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David M. Straus, Susanna Corti, and Franco Molteni


The circulation regimes in the Pacific–North American region are studied using the NCEP–NCAR reanalyses for the 18-winter period (1981/82–1998/99; NCEP18) and for the 54-winter period (1948/49–2001/02; NCEP54). The sampling properties of the regimes are estimated using very large ensembles (of size 55) of winter simulations made for the NCEP18 period with the atmospheric general circulation model of the Center for Ocean–Land–Atmosphere Studies, forced by observed SST and sea ice.

The regimes are identified using a modified version of the k-means method. From the NCEP54 dataset a set of four clusters was found [i.e., the Alaskan ridge (AR), Arctic low (AL), Pacific trough (PT), and the Arctic high (AH)], which are significant (vis-à-vis a multinormal background), and more reproducible (within randomly chosen half-length samples) than would be expected from a multinormal process. The frequency of occurrence of the PT (AH) has increased (decreased) significantly during the past two decades.

The PT cluster obtained from NCEP18 dataset more closely resembles the El Niño–forced seasonal mean pattern of recent decades than it does the traditional PNA.

The GCM simulates the AR, AL, and PT clusters (but not the AH). The simulated AR and PT patterns have errors (cf. the NCEP18 results), which are outside the range of internal variability. The simulated frequency of occurrence agrees with the NCEP18 results within sampling variability.

The differences in cluster properties of the PT and AR regimes between the NCEP18 and NCEP54 datasets are due to changes in SST forcing, not sampling error.

Year-to-year changes in the frequency of occurrence of the PT, AL, and AR clusters in the simulations and the NCEP18 dataset are generally consistent with each other.

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Franco Molteni, Susanna Corti, Laura Ferranti, and Julia M. Slingo


The effects of SST anomalies on the interannual and intraseasonal variability of the Asian summer monsoon have been studied by multivariate statistical analyses of 850-hPa wind and rainfall fields simulated in a set of ensemble integrations of the ECMWF atmospheric GCM—Predictability Experiments for the Indian Summer Monsoon (PRISM) experiments. The simulations used observed SSTs (PRISM-O), covering 9 yr, characterized by large variations of the ENSO phenomenon in the 1980s and the early 1990s. A parallel set of simulations was also performed with climatological SSTs (PRISM-C), thus enabling the influence of SST forcing on the modes of interannual and intraseasonal variability to be investigated.

As in observations, the model's interannual variability is dominated by a zonally oriented mode, which describes the north–south movement of the tropical convergence zone (TCZ). This mode appears to be independent of SST forcing, and its robustness between the PRISM-O and PRISM-C simulations suggests that it is driven by internal atmospheric dynamics. On the other hand, the second mode of variability, which again has a good correspondence with observed patterns, shows a clear relationship with the ENSO cycle. Because the mode related to ENSO accounts for only a small part of the total variance, the notion of a quasi-linear superposition of forced and unforced modes of variability may not provide an appropriate interpretation of monsoon interannual variability. Consequently, the possibility of a nonlinear influence has been investigated by exploring the relationship between interannual and intraseasonal variability.

As in other studies, a common mode of interannual and intraseasonal variability has been found, in this case describing the north–south transition of the TCZ associated with monsoon active/break cycles. Although seasonal-mean values of the principal component (PC) time series associated with the leading intraseasonal mode shows no significant correlation with ENSO, the two-dimensional probability distribution of the PC indices of the two leading modes changes from unimodal in the warm phase of ENSO to bimodal in the cold ENSO phase. These changes are suggestive of some sort of bifurcation in the monsoon properties, with multiple-regime behavior being established only when the zonal asymmetries in equatorial Pacific SST exceed a threshold value. Although an observational verification of this hypothesis is still to be achieved, the detection of regimelike behavior in simulations by a complex numerical model gives a stronger support to this dynamical framework than simple qualitative arguments based on the analogy with low-order nonlinear systems.

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Virginie Guemas, Susanna Corti, J. García-Serrano, F. J. Doblas-Reyes, Magdalena Balmaseda, and Linus Magnusson


The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.

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Pier Luigi Vidale, Kevin Hodges, Benoit Vannière, Paolo Davini, Malcolm J. Roberts, Kristian Strommen, Antje Weisheimer, Elina Plesca, and Susanna Corti


The role of model resolution in simulating geophysical vortices with the characteristics of realistic Tropical Cyclones (TCs) is well established. The push for increasing resolution continues, with General Circulation Models (GCMs) starting to use sub-10km grid spacing. In the same context it has been suggested that the use of Stochastic Physics (SP) may act as a surrogate for high resolution, providing some of the benefits at a fraction of the cost. Either technique can reduce model uncertainty, and enhance reliability, by providing a more dynamic environment for initial synoptic disturbances to be spawned and to grow into TCs. We present results from a systematic comparison of the role of model resolution and SP in the simulation of TCs, using EC-Earth simulations from project Climate-SPHINX, in large ensemble mode, spanning five different resolutions. All tropical cyclonic systems, including TCs, were tracked explicitly. As in previous studies, the number of simulated TCs increases with the use of higher resolution, but SP further enhances TC frequencies by ≈ 30%, in a strikingly similar way. The use of SP is beneficial for removing systematic climate biases, albeit not consistently so for interannual variability; conversely, the use of SP improves the simulation of the seasonal cycle of TC frequency. An investigation of the mechanisms behind this response indicates that SP generates both higher TC (and TC seed) genesis rates, and more suitable environmental conditions, enabling a more efficient transition of TC seeds into TCs. These results were confirmed by the use of equivalent simulations with the HadGEM3-GC31 GCM.

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Susanna Corti, Tim Palmer, Magdalena Balmaseda, Antje Weisheimer, Sybren Drijfhout, Nick Dunstone, Wilco Hazeleger, Jürgen Kröger, Holger Pohlmann, Doug Smith, Jin-Song von Storch, and Bert Wouters


The impact of initial conditions relative to external forcings in decadal integrations from an ensemble of state-of-the-art prediction models has been assessed using specifically designed sensitivity experiments (SWAP experiments). They consist of two sets of 10-yr-long ensemble hindcasts for two initial dates in 1965 and 1995 using either the external forcings from the “correct” decades or swapping the forcings between the two decades. By comparing the two sets of integrations, the impact of external forcing versus initial conditions on the predictability over multiannual time scales was estimated as the function of lead time of the hindcast. It was found that over time scales longer than about 1 yr, the predictability of sea surface temperatures (SSTs) on a global scale arises mainly from the external forcing. However, the correct initialization has a longer impact on SST predictability over specific regions such as the North Atlantic, the northwestern Pacific, and the Southern Ocean. The impact of initialization is even longer and extends to wider regions when below-surface ocean variables are considered. For the western and eastern tropical Atlantic, the impact of initialization for the 700-m heat content (HTC700) extends to as much as 9 years for some of the models considered. In all models the impact of initial conditions on the predictability of the Atlantic meridional overturning circulation (AMOC) is dominant for the first 5 years.

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Gerald A. Meehl, Lisa Goddard, George Boer, Robert Burgman, Grant Branstator, Christophe Cassou, Susanna Corti, Gokhan Danabasoglu, Francisco Doblas-Reyes, Ed Hawkins, Alicia Karspeck, Masahide Kimoto, Arun Kumar, Daniela Matei, Juliette Mignot, Rym Msadek, Antonio Navarra, Holger Pohlmann, Michele Rienecker, Tony Rosati, Edwin Schneider, Doug Smith, Rowan Sutton, Haiyan Teng, Geert Jan van Oldenborgh, Gabriel Vecchi, and Stephen Yeager

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.

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