Global Temperature Projections Research Articles

Global Temperature Projections from a Statistical Energy Balance Model Using Multiple Sources of Historical Data

Abstract This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955–2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models. Significance Statement We develop a statistical model to understand Earth’s energy balance and how it impacts temperature across the planet’s surface and deep oceans. Unlike previous approaches, ours is fully statistical. This means that we use maximum likelihood to calculate the estimates and evaluate the level of uncertainty in the model’s parameters. By applying the model to historical data from 1955 to 2020, we are able to make predictions about the average global surface temperature that are consistent with those from the Coupled Model Intercomparison Project. Our findings support current predictions about global temperatures using a straightforward statistical climate model, grounded in historical data.

The benefit of the Covid‐19 pandemic on global temperature projections

AbstractThis paper illustrates the benefit of the Covid‐19 pandemic on global temperature projections. Global temperature projections are obtained by forecasting Earth's temperature anomalies time series with spectral analysis. Time series of Earth's Temperature Anomalies of the Globe are collected by NASA/GISS and in the Northern and Southern Hemispheres by the Climatic Research Unit of the University of East Anglia and Hadley Centre. Spectral analysis aims at unveiling temperature anomalies time series by transforming them into simplified time series after decomposition, extrapolating information nested in these simplified series and rebuilding the forecasted time series. Earth's temperature anomalies are forecasted until 2100. Two sets of forecasts are generated, one with historical data including the Covid‐19 pandemic (from 1880 up to 2021 with NASA/GISS data or from 1850 up to 2022 with East Anglia and Hadley Centre data) and one excluding the Covid‐19 pandemic up to 2019. With the sets of data not including the pandemic, forecasts show temperature anomalies skyrocketing and bringing Earth's global warming at alarming levels in Northern and Southern Hemispheres. With the set of data including the pandemic, forecasts are reverting showing lower alarming levels by the end of the century, in the Northern and Southern Hemispheres and globally.

Approximating the Internal Variability of Bias-Corrected Global Temperature Projections with Spatial Stochastic Generators

AbstractDecision making under climate change, from vulnerability assessments to adaptation and mitigation, requires an accurate quantification of the uncertainty in the future climate. Physically constrained projections, in the presence of both observations and climate simulations, can be obtained by establishing an empirical relationship in the historical time period, and use it to correct the bias of future simulations. Traditional bias correction approaches do not account for the uncertainty in the climate simulation, and focus on regionally aggregated variables without spatial dependence, with loss of useful information such as the variability of gradients across regions. We propose a new statistical model for bias correction of monthly surface temperatures with sparse and interpretable spatial structure, and we use it to obtain future reanalysis projections with associated uncertainty, using only a small ensemble of global simulations.

An observation-based scaling model for climate sensitivity estimates and global projections to 2100

We directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green’s function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale tau approx 2 years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: 1.7^{+0.3} _{-0.2} K and 2.4^{+1.3} _{-0.6} K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880–1910 of: 1.5^{+0.4}_{-0.2}K, 2.3^{+0.7}_{-0.5} K and 4.2^{+1.3}_{-0.9} K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of [-1.0, -0.3], ,,mathrm{Wm} ^{-2} (90 % confidence interval) with median at -0.7 ,,mathrm{Wm} ^{-2}. Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.

Uncertainty in projected climate change arising from uncertain fossil-fuel emission factors

Emission inventories are widely used by the climate community, but their uncertainties are rarely accounted for. In this study, we evaluate the uncertainty in projected climate change induced by uncertainties in fossil-fuel emissions, accounting for non-CO2 species co-emitted with the combustion of fossil-fuels and their use in industrial processes. Using consistent historical reconstructions and three contrasted future projections of fossil-fuel extraction from Mohr et al we calculate CO2 emissions and their uncertainties stemming from estimates of fuel carbon content, net calorific value and oxidation fraction. Our historical reconstructions of fossil-fuel CO2 emissions are consistent with other inventories in terms of average and range. The uncertainties sum up to a ±15% relative uncertainty in cumulative CO2 emissions by 2300. Uncertainties in the emissions of non-CO2 species associated with the use of fossil fuels are estimated using co-emission ratios varying with time. Using these inputs, we use the compact Earth system model OSCAR v2.2 and a Monte Carlo setup, in order to attribute the uncertainty in projected global surface temperature change (∆T) to three sources of uncertainty, namely on the Earth system’s response, on fossil-fuel CO2 emission and on non-CO2 co-emissions. Under the three future fuel extraction scenarios, we simulate the median ∆T to be 1.9, 2.7 or 4.0 °C in 2300, with an associated 90% confidence interval of about 65%, 52% and 42%. We show that virtually all of the total uncertainty is attributable to the uncertainty in the future Earth system’s response to the anthropogenic perturbation. We conclude that the uncertainty in emission estimates can be neglected for global temperature projections in the face of the large uncertainty in the Earth system response to the forcing of emissions. We show that this result does not hold for all variables of the climate system, such as the atmospheric partial pressure of CO2 and the radiative forcing of tropospheric ozone, that have an emissions-induced uncertainty representing more than 40% of the uncertainty in the Earth system’s response.

Apparent limitations in the ability of CMIP5 climate models to simulate recent multi-decadal change in surface temperature: implications for global temperature projections

Observed surface temperature trends over the period 1998–2012/2014 have attracted a great deal of interest because of an apparent slowdown in the rate of global warming, and contrasts between climate model simulations and observations of such trends. Many studies have addressed the statistical significance of these relatively short-trends, whether they indicate a possible bias in the model values and the implications for global warming generally. Here we re-examine these issues, but as they relate to changes over much longer-term changes. We find that on multi-decadal time scales there is little evidence for any change in the observed global warming rate, but some evidence for a recent temporary slowdown in the warming rate in the Pacific. This multi-decadal slowdown can be partly explained by a cool phase of the Interdecadal Pacific Oscillation and a short-term excess of La Niña events. We also analyse historical and projected changes in 38 CMIP climate models. All of the model simulations examined simulate multi-decadal warming in the Pacific over the past half-century that exceeds observed values. This difference cannot be fully explained by observed internal multi-decadal climate variability, even if allowance is made for an apparent tendency for models to underestimate internal multi-decadal variability in the Pacific. Models which simulate the greatest global warming over the past half-century also project warming that is among the highest of all models by the end of the twenty-first century, under both low and high greenhouse gas emission scenarios. Given that the same models are poorest in representing observed multi-decadal temperature change, confidence in the highest projections is reduced.

Connecting Climate Model Projections of Global Temperature Change with the Real World

Abstract Current state-of-the-art global climate models produce different values for Earth’s mean temperature. When comparing simulations with each other and with observations, it is standard practice to compare temperature anomalies with respect to a reference period. It is not always appreciated that the choice of reference period can affect conclusions, both about the skill of simulations of past climate and about the magnitude of expected future changes in climate. For example, observed global temperatures over the past decade are toward the lower end of the range of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations irrespective of what reference period is used, but exactly where they lie in the model distribution varies with the choice of reference period. Additionally, we demonstrate that projections of when particular temperature levels are reached, for example, 2 K above “preindustrial,” change by up to a decade depending on the choice of reference period. In this article, we discuss some of the key issues that arise when using anomalies relative to a reference period to generate climate projections. We highlight that there is no perfect choice of reference period. When evaluating models against observations, a long reference period should generally be used, but how long depends on the quality of the observations available. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) choice to use a 1986–2005 reference period for future global temperature projections was reasonable, but a case-by-case approach is needed for different purposes and when assessing projections of different climate variables. Finally, we recommend that any studies that involve the use of a reference period should explicitly examine the robustness of the conclusions to alternative choices.

Drawbacks of Apriorism in Intergovernmental Climatology

IPCC (2013) replaces climate models' over-predictions of near-term global warming with its “expert assessment” that warming in the next 30 years may scarcely exceed that of the last 30. Medium-term anthropogenic forcings and global temperature projections have been all but halved since 1990. There has been no global warming this millennium. Nevertheless, at the intergovernmental level, an aprioristic approach to modeling, over-confidence in models' predictive skill, and serial misrepresentation of results has prevented agreement on what fraction of recent warming was anthropogenic, how much warming we may cause and at what risk or net welfare loss, if any. The cost of mitigating predicted warming exceeds that of later adaptation by 1–2 orders of magnitude. Given governments' pre-existing monopsony of climatology, the question arises whether intergovernmental science is either necessary or desirable.

A Review of Uncertainties in Global Temperature Projections over the Twenty-First Century

Abstract Quantification of the uncertainties in future climate projections is crucial for the implementation of climate policies. Here a review of projections of global temperature change over the twenty-first century is provided for the six illustrative emission scenarios from the Special Report on Emissions Scenarios (SRES) that assume no policy intervention, based on the latest generation of coupled general circulation models, climate models of intermediate complexity, and simple models, and uncertainty ranges and probabilistic projections from various published methods and models are assessed. Despite substantial improvements in climate models, projections for given scenarios on average have not changed much in recent years. Recent progress has, however, increased the confidence in uncertainty estimates and now allows a better separation of the uncertainties introduced by scenarios, physical feedbacks, carbon cycle, and structural uncertainty. Projection uncertainties are now constrained by observations and therefore consistent with past observed trends and patterns. Future trends in global temperature resulting from anthropogenic forcing over the next few decades are found to be comparably well constrained. Uncertainties for projections on the century time scale, when accounting for structural and feedback uncertainties, are larger than captured in single models or methods. This is due to differences in the models, the sources of uncertainty taken into account, the type of observational constraints used, and the statistical assumptions made. It is shown that as an approximation, the relative uncertainty range for projected warming in 2100 is the same for all scenarios. Inclusion of uncertainties in carbon cycle–climate feedbacks extends the upper bound of the uncertainty range by more than the lower bound.

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.

Identifying Key Sources of Uncertainty in Climate Change Projections

What sources of uncertainty shouldbe included in climate change projections and whatgains can be made if specific sources of uncertaintyare reduced through improved research?DIALOGUE, anintegrated assessment model, has been used to answerthese questions. Central in the approach of DIALOGUEis the concept of parallel modeling, i.e., for eachstep in the chain from emissions to climate change anumber of equivalent models areimplemented. The followingconclusions are drawn:The key source of uncertainty in global temperatureprojections appears to be the uncertainty inradiative forcing models. Within this group ofmodels uncertainty within aerosol forcing models isabout equal to the total forcing of greenhouse gasmodels. In the latter group CO2 is dominant.The least important source of uncertainty appears tobe the gas cycle models. Within this group of modelsthe role of carbon cycle models is dominant.Uncertainty in global temperature projections hasnot been treated consistently in the literature.First, uncertainty should be calculated as a productof all uncertainty sources. Second, aparticular choice of a base year for global warmingcalculations influences the ranking of uncertainty.Because of this, a comparison of ranking resultsacross different studies is hampered. We argue that`pre-Industrial' is the best choice for studies onuncertainty.There is a linear relationship between maximumuncertainty in the year 2100 and cumulativeemissions of CO2 over the period 1990–2100:higher emissions lead to more uncertainty.

Comparison of longterm greenhouse projections with the geologic record

Previous climate modeling studies suggest that temperature increases beyond 2100 AD due to greenhouse gas changes could be comparable to the Cretaceous. However, these projections have not been tested against a range of emission scenarios, nor have estimates of past temperature change been well restrained quantitatively. Herein we use a 1D energy balance model (EBM) with an upwelling‐diffusion ocean model to explore the temperature response to extreme cases of unrestricted and severely restricted greenhouse gas increases. Results are compared against revised estimates of global temperature change over the last 100 million years. For four of the six scenarios, global temperature projections by the years 2400–2700 equal or exceed levels of the Eocene or Cretaceous warm periods. The two exceptions involve scenarios with relatively low climate sensitivity and stringent conservation measures, but even in these projections temperatures are as warm as any time in the last 30 million years. These calculations suggest that regardless of emission scenario or system sensitivity, future greenhouse warming will be large even on a geological scale.