Claudie Beaulieu

Change-point analysis as a tool to detect abrupt climate variations

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.

Observing climate change trends in ocean biogeochemistry: when and where

Abstract 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 (<~30 years) to detect trends than other areas. In addition, we quantify the ‘footprint’ of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9–15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change‐driven responses in the marine ecosystem.

Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models

Significance One of the most concerning consequences of human-induced increases in atmospheric greenhouse gas concentrations is the potential for rapid regional transitions in the climate system. Yet, despite much public awareness of how “tipping points” may be crossed, little information is available as to exactly what may be expected in the coming centuries. We assess all Earth System Models underpinning the recent 5th Intergovernmental Panel on Climate Change report and systematically search for evidence of abrupt changes. We do find abrupt changes in sea ice, oceanic flows, land ice, and terrestrial ecosystem response, although with little consistency among the models. A particularly large number is projected for warming levels below 2°. We discuss mechanisms and include methods to objectively classify abrupt climate change. 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.

Intercomparison of homogenization techniques for precipitation data

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 Jaruskovas 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.

Trends in Daily Solar Radiation and Precipitation Coefficients of Variation since 1984

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 significantchanges in solar radiationvariabilitywere found for 35% of the globe,and particularly largeincreases were found for tropical Africa and the Maritime Continent. These increases in solar radiationvariabilitywere 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.

Intercomparison of homogenization techniques for precipitation data continued: comparison of two recent Bayesian change point models.

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.

Effects of climate on West Nile Virus transmission risk used for public health decision-making in Quebec

BackgroundIn 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.ResultsResults 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).ConclusionIn 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.

Emerging negative Atlantic Multidecadal Oscillation index in spite of warm subtropics

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.

Statistical Approach to Model the Deep Draft Ships' Squat in the St. Lawrence Waterway

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.

The North Atlantic Ocean Is in a State of Reduced Overturning

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 SST 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.