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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.

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.

Sea surface temperature (SST) anomalies in the Kuroshio Extension (KE) region have been increasingly recognized as playing a key role in shaping the extratropical air–sea interactions. However, the extent to which such extratropical SST forcing can influence large‐scale atmospheric circulation and surrounding SST remains uncertain, particularly given strong atmospheric internal variability. Here, we investigate the atmospheric response to idealized positive SST anomalies in the KE region using large‐ensemble atmospheric general circulation model experiments. The imposed forcing generates a robust sea‐level pressure anomaly over and downstream of the KE, leading to surface westerly wind anomalies extending into the subtropical North Pacific. Despite substantial atmospheric internal variability over the Aleutian Low region, these subtropical wind anomalies consistently emerge across the ensemble. Our results suggest that SST forcing in the KE region can modulate air–sea interactions not only locally but also across a broader region extending equatorward.

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.

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.

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).

In this study, we report our ocean general circulation model simulations of the global distribution of rare earth elements (REEs) in the ocean. As previously reported (Oka et al. in Glob Biogeochem Cycles 23:1–16, 2009), the vertical profiles of REEs in the North Pacific Ocean are strongly controlled by the reversible scavenging process, and the systematic differences between REEs can be reproduced in the model by selecting an appropriate model parameter which controls affinity to particles. We here demonstrate that the external REE input from the coastal regions also plays a role in controlling the vertical profiles of dissolved REE and their inter-basin differences. The role of the external inputs is especially important for light REEs, such as neodymium (Nd). The linear increase in Nd concentration in the North Pacific Ocean cannot be sufficiently reproduced by the reversible scavenging alone; rather, a combination of the reversible scavenging and the external inputs is necessary. On the other hand, the distribution of heavy REEs, such as lutetium (Lu), can be broadly reproduced without the external inputs, suggesting that Lu has similarity with conservative nutrient-like tracer. When compared with REE observations compiled from both the recently obtained GEOTRACES dataset and pre-GEOTRACES reported data, our simulations successfully reproduced the overall features of these observations. Observational data suggested that the vertical profiles of REEs are not the same among the basins; our model simulations demonstrate that this feature can be clearly reproduced by considering both the reversible scavenging and the external REE inputs from the coastal regions.

The ocean general circulation model (OGCM) is an essential tool for researching oceanography and atmospheric science. The LASG/IAP climate system ocean model version 3 (LICOM3) is a parallel version of the OGCM. Our goal is to implement and optimize a GPU version of LICOM3 based on compute unified device architecture (CUDA) called LICOM3-CUDA. Considering the characteristics of LICOM3 and CUDA, we design and implement some pivotal optimization methods, including redesigning the numerical algorithms of complicated functions, decoupling data dependency, avoiding memory write conflicts, and optimizing communication. In this paper, we selected two experiments, including 1 ∘ (small-scale) and 0.1 ∘ (large-scale) resolutions to evaluate the performance of LICOM3-CUDA. Under the experimental environment of two Intel Xeon Gold 6148 CPUs and four NVIDIA Quadro GV100s, the LICOM3-CUDA (1 ∘ ) achieves a simulation speed of 114.3 simulation-year-per-day (SYPD). Compare with the performance of LICOM3, the LICOM3-CUDA can run much faster with 6.5 times, and the compute-intensive module achieves over 70 × speedup. In addition, the energy consumption for the simulation year is reduced by 41.3%. As for high-resolution and large-scale simulation, the number of GPUs increased from 96 to 1536 as well as the LICOM3-CUDA (0.1 ∘ ) time consumption decreased from 3261 to 720 seconds with approximately 4.5 × of speedup.

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.