Simulation of ENSO-Related Surface Winds in the Tropical Pacific by an Atmospheric General Circulation Model Forced by Observed Sea Surface Temperatures

The authors present the simulation of the tropical Pacific surface wind variability by a low-resolution (R15 horizontal resolution and 18 vertical levels) version of the Center for Ocean-Land-Atmosphere Interactions, Maryland, general circulation model (GCM) when forced by observed global sea surface temperature. The authors have examined the monthly mean surface winds acid precipitation simulated by the model that was integrated from January 1979 to March 1992. Analyses of the climatological annual cycle and interannual variability over the Pacific are presented. The annual means of the simulated zonal and meridional winds agree well with observations. The only appreciable difference is in the region of strong trade winds where the simulated zonal winds are about 15%-20% weaker than observed, The amplitude of the annual harmonics are weaker than observed over the intertropical convergence zone and the South Pacific convergence zone regions. The amplitudes of the interannual variation of the simulated zonal and meridional winds are close to those of the observed variation. The first few dominant empirical orthogonal functions (EOF) of the simulated, as well as the observed, monthly mean winds are found to contain a targe amount of high-frequency intraseasonal variations, While the statistical properties of the high-frequency modes, such as their amplitude and geographical locations, agree with observations, their detailed time evolution does not. When the data are subjected to a 5-month running-mean filter, the first two dominant EOFs of the simulated winds representing the low-frequency EI Nino-Southern Oscillation fluctuations compare quite well with observations. However, the location of the center of the westerly anomalies associated with the warm episodes is simulated about 15 degrees west of the observed locations. The model simulates well the progress of the westerly anomalies toward the eastern Pacific during the evolution of a warm event. The simulated equatorial wind anomalies are comparable in magnitude to the observed anomalies. An intercomparison of the simulation of the interannual variability by a few other GCMs with comparable resolution is also presented. The success in simulation of the large-scale low-frequency part of the tropical surface winds by the atmospheric GCM seems to be related to the model's ability to simulate the large-scale low-frequency part of the precipitation. Good correspondence between the simulated precipitation and the highly reflective cloud anomalies is seen in the first two EOFs of the 5-month running means. Moreover, the strong correlation found between the simulated precipitation and the simulated winds in the first two principal components indicates the primary role of model precipitation in driving the surface winds. The surface winds simulated by a linear model forced by the GCM-simulated precipitation show good resemblance to the GCM-simulated winds in the equatorial region. This result supports the recent findings that the large-scale part of the tropical surface winds is primarily linear.

Relationship between sea surface temperatures and dolphin-associated fishing activities by the Mexican tuna fleet

Data from the Mexican purse seiner fleet operating in the eastern Tropical Pacific, for the year 1985-1990, are used to show that the fraction of surface schools of yellowfin tuna Thunnus albacares associated with dolphins (Stenella attenuata and others) increases with sea surface temperature. Possible reasons for this correlation are briefly discussed.

Studies on the seasonal cycle of sea surface temperatures, salinities and phytoplankton in Puttalam Lagoon, Dutch Bay and Portugal Bay along the west coast of Ceylon.

The monthly average temperatures at Puttalam Lagoon, Dutch Bay, Portugal Bay towards Kovilmunai and Portugal Bay towards Pallugaturai showed a distinct annual cycle. The peak was in April and values gradually fell till September. There was a further gradual fall in temperature from October to January. The highest temperatures in all four stations were in April. The highest salinities in all the stations were from May to October i.e., during the south-west monsoon. The salinities at Dutch Bay and Portugal Bay were high in March and April corresponding to the highest temperatures reached during these months. Two maxima have been observed in phytoplankton production. A primary maximum in May-June and a secondary maximum in October. The primary and secondary maxima are due to the influx of nutrient laden waters from the rivers Kal Aru and Pomparippu Aru. The phytoplankton producing blooms were Rhizosolenia alata. Rhizosolenia imbricata, Chaetoceros lascinosus, Chaetoceros pervianus, Ch,aetoceros diversus, Coscinodiscus gigas, Thallasionema nitzschioides, Thalassiosira subtilis, Thallassiothrix frauenfeldii, Asterionella japonica, Sceletonema costatum, Bacteriastrum varians and Biddulphia sinensis. Sudden outbursts of a single species were common. These diatoms were species of Chaetoceros and Rhizosolenia, and Thallassiothrix frauenfeldii. Wide fluctuations have been observed in the distribution of phytoplankton but no definite conclusions can be drawn as the period of observation was only one year.

Studies on the seasonal cycle of sea surface temperatures, salinities, oxygen and phytoplankton in Koddiyar Bay, Trincomalee in relation to the shore seine fishery

In Sri Lanka waters, Durairatnam (1963, 1969) had reported on the seasonal variation of plankton at Puttalam lagoon, Dutch Bay, Portugal Bay and in the inshore and off shore waters off Colombo. Similar investigations were carried out at Koddiyar Bay especially in the estuarine waters where the three tributaries of the river Mahaveli empty their waters into the bay. The estuary is perennial. The fishes constituting the estuarine fisheries are mostly shallow water marine species which can tolerate considerable variations of salinities. Prawns and crabs also constitute a very valuable fishery in estuarine waters.

Reevaluation of mid-Pliocene North Atlantic sea surface temperatures

Multiproxy temperature estimation requires careful attention to biological, chemical, physical, temporal, and calibration differences of each proxy and paleothermometry method. We evaluated mid-Pliocene sea surface temperature (SST) estimates from multiple proxies at Deep Sea Drilling Project Holes 552A, 609B, 607, and 606, transecting the North Atlantic Drift. SST estimates derived from faunal assemblages, foraminifer Mg/Ca, and alkenone unsaturation indices showed strong agreement at Holes 552A, 607, and 606 once differences in calibration, depth, and seasonality were addressed. Abundant extinct species and/or an unrecognized productivity signal in the faunal assemblage at Hole 609B resulted in exaggerated faunal-based SST estimates but did not affect alkenone-derived or Mg/Ca-derived estimates. Multiproxy mid-Pliocene North Atlantic SST estimates corroborate previous studies documenting high-latitude mid-Pliocene warmth and refine previous faunal-based estimates affected by environmental factors other than temperature. Multiproxy investigations will aid SST estimation in high-latitude areas sensitive to climate change and currently underrepresented in SST reconstructions. Copyright 2008 by the American Geophysical Union.

Influence of south Atlantic sea surface temperatures on rainfall variability and extremes over southern Africa

It is generally agreed that changing climate variability, and the associated change in climate extremes, may have a greater impact on environmentally vulnerable regions than a changing mean. This research investigates rainfall variability, rainfall extremes, and their associations with atmospheric and oceanic circulations over southern Africa, a region that is considered particularly vulnerable to extreme events because of numerous environmental, social, and economic pressures. Because rainfall variability is a function of scale, high-resolution data are needed to identify extreme events. Thus, this research uses remotely sensed rainfall data and climate model experiments at high spatial and temporal resolution, with the overall aim being to investigate the ways in which sea surface temperature (SST) anomalies influence rainfall extremes over southern Africa. Extreme rainfall identification is achieved by the high-resolution microwave/infrared rainfall algorithm dataset. This comprises satellite-derived daily rainfall from 1993 to 2002 and covers southern Africa at a spatial resolution of 0.1° latitude–longitude. Extremes are extracted and used with reanalysis data to study possible circulation anomalies associated with extreme rainfall. Anomalously cold SSTs in the central South Atlantic and warm SSTs off the coast of southwestern Africa seem to be statistically related to rainfall extremes. Further, through a number of idealized climate model experiments, it would appear that both decreasing SSTs in the central South Atlantic and increasing SSTs off the coast of southwestern Africa lead to a demonstrable increase in daily rainfall and rainfall extremes over southern Africa, via local effects such as increased convection and remote effects such as an adjustment of the Walker-type circulation.

Multidecadal modulation of El Nino-Southern Oscillation (ENSO) variance by Atlantic Ocean sea surface temperatures

Observations suggest a possible link between the Atlantic Multidecadal Oscillation (AMO) and El Nino Southern Oscillation (ENSO) variability, with the warm AMO phase being related to weaker ENSO variability. A coupled ocean-atmosphere model is used to investigate this relationship and to elucidate mechanisms responsible for it. Anomalous sea surface temperatures (SSTs) associated with the positive AMO lead to change in the basic state in the tropical Pacific Ocean. This basic state change is associated with a deepened thermocline and reduced vertical stratification of the equatorial Pacific ocean, which in turn leads to weakened ENSO variability. We suggest a role for an atmospheric bridge that rapidly conveys the influence of the Atlantic Ocean to the tropical Pacific. The results suggest a non-local mechanism for changes in ENSO statistics and imply that anomalous Atlantic ocean SSTs can modulate both mean climate and climate variability over the Pacific.

Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach

We explore the potential for making statistical decadal predictions of sea surface temperatures (SSTs) in a perfect model analysis, with a focus on the Atlantic basin. Various statistical methods (Lagged correlations, Linear Inverse Modelling and Constructed Analogue) are found to have significant skill in predicting the internal variability of Atlantic SSTs for up to a decade ahead in control integrations of two different global climate models (GCMs), namely HadCM3 and HadGEM1. Statistical methods which consider non-local information tend to perform best, but which is the most successful statistical method depends on the region considered, GCM data used and prediction lead time. However, the Constructed Analogue method tends to have the highest skill at longer lead times. Importantly, the regions of greatest prediction skill can be very different to regions identified as potentially predictable from variance explained arguments. This finding suggests that significant local decadal variability is not necessarily a prerequisite for skillful decadal predictions, and that the statistical methods are capturing some of the dynamics of low-frequency SST evolution. In particular, using data from HadGEM1, significant skill at lead times of 6–10 years is found in the tropical North Atlantic, a region with relatively little decadal variability compared to interannual variability. This skill appears to come from reconstructing the SSTs in the far north Atlantic, suggesting that the more northern latitudes are optimal for SST observations to improve predictions. We additionally explore whether adding sub-surface temperature data improves these decadal statistical predictions, and find that, again, it depends on the region, prediction lead time and GCM data used. Overall, we argue that the estimated prediction skill motivates the further development of statistical decadal predictions of SSTs as a benchmark for current and future GCM-based decadal climate predictions.