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A brief history of time the detection and attribution of climate change David Karoly School of Meteorology University of Oklahoma What is detection and attribution? Detection of significant observed climate change and attribution of this observed change to one or more causes is a signal-in-noise problem: identifying possible signals in the noise of natural internal climate variations in the chaotic climate system. Detection is the process of demonstrating that an observed change is significantly different (in a statistical sense) than can be explained by natural internal climate variability. What is detection and attribution? Attribution of climate change to specific causes involves statistical analysis and the careful assessment of multiple lines of evidence to demonstrate that the observed changes are: • unlikely to be due entirely to internal climate variability; • consistent with the estimated responses to a given combination of anthropogenic and natural forcing; and • not consistent with alternative, physically plausible explanations of recent climate change Why do detection and attribution? • To identify the causes of recent observed climate variations • To evaluate the performance of climate models in simulating the observed climate variations over the last century • To constrain the projections of future climate change Senator James Inhofe (R, Oklahoma), Chair, Senate Env. and Public Works Comm., in a speech to the US Senate on Jan 4, 2005 “I called the threat of catastrophic global warming the ‘greatest hoax ever perpetrated on the American people’ ” History Global Mean Temperature 0.6 Anomaly (deg C) 0.4 0.2 Madden and Ramanathan (1980) “surface warming due to increasing carbon dioxide … should be detectable … (by) the year 2000” 0 1860 -0.2 -0.4 -0.6 1880 1900 1920 1940 1960 1980 2000 History Global Mean Temperature 0.6 Anomaly (deg C) 0.4 0.2 IPCC First Assessment Report in 1990 had no attribution chapter; general statement on consistency of observed warming with model projections 0 1860 -0.2 -0.4 -0.6 1880 1900 1920 1940 1960 1980 2000 History 0.6 Anomaly (deg C) 0.4 0.2 Global Mean Temperature IPCC SAR (1995) had detection and attribution chapter; “the balance of evidence suggests a discernable human influence on climate” 0 1860 -0.2 -0.4 -0.6 1880 1900 1920 1940 1960 1980 2000 History 0.6 Anomaly (deg C) 0.4 0.2 Global Mean(2001); Temperature IPCC TAR “Most of the observed warming over the last fifty years is likely to have been due to the increase in greenhouse gas concentrations” 0 1860 -0.2 -0.4 -0.6 1880 1900 1920 1940 1960 1980 2000 History Global Mean Temperature 0.6 IPCC Fourth Assessment Report (2007): ?? Anomaly (deg C) 0.4 0.2 0 1860 -0.2 -0.4 -0.6 1880 1900 1920 1940 1960 1980 2000 Initial studies to SAR (1995) • Univariate analysis of global mean temperature comparing change with internal variability • Difficult to separate different causes that affect global radiation balance; increasing greenhouse gases, increasing solar irradiance • Use the spatial pattern of the temperature response to differentiate between different causes – fingerprint analysis • Initially consider contrast between troposphere and stratosphere using pattern correlation – Karoly et al (1994), Santer et al (1995) Changes in zonal mean atmospheric temperature (C), 1960-1995: Modelled and observed From Tett et al (1996), following Santer et al (1995) Further studies to TAR (2001) • Use optimal fingerprint analysis to reduce noise by rotating detection vector away from noise direction • Use linear regression to estimate amplitude of forced signal from model pattern and observational data • Applied to spatial pattern of surface temperature change • Use more models, including models without flux correction between the atmosphere and ocean All factors Global mean surface temperature (C) HadCM3 model (black) Observations (red) Natural only Anthropogenic only Stott et al. (2000) Apply optimal fingerprint analysis to large-scale variations of surface temperature at decadal timescales Bars show 5-95% uncertainty limits Allen et al. (2000) Optimised predictions of temperature change (C), from 1990 to 2040 under IS92a emissions (diamonds) Allen et al, 2000 Studies since the TAR • Use other variables: anthropogenic signal found in ocean heat content, mean sea level pressure, and temperature extremes • Evaluate anthropogenic signal in temperature changes at smaller scales • “Key uncertainties include … relating regional trends to anthropogenic climate change” IPCC TAR Continental-scale studies Stott (2003) showed that most of the observed warming over the last 50 years in six separate continents, including North America, Eurasia and Australia, was likely to have been due to the increase in greenhouse gases Decadal variations of North American mean temperatures 1.2 1.0 Karoly et al (2003) Anomaly (K) 0.8 0.6 0.4 0.2 0.0 1900 -0.2 1920 1940 1960 1980 2000 -0.4 -0.6 Observed HadCM2 GS PCM GS GFDL GS HadCM3 GS ECHAM4 GS HadCM2 NAT PCM NAT GFDL NAT HadCM3 NAT Detection of regional warming Calculate observed linear trend in each grid-box and test for 95% significance (marked with +) using model control simulations to provide estimate of natural variability of trends (Karoly and Wu, 2005) Simple indices of climate variability & change • Select a small number of indices of surface temperature variations that represent different aspects of natural climate variability but represent key features of patterns of anthropogenic climate change (following Braganza et al., 2003, 2004) • Want indices that show a common signal due to greenhouse climate change but are nearly independent for natural climate variations – – – – Global mean surface temperature (GM) Mean land – ocean temp difference (LO) Mean magnitude of the annual cycle over land (AC) Mean meridional temperature gradient in the NH (MTG) Describing climate variability Decadal standard deviations 0.25 Temperature (K) 0.20 Observed CCSM CSIRO ECHAM5 GFDL 2.1 GISS E-R HadCM3 MIROC Med PCM 0.15 0.10 0.05 0.00 GM LO AC MTG Assessing climate change Temperature Trends over last 50 years 1.5 Trend (K/100 yrs) 1.0 Observed CCSM CSIRO MkIII ECHAM5 GFDL 2.1 GISS E-R HadCM3 MIROC Med PCM 0.5 0.0 -0.5 -1.0 -1.5 GM LO AC MTG Detection of regional warming: California Compare observed area-mean temperature change with model simulations for 20th century from NCAR CCSM3 and GFDL CM2 Attribution of regional warming: California Probability distributions of 90-year trends in California temperature from control model simulations (solid line) and 20th century simulations with increasing greenhouse gases and aerosols (dash-dot line, 20C3M). The observed trend agrees well with the 20C3M simulations and can’t be explained by natural climate variations Attribution of regional warming: Central England temperature Probability distributions of 50-year trends in CET from control model simulations (solid line) and 20th century simulations with anthropogenic (ANT) and natural (NAT) external forcing 50-year trends in Central England Temperature 30 25 20 Probability (%) The observed trend agrees well with the ANT simulations and can’t be explained by NAT simulations HadCM3 Control Obs trend 1950-99 Obs trend 1955-04 HadCM3 ANT 1950-99 HadCM3 NAT 1950-99 15 10 5 0 -0.3 -0.2 -0.1 0 0.1 0.2 Temperature trend (deg C/decade) 0.3 0.4 Continental-scale temperature projections Uncertainty plumes for changes relative to 1990s using scalings based on continental-scale attribution. Probabilities are represented by the depth of shading. From Stott et al. (2006) Conclusions • There have been significant advances in the methods used for attribution of the causes of observed climate change over the past two decades • The predictions of Madden and Ramanathan (1980) proved to be uncannily correct • A clear anthropogenic signal can be identified in observed climate changes over the last 50 years in many variables and in temperature in almost all regions