Observations show that decadal (10-20 yr) to interdecadal (>20 yr) variability of the tropical Indian Ocean (TIO) sea surface temperature (SST) closely follows that of the Pacific until the 1960s. Since then, the TIO SST exhibits a persistent warming trend, whereas the Pacific SST shows large-amplitude fluctuations associated with the interdecadal Pacific oscillation (IPO), and the decadal variability of the TIO SST is out of phase with that of the Pacific after around 1980. Here causes for the changing behavior of the TIO SST are explored, by analyzing multiple observational datasets and the recently available large-ensemble simulations from two climate models. It is found that on interdecadal time scales, the persistent TIO warming trend is caused by emergence of anthropogenic warming overcoming internal variability, while the time of emergence occurs much later in the Pacific. On decadal time scales, two major tropical volcanic eruptions occurred in the 1980s and 1990s causing decadal SST cooling over the TIO during which the IPO was in warm phase, yielding the out-of-phase relation. The more evident fingerprints of external forcing in the TIO compared to the Pacific result from the much weaker TIO internal decadal-interdecadal variability, making the TIO prone to the external forcing. These results imply that the ongoing warming and natural external forcing may make the Indian Ocean more active, playing an increasingly important role in affecting regional and global climate.

Previous studies suggest that anthropogenic warming has affected the multi-decadal trend patterns of sea level over the Indian Ocean (IO). This effect, however, has not been quantified. Using observational datasets combined with large ensemble experiments from two climate models, this paper assesses the effects of natural internal variability versus external forcing on the observed, multi-decadal trend pattern and the decadal sea level anomaly (SLA) of the IO since the 1960s. Because the global mean sea level rise (SLR), which results largely from external forcing, has been removed before the examination, the paper focuses on the regionally uneven distribution of trend and SLA. The impacts of climate modes are quantified using a Bayesian Dynamic Linear Model. For the regional trend pattern of 1958–2005, the effects of internal variability dominate external forcing. Over the Seychelles area where sea-level variations obtain the maximum, internal variability (external forcing) contributes 81% (19 ± 2.4%) of the observed trend. For decadal SLA, internal variability is the predominant cause, with a standard deviation (STD) ratio of externally forced/observed SLA being 18 ± 17% over Seychelles and 17 ± 11% near the Indonesian Throughflow (ITF) area. Climate modes account for most observed SLA during boreal winter, with the total effects of decadal ENSO, Indian Ocean Dipole (IOD), and monsoon accounting for 78–86% of the observed STD near the Seychelles region, ITF area, and coasts of Sumatra and the Bay of Bengal. During summer, climate modes explain 95% of observed STD near the ITF but only 58–67% in other regions. Decadal ENSO dominates the SLA in the south tropical IO for both seasons and near the coasts of Sumatra and the Bay during winter. Decadal IOD and monsoon, however, control the coastal SLA during summer. Remote and local winds over the IO are the main drivers for decadal SLA, while the Pacific influence via the ITF is strong mainly in the southeast basin.

Our decadal climate prediction system, which is based on the Max-Planck-Institute Earth System Model, is initialized from a coupled assimilation run that utilizes nudging to selected state parameters from reanalyses. We apply full-field nudging in the atmosphere and either full-field or anomaly nudging in the ocean. Full fields from two different ocean reanalyses are considered. This comparison of initialization strategies focuses on the North Atlantic Subpolar Gyre (SPG) region, where the transition from anomaly to full-field nudging reveals large differences in prediction skill for sea surface temperature and ocean heat content (OHC). We show that nudging of temperature and salinity in the ocean modifies OHC and also induces changes in mass and heat transports associated with the ocean flow. In the SPG region, the assimilated OHC signal resembles well OHC from observations, regardless of using full fields or anomalies. The resulting ocean transport, on the other hand, reveals considerable differences between full-field and anomaly nudging. In all assimilation runs, ocean heat transport together with net heat exchange at the surface does not correspond to OHC tendencies, the SPG heat budget is not closed. Discrepancies in the budget in the cases of full-field nudging exceed those in the case of anomaly nudging by a factor of 2–3. The nudging-induced changes in ocean transport continue to be present in the free running hindcasts for up to 5 years, a clear expression of memory in our coupled system. In hindcast mode, on annual to inter-annual scales, ocean heat transport is the dominant driver of SPG OHC. Thus, we ascribe a significant reduction in OHC prediction skill when using full-field instead of anomaly initialization to an initialization shock resulting from the poor initialization of the ocean flow.

As a major CO 2 sink, the North Atlantic, especially its subpolar gyre region, is essential for the global carbon cycle. Decadal fluctuations of CO 2 uptake in the North Atlantic subpolar gyre region are associated with the evolution of the North Atlantic Oscillation, the Atlantic meridional overturning circulation, ocean mixing and sea surface temperature anomalies. While variations in the physical state of the ocean can be predicted several years in advance by initialization of Earth system models, predictability of CO 2 uptake has remained unexplored. Here we investigate the predictability of CO 2 uptake variations by initialization of the MPI-ESM decadal prediction system. We find large multi-year variability in oceanic CO 2 uptake and demonstrate that its potential predictive skill in the western subpolar gyre region is up to 4–7 years. The predictive skill is mainly maintained in winter and is attributed to the improved physical state of the ocean.

AbstractThe observed strengthening of the Northern Hemisphere (NH) polar vortex after tropical volcanic eruptions appears to be underestimated by coupled climate models. However, there are only a limited number of observed eruptions, which makes the attribution of volcanic signals difficult, because the polar vortex is also influenced by other external forcing factors as well as internal variability. We show with a 100‐member ensemble of historical (1850–2005) simulations with the Max Planck Institute Earth System Model that an ensemble larger than what is provided by the Coupled Model Intercomparison Project Phase 5 (CMIP5) models is needed to detect a statistically significant NH polar vortex strengthening. The most robust signal can be found when only the two strongest eruptions (Krakatau and Pinatubo) are considered in contrast to including smaller eruptions to increase the sample size. For these two strongest eruptions, the mean of 15 CMIP5 models shows a statistically significant strengthening of the NH polar vortex as well.

AbstractMittelfristige Klimaprognose (MiKlip), an 8-yr German national research project on decadal climate prediction, is organized around a global prediction system comprising the Max Planck Institute Earth System Model (MPI-ESM) together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that target future operational use.Three prediction system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multiyear-mean European summer surface temperatures, extratropical cyclone tracks, the quasi-biennial oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays h...

Retrospective prediction experiments have to be performed to estimate the skill of decadal prediction systems. These are necessarily restricted in the number due to the computational constraints. From weather and seasonal prediction it is known that the ensemble size is crucial to yield reliable predictions. Differences are expected for decadal predictions due to the differing time-scales of the involved processes and the longer prediction horizon. A conceptual model is applied that enables the systematic analysis of ensemble size dependencies in a framework close to that of decadal predictions. Differences are quantified in terms of the confidence intervals coverage and the power of statistical tests for prediction scores. In addition, the concepts are applied to decadal predicitions of the MiKlip Baseline1 system. It is shown that small ensemble, as well as hindcast sample sizes lead to biased test performances in a way that the detection of a present prediction skill is hampered. Experiments with ensemble sizes smaller than 10 are not recommended to evaluate decadal prediction skill or as basis for the prediction system developement. For regions with low signal-to-noise ratios much larger ensembles are required and it is shown that in this case successful decadal predictions are possible for the Central European summer temperatures.

An ensemble of yearly initialized decadal predictions is performed with the Max Planck InstituteEarth System Model to examine the forecast skill for the period from 1901 to 2010.Compared to the more recent period (1960 to present day), the extended period leads to an enlargement of regions with significant anomaly correlation coefficients (ACC) for predicted surface temperatures.This arises from an increased contribution of the trend, which is also found in the uninitialized runs.Additionally, in the North Atlantic decadal variability plays a larger role over the extended period, with detrended time series showing higher ACC for the extended compared to the short period.Furthermore, in contrast to the uninitialized simulations, the initialized predictions capture the North Atlantic warming events during the 1920s and 1990s, together with some of the surface climate impacts including warm European summer temperatures and a northward shift of Atlantic tropical rainfall.

The adequacy of the Gamma distribution (GD) for monthly precipitation totals is reconsidered. The motivation for this study is the observation that the GD fails to represent precipitation in considerable areas of global observed and simulated data. This misrepresentation may lead to erroneous estimates of the Standardised Precipitation Index (SPI), evaluations of models, and assessments of climate change. In this study, the GD is compared to the Weibull (WD), Burr Type III (BD), exponentiated Weibull (EWD) and generalised Gamma (GGD) distribution. These distributions extend the GD in terms of possible shapes (skewness and kurtosis) and the behaviour for large arguments. The comparison is based on the Akaike information criterion, which maximises information entropy, and reveals a trade-off between deviation and the numbers of parameters used. We use monthly sums of observed and simulated precipitation for 12 calendar months of the year. Assessing observed and simulated data (i) the Weibull type distributions give distinctly improved fits compared to the GD and (ii) the SPI resulting from the GD overestimates (underestimates) extreme dryness (wetness).

Variability of the Yangtze catchment hydrology is closely linked with the Tibetan Plateau snow cover and the large scale atmospheric circulation in East Asia. These connections are analyzed in a control simulation (2800 years) of the atmosphere ocean general circulation model (ECHAM5/MPIOM) coupled to vegetation and ocean biogeochemistry modules. Up to decadal time scales, runoff, soil wetness, and temperature show inter-annual memory while precipitation, snow depth, and snow melt reveal no memory. On the annual time scale, the correlations of the Hadley cell and ENSO on precipitation, temperature and snow melt are weak but signficant. Temperature is anticorrelated with precipitation and soil wetness in the Yangtze catchment. Singular spectrum analysis highlights the 3-4 year ENSO mode in the monsoon indices, precipitation, and snow depth, explaining their high correlations. Projecting snow depth on ENSO shows long term variations explaining conflicting results obtained from correlations in short data sets. To retrieve possible causal relationships between the climatic components, a Granger causality analysis is performed. This method determines causality by an error analysis in predictions. The results reveal a mutual causality between monsoons and the hydrology in the catchment on an annual basis. Thus the climate in the catchment is subject to complex interactions and predictability cannot be attributed to the inter-annual memory of a few individual components.

The analysis of extremes is of increasing importance in meteorological science. This is mainly due to the associated damages caused by the events and the expected changes going along with changing climate conditions. Extremes are of relevance in different fields and can be classified by the time scale of the generating processes. This circumstance complicates the analysis, beside their inherent rareness.These complications have to be overcome with adequate methods and the following are applied: Standardized Precipitation Index (SPI), extreme value statistics and extreme event return time analysis. This study contributes to the description of extreme events. The more technical oriented part discusses shortcomings of the applied methods and resolves them partly.The impact of climate variability and climate change on frequency and intensity of the extremes is analyzed for Icelandic precipitation and North Atlantic cyclones. Further, extreme event return time properties are investigated in long-term memory processes near 1/f.

Extremes of the cyclone intensity measures geopotential height (z(1000)), mean horizontal gradient (del z), cyclone depth (D), and relative vorticity (zeta(850)), are analysed in re-analysis data (ERA40) and model simulations (ECHAM5/MPI-OM) in the North Atlantic region for extended winter seasons. Generalized Pareto distributions (GPD) are estimated for model validation and climate change assessment. Covariates, linear trend and North Atlantic Oscillation (NAO) are included to analyse the dependancies of the extremes.In ERA40 no significant linear trend can be detected, while evidence for a NAO impact on z(1000), del z and zeta(850) extremes is found. Model validation yields good agreement with consistent scale and shape, but a shift to lower values is notable. Like in ERA40 no trend is found in the simulation. The evidence for an NAO impact on cyclone extremes is less corroborated in the simulation, pointing to sample size effects.In the warmer climate scenario (A1BS) extreme value statistics shows an intensification for all variables. Significant differences in GPD are obtained through testing for lower (higher) parameters. In contrast, considering all cyclones an increase is only present for z(1000), while a decrease is found for del z and zeta(850) and no change for D.