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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.

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.

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 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.

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.

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.

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 (

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.

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.