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Abstract Using a high-resolution atmospheric general circulation model simulation of unprecedented ensemble size, we examine potential predictability of monthly anomalies under El Niño Southern Oscillation (ENSO) forcing and back-ground internal variability. This study reveals the pronounced month-to-month evolution of both the ENSO forcing signal and internal variability. Internal variance in upper-level geopotential height decreases (∼ 10%) over the North Pacific during El Niño as the westerly jet extends eastward, allowing forced signals to account for a greater fraction of the total variability, and leading to increased potential predictability. We identify February and March of El Niño years as the most predictable months using a signal-to-noise analysis. In contrast, December, a month typically included in teleconnection studies, shows little-to-no potential predictability. We show that the seasonal evolution of SST forcing and variability leads to significant signal-to-noise relationships that can be directly linked to both upper-level and surface variable predictability for a given month. The stark changes in forced response, internal variability, and thus signal-to-noise across an ENSO season indicate that subseasonal fields should be used to diagnose potential predictability over North America associated with ENSO teleconnections. Using surface air temperature and precipitation as examples, this study provides motivation to pursue ‘windows of forecast opportunity’, in which statistical skill can be developed, tested, and leveraged to determine times and regions in which this skill may be elevated. 

Abstract The influence of eastern tropical Pacific (EPAC; 10°S–10°N, 140°–80°W) wind anomalies on El Niño is investigated using observations and model experiments. Extreme and moderate El Niños exhibit contrasting anomalous wind patterns in the EPAC during the peak and decay phases: westerly wind anomalies during extreme El Niño and southeasterly (southwesterly) wind anomalies south (north) of the equator during moderate El Niño. Experiments with an ocean general circulation model indicate that for extreme El Niño, the eastward intrusion of westerly wind anomalies contributes to the prolonged positive sea surface temperature (SST) anomalies in the eastern equatorial Pacific throughout boreal spring by weakened upwelling and horizontal advection. For moderate El Niño, by contrast, both the meridional and zonal anomalous winds over the EPAC are important in the rapid (slow) SST cooling south (north) of the equator through advection and wind–evaporation–SST feedback. Atmospheric model experiments confirm that these EPAC anomalous winds are primarily forced by tropical SST anomalies. The interplay between wind and SST anomalies suggests positive air–sea feedbacks over EPAC during the decay phase of El Niño. Ocean model results show that the frequency of extreme El Niño increases when EPAC wind anomalies are removed, suggesting the importance of EPAC winds for El Niño diversity. 

Variability of North Atlantic annual hurricane frequency during 1951–2010 is studied using a 100-member ensemble of climate simulations by a 60-km atmospheric general circulation model that is forced by observed sea surface temperatures (SSTs). The ensemble mean results well capture the interannual-to-decadal variability of hurricane frequency in best track data since 1970, and suggest that the current best track data might underestimate hurricane frequency prior to 1966 when satellite measurements were unavailable. A genesis potential index (GPI) averaged over the main development region (MDR) accounts for more than 80% of the SST-forced variations in hurricane frequency, with potential intensity and vertical wind shear being the dominant factors. In line with previous studies, the difference between MDR SST and tropical mean SST is a useful predictor; a 1°C increase in this SST difference produces 7.05 ± 1.39 more hurricanes. The hurricane frequency also exhibits strong internal variability that is systematically larger in the model than observations. The seasonal-mean environment is highly correlated among ensemble members and contributes to less than 10% of the ensemble spread in hurricane frequency. The strong internal variability is suggested to originate from weather to intraseasonal variability and nonlinearity. In practice, a 20-member ensemble is sufficient to capture the SST-forced variability.

 

Abstract. Palaeoclimate simulations improve our understanding ofthe climate, inform us about the performance of climate models in adifferent climate scenario, and help to identify robust features of theclimate system. Here, we analyse Arctic warming in an ensemble of 16simulations of the mid-Pliocene Warm Period (mPWP), derived from thePliocene Model Intercomparison Project Phase 2 (PlioMIP2). The PlioMIP2 ensemble simulates Arctic (60–90∘ N) annual meansurface air temperature (SAT) increases of 3.7 to 11.6 ∘Ccompared to the pre-industrial period, with a multi-model mean (MMM) increase of7.2 ∘C. The Arctic warming amplification ratio relative to globalSAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea iceextent anomalies range from −3.0 to -10.4×106 km2, with a MMManomaly of -5.6×106 km2, which constitutes a decrease of 53 %compared to the pre-industrial period. The majority (11 out of 16) of models simulatesummer sea-ice-free conditions (≤1×106 km2) in their mPWPsimulation. The ensemble tends to underestimate SAT in the Arctic whencompared to available reconstructions, although the degree of underestimationvaries strongly between the simulations. The simulations with the highestArctic SAT anomalies tend to match the proxy dataset in its current formbetter. The ensemble shows some agreement with reconstructions of sea ice,particularly with regard to seasonal sea ice. Large uncertainties limit theconfidence that can be placed in the findings and the compatibility of thedifferent proxy datasets. We show that while reducing uncertainties in thereconstructions could decrease the SAT data–model discord substantially,further improvements are likely to be found in enhanced boundary conditionsor model physics. Lastly, we compare the Arctic warming in the mPWP toprojections of future Arctic warming and find that the PlioMIP2 ensemblesimulates greater Arctic amplification than CMIP5 future climate simulationsand an increase instead of a decrease in Atlantic Meridional OverturningCirculation (AMOC) strength compared topre-industrial period. The results highlight the importance of slow feedbacks inequilibrium climate simulations, and that caution must be taken when usingsimulations of the mPWP as an analogue for future climate change. 

Abstract. The Pliocene epoch has great potential to improve ourunderstanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts permillion by volume. Here we present the large-scale features of Plioceneclimate as simulated by a new ensemble of climate models of varyingcomplexity and spatial resolution based on new reconstructions ofboundary conditions (the Pliocene Model Intercomparison Project Phase 2;PlioMIP2). As a global annual average, modelled surface air temperaturesincrease by between 1.7 and 5.2 ∘C relative to the pre-industrial erawith a multi-model mean value of 3.2 ∘C. Annual mean totalprecipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.