The seasonality of the southern annular mode (SAM) and the resulting impacts on the climate variability of New Zealand (NZ) are investigated. As with previous studies, during summer the SAM is found to be largely zonally symmetric, whereas during winter it exhibits increased zonal wavenumber 2-3 variability. This is consistent with seasonal variations in the mean state, and the authors argue that the seasonal cycle of nearsurface temperature over the Australian continent plays an important role, making the eddy-driven jet, and hence the SAM, more zonally symmetric during summer than winter. During winter, the SAM exhibits little variability over the South Pacific and southeast of Australia. Dynamical reasons for this behavior are discussed.For the NZ region this seasonality implies that fluctuations in the SAM are associated with a zonal wind speed anomaly during summer but a more meridional wind speed anomaly during winter. This behavior is well captured by temperature and rainfall station data, which serves to corroborate the seasonal changes seen in the large-scale analysis. Moreover, the mode of climate variability that corresponds to a fluctuation of the zonal wind speed is well correlated with the SAM during the summer only and exhibits less variance during the winter. This is consistent with the notion that the seasonality of the SAM significantly impacts modes of climate variability in the region.

Recent warming of the Antarctic Peninsula during austral autumn, winter, and spring has been linked to sea surface temperature (SST) trends in the tropical Pacific and tropical Atlantic, while warming of the northeast Peninsula during summer has been linked to a strengthening of westerly winds traversing the Peninsula associated with a positive trend in the Southern Annular Mode (SAM). Here we demonstrate that circulation changes associated with the SAM dominate interannual temperature variability across the entire Antarctic Peninsula during both summer and autumn, while relationships with tropical Pacific SST variability associated with the El Niño–Southern Oscillation (ENSO) are strongest and statistically significant primarily during winter and spring only. We find the ENSO‐Peninsula temperature relationship during autumn to be weak on interannual time scales and regional circulation anomalies associated with the SAM more important for interannual temperature variability across the Peninsula during autumn. Consistent with previous studies, western Peninsula temperatures during autumn, winter, and spring are closely tied to changes in the Amundsen Sea Low (ASL) and associated meridional wind anomalies. The interannual variability of ASL depth is most strongly correlated with the SAM index during autumn, while the ENSO relationship is strongest during winter and spring. Investigation of western and northeast Peninsula temperatures separately reveals that interannual variability of northeast Peninsula temperatures is primarily sensitive to zonal wind anomalies crossing the Peninsula and resultant leeside adiabatic warming rather than to meridional wind anomalies, which is closely tied to variability in the zonal portion of the SAM pattern.

To study why, where, and when deep convection

This study characterizes rainfall and temperature variability for the whole of Vietnam and for climate subregions over 40 years from 1971 to 2010. Vietnam's average temperature has increased at a rate of 0.26 ± 0.10 • C per decade since the 1970s, approximately twice the rate of global warming over the same period. The rate of increase is greater in winter than in summer. Except for the Central Highland, annual average temperatures in southern regions are increasing more rapidly than in the North. The increases in temperatures are statistically significant in most sub-regions; however this is not the case for rainfall. The locations of climate boundaries between sub-regions are also discussed and suggestions for repositioning of these are made. Temperature and rainfall variability are shown to be linked to El Niño-Southern Oscillation on both national and sub-regional scale. This relationship is stronger in lower latitudes and in winter.

No abstract

This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98-2006/07). The evaluated precipitation products are the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals.

No abstract

Fog is a high-impact weather phenomenon affecting human activity, including aviation, transport, and health. Its prediction is a longstanding issue for weather forecast models. The success of a forecast depends on complex interactions among various meteorological and topographical parameters; even very small changes in some of these can determine the difference between thick fog and good visibility. This makes prediction of fog one of the most challenging goals for numerical weather prediction. The Local and Nonlocal Fog Experiment (LANFEX) is an attempt to improve our understanding of radiation fog formation through a combined field and numerical study. The 18-month field trial was deployed in the United Kingdom with an extensive range of equipment, including some novel measurements (e.g., dew measurement and thermal imaging). In a hilly area we instrumented flux towers in four adjacent valleys to observe the evolution of similar, but crucially different, meteorological conditions at the different sites. We correlated these with the formation and evolution of fog. The results indicate new quantitative insight into the subtle turbulent conditions required for the formation of radiation fog within a stable boundary layer. Modeling studies have also been conducted, concentrating on high-resolution forecast models and research models from 1.5-km to 100-m resolution. Early results show that models with a resolution of around 100 m are capable of reproducing the local-scale variability that can lead to the onset and development of radiation fog, and also have identified deficiencies in aerosol activation, turbulence, and cloud micro- and macrophysics, in model parameterizations.