A Bayesian Climate Change Detection and Attribution Assessment

Abstract A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.

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Pan-spectral observing system simulation experiments of shortwave reflectance and long-wave radiance for climate model evaluation

Geoscientific Model Development ◽

10.5194/gmd-8-1943-2015 ◽

2015 ◽

Vol 8 (7) ◽

pp. 1943-1954 ◽

Cited By ~ 11

Author(s):

D. R. Feldman ◽

W. D. Collins ◽

J. L. Paige

Keyword(s):

Climate Change ◽

Climate Model ◽

Coupled Model ◽

Climate System ◽

Spectral Measurements ◽

Intergovernmental Panel ◽

Climate System Model ◽

Detection And Attribution ◽

Long Wave ◽

Hadley Centre

Abstract. Top-of-atmosphere (TOA) spectrally resolved shortwave reflectances and long-wave radiances describe the response of the Earth's surface and atmosphere to feedback processes and human-induced forcings. In order to evaluate proposed long-duration spectral measurements, we have projected 21st Century changes from the Community Climate System Model (CCSM3.0) conducted for the Intergovernmental Panel on Climate Change (IPCC) A2 Emissions Scenario onto shortwave reflectance spectra from 300 to 2500 nm and long-wave radiance spectra from 2000 to 200 cm−1 at 8 nm and 1 cm−1 resolution, respectively. The radiative transfer calculations have been rigorously validated against published standards and produce complementary signals describing the climate system forcings and feedbacks. Additional demonstration experiments were performed with the Model for Interdisciplinary Research on Climate (MIROC5) and Hadley Centre Global Environment Model version 2 Earth System (HadGEM2-ES) models for the Representative Concentration Pathway 8.5 (RCP8.5) scenario. The calculations contain readily distinguishable signatures of low clouds, snow/ice, aerosols, temperature gradients, and water vapour distributions. The goal of this effort is to understand both how climate change alters reflected solar and emitted infrared spectra of the Earth and determine whether spectral measurements enhance our detection and attribution of climate change. This effort also presents a path forward to understand the characteristics of hyperspectral observational records needed to confront models and inline instrument simulation. Such simulation will enable a diverse set of comparisons between model results from coupled model intercomparisons and existing and proposed satellite instrument measurement systems.

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Climate-mediated changes to mixed-layer properties in the Southern Ocean: assessing the phytoplankton response

Biogeosciences ◽

10.5194/bg-5-847-2008 ◽

2008 ◽

Vol 5 (3) ◽

pp. 847-864 ◽

Cited By ~ 55

Author(s):

P. W. Boyd ◽

S. C. Doney ◽

R. Strzepek ◽

J. Dusenberry ◽

K. Lindsay ◽

...

Keyword(s):

Climate Change ◽

Southern Ocean ◽

Climate Model ◽

Environmental Changes ◽

Environmental Control ◽

Climate System Model ◽

Climate Change Effects ◽

Detection And Attribution ◽

Key Species ◽

Phytoplankton Functional Groups

Abstract. Concurrent changes in ocean chemical and physical properties influence phytoplankton dynamics via alterations in carbonate chemistry, nutrient and trace metal inventories and upper ocean light environment. Using a fully coupled, global carbon-climate model (Climate System Model 1.4-carbon), we quantify anthropogenic climate change relative to the background natural interannual variability for the Southern Ocean over the period 2000 and 2100. Model results are interpreted using our understanding of the environmental control of phytoplankton growth rates – leading to two major findings. Firstly, comparison with results from phytoplankton perturbation experiments, in which environmental properties have been altered for key species (e.g., bloom formers), indicates that the predicted rates of change in oceanic properties over the next few decades are too subtle to be represented experimentally at present. Secondly, the rate of secular climate change will not exceed background natural variability, on seasonal to interannual time-scales, for at least several decades – which may not provide the prevailing conditions of change, i.e. constancy, needed for phytoplankton adaptation. Taken together, the relatively subtle environmental changes, due to climate change, may result in adaptation by resident phytoplankton, but not for several decades due to the confounding effects of climate variability. This presents major challenges for the detection and attribution of climate change effects on Southern Ocean phytoplankton. We advocate the development of multi-faceted tests/metrics that will reflect the relative plasticity of different phytoplankton functional groups and/or species to respond to changing ocean conditions.

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Detection and Attribution of Streamflow Timing Changes to Climate Change in the Western United States

Journal of Climate ◽

10.1175/2009jcli2470.1 ◽

2009 ◽

Vol 22 (13) ◽

pp. 3838-3855 ◽

Cited By ~ 183

Author(s):

H. G. Hidalgo ◽

T. Das ◽

M. D. Dettinger ◽

D. R. Cayan ◽

D. W. Pierce ◽

...

Keyword(s):

Climate Change ◽

United States ◽

Greenhouse Gases ◽

River Basin ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Hydrologic Model ◽

Western United States ◽

Detection And Attribution

Abstract This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.

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Optimal detection and attribution of climate change: sensitivity of results to climate model differences

Climate Dynamics ◽

10.1007/s003820000071 ◽

2000 ◽

Vol 16 (10-11) ◽

pp. 737-754 ◽

Cited By ~ 39

Author(s):

G. C. Hegerl ◽

P. A. Stott ◽

M. R. Allen ◽

J. F. B. Mitchell ◽

S. F. B. Tett ◽

...

Keyword(s):

Climate Change ◽

Climate Model ◽

Optimal Detection ◽

Detection And Attribution

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Robust detection of forced warming in the presence of potentially large climate variability

10.5194/egusphere-egu21-9611 ◽

2021 ◽

Author(s):

Sebastian Sippel ◽

Nicolai Meinshausen ◽

Eniko Székely ◽

Erich Fischer ◽

Angeline G. Pendergrass ◽

...

Keyword(s):

Climate Change ◽

Climate Variability ◽

Climate Models ◽

Climate Model ◽

Decadal Variability ◽

Internal Variability ◽

Warming Trend ◽

Robust Detection ◽

Internal Climate Variability ◽

Detection And Attribution

Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (&#8220;control&#8221;) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76&#176;C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5&#963;), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model&#8217;s natural variability estimates are used.Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.&#160;Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science.&#160;Nat Clim Change&#160;9(3), pp. 180-182.

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Weighting climate model projections using observational constraints

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2014.0425 ◽

2015 ◽

Vol 373 (2054) ◽

pp. 20140425 ◽

Cited By ~ 14

Author(s):

Nathan P. Gillett

Keyword(s):

Climate Change ◽

Radiative Forcing ◽

Climate Model ◽

Historical Period ◽

Intergovernmental Panel ◽

Transient Climate Response ◽

Detection And Attribution ◽

Future Warming ◽

Near Term ◽

Climate Model Simulations

Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081–2100 relative to 1986–2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5–95% warming range of 0.8–2.5 K is somewhat lower than the unweighted range of 1.1–2.6 K reported in the IPCC AR5.

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Pan-spectral observing system simulation experiments of shortwave reflectance and longwave radiance for climate model evaluation

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-3647-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 3647-3670

Author(s):

D. R. Feldman ◽

W. D. Collins

Keyword(s):

Climate Change ◽

Climate Model ◽

Coupled Model ◽

Measurement Model ◽

Climate System ◽

Spectral Measurements ◽

Intergovernmental Panel ◽

Climate System Model ◽

Detection And Attribution ◽

Spectrally Resolved

Abstract. Top-of-atmosphere spectrally-resolved shortwave reflectances and longwave radiances describe the evolution of the Earth's surface and atmosphere response to feedbacks in and human-induced forcings on the climate system. In order to evaluate proposed long-duration spectral measurements, we have projected 21st century changes described by the Community Climate System Model (CCSM3.0) conducted for the Intergovernmental Panel on Climate Change (IPCC) A2 Emissions Scenario onto shortwave reflectance spectra from 0.3 to 2.5 μm and longwave radiance spectra from 5 to 50 μm at 8 nm and 1 cm−1 resolution, respectively. The radiative transfer calculations have been rigorously validated against published standards and produce complementary signals describing the climate system forcings and feedbacks. Additional demonstration experiments were performed with the MIROC5 and HadGEM2-ES models for the Representative Concentration Pathway 8.5 (RCP8.5) scenario. The calculations contain readily distinguishable signatures of low clouds, snow/ice, aerosols, temperature gradients, and water vapour distributions. The goal of this effort is to understand both how climate change alters the spectrum of the Earth and determine whether spectral measurements enhance our detection and attribution of climate change. This effort also presents a path forward for hyperspectral measurement-model intercomparison by enabling a diverse set of comparisons between model results from coupled model intercomparisons and existing and proposed satellite instrument measurement systems.

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When Will We Detect Changes in Short-Duration Precipitation Extremes?

Journal of Climate ◽

10.1175/jcli-d-17-0435.1 ◽

2018 ◽

Vol 31 (7) ◽

pp. 2945-2964 ◽

Cited By ~ 21

Author(s):

Elizabeth J. Kendon ◽

Stephen Blenkinsop ◽

Hayley J. Fowler

Keyword(s):

Climate Change ◽

United Kingdom ◽

Short Duration ◽

Climate Models ◽

Climate Model ◽

Summer Rainfall ◽

Adaptation Measures ◽

Detection And Attribution ◽

Hourly Rainfall ◽

Rainfall Extremes

The question of when the influence of climate change on U.K. rainfall extremes may be detected is important from a planning perspective, providing a time scale for necessary climate change adaptation measures. Short-duration intense rainfall is responsible for flash flooding, and several studies have suggested an amplified response to warming for rainfall extremes on hourly and subhourly time scales. However, there are very few studies examining the detection of changes in subdaily rainfall. This is due to the high cost of very high-resolution (kilometer scale) climate models needed to capture hourly rainfall extremes and to a lack of sufficiently long, high-quality, subdaily observational records. Results using output from a 1.5-km climate model over the southern United Kingdom indicate that changes in 10-min and hourly precipitation emerge before changes in daily precipitation. In particular, model results suggest detection times for short-duration rainfall intensity in the 2040s in winter and the 2080s in summer, which are, respectively, 5–10 years and decades earlier than for daily extremes. Results from a new quality-controlled observational dataset of hourly rainfall over the United Kingdom do not show a similar difference between daily and hourly trends. Natural variability appears to dominate current observed trends (including an increase in the intensity of heavy summer rainfall over the last 30 years), with some suggestion of larger daily than hourly trends for recent decades. The expectation of the reverse, namely, larger trends for short-duration rainfall, as the signature of underlying climate change has potentially important implications for detection and attribution studies.

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Detection and Attribution of Multivariate Climate Change Signals Using Discriminant Analysis and Bayesian Theorem

Journal of Climate ◽

10.1175/jcli-d-16-0850.1 ◽

2017 ◽

Vol 30 (19) ◽

pp. 7757-7776

Author(s):

Heiko Paeth ◽

Felix Pollinger ◽

Christoph Ring

Keyword(s):

Climate Change ◽

Discriminant Analysis ◽

Radiative Forcing ◽

Climate Model ◽

First Century ◽

Climate Indices ◽

Bayesian Theorem ◽

Climate Anomalies ◽

Detection And Attribution ◽

Twenty First Century

Abstract Detection and attribution methods in climatological research aim at assessing whether observed climate anomalies and trends are still consistent with the range of natural climate variations or rather an indication of anthropogenic climate change. In this study, the authors pursue a novel approach by using discriminant analysis to enhance the distinction between past and future climates from state-of-the-art climate model simulations. The method is based on multivariate fingerprints that are defined in the space of several prominent climate indices representing the thermal, dynamical, and hygric aspects of climate change. Attribution is carried out by means of a Bayesian classification approach. The leading discriminant function accounts for more than 99% of total discriminability, with temperature variables, extratropical precipitation, and extratropical circulation modes mainly contributing to the discriminant power. The misclassification probability between probability density functions of past and future climates is substantially reduced by the discriminant analysis: from >50% to <15%. Since the mid-1980s, the observed anomalies of the considered climate indices are more or less consistently attributed to a climate under strong radiative forcing, projected for the first half of the twenty-first century. The authors also assess the sensitivity of their results to different emissions scenarios from the CMIP3 and CMIP5 multimodel ensembles, seasons, prior probabilities for the early twenty-first-century climate, estimates of the observational error, low-pass filters, variable compositions, group numbers, and reference data.

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Toward a New Estimate of “Time of Emergence” of Anthropogenic Warming: Insights from Dynamical Adjustment and a Large Initial-Condition Model Ensemble

Journal of Climate ◽

10.1175/jcli-d-16-0792.1 ◽

2017 ◽

Vol 30 (19) ◽

pp. 7739-7756 ◽

Cited By ~ 44

Author(s):

Flavio Lehner ◽

Clara Deser ◽

Laurent Terray

Keyword(s):

Climate Change ◽

North America ◽

Atmospheric Circulation ◽

Climate Model ◽

Model Ensemble ◽

Land Area ◽

Internal Climate Variability ◽

Detection And Attribution ◽

Model Evidence

Abstract Time of emergence of anthropogenic climate change is a crucial metric in risk assessments surrounding future climate predictions. However, internal climate variability impairs the ability to make accurate statements about when climate change emerges from a background reference state. None of the existing efforts to explore uncertainties in time of emergence has explicitly explored the role of internal atmospheric circulation variability. Here a dynamical adjustment method based on constructed circulation analogs is used to provide new estimates of time of emergence of anthropogenic warming over North America and Europe from both a local and spatially aggregated perspective. After removing the effects of internal atmospheric circulation variability, the emergence of anthropogenic warming occurs on average two decades earlier in winter and one decade earlier in summer over North America and Europe. Dynamical adjustment increases the percentage of land area over which warming has emerged by about 30% and 15% in winter (10% and 5% in summer) over North America and Europe, respectively. Using a large ensemble of simulations with a climate model, evidence is provided that thermodynamic factors related to variations in snow cover, sea ice, and soil moisture are important drivers of the remaining uncertainty in time of emergence. Model biases in variability lead to an underestimation (13%–22% over North America and <5% over Europe) of the land fraction emerged by 2010 in summer, indicating that the forced warming signal emerges earlier in observations than suggested by models. The results herein illustrate opportunities for future detection and attribution studies to improve physical understanding by explicitly accounting for internal atmospheric circulation variability.

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