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Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

Given the consequences and global significance of El Niño–Southern Oscillation (ENSO) events it is essential to understand the representation of El Niño diversity in climate models for the present day and the future. In recent decades, El Niño events have occurred more frequently in the central Pacific (CP). Eastern Pacific (EP) El Niño events have increased in intensity. However, the processes and future implications of these observed changes in El Niño are not well understood. Here, the frequency and intensity of El Niño events are assessed in models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), and results are compared to extended instrumental and multicentury paleoclimate records. Future changes of El Niño are stronger for CP events than for EP events and differ between models. Models with a projected La Niña–like mean-state warming pattern show a tendency toward more EP but fewer CP events compared to models with an El Niño–like warming pattern. Among the models with more El Niño–like warming, differences in future El Niño can be partially explained by Pacific decadal variability (PDV). During positive PDV phases, more El Niño events occur, so future frequency changes are mainly determined by projected changes during positive PDV phases. Similarly, the intensity of El Niño is strongest during positive PDV phases. Future changes to El Niño may thus depend on both mean-state warming and decadal-scale natural variability.

Seasonal climate forecasts from raw climate models at coarse grids are often biased and statistically unreliable for credible crop prediction at the farm scale. We develop a copula-based postprocessing (CPP) method to overcome this mismatch problem. The CPP forecasts are ensemble based and are generated from the predictive distribution conditioned on raw climate forecasts. CPP performs univariate postprocessing procedures at each station, lead time, and variable separately and then applies the Schaake shuffle to reorder ensemble sequence for a more realistic spatial, temporal, and cross-variable dependence structure. The use of copulas makes CPP free of strong distributional assumptions and flexible enough to describe complex dependence structures. In a case study, we apply CPP to postprocess rainfall, minimum temperature, maximum temperature, and radiation forecasts at a monthly level from the Australian Community Climate and Earth-System Simulator Seasonal model (ACCESS-S) to three representative stations in Australia. We evaluate forecast skill at lead times of 0–5 months on a cross-validation theme in the context of both univariate and multivariate forecast verification. When compared with forecasts that use climatological values as the predictor, the CPP forecast has positive skills, although the skills diminish with increasing lead times and finally become comparable at long lead times. When compared with the bias-corrected forecasts and the quantile-mapped forecasts, the CPP forecast is the overall best, with the smallest bias and greatest univariate forecast skill. As a result of the skill gain from univariate forecasts and the effect of the Schaake shuffle, CPP leads to the most skillful multivariate forecast as well. Further results investigate whether using ensemble mean or additional predictors can enhance forecast skill for CPP.

Regional climate projections in the Pacific region are potentially sensitive to a range of existing model biases. This study examines the implications of coupled model biases on regional climate projections in the tropical western Pacific. Model biases appear in the simulation of the El Niño Southern Oscillation, the location and movement of the South Pacific Convergence Zone, rainfall patterns, and the mean state of the ocean–atmosphere system including the cold tongue bias and erroneous location of the edge of the Western Pacific warm pool. These biases are examined in the CMIP3 20th century climate models and used to provide some context to the uncertainty in interpretations of regional-scale climate projections for the 21st century. To demonstrate, we provide examples for two island nations that are located in different climate zones and so are affected by different biases: Nauru and Palau. We discuss some of the common approaches to analyze climate projections and whether they are effective in reducing the effect of model biases. These approaches include model selection, calculating multi model means, downscaling and bias correcting.

The equatorial edge of the Western Pacific Warm Pool is operationally identified by one isotherm ranging between 28° and 29 °C, chosen to align with the interannual variability of strong zonal salinity gradients and the convergence of zonal ocean currents. The simulation of this edge is examined in 19 models from the World Climate Research Program Coupled Model Intercomparison Project Phase 5 (CMIP5), over the historical period from 1950 to 2000. The dynamic warm pool edge (DWPE), where the zonal currents converge, is difficult to determine from limited observations and biased models. A new analysis technique is introduced where a proxy for DWPE is determined by the isotherm that most closely correlates with the movements of the strong salinity gradient. It can therefore be a different isotherm in each model. The DWPE is simulated much closer to observations than if a direct temperature-only comparison is made. Aspects of the DWPE remain difficult for coupled models to simulate including the mean longitude, the interannual excursions, and the zonal convergence of ocean currents. Some models have only very weak salinity gradients trapped to the western side of the basin making it difficult to even identify a DWPE. The model’s DWPE are generally 1–2 °C cooler than observed. In line with theory, the magnitude of the zonal migrations of the DWPE are strongly related to the amplitudes of the Nino3.4 SST index. Nevertheless, a better simulation of the mean location of the DWPE does not necessarily improve the amplitude of a model’s ENSO. It is also found that in a few models (CSIROMk3.6, inmcm and inmcm4-esm) the warm pool displacements result from a net heating or cooling rather than a zonal advection of warm water. The simulation of the DWPE has implications for ENSO dynamics when considering ENSO paradigms such as the delayed action oscillator mechanism, the Advective-Reflective oscillator, and the zonal-advective feedback. These are also discussed in the context of the CMIP5 simulations.

Accelerated warming and hiatus periods in the long-term rise of Global Mean Surface Temperature (GMST) have, in recent decades, been associated with the Interdecadal Pacific Oscillation (IPO). Critically, decadal climate prediction relies on the skill of state-of-the-art climate models to reliably represent these low-frequency climate variations. We undertake a systematic evaluation of the simulation of the IPO in the suite of Coupled Model Intercomparison Project 5 (CMIP5) models. We track the IPO in pre-industrial (control) and all-forcings (historical) experiments using the IPO tripole index (TPI). The TPI is explicitly aligned with the observed spatial pattern of the IPO, and circumvents assumptions about the nature of global warming. We find that many models underestimate the ratio of decadal-to-total variance in sea surface temperatures (SSTs). However, the basin-wide spatial pattern of positive and negative phases of the IPO are simulated reasonably well, with spatial pattern correlation coefficients between observations and models spanning the range 0.4-0.8. Deficiencies are mainly in the extratropical Pacific. Models that better capture the spatial pattern of the IPO also tend to more realistically simulate the ratio of decadal to total variance. Of the 13% of model centuries that have a fractional bias in the decadal-to-total TPI variance of 0.2 or less, 84% also have a spatial pattern correlation coefficient with the observed pattern exceeding 0.5. This result is highly consistent across both IPO positive and negative phases. This is evidence that the IPO is related to one or more inherent dynamical mechanisms of the climate system.

Coupled general circulation models (CGCMs) simulate a diverse range of El Niño–Southern Oscillation behaviors. “Double peaked” El Niño events—where two separate centers of positive sea surface temperature (SST) anomalies evolve concurrently in the eastern and western equatorial Pacific—have been evidenced in Coupled Model Intercomparison Project version 5 CGCMs and are without precedent in observations. The characteristic CGCM double peaked El Niño may be mistaken for a central Pacific warming event in El Niño composites, shifted westwards due to the cold tongue bias. In results from the Australian Community Climate and Earth System Simulator coupled model, we find that the western Pacific warm peak of the double peaked El Niño event emerges due to an excessive westward extension of the climatological cold tongue, displacing the region of strong zonal SST gradients towards the west Pacific. A coincident westward shift in the zonal current anomalies reinforces the western peak in SST anomalies, leading to a zonal separation between the warming effect of zonal advection (in the west Pacific) and that of vertical advection (in the east Pacific). Meridional advection and net surface heat fluxes further drive growth of the western Pacific warm peak. Our results demonstrate that understanding historical CGCM El Niño behaviors is a necessary precursor to interpreting projections of future CGCM El Niño behaviors, such as changes in the frequency of eastern Pacific El Niño events, under global warming scenarios.

Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability. This so-called climate drift arises for various reasons including the following: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics. When examining trends in historical or future climate simulations, it is important to know the error introduced by drift so that action can be taken where necessary. This study assesses the importance of drift for a number of climate properties at global and local scales. To illustrate this, the present paper focuses on simulated trends over the second half of the twentieth century. While drift in globally averaged surface properties is generally considerably smaller than observed and simulated twentieth-century trends, it can still introduce nontrivial errors in some models. Furthermore, errors become increasingly important at smaller scales. The direction of drift is not systematic across different models or variables, as such drift is considerably reduced in the multimodel mean. Despite drift being primarily associated with ocean adjustment, it is also apparent in atmospheric variables. For example, most models have local drift magnitudes in surface air and ocean temperatures that are typically between 15% and 35% of the twentieth-century simulation trend magnitudes for 1950–2000. Below depths of 1000–2000 m, drift dominates over any forced trend in most regions. As such steric sea level is strongly affected and for some models and regions the sea level trend direction is reversed. Thus depending on the application, drift may be negligible or may make up an important part of the simulated trend.

Regional seasonal forecasting requires accurate simulation of the variability of local climate drivers. The South Pacific Convergence Zone (SPCZ) is a large region of low-level convergence, clouds and precipitation in the South Pacific, whose effects extend as far as northeast Australia (NEA). The location of the SPCZ is modulated by the El Niño-Southern Oscillation (ENSO) which causes rainfall variability in the region. Correctly simulating the ENSO-SPCZ teleconnection and its interplay with local conditions is essential for improving seasonal rainfall forecasts. Here we analyse the ability of the ACCESS-S1 seasonal forecast system to predict the SPCZ’s relationship with ENSO including its latitudinal shifts, zonal slope and rainfall magnitude between 1990 and 2012 for the December–January–February (DJF) season. We found improvements in ACCESS-S1’s SPCZ prediction capability compared to its predecessor (POAMA), although prediction of the slope is still limited. The inability of ACCESS-S1 to replicate seasons with a strong anti-zonal SPCZ slope is attributed to its atmospheric model. This has implications for accurate seasonal rainfall forecasts for NEA and South Pacific Islands. Future challenges in seasonal prediction facing regional communities and developers of coupled ocean–atmosphere forecast models are discussed.

The dynamics of El Niño–Southern Oscillation (ENSO) are studied in terms of the balance between energy input from the winds (via wind power) and changes in the storage of available potential energy in the tropical ocean. Presently, there are broad differences in the way global general circulation models simulate the dynamics, magnitude, and phase of ENSO events; hence, there is a need for simple, physically based metrics to allow for model evaluation. This energy description is a basinwide, integral, quantitative approach, ideal for intermodel comparison, that assesses model behavior in the subsurface ocean. Here it is applied to a range of ocean models and data assimilations within ENSO spatial and temporal scales. The onset of an El Niño is characterized by a decrease in wind power that leads to a decrease in available potential energy, and hence a flatter thermocline. In contrast, La Niña events are preceded by an increase in wind power that leads to an increase in the available potential energy and a steeper thermocline. The wind power alters the available potential energy via buoyancy power, associated with vertical mass fluxes that modify the slope of the isopycnals. Only a fraction of wind power is converted to buoyancy power. The efficiency of this conversionγis estimated in this study at 50%–60%. Once the energy is delivered to the thermocline it is subject to small, but important, diffusive dissipation. It is estimated that this dissipation sets thee-folding damping rateαfor the available potential energy on the order of 1 yr−1. The authors propose to use the efficiencyγand the damping rateαas two energy-based metrics for evaluating dissipative properties of the ocean component of general circulation models, providing a simple method for understanding subsurface ENSO dynamics and a diagnostic tool for exploring differences between the models.