
Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.
Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
Post-glacial aquatic ecosystems in Eurasia and North America, such as the Baltic Sea, evolved in the freshwater, brackish, and marine environments that fringed the melting glaciers. Warming of the climate initiated sea level and land rise and subsequent changes in aquatic ecosystems. Seminal ideas on ancient developing ecosystems were based on findings in Swedish large lakes of species that had arrived there from adjacent glacial freshwater or marine environments and established populations which have survived up to the present day. An ecosystem of the first freshwater stage, the Baltic Ice Lake initially consisted of ice-associated biota. Subsequent aquatic environments, the Yoldia Sea, the Ancylus Lake, the Litorina Sea, and the Mya Sea, are all named after mollusc trace fossils. These often convey information on the geologic period in question and indicate some physical and chemical characteristics of their environment. The ecosystems of various Baltic Sea stages are regulated primarily by temperature and freshwater runoff (which affects directly and indirectly both salinity and nutrient concentrations). Key ecological environmental factors, such as temperature, salinity, and nutrient levels, not only change seasonally but are also subject to long-term changes (due to astronomical factors) and shorter disturbances, for example, a warm period that essentially formed the Yoldia Sea, and more recently the “Little Ice Age” (which terminated the Viking settlement in Iceland).
There is no direct way to study the post-Holocene Baltic Sea stages, but findings in geological samples of ecological keystone species (which may form a physical environment for other species to dwell in and/or largely determine the function of an ecosystem) can indicate ancient large-scale ecosystem features and changes. Such changes have included, for example, development of an initially turbid glacial meltwater to clearer water with increasing primary production (enhanced also by warmer temperatures), eventually leading to self-shading and other consequences of anthropogenic eutrophication (nutrient-rich conditions). Furthermore, the development in the last century from oligotrophic (nutrient-poor) to eutrophic conditions also included shifts between the grazing chain (which include large predators, e.g., piscivorous fish, mammals, and birds at the top of the food chain) and the microbial loop (filtering top predators such as jellyfish). Another large-scale change has been a succession from low (freshwater glacier lake) biodiversity to increased (brackish and marine) biodiversity. The present-day Baltic Sea ecosystem is a direct descendant of the more marine Litorina Sea, which marks the beginning of the transition from a primeval ecosystem to one regulated by humans. The recent Baltic Sea is characterized by high concentrations of pollutants and nutrients, a shift from perennial to annual macrophytes (and more rapid nutrient cycling), and an increasing rate of invasion by non-native species. Thus, an increasing pace of anthropogenic ecological change has been a prominent trend in the Baltic Sea ecosystem since the Ancylus Lake.
Future development is in the first place dependent on regional factors, such as salinity, which is regulated by sea and land level changes and the climate, and runoff, which controls both salinity and the leaching of nutrients to the sea. However, uncertainties abound, for example the future development of the Gulf Stream and its associated westerly winds, which support the sub-boreal ecosystems, both terrestrial and aquatic, in the Baltic Sea area. Thus, extensive sophisticated, cross-disciplinary modeling is needed to foresee whether the Baltic Sea will develop toward a freshwater or marine ecosystem, set in a sub-boreal, boreal, or arctic climate.
John T. Allen
The response of severe thunderstorms to a changing climate is a rapidly growing area of research. Severe thunderstorms are one of the largest contributors to global losses in excess of USD $10 billion per year in terms of property and agriculture, as well as dozens of fatalities. Phenomena associated with severe thunderstorms such as large hail (greater than 2 cm), damaging winds (greater than 90 kmh−1), and tornadoes pose a global threat, and have been documented on every continent except Antarctica. Limitations of observational records for assessing past trends have driven a variety of approaches to not only characterize the past occurrence but provide a baseline against which future projections can be interpreted. These proxy methods have included using environments or conditions favorable to the development of thunderstorms and directly simulating storm updrafts using dynamic downscaling. Both methodologies have demonstrated pronounced changes to the frequency of days producing severe thunderstorms. Major impacts of a strongly warmed climate include a general increase in the length of the season in both the fall and spring associated with increased thermal instability and increased frequency of severe days by the late 21st century. While earlier studies noted changes to vertical wind shear decreasing frequency, recent studies have illustrated that this change appears not to coincide with days which are unstable. Questions remain as to whether the likelihood of storm initiation decreases, whether all storms which now produce severe weather will maintain their physical structure in a warmer world, and how these changes to storm frequency and or intensity may manifest for each of the threats posed by tornadoes, hail, and damaging winds. Expansion of the existing understanding globally is identified as an area of needed future research, together with meaningful consideration of both the influence of climate variability and indirect implications of anthropogenic modification of the physical environment.
D. B. Tindall, Mark C.J. Stoddart, and Candis Callison
This article considers the relationship between news media and the sociopolitical dimensions of climate change. Media can be seen as sites where various actors contend with one another for visibility, for power, and for the opportunity to communicate, as well as where they promote their policy preferences. In the context of climate change, actors include politicians, social movement representatives, scientists, business leaders, and celebrities—to name a few.
The general public obtain much of their information about climate change and other environmental issues from the media, either directly or indirectly through sources like social media. Media have their own internal logic, and getting one’s message into the media is not straightforward. A variety of factors influence what gets into the media, including media practices, and research shows that media matter in influencing public opinion.
A variety of media practices affect reporting on climate change─one example is the journalistic norm of balance, which directs that actors on both sides of a controversy be given relatively equal attention by media outlets. In the context of global warming and climate change, in the United States, this norm has led to the distortion of the public’s understanding of these processes. Researchers have found that, in the scientific literature, there is a very strong consensus among scientists that human-caused (anthropogenic) climate change is happening. Yet media in the United States often portray the issue as a heated debate between two equal sides.
Subscription to, and readership of, print newspapers have declined among the general public; nevertheless, particular newspapers continue to be important. Despite the decline of traditional media, politicians, academics, NGO leaders, business leaders, policymakers, and other opinion leaders continue to consume the media. Furthermore, articles from particular outlets have significant readership via new media access points, such as Facebook and Twitter.
An important concept in the communication literature is the notion of framing. “Frames” are the interpretive schemas individuals use to perceive, identify, and label events in the world. Social movements have been important actors in discourse about climate change policy and in mobilizing the public to pressure governments to act. Social movements play a particularly important role in framing issues and in influencing public opinion. In the United States, the climate change denial countermovement, which has strong links to conservative think tanks, has been particularly influential. This countermovement is much more influential in the United States than in other countries. The power of the movement has been a barrier to the federal government taking significant policy action on climate change in the United States and has had consequences for international agreements and processes.
The Chinese meteorological records could be traced back to the oracle-bone inscriptions of the Shang Dynasty (c. 1600
Modern meteorological knowledge began to be introduced in China during the late Ming Dynasty (1368–1644
Previous researches have reconstructed the chronologies of the temperature change in China during the past 2,000 years, and the Medieval Warm Period and Little Ice Age were identified. With regard to precipitation variability, yearly charts of dryness/wetness in China for the past 500 years were produced. Several chronologies of dust storm, plum rain (Meiyu), and typhoon were also established. Large volcanic eruptions resulted in short scale abrupt cooling in China during the past 2,000 years. Climatic change was significantly related to the war occurrences and dynastic cycles in historical China.
William K. M. Lau
Situated at the southern edge of the Tibetan Plateau (TP), the Hindu-Kush-Himalayas-Gangetic (HKHG) region is under the clear and present danger of climate change. Flash-flood, landslide, and debris flow caused by extreme precipitation, as well as rapidly melting glaciers, threaten the water resources and livelihood of more than 1.2 billion people living in the region. Rapid industrialization and increased populations in recent decades have resulted in severe atmospheric and environmental pollution in the region. Because of its unique topography and dense population, the HKHG is not only a major source of pollution aerosol emissions, but also a major receptor of large quantities of natural dust aerosols transported from the deserts of West Asia and the Middle East during the premonsoon and early monsoon season (April–June). The dust aerosols, combined with local emissions of light-absorbing aerosols, that is, black carbon (BC), organic carbon (OC), and mineral dust, can (a) provide additional powerful heating to the atmosphere and (b) allow more sunlight to penetrate the snow layer by darkening the snow surface. Both effects will lead to accelerated melting of snowpack and glaciers in the HKHG region, amplifying the greenhouse warming effect. In addition, these light-absorbing aerosols can interact with monsoon winds and precipitation, affecting extreme precipitation events in the HKHG, as well as weather variability and climate change over the TP and the greater Asian monsoon region.
Matthew A. Shapiro, Toby Bolsen, and Anna McCaghren Fleming
Public opinion plays a central role in determining the feasibility of efforts to transform energy systems in the coming years, yet scholarship on communication effects and public opinion about clean energy and energy efficiency seems to have expanded only relatively recently. There is a growing body of work that explores how targeted and strategically framed messages affect individuals’ beliefs and motivations to act on matters affecting household energy choices as well as energy policies. One must attend particularly to the principal communication-based factors that shape the public’s understanding of clean energy sources and promote efficiencies in energy use. To better understand the communication vehicles for improving both household energy efficiency and conservation, two research foci are most relevant: (1) field experiments that primarily assess how household energy consumption shifts after receiving energy consumption reports and (2) surveys/laboratory experiments that focus on the nuances of energy-related communications, paying particular attention to the role of politics and ideology. This bimodal classification of clean energy and efficiency communication research genres is not exhaustive but can be synthesized into two major contributions. First, providing households with information about specific benefits that would result from a greater reliance on clean energy may increase support for its development and move individuals toward energy efficiency outcomes; however, exposure to counter-messages that emphasize costs associated with clean energy and the associated policies can negate the effects of pro-clean energy messages. Second, there is still no reprieve from the politicization of energy, and thus the role of partisanship and motivated reasoning must be accounted for when assessing how individuals modify their decision-making processes regarding energy efficiency.