The most important anthropogenic influences on climate are the emission of greenhouse gases1 and changes in land use, such as urbanization and agriculture2. But it has been difficult to separate these two influences because both tend to increase the daily mean surface temperature3,4. The impact of urbanization has been estimated by comparing observations in cities with those in surrounding rural areas, but the results differ significantly depending on whether population data5 or satellite measurements of night light6,7,8 are used to classify urban and rural areas7,8. Here we use the difference between trends in observed surface temperatures in the continental United States and the corresponding trends in a reconstruction of surface temperatures determined from a reanalysis of global weather over the past 50 years, which is insensitive to surface observations, to estimate the impact of land-use changes on surface warming. Our results suggest that half of the observed decrease in diurnal temperature range is due to urban and other land-use changes. Moreover, our estimate of 0.27 °C mean surface warming per century due to land-use changes is at least twice as high as previous estimates based on urbanization alone7,8.

Impact of urbanization and land-use change on climate

Changes in climate extremes and their impacts on the natural physical environment: An overview of the IPCC SREX report

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kalrnan filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the time-marching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) (Parker et al., 1994) of the Global Ocean Surface Temperature Atlas (COSTA) (Bottoraley et al., 1990). These analyses are compared with each other, with COSTA, and with an analy- sis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. (1996)). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and COSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and COSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the E1 Nifio - Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

http://rainbow.ldeo.columbia.edu/~alexeyk/Papers/Kaplan_etal1998.pdf

Analyses of global sea surface temperature 1856–1991

Advanced Statistical Methods in Biometric Research.

Analysis of the 140-year historical record suggests that the inverse relationship between the El Nino-Southern Oscillation (ENSO) and the Indian summer monsoon (weak monsoon arising from warm ENSO event) has broken down in recent decades. Two possible reasons emerge from the analyses. A southeastward shift in the Walker circulation anomalies associated with ENSO events may lead to a reduced subsidence over the Indian region, thus favoring normal monsoon conditions. Additionally, increased surface temperatures over Eurasia in winter and spring, which are a part of the midlatitude continental warming trend, may favor the enhanced land-ocean thermal gradient conducive to a strong monsoon. These observations raise the possibility that the Eurasian warming in recent decades helps to sustain the monsoon rainfall at a normal level despite strong ENSO events.

/pdf/on-the-weakening-relationship-between-the-indian-monsoon-and-4y1pqv2xkm.pdf

On the Weakening Relationship Between the Indian Monsoon and ENSO

A state-of-art regional climate modelling system, known as PRECIS (Providing Regional Climates for Impacts Studies) developed by the Hadley Centre for Climate Prediction and Research, is applied for India to develop high-resolution climate change scenarios. The present-day simulation (1961-1990) with PRECIS is evaluated, including an examination of the impact of enhanced resolution and an identification of biases. The RCM is able to resolve features on finer scales than those resolved by the GCM, particularly those related to improved resolution of the topography. The most notable advantage of using the RCM is a more realistic representation of the spatial patterns of summer monsoon rainfall such as the maximum along the windward side of the Western Ghats. There are notable quantitative biases in precipitation over some regions, mainly due to similar biases in the driving GCM. PRECIS simulations under scenarios of increasing greenhouse gas concentrations and sulphate aerosols indicate marked increase in both rainfall and temperature towards the end of the 21st century. Surface air temperature and rainfall show similar patterns of projected changes under A2 and B2 scenarios, but the B2 scenario shows slightly lower magnitudes of the projected change. The warming is monotonously widespread over the country, but there are substantial spatial differences in the projected rainfall changes. West central India shows maximum expected increase in rainfall. Extremes in maximum and minimum temperatures are also expected to increase into the future, but the night temperatures are increasing faster than the day temperatures. Extreme precipitation shows substantial increases over a large area, and particularly over the west coast of India and west central India.

High-resolution climate change scenarios for India for the 21st century

METEOROLOGICAL BACKGROUND. South Asia: An Introduction to the Region. Tropical General Circulation: South Asian Context. Characteristic Features of South Asian Summer Monsoon. Other Meteorological Features and Systems of South Asia. CLIMATIC CHARACTERISTICS. Mean Climatology of South Asia. Rainfall Variability. Temperature Variability. Regional Climatic Features. The Changing Climate of South Asia: Its Environmental Impacts. Appendices. Bibliography. Index.

Climates of South Asia

Long-term changes in the Indian monsoon rainfall on regional and local scales have important social and economic consequences. To present a comprehensive picture of such changes, the monthly rainfall data at 306 stations, well spread over India, have been analysed over 114 years (1871–1984), for long-term trends. The trends are quantified by linear trend for monthly as well as seasonal rainfall for each station. Some broad contiguous areas showing statistically significant trends have been identified. Areas of increasing trend in the monsoon seasonal rainfall are found along the west coast, north Andhra Pradesh and north-west India, and those of decreasing trend over east Madhya Pradesh and adjoining areas, north-east India and parts of Gujarat and Kerala.



The subseasonal patterns indicate that the excess or deficiency of the monsoon rainfall is more frequently realized in the later half of the season. Monsoon rainfall tends to be more concentrated in August, over the west coast and central India.

Spatial and subseasonal patterns of the long‐term trends of Indian summer monsoon rainfall

Diurnal asymmetry of the recent surface warming, reported for several continental areas, is believed to be related to changes in cloudiness, humidity, atmospheric circulation patterns, winds and soil moisture. This paper presents linear trend analyses of maximum and minimum temperature data at 121 stations in India during the period 1901–87. While the mean temperature trends over India are similar to the global and hemispheric trends, there are marked differences in their diurnal manifestation from those reported for other areas. The increase in the mean temperatures over India is almost solely contributed by the maximum temperatures, with the minimum temperatures remaining practically trendless, leading to an increase in the diurnal range of temperatures. These trends do not show any significant urban or altitude bias. There are some differences in the trends on smaller spatial and temporal scales, but the increase of maximum temperature is predominant over a major part of India, particularly in winter and post-monsoon seasons.

Diurnal asymmetry of surface temperature trends over India

Association between the all-India summer monsoon (June to September) rainfall and an index of the Southern Oscillation (SO) is studied in relation to the vagaries of the monsoon rainfall and the seasonal characteristics of the SO. The SOI values of different months and standard seasons show opposite tendencies during deficient and excess years of all-India monsoon rainfall. The correlation coefficients between the all-India monsoon rainfall series and the SOIs of summer monsoon, autumn and winter minus spring seasons are significant at the 1 per cent level.

G. B. Pant

Papers

High-resolution climate change scenarios for India for the 21st century

Climates of South Asia

Spatial and subseasonal patterns of the long‐term trends of Indian summer monsoon rainfall

Diurnal asymmetry of surface temperature trends over India

Seasonal relationships between indian summer monsoon rainfall and the southern oscillation