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In this paper, we present TropFlux wind stresses and evaluate them against observations along with other widely used daily air-sea momentum flux products (NCEP, NCEP2, ERA-I and QuikSCAT). TropFlux wind stresses are computed from the COARE v3.0 algorithm, using bias and amplitude corrected ERA-I input data and an additional climatological gustiness correction. The wind stress products are evaluated against dependent data from the TAO/TRITON, PIRATA and RAMA arrays and independent data from the OceanSITES mooring networks. Wind stress products are more consistent amongst each other than surface heat fluxes, suggesting that 10 m-winds are better constrained than near-surface thermodynamical parameters (2 m-humidity and temperature) and surface downward radiative fluxes. QuikSCAT overestimates wind stresses away from the equator, while NCEP and NCEP2 underestimate wind stresses, especially in the equatorial Pacific. QuikSCAT wind stress quality is strongly affected by rain under the Inter Tropical Convergence Zones. ERA-I and TropFlux display the best agreement with in situ data, with correlations >0.93 and rms-differences <0.012 Nm. TropFlux wind stresses exhibit a small, but consistent improvement (at all timescales and most locations) over ERA-I, with an overall 17 % reduction in root mean square error. ERA-I and TropFlux agree best with long-term mean zonal wind stress observations at equatorial latitudes. All products tend to underestimate the zonal wind stress seasonal cycle by ~20 % in the western and central equatorial Pacific. TropFlux and ERA-I equatorial zonal wind stresses have clearly the best phase agreement with mooring data at intraseasonal and interannual timescales (correlation of ~0.9 versus ~0.8 at best for any other product), with TropFlux correcting the ~13 % underestimation of ERA-I variance at both timescales. For example, TropFlux was the best at reproducing westerly wind bursts that played a key role in the 1997-1998 El Niño onset. Hence, we recommend the use of TropFlux for studies of equatorial ocean dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we apply finite-mixture-model-based clustering algorithms to cluster post-landfall tracks of tropical cyclones (TCs) making landfall over China. Because existing studies find that landfall surfaces or elevations affect post-landfall TC movements, we also take account of elevations in addition to time orders in this model. Our study reveals three clusters, with cluster 1 making landfall in Hainan province and moving across the western coast of Guangdong province. Most of the TC tracks in cluster 1 move over the ocean and make secondary landfalls over Yunnan province of China and Vietnam. Cluster 1 finally dissipates inland and moves westward as a result of the westward-shift subtropical high, westward steering flow, easterly vertical wind shear and strong mountainous blocking. Cluster 2 makes landfall over Guangdong and Fujian provinces. TCs in cluster 2 subsequently move inland and disappear due largely to westward-shift subtropical high, easterly steering flow, easterly vertical wind shear and relatively strong mountainous blocking. Cluster 3 makes landfall along the Fujian and Zhejiang coast and sustains a long period of time, recurving mostly to the mid-latitude region owing to the surrounding eastward-shift subtropical high, westerly vertical wind shear, weak mountainous blocking and westerly steering flow. Because cluster 2 is significantly associated with La Niña events, TCs more likely make landfall over southeastern China coast and move westward or northwestward without recurving. Cluster 3 sustains a longer time than clusters 1 and 2 in spite of its weak horizontal and vertical water vapor supply. TCs in cluster 3 interact actively with westerlies during the post-landfall period. However, we cannot observe any analogous interactions with the mid-latitude westerlies in clusters 1 and 2. TCs of clusters 1 and 2 are influenced by summer monsoon flows. Moreover, summer monsoon exerts a greater influence on cluster 1 than cluster 2. The composite 200 hPa divergence of cluster 3 is stronger than that of clusters 1 and 2. This explains to some degree why cluster 3 sustains longer than clusters 1 and 2 after making landfall. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate several timely, daily air-sea heat flux products (NCEP, NCEP2, ERA-Interim and OAFlux/ISCCP) against observations and present the newly developed TropFlux product. This new product uses bias-corrected ERA-interim and ISCCP data as input parameters to compute air-sea fluxes from the COARE v3.0 algorithm. Wind speed is corrected for mesoscale gustiness. Surface net shortwave radiation is based on corrected ISCCP data. We extend the shortwave radiation time series by using 'near real-time' SWR estimated from outgoing longwave radiation. All products reproduce consistent intraseasonal surface net heat flux variations associated with the Madden-Julian Oscillation in the Indian Ocean, but display more disparate interannual heat flux variations associated with El Niño in the eastern Pacific. They also exhibit marked differences in mean values and seasonal cycle. Comparison with global tropical moored buoy array data, I-COADS and fully independent mooring data sets shows that the two NCEP products display lowest correlation to mooring turbulent fluxes and significant biases. ERA-interim data captures well temporal variability, but with significant biases. OAFlux and TropFlux perform best. All products have issues in reproducing observed longwave radiation. Shortwave flux is much better captured by ISCCP data than by any of the re-analyses. Our 'near real-time' shortwave radiation performs better than most re-analyses, but tends to underestimate variability over the cold tongues of the Atlantic and Pacific. Compared to independent mooring data, NCEP and NCEP2 net heat fluxes display ~0.78 correlation and >65 W m rms-difference, ERA-I performs better (~0.86 correlation and ~48 W m) while OAFlux and TropFlux perform best (~0.9 correlation and ~43 W m). TropFlux hence provides a useful option for studying flux variability associated with ocean-atmosphere interactions, oceanic heat budgets and climate fluctuations in the tropics. [ABSTRACT FROM AUTHOR]

A range of existing statistical approaches for reconstructing historical temperature variations from proxy data are compared using both climate model data and real-world paleoclimate proxy data. We also propose a new method for reconstruction that is based on a state-space time series model and Kalman filter algorithm. The state-space modelling approach and the recently developed RegEM method generally perform better than their competitors when reconstructing interannual variations in Northern Hemispheric mean surface air temperature. On the other hand, a variety of methods are seen to perform well when reconstructing surface air temperature variability on decadal time scales. An advantage of the new method is that it can incorporate additional, non-temperature, information into the reconstruction, such as the estimated response to external forcing, thereby permitting a simultaneous reconstruction and detection analysis as well as future projection. An application of these extensions is also demonstrated in the paper. [ABSTRACT FROM AUTHOR]

In this paper, we assess and compare to observations the spatial characteristics of the twentieth Century ENSO SST variability simulated by 23 models of the IPCC-AR4/CMIP3 database. The analysis is confined to the SST anomalies along the equatorial Pacific and is based on the use of a non-linear neural classification algorithm, the Self-Organizing Maps. Systematic biases include a larger than observed proportion for modelled ENSO maximum variability occurring in the Western Pacific. No clear relationship is found between this bias and the characteristics of the modelled mean state bias in the equatorial Pacific. This bias is mainly related to a misrepresentation of both El Niño and La Niña termination phases for most of the models. In contrast, the onset phase is quite well simulated. Modelled El Niño and La Niña peak phases display an asymmetric bias. Whereas the main bias of the modelled El Niño peak is to exhibit a maximum in the western Pacific, the simulated La Niña bias mainly occurs in the central Pacific. In addition, some models are able to capture the observed El Niño peak characteristics while none of them realistically simulate La Niña peaks. It also arises that the models closest to the observations score unevenly in reproducing the different phases, preventing an accurate classification of the models quality to reproduce the overall ENSO-like variability. [ABSTRACT FROM AUTHOR]