A diagnosis of twentieth century temperature records at West Lafayette,Indiana

A diagnostic study of 80 yrs(1901–80) of surface temperatures collected at West Lafayette, Indiana, has been found to be in tune with the global trend and that for the eastern two-thirds of the United States, namely, cold at the turn of the century, warming up to about 1940, and then cooling to present. The study was divided into two cold periods (1901–18, 1947–80) and a warm period (1919–46), based on the distribution of annual mean temperature. Decadal mean annual temperatures ranged from 10 °C in period I to 12.2 °C in period II, to 9.4 °C during the present cold period. Themean annual temperature for the 80 yr ranged from the coldest of 8.7 °C in 1979 to the warmest of 13.6 °C in 1939. Thedaily mean temperature for the entire 80-yr ranged from -4.7 °C on 31 January to 25.1 °C on 27 July. Thecoldest daily mean was -26.7 °C on 17 January, 1977, and thewarmest daily mean was 35 °C on 14 July, 1936. The range of values for thedaily mean maximum temperatures was -.2 °C on 31 January to 31.4 °C on 27 July. Corresponding values for thedaily mean minimum are -9.2 °C on 31 January and 18.7 °C on 27 July. The all-time extreme temperatures are -30.6 °C on 26 February, 1963 and 43.9 °C on 14 July, 1936. Climatic variability has been considered by computing the standard deviations of a) the daily mean maximum and minimum temperature per year, and b) the daily mean maximum and minimum temperatures for each day of the year for the 80-yr period. These results have shown that there is more variability in the daily mean maximum per year than in the daily mean minimum, for each year of the 80-yr period. Also the variability for both extremes has been greater in each of the two cold periods than in the warm period. Particularly noticeable has been theincrease in the variability of the daily mean minima per year during the current cooling trend. Further, it has been determined that the variability in the daily mean maxima and minima for each day of the year (based on the entire 80 yrs is a) two times greater in the winter than in the summer for both extremes, and b) about the same for each in the summer, greater for daily maximum in the spring and fall, but greater for the daily minimum during the winter. The latter result is undoubtedly related to the effect of snow cover on daily minimum temperatures. An examination of daily record maximum and minimum temperatures has been made to help establish climatic trends this century. For the warm period, 175 record maxima and 68 record minima were set, compared to 213 record minima and 105 record maxima during the recent cold period. For West Lafayette, the present climatic trend is definitely one of extreme record-breaking cold. Evidence has also been presented to show the substantial increases in snowfall amounts in the lee regions of the Great Lakes during the present cold period, due to the lake-induced snow squalls associated with cold air mass intrusions. The possible impact of the cooling trend on agricultural activities has also been noted, due to a reduced growing season.     

Recent Wyoming temperature trends,their drivers,and impacts in a 14,000-year context

Wyoming provides more fossil fuels to the remainder of the United States than any other state or country, and its citizens remain skeptical of anthropogenic influences on their climate. However, much of the state including Yellowstone National Park and the headwaters of several major river systems, may have already been affected by rising temperatures. This paper examines the historic climate record from Wyoming in the context of ∼14,000-year temperature reconstructions based on fossil pollen data. The analysis shows that 24 of 30 U.S. Historical Climatology Network records from the state show an increase in the frequency of unusually warm years since 1978. Statewide temperatures have included 15 years (50%) from 1978 to 2007 that were greater than 1σ above the mean annual temperature for 1895–1978. The frequent warm years coincide with a reduction in the frequency of extremely low (<−20°C) January temperatures, and are not well explained by factors such as solar irradiance and the Pacific Decadal Oscillation. Linear regressions require inclusion of atmospheric greenhouse gas concentrations to explain the multi-decadal temperature trends. The observed warming is large in Yellowstone National Park where 21 years (70%) from 1978 to 2007 were greater than 1σ above the 1895–1978 mean; the deviation from the mean (>1°C) is greater than any time in the past 6,000 years. Recent temperatures have become as high as those experienced from 11,000 to 6,000 years ago when summer insolation was >6% higher than today and when regional ecosystems experienced frequent severe disturbances.     

Bayesian multi-model projection of climate: bias assumptions and interannual variability

Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.     

Near-term increase in frequency of seasonal temperature extremes prior to the 2°C global warming target

Given current international efforts to reduce greenhouse gas emissions and limit human-induced global-mean near-surface temperature increases to 2°C, relative to the pre-industrial era, we seek to determine the impact such a temperature increase might have upon the frequency of seasonal-mean temperature extremes; further we seek to determine what global-mean temperature increase would prevent extreme temperature values from becoming the norm. Results indicate that given a 2°C global mean temperature increase it is expected that for 70–80% of the land surface maximum seasonal-mean temperatures will exceed historical extremes (as determined from the 95th percentile threshold value over the second half of the 20th Century) in at least half of all years, i.e. the current historical extreme values will effectively become the norm. Many regions of the globe—including much of Africa, the southeastern and central portions of Asia, Indonesia, and the Amazon—will reach this point given the “committed” future global-mean temperature increase of 0.6°C (1.4°C relative to the pre-industrial era) and 50% of the land surface will reach it given a future global-mean temperature increase of between 0.8 and 0.95°C (1.6–1.75°C relative to the pre-industrial era). These results suggest substantial fractions of the globe could experience seasonal-mean temperature extremes with high regularity, even if the global-mean temperature increase remains below the 2°C target.     

Egyptian cotton (Gossypium barbadense) flower and boll production as affected by climatic factors and soil moisture status

The information on impact of climatic factors on cotton production is not generally available, or at least not available in the required form. Understanding this impact may help physiologists determine a possible control of the flowering mechanism in the cotton plant. Two field trials were conducted to investigate the relationships between climatic factors, soil moisture status, and flower and boll production of Gossypium barbadense. The climatic factors considered were daily maximum air temperature (°C), minimum air temperature (°C), maximum–minimum temperature (diurnal temperature range) (°C), sunshine duration (h day^?1), maximum relative humidity (%), minimum relative humidity (%), and wind speed (m s^?1). Minimum relative humidity and sunshine duration were the most significant climatic factors affecting flower and boll retention and production. Temperature appeared to be less important in the reproduction growth stage of cotton in Egypt than minimum relative humidity and sunshine duration. The soil moisture status showed low and insignificant correlation to flower and boll production. High minimum relative humidity, short period of sunshine duration, and low temperatures enhanced flower and boll formation.     

Detection of Variations in Air Temperature at Different Time Scales During the Period 1889–1998 at Firenze, Italy

In an attempt to contribute to studies on global climatic change, 110 years of temperature data for Firenze, Italy, were analysed. Means and trends of annual and monthly temperatures (minimum, maximum and average) were analysed at three different time scales: short (20 years), medium (36–38 years) and long (55 years). Comparative changes in extreme events viz. frosts in the first and second parts of the 20th century were also analysed. At short time scales, climatic change was found in minimum and average temperatures but not in maximum temperatures. At all three time scales, the annual means of minimum, maximum and average temperatures were significantly warmer in the last part than in the early part of the 20th century. The monthly mean temperatures showed significant warming of winter months. Over the last four decades, minimum, maximum and average temperatures had warmed by 0.4, 0.43 and 0.4 ∘C per decade, respectively, and if this trend continues, they will be warmer by 4 ∘C by the end of the 21st century. The significant decline in days with subzero temperatures and frosts in the last half of the 20th century, further substantiated the occurrence of climate change at this site.     

On rising temperature trends of Karachi in Pakistan

Karachi is the largest city of Pakistan. The temperature change in Karachi is studied in this research by analyzing the time series data of mean maximum temperature (MMxT), mean minimum temperature (MMiT) and mean annual temperature (MAT) from 1947 to 2005 (59 years). Data is analyzed in three parts by running linear regression and by taking anomalies of all time periods: (a) whole period from 1947–2005; (b) phase one 1947–1975 and (c) phase two 1976–2005. During 1947 to 2005 MMxT has increased about 4.6°C, MMiT has no change and MAT has increased 2.25°C. During 1947–1975, MMxT increased 1.9°C, in this period there is − 1.3°C decrease in MMiT and MAT has raised upto 0.3°C. During 1976–2005, the MMxT, MMiT and MAT increased 2.7°C, 1.2°C and 1.95°C, respectively. The analysis shows significantly the role of extreme vulnerability of MMxT in rising the temperature of Karachi than the MMiT.     

The Characteristics of Cold Air Outbreaks over the Eastern Highlands of Kenya

Summary Climatological statistics of extreme temperature events over Kenya are established from the analysis of daily and monthly maximum temperatures for a representative station (Nairobi Dagoretti Corner) over the period 1956–1997. The months of June to August were shown to be the coldest with a mean monthly maximum temperature of less than 22 °C. Seasonal (June to August) mean maximum temperature was 21.5 °C. Using this seasonal mean temperature for the period 1967–1997 delineated 1968 as the coldest year in this series and 1983 as the warmest year. Spectral analysis of the seasonal data, for both the coldest and the warmest years, revealed that the major periods were the quasi-biweekly (10 days) and the Intraseasonal Oscillations (23 days). Secondary peaks occurred at periods of 4–6 and 2.5–3.5 days. A temperature threshold of 16.7 °C during July was used to define cold air outbreaks over Nairobi. This threshold temperature of 16.7 °C was obtained from the mean July maximum temperature (20.9 °C) minus two standard deviations. Notable trends include a decrease in the frequency of station-days, between 1956 and 1997, with temperatures less than 16.7 °C during July. Surface pressure patterns indicate that the origin of the cold air is near latitude 25° S and to the east of mainland South Africa. The cold air near 25° S is advected northwards ahead of the surface pressure ridge. Received July 19, 1999 Revised January 11, 2000     

Estimation of ground temperatures in permafrost regions of the Qinghai-Tibetan Plateau from climatic variables

Ground temperature is an important factor influencing ground source heat pumps, ground energy storage systems, land-atmosphere processes, and ecosystem dynamics. This paper presents an accurate development model (DM) based on a segment function: it can derive ground temperatures in permafrost regions of the Qinghai-Tibetan Plateau (QTP) from air temperature in case of shallow soil depths and without using air temperature data in case of deep soil depths. Here, we applied this model to simulate the active layer and permafrost ground temperature at the Tanggula observation station. The DM results were compared with those from the artificial neural network (ANN), support vector machine (SVM), and multiple linear regressions (MLR) models, which were based on climatic variables from prior models and on ground temperatures derived from observations at different depths. The results revealed that the effect of air temperature on simulated ground temperatures decreased with increasing depth; moreover, ground temperatures fluctuated greatly within the shallow layers but remained rather stable with deeper layers. The results also indicated that the DM has the best performance for the estimation of soil temperature compared to the MLR, SVM, and ANN models. Finally, we obtained the three average statistics indexes, i.e., mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) at TGL site: they were equal to 0.51 °C, 0.63 °C, and 0.15 °C for the ground temperature of active layer and to 0.08 °C, 0.09 °C, and 0.07 °C for the permafrost temperature.     

Climatic impacts of historical wetland drainage in Switzerland

The effects of historical land-use and land-cover changes on the climate of the Swiss Plateau in the different seasons were investigated. In the 19th century, a civil engineering project was initiated to reshape the lake and river system on the Swiss Plateau in order to ban the frequent flooding during extreme weather events. The landscape modifications consisted primarily of a conversion of wetlands with extended peat soils into a highly productive agricultural landscape. Historical maps (1800–1850) served as a basis for the reconstruction of the past land use. The “Lokal-Modell” of the Consortium for Small-Scale Modelling was used to conduct eight one-month long high-resolution simulations (1.5 × 1.5 km²) with present and past landscape conditions. The modified soil and surface properties led to distinctly altered energy and moisture exchanges at the surface and as a consequence affected the local and regional climate. The climatic changes show different characteristics and magnitudes in the cold (October – March) as compared to the warm season (April – September). The landscape modifications led to an average daytime cooling between −0.12 °C (January) and −0.61 °C (April) and a night-time warming of 0.19 °C−0.34 °C. The differences in the mean monthly temperatures show a warming of 0.1 °C−0.2 °C in the cold season and a cooling of similar magnitude in most of the study area in the warm season. The modification of the radiation budget and the surface energy balance distinctly affected the convective activity in the study area in the warm season, but had only a weak effect on convectivity in the cold season. The cloud coverage in the warm season is therefore distinctly reduced compared to the past.     

Estimation of the monthly and annual mean maximum and mean minimum air temperature values in Greece

In this study, regression equations to estimate the monthly and annual values of the mean maximum and mean minimum air temperatures in Greece are derived. For this purpose, data from 87 meteorological stations distributed all over Greece are used. Geographical parameters, i.e., altitude, latitude, longitude, minimum distance from the sea and an index of terrain morphology, are used as independent variables. These equations explain 79?C97% of the variance of the temperature values and have standard error of estimate between 0.59 and 1.20°C. Data from 37 other meteorological stations are used to validate the accuracy of the equations. Topographic or climatic factors, which could not be introduced into the equations, are responsible for most temperature residuals >0.5°C or <?0.5°C. Moreover, some particular emphasis has been given to the values of the regression coefficient for the altitude, since it is the estimator for the mean lapse rate of air temperature.     

A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate to the twenty-first century

 The potential climatic consequences of increasing atmospheric greenhouse gas (GHG) concentration and sulfate aerosol loading are investigated for the years 1900 to 2100 based on five simulations with the CCCma coupled climate model. The five simulations comprise a control experiment without change in GHG or aerosol amount, three independent simulations with increasing GHG and aerosol forcing, and a simulation with increasing GHG forcing only. Climate warming accelerates from the present with global mean temperatures simulated to increase by 1.7 °C to the year 2050 and by a further 2.7 °C by the year 2100. The warming is non-uniform as to hemisphere, season, and underlying surface. Changes in interannual variability of temperature show considerable structure and seasonal dependence. The effect of the comparatively localized negative radiative forcing associated with the aerosol is to retard and reduce the warming by about 0.9 °C at 2050 and 1.2 °C at 2100. Its primary effect on temperature is to counteract the global pattern of GHG-induced warming and only secondarily to affect local temperatures suggesting that the first order transient climate response of the system is determined by feedback processes and only secondarily by the local pattern of radiative forcing. The warming is accompanied by a more active hydrological cycle with increases in precipitation and evaporation rates that are delayed by comparison with temperature increases. There is an “El Nino-like” shift in precipitation and an overall increase in the interannual variability of precipitation. The effect of the aerosol forcing is again primarily to delay and counteract the GHG-induced increase. Decreases in soil moisture are common but regionally dependent and interannual variability changes show considerable structure. Snow cover and sea-ice retreat. A PNA-like anomaly in mean sea-level pressure with an enhanced Aleutian low in northern winter is associated with the tropical shift in precipitation regime. The interannual variability of mean sea-level pressure generally decreases with largest decreases in the tropical Indian ocean region. Changes to the ocean thermal structure are associated with a spin-down of the Atlantic thermohaline circulation together with a decrease in its variability. The effect of aerosol forcing, although modest, differs from that for most other quantities in that it does not act primarily to counteract the GHG forcing effect. The barotropic stream function in the ocean exhibits modest change in the north Pacific but accelerating changes in much of the Southern Ocean and particularly in the north Atlantic where the gyre spins down in conjunction with the decrease in the thermohaline circulation. The results differ in non-trivial ways from earlier equilibrium 2 × CO₂ results with the CCCma model as a consequence of the coupling to a fully three-dimensional ocean model and the evolving nature of the forcing. Received: 24 September 1998 / Accepted: 8 October 1999     

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R ²)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R ² 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R ² 0.93, 0.91) in estimating soil temperature in Turkey.

     

Estimating the annual mean screen temperature empirically

Summary We have examined station data from around the world to study the separate effects of the latitude (between 60° N–40° S), elevation and distance inland, on the annual-mean screen temperature. In the first 200–400 km from some west coasts, screen temperatures (after adjustment for elevation) rise inland, reaching a maximum called the ‘thermal-ridge temperature’ Tr. The rise of temperature within this littoral fringe (of width F) depends mainly on the difference between the sea-surface temperature off the west coast and the zonal mean. Further inland than such a fringe, adjusted temperatures generally decline eastwards, approximately linearly, at a rate C. The rate is related to hemisphere and latitude. Empirical relationships between latitude and the observed coastal sea-surface temperature, the near-shore screen temperature, Tr, C and F for each continent are used to estimate annual mean temperatures on land. Independent estimates of this kind for 48 places, using a look-up table, differ overall by only 0.7 K from the actual long-term average annual mean temperatures. This is less than half the error resulting from an assumption of zonal-mean temperatures. Basing estimates on coastal sea-surface temperatures, instead of the look-up table, results in an average error of 1.0 K for the 48 places. The errors are comparable with the standard deviation of annual mean temperatures during 30 years or so. Received March 6, 2001 Revised July 30, 2001     

Temperature and thermal growing season variations along elevational gradients on a sub-alpine,temperate China

Fully and accurately studying temperature variations in montane areas are conducive to a better understanding of climate modeling and climate-growth relationships on regional scales. To explore the spatio-temporal changes in air and soil temperatures and their relationship in montane areas, on-site monitoring over 2 years (2015 and 2016) was conducted at nine different elevations from 2040 to 2740 m a.s.l. on Luya Mountain in the semiarid region of China. The results showed that the annual mean of air temperature lapse rate (ATLR) was 0.67 °C/100 m. ATLR varied obviously in different months within a range of 0.57~0.79 °C/100 m. The annual mean of the soil temperature lapse rate (STLR) was 0.48 °C/100 m. Seasonally, monthly mean soil temperature did not show a consistent pattern with regard to elevation. The relationships between air and soil temperatures showed piecewise changes. Soil was decoupled from the air temperature in cold winter and early spring. The parameters of the growing season based on the two temperature types had no corresponding relations, and seasonal mean of soil temperature showed the smallest value at mid-elevation rather than in the treeline ecotone. Based on these changes, our results emphasized that altitudinal and seasonal variability caused by local factors (such as snow cover and soil moisture) should be taken into full consideration in microclimate extrapolation and treeline prediction in montane areas, especially in relation to soil temperature.     

Climate change effects on winter chill for tree crops with chilling requirements on the Arabian Peninsula

Fruit production systems that rely on winter chill for breaking of dormancy might be vulnerable to climatic change. We investigated decreases in the number of winter chilling hours (0–7.2°C) in four mountain oases of Oman, a marginal area for the production of fruit trees with chilling requirements. Winter chill was calculated from long-term hourly temperature records. These were generated based on the correlation of hourly temperature measurements in the oases with daylength and daily minimum and maximum temperatures recorded at a nearby weather station. Winter chill was estimated for historic temperature records between 1983 and 2008, as well as for three sets of synthetic 100-year weather records, generated to represent historic conditions, and climatic changes likely to occur within the next 30 years (temperatures elevated by 1°C and 2°C). Our analysis detected a decrease in the numbers of chilling hours in high-elevation oases by an average of 1.2–9.5 h/year between 1983 and 2008, a period during which, according to the scenario analysis, winter chill was sufficient for most important species in most years in the highest oasis. In the two climate change scenarios, pomegranates, the most important tree crop, received insufficient chilling in 13% and 75% of years, respectively. While production of most traditional fruit trees is marginal today, with trees barely fulfilling their chilling requirements, such production might become impossible in the near future. Similar developments are likely to affect other fruit production regions around the world.     

Interpretation of recent temperature and precipitationtrends observed in Korea

Summary  The possibility of climate change in the Korean Peninsula has been examined in view of the general increase in greenhouse gases. Analyses include changes in annual temperature and precipitation. These analyses are supplemented with our observations regarding the apparent decrease of forest areas. It was found that there was a 0.96 °C (0.42 °C per decade) increase in annual mean temperature between 1974 and 1997. The increase in large cities was 1.5 °C but only 0.58 °C at rural and marine stations. The difference in the mean temperature between large cities and rural stations was small from 1974 to 1981. However, the difference increased from 1982 to 1997. In particular, the warming appears most significant in winter. Prior to 1982, the lowest temperatures were often −18 °C in central Korea, and since then the lowest temperatures have been only −12∼−14 °C. Recently, the minimum January temperature has increased at a rate of 1.5 °C per decade. It is estimated that the increase of1 °C in annual mean temperature corresponds to about a 250 km northward shift of the subtropical zone boundary. The analysis of data from 1906 to 1997 indicates a trend of increasing annual precipitation, an increase of 182 mm during the 92-year peirod, with large year-to-year variations. More than half of the annual mean amount, 1,274 mm, occurred from June to September. Meteorological data and satellite observations suggest that changes have occurred in the characteristics of the quasi-stationary fronts that produce summer rain. In recent years scattered local heavy showers usually occur with an inactive showery front, in comparison with the classical steady rain for more than three weeks. For instance, local heavy rainfall, on 6 August 1998 was in the range of 123–481 mm. The scattered convective storms resulted in flooding with a heavy toll of approx. 500 people. The northward shift of the inactive showery front over Korea, and of a convergence zone in central China, correlate with the increase in temperature. It has been suggested that the decrease in forest areas and the change in ground cover also contribute to the warming of the Korean Peninsula. Received March 16, 2000     

Recent trends in observed temperature and precipitation extremes in the Yangtze River basin,China

Summary The present study is an analysis of the observed extreme temperature and precipitation trends over Yangtze from 1960 to 2002 on the basis of the daily data from 108 meteorological stations. The intention is to identify whether or not the frequency or intensity of extreme events has increased with climate warming over Yangtze River basin in the last 40 years. Both the Mann-Kendall (MK) trend test and simple linear regression were utilized to detect monotonic trends in annual and seasonal extremes. Trend tests reveal that the annual and seasonal mean maximum and minimum temperature trend is characterized by a positive trend and that the strongest trend is found in the winter mean minimum in the Yangtze. However, the observed significant trend on the upper Yangtze reaches is less than that found on the middle and lower Yangtze reaches and for the mean maximum is much less than that of the mean minimum. From the basin-wide point of view, significant increasing trends are observed in 1-day extreme temperature in summer and winter minimum, but there is no significant trend for 1-day maximum temperature. Moreover, the number of cold days ≤0 °C and ≤10 °C shows significant decrease, while the number of hot days (daily value ≥35 °C) shows only a minor decrease. The upward trends found in the winter minimum temperature in both the mean and the extreme value provide evidence of the warming-up of winter and of the weakening of temperature extremes in the Yangtze in last few decades. The monsoon climate implies that precipitation amount peaks in summer as does the occurrence of heavy rainfall events. While the trend test has revealed a significant trend in summer rainfall, no statistically significant change was observed in heavy rain intensity. The 1-day, 3-day and 7-day extremes show only a minor increase from a basin-wide point of view. However, a significant positive trend was found for the number of rainstorm days (daily rainfall ≥50 mm). The increase of rainstorm frequency, rather than intensity, on the middle and lower reaches contributes most to the positive trend in summer precipitation in the Yangtze.     

Relationship of Late-Winter Temperatures in Europeto North Atlantic Surface Winds: A Correlation Analysis

Summary  The relationship between European surface temperature and winds over the eastern North Atlantic are investigated for the years 1988 to 1997. Daily Special Sensor Microwave Imager SSM/I observations are used to evaluate a monthly surface wind index that quantifies the influence of southwesterly flow. Our wind index and the monthly-mean surface-air temperatures in late winter and early spring over France and northern-latitude Europe are highly correlated. In February, the year-to-year increases/decreases match every year for France (correlation of 0.82 with the Index); and every year with just one exception for Europe (correlation with the Index of 0.76 for a longitudinal strip through Europe 45–50° N, and 0.73 for the 50–60° N strip). In March, the increases/decreases of the wind Index and of the temperatures for France also match, but the correlation with the Index is lower, 0.65. The high correlation between our Index and the large interannual fluctuations in the monthly temperature in late winter and early spring indicate that the onset of the spring conditions in Europe is significantly influenced by the wind patterns over the eastern North Atlantic. Coinciding with the fluctuations from warm-Europe/high-Index winter to the opposite conditions, we observe “seesaw” effects, fluctuations over the North Atlantic, in opposite directions in the east (25–5° W), and the west (65–45° W). In the low-Index years we find that: (a) the surface-air temperatures in the west are appreciably higher than in the east (but slightly lower in the high-Index year), and (b) the difference between the 500 mb meridional wind in the west and that in the east is positive and large, exceeding 10 m s⁻¹ (but it becomes negative and small in the high-Index years). The “seesaw” effects suggest that a positive feedback exits between these cross-Atlantic temperature differences and the surface winds. Received August 7, 1998 Revised April 23, 1999     

Effect of clouds on UV and total irradiance at Paradise Bay, Antarctic Peninsula, from a summer 2000 campaign

Summary ?The analysis of ground-based measurements of solar erythemal ultraviolet (UV) irradiance with a Solar Light 501 biometer, and total (300–3000 nm) irradiance with an Eppley B&W pyranometer at the Argentine Antarctic Base “Almirante Brown”, Paradise Bay (64.9° S, 62.9° W, 10 m a.s.l.) is presented. Measurement period extends from February 16 to March 28 2000. A relatively high mean albedo and a very clean atmosphere characterise the place. Sky conditions were of generally high cloud cover percentage. Clear-sky irradiance for each day was estimated with model calculations, and the effect of the cloudiness was studied through the ratio of measured to clear-sky value (r). Two particular cases were analysed: overcast sky without precipitation and overcast sky with rain or slight snowfall, the last one presenting frequently dense fog. Total irradiance was more attenuated than UV by the homogeneous cloudiness, obtaining mean r values of 0.54 for erythemal irradiance and 0.30 for total irradiance in the first case (without precipitation) and 0.27 and 0.17 respectively in the second case (with precipitation). Mean r values for the complete period were 0.58 for erythemal irradiance and 0.43 for total irradiance. Erythemal and total daily insolations reduce quickly at this epoch due to the increase of the noon solar zenith angle and the decrease of daylight time. Additionally, they were strongly modulated by cloudiness. Measured maxima were 2.71 kJ/m² and 18.42 MJ/m² respectively. Measurements were compared with satellite data. TOMS-inferred erythemal daily insolation shows the typical underestimation with respect to ground measurements at regions of high mean albedo. Measured mean total daily insolation agrees with climatological satellite data for the months of the campaign. Received August 9, 2002; revised January 4, 2003; accepted January 28, 2003 Published online May 20, 2003