Bjørn H. Samset
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.
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.
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.
Kerry H. Cook
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.
Swadhin Behera and Toshio Yamagata
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.
David M. Straus
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.
Rasmus Fensholt, Cheikh Mbow, Martin Brandt, and Kjeld Rasmussen
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.
B.N. Goswami and Soumi Chakravorty
Lifeline for about one-sixth of the world’s population in the subcontinent, the Indian summer monsoon (ISM) is an integral part of the annual cycle of the winds (reversal of winds with seasons), coupled with a strong annual cycle of precipitation (wet summer and dry winter). For over a century, high socioeconomic impacts of ISM rainfall (ISMR) in the region have driven scientists to attempt to predict the year-to-year variations of ISM rainfall. A remarkably stable phenomenon, making its appearance every year without fail, the ISM climate exhibits a rather small year-to-year variation (the standard deviation of the seasonal mean being 10% of the long-term mean), but it has proven to be an extremely challenging system to predict. Even the most skillful, sophisticated models are barely useful with skill significantly below the potential limit on predictability. Understanding what drives the mean ISM climate and its variability on different timescales is, therefore, critical to advancing skills in predicting the monsoon. A conceptual ISM model helps explain what maintains not only the mean ISM but also its variability on interannual and longer timescales.
The annual ISM precipitation cycle can be described as a manifestation of the seasonal migration of the intertropical convergence zone (ITCZ) or the zonally oriented cloud (rain) band characterized by a sudden “onset.” The other important feature of ISM is the deep overturning meridional (regional Hadley circulation) that is associated with it, driven primarily by the latent heat release associated with the ISM (ITCZ) precipitation. The dynamics of the monsoon climate, therefore, is an extension of the dynamics of the ITCZ. The classical land–sea surface temperature gradient model of ISM may explain the seasonal reversal of the surface winds, but it fails to explain the onset and the deep vertical structure of the ISM circulation. While the surface temperature over land cools after the onset, reversing the north–south surface temperature gradient and making it inadequate to sustain the monsoon after onset, it is the tropospheric temperature gradient that becomes positive at the time of onset and remains strongly positive thereafter, maintaining the monsoon. The change in sign of the tropospheric temperature (TT) gradient is dynamically responsible for a symmetric instability, leading to the onset and subsequent northward progression of the ITCZ. The unified ISM model in terms of the TT gradient provides a platform to understand the drivers of ISM variability by identifying processes that affect TT in the north and the south and influence the gradient.
The predictability of the seasonal mean ISM is limited by interactions of the annual cycle and higher frequency monsoon variability within the season. The monsoon intraseasonal oscillation (MISO) has a seminal role in influencing the seasonal mean and its interannual variability. While ISM climate on long timescales (e.g., multimillennium) largely follows the solar forcing, on shorter timescales the ISM variability is governed by the internal dynamics arising from ocean–atmosphere–land interactions, regional as well as remote, together with teleconnections with other climate modes. Also important is the role of anthropogenic forcing, such as the greenhouse gases and aerosols versus the natural multidecadal variability in the context of the recent six-decade long decreasing trend of ISM rainfall.
A. Johannes Dolman, Luis U. Vilasa-Abad, and Thomas A. J. Janssen
Drylands cover around 40% of the land surface on Earth and are inhabited by more than 2 billion people, who are directly dependent on these lands. Drylands are characterized by a highly variable rainfall regime and inherent vegetation-climate feedbacks that can enhance the resilience of the system, but also can amplify disturbances. In that way, the system may get locked into two alternate stable states: one relatively wet and vegetated, and the other dry and barren. The resilience of dryland ecosystems derives from a number of adaptive mechanisms by which the vegetation copes with prolonged water stress, such as hydraulic redistribution. The stochastic nature of both the vegetation dynamics and the rainfall regime is a key characteristic of these systems and affects its management in relation to the feedbacks. How the ecohydrology of the African drylands will change in the future depends on further changes in climate, human disturbances, land use, and the socioeconomic system.
Sharon E. Nicholson
Classic paradigms describing meteorological phenomena and climate have changed dramatically over the last half-century. This is particularly true for the continent of Africa. Our understanding of its climate is today very different from that which prevailed as recently as the 1960s or 1970s. This article traces the development of relevant paradigms in five broad areas: climate and climate classification, tropical atmospheric circulation, tropical rain-bearing systems, climatic variability and change, and land surface processes and climate. One example is the definition of climate. Originally viewed as simple statistical averages, it is now recognized as an environmental variable with global linkages, multiple timescales of variability, and strong controls via earth surface processes. As a result of numerous field experiments, our understanding of tropical rainfall has morphed from the belief in the domination by local thunderstorms to recognition of vast systems on regional to global scales. Our understanding of the interrelationships with land surface processes has also changed markedly. The simple Charney hypothesis concerning albedo change and the related concept of desertification have given way to a broader view of land–atmosphere interaction. In summary, there has been a major evolution in the way we understand climate, climatic variability, tropical rainfall regimes and rain-bearing systems, and potential human impacts on African climate. Each of these areas has evolved in complexity and understanding, a result of an explosive growth in research and the availability of such investigative tools as satellites, computers, and numerical models.
The Sahel of Africa has been identified as having the strongest land–atmosphere (L/A) interactions on Earth. The Sahelian L/A interaction studies started in the late 1970s. However, due to controversies surrounding the early studies, in which only a single land parameter was considered in L/A interactions, the credibility of land-surface effects on the Sahel’s climate has long been challenged. Using general circulation models and regional climate models coupled with biogeophysical and dynamic vegetation models as well as applying analyses of satellite-derived data, field measurements, and assimilation data, the effects of land-surface processes on West African monsoon variability, which dominates the Sahel climate system at intraseasonal, seasonal, interannual, and decadal scales, as well as mesoscale, have been extensively investigated to realistically explore the Sahel L/A interaction: its effects and the mechanisms involved.
The Sahel suffered the longest and most severe drought on the planet in the 20th century. The devastating environmental and socioeconomic consequences resulting from drought-induced famines in the Sahel have provided strong motivation for the scientific community and society to understand the causes of the drought and its impact. It was controversial and under debate whether the drought was a natural process, mainly induced by sea-surface temperature variability, or was affected by anthropogenic activities. Diagnostic and modeling studies of the sea-surface temperature have consistently demonstrated it exerts great influence on the Sahel climate system, but sea-surface temperature is unable to explain the full scope of the Sahel climate variability and the later 20th century’s drought. The effect of land-surface processes, especially land-cover and land-use change, on the drought have also been extensively investigated. The results with more realistic land-surface models suggest land processes are a first-order contributor to the Sahel climate and to its drought during the later 1960s to the 1980s, comparable to sea surface temperature effects. The issues that caused controversies in the early studies have been properly addressed in the studies with state-of-the-art models and available data.
The mechanisms through which land processes affect the atmosphere are also elucidated in a number of studies. Land-surface processes not only affect vertical transfer of radiative fluxes and heat fluxes but also affect horizontal advections through their effect on the atmospheric heating rate and moisture flux convergence/divergence as well as horizontal temperature gradients.
Saji N. Hameed
Discovered at the very end of the 20th century, the Indian Ocean Dipole (IOD) is a mode of natural climate variability that arises out of coupled ocean–atmosphere interaction in the Indian Ocean. It is associated with some of the largest changes of ocean–atmosphere state over the equatorial Indian Ocean on interannual time scales. IOD variability is prominent during the boreal summer and fall seasons, with its maximum intensity developing at the end of the boreal-fall season. Between the peaks of its negative and positive phases, IOD manifests a markedly zonal see-saw in anomalous sea surface temperature (SST) and rainfall—leading, in its positive phase, to a pronounced cooling of the eastern equatorial Indian Ocean, and a moderate warming of the western and central equatorial Indian Ocean; this is accompanied by deficit rainfall over the eastern Indian Ocean and surplus rainfall over the western Indian Ocean. Changes in midtropospheric heating accompanying the rainfall anomalies drive wind anomalies that anomalously lift the thermocline in the equatorial eastern Indian Ocean and anomalously deepen them in the central Indian Ocean. The thermocline anomalies further modulate coastal and open-ocean upwelling, thereby influencing biological productivity and fish catches across the Indian Ocean. The hydrometeorological anomalies that accompany IOD exacerbate forest fires in Indonesia and Australia and bring floods and infectious diseases to equatorial East Africa. The coupled ocean–atmosphere instability that is responsible for generating and sustaining IOD develops on a mean state that is strongly modulated by the seasonal cycle of the Austral-Asian monsoon; this setting gives the IOD its unique character and dynamics, including a strong phase-lock to the seasonal cycle. While IOD operates independently of the El Niño and Southern Oscillation (ENSO), the proximity between the Indian and Pacific Oceans, and the existence of oceanic and atmospheric pathways, facilitate mutual interactions between these tropical climate modes.
Fred Kucharski and Muhammad Adnan Abid
The interannual variability of Indian summer monsoon is probably one of the most intensively studied phenomena in the research area of climate variability. This is because even relatively small variations of about 10% to 20% from the mean rainfall may have dramatic consequences for regional agricultural production. Forecasting such variations months in advance could help agricultural planning substantially. Unfortunately, a perfect forecast of Indian monsoon variations, like any other regional climate variations, is impossible in a long-term prediction (that is, more than 2 weeks or so in advance). The reason is that part of the atmospheric variations influencing the monsoon have an inherent predictability limit of about 2 weeks. Therefore, such predictions will always be probabilistic, and only likelihoods of droughts, excessive rains, or normal conditions may be provided. However, even such probabilistic information may still be useful for agricultural planning. In research regarding interannual Indian monsoon rainfall variations, the main focus is therefore to identify the remaining predictable component and to estimate what fraction of the total variation this component accounts for. It turns out that slowly varying (with respect to atmospheric intrinsic variability) sea-surface temperatures (SSTs) provide the dominant part of the predictable component of Indian monsoon variability. Of the predictable part arising from SSTs, it is the El Niño Southern Oscillation (ENSO) that provides the main part. This is not to say that other forcings may be neglected. Other forcings that have been identified are, for example, SST patterns in the Indian Ocean, Atlantic Ocean, and parts of the Pacific Ocean different from the traditional ENSO region, and springtime snow depth in the Himalayas, as well as aerosols. These other forcings may interact constructively or destructively with the ENSO impact and thus enhance or reduce the ENSO-induced predictable signal. This may result in decade-long changes in the connection between ENSO and the Indian monsoon. The physical mechanism for the connection between ENSO and the Indian monsoon may be understood as large-scale adjustment of atmospheric heatings and circulations to the ENSO-induced SST variations. These adjustments modify the Walker circulation and connect the rising/sinking motion in the central-eastern Pacific during a warm/cold ENSO event with sinking/rising motion in the Indian region, leading to reduced/increased rainfall.
The strongest Indian summer monsoon (ISM) on the planet features prolonged clustered spells of wet and dry conditions often lasting for two to three weeks, known as active and break monsoons. The active and break monsoons are attributed to a quasi-periodic intraseasonal oscillation (ISO), which is an extremely important form of the ISM variability bridging weather and climate variation. The ISO over India is part of the ISO in global tropics. The latter is one of the most important meteorological phenomena discovered during the 20th century (Madden & Julian, 1971, 1972). The extreme dry and wet events are regulated by the boreal summer ISO (BSISO). The BSISO over Indian monsoon region consists of northward propagating 30–60 day and westward propagating 10–20 day modes. The “clustering” of synoptic activity was separately modulated by both the 30–60 day and 10–20 day BSISO modes in approximately equal amounts. The clustering is particularly strong when the enhancement effect from both modes acts in concert. The northward propagation of BSISO is primarily originated from the easterly vertical shear (increasing easterly winds with height) of the monsoon flows, which by interacting with the BSISO convective system can generate boundary layer convergence to the north of the convective system that promotes its northward movement. The BSISO-ocean interaction through wind-evaporation feedback and cloud-radiation feedback can also contribute to the northward propagation of BSISO from the equator. The 10–20 day oscillation is primarily produced by convectively coupled Rossby waves modified by the monsoon mean flows. Using coupled general circulation models (GCMs) for ISO prediction is an important advance in subseasonal forecasts. The major modes of ISO over Indian monsoon region are potentially predictable up to 40–45 days as estimated by multiple GCM ensemble hindcast experiments. The current dynamical models’ prediction skills for the large initial amplitude cases are approximately 20–25 days, but the prediction of developing BSISO disturbance is much more difficult than the prediction of the mature BSISO disturbances. This article provides a synthesis of our current knowledge on the observed spatial and temporal structure of the ISO over India and the important physical processes through which the BSISO regulates the ISM active-break cycles and severe weather events. Our present capability and shortcomings in simulating and predicting the monsoon ISO and outstanding issues are also discussed.
Yongkang Xue, Yaoming Ma, and Qian Li
The Tibetan Plateau (TP) is the largest and highest plateau on Earth. Due to its elevation, it receives much more downward shortwave radiation than other areas, which results in very strong diurnal and seasonal changes of the surface energy components and other meteorological variables, such as surface temperature and the convective atmospheric boundary layer. With such unique land process conditions on a distinct geomorphic unit, the TP has been identified as having the strongest land/atmosphere interactions in the mid-latitudes.
Three major TP land/atmosphere interaction issues are presented in this article: (1) Scientists have long been aware of the role of the TP in atmospheric circulation. The view that the TP’s thermal and dynamic forcing drives the Asian monsoon has been prevalent in the literature for decades. In addition to the TP’s topographic effect, diagnostic and modeling studies have shown that the TP provides a huge, elevated heat source to the middle troposphere, and that the sensible heat pump plays a major role in the regional climate and in the formation of the Asian monsoon. Recent modeling studies, however, suggest that the south and west slopes of the Himalayas produce a strong monsoon by insulating warm and moist tropical air from the cold and dry extratropics, so the TP heat source cannot be considered as a factor for driving the Indian monsoon. The climate models’ shortcomings have been speculated to cause the discrepancies/controversies in the modeling results in this aspect. (2) The TP snow cover and Asian monsoon relationship is considered as another hot topic in TP land/atmosphere interaction studies and was proposed as early as 1884. Using ground measurements and remote sensing data available since the 1970s, a number of studies have confirmed the empirical relationship between TP snow cover and the Asian monsoon, albeit sometimes with different signs. Sensitivity studies using numerical modeling have also demonstrated the effects of snow on the monsoon but were normally tested with specified extreme snow cover conditions. There are also controversies regarding the possible mechanisms through which snow affects the monsoon. Currently, snow is no longer a factor in the statistic prediction model for the Indian monsoon prediction in the Indian Meteorological Department. These controversial issues indicate the necessity of having measurements that are more comprehensive over the TP to better understand the nature of the TP land/atmosphere interactions and evaluate the model-produced results. (3) The TP is one of the major areas in China greatly affected by land degradation due to both natural processes and anthropogenic activities. Preliminary modeling studies have been conducted to assess its possible impact on climate and regional hydrology. Assessments using global and regional models with more realistic TP land degradation data are imperative.
Due to high elevation and harsh climate conditions, measurements over the TP used to be sparse. Fortunately, since the 1990s, state-of-the-art observational long-term station networks in the TP and neighboring regions have been established. Four large field experiments since 1996, among many observational activities, are presented in this article. These experiments should greatly help further research on TP land/atmosphere interactions.
Mineral dust is the most important natural aerosol type by mass, with northern Africa the most prominent source region worldwide. Dust particles are lifted into the atmosphere by strong winds over arid or semiarid soils through a range of emission mechanisms, the most important of which is saltation. Dust particles are mixed vertically by turbulent eddies in the desert boundary layer (up to 6km) or even higher by convective and frontal circulations. The meteorological systems that generate winds strong enough for dust mobilization cover scales from dust devils (~100m) to large dust outbreaks related to low- and high-pressure systems over subtropical northern Africa (thousands of kilometers) and include prominent atmospheric features such as the morning breakdown of low-level jets forming in the stable nighttime boundary layer and cold pools emanating from deep convective systems (so-called haboobs). Dust particles are transported in considerable amounts from northern Africa to remote regions such as the Americas and Europe. The removal of dust particles from the atmosphere occurs through gravitational settling, molecular and turbulent diffusion (dry deposition), as well as in-cloud and sub-cloud scavenging (wet deposition). Advances in satellite technology and numerical dust models (including operational weather prediction systems) have led to considerable progress in quantifying the temporal and spatial variability of dust from Africa, but large uncertainties remain for practically all stages of the dust cycle. The annual cycle of dustiness is dominated by the seasonal shift of rains associated with the West African monsoon and the Mediterranean storm track. In summer, maximum dust loadings are observed over Mauritania and Mali, and the main export is directed toward the Caribbean Sea, creating the so-called elevated Saharan Air Layer. In winter the northeasterly harmattan winds transport dust to the tropical Atlantic and across to southern America, usually in a shallower layer.
Mineral dust has a multitude of impacts on climate and weather systems but also on humans (air pollution, visibility, erosion). Nutrients contained in dust fertilize marine and terrestrial ecosystems and therefore impact the global carbon cycle. Dust affects the energy budget directly through interactions with short- and long-wave radiation, with details depending crucially on particle size, shape, and chemical composition. Mineral dust particles are the most important ice-nuclei worldwide and can also serve as condensation nuclei in liquid clouds, but details are not well understood. The resulting modifications to cloud characteristics and precipitation can again affect the energy (and water) budget. Complicated responses and feedbacks on atmospheric dynamics are known, including impacts on regional-scale circulations, sea-surface temperatures, surface fluxes and boundary layer mixing, vertical stability, near-surface winds, soil moisture, and vegetation (and therefore again dust emission). A prominent example of such complex interactions is the anti-correlation between African dust and Atlantic hurricane activity from weekly to decadal timescales, the causes of which remain difficult to disentangle. Particularly in the early 21st century, research on African dust intensified substantially and became more interdisciplinary, leading to some significant advances in our understanding of this fascinating and multifaceted element of the Earth system.
Edward Hanna and Thomas E. Cropper
Many variations in the weather in the European and North Atlantic regions are linked with changes in the North Atlantic Oscillation (NAO). The NAO is measured using a south-minus-north index of atmospheric surface pressure variation across the North Atlantic and is closely connected with changes in the North Atlantic atmospheric polar jet stream and wider changes in atmospheric circulation. The physical, human, and biological impacts of NAO changes extend well beyond weather and climate, with major economic, social, and environmental effects. The NAO index based on barometric pressure records now extends as far back as 1850, based on recent work. Although there are few significant overall trends in monthly or seasonal NAO (i.e., for the whole record), there are many shorter-term multidecadal variations. A prominent increase in the NAO between the 1960s and 1990s was widely noted in previous work and was thought to be related to human-induced greenhouse gas forcing. However, since then this trend has reversed, with a significant decrease in the summer NAO since the 1990s and a striking increase in variability of the winter—especially December—NAO that has resulted in four of the six highest and two of the five lowest NAO Decembers occurring during 2004–2015 in the 116-year record, with accompanying more variable year-to-year winter weather conditions over the United Kingdom. These NAO changes are related to an increasing trend in the Greenland Blocking Index (GBI; equals high pressure over Greenland) in summer and a significantly more variable GBI in December. Such NAO and related jet stream and blocking changes are not generally present in the current generation of global climate models, although recent process studies offer insights into their possible causes. Several plausible climate forcings and feedbacks, including changes in the sun’s energy output and the Arctic amplification of global warming with accompanying reductions in sea ice, may help explain the recent NAO changes. Recent research also suggests significant skill in being able to make seasonal NAO predictions and therefore long-range weather forecasts for up to several months ahead for northwest Europe. However, global climate models remain unclear on longer-term NAO predictions for the remainder of the 21st century.