Release 2.5 of the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is a major update (covering 1662-2007) of the world's most extensive surface marine meteorological data collection. Building on extensive national and international partnerships, many new and improved contributing datasets have been processed into a uniform format and combined with the previous Release 2.4. The new data range from early non-instrumental ship observations to measurements initiated in the twentieth century from buoys and other automated platform types. Improvements to existing data include replacing preliminary Global Telecommunication System (GTS) receipts with more reliable, delayed mode reports for post-1997 data, and in the processing and quality control (QC) of humidity observations. Over the entire period of record, spatial and temporal coverage has been enriched and data and metadata quality has been improved. Along with the observations, now updated monthly in near real time, Release 2.5 includes quality-controlled monthly summary products for 2°latitude × 2°longitude (since 1800) and 1°× 1°boxes (since 1960), together with multiple options for access to the data and products. The measured and estimated data in Release 2.5 are subject to many technical changes, multiple archive sources, and historical events throughout the more than three-century record. Some of these data characteristics are highlighted, including known unresolved errors and inhomogeneities, which may impact climate and other research applications. Anticipated future directions for ICOADS aim to continue adding scientific value to the observations, products, and metadata, as well as strengthen the cooperative enterprise through expanded linkages to international initiatives and organisations.

ABSTRACT:We highlight improvements to the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) in the latest Release 3.0 (R3.0; covering . ICOADS is the most widely used freely available collection of surface marine observations, providing data for the construction of gridded analyses of sea surface temperature, estimates of air-sea interaction and other meteorological variables. ICOADS observations are assimilated into all major atmospheric, oceanic and coupled reanalyses, further widening its impact. R3.0 therefore includes changes designed to enable effective exchange of information describing data quality between ICOADS, reanalysis centres, data set developers, scientists and the public. These user-driven innovations include the assignment of a unique identifier (UID) to each marine report -to enable tracing of observations, linking with reports and improved data sharing. Other revisions and extensions of the ICOADS' International Maritime Meteorological Archive common data format incorporate new near-surface oceanographic data elements and cloud parameters. Many new input data sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. Coupled with enhanced 'preliminary' monthly data and product extensions past 2014, R3.0 provides improved support of climate assessment and monitoring, reanalyses and near-real-time applications.

El Niño-Southern Oscillation (ENSO) is a natural, coupled atmospheric-oceanic cycle that occurs in the tropical Pacific Ocean on an approximate time scale of 2-7 years. ENSO events have been shown in previous studies to be related to regional extremes in weather (e.g., hurricane occurrences, frequency and severity of tornadoes, droughts, and floods). The teleconnection of ENSO events to extreme weather events means the ability to classify an event as El Niño or La Niña is of interest in scientific and other applications. ENSO is most often classified using indices that indicate the warmth and coolness of equatorial tropical Pacific Ocean sea-surface temperatures (SSTs). Another commonly used index is based on sea-level pressure differences measured across the tropical Pacific Ocean. More recently, other indices have been proposed and have been shown to be effective in describing ENSO events. There is currently no consensus within the scientific community as to which of many indices best captures ENSO phases. The goal of this study is to compare several commonly used ENSO indices and to determine whether or not one index is superior in defining ENSO events; or alternatively, to determine which indices are best for various applications. The response and sensitivity of the SST-based indices and pressure-based indices are compared. The Niño 4 index has a relatively weak response to El Niño; the Niño 1+2 index has a relatively strong response to La Niña. Analysis of the sensitivity of the indices relative to one another suggests that the choice of index to use in ENSO studies is dependent upon the phase of ENSO that is to be studied. The JMA index is found to be more sensitive to La Niña events than all other indices. The SOI, Niño 3.4, and Niño 4 indices are almost equally sensitive to El Niño events and are more sensitive than the JMA, Niño 1+2, and Niño 3 indices.

No abstract

[1] The accuracy of the SeaWinds scatterometer's vector winds is assessed through comparison with research vessel observations. Factors that contribute to uncertainty in scatterometer winds are isolated and examined as functions of wind speed. For SeaWinds on QuikSCAT, ambiguity selection is found to be near perfect for surface wind speed (w) > 8 m s À1 ; however, ambiguity selection errors cause directional uncertainty to exceed 20°for w < $5 m s À1 . These average uncertainties for wind speed and direction are found to be 0.45 m s À1 and 5°for the QSCAT-1 model function and 0.3 m s À1 and 3°for the Ku-2000 model function. The QuikSCAT winds are examined as vectors through two new approaches. The first is a method for determining vector correlations that considers uncertainty in the comparison data set. The second approach is a wind speeddependent model for the uncertainty in the magnitude of vector errors. For the QSCAT-1 (Ku-2000) model function this approach shows ambiguity selection dominates uncertainty for 2.5 < w < 5.5 m s À1 (0.6 < w < 5.5 m s À1 ), uncertainty in wind speed dominates for w < 2.5 m s À1 and 5.5 < w < 7.5 m s À1 (w < 0.6 m s À1 and 5.5 < w < 18 m s À1 ), and uncertainty in wind direction (for correctly selected ambiguities) dominates for w > 7.5 m s À1 (w > 18 m s À1 ). This approach also shows that spatial variability in the wind direction, related to inexact spatial co-location, is likely to dominate rms differences between scatterometer wind vectors and in situ comparison measurements for w > 4.5 m s À1 . The techniques used herein are applicable to any validation effort with uncertainty in the comparison data set or with inexact co-location. INDEX TERMS: 3339