scholarly journals Estimating trends in the global mean temperature record

2017 ◽  
Vol 3 (1) ◽  
pp. 33-53 ◽  
Author(s):  
Andrew Poppick ◽  
Elisabeth J. Moyer ◽  
Michael L. Stein
Keyword(s):  
Radiative Forcing ◽  
Temporal Correlation ◽  
Internal Variability ◽  
Historical Record ◽  
Temperature Record ◽  
Empirical Methods ◽  
Global Mean Temperature ◽  
Data Record ◽  
Historical Temperature ◽  
Global Mean

Abstract. Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.

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

2014 ◽  
Vol 5 (1) ◽  
pp. 139-175 ◽  
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.

scholarly journals Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

2020 ◽  
Vol 33 (1) ◽  
pp. 397-404 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Time Windows ◽  
Internal Variability ◽  
Historical Record ◽  
Cmip5 Models ◽  
Temperature Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Total Uncertainty ◽  
The Impact ◽  
The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

scholarly journals Estimates of Uncertainty in Predictions of Global Mean Surface Temperature

2007 ◽  
Vol 20 (5) ◽  
pp. 843-855 ◽  
Author(s):  
J. A. Kettleborough ◽  
B. B. B. Booth ◽  
P. A. Stott ◽  
M. R. Allen
Keyword(s):  
Twentieth Century ◽  
Energy Balance ◽  
Temperature Change ◽  
Radiative Forcing ◽  
Balance Model ◽  
Future Climate Change ◽  
Model Parameters ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

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.

Global and amplified land warming trends over the past 40+ years are entirely human-driven.

2021 ◽  
Author(s):  
Karsten Haustein
Keyword(s):  
Radiative Forcing ◽  
Volcanic Eruptions ◽  
Internal Variability ◽  
Forced Response ◽  
Global Temperature ◽  
Extreme Weather Events ◽  
Temperature Record ◽  
Model Framework ◽  
Anthropogenic Aerosol ◽  
The Past

<p class="p1">The role of external (radiative) forcing factors and internal unforced (ocean) low-frequency variations in the instrumental global temperature record are still hotly debated. More recent findings point towards a larger contribution from changes in external forcing, but the jury is still out. While the estimation of the human-induced total global warming fraction since pre-industrial times is fairly robust and mostly independent of multidecadal internal variability, this is not necessarily the case for key regional features such as Arctic amplification or enhanced warming over continental land areas. Accounting for the slow global temperature adjustment after strong volcanic eruptions, the spatially heterogeneous nature of anthropogenic aerosol forcing and known biases in the sea surface temperature record, almost all of the multidecadal fluctuations observed over at least the last 160+ years can be explained without a relevant role for internal variability. Using a two-box response model framework, I will demonstrate that not only multidecadal variability is very likely a forced response, but warming trends over the past 40+ years are entirely attributable to human factors. Repercussions for amplifed European (or D-A-CH for that matter) warming and associated implications for extreme weather events are discussed. Further consideration is given to the communications aspect of such critical results as well as the question of wider societal impacts.</p>

scholarly journals Impact of methane and black carbon mitigation on forcing and temperature: a multi-model scenario analysis

2020 ◽  
Vol 163 (3) ◽  
pp. 1427-1442 ◽  
Author(s):  
Steven J Smith ◽  
Jean Chateau ◽  
Kalyn Dorheim ◽  
Laurent Drouet ◽  
Olivier Durand-Lasserve ◽  
...  
Keyword(s):  
Black Carbon ◽  
Radiative Forcing ◽  
Climate Model ◽  
Economic Structure ◽  
Time Frame ◽  
Focused Attention ◽  
Carbon Mitigation ◽  
Global Mean Temperature ◽  
Model Parameter Uncertainty ◽  
Global Mean

AbstractThe relatively short atmospheric lifetimes of methane (CH4) and black carbon (BC) have focused attention on the potential for reducing anthropogenic climate change by reducing Short-Lived Climate Forcer (SLCF) emissions. This paper examines radiative forcing and global mean temperature results from the Energy Modeling Forum (EMF)-30 multi-model suite of scenarios addressing CH4 and BC mitigation, the two major short-lived climate forcers. Central estimates of temperature reductions in 2040 from an idealized scenario focused on reductions in methane and black carbon emissions ranged from 0.18–0.26 °C across the nine participating models. Reductions in methane emissions drive 60% or more of these temperature reductions by 2040, although the methane impact also depends on auxiliary reductions that depend on the economic structure of the model. Climate model parameter uncertainty has a large impact on results, with SLCF reductions resulting in as much as 0.3–0.7 °C by 2040. We find that the substantial overlap between a SLCF-focused policy and a stringent and comprehensive climate policy that reduces greenhouse gas emissions means that additional SLCF emission reductions result in, at most, a small additional benefit of ~ 0.1 °C in the 2030–2040 time frame.

scholarly journals How Robust Are Trends in the Brewer–Dobson Circulation Derived from Observed Stratospheric Temperatures?

2015 ◽  
Vol 28 (8) ◽  
pp. 3024-3040 ◽  
Author(s):  
Albert Ossó ◽  
Yolanda Sola ◽  
Karen Rosenlof ◽  
Birgit Hassler ◽  
Joan Bech ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
First Century ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Global Mean Temperature ◽  
Stratospheric Temperature ◽  
Spatially Inhomogeneous ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Global Mean

Abstract Most global circulation models and climate–chemistry models forced with increasing greenhouse gases predict a strengthening of the Brewer–Dobson circulation (BDC) in the twenty-first century, and some of them claim that such strengthening has already begun at the end of the twentieth century. However, observational evidence for such a trend remains inconclusive. The goal of this paper is to examine the evidence for observed trends in the stratospheric overturning circulation using a suite of currently available observational stratospheric temperature data. Trends are examined as “departures” from the global mean temperature, since such trends reflect the effects of dynamics and spatially inhomogeneous radiative forcing and are to first order independent of the direct radiative effects of increasing well-mixed greenhouse gas concentrations. The primary conclusion of the study is that temperature observations do not reveal statistically significant trends in the Brewer–Dobson circulation over the period from 1979 to the present, as covered by Microwave Sounding Unit and Stratospheric Sounding Unit temperatures. The estimated trends in the BDC are weak in all datasets and not statistically significant at the 95% confidence level. In many cases, different data products yield very different results, particularly when the trends are stratified by season. Implications for the interpretation of recent stratospheric climate change are discussed. The results illustrate the essential need to better constrain the accuracy of future stratospheric temperature datasets.

scholarly journals A Re-evaluation of Climate Sensitivity

2021 ◽  
Author(s):  
Ian Harold Wilson
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Co2 Concentration ◽  
Systematic Errors ◽  
Short Term ◽  
The Past ◽  
Global Mean Temperature ◽  
Recent Climate ◽  
Global Mean ◽  
Paleoclimate Data

Equilibrium climate sensitivity (ECS) is the change in global mean temperature expected to result from doubling atmospheric CO2 concentration from pre-industrial levels. Extensive research during the past 40 years has not reduced the uncertainty associated with ECS. Sherwood et al. [1] applied Bayesian statistics to evidence from climate-process physics, historical observations and earlier proxies to reduce the range of ECS from 1.5 – 4.5 K to 2.6 – 4.1 K. This paper examines their methods and many of the assumptions they made. It also evaluates two additional periods in the Holocene to show that factors other than CO2 drove recent climate change. It identifies potential systematic errors resulting from adding non-equilibrium short-term adjustments to the radiative forcing of greenhouse gases and from underestimating the effects of solar irradiance, ocean currents and aerosols. These factors have resulted in estimates of the forcing by CO2 that far exceed the apparent effects in paleoclimate data.

scholarly journals Emulating Earth System Model land temperature fields with MESMER

2021 ◽  
Author(s):  
Lea Beusch ◽  
Lukas Gudmundsson ◽  
Sonia I. Seneviratne
Keyword(s):  
Emission Scenario ◽  
Computational Cost ◽  
Internal Variability ◽  
Temperature Fields ◽  
Natural Climate Variability ◽  
Sources Of Uncertainty ◽  
Global Mean Temperature ◽  
Scenario Uncertainty ◽  
Natural Climate ◽  
Global Mean

<p>Earth System Models (ESMs) are invaluable tools to study the climate system’s response to greenhouse gas emissions. But their projections are affected by three major sources of uncertainty: (i) internal variability, i.e., natural climate variability, (ii) ESM structural uncertainty, i.e., uncertainty in the response of the climate system to given greenhouse gas concentrations, and (iii) emission scenario uncertainty, i.e., which emission pathway the world chooses. The large computational cost of running full ESMs limits the exploration of this uncertainty phase space since it is only feasible to create a limited number of ESM runs. However, climate change impact and integrated assessment models, which require ESM projections as their input, could profit from a more complete sampling of the climate change uncertainty phase space. In this contribution, we present MESMER (Beusch et al., 2020), a Modular ESM Emulator with spatially Resolved output, which allows for a computationally efficient exploration of the uncertainty space of yearly temperatures. MESMER approximates ESM land temperature fields at a negligible computational cost by expressing grid-point-level temperatures as a function of global mean temperature and an overlaid spatio-temporally correlated variability term. Within MESMER all three major sources of uncertainty can be accounted for. Stochastic simulation of natural climate variability allows to account for internal variability. ESM structural uncertainty can be addressed by calibrating MESMER on different ESMs from the Coupled Model Intercomparison Project (CMIP) archives. Finally, emission scenario uncertainty can be accounted for by ingesting forced global mean temperature trajectories from global climate model emulators, such as MAGICC or FaIR. MESMER is a flexible statistical tool which is under active development and in the process of becoming an open-source software.</p><p>Beusch, L., Gudmundsson, L., and Seneviratne, S. I. (ESD, 2020): https://doi.org/10.5194/esd-11-139-2020</p>

scholarly journals The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity

2018 ◽  
Author(s):  
Andrew E. Dessler ◽  
Thorsten Mauritsen ◽  
Bjorn Stevens
Keyword(s):  
Energy Balance ◽  
Solar Energy ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Internal Variability ◽  
Ensemble Member ◽  
Temperature Record ◽  
Central Hypothesis ◽  
Climate Model Simulations ◽  
Historical Temperature

Abstract. Our climate is constrained by the balance between solar energy absorbed by the Earth and terrestrial energy radiated to space. This energy balance has been widely used to infer equilibrium climate sensitivity (ECS) from observations of 20th-century warming. Such estimates yield lower values than other methods and these have been influential in pushing down the consensus ECS range in recent assessments. Here we test the method using a 100-member ensemble of the MPI-ESM1.1 climate model simulations of the period 1850–2005 with known forcing. We calculate ECS in each ensemble member using energy balance, yielding values ranging from 2.1 to 3.9 K. The spread in the ensemble is related to the central hypothesis in the energy budget framework: that global average surface temperature anomalies are indicative of anomalies in outgoing energy (either of terrestrial origin or reflected solar energy). We find that assumption is not well supported over the historical temperature record in the model ensemble or more recent satellite observations. We find that framing energy balance in terms of 500-hPa tropical temperature better describes the planet's energy balance.

scholarly journals Power-law behavior in millennium climate simulations

2012 ◽  
Vol 3 (1) ◽  
pp. 391-416
Author(s):  
S. V. Henriksson ◽  
P. Räisänen ◽  
J. Silen ◽  
H. Järvinen ◽  
A. Laaksonen
Keyword(s):  
Power Law ◽  
El Niño ◽  
El Nino ◽  
Temperature Record ◽  
Frequency Range ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Starting Point ◽  
The Impact ◽  
Global Mean

Abstract. Using a method of discrete Fourier transform with varying starting point and length of time window and the long time series provided by millennium Earth System Model simulations, we get good fits to power laws between two characteristic oscillatory timescales of the model climate: multidecadal (50–80 yr) and El Nino (3–6 yr) timescales. For global mean temperature, we fit β ~ 0.35 in a relation S(f) ~ f−β in a simulation without external climate forcing and β over 0.7 in a simulation with external forcing included. We also fit a power law with β ~ 8 to the narrow frequency range between El Nino frequencies and the Nyquist frequency. Regional variability in best-fit β is explored and the impact of choosing the frequency range on the result is illustrated. When all resolved frequencies are used, land areas seem to have lower βs than ocean areas on average, but when fits are restricted to frequencies below 1/(6 yr), this difference disappears, while regional differences still remain. Results compare well with measurements both for global mean temperature and for the Central England temperature record.

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