Area-averaged mean seasonal cycles of chlorophyll-a concentration, sea surface temperature, alongshore wind stress, westward wind speed and dust component of the aerosol optical depth at 550 nm

Nutrient supply in the area off Northwest Africa is mainly regulated by two processes, coastal upwelling and deposition of Saharan dust. In the present study, both processes were analyzed and evaluated by different methods, including cross-correlation, multiple correlation, and event statistics, using remotely sensed proxies of the period from 2000 to 2008 to investigate their influence on the marine environment. The remotely sensed chlorophyll-a concentration was used as a proxy for the phytoplankton biomass stimulated by nutrient supply into the euphotic zone from deeper water layers and from the atmosphere. Satellite-derived alongshore wind stress and sea-surface temperature were applied as proxies for the strength and reflection of coastal upwelling processes. The westward wind and the dust component of the aerosol optical depth describe the transport direction of atmospheric dust and the atmospheric dust column load. Alongshore wind stress and induced upwelling processes were most significantly responsible for the surface chlorophyll-a variability, accounting for about 24% of the total variance, mainly in the winter and spring due to the strong north-easterly trade winds. The remotely sensed proxies allowed determination of time lags between biological response and its forcing processes. A delay of up to 16 days in the surface chlorophyll-a concentration due to the alongshore wind stress was determined in the northern winter and spring. Although input of atmospheric iron by dust storms can stimulate new phytoplankton production in the study area, only 5% of the surface chlorophyll-a variability could be ascribed to the dust component in the aerosol optical depth. All strong desert storms were identified by an event statistics in the time period from 2000 to 2008. The 57 strong storms were studied in relation to their biological response. Six events were clearly detected in which an increase of chlorophyll-a was caused by Saharan dust input and not by coastal upwelling processes. Time lags of <8 days, 8 days, and 16 days were determined. An increase in surface chlorophyll-a concentration of up to 2.4 mg m**3 after dust storms in which the dust component of the aerosol optical depth was up to 0.9 was observed.

Spatial and seasonal distribution of Thaumarchaeota in the water column and sediment of the southern North Sea

We have examined the spatial and seasonal distribution of Thaumarchaeota in the water column and sediment of the southern North Sea using the specific intact polar lipid (IPL) hexose, phosphohexose (HPH) crenarchaeol, as well as thaumarchaeotal 16S rRNA gene abundances and expression. In the water column, a higher abundance of Thaumarchaeota was observed in the winter season than in the summer, which is in agreement with previous studies, but this was not the case in the sediment where Thaumarchaeota were most abundant in spring and summer. This observation corresponds well with the idea that ammonia availability is a key factor in thaumarchaeotal niche determination. In the surface waters of the southern North Sea, we observed a spatial variability in HPH crenarchaeol, thaumarchaeotal 16S rRNA gene abundance and transcriptional activity that corresponded well with the different water masses present. In bottom waters, a clear differentiation based on water masses was not observed; instead, we suggest that observed differences in thaumarchaeotal abundance with depth may be related to resuspension from the sediment. This could be due to suspension of benthic Thaumarchaeota to the water column or due to delivery of e.g. resuspended sediment or ammonium to the water column, which could be utilized by pelagic Thaumarchaeota. This study has shown that the seasonality of Thaumarchaeota in water and sediment is different and highlights the importance of water masses, currents and sedimentary processes in determining the spatial abundance of Thaumarchaeota in the southern North Sea.

Mg/Ca variability in Globigerinoides ruber tests

Laser ablation inductively coupled plasma-mass spectrometry microanalysis of fossil and live Globigerinoides ruber from the eastern Indian Ocean reveals large variations of Mg/Ca composition both within and between individual tests from core top or plankton pump samples. Although the extent of intertest and intratest compositional variability exceeds that attributable to calcification temperature, the pooled mean Mg/Ca molar values obtained for core top samples between the equator and >30°S form a strong exponential correlation with mean annual sea surface temperature (Mg/Ca mmol/mol = 0.52 exp**0.076SST°C, r**2 = 0.99). The intertest Mg/Ca variability within these deep-sea core top samples is a source of significant uncertainty in Mg/Ca seawater temperature estimates and is notable for being site specific. Our results indicate that widely assumed uncertainties in Mg/Ca thermometry may be underestimated. We show that statistical power analysis can be used to evaluate the number of tests needed to achieve a target level of uncertainty on a sample by sample case. A varying bias also arises from the presence and varying mix of two morphotypes (G. ruber ruber and G. ruber pyramidalis), which have different mean Mg/Ca values. Estimated calcification temperature differences between these morphotypes range up to 5°C and are notable for correlating with the seasonal range in seawater temperature at different sites.

Climate variability effects on field crops in Iberian Peninsula. Predictability

La presente Tesis constituye un avance en el conocimiento de los efectos de la variabilidad climática en los cultivos en la Península Ibérica (PI). Es bien conocido que la temperatura del océano, particularmente de la región tropical, es una de las variables más convenientes para ser utilizado como predictor climático. Los océanos son considerados como la principal fuente de almacenamiento de calor del planeta debido a la alta capacidad calorífica del agua. Cuando se libera esta energía, altera los regímenes globales de circulación atmosférica por mecanismos de teleconexión. Estos cambios en la circulación general de la atmósfera afectan a la temperatura, precipitación, humedad, viento, etc., a escala regional, los cuales afectan al crecimiento, desarrollo y rendimiento de los cultivos. Para el caso de Europa, esto implica que la variabilidad atmosférica en una región específica se asocia con la variabilidad de otras regiones adyacentes y/o remotas, como consecuencia Europa está siendo afectada por los patrones de circulaciones globales, que a su vez, se ven afectados por patrones oceánicos. El objetivo general de esta tesis es analizar la variabilidad del rendimiento de los cultivos y su relación con la variabilidad climática y teleconexiones, así como evaluar su predictibilidad. Además, esta Tesis tiene como objetivo establecer una metodología para estudiar la predictibilidad de las anomalías del rendimiento de los cultivos. El análisis se centra en trigo y maíz como referencia para otros cultivos de la PI, cultivos de invierno en secano y cultivos de verano en regadío respectivamente. Experimentos de simulación de cultivos utilizando una metodología en cadena de modelos (clima + cultivos) son diseñados para evaluar los impactos de los patrones de variabilidad climática en el rendimiento y su predictibilidad. La presente Tesis se estructura en dos partes: La primera se centra en el análisis de la variabilidad del clima y la segunda es una aplicación de predicción cuantitativa de cosechas. La primera parte está dividida en 3 capítulos y la segundo en un capitulo cubriendo los objetivos específicos del presente trabajo de investigación. Parte I. Análisis de variabilidad climática El primer capítulo muestra un análisis de la variabilidad del rendimiento potencial en una localidad como indicador bioclimático de las teleconexiones de El Niño con Europa, mostrando su importancia en la mejora de predictibilidad tanto en clima como en agricultura. Además, se presenta la metodología elegida para relacionar el rendimiento con las variables atmosféricas y oceánicas. El rendimiento de los cultivos es parcialmente determinado por la variabilidad climática atmosférica, que a su vez depende de los cambios en la temperatura de la superficie del mar (TSM). El Niño es el principal modo de variabilidad interanual de la TSM, y sus efectos se extienden en todo el mundo. Sin embargo, la predictibilidad de estos impactos es controversial, especialmente aquellos asociados con la variabilidad climática Europea, que se ha encontrado que es no estacionaria y no lineal. Este estudio mostró cómo el rendimiento potencial de los cultivos obtenidos a partir de datos de reanálisis y modelos de cultivos sirve como un índice alternativo y más eficaz de las teleconexiones de El Niño, ya que integra las no linealidades entre las variables climáticas en una única serie temporal. Las relaciones entre El Niño y las anomalías de rendimiento de los cultivos son más significativas que las contribuciones individuales de cada una de las variables atmosféricas utilizadas como entrada en el modelo de cultivo. Además, la no estacionariedad entre El Niño y la variabilidad climática europea se detectan con mayor claridad cuando se analiza la variabilidad de los rendimiento de los cultivos. La comprensión de esta relación permite una cierta predictibilidad hasta un año antes de la cosecha del cultivo. Esta predictibilidad no es constante, sino que depende tanto la modulación de la alta y baja frecuencia. En el segundo capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de verano en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de maíz en la PI para todo el siglo veinte, usando un modelo de cultivo calibrado en 5 localidades españolas y datos climáticos de reanálisis para obtener series temporales largas de rendimiento potencial. Este estudio evalúa el uso de datos de reanálisis para obtener series de rendimiento de cultivos que dependen solo del clima, y utilizar estos rendimientos para analizar la influencia de los patrones oceánicos y atmosféricos. Los resultados muestran una gran fiabilidad de los datos de reanálisis. La distribución espacial asociada a la primera componente principal de la variabilidad del rendimiento muestra un comportamiento similar en todos los lugares estudiados de la PI. Se observa una alta correlación lineal entre el índice de El Niño y el rendimiento, pero no es estacionaria en el tiempo. Sin embargo, la relación entre la temperatura del aire y el rendimiento se mantiene constante a lo largo del tiempo, siendo los meses de mayor influencia durante el período de llenado del grano. En cuanto a los patrones atmosféricos, el patrón Escandinavia presentó una influencia significativa en el rendimiento en PI. En el tercer capítulo se identifica los patrones oceánicos y atmosféricos de variabilidad climática que afectan a los cultivos de invierno en la PI. Además, se presentan hipótesis acerca del mecanismo eco-fisiológico a través del cual el cultivo responde. Este estudio se centra en el análisis de la variabilidad del rendimiento de trigo en secano del Noreste (NE) de la PI. La variabilidad climática es el principal motor de los cambios en el crecimiento, desarrollo y rendimiento de los cultivos, especialmente en los sistemas de producción en secano. En la PI, los rendimientos de trigo son fuertemente dependientes de la cantidad de precipitación estacional y la distribución temporal de las mismas durante el periodo de crecimiento del cultivo. La principal fuente de variabilidad interanual de la precipitación en la PI es la Oscilación del Atlántico Norte (NAO), que se ha relacionado, en parte, con los cambios en la temperatura de la superficie del mar en el Pacífico Tropical (El Niño) y el Atlántico Tropical (TNA). La existencia de cierta predictibilidad nos ha animado a analizar la posible predicción de los rendimientos de trigo en la PI utilizando anomalías de TSM como predictor. Para ello, se ha utilizado un modelo de cultivo (calibrado en dos localidades del NE de la PI) y datos climáticos de reanálisis para obtener series temporales largas de rendimiento de trigo alcanzable y relacionar su variabilidad con anomalías de la TSM. Los resultados muestran que El Niño y la TNA influyen en el desarrollo y rendimiento del trigo en el NE de la PI, y estos impactos depende del estado concurrente de la NAO. Aunque la relación cultivo-TSM no es igual durante todo el periodo analizado, se puede explicar por un mecanismo eco-fisiológico estacionario. Durante la segunda mitad del siglo veinte, el calentamiento (enfriamiento) en la superficie del Atlántico tropical se asocia a una fase negativa (positiva) de la NAO, que ejerce una influencia positiva (negativa) en la temperatura mínima y precipitación durante el invierno y, por lo tanto, aumenta (disminuye) el rendimiento de trigo en la PI. En relación con El Niño, la correlación más alta se observó en el período 1981 -2001. En estas décadas, los altos (bajos) rendimientos se asocian con una transición El Niño - La Niña (La Niña - El Niño) o con eventos de El Niño (La Niña) que están finalizando. Para estos eventos, el patrón atmosférica asociada se asemeja a la NAO, que también influye directamente en la temperatura máxima y precipitación experimentadas por el cultivo durante la floración y llenado de grano. Los co- efectos de los dos patrones de teleconexión oceánicos ayudan a aumentar (disminuir) la precipitación y a disminuir (aumentar) la temperatura máxima en PI, por lo tanto el rendimiento de trigo aumenta (disminuye). Parte II. Predicción de cultivos. En el último capítulo se analiza los beneficios potenciales del uso de predicciones climáticas estacionales (por ejemplo de precipitación) en las predicciones de rendimientos de trigo y maíz, y explora métodos para aplicar dichos pronósticos climáticos en modelos de cultivo. Las predicciones climáticas estacionales tienen un gran potencial en las predicciones de cultivos, contribuyendo de esta manera a una mayor eficiencia de la gestión agrícola, seguridad alimentaria y de subsistencia. Los pronósticos climáticos se expresan en diferentes formas, sin embargo todos ellos son probabilísticos. Para ello, se evalúan y aplican dos métodos para desagregar las predicciones climáticas estacionales en datos diarios: 1) un generador climático estocástico condicionado (predictWTD) y 2) un simple re-muestreador basado en las probabilidades del pronóstico (FResampler1). Los dos métodos se evaluaron en un caso de estudio en el que se analizaron los impactos de tres escenarios de predicciones de precipitación estacional (predicción seco, medio y lluvioso) en el rendimiento de trigo en secano, sobre las necesidades de riego y rendimiento de maíz en la PI. Además, se estimó el margen bruto y los riesgos de la producción asociada con las predicciones de precipitación estacional extremas (seca y lluviosa). Los métodos predWTD y FResampler1 usados para desagregar los pronósticos de precipitación estacional en datos diarios, que serán usados como inputs en los modelos de cultivos, proporcionan una predicción comparable. Por lo tanto, ambos métodos parecen opciones factibles/viables para la vinculación de los pronósticos estacionales con modelos de simulación de cultivos para establecer predicciones de rendimiento o las necesidades de riego en el caso de maíz. El análisis del impacto en el margen bruto de los precios del grano de los dos cultivos (trigo y maíz) y el coste de riego (maíz) sugieren que la combinación de los precios de mercado previstos y la predicción climática estacional pueden ser una buena herramienta en la toma de decisiones de los agricultores, especialmente en predicciones secas y/o localidades con baja precipitación anual. Estos métodos permiten cuantificar los beneficios y riesgos de los agricultores ante una predicción climática estacional en la PI. Por lo tanto, seríamos capaces de establecer sistemas de alerta temprana y diseñar estrategias de adaptación del manejo del cultivo para aprovechar las condiciones favorables o reducir los efectos de condiciones adversas. La utilidad potencial de esta Tesis es la aplicación de las relaciones encontradas para predicción de cosechas de la próxima campaña agrícola. Una correcta predicción de los rendimientos podría ayudar a los agricultores a planear con antelación sus prácticas agronómicas y todos los demás aspectos relacionados con el manejo de los cultivos. Esta metodología se puede utilizar también para la predicción de las tendencias futuras de la variabilidad del rendimiento en la PI. Tanto los sectores públicos (mejora de la planificación agrícola) como privados (agricultores, compañías de seguros agrarios) pueden beneficiarse de esta mejora en la predicción de cosechas. ABSTRACT The present thesis constitutes a step forward in advancing of knowledge of the effects of climate variability on crops in the Iberian Peninsula (IP). It is well known that ocean temperature, particularly the tropical ocean, is one of the most convenient variables to be used as climate predictor. Oceans are considered as the principal heat storage of the planet due to the high heat capacity of water. When this energy is released, it alters the global atmospheric circulation regimes by teleconnection1 mechanisms. These changes in the general circulation of the atmosphere affect the regional temperature, precipitation, moisture, wind, etc., and those influence crop growth, development and yield. For the case of Europe, this implies that the atmospheric variability in a specific region is associated with the variability of others adjacent and/or remote regions as a consequence of Europe being affected by global circulations patterns which, in turn, are affected by oceanic patterns. The general objective of this Thesis is to analyze the variability of crop yields at climate time scales and its relation to the climate variability and teleconnections, as well as to evaluate their predictability. Moreover, this Thesis aims to establish a methodology to study the predictability of crop yield anomalies. The analysis focuses on wheat and maize as a reference crops for other field crops in the IP, for winter rainfed crops and summer irrigated crops respectively. Crop simulation experiments using a model chain methodology (climate + crop) are designed to evaluate the impacts of climate variability patterns on yield and its predictability. The present Thesis is structured in two parts. The first part is focused on the climate variability analyses, and the second part is an application of the quantitative crop forecasting for years that fulfill specific conditions identified in the first part. This Thesis is divided into 4 chapters, covering the specific objectives of the present research work. Part I. Climate variability analyses The first chapter shows an analysis of potential yield variability in one location, as a bioclimatic indicator of the El Niño teleconnections with Europe, putting forward its importance for improving predictability in both climate and agriculture. It also presents the chosen methodology to relate yield with atmospheric and oceanic variables. Crop yield is partially determined by atmospheric climate variability, which in turn depends on changes in the sea surface temperature (SST). El Niño is the leading mode of SST interannual variability, and its impacts extend worldwide. Nevertheless, the predictability of these impacts is controversial, especially those associated with European climate variability, which have been found to be non-stationary and non-linear. The study showed how potential2 crop yield obtained from reanalysis data and crop models serves as an alternative and more effective index of El Niño teleconnections because it integrates the nonlinearities between the climate variables in a unique time series. The relationships between El Niño and crop yield anomalies are more significant than the individual contributions of each of the atmospheric variables used as input in the crop model. Additionally, the non-stationarities between El Niño and European climate variability are more clearly detected when analyzing crop-yield variability. The understanding of this relationship allows for some predictability up to one year before the crop is harvested. This predictability is not constant, but depends on both high and low frequency modulation. The second chapter identifies the oceanic and atmospheric patterns of climate variability affecting summer cropping systems in the IP. Moreover, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of maize yield variability in IP for the whole twenty century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate dependent time series of simulated crop yield for the whole region, and to use these yield to analyze the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non-stationary on time, although the air temperature-yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The third chapter identifies the oceanic and atmospheric patterns of climate variability affecting winter cropping systems in the IP. Also, hypotheses about the eco-physiological mechanism behind crop response are presented. It is focused on an analysis of rainfed wheat yield variability in IP. Climate variability is the main driver of changes in crop growth, development and yield, especially for rainfed production systems. In IP, wheat yields are strongly dependent on seasonal rainfall amount and temporal distribution of rainfall during the growing season. The major source of precipitation interannual variability in IP is the North Atlantic Oscillation (NAO) which has been related in part with changes in the Tropical Pacific (El Niño) and Atlantic (TNA) sea surface temperature (SST). The existence of some predictability has encouraged us to analyze the possible predictability of the wheat yield in the IP using SSTs anomalies as predictor. For this purpose, a crop model with a site specific calibration for the Northeast of IP and reanalysis climate datasets have been used to obtain long time series of attainable wheat yield and relate their variability with SST anomalies. The results show that El Niño and TNA influence rainfed wheat development and yield in IP and these impacts depend on the concurrent state of the NAO. Although crop-SST relationships do not equally hold on during the whole analyzed period, they can be explained by an understood and stationary ecophysiological mechanism. During the second half of the twenty century, the positive (negative) TNA index is associated to a negative (positive) phase of NAO, which exerts a positive (negative) influence on minimum temperatures (Tmin) and precipitation (Prec) during winter and, thus, yield increases (decreases) in IP. In relation to El Niño, the highest correlation takes place in the period 1981-2001. For these decades, high (low) yields are associated with an El Niño to La Niña (La Niña to El Niño) transitions or to El Niño events finishing. For these events, the regional associated atmospheric pattern resembles the NAO, which also influences directly on the maximum temperatures (Tmax) and precipitation experienced by the crop during flowering and grain filling. The co-effects of the two teleconnection patterns help to increase (decrease) the rainfall and decrease (increase) Tmax in IP, thus on increase (decrease) wheat yield. Part II. Crop forecasting The last chapter analyses the potential benefits for wheat and maize yields prediction from using seasonal climate forecasts (precipitation), and explores methods to apply such a climate forecast to crop models. Seasonal climate prediction has significant potential to contribute to the efficiency of agricultural management, and to food and livelihood security. Climate forecasts come in different forms, but probabilistic. For this purpose, two methods were evaluated and applied for disaggregating seasonal climate forecast into daily weather realizations: 1) a conditioned stochastic weather generator (predictWTD) and 2) a simple forecast probability resampler (FResampler1). The two methods were evaluated in a case study where the impacts of three scenarios of seasonal rainfall forecasts on rainfed wheat yield, on irrigation requirements and yields of maize in IP were analyzed. In addition, we estimated the economic margins and production risks associated with extreme scenarios of seasonal rainfall forecasts (dry and wet). The predWTD and FResampler1 methods used for disaggregating seasonal rainfall forecast into daily data needed by the crop simulation models provided comparable predictability. Therefore both methods seem feasible options for linking seasonal forecasts with crop simulation models for establishing yield forecasts or irrigation water requirements. The analysis of the impact on gross margin of grain prices for both crops and maize irrigation costs suggests the combination of market prices expected and the seasonal climate forecast can be a good tool in farmer’s decision-making, especially on dry forecast and/or in locations with low annual precipitation. These methodologies would allow quantifying the benefits and risks of a seasonal weather forecast to farmers in IP. Therefore, we would be able to establish early warning systems and to design crop management adaptation strategies that take advantage of favorable conditions or reduce the effect of adverse conditions. The potential usefulness of this Thesis is to apply the relationships found to crop forecasting on the next cropping season, suggesting opportunity time windows for the prediction. The methodology can be used as well for the prediction of future trends of IP yield variability. Both public (improvement of agricultural planning) and private (decision support to farmers, insurance companies) sectors may benefit from such an improvement of crop forecasting.

Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield

The El Nino-Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Nino events are often associated with severe drought conditions. However, El Nino events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Nino are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (footprints) found in the El Nino impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean-atmosphere dynamics identifies three types of El Nino differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Nino events on agricultural systems.

Correlations between the satellite-derived seasonal cycle of phytoplankton biomass and aerosol optical depth in the Southern Ocean: Evidence for the influence of sea ice

The relationship between the production of dimethylsulfide (DMS) in the upper ocean and atmospheric sulfate aerosols has been confirmed through local shipboard measurements, and global modeling studies alike. In order to examine whether such a connection may be recoverable in the satellite record, we have analyzed the correlation between mean surface chlorophyll (CHL) and aerosol optical depth (AOD) in the Southern Ocean, where the marine atmosphere is relatively remote from anthropogenic and continental influences. We carried out the analysis in 5-degree zonal bands between 50 degrees S and 70 degrees S, for the period ( 1997 - 2004), and in smaller meridional sectors in the Eastern Antarctic, Ross and Weddell seas. Seasonality is moderate to strong in both CHL and AOD signatures throughout the study regions. Coherence in the CHL and AOD time series is strong in the band between 50 degrees S and 60 degrees S, however this synchrony is absent in the sea-ice zone (SIZ) south of 60 degrees S. Marked interannual variability in CHL occurs south of 60 degrees S, presumably related to variability in sea-ice production during the previous winter. We find a clear latitudinal difference in the cross correlation between CHL and AOD, with the AOD peak preceding the CHL bloom by up to 6 weeks in the SIZ. This suggests that substantial trace gas emissions ( aerosol precursors) are being produced over the SIZ in spring ( October - December) as sea ice melts. This hypothesis is supported by field data that record extremely high levels of sulfur species in sea ice, surface seawater, and the overlying atmosphere during ice melt.

Mid-late Holocene monsoon climate retrieved from seasonal Sr/Ca and delta O-18 records of Porites lutea corals at Leizhou Peninsula, northern coast of South China Sea

South China Sea (SCS) is a major moisture source region, providing summer monsoon rainfall throughout Mainland China, which accounts for more than 80% total precipitation in the region. We report seasonal to monthly resolution Sr/Ca and delta(18)O data for five Holocene and one modem Porites corals, each covering a growth history of 9-13 years. The results reveal a general decreasing trend in sea surface temperature (SST) in the SCS from similar to 6800 to 1500 years ago, despite shorter climatic cycles. Compared with the mean Sr/Ca-SST in the 1990s (24.8 degrees C), 10-year mean Sr/Ca-SSTs were 0.9-0.5 degrees C higher between 6.8 and 5.0 thousand years before present (ky BP), dropped to the present level by similar to 2.5 ky BP, and reached a low of 22.6 degrees C (2.2 degrees C lower) by similar to 1.5 ky BP. The summer Sr/Ca-SST maxima, which are more reliable due to faster summer-time growth rates and higher sampling resolution, follow the same trend, i.e. being 1-2 degrees C higher between 6.8 and 5.0 ky BP, dropping to the present level by -2.5 ky BP, and reaching a low of 28.7 degrees C (0.7 degrees C lower) by similar to 1.5 ky BP. Such a decline in SST is accompanied by a similar decrease in the amount of monsoon moisture transported out of South China Sea, resulting in a general decrease in the seawater delta(18)O values, reflected by offsets of mean 6 180 relative to that in the 1990s. This observation is consistent with general weakening of the East Asian summer monsoon since early Holocene, in response to a continuous decline in solar radiation, which was also found in pollen, lake-level and loess/paleosol records throughout Mainland China. The climatic conditions similar to 2.5 and similar to 1.5 ky ago were also recorded in Chinese history. In contrast with the general cooling trend of the monsoon climate in East Asia, SST increased dramatically in recent time, with that in the 1990s being 2.2 degrees C warmer than that similar to 1.5 ky ago. This clearly indicates that the increase in the concentration of anthropogenic greenhouse gases played a dominant role in recent global warming, which reversed the natural climatic trend in East Asian monsoon regime. (c) 2004 Elsevier B.V. All rights reserved.

Spatial and Temporal Variability of Dissolved Organic Matter Quantity and Composition in an Oilgotrophic Subtropical Coastal Wetland

Dissolved organic matter (DOM) is an essential component of the carbon cycle and a critical driver in controlling variety of biogeochemical and ecological processes in wetlands. The quality of this DOM as it relates to composition and reactivity is directly related to its sources and may vary on temporal and spatial scales. However, large scale, long-term studies of DOM dynamics in wetlands are still scarce in the literature. Here we present a multi-year DOM characterization study for monthly surface water samples collected at 14 sampling stations along two transects within the greater Everglades, a subtropical, oligotrophic, coastal freshwater wetland-mangrove-estuarine ecosystem. In an attempt to assess quantitative and qualitative variations of DOM on both spatial and temporal scales, we determined dissolved organic carbon (DOC) values and DOM optical properties, respectively. DOM quality was assessed using, excitation emission matrix (EEM) fluorescence coupled with parallel factor analysis (PARAFAC). Variations of the PARAFAC components abundance and composition were clearly observed on spatial and seasonal scales. Dry versus wet season DOC concentrations were affected by dry-down and re-wetting processes in the freshwater marshes, while DOM compositional features were controlled by soil and higher plant versus periphyton sources respectively. Peat-soil based freshwater marsh sites could be clearly differentiated from marl-soil based sites based on EEM–PARAFAC data. Freshwater marsh DOM was enriched in higher plant and soil-derived humic-like compounds, compared to estuarine sites which were more controlled by algae- and microbial-derived inputs. DOM from fringe mangrove sites could be differentiated between tidally influenced sites and sites exposed to long inundation periods. As such coastal estuarine sites were significantly controlled by hydrology, while DOM dynamics in Florida Bay were seasonally driven by both primary productivity and hydrology. This study exemplifies the application of long term optical properties monitoring as an effective technique to investigate DOM dynamics in aquatic ecosystems. The work presented here also serves as a pre-restoration condition dataset for DOM in the context of the Comprehensive Everglades Restoration Plan (CERP).

Exploring Mechanisms of Bacterial Adaptation to Seasonal Temperature Change

This research examines three potential mechanisms by which bacteria can adapt to different temperatures: changes in strain-level population structure, gene regulation and particle colonization. For the first two mechanisms, I utilize bacterial strains from the Vibrionaceae family due to their ease of culturability, ubiquity in coastal environments and status as a model system for marine bacteria. I first examine vibrio seasonal dynamics in temperate, coastal water and compare the thermal performance of strains that occupy different thermal environments. Our results suggest that there are tradeoffs in adaptation to specific temperatures and that thermal specialization can occur at a very fine phylogenetic scale. The observed thermal specialization over relatively short evolutionary time-scales indicates that few genes or cellular processes may limit expansion to a different thermal niche. I then compare the genomic and transcriptional changes associated with thermal adaptation in closely-related vibrio strains under heat and cold stress. The two vibrio strains have very similar genomes and overall exhibit similar transcriptional profiles in response to temperature stress but their temperature preferences are determined by differential transcriptional responses in shared genes as well as temperature-dependent regulation of unique genes. Finally, I investigate the temporal dynamics of particle-attached and free-living bacterial community in coastal seawater and find that microhabitats exert a stronger forcing on microbial communities than environmental variability, suggesting that particle-attachment could buffer the impacts of environmental changes and particle-associated communities likely respond to the presence of distinct eukaryotes rather than commonly-measured environmental parameters. Integrating these results will offer new perspectives on the mechanisms by which bacteria respond to seasonal temperature changes as well as potential adaptations to climate change-driven warming of the surface oceans.

Sea ice and wind variability during the Holocene in East Antarctica

A high-resolution continuous record of diatom census counts and diatom specific biomarkers in sediment core NBP0101-JPC24 allows assessment of oceanographic and environmental conditions in eastern Prydz Bay during the deglaciation (11 100-9000 cal yr BP) at decadal timescale. Our study improves previous snapshots investigations based on resin-embedded thin sections and presents a new proxy that compliments the diatom census counts. Our results suggest that the ice sheet retreat over the core site is dated at ~11 100 cal yr BP, setting the onset of local deglaciation and subsequent open marine conditions. The glacial retreat in Prydz Bay is due to global warming initiated at 18 cal ka BP and the regional development of the Prydz Bay cyclonic gyre. Our results further demonstrate that the deglaciation in eastern Prydz Bay can be separated in four phases: the first between 11 100 and 10 900 cal yr BP when the ice shelf was proximal and sea ice was almost perennial; the second and the third phases between 10 900-10 400 cal yr BP and 10 400-9900 cal yr BP, respectively, when the ice shelf retreated and seasonal sea ice cycle consequently developed promoting warmer water to pump into the bay within the gyre, which in turn forced the ice shelf recession and the yearly sea ice cycle establishment; and the fourth between 9900 and 9000 cal yr BP when Holocene condition were set with a recurrent seasonal sea ice cycle and a well established Prydz Bay gyre.