Chris Brierley

Bio: Chris Brierley is an academic researcher from University College London. The author has contributed to research in topics: Sea surface temperature & Climate model. The author has an hindex of 21, co-authored 60 publications receiving 2214 citations. Previous affiliations of Chris Brierley include University of Reading & Yale University.

Patterns and mechanisms of early Pliocene warmth

Abstract: About five to four million years ago, in the early Pliocene epoch, Earth had a warm, temperate climate. The gradual cooling that followed led to the establishment of modern temperature patterns, possibly in response to a decrease in atmospheric CO2 concentration, of the order of 100 parts per million, towards preindustrial values. Here we synthesize the available geochemical proxy records of sea surface temperature and show that, compared with that of today, the early Pliocene climate had substantially lower meridional and zonal temperature gradients but similar maximum ocean temperatures. Using an Earth system model, we show that none of the mechanisms currently proposed to explain Pliocene warmth can simultaneously reproduce all three crucial features. We suggest that a combination of several dynamical feedbacks underestimated in the models at present, such as those related to ocean mixing and cloud albedo, may have been responsible for these climate conditions.

Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years

Abstract: The Atlantic meridional overturning circulation (AMOC) is a system of ocean currents that has an essential role in Earth’s climate, redistributing heat and influencing the carbon cycle1, 2. The AMOC has been shown to be weakening in recent years1; this decline may reflect decadal-scale variability in convection in the Labrador Sea, but short observational datasets preclude a longer-term perspective on the modern state and variability of Labrador Sea convection and the AMOC1, 3,4,5. Here we provide several lines of palaeo-oceanographic evidence that Labrador Sea deep convection and the AMOC have been anomalously weak over the past 150 years or so (since the end of the Little Ice Age, LIA, approximately ad 1850) compared with the preceding 1,500 years. Our palaeoclimate reconstructions indicate that the transition occurred either as a predominantly abrupt shift towards the end of the LIA, or as a more gradual, continued decline over the past 150 years; this ambiguity probably arises from non-AMOC influences on the various proxies or from the different sensitivities of these proxies to individual components of the AMOC. We suggest that enhanced freshwater fluxes from the Arctic and Nordic seas towards the end of the LIA—sourced from melting glaciers and thickened sea ice that developed earlier in the LIA—weakened Labrador Sea convection and the AMOC. The lack of a subsequent recovery may have resulted from hysteresis or from twentieth-century melting of the Greenland Ice Sheet6. Our results suggest that recent decadal variability in Labrador Sea convection and the AMOC has occurred during an atypical, weak background state. Future work should aim to constrain the roles of internal climate variability and early anthropogenic forcing in the AMOC weakening described here.

Greatly Expanded Tropical Warm Pool and Weakened Hadley Circulation in the Early Pliocene

Abstract: The Pliocene warm interval has been difficult to explain. We reconstructed the latitudinal distribution of sea surface temperature around 4 million years ago, during the early Pliocene. Our reconstruction shows that the meridional temperature gradient between the equator and subtropics was greatly reduced, implying a vast poleward expansion of the ocean tropical warm pool. Corroborating evidence indicates that the Pacific temperature contrast between the equator and 32°N has evolved from ∼2°C 4 million years ago to ∼8°C today. The meridional warm pool expansion evidently had enormous impacts on the Pliocene climate, including a slowdown of the atmospheric Hadley circulation and El Nino–like conditions in the equatorial region. Ultimately, sustaining a climate state with weak tropical sea surface temperature gradients may require additional mechanisms of ocean heat uptake (such as enhanced ocean vertical mixing).

Tropical cyclones and permanent El Niño in the early Pliocene epoch

Abstract: Tropical cyclones (also known as hurricanes and typhoons) are now believed to be an important component of the Earth's climate system. In particular, by vigorously mixing the upper ocean, they can affect the ocean's heat uptake, poleward heat transport, and hence global temperatures. Changes in the distribution and frequency of tropical cyclones could therefore become an important element of the climate response to global warming. A potential analogue to modern greenhouse conditions, the climate of the early Pliocene epoch (approximately 5 to 3 million years ago) can provide important clues to this response. Here we describe a positive feedback between hurricanes and the upper-ocean circulation in the tropical Pacific Ocean that may have been essential for maintaining warm, El Nino-like conditions during the early Pliocene. This feedback is based on the ability of hurricanes to warm water parcels that travel towards the Equator at shallow depths and then resurface in the eastern equatorial Pacific as part of the ocean's wind-driven circulation. In the present climate, very few hurricane tracks intersect the parcel trajectories; consequently, there is little heat exchange between waters at such depths and the surface. More frequent and/or stronger hurricanes in the central Pacific imply greater heating of the parcels, warmer temperatures in the eastern equatorial Pacific, warmer tropics and, in turn, even more hurricanes. Using a downscaling hurricane model, we show dramatic shifts in the tropical cyclone distribution for the early Pliocene that favour this feedback. Further calculations with a coupled climate model support our conclusions. The proposed feedback should be relevant to past equable climates and potentially to contemporary climate change.

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.

Tropical cyclones and climate change

Abstract: Whether the characteristics of tropical cyclones have altered, or will alter, in a changing climate has been subject of considerable debate. An overview of recent research indicates that greenhouse warming will cause stronger storms, on average, but a decrease in the frequency of tropical cyclones. Whether the characteristics of tropical cyclones have changed or will change in a warming climate — and if so, how — has been the subject of considerable investigation, often with conflicting results. Large amplitude fluctuations in the frequency and intensity of tropical cyclones greatly complicate both the detection of long-term trends and their attribution to rising levels of atmospheric greenhouse gases. Trend detection is further impeded by substantial limitations in the availability and quality of global historical records of tropical cyclones. Therefore, it remains uncertain whether past changes in tropical cyclone activity have exceeded the variability expected from natural causes. However, future projections based on theory and high-resolution dynamical models consistently indicate that greenhouse warming will cause the globally averaged intensity of tropical cyclones to shift towards stronger storms, with intensity increases of 2–11% by 2100. Existing modelling studies also consistently project decreases in the globally averaged frequency of tropical cyclones, by 6–34%. Balanced against this, higher resolution modelling studies typically project substantial increases in the frequency of the most intense cyclones, and increases of the order of 20% in the precipitation rate within 100 km of the storm centre. For all cyclone parameters, projected changes for individual basins show large variations between different modelling studies.

Chapter 12 - Long-term climate change: Projections, commitments and irreversibility

Abstract: This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated.