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Climate feedbacks over the historical period (here defined as 1850–2014) have been investigated in large ensembles of historical and single forcing experiments (hist‐ghg, hist‐aer, and hist‐nat), 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 strongly correlated with the amount of sea ice melt per degree of global warming. This mechanism is related to 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 strongly correlated with the longwave clear‐sky feedbacks, and the local surface‐air‐temperatures well correlated with the spread in cloud radiative effect 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 modeled estimate of the feedbacks seen in the real world derived from an atmosphere‐only experiment prescribed with observed SSTs (termed amip‐piForcing). Plain Language Summary: Understanding how the Earth's climate responds to an imposed forcing such as an increase in greenhouse gases or aerosols is an important issue relevant to climate mitigation and adaptation policies on the global scale. One way we can understand this is by analysing the historical period (1850–2014), a period over which the climate has already changed substantially due to human induced forcings, and also a period over which observations allow us to compare modeled changes in climate with the changes seen in the real world. Here, we use a large ensemble of climate model simulations of the historical period where we aim to understand (a) how natural variability causes differences in the global temperature response to the same imposed forcing, (b) what causes different forcing agents (e.g., greenhouse gases or aerosols) to be more or less effective at warming or cooling the planet, and (c) whether historical simulations—where the climate model simulates its own sea surface temperatures—capture the same response to historical forcings as an atmosphere‐only simulation prescribed with observed sea surface temperatures. We find that the pattern of sea surface temperatures (particularly the levels of tropical and polar warming) is key to understanding each of these points. Key Points: Natural variability causes a 3–6 K range in Effective Climate Sensitivity in a large single model ensemble of historical simulationsTropical and polar warming are strongly related to the spread in longwave clear‐sky and shortwave clear‐sky feedbacks respectivelyDeficiencies in simulating observed tropical and polar warming cause different feedbacks in historical and amip‐piForcing experiments [ABSTRACT FROM AUTHOR]

In recent decades, three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal to decadal timescales that could have been potentially predicted with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of volcanic aerosols on the climate experienced during the years that followed those eruptions through the use of decadal predictions. The interest of conducting this exercise with climate predictions is that, thanks to the initialisation, they start from the observed climate conditions at the time of the eruptions, which helps to disentangle the climatic changes due to the initial conditions and internal variability from the volcanic forcing. The protocol consists of repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of their volcanic forcing, which are then compared with the baseline predictions to disentangle the simulated volcanic effects upon climate. We present the results from six Coupled Model Intercomparison Project Phase 6 (CMIP6) decadal prediction systems. These systems show strong agreement in predicting the well-known post-volcanic radiative effects following the three eruptions, which induce a long-lasting cooling in the ocean. Furthermore, the multi-model multi-eruption composite is consistent with previous work reporting an acceleration of the Northern Hemisphere polar vortex and the development of El Niño conditions the first year after the eruption, followed by a strengthening of the Atlantic Meridional Overturning Circulation the subsequent years. Our analysis reveals that all these dynamical responses are both model- and eruption-dependent. A novel aspect of this study is that we also assess whether the volcanic forcing improves the realism of the predictions. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that, overall, including the volcanic forcing results in better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed sea surface temperature (SST) variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo. [ABSTRACT FROM AUTHOR]

Ocean reanalyses covering many decades, including those with few observations, are needed to understand climate variability and to initialize and assess interannual to decadal climate predictions. The Met Office Statistical Ocean Re‐Analysis (MOSORA) exploits long‐range covariances to generate full‐depth reanalyses of monthly ocean temperature and salinity even from sparse observations. We extend MOSORA by generating an ensemble that samples uncertainties in long‐range covariances. Initial covariances are taken from model runs and these are improved with observations using an iterative process. We demonstrate that covariances are improved by iteration, and that this procedure, using very sparse observations typical of the 1960s, captures many features of analyses benefiting from modern observation density. We investigate the ensemble spread and find that salinity trends in the covariances from model runs can introduce unexpected changes in the reanalyses. We nudge the reanalyses into an ensemble of coupled climate models to produce estimates of the Atlantic Meridional Overturning Circulation (AMOC). At 26°N, the AMOC shows decadal variability consistent with observations at this latitude and shows signs of strengthening in recent years. The ensemble spread in AMOC reconstructions increases with time as more observations interact with uncertain covariances. At 45°N, the amount of decadal variability in the AMOC varies between members, but on shorter timescales the variability is similar across the ensemble. At 45°N, the AMOC can be constrained better with more observations on the western boundary, but longer continuous observations are needed to improve covariances and reduce uncertainties in the AMOC. [ABSTRACT FROM AUTHOR]

In recent decades three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal-to-decadal timescales that could have been potentially predictable with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of such volcanic eruptions on decadal prediction. It consists in repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of the volcanic forcing, which are then compared with the baseline predictions to disentangle their effect. We present the results from six CMIP6 decadal prediction systems. These systems show a strong agreement in predicting the radiative response to the volcanic eruptions, simulating a reduction in global mean top-of-atmosphere radiation fluxes, surface temperature and ocean heat content. The characteristic geographical patterns of the response are con10 sistent across the models and share similarities across the volcanic eruptions, however some differences across models and eruptions arise due to the varying magnitude and spatiotemporal structure of the volcanic forcing. Taking advantage of the large multi-model ensemble we additionally analyse the dynamical responses in the Northern Hemisphere atmospheric circulation, in the tropical Pacific Ocean and the North Atlantic Ocean. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that including the volcanic forcing results in overall better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed SST variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo, however in the tropical Pacific Ocean the predicted SST anomalies are degraded. [ABSTRACT FROM AUTHOR]

North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful. Significance Statement: The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general. [ABSTRACT FROM AUTHOR]