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A Bayesian structure learning approach is employed to compare and contrast interactions between the major climate teleconnections over the recent past as revealed in reanalyses and climate model simulations from leading Meteorological Centers. In a previous study, the authors demonstrated a general framework using homogeneous Dynamic Bayesian Network models constructed from reanalyzed time series of empirical climate indices to compare probabilistic graphical models. Reversible jump Markov Chain Monte Carlo is used to provide uncertainty quantification for selecting the respective network structures. The incorporation of confidence measures in structural features provided by the Bayesian approach is key to yielding informative measures of the differences between products if network‐based approaches are to be used for model evaluation, particularly as point estimates alone may understate the relevant uncertainties. Here we compare models fitted from the NCEP/NCAR and JRA‐55 reanalyses and Coupled Model Intercomparison Project version 5 (CMIP5) historical simulations in terms of associations for which there is high posterior confidence. Examination of differences in the posterior probabilities assigned to edges of the directed acyclic graph provides a quantitative summary of departures in the CMIP5 models from reanalyses. In general terms the climate model simulations are in better agreement with reanalyses where tropical processes dominate, and autocorrelation time scales are long. Seasonal effects are shown to be important when examining tropical‐extratropical interactions with the greatest discrepancies and largest uncertainties present for the Southern Hemisphere teleconnections. Plain Language Summary Climate model biases and performance is typically assessed against observational products via systematic comparison of individual metrics, usually focused on the mean climate, over the recent historical period. We demonstrate how Bayesian structure learning can enable a systematic probabilistic framework for process‐based model evaluation of both the temporal behavior of individual climate modes but also to identify and assess the teleconnections between those modes. We show that network structures can be fitted simultaneously and feasibly across a representative sample of climate model simulations affording uncertainty estimation of the robustness of differences across models and observations and robustly identify model biases between teleconnections in the climate. Key Points Bayesian structure learning is used to quantify uncertainty in estimated network structures describing climate mode teleconnections Dynamic Bayesian networks estimated from reanalyses are compared to CMIP5 model simulations over the historical period Differences in network structures between models and reanalyses quantify complex interacting biases in climate model dynamics

Regime dependencies and Granger causal relationships between tropical and extratropical teleconnections are inferred using Bayesian structure learning. Using ERA5 data, an examination of the differences between the learned graphical structures during particular phases of the Interdecadal Pacific Oscillation (IPO) are used to infer the role of the background state on interactions between the major climate teleconnections. These relationships present a clear regime dependency on the phase of IPO. In the positive phase, IPO autocorrelations are weak whereas Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) autocorrelations and the influence of the Madden Julian Oscillation (MJO) are indicative of an enhanced Walker circulation. In contrast, during the negative phase, IPO autocorrelations are strongest with evidence of an enhanced role for extratropical teleconnections on the tropics. Exclusion of MJO removes important tropical-extratropical influences while increasing posterior edge weights between ENSO, the IPO and IOD. Our analysis reveals the dependence of the ENSO autocorrelation on the phase of the background IPO state, and the role of the MJO as being key to link the extratropical tropospheric modes Pacific North American and North Atlantic Oscillation (PNA, NAO) and equatorial surface ocean temperatures (IOD, ENSO) and as a consequence convection.

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.

Linear regression is used to examine the relationship between simulated changes in historical global-mean surface temperature (GMST) and global-mean aerosol effective radiative forcing (ERF) in 14 climate models from CMIP5. The models have global-mean aerosol ERF that ranges from −0.35 to −1.60 W m−2for 2000 relative to 1850. It is shown that aerosol ERF is the dominant factor that determines intermodel variations in simulated GMST change: correlations between aerosol ERF and simulated changes in GMST exceed 0.9 for linear trends in GMST over all periods that begin between 1860 and 1950 and end between 1995 and 2005. Comparison of modeled and observed GMST trends for these time periods gives an inferred global-mean aerosol ERF of −0.92 W m−2. On average, transient climate sensitivity is roughly 40% larger with respect to historical forcing from aerosols than well-mixed greenhouse gases. This enhanced sensitivity explains the dominant effect of aerosol forcing on simulated changes in GMST: it is estimated that 85% of the intermodel variance of simulated GMST change is explained by variations in aerosol ERF, but without the enhanced sensitivity less than half would be explained. Physically, the enhanced sensitivity is caused by a combination of 1) the larger concentration of aerosol forcing in the midlatitudes of the Northern Hemisphere, where positive feedbacks are stronger and transient warming is faster than in the Southern Hemisphere, and 2) the time evolution of aerosol forcing, which levels out earlier than forcing from well-mixed greenhouse gases.

We detail the system design, model configuration, and data assimilation evaluation for the CSIRO Climate retrospective Analysis and Forecast Ensemble system, version 1 (CAFE60v1). CAFE60v1 has been designed with the intention of simultaneously generating both initial conditions for multiyear climate forecasts and a large ensemble retrospective analysis of the global climate system from 1960 to the present. Strongly coupled data assimilation(SCDA)is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are subsampled from the JRA-55 atmospheric reanalysis. Strong coupling is implemented via explicit cross-domain covariances between ocean, atmosphere, sea ice, and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean, and sea ice. The system produces 96 climate trajectories (state estimates) over the most recent six decades as well as a complete data archive of initial conditions, potentially enabling individual forecasts for all members each month over the 60-yr period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses.

Recent studies of various stochastic forcing and subgrid-scale parameterization schemes applied to climate and atmospheric models have revealed a diversity of model responses. These responses include degeneracy in the response to different forcings and compensating model errors. While stochastic parameterization of the ocean eddies is an active area, this has mainly involved idealized models with fewer studies employing ocean general circulation models. Here we examine the sensitivity of a low-resolution climate model to stochastic forcing of the momentum fluxes restricted to regions of the three-dimensional ocean where an eddy-resolving ocean model configuration has high variability. We consider the changes in the modeled energetics of low-resolution simulations in response to increased stochastic forcing. We find that as forcing amplitudes are increased there is enhanced conversion of transient to seasonal potential energy. Additionally, there is a systematic redistribution from seasonal to small-scale transient kinetic energy. Our approach has zero mean noise such that the total kinetic energy spectra remain largely unchanged even as small-scale eddy kinetic energy is increased in the targeted regions. However, we also show that strong stochastic forcing, particularly when applied in the tropics, can induce substantial changes to the ocean steady state that are undesirable. These changes include overly strong vertical mixing leading to unrealistic increases in ocean heat content and latitudinally dependent changes to sea level. We show that judicious selection of the magnitude and spatial extent of the stochastic forcing is required for desirable results. Our results point to the importance of a comprehensive evaluation of ocean model responses to stochastic parameterizations.

We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupledDAwas employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting.

Ongoing greenhouse gas emissions can modify climate processes and induce shifts in ocean temperature, pH, oxygen concentration, and productivity, which in turn could alter biological and social systems. Here, we provide a synoptic globalassessment of the simultaneous changes in future ocean biogeochemical variables over marine biota and their broader implications for people. We analyzed modern Earth System Models forced by greenhouse gas concentration pathways until 2100 and showed that the entire world’s ocean surface will be simultaneously impacted by varying intensities of ocean warming, acidification, oxygen depletion, or shortfalls in productivity. In contrast, only a small fraction of the world’s ocean surface, mostly in polar regions, will experience increased oxygenation and productivity, while almost nowhere will there be ocean cooling or pH elevation. We compiled the global distribution of 32 marine habitats and biodiversity hotspots and found that they would all experience simultaneous exposure to changes in multiple biogeochemical variables. This superposition highlights the high risk for synergistic ecosystem responses, the suite of physiological adaptations needed to cope with future climate change, and the potential for reorganization of global biodiversity patterns. If co-occurring biogeochemical changes influence the delivery of ocean goods and services, then they could also have a considerable effect on human welfare. Approximately 470 to 870 million of the poorest people in the world rely heavily on the ocean for food, jobs, and revenues and live in countries that will be most affected by multaneous changes in ocean biogeochemistry. These results highlight the high risk of degradation of marine ecosystems and associated human hardship expected in a future following current trends in anthropogenic greenhouse gas emissions.

Offshore wind farms (OWFs) are a key part of efforts to mitigate the impacts of climate change. However, they have the potential to negatively impact seabird species through collisions with turbine blades, displacement from preferred foraging habitat and the perception of wind farms as a barrier to migrating or foraging birds. Whilst the data available to model these impacts are increasing, many data gaps remain, particularly in relation to the impacts of barrier effects. We analyse the movements of Sandwich terns in relation to an offshore wind farm cluster using data collected as part of a multi-year GPS tracking study. Over the course of the study, two additional wind farms were built within the home range of the breeding colony. The construction of these wind farms coincided with a change in the foraging and commuting areas used by breeding terns. Whilst birds entered OWFs when foraging, they appeared to avoid them when commuting, driving an apparent ‘funnelling’ effect to important feeding locations. We discuss if this could be driven by changes to the prey base, subsequent displacement and potentially altered routes reflecting new favourable airflow patterns following OWF construction. Our results suggest that behavioural responses of birds to OWFs may be the result of a complex series of ecological and environmental interactions, as opposed to simplistic assumptions around the perception of the OWF as a barrier to movement.