Kevin J. Anchukaitis

Bio: Kevin J. Anchukaitis is an academic researcher from University of Arizona. The author has contributed to research in topics: Climate change & Paleoclimatology. The author has an hindex of 48, co-authored 129 publications receiving 9246 citations. Previous affiliations of Kevin J. Anchukaitis include Woods Hole Oceanographic Institution & University of Tennessee.

Asian monsoon failure and megadrought during the last millennium.

Abstract: The Asian monsoon system affects more than half of humanity worldwide, yet the dynamical processes that govern its complex spatiotemporal variability are not sufficiently understood to model and predict its behavior, due in part to inadequate long-term climate observations. Here we present the Monsoon Asia Drought Atlas (MADA), a seasonally resolved gridded spatial reconstruction of Asian monsoon drought and pluvials over the past millennium, derived from a network of tree-ring chronologies. MADA provides the spatiotemporal details of known historic monsoon failures and reveals the occurrence, severity, and fingerprint of previously unknown monsoon megadroughts and their close linkages to large-scale patterns of tropical Indo-Pacific sea surface temperatures. MADA thus provides a long-term context for recent monsoon variability that is critically needed for climate modeling, prediction, and attribution.

Continental-scale temperature variability during the past two millennia

Abstract: Past global climate changes had strong regional expression To elucidate their spatio-temporal pattern, we reconstructed past temperatures for seven continental-scale regions during the past one to two millennia The most coherent feature in nearly all of the regional temperature reconstructions is a long-term cooling trend, which ended late in the nineteenth century At multi-decadal to centennial scales, temperature variability shows distinctly different regional patterns, with more similarity within each hemisphere than between them There were no globally synchronous multi-decadal warm or cold intervals that define a worldwide Medieval Warm Period or Little Ice Age, but all reconstructions show generally cold conditions between ad 1580 and 1880, punctuated in some regions by warm decades during the eighteenth century The transition to these colder conditions occurred earlier in the Arctic, Europe and Asia than in North America or the Southern Hemisphere regions Recent warming reversed the long-term cooling; during the period ad 1971–2000, the area-weighted average reconstructed temperature was higher than any other time in nearly 1,400 years

How unusual is the 2012–2014 California drought?

Abstract: For the past three years (2012–2014), California has experienced the most severe drought conditions in its last century. But how unusual is this event? Here we use two paleoclimate reconstructions of drought and precipitation for Central and Southern California to place this current event in the context of the last millennium. We demonstrate that while 3 year periods of persistent below-average soil moisture are not uncommon, the current event is the most severe drought in the last 1200 years, with single year (2014) and accumulated moisture deficits worse than any previous continuous span of dry years. Tree ring chronologies extended through the 2014 growing season reveal that precipitation during the drought has been anomalously low but not outside the range of natural variability. The current California drought is exceptionally severe in the context of at least the last millennium and is driven by reduced though not unprecedented precipitation and record high temperatures.

Climate as a contributing factor in the demise of Angkor, Cambodia

Abstract: The “hydraulic city” of Angkor, the capitol of the Khmer Empire in Cambodia, experienced decades-long drought interspersed with intense monsoons in the fourteenth and fifteenth centuries that, in combination with other factors, contributed to its eventual demise. The climatic evidence comes from a seven-and-a-half century robust hydroclimate reconstruction from tropical southern Vietnamese tree rings. The Angkor droughts were of a duration and severity that would have impacted the sprawling city’s water supply and agricultural productivity, while high-magnitude monsoon years damaged its water control infrastructure. Hydroclimate variability for this region is strongly and inversely correlated with tropical Pacific sea surface temperature, indicating that a warm Pacific and El Nino events induce drought at interannual and interdecadal time scales, and that low-frequency variations of tropical Pacific climate can exert significant influence over Southeast Asian climate and society.

Atlantic Forcing of Persistent Drought in West Africa

Abstract: Although persistent drought in West Africa is well documented from the instrumental record and has been primarily attributed to changing Atlantic sea surface temperatures, little is known about the length, severity, and origin of drought before the 20th century. We combined geomorphic, isotopic, and geochemical evidence from the sediments of Lake Bosumtwi, Ghana, to reconstruct natural variability in the African monsoon over the past three millennia. We find that intervals of severe drought lasting for periods ranging from decades to centuries are characteristic of the monsoon and are linked to natural variations in Atlantic temperatures. Thus the severe drought of recent decades is not anomalous in the context of the past three millennia, indicating that the monsoon is capable of longer and more severe future droughts.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Climate Change 2007: The Physical Science Basis.

Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

Anthropogenic and Natural Radiative Forcing

Abstract: This chapter should be cited as: Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Coordinating Lead Authors: Gunnar Myhre (Norway), Drew Shindell (USA)