Estimates of Uncertainty in Predictions of Global Mean Surface Temperature

Abstract A method for estimating uncertainty in future climate change is discussed in detail and applied to predictions of global mean temperature change. The method uses optimal fingerprinting to make estimates of uncertainty in model simulations of twentieth-century warming. These estimates are then projected forward in time using a linear, compact relationship between twentieth-century warming and twenty-first-century warming. This relationship is established from a large ensemble of energy balance models. By varying the energy balance model parameters an estimate is made of the error associated with using the linear relationship in forecasts of twentieth-century global mean temperature. Including this error has very little impact on the forecasts. There is a 50% chance that the global mean temperature change between 1995 and 2035 will be greater than 1.5 K for the Special Report on Emissions Scenarios (SRES) A1FI scenario. Under SRES B2 the same threshold is not exceeded until 2055. These results should be relatively robust to model developments for a given radiative forcing history.

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A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

Earth System Dynamics ◽

10.5194/esd-5-139-2014 ◽

2014 ◽

Vol 5 (1) ◽

pp. 139-175 ◽

Cited By ~ 40

Author(s):

R. B. Skeie ◽

T. Berntsen ◽

M. Aldrin ◽

M. Holden ◽

G. Myhre

Keyword(s):

Time Series ◽

Temperature Change ◽

Climate Sensitivity ◽

Radiative Forcing ◽

Internal Variability ◽

Posterior Mean ◽

Global Mean Temperature ◽

Mean Temperature ◽

Decadal Scale ◽

Global Mean

Abstract. Equilibrium climate sensitivity (ECS) is constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model (EBM) and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.0 Wm−2, with a 90% credible interval (C.I.) of 1.3 to 2.8 Wm−2, excluding present-day total aerosol effects (direct + indirect) stronger than −1.7 Wm−2. The posterior mean of the ECS is 1.8 °C, with 90% C.I. ranging from 0.9 to 3.2 °C, which is tighter than most previously published estimates. We find that using three OHC data sets simultaneously and data for global mean temperature and OHC up to 2010 substantially narrows the range in ECS compared to using less updated data and only one OHC data set. Using only one OHC set and data up to 2000 can produce comparable results as previously published estimates using observations in the 20th century, including the heavy tail in the probability function. The analyses show a significant contribution of internal variability on a multi-decadal scale to the global mean temperature change. If we do not explicitly account for long-term internal variability, the 90% C.I. is 40% narrower than in the main analysis and the mean ECS becomes slightly lower, which demonstrates that the uncertainty in ECS may be severely underestimated if the method is too simple. In addition to the uncertainties represented through the estimated probability density functions, there may be uncertainties due to limitations in the treatment of the temporal development in RF and structural uncertainties in the EBM.

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Evidence of Decadal Climate Prediction Skill Resulting from Changes in Anthropogenic Forcing

Journal of Climate ◽

10.1175/jcli3912.1 ◽

2006 ◽

Vol 19 (20) ◽

pp. 5305-5318 ◽

Cited By ~ 33

Author(s):

Terry C. K. Lee ◽

Francis W. Zwiers ◽

Xuebin Zhang ◽

Min Tsao

Keyword(s):

Twentieth Century ◽

Radiative Forcing ◽

Climate Models ◽

Global Climate ◽

Spatial Scales ◽

Global Climate Models ◽

Anthropogenic Forcing ◽

Global Mean Temperature ◽

Mean Temperature ◽

Global Mean

Abstract It is argued that simulations of the twentieth century performed with coupled global climate models with specified historical changes in external radiative forcing can be interpreted as climate hindcasts. A simple Bayesian method for postprocessing such simulations is described, which produces probabilistic hindcasts of interdecadal temperature changes on large spatial scales. Hindcasts produced for the last two decades of the twentieth century are shown to be skillful. The suggestion that skillful decadal forecasts can be produced on large regional scales by exploiting the response to anthropogenic forcing provides additional evidence that anthropogenic change in the composition of the atmosphere has influenced the climate. In the absence of large negative volcanic forcing on the climate system (which cannot presently be forecast), it is predicted that the global mean temperature for the decade 2000–09 will lie above the 1970–99 normal with a probability of 0.94. The global mean temperature anomaly for this decade relative to 1970–99 is predicted to be 0.35°C with a 5%–95% confidence range of 0.21°–0.48°C.

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Variability of surface climate in simulations of past and future

10.5194/egusphere-egu2020-8592 ◽

2020 ◽

Author(s):

Kira Rehfeld ◽

Raphaël Hébert ◽

Juan M. Lora ◽

Marcus Lofverstrom ◽

Chris M. Brierley

Keyword(s):

Climate Variability ◽

Temperature Change ◽

Temperature Variability ◽

Last Interglacial ◽

Global Mean Temperature ◽

Extreme Precipitation Events ◽

Mean Temperature ◽

Project Phases ◽

Intercomparison Project ◽

Global Mean

It is virtually certain that the mean surface temperature of the Earth will continue to increase under realistic emission scenarios. Yet comparatively little is known about future changes in climate variability. We explore changes in climate variability over the large range of climates simulated in the framework the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) and the Paleoclimate Modeling Intercomparison Project Phases 3 and 4 (PMIP3/4).&#160; This consists of time slice simulations for the Pliocene, Last Interglacial, Last Glacial Maximum, the Mid Holocene and idealized warming experiments (1% CO2 and abrupt 4xCO2), and encompasses climates with a range of 12&#176;C of global mean temperature change. We examine climate variability from different perspectives: from local interannual change, to coherent climate modes and through compositing extremes. The change in the interannual variability of precipitation is strongly dependent upon the local change in the total amount of precipitation. Meanwhile only over tropical land is the change in the interannual temperature variability positively correlated to temperature change, and then weakly. In general, temperature variability is inversely related to mean temperature change - with analysis of power spectra demonstrating that this holds from intra-seasonal to multi-decadal timescales. We systematically investigate changes in the standard deviation of modes of climate variability. Overall, no generalisable pattern emerges. Several modes do show, sometimes weak, increasing variability with global mean temperature change (most notably the Atlantic Zonal Mode), but also the El Ni&#241;o/Southern Oscillation indices (NINO3.4 and NINO4). The annular modes in the Northern (Southern) hemisphere show only weakly increasing (decreasing) relationships. By compositing extreme precipitation events across the ensemble, we demonstrate that the atmospheric drivers dominating rainfall variability in Mediterranean climates persist throughout palaeoclimate and future simulations. The robust nature of the response of climate variability in model simulations, between both cold and warm climates and across multiple timescales, suggests that observations and proxy reconstructions could provide a meaningful constraint on climate variability in future projections.

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Pacific variability reconciles observed and modelled global mean temperature increase since 1950

Climate Dynamics ◽

10.1007/s00382-020-05493-y ◽

2020 ◽

Author(s):

Martin B. Stolpe ◽

Kevin Cowtan ◽

Iselin Medhaug ◽

Reto Knutti

Keyword(s):

Global Warming ◽

Temperature Change ◽

Temperature Increase ◽

Global Temperature ◽

Global Mean Temperature ◽

Mean Temperature ◽

The Pacific ◽

Global Warming Hiatus ◽

Warming Hiatus ◽

Global Mean

Abstract Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean.

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Variability of surface climate in simulations of past and future

Earth System Dynamics ◽

10.5194/esd-11-447-2020 ◽

2020 ◽

Vol 11 (2) ◽

pp. 447-468 ◽

Cited By ~ 1

Author(s):

Kira Rehfeld ◽

Raphaël Hébert ◽

Juan M. Lora ◽

Marcus Lofverstrom ◽

Chris M. Brierley

Keyword(s):

Climate Variability ◽

Temperature Change ◽

Southern Oscillation ◽

Rainfall Variability ◽

Temperature Variability ◽

Climate Modes ◽

Global Mean Temperature ◽

Mean Temperature ◽

Intercomparison Project ◽

Global Mean

Abstract. It is virtually certain that the mean surface temperature of the Earth will continue to increase under realistic emission scenarios, yet comparatively little is known about future changes in climate variability. This study explores changes in climate variability over the large range of climates simulated by the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5/6) and the Paleoclimate Modeling Intercomparison Project Phase 3 (PMIP3), including time slices of the Last Glacial Maximum, the mid-Holocene, and idealized experiments (1 % CO2 and abrupt4×CO2). These states encompass climates within a range of 12 ∘C in global mean temperature change. We examine climate variability from the perspectives of local interannual change, coherent climate modes, and through compositing extremes. The change in the interannual variability of precipitation is strongly dependent upon the local change in the total amount of precipitation. At the global scale, temperature variability is inversely related to mean temperature change on intra-seasonal to multidecadal timescales. This decrease is stronger over the oceans, while there is increased temperature variability over subtropical land areas (40∘ S–40∘ N) in warmer simulations. We systematically investigate changes in the standard deviation of modes of climate variability, including the North Atlantic Oscillation, the El Niño–Southern Oscillation, and the Southern Annular Mode, with global mean temperature change. While several climate modes do show consistent relationships (most notably the Atlantic Zonal Mode), no generalizable pattern emerges. By compositing extreme precipitation years across the ensemble, we demonstrate that the same large-scale modes influencing rainfall variability in Mediterranean climates persist throughout paleoclimate and future simulations. The robust nature of the response of climate variability, between cold and warm climates as well as across multiple timescales, suggests that observations and proxy reconstructions could provide a meaningful constraint on climate variability in future projections.

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Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models

Earth System Dynamics ◽

10.5194/esd-4-95-2013 ◽

2013 ◽

Vol 4 (1) ◽

pp. 95-108 ◽

Cited By ~ 24

Author(s):

B. B. B. Booth ◽

D. Bernie ◽

D. McNeall ◽

E. Hawkins ◽

J. Caesar ◽

...

Keyword(s):

Carbon Cycle ◽

Temperature Change ◽

Climate Sensitivity ◽

Global Climate ◽

Global Climate Model ◽

Design Decision ◽

Global Mean Temperature ◽

Mean Temperature ◽

Atmospheric Feedbacks ◽

Global Mean

Abstract. We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission-driven rather than concentration-driven perturbed parameter ensemble of a global climate model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration-driven simulations (with 10–90th percentile ranges of 1.7 K for the aggressive mitigation scenario, up to 3.9 K for the high-end, business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 K (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission-driven experiments, they do not change existing expectations (based on previous concentration-driven experiments) on the timescales over which different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in the case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration scenarios used to drive GCM ensembles, lies towards the lower end of our simulated distribution. This design decision (a legacy of previous assessments) is likely to lead concentration-driven experiments to under-sample strong feedback responses in future projections. Our ensemble of emission-driven simulations span the global temperature response of the CMIP5 emission-driven simulations, except at the low end. Combinations of low climate sensitivity and low carbon cycle feedbacks lead to a number of CMIP5 responses to lie below our ensemble range. The ensemble simulates a number of high-end responses which lie above the CMIP5 carbon cycle range. These high-end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real-world climate-sensitivity constraints which, if achieved, would lead to reductions on the upper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present-day observables and future changes, while the large spread of future-projected changes highlights the ongoing need for such work.

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Sensitivity of simulated climate to latitudinal distribution of solar insolation reduction in SRM geoengineering methods

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-13-25387-2013 ◽

2013 ◽

Vol 13 (10) ◽

pp. 25387-25415 ◽

Cited By ~ 2

Author(s):

A. Modak ◽

G. Bala

Keyword(s):

Climate Change ◽

Radiative Forcing ◽

Aerosol Concentration ◽

Solar Radiation Management ◽

Latitudinal Distribution ◽

Solar Insolation ◽

Global Mean Temperature ◽

Mean Temperature ◽

Mean Climate ◽

Global Mean

Abstract. Solar radiation management (SRM) geoengineering has been proposed as a potential option to counteract climate change. We perform a set of idealized geoengineering simulations to understand the global hydrological implications of varying the latitudinal distribution of solar insolation reduction in SRM methods. We find that for a fixed total mass of sulfate aerosols (12.6 Mt of SO4), relative to a uniform distribution which mitigates changes in global mean temperature, global mean radiative forcing is larger when aerosol concentration is maximum at the poles leading to a warmer global mean climate and consequently an intensified hydrological cycle. Opposite changes are simulated when aerosol concentration is maximized in the tropics. We obtain a range of 1 K in global mean temperature and 3% in precipitation changes by varying the distribution pattern: this range is about 50% of the climate change from a doubling of CO2. Hence, our study demonstrates that a range of global mean climate states, determined by the global mean radiative forcing, are possible for a fixed total amount of aerosols but with differing latitudinal distribution, highlighting the need for a careful evaluation of SRM proposals.

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Scenario and modelling uncertainty in global mean temperature change derived from emission driven Global Climate Models

Earth System Dynamics Discussions ◽

10.5194/esdd-3-1055-2012 ◽

2012 ◽

Vol 3 (2) ◽

pp. 1055-1084 ◽

Cited By ~ 5

Author(s):

B. B. B. Booth ◽

D. Bernie ◽

D. McNeall ◽

E. Hawkins ◽

J. Caesar ◽

...

Keyword(s):

Carbon Cycle ◽

Temperature Change ◽

Climate Sensitivity ◽

Global Climate ◽

Global Climate Model ◽

Design Decision ◽

Global Mean Temperature ◽

Mean Temperature ◽

Atmospheric Feedbacks ◽

Global Mean

Abstract. We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission driven rather than concentration driven perturbed parameter ensemble of a Global Climate Model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration driven simulations (with 10–90 percentile ranges of 1.7 K for the aggressive mitigation scenario up to 3.9 K for the high end business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 degrees (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission driven experiments, they do not change existing expectations (based on previous concentration driven experiments) on the timescale that different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration pathways used to drive GCM ensembles lies towards the lower end of our simulated distribution. This design decision (a legecy of previous assessments) is likely to lead concentration driven experiments to under-sample strong feedback responses in concentration driven projections. Our ensemble of emission driven simulations span the global temperature response of other multi-model frameworks except at the low end, where combinations of low climate sensitivity and low carbon cycle feedbacks lead to responses outside our ensemble range. The ensemble simulates a number of high end responses which lie above the CMIP5 carbon cycle range. These high end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real world climate sensitivity constraints which, if achieved, would lead to reductions on the uppper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present day observables and future changes while the large spread of future projected changes, highlights the ongoing need for such work.

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