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We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.

Extreme El Niño events cause global disruption of weather patterns and affect ecosystems and agriculture through changes in rainfall. Model projections show that a doubling in the occurrence of such extreme episodes is caused by increased surface warming of the eastern equatorial Pacific Ocean, which results in the atmospheric conditions required for these event to occur. El Niño events are a prominent feature of climate variability with global climatic impacts. The 1997/98 episode, often referred to as ‘the climate event of the twentieth century’ 1 , 2 , and the 1982/83 extreme El Niño 3 , featured a pronounced eastward extension of the west Pacific warm pool and development of atmospheric convection, and hence a huge rainfall increase, in the usually cold and dry equatorial eastern Pacific. Such a massive reorganization of atmospheric convection, which we define as an extreme El Niño, severely disrupted global weather patterns, affecting ecosystems 4 , 5 , agriculture 6 , tropical cyclones, drought, bushfires, floods and other extreme weather events worldwide 3 , 7 , 8 , 9 . Potential future changes in such extreme El Niño occurrences could have profound socio-economic consequences. Here we present climate modelling evidence for a doubling in the occurrences in the future in response to greenhouse warming. We estimate the change by aggregating results from climate models in the Coupled Model Intercomparison Project phases 3 (CMIP3; ref.  10 ) and 5 (CMIP5; ref.  11 ) multi-model databases, and a perturbed physics ensemble 12 . The increased frequency arises from a projected surface warming over the eastern equatorial Pacific that occurs faster than in the surrounding ocean waters 13 , 14 , facilitating more occurrences of atmospheric convection in the eastern equatorial region.

The boreal summer Asian monsoon has been evaluated in 25 Coupled Model Intercomparison Project-5 (CMIP5) and 22 CMIP3 GCM simulations of the late twentieth Century. Diagnostics and skill metrics have been calculated to assess the time-mean, climatological annual cycle, interannual variability, and intraseasonal variability. Progress has been made in modeling these aspects of the monsoon, though there is no single model that best represents all of these aspects of the monsoon. The CMIP5 multi-model mean (MMM) is more skillful than the CMIP3 MMM for all diagnostics in terms of the skill of simulating pattern correlations with respect to observations. Additionally, for rainfall/convection the MMM outperforms the individual models for the time mean, the interannual variability of the East Asian monsoon, and intraseasonal variability. The pattern correlation of the time (pentad) of monsoon peak and withdrawal is better simulated than that of monsoon onset. The onset of the monsoon over India is typically too late in the models. The extension of the monsoon over eastern China, Korea, and Japan is underestimated, while it is overestimated over the subtropical western/central Pacific Ocean. The anti-correlation between anomalies of all-India rainfall and Niño3.4 sea surface temperature is overly strong in CMIP3 and typically too weak in CMIP5. For both the ENSO-monsoon teleconnection and the East Asian zonal wind-rainfall teleconnection, the MMM interannual rainfall anomalies are weak compared to observations. Though simulation of intraseasonal variability remains problematic, several models show improved skill at representing the northward propagation of convection and the development of the tilted band of convection that extends from India to the equatorial west Pacific. The MMM also well represents the space–time evolution of intraseasonal outgoing longwave radiation anomalies. Caution is necessary when using GPCP and CMAP rainfall to validate (1) the time-mean rainfall, as there are systematic differences over ocean and land between these two data sets, and (2) the timing of monsoon withdrawal over India, where the smooth southward progression seen in India Meteorological Department data is better realized in CMAP data compared to GPCP data.

CMIP5 multimodel ensemble projection of midlatitude storm track changes has been examined. Storm track activity is quantified by temporal variance of meridional wind and sea level pressure (psl), as well as cyclone track statistics. For the Southern Hemisphere (SH), CMIP5 models project clear poleward migration, upward expansion, and intensification of the storm track. For the Northern Hemisphere (NH), the models also project some poleward shift and upward expansion of the storm track in the upper troposphere/lower stratosphere, but mainly weakening of the storm track toward its equatorward flank in the troposphere. Consistent with these, CMIP5 models project significant increase in the frequency of extreme cyclones during the SH cool season, but significant decrease in such events in the NH. Comparisons with CMIP3 projections indicate high degrees of consistency for SH projections, but significant differences are found in the NH. Overall, CMIP5 models project larger decrease in storm track activity in the NH troposphere, especially over North America in winter, where psl variance as well as cyclone frequency and amplitude are all projected to decrease significantly. In terms of climatology, similar to CMIP3, most CMIP5 models simulate storm tracks that are too weak and display equatorward biases in their latitude. These biases have also been related to future projections. In the NH, the strength of a model's climatological storm track is negatively correlated with its projected amplitude change under global warming, while in the SH, models with large equatorward biases in storm track latitude tend to project larger poleward shifts. Key Points CMIP5 ensemble projection of mid‐latitude storm track changes are documented Projections consistent with CMIP3 in SH, but significant differences in the NH Models have biases in climatology which are correlated with future projections

Snow-albedo feedback (SAF) is examined in 25 climate change simulations participating in the Coupled Model Intercomparison Project version 5 (CMIP5). SAF behavior is compared to the feedback’s behavior in the previous (CMIP3) generation of global models. SAF strength exhibits a fivefold spread across CMIP5 models, ranging from 0.03 to 0.16 W m −2  K −1  (ensemble-mean = 0.08 W m −2  K −1 ). This accounts for much of the spread in 21st century warming of Northern Hemisphere land masses, and is very similar to the spread found in CMIP3 models. As with the CMIP3 models, there is a high degree of correspondence between the magnitudes of seasonal cycle and climate change versions of the feedback. Here we also show that their geographical footprint is similar. The ensemble-mean SAF strength is close to an observed estimate of the real climate’s seasonal cycle feedback strength. SAF strength is strongly correlated with the climatological surface albedo when the ground is covered by snow. The inter-model variation in this quantity is surprisingly large, ranging from 0.39 to 0.75. Models with large surface albedo when these regions are snow-covered will also have a large surface albedo contrast between snow-covered and snow-free regions, and therefore a correspondingly large SAF. Widely-varying treatments of vegetation masking of snow-covered surfaces are probably responsible for the spread in surface albedo where snow occurs, and the persistent spread in SAF in global climate models.

All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the six-hourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO 2 ) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO 2 radiative forcing. By considering tropospheric adjustments to CO 2 as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO 2 is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO 2 ), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.

Using a fully coupled global ocean–atmosphere general circulation model assimilating only sea surface temperature, the authors found for the first time that several El Niño–Southern Oscillation (ENSO) events over the past two decades can be predicted at lead times of up to 2 yr. The El Niño condition in the 1997/98 winter can be predicted to some extent up to about a 1½-yr lead but with a weak intensity and large phase delay in the prediction of the onset of this exceptionally strong event. This is attributed to the influence of active and intensive stochastic westerly wind bursts during late 1996 to mid-1997, which are generally unpredictable at seasonal time scales. The cold signals in the 1984/85 and 1999/2000 winters during the peak phases of the past two long-lasting La Niña events are predicted well up to a 2-yr lead. Amazingly, the mild El Niño–like event of 2002/03 is also predicted well up to a 2-yr lead, suggesting a link between the prolonged El Niño and the tropical Pacific decadal variability. Seasonal climate anomalies over vast parts of the globe during specific ENSO years are also realistically predicted up to a 2-yr lead for the first time.

In the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO₂ concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO₂ emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations. When forced by RCP8.5 CO₂ emissions, models simulate a large spread in atmospheric CO₂; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO₂ (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m−2) when driven by CO₂ emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO₂, with the present-day biases reasonably well correlated with future atmospheric concentrations’ departure from the prescribed concentration. The uncertainty in CO₂ projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO₂ concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO₂ emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks.

We develop a summer temperature reconstruction for temperate East Asia based on a network of annual tree-ring chronologies covering the period 800–1989 C.E. The East Asia reconstruction is the regional average of 585 individual grid point summer temperature reconstructions produced using an ensemble version of point-by-point regression. Statistical calibration and validation tests indicate that the regional average possesses sufficient overall skill to allow it to be used to study the causes of temperature variability and change over the region. The reconstruction suggests a moderately warm early medieval epoch (ca. 850–1050 C.E.), followed by generally cooler ‘Little Ice Age’ conditions (ca. 1350–1880 C.E.) and 20th century warming up to the present time. Since 1990, average temperature has exceeded past warm epochs of comparable duration, but it is not statistically unprecedented. Superposed epoch analysis reveals a volcanic forcing signal in the East Asia summer temperature reconstruction, resulting in pulses of cooler summer conditions that may persist for several years. Substantial uncertainties remain, however, particularly at lower frequencies, thus requiring caution and scientific prudence in the interpretation of this record.