The expression of mouse CLEC-2 on leucocyte subsets varies according to their anatomical location and inflammatory state

Expression of mouse C‐type lectin‐like receptor 2 (CLEC‐2) has been reported on circulating CD11b(high) Gr‐1(high) myeloid cells and dendritic cells (DCs) under basal conditions, as well as on a variety of leucocyte subsets following inflammatory stimuli or in vitro cell culture. However, previous studies assessing CLEC‐2 expression failed to use CLEC‐2‐deficient mice as negative controls and instead relied heavily on single antibody clones. Here, we generated CLEC‐2‐deficient adult mice using two independent approaches and employed two anti‐mouse CLEC‐2 antibody clones to investigate surface expression on hematopoietic cells from peripheral blood and secondary lymphoid organs. We rule out constitutive CLEC‐2 expression on resting DCs and show that CLEC‐2 is upregulated in response to LPS‐induced systemic inflammation in a small subset of activated DCs isolated from the mesenteric lymph nodes but not the spleen. Moreover, we demonstrate for the first time that peripheral blood B lymphocytes present exogenously derived CLEC‐2 and suggest that both circulating B lymphocytes and CD11b(high) Gr‐1(high) myeloid cells lose CLEC‐2 following entry into secondary lymphoid organs. These results have significant implications for our understanding of CLEC‐2 physiological function

Climate fluctuations of tropical coupled system: The role of ocean dynamics

The tropical oceans have long been recognized as the most important region for large-scale ocean–atmosphere interactions, giving rise to coupled climate variations on several time scales. During the Tropical Ocean Global Atmosphere (TOGA) decade, the focus of much tropical ocean research was on understanding El Niño–related processes and on development of tropical ocean models capable of simulating and predicting El Niño. These studies led to an appreciation of the vital role the ocean plays in providing the memory for predicting El Niño and thus making seasonal climate prediction feasible. With the end of TOGA and the beginning of Climate Variability and Prediction (CLIVAR), the scope of climate variability and predictability studies has expanded from the tropical Pacific and ENSO-centric basis to the global domain. In this paper the progress that has been made in tropical ocean climate studies during the early years of CLIVAR is discussed. The discussion is divided geographically into three tropical ocean basins with an emphasis on the dynamical processes that are most relevant to the coupling between the atmosphere and oceans. For the tropical Pacific, the continuing effort to improve understanding of large- and small-scale dynamics for the purpose of extending the skill of ENSO prediction is assessed. This paper then goes beyond the time and space scales of El Niño and discusses recent research activities on the fundamental issue of the processes maintaining the tropical thermocline. This includes the study of subtropical cells (STCs) and ventilated thermocline processes, which are potentially important to the understanding of the low-frequency modulation of El Niño. For the tropical Atlantic, the dominant oceanic processes that interact with regional atmospheric feedbacks are examined as well as the remote influence from both the Pacific El Niño and extratropical climate fluctuations giving rise to multiple patterns of variability distinguished by season and location. The potential impact of Atlantic thermohaline circulation on tropical Atlantic variability (TAV) is also discussed. For the tropical Indian Ocean, local and remote mechanisms governing low-frequency sea surface temperature variations are examined. After reviewing the recent rapid progress in the understanding of coupled dynamics in the region, this study focuses on the active role of ocean dynamics in a seasonally locked east–west internal mode of variability, known as the Indian Ocean dipole (IOD). Influences of the IOD on climatic conditions in Asia, Australia, East Africa, and Europe are discussed. While the attempt throughout is to give a comprehensive overview of what is known about the role of the tropical oceans in climate, the fact of the matter is that much remains to be understood and explained. The complex nature of the tropical coupled phenomena and the interaction among them argue strongly for coordinated and sustained observations, as well as additional careful modeling investigations in order to further advance the current understanding of the role of tropical oceans in climate

Seasonal and El Nino variability in weekly satellite evaporation over the global ocean during 1996-98

The seasonal and anomaly variability of satellite-derived weekly latent heat fluxes occurring over the global oceans during a 3-yr period (January 1996-December 1998) is investigated using EOF and harmonic analyses. The seasonal cycle of latent heat flux is estimated by least squares fitting the first three (annual, semiannual. and 4 month) harmonics to the data. The spatial patterns of amplitudes of these harmonics agree well with the Corresponding patterns for wind speed. The annual harmonic captures an oscillation that reflects high evaporation in late fall/early winter and low evaporation in late spring/early summer in both hemispheres. with larger amplitudes in the Northern Hemisphere over the western side of the oceans and significant phase differences within each hemisphere. The main feature of the semiannual harmonic is its large amplitude in the Asian monsoon region (e.g.. in the Arabian Sea its amplitude is about 1.5 larger than the annual) and the out-of-phase relationship of this region with the high latitudes of the North Pacific, consistent with other studies. The third harmonic shows three main regions with relatively large amplitudes, one in the Arabian Sea and two out-of-phase regions in the central midlatitude North and South Pacific. After removing this estimate of the seasonal cycle from the data, the leading EOF of the anomalies isolates the 1997-98 El Nino signal, with enhanced evaporation in the eastern tropical Pacific, around the Maritime Continent, in the midlatitude North and South Pacific. and the equatorial Indian Ocean. and reduced evaporation elsewhere around the global ocean during April 1997-April 1998. This pattern is consistent with known patterns of ENSO variability and with the "atmospheric bridge" teleconnection concept. The current study illustrates the usefulness of satellite-derived latent heat fluxes for climatic applications

Satellite Estimates of Wind Speed and Latent Heat Flux over the Global Oceans

Surface fluxes of momentum, freshwater, and energy across the air-sea interface determine oceanic circulation and its variability at all timescales. The goal of this paper is to estimate and examine some ocean surface flux variables using satellite measurements. The remotely sensed data come from the European Remote Sensing (ERS) satellite scatterometer on ERS-2, NASA scatterometer (NSCAT), and several Defense Meteorological Satellite Program (DMSP) radiometers [Special Sensor Microwave Imager (SSM/I)] on board the satellites F10 -F14. The sea surface temperature comes from daily analysis calculated from Advanced Very High Resolution Radiometer (AVHRR) measurements. This study focuses on the 9-month period (October 1996-June 1997) of the NSCAT mission. To ensure high quality of the merged surface parameter fields, comparisons between different satellite estimates for the same variable have been performed, and bias corrections have been applied so that they are compatible with each other. The satellite flux fields are compared to in situ observations from buoys and ships globally and in different regions of the ocean. It is found that the root-mean-square (rms) difference with weekly averaged wind speeds is less than 2.5 m s-1 and the correlation coefficient is higher than 0.8. For weekly latent heat flux, the rms difference between satellite and buoys does not exceed 30 W m-2. The comparisons with weekly ship latent heat flux estimates gives an rms difference approaching 40 W m-2. Comparisons are also made between satellite fields and atmospheric analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and reanalyses from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR). The wind speeds and latent heat fluxes from these atmospheric analyses compare reasonably well with the satellite estimates. The main discrepancies are found in regions and seasons of large air-sea temperature difference and high wind speed, such as the Gulf Stream during the winter season

Interpreting variability in global SST data using independent component analysis and principal component analysis

Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ‘source signals’ which drive the multivariate ‘mixed’ dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally, our analysis suggests that the interpretability of independent components (ICs) appears to be low. This does not diminish the statistical advantages of deriving components that are mutually independent, with potential applications ranging from synthetically generating multivariate datasets, developing statistical forecasts, and reconstructing spatial datasets from patchy observations at multiple point locations.Seth Westra, Casey Brown, Upmanu Lall, Inge Koch and Ashish Sharm