Monthly Sea Surface Research Articles

Relative Roles of Circumnavigating Waves and Extratropics on the MJO and Its Relationship with the Mean State*

Abstract A set of atmospheric general circulation model (GCM) experiments is designed to explore the relative roles of the circumnavigating waves and the extratropics on the Madden–Julian oscillation (MJO). In a “control” simulation, the model is forced by the climatological monthly sea surface temperature for 20 yr. In the first sensitivity experiment, model prognostic variables are relaxed in the tropical Atlantic region (20°S–20°N, 80°W–0°) toward the “controlled” climatological annual cycle to suppress the influences from the circumnavigating waves. In the second sensitivity experiment, model prognostic variables are relaxed in the 20°–30° latitude zones toward the controlled climatological annual cycle to suppress the influences from the extratropics (or the tropics–extratropics interactions). The numerical results demonstrate that the extratropics play a more important role in maintaining the MJO variance than the circumnavigating waves. The simulations further show that both the tropical mean precipitation and the intraseasonal precipitation variability are reduced when the extratropical influences are suppressed. The in-phase relationship is primarily attributed to the effect of the mean state on perturbations. A moisture budget analysis indicates that a positive feedback to the mean precipitation by the anomalous moisture convergence is offset by a negative feedback due to the anomalous moisture advection. The change in the mean precipitation in the absence of extratropical influences is primarily determined by the change in the mean moisture convergence, which in turn is due to the change in circulation. This study is the first attempt to quantitatively separate the effects of the circumnavigating waves and the extratropics on the MJO. Implications and limitations of this study are discussed.

Ocean–Atmosphere Forcing of Summer Streamflow Drought in Great Britain

Abstract Droughts are high-impact events that have substantial implications for both human and natural systems. As such, improved understanding of the hydroclimatological processes involved in drought development is a major scientific imperative of direct practical relevance. To address this research need, this paper investigates the chain of processes linking antecedent ocean–atmosphere variation to summer streamflow drought in Great Britain. Analyses are structured around four distinct drought regions (defined using hierarchical cluster analysis) for the period 1964–2001. Droughts were identified using a novel regional drought area index. Composite analysis of monthly sea surface temperature (SST) prior to drought onset reveals a horseshoe- or tripole-shaped pattern of North Atlantic SST anomalies that is similar to patterns of SST anomalies associated with the North Atlantic Oscillation (NAO). Patterns in geopotential height, wind, moisture vapor flux, and precipitation prior to drought onset support the influence of the NAO but also demonstrate that the atmospheric bridge linking North Atlantic SST to drought development is too complex to be described solely by indices of the NAO. In revealing new information on the chain of processes leading to the development of hydrological drought in Great Britain, this paper has the potential to inform drought-forecasting research and so improve drought preparedness and management.

Jumbo squid (Dosidicus gigas) landings in the Gulf of California related to remotely sensed SST and concentrations of chlorophyll a (1998–2012)

The jumbo squid Dosidicus gigas supports an important fishery in the central region of the Gulf of California, Mexico. However, in recent years, landings of this species in the port of Guaymas, Sonora has declined significantly. We examined monthly landing records from January 1998 through March 2012 and related this record to monthly sea surface temperature (SST) and chlorophyll a from satellite imagery and coastal upwelling index (CUI) in the fishing area as proxies of inter-annual changes in the epipelagic habitat. Results indicate that jumbo squid catches were high between June 1999 and December 2004 (328,903t) associated with an extended period of prevailing cold SSTs (La Niña 1999) and high chlorophyll a concentrations with intense CUI. From January 2005 through March 2012, a progressive catches decrease; landings were about 31.8% of previous captures (104,829t). Decline is associated with a progressively warmer habitat with less chlorophyll a and considerably lower CUI. Six fishing surveys carried out into the central Gulf of California with jig sampling indicate that jumbo squid tend to be in regions with low SST and high chlorophyll a, confirming its association with cold and productive areas.

Temporal–spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans

AbstractTo examine atmospheric and oceanic predictability based on nonlinear error growth dynamics, the authors introduced recently a new method using the nonlinear local Lyapunov exponent (NLLE). In this study, the NLLE method is employed to investigate the temporal–spatial distribution of the limit of sea surface temperature (SST) predictability, based on reanalysis monthly SST data. The results show that the annual mean limit of SST predictability is the greatest in the tropical central–eastern Pacific (>8 months). Relatively high values were also obtained for the tropical Indian and Atlantic Oceans (5–8 months). In the northern and southern mid‐high latitude oceans, the limit of SST predictability is less than 6 months, with a minimum value of only 2–3 months. The limit of SST predictability in different oceanic regions shows significant seasonal variations, related to the persistence barriers that occur during particular seasons. In addition to the well‐known spring persistence barrier (SPB) in the tropical central–eastern Pacific, persistence barriers also occur in other ocean areas during seasons other than spring. A winter persistence barrier (WPB) exists in the southeastern tropical Indian Ocean and the northern tropical Atlantic. In the North Pacific and North Atlantic, a persistence barrier exists around July–September. These seasonal persistence barriers cause a relatively low limit of SST predictability when predictions are made across the season in which the barriers occur. In contrast, when predictions are made initiated from the season with a persistence barrier, the SST errors show rapid initial growth but slow growth in the following seasons, resulting in a relatively high limit in predictability. Analyses also indicate that the possibility of really eliminating the effects of persistence barriers on SST errors by improving ocean–atmosphere coupled general circulation models (CGCMs) or the data assimilation procedure is very low. Copyright © 2012 Royal Meteorological Society

The influence of Indian Ocean sea surface temperature on the variability of monsoon rainfall over India

This study focuses on the spatial and temporal variability of southwest monsoon (SWM) and northeast monsoon (NEM) rainfall over India, after the 1976–1977 regime shift. Through wavelet analysis the dominant mode of variability in the SWM and NEM rainfall is found to be in the 2–8-year scale (ENSO band). Therefore, scale-averaged wavelet power (SAP) of SWM (SAPSWM) and NEM (SAPNEM) rainfall obtained from a high-resolution dataset for the period 1977–2001 was subjected to empirical orthogonal function analysis, in order to understand the homogenous regions of rainfall variability. As the study utilizes SAP, the decadal variability is observed in the spatial and temporal patterns of rainfall. The maximum variability of SAPSWM rainfall is found to be in the 2–4-year period, whereas, for the SAPNEM rainfall, the biennial mode explains the variability over the core region of NEM rainfall. Significant power is also observed in the 4-year period. The evolution of the principal components (PCs) is consistent with the above/below normal rainfall received over the region. Similar analysis has been carried out also for the SAP of monthly sea surface temperature (SAPSST) over Indian Ocean for the months from January to September. Compared with any other month, the decadal changes of the PCs of SAPSWM and SAPNEM rainfall are highly correlated with the decadal variations of PC1 of SAPSST for the month of March. Therefore, the relation between the rainfall and SST was explained using SAPSST for the month of March. Significant biennial power is also observed in March SST over southeast Indian Ocean (85–110°E and 5–40°S) after the climate shift of 1976. In order to identify any change in the relation between NEM rainfall and SAPSST (March) before and after 1976, a correlation between NEM rainfall and PC1 of SAPSST (March) was found for the period 1951–1975 and 1977–2001. Results show that correlation has increased after 1976. Therefore, this PC can be made use of in the prediction of NEM rainfall. Copyright © 2012 Royal Meteorological Society

Recruitment of catarina scallop (Argopecten ventricosus) larvae on artificial collectors off the NE coast of the Gulf of California

We evaluated recruitment of larvae of catarina scallop, Argopecten ventricosus, in the area of Puerto Penasco, NE Gulf of California. We moored artificial collectors in six sites from June 2007 to August 2008 and replaced them every 2 months. We used monthly (July 2002-September 2011) sea surface temperature (SST, °C) and surface chlorophyll-a concentration (SSChl, mg m � 3 ) Aqua/MODIS satellite data to describe seasonal environmental behaviour study area. Also, we recorded bottom temperature at each site every 4 h, and every 2 months measured sea surface salinity, temperature and dissolved oxygen. We used a repeated measures ANOVA to evaluate differ- ences in the number of recruited spat between main factors, and analysed the presence of multi- modal spat shell size frequency distributions. Over- all, spat recruitment was negatively correlated with seawater temperature and showed higher spat recruitment abundances throughout winter, which is the season with the highest surface chlo- rophyll a concentration. We estimated multimodal shell size frequency distributions characterized by more than one modal size. The natural collection of A. ventricosus spat on artificial collectors in the area can be successfully performed over a pro- tracted period (November-December to May-June). Our results extend the area where collection of A. ventricosus spat can be successful.

Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Niño-Southern Oscillation

A principal component decomposition of monthly sea surface temperature (SST) variability in the tropical Pacific Ocean demonstrates that nearly all of the linear trends during 1950–2010 are found in two leading patterns. The first SST pattern is strongly related to the canonical El Niño-Southern Oscillation (ENSO) pattern. The second pattern shares characteristics with the first pattern and its existence solely depends on the presence of linear trends across the tropical Pacific Ocean. The decomposition also uncovers a third pattern, often referred to as ENSO Modoki, but the linear trend is small and dataset dependent over the full 61-year record and is insignificant within each season. ENSO Modoki is also reflected in the equatorial zonal SST gradient between the Niño-4 region, located in the west-central Pacific, and the Niño-3 region in the eastern Pacific. It is only in this zonal SST gradient that a marginally significant trend arises early in the Northern Hemisphere spring (March–May) during El Niño and La Niña and also in the late summer (July–September) during El Niño. Yet these SST trends in the zonal gradient do not unequivocally represent an ENSO Modoki-like dipole because they are exclusively associated with significant positive SST trends in either the eastern or western Pacific, with no corresponding significant negative trends. Insignificant trends in the zonal SST gradient are evident during the boreal wintertime months when ENSO events typically mature. Given the presence of positive SST trends across much of the equatorial Pacific Ocean, using fixed SST anomaly thresholds to define ENSO events likely needs to be reconsidered.

The seaweeds of Angola: the transition between tropical and temperate marine floras on the west coast of southern Africa

The seaweed flora of Angola is relatively poorly known. Most of the 124 records listed for the country come from a 1974 British Natural History Museum expedition to the central and southern parts of that country. Previous biogeographic studies treated the Angolan seaweed flora as a whole and grouped it with those of certain West African islands as transitional between a Tropical West African seaweed flora (essentially extending from Senegal to Gabon) and temperate floras to the north and south of this truly tropical region. In the present study a total of 99 species and subspecies of seaweeds was collected from the intertidal zone and shallow sublittoral zones at five sites in the north of Angola and four sites in the south. The biogeographic distributions of our records were examined and compared with the temperate flora of Namibia to the south and the flora of Ghana to the north (as an example of a well-studied Tropical West African flora). Multivariate analyses of our presence/absence records showed differences between northern and southern sites (cluster analysis [Jaccard, average linkage] and detrended correspondence analysis). All Angolan sites were clearly different from floras of south, central and northern Namibia, which are considered to be strictly temperate in nature. Northern Angolan sites grouped more closely with Ghana than with southern Angolan sites. Distribution patterns within Angola are discussed in relation to monthly sea surface temperature data that were processed for this region. We conclude that the overall affinities of the Angolan seaweed flora, as represented by our collections, are Tropical West African, but with a well-developed temperate element in southern Angola (from about 13° S) comprising mainly cooler-water species from the Benguela Marine Province of Namibia and western South Africa. Our collections add about 45 species to the Angolan seaweed flora, raising the total number of species to around 169. This total approaches that of Ghana which, with about 200 species, is considered to have the richest seaweed flora in Tropical West Africa.

Vegetation feedback under future global warming

It has been well documented that vegetation plays an important role in the climate system. However, vegetation is typically kept constant when climate models are used to project anthropogenic climate change under a range of emission scenarios in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios. Here, an atmospheric general circulation model, and an asynchronously coupled system of an atmospheric and an equilibrium terrestrial biosphere model are forced by monthly sea surface temperature and sea ice extent for the periods 2051–2060 and 2090–2098 as projected with 17 atmosphere–ocean general circulation models participating in the IPCC Fourth Assessment Report, and by appropriate atmospheric carbon dioxide concentrations under the A2 emission scenario. The effects of vegetation feedback under future global warming are then investigated. It is found that the simulated composition and distribution of vegetation during 2051–2060 (2090–2098) differ greatly from the present, and global vegetation tends to become denser as expressed by a 21% (36%) increase in global mean leaf area index, which is most pronounced at the middle and high northern latitudes. Vegetation feedback has little effect on globally averaged surface temperature. On a regional scale, however, it induces statistically significant changes in surface temperature, in particular over most parts of continental Eurasia east of about 60°E where annual surface temperature is expected to increase by 0.1–1.0 K, with an average of about 0.4 K for each future period. These changes can mostly be explained by changes in surface albedo resulting from vegetation changes in the context of future global warming.

Influence of the Meridional Shifts of the Kuroshio and the Oyashio Extensions on the Atmospheric Circulation

Abstract The meridional shifts of the Oyashio Extension (OE) and of the Kuroshio Extension (KE), as derived from high-resolution monthly sea surface temperature (SST) anomalies in 1982–2008 and historical temperature profiles in 1979–2007, respectively, are shown based on lagged regression analysis to significantly influence the large-scale atmospheric circulation. The signals are independent from the ENSO teleconnections, which were removed by seasonally varying, asymmetric regression onto the first three principal components of the tropical Pacific SST anomalies. The response to the meridional shifts of the OE front is equivalent barotropic and broadly resembles the North Pacific Oscillation/western Pacific pattern in a positive phase for a northward frontal displacement. The response may reach 35 m at 250 hPa for a typical OE shift, a strong sensitivity since the associated SST anomaly is 0.5 K. However, the amplitude, but not the pattern or statistical significance, strongly depends on the lag and an assumed 2-month atmospheric response time. The response is stronger during fall and winter and when the front is displaced southward. The response to the northward KE shifts primarily consists of a high centered in the northwestern North Pacific and hemispheric teleconnections. The response is also equivalent barotropic, except near Kamchatka, where it tilts slightly westward with height. The typical amplitude is half as large as that associated with OE shifts.

Simulation and estimation of probabilities of phases of the Pacific Decadal Oscillation

The Pacific Decadal Oscillation (PDO) index defines the leading mode of monthly sea surface temperature (SST) anomalies in the North Pacific Ocean. Time series analysis in both the frequency and time domains is applied to 107 years of monthly PDO index values. Simulations of a model fitted to the data are used to estimate p-values associated with particular events observed in the raw data. The simulations are further used to estimate the distribution of various quantities, such as the length (in years) of a positive phase, or the absolute difference between the longest positive and negative phase (in years). The results show that the probability of occurrence of a negative phase surrounded by two positive phases within a 107-year period is approximately 9.9%. The raw data's mean positive phase length is close to the simulation mean and median, while the absolute difference in maximum positive/negative phase lengths corresponds to a p-value of 14.9%. The methodology developed in this paper can be useful to ecologists in assessing the potential ecological effects due to PDO variation, and for estimating the probabilities associated with future phases or other events. Copyright © 2009 John Wiley & Sons, Ltd.

<b>PREDICTION OF PLANTING DATE AND GROWING PERIOD USING SEA SURFACE TEMPERATURE (SST) ANOMALIES IN NINO 3.4 FOR INDRAMAYU DISTRICT </b>

<div>Agriculture is a very important sector in Indramayu’s economy. Approximately 53.52% of Indramayu residents are involved in agriculture. Indramayu is a center of rice production in Indonesia. Most of the </div><div>farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic natural events, but not consider climate variability well. Climate variability has become a major obstacle to achieving a successful harvest, because it can affect the timing of planting and length of the growing season, which leads to drought and flood vulnerability. The planting date and growing season predicted by using monthly sea surface temperature anomalies (SSTa) in Nino 3.4. The August SSTa can describe the planting date better than the growing season, which are demonstrated best in Lohbener with R</div><div>2</div><div> = 45% with forecast skill reach = 84% and 92% for advanced and delayed planting dates. Knowing the planting date and growing season length produce a more effective cropping calendar, which includes details such as when to prepare the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of climate variability by providing a more efficient cropping pattern and avoiding potential harvest failures.Agriculture is a very important sector in Indramayu's economy. Approximately 53.52% of Indramayu residents are involved in agriculture. Indramayu is a center of rice production in Indonesia. Most of the farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic natural events, but not consider climate variability well. Climate variability has become a major obstacle to achieving a successful harvest, because it can affect the timing of planting and length of the growing season, which leads to drought and flood vulnerability. The planting date and growing season predicted by using monthly sea surface temperature anomalies (SSTa) in Nino 3.4. The August SSTa can describe the planting date better than the growing season, which are demonstrated best in Lohbener with R2 = 45% with forecast skill reach = 84% and 92% for advanced and delayed planting dates. Knowing the planting date and growing season length produce a more effective cropping calendar, which includes details such as when to prepare the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of climate variability by providing a more efficient cropping pattern and avoiding potential harvest failures.</div>

Nonparametric time series forecasting with dynamic updating

We present a nonparametric method to forecast a seasonal univariate time series, and propose four dynamic updating methods to improve point forecast accuracy. Our methods consider a seasonal univariate time series as a functional time series. We propose first to reduce the dimensionality by applying functional principal component analysis to the historical observations, and then to use univariate time series forecasting and functional principal component regression techniques. When data in the most recent year are partially observed, we improve point forecast accuracy by using dynamic updating methods. We also introduce a nonparametric approach to construct prediction intervals of updated forecasts, and compare the empirical coverage probability with an existing parametric method. Our approaches are data-driven and computationally fast, and hence they are feasible to be applied in real time high frequency dynamic updating. The methods are demonstrated using monthly sea surface temperatures from 1950 to 2008.

Modality of semiannual to multidecadal oscillations in global sea surface temperature variability

Repeating the history of study on El Niño‐Southern Oscillation (ENSO) in the 1980s, interdecadal oscillation (IDO) in climate variability is currently an area of active research and debate, following the recognition of its emerging significance in nature and science. In this work, a two‐dimensional propagating modal extraction technique is applied to a reconstructed global monthly sea surface temperature (SST) data set spanning 1854 through 2007, to examine the spatiotemporal structure of SST variability with an emphasis on the fine modal pattern of IDOs. In the time domain, it is revealed that a canonical modal spectrum of decadal‐to‐centennial SST variability constitutes four most distinct oscillations with periodicities at 9.0, 13.0, 21.2, and 62.2 years, which are naturally defined as primary modes and are, respectively, termed as the subdecadal mode, the quasidecadal mode, the interdecadal mode, and the multidecadal mode (modes S, Q, I, and M). They join the energetic annual mode (mode A) and two major ENSO modes at 3.7 and 5.8 years (modes B and C), as well as a dozen of secondary modes ranging from semiannual to multidecadal, in determining the key pattern of SST‐related climate variability. In the space domain, seven modally dynamic areas, analogous to the Niño regions for ENSO, are clearly identified and are named as IDO zones. Contrary to ENSO, dominant IDO zones are most visible in the extratropical oceans, especially in the North Atlantic/North Pacific sectors, while secondary signatures are observed in the tropical oceans. The array of (four) primary modes with respect to (seven) major zones yields a sophisticated yet canonical pattern of IDOs, leading to a basic conclusion that multimodality (for a given region) and multiregionality (for a given mode) are fundamental features of the IDO system.

Forecasts of South China Sea surface temperature anomalies using the Niño indices and dipole mode index as predictors

Based on an empirical orthogonal function (EOF) analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature (OISST) data in the South China Sea (SCS) after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index (DMI), Nino1+2, Nino3.4, Nino3, and Nino4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis (CCA) model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1–12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1–12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1–12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models: one using both DMI and Nino indices and the other using only Nino indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Nino indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Nino indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years (1993/94 to October 2004) by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1–12 month lead time (the persisted prediction). Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time (longer than 3 months). The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1–12 month lead time are also examined.

PRINCIPAL COMPONENT ANALYSIS OF SEA SURFACE TEMPERATURE IN THE NORTH ATLANTIC OCEAN

The framework of principal component analysis (PCA) based on singular value decomposition (SVD) is applied to the monthly sea surface temperature (SST) observations in the North Atlantic Ocean for the time interval 1856–2008. Multiyear time series of SST for each month are used to investigate the statistical relationship between SST variations from the 12 months. To obtain approximate stationary conditions, the trend and a multidecadal oscillation are removed from the data. The remaining SST residuals exhibit remarkable correlation between successive months, due largely to persistence. PCA demonstrates the dimension reduction of the data sets and provides a robust way of analyzing multivariate observations describing the climate system.

Asociación entre la Corriente de Deriva de los Vientos del Oeste y la abundancia relativa del pez espada (Xiphias gladius) frente a la costa de Chile

The relationship between the relative abundance of swordfish ( Xiphias gladius ) off the Chilean coast and the latitudinal location of the West Wind Drift (WWD) was analyzed from a macro scale standpoint for the period between 1989 and 1996. The position of the axis of the eastern boundary of the WWD was estimated by determining the latitude of the bifurcation of the isotherms obtained from the average monthly sea surface temperatures. The capture per unit effort (CPUE) of the small-scale driftnet fleet was used as a relative abundance index for the resource. The WWD and CPUE time series were analyzed on a monthly scale with different lags between the series. Two methodological approaches were used to determine the relationship between these two time series. The first was the methodology proposed by Pyper & Peterman (1998), which was used to correct the actual degrees of freedom in the cross-correlation test. The second approach consisted in analyzing the correlation of two stationary time series at different lags. Those approaches indicated a significant relationship between the latitudinal location of the WWD and the CPUE on a monthly scale. The relationship found between the WWD and the swordfish relative abundance suggests that the WWD could be a macro scale indicator of oceanographic and meteorological variability that could affect swordfish distribution patterns and availability.

Long-Range Niño-3.4 Predictions Using Pairwise Dynamic Combinations of Multiple Models

Abstract The interest in climate prediction has seen a rise in the number of modeling alternatives in recent years. One way to reduce the predictive uncertainty from any such modeling procedure is to combine or average the modeled outputs. Multiple model results can be combined such that the combination weights may either be static or vary over time. This research develops a methodology for combining forecasts from multiple models in a dynamic setting. The authors mix models on a pairwise basis using importance weights that vary in time, reflecting the persistence of individual model skills. Such an approach is referred to here as a dynamic pairwise combination tree and is presented as an improvement over the case where the importance weights are static or constant over time. The pairwise importance weight is modeled as a product of a “mixture ratio” and a “bias direction,” the former representing the fraction of the absolute residual error associated with each of the paired models, and the latter representing an indicator of the sign of the two residual errors. The mixture ratio is modeled using a generalized autoregressive model and the bias direction using ordered logistic regression. The method is applied to combine three climate models, the variables of interest being the monthly sea surface temperature anomalies averaged over the Niño-3.4 region from 1956 to 2001. The authors test the combined model skill using a “leave ± 6 months out cross-validation” approach along with validation in 10-yr blocks. This study attained a small but consistent improvement of the predictive skill of the dynamically combined models compared to the existing practice of static weight combination.

SPATIAL AND TEMPORAL PATTERNS OF INVERTEBRATE RECRUITMENT ALONG THE WEST COAST OF THE UNITED STATES

Patterns of recruitment in marine ecosystems can reflect the distribution of adults, dispersal by ocean currents, or patterns of mortality after settlement. In turn, patterns of recruitment can play an important role in determining patterns of adult abundance and community dynamics. Here we examine the biogeographic structure of recruitment variability along the U.S. West Coast and examine its association with temperature variability. From 1997 to 2004 we monitored monthly recruitment rates of dominant intertidal invertebrates, mussels and barnacles, at 26 rocky shore sites on the West Coast of the United States, from northern Oregon to southern California, a span of 1750 km of coastline. We examined spatial variation in the dynamics of recruitment rates and their relationship to coastal oceanography using satellite-derived time series of monthly sea surface temperature (SST). Recruitment rates showed a biogeographic structure with large regions under similar dynamics delimited by abrupt transitions. The seasonal peak in recruitment rates for both mussels and barnacles changed from a late summer–early fall peak in Oregon to winter or early spring in northern California, and then back toward summer in southern California. Recruitment rates varied greatly in magnitude across the latitudinal range. The barnacle Balanus glandula and mussels (Mytilus spp.) showed a decline of two orders of magnitude south of Oregon. In contrast, recruitment rates of barnacles of the genus Chthamalus showed a variable pattern across the region examined. The spatial distribution of associations between raw SST and recruitment rates for all species showed positive associations, indicating recruitment during warm months, for all species in Oregon, northern California, and several sites in south-central California. By considerably extending the spatial and temporal scales beyond that of previous studies on larval recruitment rates in this system, our study has identified major biogeographic breaks around Cape Blanco and Point Conception despite considerable spatial and temporal variation within each region and among species. These large differences in recruitment rates across biogeographic scales highlight the need for a better understanding of larval responses to ocean circulation patterns in the conservation and management of coastal ecosystems.

Coral bleaching indices and thresholds for the Florida Reef Tract, Bahamas, and St. Croix, US Virgin Islands

It is well established that elevated sea temperatures cause widespread coral bleaching, yet confusion lingers as to what facet of extreme temperatures is most important. Utilizing long-term in situ datasets, we calculated nine thermal stress indices and tested their effectiveness at segregating bleaching years a posteriori for multiple reefs on the Florida Reef Tract. The indices examined represent three aspects of thermal stress: (1) short-term, acute temperature stress; (2) cumulative temperature stress; and (3) temperature variability. Maximum monthly sea surface temperature (SST) and the number of days >30.5 °C were the most significant; indicating that cumulative exposure to temperature extremes characterized bleaching years. Bleaching thresholds were warmer for Florida than the Bahamas and St. Croix, US Virgin Islands reflecting differences in seasonal maximum SST. Hind-casts showed that monthly mean SST above a local threshold explained all bleaching years in Florida, the Bahamas, and US Virgin Islands.