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Ying K.,Beijing Normal University | Zheng X.,Beijing Normal University | Quan X.-W.,University of Colorado at Boulder | Frederiksen C.S.,CAWCR
International Journal of Climatology | Year: 2013

An effort is made to identify the 'more predictable' signals in seasonal precipitation in the Eastern Yangtze-Huaihe River Valley of China based on the observations recorded for the period of 1951-2004 at a network of 23 stations in this region. A recently developed methodology for decomposing the interannual variance of seasonal mean climate fields is applied to precipitation time series at the stations. This allows the total interannual variance to be separated into the variance of a slow component, or predictable signal, and the variance of a more noisier component of rainfall associated with intraseasonal variability. The potential predictability (signal-to-total ratio) is generally moderate in this region and is lower during winter from January to March (JFM) and higher in summer from May to July (MJJ) and autumn from November to January (NDJ). Empirical orthogonal function (EOF) analysis is then applied to the predictable-, intraseasonal- and total-covariance matrices, respectively. Leading EOF modes of the total component more often resemble the EOF modes of the intraseasonal component when the potential predictability is lower and vice versa. Temporal variation of the leading modes in the predictable components is more closely linked to the interannual variability of Eastern Pacific sea surface temperature (SST) in early summer (MJJ), North Atlantic SST in late summer (JAS) and El Niño/Southern oscillation in late autumn (NDJ). The rainfall SST connections found in these seasons persist throughout the entire 54-year period. The predictable rainfall modes also have apparent linkages to the intensity and position of the Western Pacific subtropical high during the early summer and late autumn with more rainfall occurring in the region when the height pattern is more intensive and extends further west. During the late summer, the predictable rainfall variability is accompanied by a large variation of tropospheric advection from north of the region, indicating the impact of an anomalous atmospheric circulation associated with the variation of summer North Atlantic Oscillation. © 2013 Royal Meteorological Society. Source

Zhang C.,University of Miami | Gottschalck J.,College Park | Maloney E.D.,Colorado State University | Moncrieff M.W.,NCAR | And 4 more authors.
Geophysical Research Letters | Year: 2013

The Madden-Julian oscillation poses great challenges to our understanding and prediction of tropical convection and the large-scale circulation. Several internationally coordinated activities were recently formed to meet the challenges from the perspectives of numerical simulations, prediction, diagnostics, and virtual and actual field campaigns. This article provides a brief description of these activities and their connections, with the motivation in part to encourage the next generation of physical scientists to help solve the grand challenging problem of the Madden-Julian oscillation. Key Points Several unprecedented international studies of the MJO are underway They provide opportunities to further our understanding of the MJO Advanced prediction skill of the MJO will have tremendous societal benefits. ©2013 American Geophysical Union. All Rights Reserved. Source

Collins M.,University of Exeter | Collins M.,UK Met Office | An S.-I.,Yonsei University | Cai W.,CSIRO | And 12 more authors.
Nature Geoscience | Year: 2010

The El Nĩo-Southern Oscillation (ENSO) is a naturally occurring fluctuation that originates in the tropical Pacific region and affects ecosystems, agriculture, freshwater supplies, hurricanes and other severe weather events worldwide. Under the influence of global warming, the mean climate of the Pacific region will probably undergo significant changes. The tropical easterly trade winds are expected to weaken; surface ocean temperatures are expected to warm fastest near the equator and more slowly farther away; the equatorial thermocline that marks the transition between the wind-mixed upper ocean and deeper layers is expected to shoal; and the temperature gradients across the thermocline are expected to become steeper. Year-to-year ENSO variability is controlled by a delicate balance of amplifying and damping feedbacks, and one or more of the physical processes that are responsible for determining the characteristics of ENSO will probably be modified by climate change. Therefore, despite considerable progress in our understanding of the impact of climate change on many of the processes that contribute to El Nĩo variability, it is not yet possible to say whether ENSO activity will be enhanced or damped, or if the frequency of events will change. © 2010 Macmillan Publishers Limited. All rights reserved. Source

Oke P.R.,CSIRO | Brassington G.B.,CAWCR | Cummings J.,U.S. Navy | Martin M.,UK Met Office | Hernandez F.,Mercator Ocean
Journal of Operational Oceanography | Year: 2012

This paper compares the performance of operational short-range ocean forecast systems developed under the Global Ocean Data Assimilation Experiment (GODAE) - an international effort to demonstrate the feasibility of operational ocean forecasting. 'Best estimates' from four different operational forecast systems (either analyses, hindcasts, or nowcasts) are inter-compared for the Tasman and Coral Seas, off eastern Australia. Systems considered include those developed in Australia, France, the USA, and the UK. Each system is compared to observations of along-track sea-level anomaly, sea-surface temperature, near-surface velocity, and sub-surface temperature and salinity. All have their strengths and weaknesses, and each system out-performs all others in one aspect or another. With few exceptions, all systems demonstrate signal-to-noise ratios greater than one for all variables. Due to the Australian focus, in addition to the best estimates from the operational systems, operational forecasts and a delayed-mode reanalysis are also inter-compared using the Australian system. The Australian system generally performs the best for sea-level anomaly; the French system is best for near-surface velocities; the USA system generally performs the best for sea-surface temperature; and the UK system is best for sub-surface temperature and salinity. These findings provide useful indicators of deficiencies in each system and clear metrics by which future developments should be assessed. Based on these results and other practical considerations the adoption of multimodel consensus forecasting, using all available forecasts from all systems, is promoted as the most robust approach for the user community. Such developments are being pursued under GODAE OceanView - the successor to GODAE. The results show the success of GODAE in demonstrating the feasibility of operational oceanography. Source

Martin M.,UK Met Office | Dash P.,National Oceanic and Atmospheric Administration | Dash P.,Cooperative Institute for Research in the Atmospheres CIRA | Ignatov A.,National Oceanic and Atmospheric Administration | And 12 more authors.
Deep-Sea Research Part II: Topical Studies in Oceanography | Year: 2012

Many sea surface temperature (SST) gap-free gridded analysis (Level 4, or L4) fields are produced by various groups in different countries. The Group for High Resolution SST (GHRSST) is an international collaboration body which has formed the inter-comparison technical advisory group (IC-TAG), to advise SST producers and users on the relative performance of these SST fields. This two-part paper describes two of the three major systems developed under GHRSST coordination towards this goal. Part one (this paper) describes the GHRSST Multi-Product Ensemble (GMPE) system, which runs on a daily basis at the UK Met Office, taking various L4 analyses as inputs, transferring them onto a common grid, and producing an ensemble median and standard deviation. The various analysis systems contributing to the GHRSST inter-comparisons are discussed, highlighting areas of commonality between the systems as well as those parts of the systems where there is less agreement on the appropriate algorithmic or parametric choices. The characteristics of the contributing L4 analyses are demonstrated by comparing them to near-surface Argo profile temperature data, which provide an independent measurement of SST and have been shown to provide a good estimate of foundation SST (the SST free of diurnal warming). The feature resolution characteristics of the L4 analyses are demonstrated by calculating horizontal gradients of the SST fields (on their original grid). The accuracy and resolution of the GMPE median are compared with those of the input analyses using the same metrics, showing that the GMPE median is more accurate than any of the contributing analyses with a standard deviation error of 0.40. K globally with respect to near-surface Argo data. For use in climate applications such as trend analysis or assimilation into climate models, it is important to have a good measure of uncertainty, so the suitability of the GMPE standard deviation as a measure of uncertainty is explored. This assessment shows that, over large spatial and temporal scales, the spread in the ensemble does have a strong relationship with the error in the median, although it underestimates the error by about one third. © 2012 . Source

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