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Primary afferents are responsible for transmitting signals produced by noxious stimuli from the periphery to the spinal cord. Mu and delta opioid receptors (MOP and DOP) have analgesic properties and are highly expressed in dorsal root ganglia (DRG) neurons. In humans, spinal DOP is almost exclusively located on central terminals of DRG neurons, whereas in rodents, it is expressed both on presynaptic terminals and spinal neurons. In this study, we aimed to assess the distribution of MOP and DOP in the DRGs of mice and rats. Using in situ hybridization and immunofluorescence, we visualized MOP and DOP mRNA together with various neuronal markers. In rats and mice, we show that both receptors are expressed, albeit to different extents, in all types of neurons, namely, large and medium myelinated neurons (NF200-positive), small nonpeptidergic (IB4- or P2X3R-positive) and peptidergic C fibres (Tac1-positive). Overall, DOP mRNA was found to be mainly expressed in large and medium myelinated neurons, whereas MOP mRNA was mainly found in C fibres. The distribution of MOP and DOP, however, slightly differs between rats and mice, with a higher proportion of small nonpeptidergic C fibres expressing DOP mRNA in mice than in rats. We further found that neither morphine nor inflammation affected the distribution of the receptor mRNA. Because of their location, our results confirm that MOP and DOP have the potential to alleviate similar types of pain and that this effect could slightly differ between species.

Spinal sensorimotor circuits interact with supraspinal and peripheral inputs to generate quadrupedal locomotion. Ascending and descending spinal pathways ensure coordination between the fore-and hindlimbs. Spinal cord injury disrupts these pathways. To investigate the control of interlimb coordination and hindlimb locomotor recovery, we performed two lateral thoracic hemisections placed on opposite sides of the cord (right T5-T6 and left T10-T11) at an interval of approximately two months in eight adult cats. In three cats, we then made a complete spinal transection caudal to the second hemisection at T12-T13. We collected electromyography and kinematic data during quadrupedal and hindlimb-only locomotion before and after spinal lesions. We show that 1) cats spontaneously recover quadrupedal locomotion following staggered hemisections but require balance assistance after the second one, 2) coordination between the fore-and hindlimbs displays 2:1 patterns and becomes weaker and more variable after both hemisections, 3) left-right asymmetries in hindlimb stance and swing durations appear after the first hemisection and reverse after the second, and 4) support periods reorganize after staggered hemisections to favor support involving both forelimbs and diagonal limbs. Cats expressed hindlimb locomotion the day following spinal transection, indicating that lumbar sensorimotor circuits play a prominent role in hindlimb locomotor recovery after staggered hemisections. These results reflect a series of changes in spinal sensorimotor circuits that allow cats to maintain and recover some level of quadrupedal locomotor functionality with diminished motor commands from the brain and cervical cord, although the control of posture and interlimb coordination remains impaired.Significance StatementCoordinating the limbs during locomotion depends on pathways in the spinal cord. We used a spinal cord injury model that disrupts communication between the brain and spinal cord by sectioning half of the spinal cord on one side and then about two months later, half the spinal cord on the other side at different levels of the thoracic cord in cats. We show that despite a strong contribution from neural circuits located below the second spinal cord injury in the recovery of hindlimb locomotion, the coordination between the forelimbs and hindlimbs weakens and postural control is impaired. We can use our model to test approaches to restore the control of interlimb coordination and posture during locomotion after spinal cord injury.

Most previous studies investigated the recovery of locomotion in animals and people with incomplete spinal cord injury (SCI) during relatively simple tasks (e.g., walking in a straight line on a horizontal surface or a treadmill). We know less about the recovery of locomotion after incomplete SCI in left-right asymmetric conditions, such as turning or stepping along circular trajectories. To investigate this, we collected kinematic and electromyography data during split-belt locomotion at different left-right speed differences before and after a right thoracic lateral spinal cord hemisection in nine adult cats. After hemisection, although cats still performed split-belt locomotion, we observed several changes in the gait pattern compared with the intact state at early (1-2 wk) and late (7-8 wk) time points. Cats with larger lesions showed new coordination patterns between the fore- and hindlimbs, with the forelimbs taking more steps. Despite this change in fore-hind coordination, cats maintained consistent phasing between the fore- and hindlimbs. Adjustments in cycle and phase (stance and swing) durations between the slow and fast sides allowed animals to maintain 1:1 left-right coordination. Periods of triple support involving the right (ipsilesional) hindlimb decreased in favor of quad support and triple support involving the other limbs. Step and stride lengths decreased with concurrent changes in the right fore- and hindlimbs, possibly to avoid interference. The above adjustments in the gait pattern allowed cats to retain the ability to locomote in asymmetric conditions after incomplete SCI. We discuss potential plastic neuromechanical mechanisms involved in locomotor recovery in these conditions.

The Atlantic Meridional Overturning Circulation (AMOC) is a key player in the global coupled ocean-atmosphere climate system. To characterise the potential of an AMOC slowdown, a past and future trend probability analysis is applied using 16 models from the Coupled Model Intercomparison Project Phase 5. We determine the probability of AMOC annual to multidecadal trends under the historical period and two future climate scenarios (`business-as-usual’ scenario - RCP8.5 and `stabilisation’ scenario - RCP4.5). We show that the probability of a AMOC decline in model data shifts outside its range of intrinsic variability (determined from the pre-industrial control runs) for sustained 5-year trend or longer. This suggests that interannual AMOC events are not significantly affected by future climate scenario, and so potentially neither by anthropogenic forcing. Furthermore, under the ‘business-as-usual’ scenario the probability of a 20-year decline remains high (87\%) until 2100, however in a ‘stabilisation’ scenario the trend probability recovers its pre-industrial values by 2100. A 20-year unique event is identified from 1995 to 2015, marked by simultaneous unique features in the AMOC and salinity transport that are not replicated over any other 20-year period within the 250 years studied. These features include the maximum probability and magnitude of an `intense’ AMOC decline, and a sustained 20-year decline in subpolar salinity transport caused by internal oceanic processes (as opposed to external atmospheric forcing). This work therefore highlights the potential use of direct salinity transport observations, and ensemble mean numerical models to represent and understand changes in past, present, and future AMOC.

Extreme Aleutian Low (AL) events have been associated with major ecosystem reorganisations and unusual weather patterns in the Pacific region, with serious socio-economic consequences. Yet, their future evolution and impacts on atmosphere–ocean interactions remain uncertain. Here, a large ensemble of historical and future runs from the Community Earth System Model is used to investigate the evolution of AL extremes. The frequency and persistence of AL extremes are quantified and their connection with climatic variables is examined. AL extremes become more frequent and persistent under the RCP8.5 scenario, associated with changes in precipitation and air temperature patterns over North America. Future changes in AL extremes also increase the variability of the sea surface temperature and net heat fluxes in the Kuroshio Extension, the most significant heat and energy flux region of the basin. The increased frequency and persistence of future AL extremes may potentially cause substantial changes in fisheries and ecosystems of the entire Pacific region as a knock-on effect.

As primary producers, phytoplankton play a pivotal role in the marine environment and are central to many biogeochemical processes. Changes to phytoplankton community composition could have major consequences for wider ecosystem functioning and may occur in response to climate change. Here we describe multi-decadal variability in phytoplankton community structure using taxonomic data from the Continuous Plankton Recorder collected in the North-East Atlantic from 1969-2013, using a total of 42 diatom and dinoflagellate taxa. We considered a range of characteristics of community structure, including taxonomic diversity and community stability and disorder, and how these characteristics change in response to sea surface temperature, mixed layer depth and the North Atlantic Oscillation. We found that phytoplankton community composition was largely stable on interannual timescales. A change in community composition occurred between 1985 and 1995 due to an increased dominance of 2 diatom taxa (Rhizosolenia styliformis and Thalassiosira spp.); however, after this period, the community returned to its previous composition. Further, a community disorder analysis found that phytoplankton compositional structure became more rigid in recent years, which may lead to an eventual community shift in the future. In contrast to previous studies that revealed relationships between total phytoplankton abundance or biomass and environmental forcing, we found that community structure had, at most, a very weak relationship with the environmental parameters tested. Changes to the physical environment may therefore have less influence at interannual timescales on phytoplankton community structure than previously thought.

Determining whether the Atlantic Meridional Overturning Circulation (AMOC)'s transport is in decline is challenging due to the short duration of continuous observations. To estimate how many years are needed to detect a decline, we conduct a simulation study using synthetic data that mimics an AMOC time series. The time series' characteristics are reproduced using the trend, variance, and autocorrelation coefficient of the AMOC strength at 26.5°,N from 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models under the RCP8.5 future scenario, and from RAPID observations (2004‐‐2018). Our results suggest that the 14‐year RAPID length has just entered the lower limits of the trend’s `detection window’ based on synthetic data generated using CMIP5 trends and variability (14‐‐42 years; median = 24 years), but twice the length is required for detectability based on RAPID variability (29‐‐67 years; median = 43 years). The annual RAPID trend is currently not statistically significant (‐0.11 Sv yr‐1, p>0.05). Plain Language Summary There are ongoing discussions in the scientific community about whether the Atlantic Meridional Overturning Circulation transport is slowing down. This is of interest due to the importance of this circulation in transporting heat from the tropics to the northern latitudes. A consensus about its decline is hard to reach due to the limited direct observational data available; with the longest continuous data being 14 years long from 2004 to 2018. We therefore conduct a simulation experiment to examine how many years of data are required to detect a decline in the circulation. We create simulations of the North Atlantic transport based on statistical properties from 20 general circulation models with future climate change projections (until 2100) and from the RAPID array observations (since 2004). Our results demonstrate that the length of data we currently have from observations has just entered the `detection window’ of 14—42 years (based on model simulations). However, the RAPID observations do not currently exhibit a statistically significant trend.

Changes in marine primary productivity are key to determine how climate change might impact marine ecosystems and fisheries. Satellite ocean color sensors provide coverage of global ocean chlorophyll with a combined record length of ~ 20 years. Coupled physical–biogeochemical models can inform on expected changes and are used here to constrain observational trend estimates and their uncertainty. We produce estimates of ocean surface chlorophyll trends, by using Coupled Model Intercomparison Project (CMIP5) models to form priors as a “first guess”, which are then updated using satellite observations in a Bayesian spatio-temporal model. Regional chlorophyll trends are found to be significantly different from zero in 18/23 regions, in the range ± 1.8% year−1. A global average of these regional trends shows a net positive trend of 0.08 ± 0.35% year−1, highlighting the importance of considering chlorophyll changes at a regional level. We compare these results with estimates obtained with the commonly used “vague” prior, representing no independent knowledge; coupled model priors are shown to slightly reduce trend magnitude and uncertainties in most regions. The statistical model used here provides a robust framework for making best use of all available information and can be applied to improve understanding of global change.

The strength of the Atlantic meridional overturning circulation (AMOC) at 26∘ N has now been continuously measured by the RAPID array over the period April 2004–September 2018. This record provides unique insight into the variability of the large-scale ocean circulation, previously only measured by sporadic snapshots of basin-wide transport from hydrographic sections. The continuous measurements have unveiled striking variability on timescales of days to a decade, driven largely by wind forcing, contrasting with previous expectations about a slowly varying buoyancy-forced large-scale ocean circulation. However, these measurements were primarily observed during a warm state of the Atlantic multidecadal variability (AMV) which has been steadily declining since a peak in 2008–2010. In 2013–2015, a period of strong buoyancy forcing by the atmosphere drove intense water-mass transformation in the subpolar North Atlantic and provides a unique opportunity to investigate the response of the large-scale ocean circulation to buoyancy forcing. Modelling studies suggest that the AMOC in the subtropics responds to such events with an increase in overturning transport, after a lag of 3–9 years. At 45∘ N, observations suggest that the AMOC may already be increasing. Examining 26∘ N, we find that the AMOC is no longer weakening, though the recent transport is not above the long-term mean. Extending the record backwards in time at 26∘ N with ocean reanalysis from GloSea5, the transport fluctuations at 26∘ N are consistent with a 0- to 2-year lag from those at 45∘ N, albeit with lower magnitude. Given the short span of time and anticipated delays in the signal from the subpolar to subtropical gyres, it is not yet possible to determine whether the subtropical AMOC strength is recovering nor how the AMOC at 26∘ N responds to intense buoyancy forcing.

Assessing ongoing changes in marine primary productivity is essential to determine the impacts of climate change on marine ecosystems and fisheries. Satellite ocean color sensors provide detailed coverage of ocean chlorophyll in space and time, now with a combined record length of just over 20 years. Detecting climate change impacts is hindered by the shortness of the record and the long timescale of memory within the ocean such that even the sign of change in ocean chlorophyll is still inconclusive from time-series analysis of satellite data. Here we use a Bayesian hierarchical space-time model to estimate long-term trends in ocean chlorophyll. The main advantage of this approach comes from the principle of ”borrowing strength” from neighboring grid cells in a given region to improve overall detection. We use coupled model simulations from the CMIP5 experiment to form priors to provide a “first guess” on observational trend estimates and their uncertainty that we then update using satellite observations. We compare the results with estimates obtained with the commonly used vague prior, reflecting the case where no independent knowledge is available. A global average net positive chlorophyll trend is found, with stronger regional trends that are typically positive in high and mid latitudes, and negative at low latitudes outside the Atlantic. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.

The strength of the Atlantic meridional overturning circulation (AMOC) at 26°N has now been continuously measured by the RAPID array over the period April 2004 - Sept 2018. This record provides unique insight into the variability of the large-scale ocean circulation, previously only measured by sporadic snapshots of basin-wide transports from hydrographic sections. The continuous measurements have unveiled striking variability on timescales of days to a decade, driven largely by wind-forcing, contrasting with previous expectations about a slowly-varying, buoyancy forced large-scale ocean circulation. However, these measurements were primarily observed during a warm state of the Atlantic Multidecadal Variability (AMV) which has been steadily declining since a peak in 2008-2010. In 2013-2015, a period of strong buoyancy- forcing by the atmosphere drove intense watermass transformation in the subpolar North Atlantic and provides a unique opportunity to investigate the response of the large-scale ocean circulation to buoyancy forcing. Modelling studies suggest that the AMOC in the subtropics responds to such events with an increase in overturning transport, after a lag of 3-9 years. At 45°N, observations suggest that the AMOC my already be increasing. We have therefore examined the record of transports at 26°N to see whether the AMOC in the subtropical North Atlantic is now recovering from a previously reported low period commencing in 2009. Comparing the two latitudes, the AMOC at 26°N is higher than its previous low. Extending the record at 26°N with ocean reanalysis from GloSea5, the transport fluctuations follow those at 45°N by 0-2 years, albeit with lower magnitude. Given the short span of time and anticipated delays in the signal from the subpolar to subtropical gyres, it is not yet possible to determine whether the subtropical AMOC strength is recovering.

The strength of the Atlantic meridional overturning circulation (AMOC) at 26° N has now been continuously measured by the RAPID array over the period Apr 2004–Sept 2018. This record provides unique insight into the variability of the large-scale ocean circulation, previously only measured by sporadic snapshots of basin-wide transports from hydrographic sections. The continuous measurements have unveiled striking variability on timescales of days to a decade, driven largely by wind-forcing, contrasting with previous expectations about a slowly-varying, buoyancy forced large-scale ocean circulation. However, these measurements were primarily observed during a warm state of the Atlantic Multidecadal Variability (AMV) which has been steadily declining since a peak in 2008–2010. In 2013–2015, a period of strong buoyancy-forcing by the atmosphere drove intense watermass transformation in the subpolar North Atlantic and provides a unique opportunity to investigate the response of the large-scale ocean circulation to buoyancy forcing. Modelling studies suggest that the AMOC in the subtropics responds to such events with an increase in overturning transport, after a lag of 3–9 years. At 45° N, observations suggest that the AMOC my already be increasing. We have therefore examined the record of transports at 26° N to see whether the AMOC in the subtropical North Atlantic is now recovering from a previously reported low period commencing in 2009. Comparing the two latitudes, the AMOC at 26° N is higher than its previous low. Extending the record at 26° N with ocean reanalysis from GloSea5, the transport fluctuations follow those at 45° N by 0–2 years, albeit with lower magnitude. Given the short span of time and anticipated delays in the signal from the subpolar to subtropical gyres, it is not yet possible to determine whether the subtropical AMOC strength is recovering.

The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change. © 2018 American Meteorological Society.

The presence, magnitude, and even direction of long-term trends in phytoplankton abundance over the past few decades is still debated in the literature, primarily due to differences in the data sets and methodologies used. Recent work has suggested that the satellite chlorophyll record is not yet long enough to distinguish climate change trends from natural variability, despite the high density of coverage in both space and time. Previous work has typically focused on using linear models to determine the presence of trends, where each grid cell is considered independently from its neighbors. However, trends can be more thoroughly evaluated using a spatially resolved approach. Here a Bayesian hierarchical spatio-temporal model is fitted to quantify trends in ocean chlorophyll from September 1997 to December 2013. The approach used in this study explicitly accounts for the dependence between neighboring grid cells, which allows us to estimate trend by ‘borrowing strength’ from the spatial correlation. By way of comparison, a model without spatial correlation is also fitted. This results in a notable loss of accuracy in model fit. Additionally, we find an order of magnitude smaller global trend, and larger uncertainty, when using the spatio-temporal model: -0.023 ± 0.12 % yr-1 as opposed to -0.38 ± 0.045 % yr-1 when the spatial correlation is not taken into account. The improvement in accuracy of trend estimates, and the more complete account of their uncertainty emphasizes the solution that space-time modeling offers for studying global long-term change.

Changepoint models are increasingly used to represent changes in the rate of warming in surface temperature records. On the opposite hand, a large body of literature has suggested long‐memory processes to characterize long‐term behavior in surface temperatures. While these two model representations provide different insights into the underlying mechanisms, they share similar spectrum properties that create “ambiguity” and challenge distinguishing between the two classes of models. This study aims to compare the two representations to explain temporal changes and variability in surface temperatures. To address this question, we extend a recently developed time‐varying spectral procedure and assess its accuracy through a synthetic series mimicking observed global monthly surface temperatures. We vary the length of the synthetic series to determine the number of observations needed to be able to accurately distinguish between changepoints and long‐memory models. We apply the approach to two gridded surface temperature data sets. Our findings unveil regions in the oceans where long‐memory is prevalent. These results imply that the presence of long‐memory in monthly sea surface temperatures may impact the significance of trends, and special attention should be given to the choice of model representing memory (short versus long) when assessing long‐term changes.

Regime shifts have been reported in many marine ecosystems, and are often expressed as an abrupt change occurring in multiple physical and biological components of the system. In the Gulf of Alaska, a regime shift in the late 1970s was observed, indicated by an abrupt increase in sea surface temperature and major shifts in the catch of many fish species. This late 1970s regime shift in the Gulf of Alaska was followed by another shift in the late 1980s, not as pervasive as the 1977 shift, but which nevertheless did not return to the prior state. A thorough understanding of the extent and mechanisms leading to such regime shifts is challenged by data paucity in time and space. We investigate the ability of a suite of ocean biogeochemistry models of varying complexity to simulate regime shifts in the Gulf of Alaska by examining the presence of abrupt changes in time series of physical variables (sea surface temperature and mixed layer depth), nutrients and biological variables (chlorophyll, primary productivity and plankton biomass) using change-point analysis. Our study demonstrates that ocean biogeochemical models are capable of simulating the late 1970s shift, indicating an abrupt increase in sea surface temperature forcing followed by an abrupt decrease in nutrients and biological productivity. This predicted shift is consistent among all the models, although some of them exhibit an abrupt transition (i.e. a significant shift from one year to the next), whereas others simulate a smoother transition. Some models further suggest that the late 1980s shift was constrained by changes in mixed layer depth. Our study demonstrates that ocean biogeochemical can successfully simulate regime shifts in the Gulf of Alaska region, thereby providing better understanding of how changes in physical conditions are propagated from lower to upper trophic levels through bottom-up controls.

The world's ocean and land ecosystems act as sinks for anthropogenic CO2, and over the last half century their combined sink strength grew steadily with increasing CO2 emissions. Recent analyses of the global carbon budget, however, have uncovered an abrupt, substantial ( ∼ 1 PgC yr−1) and sustained increase in the land sink in the late 1980s whose origin remains unclear. In the absence of this prominent shift in the land sink, increases in atmospheric CO2 concentrations since the late 1980s would have been ∼ 30 % larger than observed (or ∼ 12 ppm above current levels). Global data analyses are limited in regards to attributing causes to changes in the land sink because different regions are likely responding to different drivers. Here, we address this challenge by using terrestrial biosphere models constrained by observations to determine if there is independent evidence for the abrupt strengthening of the land sink. We find that net primary production significantly increased in the late 1980s (more so than heterotrophic respiration), consistent with the inferred increase in the global land sink, and that large-scale climate anomalies are responsible for this shift. We identify two key regions in which climatic constraints on plant growth have eased: northern Eurasia experienced warming, and northern Africa received increased precipitation. Whether these changes in continental climates are connected is uncertain, but North Atlantic climate variability is important. Our findings suggest that improved understanding of climate variability in the North Atlantic may be essential for more credible projections of the land sink under climate change.

Ocean color sensors are crucial for understanding global phytoplankton dynamics. However, the limited life spans of sensors make multisensor data sets necessary for estimating long‐term trends. Discontinuities may be introduced when merging data between sensors, potentially affecting trend estimates and their uncertainties. We use a Bayesian spatiotemporal model to investigate the presence of discontinuities and their impacts on estimated chlorophyll trends. The discontinuities considered are the introduction of Medium Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer‐Aqua, and Visible Infrared Imaging Radiometer Suite and the termination of Sea‐Viewing Wide Field‐of‐View Sensor. Discontinuities are detected in ~70% of regions, affecting trend estimates (~60% of regions have statistically different trends) and potentially even biasing trend estimates (opposite sign in ~13% of regions). Considering a single discontinuity increases trend uncertainty by an average of 0.20%/year (0.59%/year for two discontinuities). This difference in trend magnitude and uncertainty highlights the importance of minimizing discontinuities in multisensor records and taking into account discontinuities when analyzing trends. Plain Language Summary Ocean color sensors are crucial for understanding global phytoplankton dynamics as they detect chlorophyll, a proxy for phytoplankton abundance. However, the limited life spans of sensors make multisensor data sets necessary for estimating long‐term trends. Differences between sensors may be seen when combining them to form multisensor data sets, creating discontinuities in the record (a point in the record representing a sudden change) which could potentially affect estimates of trend and their uncertainties. We use a statistical model that explicitly considers the spatial relationship between observation locations to investigate the presence of discontinuities and their impacts on estimated chlorophyll trends. We consider three discontinuities relating to the introduction and termination of important ocean color sensors. Discontinuities are detected in the majority of regions, affecting trend estimates and potentially even changing the direction of trend. Considering a single discontinuity increases trend uncertainty, and considering multiple discontinuities increases this further. This difference in trend magnitude and uncertainty highlights the importance of minimizing discontinuities in multisensor data sets and taking into account discontinuities when analyzing trends.

International audience; Regime shifts are abrupt changes in an ecosystem that may propagate through multiple tro-phic levels and have pronounced effects on the biotic and abiotic environment, potentially resulting in ecosystem reorganization. There are multiple mechanisms that could cause such abrupt events including natural and anthropogenic factors. In the North Pacific, a major shift in the physics of the system, including a sudden increase in sea surface temperature, was reported in 1977 with a prominent biological response in the lower trophic levels and subsequent effects on the fisheries and economy of the region. Here we investigate the statistics of physical processes that could have triggered and maintained the late 1970s shift. The hypothesis of an extreme sea level pressure event abruptly changing the oceanic conditions in winter 1976-1977, which was maintained by long-term changes in air-sea interaction processes, is tested. Using dynamical proxies, we show the occurrence of an extreme atmospheric event, specifically a persistent Aleu-tian Low during winter 1976-1977, which constitutes a substantial part of the triggering mechanism of the regime shift. Subsequent sudden changes in the net heat flux occurred in the western North Pacific, particularly in the Kuroshio Extension region, which contributed to the maintenance of the new regime.

The Atlantic Meridional Overturning Circulation (AMOC) is responsible for a variable and climatically important northward transport of heat. Using data from an array of instruments that span the Atlantic at 26°N, we show that the AMOC has been in a state of reduced overturning since 2008 as compared to 2004–2008. This change of AMOC state is concurrent with other changes in the North Atlantic such as a northward shift and broadening of the Gulf Stream and altered patterns of heat content and sea surface temperature. These changes resemble the response to a declining AMOC predicted by coupled climate models. Concurrent changes in air‐sea fluxes close to the western boundary reveal that the changes in ocean heat transport and sea surface temperature have altered the pattern of ocean‐atmosphere heat exchange over the North Atlantic. These results provide strong observational evidence that the AMOC is a major factor in decadal‐scale variability of North Atlantic climate.

The terrestrial biosphere is currently a strong carbon (C) sink but may switch to a source in the 21st century as climate-driven losses exceed CO2-driven C gains, thereby accelerating global warming. Although it has long been recognized that tropical climate plays a critical role in regulating interannual climate variability, the causal link between changes in temperature and precipitation and terrestrial processes remains uncertain. Here, we combine atmospheric mass balance, remote sensing-modeled datasets of vegetation C uptake, and climate datasets to characterize the temporal variability of the terrestrial C sink and determine the dominant climate drivers of this variability. We show that the interannual variability of global land C sink has grown by 50–100% over the past 50 y. We further find that interannual land C sink variability is most strongly linked to tropical nighttime warming, likely through respiration. This apparent sensitivity of respiration to nighttime temperatures, which are projected to increase faster than global average temperatures, suggests that C stored in tropical forests may be vulnerable to future warming.

Sea surface temperatures in the northern North Atlantic have shown a marked decrease over the past several years. The sea surface in the subpolar gyre is now as cold as it was during the last cold phase of the Atlantic Multidecadal Oscillation index in the 1990s. This climate index is associated with shifts in hurricane activity, rainfall patterns and intensity, and changes in fish populations. However, unlike the last cold period in the Atlantic, the spatial pattern of sea surface temperature anomalies in the Atlantic is not uniformly cool, but instead has anomalously cold temperatures in the subpolar gyre, warm temperatures in the subtropics and cool anomalies over the tropics. The tripole pattern of anomalies has increased the subpolar to subtropical meridional gradient in SSTs, which are not represented by the AMO index value, but which may lead to increased atmospheric baroclinicity and storminess. Here we show that the recent Atlantic cooling is likely to persist, as predicted by a statistical forecast of subsurface ocean temperatures and consistent with the irreversible nature of watermass changes involved in the recent cooling of the subpolar gyre.

Climate change is expected to modify ecological responses in the ocean, with the potential for important effects on the ecosystem services provided to humankind. Here we address the question of how rapidly multiple drivers of marine ecosystem change develop in the future ocean. By analysing an ensemble of models we find that, within the next 15 years, the climate change-driven trends in multiple ecosystem drivers emerge from the background of natural variability in 55% of the ocean and propagate rapidly to encompass 86% of the ocean by 2050 under a ‘business-as-usual' scenario. However, we also demonstrate that the exposure of marine ecosystems to climate change-induced stress can be drastically reduced via climate mitigation measures; with mitigation, the proportion of ocean susceptible to multiple drivers within the next 15 years is reduced to 34%. Mitigation slows the pace at which multiple drivers emerge, allowing an additional 20 years for adaptation in marine ecological and socio-economic systems alike., Climate change is expected to alter ocean ecology, and to potentially impact the ecosystem services provided to humankind. Here, the authors address how rapidly multiple factors that affect marine ecosystems are likely to develop in the future ocean and the remedial effects climate mitigation might have.

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change‐driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long‐term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long‐term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (

Recently, there have been an increasing number of studies using change-point methods to detect artificial or natural discontinuities and regime shifts in climate. However, a major drawback with most of the currently used change-point methods is the lack of flexibility (able to detect one specific type of shift under the assumption that the residuals are independent). As temporal variations in climate are complex, it may be difficult to identify change points with very simple models. Moreover, climate time series are known to exhibit autocorrelation, which corresponds to a model misspecification if not taken into account and can lead to the detection of non-existent shifts. In this study, we extend a method known as the informational approach for change-point detection to take into account the presence of autocorrelation in the model. The usefulness and flexibility of this approach are demonstrated through applications. Furthermore, it is highly desirable to develop techniques that can detect shifts soon after they occur for climate monitoring. To address this, we also carried out a simulation study in order to investigate the number of years after which an abrupt shift is detectable. We use two decision rules in order to decide whether a shift is detected or not, which represents a trade-off between increasing our chances of detecting a shift and reducing the risk of detecting a shift while in reality there is none. We show that, as of now, we have good chances to detect an abrupt shift with a magnitude that is larger than that of the standard deviation in the series of observations. For shifts with a very large magnitude (three times the standard deviation), our simulation study shows that after only 4 years the probabilities of shift detection reach nearly 100 per cent. This reveals that the approach has potential for climate monitoring.

To maintain an optimum balance between security and efficiency of maritime transport in shallow waterways with a lot of deep-draft ship traffic such as in the St. Lawrence Waterway, it is particularly important to accurately estimate the ship squat, which is the reduction of the underkeel clearance between a vessel at rest and in motion. Recently, a squat model based on a regression tree was developed. The skill of this model to predict squat in the St. Lawrence Waterway exceeded the performance of 10 empirical models commonly used by the operational and regularity agencies. Although this approach is promising, two main problems were noticed: (1) the predictions obtained by the regression tree are not smooth and (2) the squat predicted with this model is not always monotonically increasing with ship speed (Froude number). In this paper, a stepwise regression tree algorithm is used to model squat. This approach has the same advantages as the regression tree (allowing the representation of complex and nonlinear relationships) and solves both of the aforementioned problems. Furthermore, the squat predictions of the new stepwise regression model outperform the predictions of the regression tree model and the Eryuzlu model, which is currently used by the Canadian Coast Guard. This new model could provide a handy tool for mariners to get real-time squat predictions in the St. Lawrence River. We also provide an algorithm that can be used to fit a squat model for any other economically important shallow waterway.

This study investigates the possibility of changes in daily scale solar radiation and precipitation variability. Coefficients of variation (CVs) were computed for the daily downward surface solar radiation product from the International Satellite Cloud Climatology Project and the daily precipitation product from the Global Precipitation Climatology Project. Regression analysis was used to identify trends in CVs. Statistically significant changes in solar radiation variability were found for 35% of the globe, and particularly large increases were found for tropical Africa and the Maritime Continent. These increases in solar radiation variability were correlated with increases in precipitation variability and increases in deep convective cloud amount. The changes in high-frequency climate variability identified here have consequences for any process depending nonlinearly on climate, including solar energy production and terrestrial ecosystem photosynthesis. To assess these consequences, additional work is needed to understand how high-frequency climate variability will change in the coming decades.

A Bayesian Normal Homogeneity Test (BNHT) for the detection of artificial discontinuities in climatic series is presented. The test is simple to use and allows the integration of prior knowledge on the date of change from various sources of information (e.g. metadata or expert belief) in the analysis. The performance of the new test was evaluated on synthetic series with similar statistical properties as observed total annual precipitation in the southern and central parts of the province of Quebec, Canada. Different priors were used to investigate the sensitivity of the test to the choice of priors. It was found that (1) high-prior probability of no change yields low false detection rates on the homogeneous series; (2) the test has a very high power of detection on series with a single shift (the best power of detection if compared with previous methods applied to the same synthetic series); (3) shifts having a small magnitude are detectable with a low prior probability of no change and (4) when applied to series with multiple shifts with a segmentation procedure and a high probability of no change, the test proved to be performing well in detecting multiple shifts (as performing as the best techniques previously applied to the same synthetic series). An example of application to total annual precipitation in Quebec City, Canada is also presented to illustrate (1) a case for which the results are not affected by the choice of the prior parameters and (2) a case for which information about potential changes found in the metadata was integrated in the analysis and allowed the detection of a change that would not have been detected with a non-informative prior.

We show here an updated estimate of the net land carbon sink (NLS) as a function of time from 1960 to 2007 calculated from the difference between fossil fuel emissions, the observed atmospheric growth rate, and the ocean uptake obtained by recent ocean model simulations forced with reanalysis wind stress and heat and water fluxes. Except for interannual variability, the net land carbon sink appears to have been relatively constant at a mean value of −0.27 Pg C yr−1 between 1960 and 1988, at which time it increased abruptly by −0.88 (−0.77 to −1.04) Pg C yr−1 to a new relatively constant mean of −1.15 Pg C yr−1 between 1989 and 2003/7 (the sign convention is negative out of the atmosphere). This result is detectable at the 99% level using a t-test. The land use source (LU) is relatively constant over this entire time interval. While the LU estimate is highly uncertain, this does imply that most of the change in the net land carbon sink must be due to an abrupt increase in the land sink, LS = NLS – LU, in response to some as yet unknown combination of biogeochemical and climate forcing. A regional synthesis and assessment of the land carbon sources and sinks over the post 1988/1989 period reveals broad agreement that the Northern Hemisphere land is a major sink of atmospheric CO2, but there remain major discrepancies with regard to the sign and magnitude of the net flux to and from tropical land., Biogeosciences, 7 (8), ISSN:1726-4170

In shallow waterways such as the St. Lawrence River, an accurate prediction of the squat is important to ensure a balance between the security and the efficiency of traffic. The Canadian Coast Guard is now studying the squat phenomenon and considering to reassess the actual underkeel clearance standards of the St. Lawrence Waterway. Hence, a field campaign was conducted with 12 deep draft ship sailings, during which the maximal squat was measured with on-the-fly global positioning system. All the variables that may influence the squat (speed, draught, water level, etc.) were also measured. Twenty of the empirical models that are used in practice to predict the squat were tested and the Canadian Coast Guard recommended to either optimize these models or develop new models. Therefore, statistical approaches to model the squat of deep draft ships that navigate on the St. Lawrence Waterway are proposed in this paper. The Eryuzlu model, which is presently used by the Canadian Coast Guard, was optimized by modeling its errors with a stepwise regression. New models were also developed with the regression tree technique. The performance of the statistical models was better than 10 empirical models that are considered the most suitable to predict the maximal squat in the St. Lawrence Waterway. The models built by regression tree gave the best predictions.

Resume Au cours des dernieres decennies, plusieurs methodes d'homogeneisation ont ete developpees pour corriger les ruptures artificielles dans les series climatiques. Elles ont ete developpees dans divers pays, pour differentes situations et avec des domaines d'application differents. Cet article presente une revue exhaustive des methodes d'homogeneisation des series climatiques publiees dans la litterature. Une analyse critique de ces methodes ainsi qu'une reflexion sur leur applicabilite aux series de precipitations totales annuelles sont egalement presentees.

Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change.

The seasonal cycle (i.e. phenology) of oceanic primary production (PP) is expected to change in response to climate warming. Here, we use output from 6 global biogeochemical models to examine the response in the seasonal amplitude of PP and timing of peak PP to the IPCC AR5 warming scenario. We also investigate whether trends in PP phenology may be more rapidly detectable than trends in annual mean PP. The seasonal amplitude of PP decreases by an average of 1–2% per year by 2100 in most biomes, with the exception of the Arctic which sees an increase of ~1% per year. This is accompanied by an advance in the timing of peak PP by ~0.5–1 months by 2100 over much of the globe, and particularly pronounced in the Arctic. These changes are driven by an increase in seasonal amplitude of sea surface temperature (where the maxima get hotter faster than the minima) and a decrease in the seasonal amplitude of the mixed layer depth and surface nitrate concentration. Our results indicate a transformation of currently strongly seasonal (bloom forming) regions, typically found at high latitudes, into weakly seasonal (non-bloom) regions, characteristic of contemporary subtropical conditions. On average, 36 yr of data are needed to detect a climate-change-driven trend in the seasonal amplitude of PP, compared to 32 yr for mean annual PP. Monthly resolution model output is found to be inadequate for resolving phenological changes. We conclude that analysis of phytoplankton seasonality is not necessarily a shortcut to detecting climate change impacts on ocean productivity.

[1] A recent study of the net land carbon sink estimated using the Mauna Loa, Hawaii atmospheric CO2record, fossil fuel estimates, and a suite of ocean models suggests that the mean of the net land carbon uptake remained approximately constant for three decades and increased after 1988/1989. Due to the large variability in the net land uptake, it is not possible to determine the exact timing and nature of the increase robustly by visual inspection. Here, we develop a general methodology to objectively determine the nature and timing of the shift in the net land uptake based on the Schwarz Information Criterion. We confirm that it is likely that an abrupt shift in the mean net land carbon uptake occurred in 1988. After taking into account the variability in the net land uptake due to the influence of volcanic aerosols and the El Nino Southern Oscillation, we find that it is most likely that there is a remaining step increase at the same time (p-values of 0.01 and 0.04 for Mauna Loa and South Pole, respectively) of about 1 Pg C/yr. Thus, we conclude that neither the effect of volcanic eruptions nor the El Nino Southern Oscillation are the causes of the sudden increase of the land carbon sink. By also applying our methodology to the atmospheric growth rate of CO2, we demonstrate that it is likely that the atmospheric growth rate of CO2 exhibits a step decrease between two fitted lines in 1988–1989, which is most likely due to the shift in the net land uptake of carbon.

Tree ring Δ14C data (Reimer et al., 2004; McCormac et al., 2004) indicate that atmospheric Δ14C varied on multi-decadal to centennial timescales, in both hemispheres, over the period between AD 950 and 1830. The Northern and Southern Hemispheric Δ14C records display similar variability, but from the data alone is it not clear whether these variations are driven by the production of 14C in the stratosphere (Stuiver and Quay, 1980) or by perturbations to exchanges between carbon reservoirs (Siegenthaler et al., 1980). As the sea-air flux of 14CO2 has a clear maximum in the open ocean regions of the Southern Ocean, relatively modest perturbations to the winds over this region drive significant perturbations to the interhemispheric gradient. In this study, model simulations are used to show that Southern Ocean winds are likely a main driver of the observed variability in the interhemispheric gradient over AD 950–1830, and further, that this variability may be larger than the Southern Ocean wind trends that have been reported for recent decades (notably 1980–2004). This interpretation also implies that there may have been a significant weakening of the winds over the Southern Ocean within a few decades of AD 1375, associated with the transition between the Medieval Climate Anomaly and the Little Ice Age. The driving forces that could have produced such a shift in the winds at the Medieval Climate Anomaly to Little Ice Age transition remain unknown. Our process-focused suite of perturbation experiments with models raises the possibility that the current generation of coupled climate and earth system models may underestimate the natural background multi-decadal- to centennial-timescale variations in the winds over the Southern Ocean.

Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

In this paper, two new Bayesian change point techniques are described and compared to eight other techniques presented in previous work to detect inhomogeneities in climatic series. An inhomogeneity can be defined as a change point (a time point in a series such that the observations have a different distribution before and after this time) in the data series induced from changes in measurement conditions at a given station. It is important to be able to detect and correct an inhomogeneity, as it can interfere with the real climate change signal. The first technique is a Bayesian method of multiple change point detection in a multiple linear regression. The second one allows the detection of a single change point in a multiple linear regression. These two techniques have never been used for homogenization purposes. The ability of the two techniques to discriminate homogeneous and inhomogeneous series was evaluated using simulated data series. Various sets of synthetic series (homogeneous, with a single shift, and with multiple shifts) representing the typical total annual precipitation observed in the southern and central parts of the province of Quebec, Canada, and nearby areas were generated for the purpose of this study. The two techniques gave small false detection rates on the homogeneous series. Furthermore, the two techniques proved to be efficient for the detection of a single shift in a series. For the series with multiple shifts, the Bayesian method of multiple change point detection performed better. An application to a real data set is also provided and validated with the available metadata.

This paper presents an intercomparison of eight statistical tests to detect inhomogeneities in climatic data. The objective was to select those that are more suitable for precipitation data in the southern and central regions of the province of Quebec, Canada. The performances of these methods were evaluated by simulation on several thousands of homogeneous and inhomogeneous synthetic series. These series were generated to reproduce the statistical characteristics of typical precipitations observed in the southern and central parts of the province of Quebec and nearby areas, Canada. It was found that none of these methods was efficient for all types of inhomogeneities, but some of them performed substantially better than others: the bivariate test, the Jaruskova's method, and the standard normal homogeneity test. Techniques such as the Student sequential test and the two-phase regression led to the worst performances. The analysis of the performances of each method in several situations allowed the design of an optimal procedure that takes advantage of the strengths of the best performing techniques.

Background In 2002, major human epidemics of West Nile Virus (WNV) were reported in five cities in the North East region of North America. The present analysis examines the climatic conditions that were conducive to the WNV epidemic, in order to provide information to public health managers who eventually must decide on the implementation of a preventive larvicide spraying program in Quebec, Canada. Two sets of variables, the first observed in the summer of 2002 and the second in the preceding winter were analysed to study their potential as explanatory variables for the emergence of the virus at epidemic levels. Results Results show that the climatic conditions observed in the year 2002 have contributed to the emergence of the virus and can be observed once every forty years on average. The analysis has shown that the 2002 events observed in several North East North American cities are characterized by two main variables: the number of degree-days below -5°C in the winter (DD-5) and the number of degree-days greater than 25°C in the summer (DD25). Conclusion In the context of a declining rate of human and aviary infection to WNV, this element contributed to the decision to suspend the use of preventive larvicides in the province of Quebec in 2006 and for the foreseeable future. The second part of this study indicates that it is very important to estimate the risk that extreme values can be observed simultaneously in the summer and in the winter preceding the appearance of the virus. The proposed models provide important information to public health officials, weeks before the appearance of the virus, and can therefore be useful to help prevent human epidemics.