Could detection and attribution of climate change trends be spurious regression?

Since the 1970s, scientists have developed statistical methods intended to formalize detection of changes in global climate and to attribute such changes to relevant causal factors, natural and anthropogenic. Detection and attribution (D&A) of climate change trends is commonly performed using a variant of Hasselmann's "optimal fingerprinting" method, which involves a linear regression of historical climate observations on corresponding output from numerical climate models. However, it has long been known in the field of time series analysis that regressions of "non-stationary" or "trending" variables are, in general, statistically inconsistent and often spurious. When non-stationarity is caused by "integrated" processes, as is likely the case for climate variables, consistency of least-squares estimators depends on "cointegration" of regressors. This study has shown, using an idealized linear-response-model framework, that if standard assumptions hold then the optimal fingerprinting estimator is consistent, and hence robust against spurious regression. In the case of global mean surface temperature (GMST), parameterizing abstract linear response models in terms of energy balance provides this result with physical interpretability. Hypothesis tests conducted using observations of historical GMST and simulation output from 13 CMIP6 general circulation models produced no evidence that standard assumptions required for consistency were violated. It is therefore concluded that, at least in the case of GMST, detection and attribution of climate change trends is very likely not spurious regression. Furthermore, detection of significant cointegration between observations and model output indicates that the least-squares estimator is "superconsistent", with better convergence properties than might previously have been assumed. Finally, a new method has been developed for quantifying D&A uncertainty, exploiting the notion of cointegration to eliminate the need for pre-industrial control simulations.

Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius-Clapeyron (CC) rate (6-7% K-1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

The influence of anthropogenic climate change on wet and dry summers in Europe.

Understanding the role of anthropogenic forcings in regional hydrological changes can help communities plan their adaptation in an informed manner. Here we apply attribution research methods to investigate the effect of human influence on historical trends in wet and dry summers and changes in the likelihood of extreme events in Europe. We employ an ensemble of new climate models and compare experiments with and without the effect of human influence to assess the anthropogenic contribution. Future changes are also analysed with projections to year 2100. We employ two drought indices defined relative to the pre-industrial climate: one driven by changes in rainfall only and one that also includes the effect of temperature via changes in potential evapotranspiration. Both indices suggest significant changes in European summers have already emerged above variability and are expected to intensify in the future, leading to widespread dryer conditions which are more extreme in the south. When only the effect of rainfall is considered, there is a distinct contrast between a shift towards wetter conditions in the north and dryer in the south of the continent, as well as an overall increase in variability. However, when the effect of warming is also included, it largely masks the wet trends in the north, resulting in increasingly drier summers across most of the continent. Historical index trends are already detected in the observations, while models suggest that what were extremely dry conditions in the pre-industrial climate will become normal in the south by the end of the century.

The increasing likelihood of temperatures above 30 to 40 °C in the United Kingdom.

As European heatwaves become more severe, summers in the United Kingdom (UK) are also getting warmer. The UK record temperature of 38.7 °C set in Cambridge in July 2019 prompts the question of whether exceeding 40 °C is now within reach. Here, we show how human influence is increasing the likelihood of exceeding 30, 35 and 40 °C locally. We utilise observations to relate local to UK mean extremes and apply the resulting relationships to climate model data in a risk-based attribution methodology. We find that temperatures above 35 °C are becoming increasingly common in the southeast, while by 2100 many areas in the north are likely to exceed 30 °C at least once per decade. Summers which see days above 40 °C somewhere in the UK have a return time of 100-300 years at present, but, without mitigating greenhouse gas emissions, this can decrease to 3.5 years by 2100.

Attribution of extreme weather and climate-related events.

Extreme weather and climate-related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human-induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23-41. doi: 10.1002/wcc.380 For further resources related to this article, please visit the WIREs website.

Identifying human influences on atmospheric temperature.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000.

Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing. Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events such as the UK floods of October and November 2000. Occurring during the wettest autumn in England and Wales since records began in 1766, these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at £1.3 billion (refs 5, 6). Although the flooding was deemed a 'wake-up call' to the impacts of climate change at the time, such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail to account fully for the complex hydrometeorology associated with flooding. Here we present a multi-step, physically based 'probabilistic event attribution' framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing, we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth-century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%.

The proportionality of global warming to cumulative carbon emissions.

The global temperature response to increasing atmospheric CO(2) is often quantified by metrics such as equilibrium climate sensitivity and transient climate response. These approaches, however, do not account for carbon cycle feedbacks and therefore do not fully represent the net response of the Earth system to anthropogenic CO(2) emissions. Climate-carbon modelling experiments have shown that: (1) the warming per unit CO(2) emitted does not depend on the background CO(2) concentration; (2) the total allowable emissions for climate stabilization do not depend on the timing of those emissions; and (3) the temperature response to a pulse of CO(2) is approximately constant on timescales of decades to centuries. Here we generalize these results and show that the carbon-climate response (CCR), defined as the ratio of temperature change to cumulative carbon emissions, is approximately independent of both the atmospheric CO(2) concentration and its rate of change on these timescales. From observational constraints, we estimate CCR to be in the range 1.0-2.1 degrees C per trillion tonnes of carbon (Tt C) emitted (5th to 95th percentiles), consistent with twenty-first-century CCR values simulated by climate-carbon models. Uncertainty in land-use CO(2) emissions and aerosol forcing, however, means that higher observationally constrained values cannot be excluded. The CCR, when evaluated from climate-carbon models under idealized conditions, represents a simple yet robust metric for comparing models, which aggregates both climate feedbacks and carbon cycle feedbacks. CCR is also likely to be a useful concept for climate change mitigation and policy; by combining the uncertainties associated with climate sensitivity, carbon sinks and climate-carbon feedbacks into a single quantity, the CCR allows CO(2)-induced global mean temperature change to be inferred directly from cumulative carbon emissions.

Detection of human influence on twentieth-century precipitation trends.

Human influence on climate has been detected in surface air temperature, sea level pressure, free atmospheric temperature, tropopause height and ocean heat content. Human-induced changes have not, however, previously been detected in precipitation at the global scale, partly because changes in precipitation in different regions cancel each other out and thereby reduce the strength of the global average signal. Models suggest that anthropogenic forcing should have caused a small increase in global mean precipitation and a latitudinal redistribution of precipitation, increasing precipitation at high latitudes, decreasing precipitation at sub-tropical latitudes, and possibly changing the distribution of precipitation within the tropics by shifting the position of the Intertropical Convergence Zone. Here we compare observed changes in land precipitation during the twentieth century averaged over latitudinal bands with changes simulated by fourteen climate models. We show that anthropogenic forcing has had a detectable influence on observed changes in average precipitation within latitudinal bands, and that these changes cannot be explained by internal climate variability or natural forcing. We estimate that anthropogenic forcing contributed significantly to observed increases in precipitation in the Northern Hemisphere mid-latitudes, drying in the Northern Hemisphere subtropics and tropics, and moistening in the Southern Hemisphere subtropics and deep tropics. The observed changes, which are larger than estimated from model simulations, may have already had significant effects on ecosystems, agriculture and human health in regions that are sensitive to changes in precipitation, such as the Sahel.

Ensemble climate predictions using climate models and observational constraints.

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean-atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

Human contribution to the European heatwave of 2003.

The summer of 2003 was probably the hottest in Europe since at latest ad 1500, and unusually large numbers of heat-related deaths were reported in France, Germany and Italy. It is an ill-posed question whether the 2003 heatwave was caused, in a simple deterministic sense, by a modification of the external influences on climate--for example, increasing concentrations of greenhouse gases in the atmosphere--because almost any such weather event might have occurred by chance in an unmodified climate. However, it is possible to estimate by how much human activities may have increased the risk of the occurrence of such a heatwave. Here we use this conceptual framework to estimate the contribution of human-induced increases in atmospheric concentrations of greenhouse gases and other pollutants to the risk of the occurrence of unusually high mean summer temperatures throughout a large region of continental Europe. Using a threshold for mean summer temperature that was exceeded in 2003, but in no other year since the start of the instrumental record in 1851, we estimate it is very likely (confidence level >90%) that human influence has at least doubled the risk of a heatwave exceeding this threshold magnitude.

Detection of a human influence on North American climate.

Several indices of large-scale patterns of surface temperature variation were used to investigate climate change in North America over the 20th century. The observed variability of these indices was simulated well by a number of climate models. Comparison of index trends in observations and model simulations shows that North American temperature changes from 1950 to 1999 were unlikely to be due to natural climate variation alone. Observed trends over this period are consistent with simulations that include anthropogenic forcing from increasing atmospheric greenhouse gases and sulfate aerosols. However, most of the observed warming from 1900 to 1949 was likely due to natural climate variation.

Detection of human influence on sea-level pressure.

Greenhouse gases and tropospheric sulphate aerosols--the main human influences on climate--have been shown to have had a detectable effect on surface air temperature, the temperature of the free troposphere and stratosphere and ocean temperature. Nevertheless, the question remains as to whether human influence is detectable in any variable other than temperature. Here we detect an influence of anthropogenic greenhouse gases and sulphate aerosols in observations of winter sea-level pressure (December to February), using combined simulations from four climate models. We find increases in sea-level pressure over the subtropical North Atlantic Ocean, southern Europe and North Africa, and decreases in the polar regions and the North Pacific Ocean, in response to human influence. Our analysis also indicates that the climate models substantially underestimate the magnitude of the sea-level pressure response. This discrepancy suggests that the upward trend in the North Atlantic Oscillation index (corresponding to strengthened westerlies in the North Atlantic region), as simulated in a number of global warming scenarios, may be too small, leading to an underestimation of the impacts of anthropogenic climate change on European climate.

Origins and estimates of uncertainty in predictions of twenty-first century temperature rise.

Predictions of temperature rise over the twenty-first century are necessarily uncertain, both because the sensitivity of the climate system to changing atmospheric greenhouse-gas concentrations, as well as the rate of ocean heat uptake, is poorly quantified and because future influences on climate-of anthropogenic as well as natural origin-are difficult to predict. Past observations have been used to help constrain the range of uncertainties in future warming rates, but under the assumption of a particular scenario of future emissions. Here we investigate the relative importance of the uncertainty in climate response to a particular emissions scenario versus the uncertainty caused by the differences between future emissions scenarios for our estimates of future change. We present probabilistic forecasts of global-mean temperatures for four representative scenarios for future emissions, obtained with a comprehensive climate model. We find that, in the absence of policies to mitigate climate change, global-mean temperature rise is insensitive to the differences in the emissions scenarios over the next four decades. We also show that in the future, as the signal of climate change emerges further, the predictions will become better constrained.