Climate Systems and Climate Dynamics

Among the factors that affect the climate, few are as diverse and challenging to understand as aerosols. Minute particles suspended in the atmosphere, aerosols are emitted through a wide range of natural and industrial processes, and are transported around the globe by winds and weather. Once airborne, they affect the climate both directly, through scattering and absorption of solar radiation, and indirectly, through their impact on cloud properties. Combining all their effects, anthropogenic changes to aerosol concentrations are estimated to have had a climate impact over the industrial era that is second only to CO2. Their atmospheric lifetime of only a few days, however, makes their climate effects substantially different from those of well-mixed greenhouse gases.

Major aerosol types include sea salt, dust, sulfate compounds, and black carbon—or soot—from incomplete combustion. Of these, most scatter incoming sunlight back to space, and thus mainly cool the climate. Black carbon, however, absorbs sunlight, and therefore acts as a heating agent much like a greenhouse gas. Furthermore, aerosols can act as cloud condensation nuclei, causing clouds to become whiter—and thus more reflecting—further cooling the surface. Black carbon is again a special case, acting to change the stability of the atmosphere through local heating of the upper air, and also changing the albedo of the surface when it is deposited on snow and ice, for example.

The wide range of climate interactions that aerosols have, and the fact that their distribution depends on the weather at the time and location of emission, lead to large uncertainties in the scientific assessment of their impact. This in turn leads to uncertainties in our present understanding of the climate sensitivity, because while aerosols have predominantly acted to oppose 20th-century global warming by greenhouse gases, the magnitude of aerosol effects on climate is highly uncertain.

Finally, aerosols are important for large-scale climate events such as major volcanoes, or the threat of nuclear winter. The relative ease with which they can be produced and distributed has led to suggestions for using targeted aerosol emissions to counteract global warming—so-called climate engineering.

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.

Precipitation levels in southern Africa exhibit a marked east–west gradient and are characterized by strong seasonality and high interannual variability. Much of the mainland south of 15°S exhibits a semiarid to dry subhumid climate. More than 66 percent of rainfall in the extreme southwest of the subcontinent occurs between April and September. Rainfall in this region—termed the winter rainfall zone (WRZ)—is most commonly associated with the passage of midlatitude frontal systems embedded in the austral westerlies. In contrast, more than 66 percent of mean annual precipitation over much of the remainder of the subcontinent falls between October and March. Climates in this summer rainfall zone (SRZ) are dictated by the seasonal interplay between subtropical high-pressure systems and the migration of easterly flows associated with the Intertropical Convergence Zone. Fluctuations in both SRZ and WRZ rainfall are linked to the variability of sea-surface temperatures in the oceans surrounding southern Africa and are modulated by the interplay of large-scale modes of climate variability, including the El Niño-Southern Oscillation (ENSO), Southern Indian Ocean Dipole, and Southern Annular Mode.

Ideas about long-term rainfall variability in southern Africa have shifted over time. During the early to mid-19th century, the prevailing narrative was that the climate was progressively desiccating. By the late 19th to early 20th century, when gauged precipitation data became more readily available, debate shifted toward the identification of cyclical rainfall variation. The integration of gauge data, evidence from historical documents, and information from natural proxies such as tree rings during the late 20th and early 21st centuries, has allowed the nature of precipitation variability since ~1800 to be more fully explored.

Drought episodes affecting large areas of the SRZ occurred during the first decade of the 19th century, in the early and late 1820s, late 1850s–mid-1860s, mid-late 1870s, earlymid-1880s, and mid-late 1890s. Of these episodes, the drought during the early 1860s was the most severe of the 19th century, with those of the 1820s and 1890s the most protracted. Many of these droughts correspond with more extreme ENSO warm phases.

Widespread wetter conditions are less easily identified. The year 1816 appears to have been relatively wet across the Kalahari and other areas of south central Africa. Other wetter episodes were centered on the late 1830s–early 1840s, 1855, 1870, and 1890. In the WRZ, drier conditions occurred during the first decade of the 19th century, for much of the mid-late 1830s through to the mid-1840s, during the late 1850s and early 1860s, and in the early-mid-1880s and mid-late 1890s. As for the SRZ, markedly wetter years are less easily identified, although the periods around 1815, the early 1830s, mid-1840s, mid-late 1870s, and early 1890s saw enhanced rainfall. Reconstructed rainfall anomalies for the SRZ suggest that, on average, the region was significantly wetter during the 19th century than the 20th and that there appears to have been a drying trend during the 20th century that has continued into the early 21st. In the WRZ, average annual rainfall levels appear to have been relatively consistent between the 19th and 20th centuries, although rainfall variability increased during the 20th century compared to the 19th.

Climate and carbon cycle are tightly coupled on many time scales, from the interannual to the multimillennial. Observation always shows a positive feedback between climate and the carbon cycle: elevated atmospheric CO2 leads to warming, but warming is expected to further release of carbon to the atmosphere, enhancing the atmospheric CO2 increase. Earth system models do represent these climate–carbon cycle feedbacks, always simulating a positive feedback over the 21st century; that is, climate change will lead to loss of carbon from the land and ocean reservoirs. These processes partially offset the increases in land and ocean carbon sinks caused by rising atmospheric CO2. As a result, more of the emitted anthropogenic CO2 will remain in the atmosphere. There is, however, a large uncertainty on the magnitude of this feedback. Recent studies now help to reduce this uncertainty. On short, interannual, time scales, El Niño years record larger-than-average atmospheric CO2 growth rate, with tropical land ecosystems being the main drivers. These climate–carbon cycle anomalies can be used as emerging constraint on the tropical land carbon response to future climate change. On a longer, centennial, time scale, the variability of atmospheric CO2 found in records of the last millennium can be used to constrain the overall global carbon cycle response to climate. These independent methods confirm that the climate–carbon cycle feedback is positive, but probably more consistent with the lower end of the comprehensive models range, excluding very large climate–carbon cycle feedbacks.

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.

The El Niño Modoki/La Niña Modoki (ENSO Modoki) is a newly acknowledged face of ocean-atmosphere coupled variability in the tropical Pacific Ocean. The oceanic and atmospheric conditions associated with the El Niño Modoki are different from that of canonical El Niño, which is extensively studied for its dynamics and worldwide impacts. A typical El Niño event is marked by a warm anomaly of sea surface temperature (SST) in the equatorial eastern Pacific. Because of the associated changes in the surface winds and the weakening of coastal upwelling, the coasts of South America suffer from widespread fish mortality during the event. Quite opposite of this characteristic change in the ocean condition, cold SST anomalies prevail in the eastern equatorial Pacific during the El Niño Modoki events, but with the warm anomalies intensified in the central Pacific. The boreal winter condition of 2004 is a typical example of such an event, when a tripole pattern is noticed in the SST anomalies; warm central Pacific flanked by cold eastern and western regions. The SST anomalies are coupled to a double cell in anomalous Walker circulation with rising motion in the central parts and sinking motion on both sides of the basin. This is again a different feature compared to the well-known single-cell anomalous Walker circulation during El Niños. La Niña Modoki is the opposite phase of the El Niño Modoki, when a cold central Pacific is flanked by warm anomalies on both sides.

The Modoki events are seen to peak in both boreal summer and winter and hence are not seasonally phase-locked to a single seasonal cycle like El Niño/La Niña events. Because of this distinction in the seasonality, the teleconnection arising from these events will vary between the seasons as teleconnection path will vary depending on the prevailing seasonal mean conditions in the atmosphere. Moreover, the Modoki El Niño/La Niña impacts over regions such as the western coast of the United States, the Far East including Japan, Australia, and southern Africa, etc., are opposite to those of the canonical El Niño/La Niña. For example, the western coasts of the United States suffer from severe droughts during El Niño Modoki, whereas those regions are quite wet during El Niño. The influences of Modoki events are also seen in tropical cyclogenesis, stratosphere warming of the Southern Hemisphere, ocean primary productivity, river discharges, sea level variations, etc. A remarkable feature associated with Modoki events is the decadal flattening of the equatorial thermocline and weakening of zonal thermal gradient. The associated ocean-atmosphere conditions have caused frequent and persistent developments of Modoki events in recent decades.

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.

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.

Clustering techniques are used in the analysis of weather and climate to identify distinct, discrete groups of atmospheric and oceanic structures and evolutions from observations, reanalyses, and numerical model simulations and predictions. The goal of cluster analysis is to provide physical insight into states (and trajectories) that are preferred and also possibly unusually persistent, when such states can be identified and distinguished from the continuous background distribution of geophysical variables. “Preferred” states (or evolutions) are those that are significantly more likely to occur than would be predicted by a suitable background distribution (such as a multivariate Gaussian distribution), while “persistent” states are those with lifetimes distinctly longer than those of the background states.

The choice of technique depends to a large extent on its application. For example, the identification of a small number of distinct patterns of the seasonal mean mid-latitude response to large seasonal mean shifts in tropical diabatic heating (perhaps due to the El Niño–Southern Oscillation) can be accomplished with the use of either a partitioning or hierarchical cluster analysis. The partitioning cluster method groups all states (maps of a given variable) into clusters so as to minimize the within-cluster variance, while the hierarchical analysis merges fields into groups based on their similarities. The identification of preferred patterns (whether or not they are tropically forced) on intra-seasonal time scales can also be accomplished in this way. The partitioning approach can easily be adapted to include multiple variables, and to describe tracks of localized features (such as cyclones). A variant of the partitioning cluster analysis, the “self -organizing map” approach, allows for a greater richness in cluster patterns and so can be useful on shorter, weather-related time scales.

In either the partitioning or hierarchical analysis, each state (map) is identified uniquely with a given cluster. However, in certain applications it may be desirable to allow a given state to belong to multiple clusters with differing probabilities. In such cases one can estimate the underlying probability distribution function with a mixture model, which is a sum of a (usually small) number of component multivariate Gaussian distributions.

The partitioning, hierarchical, and mixture model approaches, applied to a sequence of maps, all have one common feature: the sequencing (order in time) of the maps is not taken into account. This is not the case with the hidden Markov method, an approach that identifies not only preferred states but ones that are also unusually persistent. This approach, based on a simple neural network approach, makes use of an underlying “hidden variable” whose evolution is modeled by a Markov process. Each state is assigned to a number of clusters with a certain probability, but the most likely evolution of states from one cluster to another can be estimated. This approach can be generalized by letting the evolution of the hidden state be governed by a nonstationary multivariate autoregressive factor process. The resulting cluster analysis can then also detect long-term changes in the population of the clusters.

In the past 50 years, human activities and climatic variability have caused major environmental changes in the semi-arid Sahelian zone and desertification/degradation of arable lands is of major concern for livelihoods and food security. In the wake of the Sahel droughts in the early 1970s and 1980s, the UN focused on the problem of desertification by organizing the UN Conference on Desertification (UNCOD) in Nairobi in 1976. This fuelled a significant increase in the often alarmist popular accounts of desertification as well as scientific efforts in providing an understanding of the mechanisms involved. The global interest in the subject led to the nomination of desertification as focal point for one of three international environmental conventions: the UN Convention to Combat Desertification (UNCCD), emerging from the Rio conference in 1992. This implied that substantial efforts were made to quantify the extent of desertification and to understand its causes. Desertification is a complex and multi-faceted phenomenon aggravating poverty that can be seen as both a cause and a consequence of land resource depletion. As reflected in its definition adopted by the UNCCD, desertification is “land degradation in arid, semi-arid[,] and dry sub-humid areas resulting from various factors, including climate variation and human activities” (UN, 1992). While desertification was seen as a phenomenon of relevance to drylands globally, the Sahel-Sudan region remained a region of specific interest and a significant amount of scientific efforts have been invested to provide an empirically supported understanding of both climatic and anthropogenic factors involved. Despite decades of intensive research on human–environmental systems in the Sahel, there is no overall consensus about the severity of desertification and the scientific literature is characterized by a range of conflicting observations and interpretations of the environmental conditions in the region. Earth Observation (EO) studies generally show a positive trend in rainfall and vegetation greenness over the last decades for the majority of the Sahel and this has been interpreted as an increase in biomass and contradicts narratives of a vicious cycle of widespread degradation caused by human overuse and climate change. Even though an increase in vegetation greenness, as observed from EO data, can be confirmed by ground observations, long-term assessments of biodiversity at finer spatial scales highlight a negative trend in species diversity in several studies and overall it remains unclear if the observed positive trends provide an environmental improvement with positive effects on people’s livelihood.

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.