Climate feedbacks over the historical period (1850–2014) have been investigated in large ensembles of historical, hist-ghg, hist-aer, and hist-nat experiments, with 47 members for each experiment. Across the historical ensemble with all forcings, a range in estimated Effective Climate Sensitivity (EffCS) between approximately 3–6 K is found, a considerable spread stemming solely from initial condition uncertainty. The spread in EffCS is associated with varying Sea Surface Temperature (SST) patterns seen across the ensemble due to their influence on different feedback processes. For example, the level of polar amplification is shown to strongly control the amount of sea ice melt per degree of global warming. This mechanism is responsible for the large spread in shortwave clear-sky feedbacks and is the main contributor to the different forcing efficacies seen across the different forcing agents, although in HadGEM3-GC3.1-LL these differences in forcing efficacy are shown to be small. The spread in other feedbacks is also investigated, with the level of tropical SST warming shown to strongly control the longwave clear-sky feedbacks, and the local surface-air-temperatures and large scale tropospheric temperatures shown to influence cloud feedbacks. The metrics used to understand the spread in feedbacks can also help to explain the disparity between feedbacks seen in the historical experiment simulations and a more accurate modeled estimate of the feedbacks seen in the real world derived from an atmosphere-only experiment prescribed with observed SSTs (termed amip-piForcing).

Effective radiative forcing (ERF) is evaluated in the ACCESS1.0 General Circulation Model (GCM) with fixed land and sea-surface-temperatures as well as sea-ice. The 4xCO2 ERF is 8.0 Wm-2. In contrast, a typical ERF experiment with only fixed sea-surface-temperatures (SST) and sea-ice gives rise to an ERF of only 7.0 Wm-2. This difference arises due to the influence of land warming in the commonly used fixed-SST ERF experimental design, which results in: (i) increased emission of longwave radiation to space from the land surface (-0.45 Wm-2) and troposphere (-0.90 Wm-2), (ii) reduced land snow-cover and albedo (+0.17 Wm-2), (iii) increased water-vapour (+0.49 Wm-2), and (iv) a cloud adjustment (-0.26 Wm-2) due to reduced stability and cloudiness over land (positive ERF) counteracted by increased lower tropospheric stability and marine cloudiness over oceans (negative ERF) . The sum of these radiative adjustments to land warming is to reduce the 4xCO2 ERF in fixed-SST experiments by ~1.0 Wm-2. CO2 stomatal effects are quantified and found to contribute just over half of the land warming effect and adjustments in the fixed-SST ERF experimental design in this model. The basic physical mechanisms in response to land warming are confirmed in a solar ERF experiment. We test various methods that have been proposed to account for land warming in fixed-SST ERFs against our GCM results and discuss their strengths and weaknesses.

We investigate the dependence of radiative feedback on the pattern of sea-surface temperature (SST) change in fourteen Atmospheric General Circulation Models (AGCMs) forced with observed variations in SST and sea-ice over the historical record from 1871 to near-present. We find that over 1871-1980, the Earth warmed with feedbacks largely consistent and strongly correlated with long-term climate sensitivity feedbacks (diagnosed from corresponding atmosphere-ocean GCM abrupt-4xCO2 simulations). Post 1980 however, the Earth warmed with unusual trends in tropical Pacific SSTs (enhanced warming in the west, cooling in the east) that drove climate feedback to be uncorrelated with – and indicating much lower climate sensitivity than – that expected for long-term CO2 increase. We show that these conclusions are not strongly dependent on the AMIP II SST dataset used to force the AGCMs, though the magnitude of feedback post 1980 is generally smaller in eight AGCMs forced with alternative HadISST1 SST boundary conditions. We quantify a ‘pattern effect’ (defined as the difference between historical and long-term CO2 feedback) equal to 0.44 ± 0.47 [5-95%] W m-2 K-1 for the time-period 1871-2010, which increases by 0.05 ± 0.04 W m-2 K-1 if calculated over 1871-2014. Assessed changes in the Earth’s historical energy budget are in agreement with the AGCM feedback estimates. Furthermore satellite observations of changes in top-of-atmosphere radiative fluxes since 1985 suggest that the pattern effect was particularly strong over recent decades, though this may be waning post 2014 due to a warming of the eastern Pacific.

The pre-industrial (Year 1850) to present-day (Year 2014) increase in methane from 808 to 1831 ppb leads to an effective radiative forcing (ERF) of 0.97±0.04 Wm‑2 in the United Kingdom’s Earth System Model, UKESM1. The direct methane contribution is 0.54±0.04 Wm‑2. It is better represented in UKESM1 than in its predecessor due to the inclusion of shortwave absorption, updates to the longwave spectral properties, and no interference from dust. An indirect ozone ERF of 0.13-0.20 Wm-2 is largely due to the radiative effect of the tropospheric ozone increase outweighing that of the stratospheric ozone decrease. An indirect water vapor ERF of 0.07±0.05/0.02±0.04 Wm‑2 is consistent with previous estimates based on the stratospherically-adjusted radiative forcing metric. The methane increase also leads to a cloud radiative effect of 0.12±0.02 Wm‑2 from aerosol-cloud interactions and thermodynamic adjustments. The aerosol-mediated contribution (0.28‑0.30 Wm‑2) arises because methane-driven oxidant changes alter the rate of new particle formation (-8 %), causing a change in the aerosol size distribution towards fewer larger particles. There is a resulting decrease in cloud droplet number concentration and an increase in cloud droplet effective radius. There are additional shortwave and longwave contributions of 0.23 and ‑0.35 Wm-2 to the cloud forcing which are dynamically-driven. They arise from radiative heating and stabilization of the upper troposphere, resulting in a reduction in global cloud cover and convection. These results highlight the importance of chemistry-aerosol-cloud interactions and dynamical adjustments in climate forcing and can explain some of the diversity in multi-model estimates of methane forcing.

This study assesses the effective climate sensitivity (EffCS) and transient climate response (TCR) derived from global energy budget constraints within historical simulations of 8 CMIP6 global climate models (GCMs). These calculations are enabled by use of the Radiative Forcing Model Intercomparison Project (RFMIP) simulations, which permit accurate quantification of the historical effective radiative forcing. We find that long-term historical energy budget constraints generally underestimate EffCS from CO2 quadrupling and TCR from CO2 ramping, both by 12%, owing to changes in radiative feedbacks and changes in ocean heat uptake efficiency. Atmospheric GCMs forced by observed warming patterns produce lower values of EffCS that are more in line with those inferred from observed historical energy budget constraints. Understanding the discrepancies between modeled and observed historical surface warming patterns remains critical for constraining EffCS and TCR from the historical record.

Climate model emulators are widely used to generate temperature projections for climate scenarios, including in the recent IPCC Sixth Assessment Report. Here we evaluate the performance of a two-layer energy balance model in emulating historical and future temperature projections from CMIP6 models. We find that prediction errors can be large (greater than 0.5oC in a given year) and differ markedly between climate models, forcing scenarios and time periods. Errors arise in emulating the near-surface temperature response to both greenhouse gas and aerosol forcing; in some periods the errors due to these forcings oppose one another, giving the spurious impression of better emulator performance. Time-varying and state-dependent feedbacks may contribute to prediction errors. Close emulations can be produced for a given period but, crucially, this does not guarantee reliable emulations of other scenarios and periods. Therefore, rigorous out-of-sample evaluation is necessary to characterize emulator performance.