Gerrit Hansen
email: hansen@pik-potsdam.de

Daithi Stone
email: dstone@lbl.gov

Maximilian Auffhammer
email: auffhammer@berkeley.edu

Christian Huggel
email: christian.huggel@geo.uzh.ch

Dáithí Stone

Wolfgang Cramer
email: wolfgang.cramer@imbe.fr

Linking local impacts to changes in climate -a guide to attribution

Keywords: Observed Impacts of Climate Change, Impact Detection, Attribution, Human and Managed Systems, Multiple drivers

come

Introduction

Human interference with the climate system has been visible at global scales for some time, and is increasingly becoming apparent at regional scales [START_REF] Stott | Detection and attribution of climate change: a regional perspective[END_REF][START_REF] Bindoff | Detection and Attribution of Climate Change: from Global to Regional[END_REF]. Consequently, the rigorous attribution of changes in local environmental conditions to changes in climate, and specifically the detection of climate change impacts in human systems and sectors interlinked with them, is gaining importance and public attention. Recent assessments of historical responses to climate change have drawn upon large amounts of direct observational evidence, applying formalized procedures for the detection and attribution of observed impacts [START_REF] Rosenzweig | Detection and attribution of anthropogenic climate change impacts[END_REF][START_REF] Cramer | Detection and attribution of observed impacts[END_REF].

While impacts of recent climate change are now documented for all continents and across the oceans, geographical imbalances and gaps in the documentation of impacts for human and managed systems remain. Based on scientific knowledge about the sensitivity of many human and managed systems to weather and climate variability, it is plausible to expect that recent climate change will have had a role in locally observed changes.

However, confident detection of local effects in historical data remains challenging due to naturally occurring variability in both climate and potentially impacted systems, and the influence of other important drivers of change, such as land use, pollution, economic development and autonomous or planned adaptation [START_REF] Nicholls | Coastline degradation as an indicator of global change[END_REF][START_REF] Bouwer | Have disaster losses increased due to anthropogenic climate change?[END_REF][START_REF] Hockey | Interrogating recent range changes in South African birds: confounding signals from land use and climate change present a challenge for attribution[END_REF]. Often, the specification of a numerical model representing the entire climate-impact system may not be feasible. In those cases, the careful examination of the individual steps of the causal chain linking climate to impacts can still provide insight into the role of recent climate change for the system in question. The goal of this paper is to provide guidance for such an approach to the detection and attribution of impacts of observed changes in climate.

Detection and attribution refer to the identification of responses to one or several drivers in historical observations, and a range of corresponding methods exists across research disciplines [START_REF] Stone | The challenge to detect and attribute effects of climate change on human and natural systems[END_REF]. In the context of climate change research, detection and attribution methodologies have been developed mostly in the field of physical climate science, where a substantial literature presents various model based statistical approaches to the question how effects of anthropogenic forcing can be identified in historical climate data (see [START_REF] Barnett | Detection and Attribution of Recent Climate Change : A Status Report[END_REF][START_REF] Hegerl | Understanding and attributing climate change[END_REF][START_REF] Bindoff | Detection and Attribution of Climate Change: from Global to Regional[END_REF].

In contrast, efforts to develop overarching methods for the detection and attribution of observed impacts to climate change are limited [START_REF] Stone | The detection and attribution of human influence on climate[END_REF][START_REF] Hegerl | Good practice guidance paper on detection and attribution related to anthropogenic climate change[END_REF][START_REF] Stone | The challenge to detect and attribute effects of climate change on human and natural systems[END_REF]. Studies explicitly attributing individual observed impacts of climate change to anthropogenic forcing of the climate system are rare, and usually based on a combination of process-or statistical models and climate models [START_REF] Gillett | Detecting the effect of climate change on Canadian forest fires[END_REF][START_REF] Barnett | Human-induced changes in the hydrology of the western United States[END_REF][START_REF] Christidis | Causes for the recent changes in cold-and heat-related mortality in England and Wales[END_REF][START_REF] Marzeion | Attribution of global glacier mass loss to anthropogenic and natural causes[END_REF]. In addition, methods have been developed to evaluate the role of anthropogenic forcing in large-scale patterns of multiple local impacts, mainly in ecology. These include the identification of so-called fingerprints of anthropogenic climate change in large sets of biological data [START_REF] Parmesan | A globally coherent fingerprint of climate change impacts across natural systems[END_REF][START_REF] Root | Fingerprints of global warming on wild animals and plants[END_REF][START_REF] Poloczanska | Global imprint of climate change on marine life[END_REF], joint attribution [START_REF] Root | Human-modified temperatures induce species changes: Joint attribution[END_REF], and joint attribution combined with spatial pattern congruence testing [START_REF] Rosenzweig | Assessment of observed changes and responses in natural and managed systems[END_REF]2008). Generally, these approaches aim at the identification of a generic impact of anthropogenic climate change which would emerge from analyzing a large number of cases in parallel, given that it is often not possible to confidently attribute changes in individual local records to anthropogenic forcing for technical reasons [START_REF] Rosenzweig | Detection and attribution of anthropogenic climate change impacts[END_REF][START_REF] Parmesan | Beyond climate change attribution in conservation and ecological research[END_REF].

The vast majority of impact studies are concerned with the identification of effects of regional changes in one or several climate variables in the context of multiple interacting drivers of change [START_REF] Cramer | Detection and attribution of observed impacts[END_REF]. Methods for detecting and explaining change are a key part of many disciplines studying natural, human and managed systems, and can be applied in the context of attribution to climate change. For example, reliable processbased models have been developed and applied in climate attribution analysis for some species and crops [START_REF] Battisti | Expansion of geographic range in the pine precessionary moth caused by increased winter temperatures[END_REF]e.g., Brisson et al 2010;[START_REF] Gregory | Attribution of climate change: a methodology to estimate the potential contribution to increases in potato yield in Scotland since 1960[END_REF].

Statistical models are increasingly being used to assess large scale effects of recent climate change (e.g., Lobell et al 2011b;[START_REF] Cheung | Signature of ocean warming in global fisheries catch[END_REF]. However, explicit numerical modeling of the climate -impact system is not always feasible (see also section 2).

Instead, conclusions about cause and effect are often inferred from a combination of multiple lines of evidence, such as process understanding, local knowledge, field and model experiments, observations from similar systems in other locations, or statistical analysis of observational data (see section 3).

Below, we will focus on impact detection and attribution in a multi-step analysis, based on a structured examination of multiple lines of evidence. In doing so, we follow the approach proposed by [START_REF] Stone | The challenge to detect and attribute effects of climate change on human and natural systems[END_REF], and applied in [START_REF] Cramer | Detection and attribution of observed impacts[END_REF] and elsewhere in the WGII contribution to the fifth assessment report (IPCC 2014a;[START_REF] Ipcc | Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects[END_REF]. This approach is inspired by the framework laid out by the IPCC good practice guidelines for detection and attribution related to anthropogenic climate change [START_REF] Hegerl | Good practice guidance paper on detection and attribution related to anthropogenic climate change[END_REF], but introduces the important modification that impact detection "addresses the question of whether a system is changing beyond a specified baseline that characterizes its behavior in the absence of climate change" (see also IPCC 2014c).

Detection of change in the climate system is concerned with the identification of a signal or trend beyond the short term variability caused by internal processes. However, the underlying assumption of a stable natural baseline state, with stochastic-like variability superimposed may not be valid or practical in the case of some impact systems, particularly those involving humans. Many impact systems are undergoing constant change due to internal dynamics as well as external drivers which often interact and change over time. The observation of a trend in the overall behavior of such a system, or a lack thereof, may not, on its own, be informative for assessing whether a response to climate change or any other driver has been detected (see also section 2). The main concern of impact detection is to identify the effect of climate change against that of other drivers of change. Therefore, the detection of a climate change impact must involve the explicit testing for confounding factors. In that sense, impact detection can't be entirely separated from attribution (see [START_REF] Stone | The challenge to detect and attribute effects of climate change on human and natural systems[END_REF].

In this paper we discuss the major steps involved in a complete evaluation of the causal chain from recent changes in climate to locally observed impacts. Following this introduction, we outline the required steps for a comprehensive impact detection and attribution analysis in section 2. We focus on distinguishing the effects of climate change from those of non-climate drivers, rather than evaluating the anthropogenic contribution to the observed change in climate. In section 3, we apply the resulting procedure in an analysis of several examples from human and managed systems, based on available literature. Those cases illustrate some of the major challenges involved, including the treatment of systems undergoing change from multiple drivers, and the integration of different types of evidence. We further discuss those challenges, and the limits and values of the detection and attribution of climate change impacts in section 4, and provide brief conclusions in section 5.

The five steps of an impact detection and attribution analysis

The logic of quantitative detection and attribution analysis -if not the methods -can also be applied to qualitative studies, and those that combine various sources of evidence.

That logical flow follows from a classical hypothesis test. Briefly, to test whether climate change has had an effect on a system, a suitable regression or other model reflecting the knowledge of the system is specified. This model includes a possible effect due to climate change as well as other potentially influential factors. The statistical test is then based on comparing the goodness of fit of the model with climate change to that of the model without climate change. In both cases, the model is fitted by optimizing a measure of the goodness of fit. If the correctly specified model that includes the effect of a changing climate provides a significantly superior fit than the model that does not, we conclude that the data are not consistent with the null hypothesis that climate change has not had an effect: in other words, we have detected a climate change impact. If we are also interested in the magnitude of the contributions of the various drivers, the fitted model provides a way of assessing these (e.g., based on the regression parameters).

The focus on impacts of recent climate change mostly restricts attention to cases in which the design involves a trend in climate (which may, in turn, be consistent with the effect of anthropogenic forcing). The identification of a trend over time in relevant climate variables is therefore part of the analysis. It is important to note that in order to avoid bias, the hypothesis taken as the starting point should not be formulated from the same data used to test it. Rather, it may be drawn from theory, e.g. model predictions, or independent data, such as observations in a similar system in a different location. It can also be helpful to differentiate between known external drivers of a system, which are explicitly accounted for in the specification of the baseline behavior, and confounding factors such as measurement errors, data bias, model uncertainty, and influences from other potential drivers that are not explicitly considered in the study set-up, but need to be controlled for [START_REF] Hegerl | Good practice guidance paper on detection and attribution related to anthropogenic climate change[END_REF].

Below, we outline the major steps involved in a comprehensive detection and attribution analysis in the context of climate change impacts (see figure 1). explicit examination of all drivers of change in the system.

Hypothesis

A common source of hypothesis is a prediction of an effect of expected anthropogenic climate change based on system understanding. For example, if an impact of future anthropogenic climate change has been predicted in an earlier analysis, one could test whether that effect is now detectable in accumulated observations. Another source might be the detection of impacts in similar systems in other locations, or observations from the recent past, or from paleo records. Naturally, studies will also be motivated by observations of change in the climate-sensitive system; while it is unrealistic to ignore that motivation, efforts need to then be made to minimise the effect of the resulting selection bias or to evaluate its importance [START_REF] Menzel | European phenological response to climate change matches the warming pattern[END_REF]. A central part of this first step is the identification of metrics that characterise the expected response of the system to climate change.

Climate trend

In order to detect an impact of observed climate change on a system, the climate must actually have changed and also have been observed to have changed for the relevant location and period. This condition distinguishes an impact study from a pure sensitivity analysis. Climate change is defined by the Intergovernmental Panel on Climate Change (IPCC) as "a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer" (IPCC 2014c). In that sense, we consider a change in climate any long-term (e.g. 20 years and more) trend in a climate variable that is substantial in relation to short time scale variability, regardless of the cause of that trend.

A local climate trend is not necessarily caused by anthropogenic climate change. While it is plausible to assume that a local temperature trend that is consistent with the temperature trend in the larger area, which in turn has been attributed to global climate change, may also be caused by anthropogenic forcing, this must not be taken as proven. In general, individual and local climate records show higher variability than aggregated or global measures [START_REF] Bindoff | Detection and Attribution of Climate Change: from Global to Regional[END_REF]. Local climate is influenced by topography and turbulence, but also by other local factors such as water management or land use change.

As a result, local trends may run contrary to or enhance the global warming signal, or may not emerge at all. Changes in atmospheric circulation patterns, or multidecadal natural variability could also generate local trends that differ from global ones. The question of how one might determine whether an observed trend is anthropogenically forced is beyond the scope of this paper, but has been considered elsewhere [START_REF] Stott | Detection and attribution of climate change: a regional perspective[END_REF].

Systems may be sensitive to aspects of the climate other than the average, such as temperature exceeding 30°C during a certain period in plant development (e.g., Lobell et al 2011a). The chosen metric needs to reflect this aspect of the expected climate change.

Baseline

For some situations, the identification of a deviation from baseline behavior is relatively straightforward: the metric shows a trend consistent in direction and magnitude with what one would expect under climate change, and that trend is also inconsistent with what could be plausibly expected as the effect of one or a combination of other known drivers in a stationary climate, either because those drivers are of insufficient magnitude or they mutually cancel. However in most human and managed systems, we expect the observed overall response to be consistent with the combined effect of climate change and other drivers, but not with that of climate change alone. The failure to account for all drivers in the baseline may lead to erroneous conclusions about the influence of climate change on a system, as illustrated in Figure 2 So, in order to evaluate whether a climate change effect has been observed the baseline behavior of the system in the absence of climate change has to be specified [START_REF] Stone | The challenge to detect and attribute effects of climate change on human and natural systems[END_REF]. For some systems, that behavior may be non-stationary even in the absence of all drivers.

As a world without climate change cannot be observed directly, the baseline must be constructed using statistical techniques, observations of analogous systems, and/or system understanding expressed in the form of numerical or conceptual models.

Specifying a reliable model is often hampered by lack of data, incomplete knowledge on processes and mechanisms involved in systems undergoing change from multiple stressors, limited understanding of causality within complex networks of social systems, and how climate drivers and their perception influence those. In addition, research in qualitative social sciences focuses on descriptive, non-numerical understanding of how systems behave and interact and is often site-or case specific. For a comprehensive assessment of impacts on humans systems, expectations of baseline behaviour may have to be developed and adopted based on qualitative methods.

Impact Detection

For natural, human and managed systems, impact detection addresses the question whether a system is changing beyond a specified baseline that characterizes behavior in the absence of climate change (IPCC 2014c). In other words, impact detection requires the demonstration that an observed long-term change in a system cannot be fully accounted for by non-climate drivers. So, in order to detect an impact, it is not sufficient for climate change to be a plausible explanation, but it must also be shown that there is no (equally valid) alternative mechanism for the observed change (see also Figure 2). In well-observed systems, a common way to investigate the effect of a driver on an outcome in the presence of other drivers is multiple regression analysis. To detect a climate change impact, the null hypothesis that climate change has not affected the outcome has to be tested, controlling for the impact of other drivers and confounding factors, including autonomous and planned adaptation. If the null hypothesis is rejected using a correctly specified model, a climate change impact has been detected. Following this statistical approach, a detection statement is always binary: an impact has (or has not) been detected at a chosen level of significance.

However, in many systems of interest, quantitative models representing causal relationships will be either impossible to construct or incompatible with the type of data available. In these situations not amenable to statistical testing, a detailed discussion of the role of other drivers and potential confounding factors such as measurement errors or data bias may provide a thorough evaluation of the various hypotheses. Though not directly comparable to the results of a rigorous analysis of long-term data, a clear and comprehensive qualitative analysis represents a valid form of evidence that should not be dismissed.

Attribution

Attribution needs to examine all drivers of change that influence the system, and evaluate their relative contribution to the detected change. Impact detection implies that climate change has had at least a minor role in the observed outcome. Assessing the magnitude of the contribution of climate change to an impact is a separate, but equally important matter in a detection and attribution exercise.

An attribution statement needs a qualifier describing the relative importance of climate change to an observed impact. This involves either simply an ordinal statement (e.g. climate is the main influence responsible for a change) or a cardinal statement, which of course requires estimation of the exact relative magnitude of the contribution of climate change in relation to other drivers (see also [START_REF] Stone | The challenge to detect and attribute effects of climate change on human and natural systems[END_REF]. The descriptor relates to the size of the response to the climate driver relative to that to other drivers of change in the system, regardless of the direction of that change. While it may be relevant in other ways, the absolute size of the impact is not vital to the attribution statement.

A key challenge for all attribution exercises consists of accounting for non-additive effects of multiple drivers interacting on several temporal and spatial scales (see [START_REF] Parmesan | Beyond climate change attribution in conservation and ecological research[END_REF][START_REF] Oliver | Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities[END_REF]. While of particular concern for human and managed systems, such effects have also been shown in analyses of large datasets of biological changes [START_REF] Crain | Interactive and cumulative effects of multiple human stressors in marine systems[END_REF][START_REF] Darling | Quantifying the evidence for ecological synergies[END_REF].

Impact attribution assessments -examples from human and managed systems

In this section we provide examples which illustrate the challenges of thorough assessments of climate change impacts. The examples were chosen to cover a range of different conditions in terms of quality and type of evidence, and clarity of climate trends and observations. In line with the focus of this paper, we selected examples from human and managed systems, and from world regions that are currently underrepresented in the literature. The assessments are based on available literature at the time of writing, and provide a summary of the more complex considerations detailed in the underlying literature. As detection is a necessary condition for attribution, the attribution step is omitted in cases where a climate impact has not been detected.

Fisheries productivity on Lake Victoria

Hypothesis

The inland fisheries of the Great Lakes are an important food source for the human population of Eastern and Southern Africa, with Lake Victoria having the largest freshwater lake fishery in the world. An expected outcome of anthropogenic climate change is warming of the Great Lakes, with faster warming at the surface increasing stratification [START_REF] Lehman | Lake Victoria plankton ecology: Mixing depth and climatedriven control of lake condition[END_REF][START_REF] Verburg | The physics of the warming of Lake Tanganyika by climate change[END_REF]. Along with direct effects of the warming, the increased stratification is expected to limit nutrient recycling, consequently leading to increased abundance of algae and hypoxic conditions detrimental for the large fish which support the regional fishery industry [START_REF] Lehman | Lake Victoria plankton ecology: Mixing depth and climatedriven control of lake condition[END_REF]. Hence, the fishery catch per unit effort would be expected to have decreased on Lake Victoria.

Climate trends

Baseline

The Great Lakes region has experienced a number of major environmental changes over the past few decades. The Nile Perch, a large predatory fish, and the Nile Tilapia were introduced in 1954-1964, and now comprise the bulk of the catch on Lake Victoria [START_REF] Hecky | Multiple stressors cause rapid ecosystem change in Lake Victoria[END_REF]. A fundamental and rapid change in the fish community occurred in the early 1980s, and fishing effort has increased in recent decades [START_REF] Kolding | Are the Lake Victoria fisheries threatened by exploitation or eutrophication? Towards an ecosystem based approach to management[END_REF]. The invasive spread of the water hyacinth had disrupted lake access and transport on Lake Victoria in the 1990s until the more recent introduction of the weevil [START_REF] Hecky | Multiple stressors cause rapid ecosystem change in Lake Victoria[END_REF].

Much of the land surrounding Lake Victoria has been converted to agriculture, leading to increased runoff of nutrients [START_REF] Stager | Diatom evidence for the timing and causes of eutrophication in Lake Victoria, East Africa[END_REF][START_REF] Hecky | Multiple stressors cause rapid ecosystem change in Lake Victoria[END_REF]. Like warming, this would be expected to contribute to increased eutrophication, increased thermal stratification (by increasing algal abundance), and a shift in species composition and decreased species diversity.

Impact Detection

The dramatic rise in both absolute fish catch and catch per unit effort observed on Lake Victoria during the 1980s coincided with the large-scale establishment of the introduced Nile Perch.

Altered predation dynamics due to a change in the light regime caused by the increased abundance of algae facilitated the success of the Nile perch [START_REF] Kolding | Are the Lake Victoria fisheries threatened by exploitation or eutrophication? Towards an ecosystem based approach to management[END_REF][START_REF] Hecky | Multiple stressors cause rapid ecosystem change in Lake Victoria[END_REF].

Another marked rise in catch of a native species in the 2000s is temporally linked to improved lake access after the establishment of efficient control of the water hyacinth [START_REF] Hecky | Multiple stressors cause rapid ecosystem change in Lake Victoria[END_REF]. That rise is not reflected in other species and the relation to catch per unit effort is not documented; the Nile perch catch has been stable since the 1980s despite increased effort.

These catch changes are linked to other changes in the ecology of the lake which indicate the possible ultimate causes. Increases in primary productivity and algal abundance were documented in the decades before 2000, though both may have decreased since [START_REF] Stager | Diatom evidence for the timing and causes of eutrophication in Lake Victoria, East Africa[END_REF][START_REF] Hecky | Multiple stressors cause rapid ecosystem change in Lake Victoria[END_REF][START_REF] Sitoki | The Environment of Lake Victoria (East Africa): Current Status and Historical Changes[END_REF][START_REF] Loiselle | Decadal trends and common dynamics of the bio-optical and thermal characteristics of the African Great Lakes[END_REF]. Increases are consistent with warming, increased nutrient supply from agricultural development, and decreased abundance of planktivorous fish species caused by the introduced predators (Hecky et al 2010); the possible recent decrease in algal biomass could be indicative of a decreased catch per unit effort, as decreases in abundance of large predators allows populations of smaller fish species to recover.

While the expected effects of species introductions can be distinguished from the expected response to warming, the responses to increased agricultural runoff and increasing fishing effort are harder to differentiate. Thus, while current evidence may suggest a response to warming beyond the responses to other drivers, considerable uncertainties remain.

Attribution

While anthropogenic climate change may become the dominant driver of the biology and productivity of the Great Lakes in future decades, current evidence is unable to distinguish whether the influence of warming has already been comparable to or much smaller than that of other drivers of environmental change in the region.

Crop production in Southeast South America

Hypothesis

In Southeast South America, significant increases in summer crop productivity, and the expansion of agricultural areas have been observed over the last decades. Given that agricultural activity in the region is often constrained by the amount of rainfall, wetter conditions are expected to have contributed to these trends.

Climate trends

Southeast South America refers to the South American area south of 20°S and east of the Andes, excluding Patagonia, and includes the important agricultural production centre of the Argentinean Pampas, South-Eastern Brazil, Paraguay and Uruguay. Past precipitation and temperature trends are well documented over the area [START_REF] Giorgi | Variability and trends of sub-continental scale surface climate in the twentieth century. Part I: observations[END_REF][START_REF] Barros | El cambio climático en Argentina (Chapter 3). Agro y Ambient. Una agenda Compart. para el Desarro. sustentable[END_REF][START_REF] Magrin | Central and South America[END_REF]. The region has warmed by roughly 1°C since the mid 1970s, and the frequency of warm nights has increased. Over the same period, there has been a reduction in the number of overall dry days [START_REF] Rivera | Inter-annual and inter-decadal variability of dry days in Argentina[END_REF] and dry months in the warm season [START_REF] Vargas | Dry spells in the River Plata Basin: an approximation of the diagnosis of droughts using daily data[END_REF], and increases in precipitation, leading to a westward shift of the 600 and 800 mm isohyetal lines [START_REF] Barros | El cambio climático en Argentina (Chapter 3). Agro y Ambient. Una agenda Compart. para el Desarro. sustentable[END_REF][START_REF] Doyle | Trends in the distributions of aggregated monthly precipitation over the La Plata Basin[END_REF].

Baseline

Impact Detection

Agricultural activity in the region is predominantly rain fed. The wetter and partly warmer conditions observed since the 1970s are consistent with varying, but substantial increases in yields observed in particular in those areas of Argentina, Uruguay and Southern Brazil where precipitation was the limiting factor in the first half of the century [START_REF] Magrin | Changes in Climate and Crop Production During the 20th Century in Argentina[END_REF][START_REF] Magrin | Past and Future Changes in Climate and their Impacts on Annual Crops Yield in South East South America[END_REF]. In the semi-arid and sub-humid areas at the western and northern fringe of the Argentinean Pampas, increases in precipitation enabled a shift of the "agricultural frontier" of about 100 km to the West into formally semi-arid land [START_REF] Barros | El cambio climático en Argentina (Chapter 3). Agro y Ambient. Una agenda Compart. para el Desarro. sustentable[END_REF].

In order to examine the role of different drivers in the expansion of agricultural land, [START_REF] Zak | What drives accelerated land cover change in central Argentina? Synergistic consequences of climatic, socioeconomic, and technological factors[END_REF] and [START_REF] Hoyos | Deforestation and precipitation patterns in the arid Chaco forests of central Argentina[END_REF] study the conversion of Chaco forest into crop-and rangelands in an area at the Northern fringes of the Argentinean Pampas. They show that conversion rates in the Western part of their study region, which did not experience increases in precipitation, are considerably lower than those in the Eastern part, where they document upward trends in precipitation. As both regions exhibit otherwise very similar conditions, they conclude that climate change is an important enabling factor of the observed agricultural expansion, synergistically with technological changes and socioeconomic drivers. The case is less clear for the La Plata basin, where no such natural comparative area has been identified and studied, and the pattern of land types converted does not allow for a clear distinction of the role of the climate trends [START_REF] Baldi | Land-Use and Land Cover Dynamics in South American Temperate Grasslands[END_REF] as opposed to other factors. [START_REF] Magrin | Changes in Climate and Crop Production During the 20th Century in Argentina[END_REF] use crop models to study the relative effects of observed changes in temperature and precipitation on yields in the Argentinean Pampas. They examine observed yields of four main crops (sunflower, wheat, maize and soy) in nine representative zones across the region. They conclude that climate change had nonnegligible favorable effects beyond that of technological changes. In a similar exercise for six zones that extended to locations in Uruguay and Brazil, [START_REF] Magrin | Past and Future Changes in Climate and their Impacts on Annual Crops Yield in South East South America[END_REF] found substantial positive climate change effects on yields in particular for summer crops. Effects were strongest in the originally drier regions.

Attribution

Recognizing what [START_REF] Zak | What drives accelerated land cover change in central Argentina? Synergistic consequences of climatic, socioeconomic, and technological factors[END_REF], call "synergistic consequences of climatic, socioeconomic, and technological factors", climate change is estimated to be a major driver of the observed increases in summer crop yields and of the expansion of agricultural land into the formally semi-arid regions of South Eastern South America, while the magnitude of its role for other areas and crops is less clear.

Agroforestry systems in the Sahel

Hypothesis

Drought and heat induced tree mortality is increasingly reported from many locations worldwide [START_REF] Allen | A global overview of drought and heatinduced tree mortality reveals emerging climate change risks for forests[END_REF]. The pronounced drought over the Western Sahel for much of the second half of the 20 th century would be expected to result in negative impacts on agroforestry systems.

Climate trends

Rainfall decreased markedly over the western Sahel in the few decades after 1950, resulting in extremely dry conditions during the 1970s and 1980s; there has been some recovery of the rains since 1990 but totals remain well below the mid-20th century values [START_REF] Greene | Drought return times in the Sahel: a question of attribution[END_REF][START_REF] Lebel | Recent trends in the Central and Western Sahel rainfall regime (1990-2007)[END_REF][START_REF] Biasutti | Forced Sahel rainfall trends in the CMIP5 archive[END_REF]. Like many regions of the world, the western Sahel has also warmed on the order of 1°C during that time [START_REF] Niang | Africa[END_REF], promoting drought conditions.

Baseline

With a growing population, there has been a large increase in agricultural area in the western Sahel at the expense of wooded vegetation [START_REF] Brink | Monitoring 25 years of land cover change dynamics in Africa: a sample based remote sensing approach[END_REF][START_REF] Ruelland | Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a Sahelian catchment[END_REF]. The growing population may also be harvesting a larger amount of firewood. The basic structure of the agroforestry system and its management by local farmers have been reported to be fairly stable over the period covered here [START_REF] Maranz | Tree mortality in the African Sahel indicates an anthropogenic ecosystem displaced by climate change[END_REF].

Impact Detection

Over the past half century there has been a decrease in tree density in the western Sahel noted through field survey as well as aerial and satellite imagery [START_REF] Vincke | Long term dynamics and structure of woody vegetation in the Ferlo (Senegal)[END_REF][START_REF] Ruelland | Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a Sahelian catchment[END_REF][START_REF] Gonzalez | Tree density and species decline in the African Sahel attributable to climate[END_REF], and by local populations [START_REF] Wezel | Woody vegetation change in Sahelian West Africa: Evidence from local knowledge[END_REF]. Because of their sensitivity to moisture deficits, trees would be expected to become less densely spaced during long-term soil-moisture drought. Tree mortality has been more pronounced for introduced or managed fruit-bearing trees, which may be less adapted to decadal-scale drought conditions which appear typical of the western Sahel than the native vegetation [START_REF] Wezel | Woody vegetation change in Sahelian West Africa: Evidence from local knowledge[END_REF][START_REF] Maranz | Tree mortality in the African Sahel indicates an anthropogenic ecosystem displaced by climate change[END_REF].

The patterns of tree cover changes remain correlated with the combined effects of the warming and drying trends after accounting for the effects of other factors [START_REF] Gonzalez | Tree density and species decline in the African Sahel attributable to climate[END_REF]. Moreover, the enhanced mortality among introduced species in relation to indigenous species is more consistent with the effect of climate change than with that of the other drivers listed above [START_REF] Wezel | Woody vegetation change in Sahelian West Africa: Evidence from local knowledge[END_REF][START_REF] Maranz | Tree mortality in the African Sahel indicates an anthropogenic ecosystem displaced by climate change[END_REF].

Attribution

The harvesting of firewood does not appear to have played a substantial role in the decrease in tree density [START_REF] Gonzalez | Tree density and species decline in the African Sahel attributable to climate[END_REF]. The shift from wooded to agricultural areas is substantial [START_REF] Brink | Monitoring 25 years of land cover change dynamics in Africa: a sample based remote sensing approach[END_REF][START_REF] Ruelland | Comparison of methods for LUCC monitoring over 50 years from aerial photographs and satellite images in a Sahelian catchment[END_REF], and the decreases in tree density are correlated with proximity to human presence [START_REF] Vincke | Long term dynamics and structure of woody vegetation in the Ferlo (Senegal)[END_REF]. However, both the warming and decreased rainfall trends appear to have played at least as large a role in the overall decrease in tree density [START_REF] Gonzalez | Tree density and species decline in the African Sahel attributable to climate[END_REF], though this has not been examined specifically for fruit-bearing trees.

Wildfire in Australia

Hypothesis

Many high-impact fires occurred over the last decade, amongst them the 2009 "Black Saturday" Bushfires, which were reported as one of the worst natural disasters in the history of Australia, with 173 lives lost, and around 2300 homes plus other structures destroyed [START_REF] Crompton | Influence of Location, Population, and Climate on Building Damage and Fatalities due to Australian Bushfire: 1925-2009[END_REF]. Bushfires occur naturally in Australia, and many of the influencing parameters are directly (temperature, precipitation and windiness) or indirectly (available fuel, land use and cover, fire history) susceptible to climate change [START_REF] Williams | Interactions between climate change, fire regimes and biodiversity in Australia -a preliminary assessment[END_REF], with fire risk expected to increase under climate change (Reisinger et al 2014, Box 25-6). Hence a possible increase in fire hazard due to recent climate change may have translated into increased damages from wildfire.

Climate trend

Increases in aggregate climate indices such as average temperature, maximum temperatures, and the length of hot-spells have been detected on continental scale, albeit with strong seasonal and regional variations [START_REF] Alexander | Assessing trends in observed and modelled climate extremes over Australia in relation to future projections[END_REF][START_REF] Trewin | Changes in the frequency of record temperatures in Australia, 1957-2009[END_REF]. Composite indices such as the McArthur Forest Fire Danger Index (FFDI) have been developed to capture the combined influence of relevant meteorological variables such as temperature, relative humidity, wind speed and direction and antecedent precipitation for the assessment of fire risk. A trend in the FFDI toward increasing danger has been observed since 1970 over large parts of Australia, especially in the South and South East, with a clear signature of annual and decadal climate modes such as the El Niño/Southern Oscillation and the positive phase of the Indian Ocean Dipole [START_REF] Mills | Assessing the impact of climate change on extreme fire weather in southeast Australia[END_REF][START_REF] Clarke | Changes in Australian fire weather between 1973 and 2010[END_REF].

Baseline

Damages from wildfire have increased over the course of the century, consistent with the observed climate trends, but also with the effects of an increased number of exposed assets (such as settlements built in or close to fire prone bush land), and increases in population. Better fire management and improved forecasting may counteract these trends, however their influence has not been quantified [START_REF] Crompton | Reply[END_REF][START_REF] Nicholls | Comments on "Influence of Location, Population, and Climate on Building Damage and Fatalities due to Australian Bushfire: 1925-2009[END_REF]).

Impact Detection

No detectable trend has been found in building damages or losses of life normalized against trends in population and number of dwellings over the last century or decades [START_REF] Crompton | Normalised Australian insured losses from meteorological hazards: 1967-2006[END_REF][START_REF] Mcaneney | 100-years of Australian bushfire property losses: is the risk significant and is it increasing?[END_REF][START_REF] Crompton | Influence of Location, Population, and Climate on Building Damage and Fatalities due to Australian Bushfire: 1925-2009[END_REF]. The normalization process does not account for all factors that influence vulnerability, e.g. human behavior such as precautionary measures of individual home owners, or collective measures of changing spatial planning in order to reduce risk. Several of these factors have been explored in the literature, often with a focus on specific regions or events.

Examples include the role of the "prepare, leave early or stay and defend" policy in New South Wales, or the reduction of community vulnerability through improved risk management [START_REF] Haynes | Australian bushfire fatalities 1900-2008: exploring trends in relation to the "Prepare, stay and defend or leave early" policy[END_REF][START_REF] O'neill | Responding to bushfire risk: the need for transformative adaptation[END_REF][START_REF] Whittaker | Community safety during the 2009 Australian "Black Saturday" bushfires: an analysis of household preparedness and response[END_REF].

Damage from extreme fires is mainly controlled by exposure, as structures built in close proximity to or within bush land are virtually impossible to defend during extreme fire conditions [START_REF] Chen | Quantifying bushfire penetration into urban areas in Australia[END_REF]). In the Greater Melbourne area, encroachment of suburban dwellings into bush land has led to an increase in the number of exposed dwellings [START_REF] Butt | Peri-urban growth, planning and bushfire in the Melbourne city-region[END_REF][START_REF] Buxton | Vulnerability to Bushfire Risk at Melbourne's Urban Fringe: The Failure of Regulatory Land Use Planning[END_REF]. [START_REF] Crompton | Reply[END_REF] in a reply to [START_REF] Nicholls | Comments on "Influence of Location, Population, and Climate on Building Damage and Fatalities due to Australian Bushfire: 1925-2009[END_REF] discusses and dismisses several factors (incl. improved fire management, forecasting, individual home owners defence measures) that could be masking a trend consistent with a climate signal in the overall loss statistics.

They conclude that an influence of anthropogenic climate change "is not ruled out by our analysis, but, if it does exist, it is clearly dwarfed by the magnitude of the societal change and the large year-to-year variation in impacts". In summary, an impact of climate change on observed damages from bushfires in Australia has not been detected.

Urban coastal erosion and flooding in West Africa

Hypothesis

Anthropogenic warming of the climate system is expected to cause widespread rises in sea level. West Africa has a number of low-lying urban areas particularly exposed to sea level rise, with increases in coastal erosion and flooding expected [START_REF] Dossou | The vulnerability to climate change of Cotonou (Benin): the rise in sea level[END_REF][START_REF] Douglas | Unjust waters. Climate change, flooding and the urban poor in Africa[END_REF][START_REF] Adelekan | Vulnerability of poor urban coastal communities to flooding in Lagos, Nigeria[END_REF].

Climate trends

There has been a lack of sustained tide gauge monitoring in West Africa over the past few decades [START_REF] Church | Sea-level rise from the late 19th century to the early 21st century[END_REF][START_REF] Fashae | Impact of climate change on sea level rise in Lagos, Nigeria[END_REF]. While satellite monitoring suggests rising total sea levels in the Gulf of Guinea, actual relative sea level changes at specific locations along the coast will depend on additional factors, such as human induced subsidence, or natural variations in ocean currents [START_REF] Stammer | Causes for contemporary regional sea level changes[END_REF].

Baseline

The construction of ports has diverted coastal sediment transport around Cotonou, Benin, while marine sand quarries have already reduced the supply of sand to the city [START_REF] Dossou | The vulnerability to climate change of Cotonou (Benin): the rise in sea level[END_REF]. Other plausible drivers of increased erosion have also been posited, and include subsidence due to oil exploration for Lagos, Nigeria, and sediment trapping in reservoirs for most of the West African Coast [START_REF] Ericson | Effective sea-level rise and deltas: Causes of change and human dimension implications[END_REF][START_REF] Douglas | Unjust waters. Climate change, flooding and the urban poor in Africa[END_REF].

Impact Detection

Based on photographic evidence and comparison with satellite imagery, coastlines in some urban areas in the Gulf of Guinea seem to have been retreating over the past few decades [START_REF] Dossou | The vulnerability to climate change of Cotonou (Benin): the rise in sea level[END_REF][START_REF] Fashae | Impact of climate change on sea level rise in Lagos, Nigeria[END_REF]. [START_REF] Ericson | Effective sea-level rise and deltas: Causes of change and human dimension implications[END_REF] found that sediment trapping is the dominant cause of contemporary effective sea level rise for the Niger delta, with contributions from land subsidence due to oil exploration. Also, the construction of reservoirs on the Volta has led to a sharp decrease in sediments moving across the West African coast, passing cities such as Cotonou and Lagos. Given the lack of long-term monitoring of local sea level, coastal erosion, and the various possible drivers of coastal erosion, it is currently not possible to examine whether an anthropogenic climate change signal has been detected.

Discussion

This paper was motivated by an apparent inconsistency between the accepted view that climate change is already impacting a number of vulnerable human and managed systems, and the relative lack of documented evidence of observed impacts of climate change for those vulnerable systems. There is a large literature concerning the sensitivity of such systems to climate, and to future climate change, but there is comparatively little documentation of observed impacts of climate change [START_REF] Cramer | Detection and attribution of observed impacts[END_REF].

A major factor explaining this gap consists in the lack of calibrated long-term monitoring across sensitive systems and regions, which would provide the observational basis that underpins detection and attribution analysis. Under the United Nations Framework Convention on Climate Change (UNFCCC), nations are obligated to monitor their respective contributions to anthropogenic forcing through standardized national greenhouse gas inventories, but no such inventory scheme or standard exists for impacts of climate change.

Detection and attribution studies are virtually impossible for impacts in some regions due to the absence of an observational basis. For example, to determine how sea level rise might be affecting urban coastal areas in West Africa (see 3.5) the current ambiguity over whether relative sea level has actually risen along the urban coastlines is a hindrance.

Innovate methods exist to fill in such gaps, for instance through analysis of archival footage or consulting local and indigenous knowledge, and can provide valuable tools in some cases [START_REF] Rosenzweig | Detection and attribution of anthropogenic climate change impacts[END_REF].

The five examples discussed in Section 3 draw on disparate studies across disciplines for a comprehensive analysis of the role of observed climate change in the changes that various systems have experienced during recent decades. However, they also illustrate some of the challenges involved in the detection and attribution of impacts of climate change. For example, the ecosystem of Lake Victoria faced the introduction of large predatory species, and subsequently a regime shift occurred. Predicting the ecosystem response to such major unprecedented change would be challenging even if the underlying ecosystem dynamics were well understood. While it is plausible to assume that increased precipitation will have contributed to increases in agricultural productivity in Southeast South America, it is very difficult to disentangle the influence of the climate trend from that of technological development and socioeconomic conditions for parts of the region.

Similarly, complex factors related to exposure preclude the detection of a climate related signal in damages from bushfire in Australia. In the case of West Africa, the monitoring of all drivers contributing to coastal erosion and flooding, as well as the documentation of the actual changes remains insufficient.

In some cases though, the examples also point to ways forward. Local knowledge has been valuable in assessing the role of rainfall decreases in the thinning of western A fundamental issue we have only touched upon briefly concerns the end point of attribution studies. For large parts of the community studying climate change and its impacts, as well as many stakeholders, "attribution" is used as a synonym for "attribution to anthropogenic forcing". As one of the key motivations for detection and attribution research is to inform the UNFCCC, this end-point has often been considered the main goal [START_REF] Zwiers | Climate change: attributing cause and effect[END_REF] It is also essential to be clear about the difference between the estimation of sensitivity to weather, and the observation of an impact of climate change. This applies especially with regard to the perception of manifestations of climate variability, such as severe drought or storms. For many human and managed systems, impacts of extreme weather or climate shocks are the rare occasion where a clear climate related signal can be detected. However, while the impact of a particular extreme can be an important indicator of sensitivity to climate it does not by itself constitute a climate change impact [START_REF] Allen | Scientific challenges in the attribution of harm to human influence on climate[END_REF][START_REF] Stott | Attribution of weather and climate-related extreme events[END_REF][START_REF] Hulme | Attributing weather extremes to "climate change": A review[END_REF].

Conclusions

Detection and attribution of climate change impacts provides the most complete and consistent analysis possible of the cause-effect chain, combining all possible sources of information in a coherent evaluation. While setting a high bar, the distinction between impacts that have been observed in data and linked to climate change with confidence, and those that are predicted to occur but cannot be detected and attributed by science (as yet) has proven a useful distinction. However, caution must be applied both ways when interpreting results. The lack of documented impacts attributable to climate change should not be misread as evidence for the absence of such impacts [START_REF] Hansen | The global distribution of climate change impacts[END_REF]. On the other hand, it is true that for many historic impacts on human systems, non-climate related drivers are equally or more important than recent climate change, and must be accounted for.

There may be cases where data are insufficient to detect an impact, while given climate trends and known sensitivity strongly suggests that climate change will have affected the system. While we support the use of different types of evidence, and the application of interdisciplinary methods to establish causality, the fact remains that observational evidence demonstrating a long term effect is needed for impact attribution. Or to put it another way -you can't attribute something you have not detected.

Detection and attribution analysis can be a powerful tool in understanding how and why our world is changing, albeit its cost is the need to possess the necessary observations and understanding, which remains poor in many areas. To identify those gaps, to determine whether they can be filled, and if so to prioritize research to address them, will lead to a more comprehensive and inclusive understanding of the impacts of climate change.

1 )

 1 Hypothesis formulation: Identification of a potential climate change impact; 2) Observation of a climate trend in the relevant spatial and temporal domain; 3) Identification of the baseline behavior of the climate-sensitive system in the absence of climate change; 4) Demonstration that the observed change is consistent with the expected response to the climate trend, and inconsistent with all plausible responses to non-climate drivers alone (impact detection); 5) Assessment of the magnitude of the climate change contribution to overall change, relative to contributions from other drivers (attribution).

Figure 1 :

 1 Figure 1: Schematic of the five steps of detection and attribution of observed climate change impacts. Note that in practice the specification of the baseline behavior and the detection and attribution steps may be performed in parallel, given they all require

Figure 2

 2 Figure 2 Stylized examples of the time series of some measure representing a climate sensitive system which is responding in time to multiple drivers, one of them climate change (the corresponding time series of the climate variable for both cases is shown in panel c). The black line depicts the overall behavior of the system, while the dark, vertically striped area represents the combined effect of non-climate drivers under stationary climatic conditions, and the light area represents the additional effect due to recent climate change. In panel a, the baseline condition (dark area) shows a clear change midway through the record (e.g. due to a policy measure) but this is compensated by the influence of climate change. However the resulting overall measure does not show a deviation from its historical pre-climate change trend, thus masking the existing climate change effect (potential type I error). In panel b, the observed behavior shows a change that is consistent in direction with a predicted climate change impact;

  Atmospheric warming has occurred in the Great Lakes region[START_REF] Verburg | The physics of the warming of Lake Tanganyika by climate change[END_REF][START_REF] Ndebele-Murisa | The implications of a changing climate on the Kapenta fish stocks of Lake Kariba[END_REF], and lake surface waters appear to have warmed, too[START_REF] Sitoki | The Environment of Lake Victoria (East Africa): Current Status and Historical Changes[END_REF][START_REF] Loiselle | Decadal trends and common dynamics of the bio-optical and thermal characteristics of the African Great Lakes[END_REF]. Analyses of sediment cores suggest that the surface waters of other large Great Lakes have warmed to temperatures unprecedented in at least the last 500 years[START_REF] Tierney | Late-twentieth-century warming in Lake Tanganyika unprecedented since AD 500[END_REF][START_REF] Powers | Organic geochemical records of environmental variability in Lake Malawi during the last 700 years, Part I: the TEX86 temperature record[END_REF]. A strengthening of the thermocline (and hence increase in stratification) has been observed before 2000, but appears to have weakened since, possibly due to variability in local wind regimes[START_REF] Stager | Diatom evidence for the timing and causes of eutrophication in Lake Victoria, East Africa[END_REF][START_REF] Sitoki | The Environment of Lake Victoria (East Africa): Current Status and Historical Changes[END_REF].

  Across the region, socioeconomic factors such as policy incentives, market conditions, population growth and agronomic developments have positively affected cultivated area and agricultural productivity. The introduction of short-cycle soy varieties, no-till cropping systems, and a general intensification of agriculture following macro-economic development contributed to the expansion of agricultural activities into formally marginal land[START_REF] Baldi | Land-Use and Land Cover Dynamics in South American Temperate Grasslands[END_REF][START_REF] Asseng | Has climate change opened new opportunities for wheat cropping in Argentina?[END_REF][START_REF] Hoyos | Deforestation and precipitation patterns in the arid Chaco forests of central Argentina[END_REF].

  photography provided important insights about the baseline in several of the case studies.The roles of some potential drivers for Australian bushfire damage were elucidated by comparative analyses across fire events, regions, and other dimensions.

  . It is important in the context of potential litigation for adverse impacts of climate change (Grossman 2003), and may become relevant for the recently established "Warsaw International Mechanism for Loss and Damage" under the UNFCCC (James et al 2014). To assess the relative role of anthropogenic versus natural forcing in observations provides a means to estimate whether recent and current impacts might be expected to persist, and to calibrate predictions of future impacts made with other methods. However, as we have shown, it is often very difficult to detect climate change effects in observed records, and to disentangle the impacts of climate change from those of other drivers of change. Clearly, attribution of observed impacts to anthropogenic climate change adds another layer of complexity to an already challenging exercise. Impact attribution research improves the understanding of vulnerabilities to long-term climatic trends, including interactions and non-additive effects of multiple drivers, for which identification of the underlying driver of the observed climate change may not be relevant (Parmesan et al 2011; Parmesan et al 2013). Impact detection and attribution provides important insights from "real world" conditions as compared to experimental conditions or idealized models. Such knowledge is essential to identify the most adequate adaptation strategies and resilient pathways. Given the increasing rate of climate change and possible threshold behaviour in impacted systems, as well as ongoing adaptation and general development, caution must be applied when inferring conclusions about future climate change impacts from observations.

Figure 2

 2 Figure 2 Stylized examples of the time series of some measure representing a climate sensitive system which is responding in time to multiple drivers, one of them climate change (the corresponding time series of the climate variable for both cases is shown in panel c). The black line depicts the overall behavior of the system, while the dark, vertically striped area represents the combined effect of non-climate drivers under stationary climatic conditions, and the light area represents the additional effect due to recent climate change. In panel a, the baseline condition (dark area) shows a clear change midway through the record (e.g. due to a policy measure) but this is compensated by the influence of climate change. However the resulting overall measure does not show a deviation from its historical pre-climate change trend, thus masking the existing climate change effect (potential type I error). In panel b, the observed behavior shows a change that is consistent in direction with a predicted climate change impact; however, the majority of that change happens due to a change in the baseline arising from other factors. This situation could lead to erroneous detection (potential type II error) or an overstatement of the climate effect.

Acknowledgments

We thank all members of the IPCC WG2 cross chapter working group on detection and attribution for valuable input and inspiring discussions during the fifth assessment report cycle. Multifaceted support during preparation of the manuscript by Andy R. Solow is acknowledged with deep gratitude. GH was supported by the German Ministry for Education and Research. DS was supported by the United States Department of Energy, Office of Science, Office of Biological and Environmental Research's Regional and Global Climate Modeling Program under contract number DE-AC02-05CH11231.