Forcing, feedback and internal variability in global temperature trends

Most present-generation climate models simulate an increase in global-mean surface temperature (GMST) since 1998, whereas observations suggest a warming hiatus. It is unclear to what extent this mismatch is caused by incorrect model forcing, by incorrect model response to forcing or by random factors. Here we analyse simulations and observations of GMST from 1900 to 2012, and show that the distribution of simulated 15-year trends shows no systematic bias against the observations. Using a multiple regression approach that is physically motivated by surface energy balance, we isolate the impact of radiative forcing, climate feedback and ocean heat uptake on GMST—with the regression residual interpreted as internal variability—and assess all possible 15- and 62-year trends. The differences between simulated and observed trends are dominated by random internal variability over the shorter timescale and by variations in the radiative forcings used to drive models over the longer timescale. For either trend length, spread in simulated climate feedback leaves no traceable imprint on GMST trends or, consequently, on the difference between simulations and observations. The claim that climate models systematically overestimate the response to radiative forcing from increasing greenhouse gas concentrations therefore seems to be unfounded

Energy budget constraints on historical radiative forcing

Radiative forcing is a fundamental quantity for understanding anthropogenic and natural drivers of past and future climate change1, yet significant uncertainty remains in our quantification of radiative forcing and its model representation2,3,4. Here we use instrumental measurements of historical global mean surface temperature change and Earth’s total heat uptake, alongside estimates of the Earth’s radiative response, to provide a top-down energy budget constraint on historical (1861–1880 to near-present) effective radiative forcing of 2.3 W m−2 (1.7–3.0W m−2; 5–95% confidence interval). This represents a near 40% reduction in the 5–95% uncertainty range assessed by the IPCC Fifth Assessment Report2. Although precise estimates of effective radiative forcing in models do not widely exist, our results suggest that the effective radiative forcing may be too small in as many as one-third of climate models in the fifth phase of the Coupled Model Intercomparison Project. Improving model representation of radiative forcing should be a priority for modelling centres. This will reduce uncertainties in climate projections that have persisted for decades4,5

Quantifying forest growth uncertainty on carbon payback times in a simple biomass carbon model

In 2018 Sterman et al (2018a) published a simple dynamic lifecycle analysis(DLCA) model for forest-sourced bioenergy. The model has been widely cited since its publication, including widespread reporting of the model’s headline results within the media. In adapting a successful replication of the Sterman et al (2018a) model with open-source software, we identified a number of changes to input parameters which improved the fit of the model’s forest site growth function with its training data. These relatively small changes to the input parameters result in relatively large changes to the model predictions of forest site carbon uptake: up to 92 tC.ha−1 or 18% of total site carbon at year 500. This change in estimated site carbon resulted in calculated payback periods (carbon sequestration parity) which differed by up to 54 years in a clear-fell scenario when compared with results obtained using previously published parameters. Notably, this uncertainty was confined to forests which were slower growing and where the model’s training dataset was not sufficiently long for forests to reach maturity. We provide improved parameterisations for all forest types used within the original Sterman et al (2018a) paper, and propose that these provide better fits to the underlying data. We also provide margins of error for the generated growth curves to indicate the wide range of possible results possible with the model for some forest types. We conclude that, while the revised model is able to reproduce the earlier Sterman et al (2018a)results, the headline figures from that paper depend heavily on how the forest growth curve is fitted to the training data. The resulting uncertainty in payback periods could be reduced by either obtaining more extensive training data (including mature forests of all types) or by modification of the forest growth function

Guidance on emissions metrics for nationally determined contributions under the Paris Agreement

Many nationally determined contributions (NDCs) under the Paris Agreement follow the established practice of specifying emissions levels in tonnes of CO2 equivalent emissions. The Global Warming Potential (GWP) is the emissions metric used most often to aggregate contributions from different greenhouse gases (GHGs). However, the climate impact of pathways expressed in this way is known to be ambiguous. For this reason, alternatives have been proposed but the ambiguity has not been quantified in the context of the Paris Agreement. Here we assess the variation in temperature using pathways consistent with the ambition of limiting temperature increases to well below 2 °C. These are taken from the IPCC Special Report on Global Warming of 1.5 °C (SR15). The CO2 emission levels are adjusted so that the pathways all have the same total CO2 equivalent emissions for a given emissions metric but have different proportions of short-lived and long-lived pollutants. We show that this difference affects projections by up to 0.17 °C when GWP100 is used. Options of reducing this ambiguity include using a different emissions metric or adding supplementary information in NDCs about the emissions levels of individual GHGs. We suggest the latter on the grounds of simplicity and because it does not require agreement on the use of a different emissions metric

A topography of climate change research

The massive expansion of scientific literature on climate change1 poses challenges for global environmental assessments and our understanding of how these assessments work. Big data and machine learning can help us deal with large collections of scientific text, making the production of assessments more tractable, and giving us better insights about how past assessments have engaged with the literature. We use topic modelling to draw a topic map, or topography, of over 400,000 publications from the Web of Science on climate change. We update current knowledge on the IPCC, showing that compared with the baseline of the literature identified, the social sciences are in fact over-represented in recent assessment reports. Technical, solutions-relevant knowledge—especially in agriculture and engineering—is under-represented. We suggest a variety of other applications of such maps, and our findings have direct implications for addressing growing demands for more solution-oriented climate change assessments that are also more firmly rooted in the social sciences2,3. The perceived lack of social science knowledge in assessment reports does not necessarily imply an IPCC bias, but rather suggests a need for more social science research with a focus on technical topics on climate solutions

Cloud adjustment and its role in CO 2 radiative forcing and climate sensitivity: a review

Understanding the role of clouds in climate change remains a considerable challenge. Traditionally, this challenge has been framed in terms of understanding cloud feedback. However, recent work suggests that under increasing levels of atmospheric carbon dioxide, clouds not only amplify or dampen climate change through global feedback processes, but also through rapid (days to weeks) tropospheric temperature and land surface adjustments. In this article, we use the Met Office Hadley Centre climate model HadGSM1 to review these recent developments and assess their impact on radiative forcing and equilibrium climate sensitivity. We estimate that cloud adjustment contributes ~0.8 K to the 4.4 K equilibrium climate sensitivity of this particular model. We discuss the methods used to evaluate cloud adjustments, highlight the mechanisms and processes involved and identify low level cloudiness as a key cloud type. Looking forward, we discuss the outstanding issues, such as the application to transient forcing scenarios. We suggest that the upcoming CMIP5 multi-model database will allow a comprehensive assessment of the significance of cloud adjustments in fully coupled atmosphere-ocean-general-circulation models for the first time, and that future research should exploit this opportunity to understand cloud adjustments/feedbacks in non-idealised transient climate change scenarios

Adaptation planning and the use of climate change projections in local government in England and Germany

Planning for adaptation to climate change is often regarded to be a local imperative and considered to be more effective if grounded on a solid evidence base and recognisant of relevant climate projections. Research has already documented some of the challenges of making climate information usable in decision-making but has not yet sufficiently reflected on the role of the wider institutional and regulatory context. This article examines the impact of the external institutional context on the use and usability of climate projections in local government through an analysis of 44 planning and climate change (adaptation) documents and 54 semi-structured interviews with planners in England and Germany conducted between July 2013 and May 2014. We show that there is little demand for climate projections in local adaptation planning in either country due to existing policy, legal and regulatory frameworks. Local government in England has not only experienced a decline in use of climate projections, but also the waning of the climate change adaptation agenda more widely, amidst changes in the planning and regulatory framework and severe budget cuts. In Germany, spatial planning makes substantial use of past and present climate data, but the strictly regulated nature of planning prevents the use of climate projections, due to their inherent uncertainties. Findings from the two countries highlight that if we are to better understand the usability of climate projections, we need to be more aware of the institutional context within which planning decisions are made. Otherwise we run the risk of continuing to provide tools and information that are of limited use within their intended context

Slow and fast response of mean and extreme precipitation to different forcing in CMIP5 simulations

We are investigating the fast and slow responses of changes in mean and extreme precipitation to different climate forcing mechanisms, such as greenhouse gas and solar forcing, to understand whether rapid adjustments are important for extreme precipitation. To disentangle the effect of rapid adjustment to a given forcing on the overall change in extreme precipitation we use a linear regression method that has been previously applied to mean precipitation. Equilibrium experiments with preindustrial CO2 concentrations and reduced solar constant were compared with a four times CO2 concentration experiment for 10 state-of-the-art climate models. We find that the two forcing mechanisms, greenhouse gases and solar, impose clearly different rapid adjustment signals in the mean precipitation, while such difference is difficult to discern for extreme precipitation due to large internal variability. In contrast to mean precipitation, changes in extreme precipitation scale with surface temperature trends and do not seem to depend on the forcing mechanism

Importance of tropospheric volcanic aerosol for indirect radiative forcing of climate

Observations and models have shown that continuously degassing volcanoes have a potentially large effect on the natural background aerosol loading and the radiative state of the atmosphere. We use a global aerosol microphysics model to quantify the impact of these volcanic emissions on the cloud albedo radiative forcing under pre-industrial (PI) and present-day (PD) conditions. We find that volcanic degassing increases global annual mean cloud droplet number concentrations by 40% under PI conditions, but by only 10% under PD conditions. Consequently, volcanic degassing causes a global annual mean cloud albedo effect of −1.06 W m−2 in the PI era but only −0.56 W m−2 in the PD era. This non-equal effect is explained partly by the lower background aerosol concentrations in the PI era, but also because more aerosol particles are produced per unit of volcanic sulphur emission in the PI atmosphere. The higher sensitivity of the PI atmosphere to volcanic emissions has an important consequence for the anthropogenic cloud radiative forcing because the large uncertainty in volcanic emissions translates into an uncertainty in the PI baseline cloud radiative state. Assuming a −50/+100% uncertainty range in the volcanic sulphur flux, we estimate the annual mean anthropogenic cloud albedo forcing to lie between −1.16 W m−2 and −0.86 W m−2. Therefore, the volcanically induced uncertainty in the PI baseline cloud radiative state substantially adds to the already large uncertainty in the magnitude of the indirect radiative forcing of climate

Latest climate models confirm need for urgent mitigation

Many recently updated climate models show greater future warming than previously. Separate lines of evidence suggest that their warming rates may be unrealistically high, but the risk of such eventualities only emphasizes the need for rapid and deep reductions in emissions