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We examined the effects of land cover change over the Indian subcontinent during pre-monsoon season (March, April, and May—MAM) on early Indian summer monsoon (ISM) rainfall using observed Normalized Difference Vegetation Index (NDVI) and July precipitation for the period of 1982–2003. MAM NDVI anomalies have increased in the Indian subcontinent and the increases are significantly correlated with increases in the irrigated area, not preceding rainfall. July rainfall significantly decreased in central and southern India, and the decrease is statistically related to the increase in the preceding MAM NDVI anomalies. Decreased July surface temperature in the Indian subcontinent (an expected result of increased evapotranspiration due to irrigation and increased vegetation) leads to a reduced land–sea thermal contrast, which is one of the factors driving the monsoon, and therefore weakens the monsoon circulation. A weak early ISM appears to be at least partially a result of irrigation and the resultant increased vegetation and crop activity prior to the monsoon. Copyright © 2008 Royal Meteorological Society

This study investigates dominant patterns of snow variability and their relationship to large-scale climate circulations over the eastern half of the United States. Two snowfall variables—total seasonal snowfall (TSF) and number of snow days (NSD)—are examined. A principal components (PC) analysis is conducted on data from 124 snowfall stations. The leading mode of variability for both TSF and NSD is driven by the North Atlantic Oscillation (NAO). The secondary mode of variability for TSF is driven by the Pacific/North American pattern (PNA), while the secondary mode of variability for NSD is driven by a dipole pattern and is attributable to regional influences and noise. These patterns exhibit persistence, which provides prospects for seasonal predictions of snowfall variables. This research compliments and extends the work of Serreze et al(1998), who performed a PC analysis of geopotential heights during the winter season and correlated the spatial patterns of the leading modes of variability with seasonal snowfall values. Copyright © 2007 Royal Meteorological Society

We use the upper-level divergence zone at 150 hPa to define the areas of study for the East Asian summer monsoon (EASM) and to show the advances and retreats of the EASM. We find that the EASM can be subdivided into a northern and southern component with distinctly different driving mechanisms. The northern EASM (NEASM) is affected by heat sources in the tropical oceans related to El Nino events while the southern EASM (SEASM) is affected by the subtropical oceans related to a North Pacific sea surface temperature (SST) dipole mode. A stronger NEASM is related to above-normal western North Pacific (WNP) anticyclonic anomalies, while a stronger SEASM is related to below-normal WNP anticyclonic anomalies. These WNP anticyclonic anomalies are connected to SST anomalies in the tropical and subtropical Pacific during the pre-monsoon season (December∼May). We also find that NEASM precipitation can be predicted from regional oceanic heat sources, i.e. SST and ocean heat content, in the tropical Pacific and Indian Oceans during the pre-monsoon season using a linear regression model. SEASM precipitation can be predicted from pre-monsoon SST in the eastern North Pacific. The NEASM forecast model is more skillful than that for the SEASM. Copyright © 2007 Royal Meteorological Society

Decadal trends in cloudiness are shown to affect incoming solar radiation (SWSFC) in the Krishna River basin (13–20°N, 72–82°E), southern India, from 1952 to 1997. Annual average cloudiness at 14 meteorological stations across the basin decreased by 0.09% of the sky per year over 1952–1997. The decreased cloudiness partly balanced the effects of aerosols on incoming solar radiation (SWSFC), resulting in a small net increase in SWSFC in monsoon months (0.1–2.9 W m−2 per decade). During the non-monsoon, aerosol forcing dominated over trends in cloud forcing, resulting in a net decrease in SWSFC (−2.8 to − 5.5 W m−2 per decade). Monthly satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) covering 1983–1995 were used to screen the visual cloudiness measurements at 26 meteorological stations, which reduced the data set to 14 stations and extended the cloudiness record back to 1952. SWSFC measurements were available at only two stations, so the SWSFC record was extended in time and to the other stations using a combination of the Angstrom and Hargreaves-Supit equations. The Hargreaves-Supit estimates of SWSFC were then corrected for trends in aerosols using the literature values of aerosol forcing over India. Monthly values and trends in satellite measurements of SWSFC from National Aeronautics and Space Administration's (NASA's) surface radiation budget (SRB) matched the aerosol-corrected Hargreaves-Supit estimates over 1984–1994 (RMSE = 11.9 W m−2, 5.2%). We conclude that meteorological station measurements of cloudiness, quality checked with satellite imagery and calibrated to local measurements of incoming radiation, provide an opportunity to extend radiation measurements in space and time. Reports of decreased cloudiness in other parts of continental Asia suggest that the cloud-aerosol trade-off observed in the Krishna basin may be widespread, particularly during the rainy seasons when changes in clouds have large effects on incoming radiation compared with aerosol forcing. Copyright © 2007 Royal Meteorological Society

This paper describes the development of a statistical forecasting method for summer monsoon rainfall over Thailand. Predictors of Thailand summer (August-October) monsoon rainfall are identified from the large-scale ocean-atmospheric circulation variables (i.e., sea surface temperature and sea level pressure) in the Indo-Pacific region. The identified predictors are part of the broader El Nino Southern Oscillation (ENSO) phenomenon. The predictors exhibit significant relationship to the summer rainfall only during the post-1980 period when the Thailand summer rainfall also shows a relationship with ENSO. Two methods for generating ensemble forecasts are adapted. The first is the traditional linear regression, and the second is a local polynomial based non-parametric method. The associated predictive standard errors are used for generating ensembles. Both the methods exhibit significant comparable skills in a cross-validated mode. However, the nonparametric method shows improved skill during extreme years (i.e. wet and dry years). Furthermore, the models provide useful skill at 1~3 month lead time that can have strong impact on resources planning and management.

Recent advances in understanding the role of the El Nino-Southern Oscillation (ENSO) in climate variability present opportunities for improving efficiency in agricultural production. We investigated the relationships between ENSO, climate and maize yields in the U.S. cornbelt, using both observed data and crop simulations. Using a time-series of sea-surface temperature anomalies (SSTA) from the NINO3 region of the Pacific Ocean and historical records of temperature and precipitation spatially averaged across 51 mid-western climate divisions from 1950 to 1995, we ran linear correlation tests at three different lags. Northern hemisphere wintertime SSTAs were significantly correlated with air temperature at the 95% level of confidence in both the previous (r 0.32) and following (r 0.41) summer, but had opposite signs. Correlations with precipitation were significant only in the summer preceding the ENSO event (r0.31). Detrended maize yield for the same area and time period was also significantly related to SSTAs in the winter after harvest, with a correlation coefficient of 0.39, indicating that ENSO accounts for : 15% of interannual maize yield variability in the cornbelt. Crop growth simulations at seven sites across the region suggest that water stress in July and August is the primary cause of lowered corn yield in La Nina years, but shortened grainfill period due to higher temperatures is also important. The benefits of El Nino-related rainfall and cooler temperatures are less pronounced than the negative impacts of warmer and dryer La Ninas. However, advance warning of both ENSO phases may present opportunities for improved crop management in the cornbelt. Copyright © 1999 Royal Meteorological Society.