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A new operational ocean analysis/reanalysis system (ORA-S3) has been implemented at ECMWF. The reanalysis, started from 1 January 1959, is continuously maintained up to 11 days behind real time and is used to initialize seasonal forecasts as well as to provide a historical representation of the ocean for climate studies. It has several innovative features, including an online bias-correction algorithm, the assimilation of salinity data on temperature surfaces, and the assimilation of altimeter-derived sea level anomalies and global sea level trends. It is designed to reduce spurious climate variability in the resulting ocean reanalysis due to the nonstationary nature of the observing system, while still taking advantage of the observation information. The new analysis system is compared with the previous operational version; the equatorial temperature biases are reduced and equatorial currents are improved. The impact of assimilation in the ocean state is discussed by diagnosis of the assimilation increment and bias correction terms. The resulting analysis not only improves the fit to the data, but also improves the representation of the interannual variability. In addition to the basic analysis, a real-time analysis is produced (RT-S3). This is needed for monthly forecasts and in the future may be needed for shorter-range forecasts. It is initialized from the near-real-time ORA-S3 and run each day from it.

In 2010 eastern Australia received its highest springtime (September–November) rainfall since 1900. Based on historical relationships with sea surface temperatures (SST) and other climate indices, this record rainfall in 2010 was shown to be largely commensurate with the occurrence of a very strong La Niña event and an extreme positive excursion of the SAM. The pattern and magnitude of the tropical SST anomalies in austral spring 2010 were diagnosed to be nearly perfect to produce high rainfall across eastern Australia. Key aspects of this SST pattern were the strong cold anomaly in the central equatorial Pacific, and the strong warm anomalies in the eastern Indian Ocean and the far western Pacific to the north of Australia. Although the recent upward trend in SSTs in the western Pacific/eastern Indian Ocean warm pool accounted for about 50 % of the SST anomaly surrounding northern Australia in 2010, the contribution by the warming trend in these SSTs to the Australian rainfall anomaly in 2010 was assessed to be relatively modest. The strong positive swing in SAM was estimated to have accounted for upwards of 40 % of the regional anomaly along the central east coastal region and about 10 % of the area mean anomaly across eastern Australia. This contribution by the SAM suggests that a significant portion of the rainfall in 2010 may not have been seasonally predictable. However, predictability arising from the promotion of high SAM by the extreme La Nina event can not be ruled out.

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1 year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3–6 months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean.

A new and provocative analysis of \"The Snows of Kilimanjaro\" Hemingway's short story, \"The Snows of Kilimanjaro,\" has secured a place among the greatest works in that genre--the story is widely considered Hemingway's greatest.To explore the richness of this work, David L.

Arctic habitats are changing rapidly and altering trophic webs and ecosystem functioning. Understanding how species' abundances and distributions differ among Arctic habitats is important in predicting future species shifts and trophic‐web consequences. We aimed to determine the habitat–abundance relationships for three small herbivores on the Seward Peninsula of Alaska, USA by fitting data from 983 point counts (collected during 2019, 2021, and 2022) with N‐mixture models that account for imperfect detection. These herbivore species, Willow Ptarmigan (Lagopus lagopus), Rock Ptarmigan (L. muta), and Arctic ground squirrels (Urocitellus parryii), are fundamental to tundra food webs, and primary prey for Arctic raptors including Gyrfalcons (Falco rusticolus). Second, we aimed to map herbivore densities within Gyrfalcon breeding territories. Third, we aimed to evaluate whether Gyrfalcons were more likely to occupy territories with higher prey densities using a multi‐season occupancy model coupled with occupancy observations from helicopter surveys conducted during 2016–2022 at 97 Gyrfalcon territories. We found that male Willow Ptarmigan were more abundant in areas with greater cover of tundra, tall shrubs, and tussock tundra. Conversely, male Rock Ptarmigan were more abundant in areas with greater cover of sparse vegetation and tundra. Arctic ground squirrels were more abundant at higher elevations with greater cover of sparse vegetation and low shrubs. Willow Ptarmigan were widespread within Gyrfalcon breeding territories, whereas Rock Ptarmigan and Arctic ground squirrels had patchier distributions with few areas of high abundance. Lastly, Gyrfalcons were more likely to occupy territories with higher densities of Willow Ptarmigan and Arctic ground squirrels. As the Artic continues to warm, Rock Ptarmigan and Arctic ground squirrels may be vulnerable to ongoing shrub encroachment, whereas Willow Ptarmigan may benefit. By tying abundances of three prey to Gyrfalcon occupancy, our results contribute to understanding potential impacts on higher levels of this Arctic trophic web. Arctic habitats are changing rapidly, impacting trophic webs and ecosystem functions. We examined the habitat–abundance relationships of three small herbivores on the Seward Peninsula, Alaska, and their distribution within Gyrfalcon breeding territories. We found that Gyrfalcons preferred territories with higher densities of Willow Ptarmigan and Arctic ground squirrels, and that changes in habitat may benefit some prey species, like Willow Ptarmigan, while making others, such as Rock Ptarmigan and Arctic ground squirrels, more vulnerable to warming and shrub encroachment.

Understanding species–environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. However, for many species, this baseline information does not exist. With reliable presence data, spatial models that predict geographic ranges and identify environmental processes regulating distribution are a cost‐effective and rapid method to achieve this. Yet these spatial models are lacking for many rare and threatened species, particularly in tropical regions. The harpy eagle (Harpia harpyja) is a Neotropical forest raptor of conservation concern with a continental distribution across lowland tropical forests in Central and South America. Currently, the harpy eagle faces threats from habitat loss and persecution and is categorized as Near‐Threatened by the International Union for the Conservation of Nature (IUCN). Within a point process modeling (PPM) framework, we use presence‐only occurrences with climatic and topographical predictors to estimate current and past distributions and define environmental requirements using Ecological Niche Factor Analysis. The current PPM prediction had high calibration accuracy (Continuous Boyce Index = 0.838) and was robust to null expectations (pROC ratio = 1.407). Three predictors contributed 96% to the PPM prediction, with Climatic Moisture Index the most important (72.1%), followed by minimum temperature of the warmest month (15.6%) and Terrain Roughness Index (8.3%). Assessing distribution in environmental space confirmed the same predictors explaining distribution, along with precipitation in the wettest month. Our reclassified binary model estimated a current range size 11% smaller than the current IUCN range polygon. Paleoclimatic projections combined with the current model predicted stable climatic refugia in the central Amazon, Guyana, eastern Colombia, and Panama. We propose a data‐driven geographic range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements. Understanding species–environment relationships is key to defining the spatial structure of species distributions and develop effective conservation plans. The harpy eagle (Harpia harpyja) is a large raptor with a continental distribution across the Neotropics, categorized as Near‐Threatened by the International Union for the Conservation of Nature (IUCN). We propose a data‐driven geographical range to complement the current IUCN range estimate and that despite its continental distribution, this tropical forest raptor is highly specialized to specific environmental requirements.

The prediction skill of the Australian Bureau of Meteorology dynamical seasonal forecast model Predictive Ocean Atmosphere Model for Australia (POAMA) is assessed for probabilistic forecasts of spring season rainfall in Australia and the feasibility of increasing forecast skill through statistical postprocessing is examined. Two statistical postprocessing techniques are explored: calibrating POAMA prediction of rainfall anomaly against observations and using dynamically predicted mean sea level pressure to infer regional rainfall anomaly over Australia (referred to as “bridging”). A “homogeneous” multimodel ensemble prediction method (HMME) is also introduced that consists of the combination of POAMA’s direct prediction of rainfall anomaly together with the two statistically postprocessed predictions. Using hindcasts for the period 1981–2006, the direct forecasts from POAMA exhibit skill relative to a climatological forecast over broad areas of eastern and southern Australia, where El Niño and the Indian Ocean dipole (whose behavior POAMA can skillfully predict at short lead times) are known to exert a strong influence in austral spring. The calibrated and bridged forecasts, while potentially offering improvement over the direct forecasts because of POAMA’s ability to predict the main drivers of springtime rainfall (e.g., El Niño and the Southern Oscillation), show only limited areas of improvement, mainly because strict cross-validation limits the ability to capitalize on relatively modest predictive signals with short record lengths. However, when POAMA and the two statistical–dynamical rainfall forecasts are combined in the HMME, higher deterministic and probabilistic skill is achieved over any of the single models, which suggests the HMME is another useful method to calibrate dynamical model forecasts.

The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification. Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.