Explore the vast range of titles available.

The Madden–Julian oscillation (MJO) represents a primary source of predictability on the intraseasonal time scales and its influence extends from seasonal variations to weather and extreme events. While the last decade has witnessed marked improvement in dynamical MJO prediction, an updated estimate of MJO predictability from a contemporary suite of dynamic models, in conjunction with an estimate of their corresponding prediction skill, is crucial for guiding future research and development priorities. In this study, the predictability of the boreal winter MJO is revisited based on the Intraseasonal Variability Hindcast Experiment (ISVHE), a set of dedicated extended-range hindcasts from eight different coupled models. Two estimates of MJO predictability are made, based on single-member and ensemble-mean hindcasts, giving values of 20–30 days and 35–45 days, respectively. Exploring the dependence of predictability on the phase of MJO during hindcast initiation reveals a slightly higher predictability for hindcasts initiated from MJO phases 2, 3, 6, or 7 in three of the models with higher prediction skill. The estimated predictability of MJO initiated in phases 2 and 3 (i.e., convection in Indian Ocean with subsequent propagation across Maritime Continent) being equal to or higher than other MJO phases implies that the so-called Maritime Continent prediction barrier may not actually be an intrinsic predictability limitation. For most of the models, the skill for single-member (ensemble mean) hindcasts is less than the estimated predictability limit by about 5–10 days (15–25 days), implying that significantly more skillful MJO forecasts can be afforded through further improvements of dynamical models and ensemble prediction systems (EPS).

A database of surface Antarctic snow isotopic composition is constructed using available measurements, with an estimate of data quality and local variability. Although more than 1000 locations are documented, the spatial coverage remains uneven with a majority of sites located in specific areas of East Antarctica. The database is used to analyze the spatial variations in snow isotopic composition with respect to geographical characteristics (elevation, distance to the coast) and climatic features (temperature, accumulation) and with a focus on deuterium excess. The capacity of theoretical isotopic, regional, and general circulation atmospheric models (including “isotopic” models) to reproduce the observed features and assess the role of moisture advection in spatial deuterium excess fluctuations is analyzed.

Species distribution models, linked to climate projections, are widely used in extinction-risk assessment and conservation planning. However, the degree of confidence that we can place on future climate-change projections depends on global climate-model performance and involves uncertainties that need to be assessed rigorously via climate-model evaluation. Performance assessments are important because the choice of climate model influences projections of species' range movement and extinction risk. A consensus view from the climate modeling community is that no single climate model is superior in its ability to forecast key climatic features. Despite this, the advantages of using multi-model ensemble-averaged climate forecasts to account for climate-model uncertainties have not been recognized by ecologists. Here we propose a method to use a range of skill and convergence metrics to rank commonly used atmosphere-ocean general circulation models (AOGCMs) according to their skill in reproducing 20-year observed patterns of regional and global climates of interest, and to assess their consistency with other AOGCMs. By eliminating poorly performing models and averaging the remainder with equal weights, we show how downscaled annual multi-climate-model ensemble-averaged forecasts, which have a strong regional focus, can be generated. We demonstrate that: (1) model ranking (match of simulated to observed conditions) differs according to the skill metric used, as well as the climate variable and season considered; (2) although the multi-model averaged result tends to outperform single models at a global scale, at the continental scale at least some models can perform better than the multi-model average; and (3) forecasts for the Australian region, which are often based on a single AOGCM (CSIRO-3.0), show spatial patterns of change that differ noticeably from ensemble-average projections based on a subset of better-performing AOGCMs. Our suggested approach-novel in the ecology discipline-provides a straightforward, consistent, and defensible method for conservation practitioners and natural-resource managers to generate estimates of future climate change at a spatial resolution suitable for biodiversity impact studies.

An ocean–sea ice model reconstruction spanning the period 1990–2009 is used to initialize ensemble seasonal forecasts with the Centre National de Recherches Météorologiques Coupled Global Climate Model version 5.1 (CNRM-CM5.1) coupled atmosphere–ocean general circulation model. The aim of this study is to assess the skill of fully initialized September and March pan-Arctic sea ice forecasts in terms of climatology and interannual anomalies. The predictions are initialized using “full field initialization” of each component of the system. In spite of a drift due to radiative biases in the coupled model during the melt season, the full initialization of the sea ice cover on 1 May leads to skillful forecasts of the September sea ice extent (SIE) anomalies. The skill of the prediction is also significantly high when considering anomalies of the SIE relative to the long-term linear trend. It confirms that the anomaly of spring sea ice cover in itself plays a role in preconditioning a September SIE anomaly. The skill of predictions for March SIE initialized on 1 November is also encouraging, and it can be partly attributed to persistent features of the fall sea ice cover. The present study gives insight into the current ability of state-of-the-art coupled climate systems to perform operational seasonal forecasts of the Arctic sea ice cover up to 5 months in advance.

Previous studies indicate observed influences of autumn and winter Tibetan Plateau (TP) snow-cover anomalies on the winter Pacific–North American (PNA) teleconnection. This study simulates atmospheric and oceanic responses to persistent autumn–winter TP snow forcing using an atmospheric general circulation model (AGCM) and a coupled atmospheric-oceanic general circulation model (AOGCM), and quantifies the role of atmosphere–ocean interactions over North Pacific in TP snow effects. The AOGCM experiment induces a stronger and more realistic remote PNA response to heavy TP snow anomalies, and also a significant winter horseshoe-like North Pacific sea surface temperature (SST) pattern resulting from an anomalous equivalent barotropic cyclone, or a strengthened Aleutian low, with associated cyclonic wind stress anomalies. The horseshoe-like SST anomaly pattern is used as boundary forcing (without prescribed heavy TP snow) in another AOGCM experiment, which simulates an enhanced winter Aleutian low and a PNA-like response similar to the original AOGCM responses, indicating that that the direct Pacific–North American atmospheric response to persistent TP snow forcing in the AGCM is amplified in the AOGCM by the North Pacific midlatitude atmosphere–ocean interactions. This suggests that the mechanisms of the winter PNA responses to TP snow forcing involve dynamical atmospheric processes such as horizontal propagation of Rossby wave energy and transient eddy feedbacks, and also North Pacific atmosphere–ocean interactions, which provide a positive feedback on the development of the remote PNA teleconnection.

The midlatitude poleward atmospheric energy transport increases in radiatively forced simulations of warmed climates across a range of models from comprehensive coupled general circulation models (GCMs) to idealized aquaplanet moist GCMs to diffusive moist energy balance models. These increases have been rationalized from two perspectives. The energetic (or radiative) perspective takes the atmospheric energy budget and decomposes energy flux changes (radiative forcing, feedbacks, or surface fluxes) to determine the energy transport changes required by the budget. The diffusive perspective takes the net effect of atmospheric macroturbulence to be a diffusive energy transport down-gradient, so transport changes can arise from changes in mean energy gradients or turbulent diffusivity. Here, we compare these perspectives in idealized moist, gray-radiation GCM simulations over a wide range of climates. The energetic perspective has a dominant role for radiative forcing in this GCM, with cancellation between the temperature feedback components that account for the GCM’s nonmonotonic energy transport changes in response to warming. Comprehensive CMIP5 simulations have similarities in the Northern Hemisphere to the idealized GCM, although a comprehensive GCM over several CO2 doublings has a distinctly different feedback evolution structure. The diffusive perspective requires a non-constant diffusivity to account for the idealized GCM-simulated changes, with important roles for the eddy velocity, dry static stability, and horizontal energy gradients. Beyond diagnostic analysis, GCM-independent a priori theories for components of the temperature feedback are presented that account for changes without knowledge of a perturbed climate state, suggesting that the energetic perspective is the more parsimonious one.

During the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident on March 11, 2011, radionuclides such as tritium were released into the environment across Japan, obscuring the natural background signal of tritium in precipitation. This anthropogenic component was rapidly washed out by precipitation according to measurements in Japan. However, the impact of the accident on the natural tritium-based estimation of water system transit times in Fukushima and other prefectures in Japan remains uncertain. For the first time, anthropogenic tritium from the FDNPP accident together with natural tritium were simulated in an atmospheric general circulation model with a good ability to represent tritium variations in daily and monthly precipitation. For the FDNPP accident, we estimate the maximum tritium atmospheric emission of 0.815 PBq with a tritium in precipitation peak of 68.7 Bq/L (582 tritium units) on March 2011 at Fukushima, which are consistent with previous estimations. Using our modeled outputs with tritium measurements, we improve tritium-tracer application for estimating mean transit times of Fukushima surface and groundwater systems impacted by the anthropogenic tritium from the FDNPP accident. While the anthropogenic impact of the FDNPP accident was limited compared to the tritium peak due to the thermonuclear testing, globally modeled tritium in precipitation is useful to apply for other areas of tritium-tracer studies.

Since the 1970s, Atlantic Niños during boreal summer have been linked to Pacific La Niñas the following winter. Earlier studies have explained the appearance of the Atlantic-Pacific teleconnection with changing Atlantic Niño configurations. Here we find that the non-stationarity of this teleconnection can also be explained by changes in the ocean background state, without changing the Atlantic Niño configuration. Experiments with different atmospheric general circulation models are performed where the same Atlantic Niño pattern is prescribed to different global ocean background states. The 1975–1985 global mean sea surface temperature forces a Walker Circulation response and low-level convergence over the Maritime Continent, increasing the chance of triggering a La Niña-like event in the Pacific. These results suggest that ENSO-predictions could be improved in certain periods by considering tropical Atlantic variability.

Using outputs from an ocean general circulation model, the relative importance of the processes contributing to the development of the Indian Ocean Dipole (IOD) is examined systematically based on two metrics. One metric quantifies the relative importance of the surface heat flux term against the oceanic terms, while the other metric compares the contribution from the horizontal advection and vertical diabatic terms. It is revealed that the anomalous cooling in the eastern tropical Indian Ocean associated with the positive IOD varies with events and four representative events are investigated in more detail. During the 1991 IOD, the horizontal advection term made the largest contribution to the eastern cooling, and the vertical diabatic term was equally important in the early stage of the development. In the 1994 event, negative SST anomalies were generated by the surface heat flux term at first, and then matured by the vertical diabatic term. Anomalous cooling during the 1997 IOD was mainly produced by the vertical diabatic term. In 2012, anomalous surface heat flux and horizontal advection played the crucial role in the development of the eastern pole, but the vertical diabatic term opposed to the anomalous cooling. Furthermore, the dependence of the seasonal prediction skill by a global ocean–atmosphere coupled general circulation model on the generation mechanisms was examined. It is demonstrated that events with the vertical diabatic term playing a more important role in the development of the eastern pole are better predicted than those with the vertical diabatic term making relatively small contribution or opposing the occurrence.

Three different trajectory schemes for oceanic and atmospheric general circulation models are compared in two different experiments. The theories of the trajectory schemes are presented showing the differential equations they solve and why they are mass conserving. One scheme assumes that the velocity fields are stationary for set intervals of time between saved model outputs and solves the trajectory path from a differential equation only as a function of space, i.e. stepwise stationary. The second scheme is a special case of the stepwise-stationary scheme, where velocities are assumed constant between general circulation model (GCM) outputs; it uses hence a fixed GCM time step. The third scheme uses a continuous linear interpolation of the fields in time and solves the trajectory path from a differential equation as a function of both space and time, i.e. a time-dependent scheme. The trajectory schemes are tested offline, i.e. using the already integrated and stored velocity fields from a GCM. The first comparison of the schemes uses trajectories calculated using the velocity fields from a high-resolution ocean general circulation model in the Agulhas region. The second comparison uses trajectories calculated using the wind fields from an atmospheric reanalysis. The study shows that using the time-dependent scheme over the stepwise-stationary scheme greatly improves accuracy with only a small increase in computational time. It is also found that with decreasing time steps the stepwise-stationary scheme becomes increasingly more accurate but at increased computational cost. The time-dependent scheme is therefore preferred over the stepwise-stationary scheme. However, when averaging over large ensembles of trajectories, the two schemes are comparable, as intrinsic variability dominates over numerical errors. The fixed GCM time step scheme is found to be less accurate than the stepwise-stationary scheme, even when considering averages over large ensembles.