Local climatic drivers of changes in phenology at a boreal-temperate ecotone in eastern North America

Ecosystems in biogeographical transition zones, or ecotones, tend to be highly sensitive to climate and can provide early indications of future change. To evaluate recent climatic changes and their impacts in a boreal-temperate ecotone in eastern North America, we analyzed ice phenology records (1975?C2007) for five lakes in the Adirondack Mountains of northern New York State. We observed rapidly decreasing trends of up to 21?days less ice cover, mostly due to later freeze-up and partially due to earlier break-up. To evaluate the local drivers of these lake ice changes, we modeled ice phenology based on local climate data, derived climatic predictors from the models, and evaluated trends in those predictors to determine which were responsible for observed changes in lake ice. November and December temperature and snow depth consistently predicted ice-in, and recent trends of warming and decreasing snow during these months were consistent with later ice formation. March and April temperature and snow depth consistently predicted ice-out, but the absence of trends in snow depth during these months, despite concurrent warming, resulted in much weaker trends for ice-out. Recent rates of warming in the Adirondacks are among the highest regionally, although with a different seasonality of changes (early winter > late winter) that is consistent with other lake ice records in the surrounding area. Projected future declines in snow cover could create positive feedbacks and accelerate current rates of ice loss due to warming. Climate sensitivity was greatest for the larger lakes in our study, including Wolf Lake, considered one of the most ecologically intact ??wilderness lakes?? in eastern North America. Our study provides further evidence of climate sensitivity of the boreal-temperate ecotone of eastern North America and points to emergent conservation challenges posed by climate change in legally protected yet vulnerable landscapes like the Adirondack Park.     

Changes in ice-season characteristics of a European Arctic lake from 1964 to 2008

The long-term ice record (from 1964 to 2008) of an Arctic lake in northern Europe (Lake Kilpisj?rvi) reveals the response of lake ice to climate change at local and regional scales. Average freeze-up and ice breakup occurred on 9 November and 19 June, respectively. The freeze-up has been significantly delayed at a rate of 2.3 d per decade from 1964 onward (P?<?0.05). No significant change has taken place in ice breakup. Annual average ice thickness has become smaller since the mid-1980s (P?<?0.05). Air temperature during the early ice season significantly affected the ice thickness. The freeze-up date exhibits the highest correlation with the 2-month average daily minimum air temperature centered at the end of October, while the ice breakup date exhibits the highest correlation with the 2-month average daily maximal air temperature centered in mid May. A 1°C increase in the surface air temperature corresponds to a freeze-up later by 3.4?days and an ice breakup earlier by 3.6?days. Snow cover is a critical factor in lake-ice climatology. For cumulative November to March precipitation of less than 0.13?m, the insulating effect of the snow dominated, while higher rates of precipitation favored thicker ice due to the formation of snow ice. Variations in ice records of Lake Kilpisj?rvi can serve as an indicator of climate variations across the northern Europe. The North Atlantic Oscillation (NAO) does not significantly affect the ice season there, although both the local air temperatures and winter precipitation contain a strong NAO signal.     

Variations in surface ozone and NOx at Kannur: a tropical, coastal site in India

Continuous measurements of surface ozone (O₃), NOx (NO + NO₂) and meteorological parameters have been made in Kannur (11.9?°N, 75.4?°E, 5?m asl), India from November 2009 to October 2010. It was observed that O₃ and NOx showed distinct diurnal and seasonal variabilities at this site. The annual average diurnal profile of O₃ showed a peak of (30.3?±?10.4) ppbv in the late afternoon and a minimum of (3.2?±?0.7) ppbv in the early morning. The maximum value of O₃ mixing ratio was observed in winter (44?±?3.1) ppbv and minimum during monsoon (18.46?±?3.5) ppbv. The rate of production of O₃ was found to be higher in December (10.1?ppbv/h) and lower in July (1.8?ppbv/h) during the time interval 0800?C1000?h. A correlation coefficient of 0.52 for the relationship between O₃ and [NO₂]/[NO] reveals the role of NO₂ photolysis that generates O₃ at this site. The correlation between O₃ and meteorological parameters indicate the influence of seasonal changes on O₃ production. Investigations were further extended to explore the week day weekend variations in O₃ mixing ratio at an urban site reveals the enhancement of O₃. The variations of O₃ mixing ratio with seasonal air mass flows were elucidated with the aid of backward air trajectories. This study also indicates how vapor phase organic species present in the ambient air at this location may influence the complex chemistry involving (VOCs) that enhances the production of O₃ at this location.     

Estimation of relative humidity based on artificial neural network approach in the Aegean Region of Turkey

The aim of this study is to estimate the monthly mean relative humidity (MRH) values in the Aegean Region of Turkey with the help of the topographical and meteorological parameters based on artificial neural network (ANN) approach. The monthly MRH values were calculated from the measurement in the meteorological observing stations established in Izmir, Mugla, Aydin, Denizli, Usak, Manisa, Kutahya and Afyonkarahisar provinces between 2000 and 2006. Latitude, longitude, altitude, precipitation and months of the year were used in the input layer of the ANN network, while the MRH was used in output layer of the network. The ANN model was developed using MATLAB software, and then actual values were compared with those obtained by ANN and multi-linear regression methods. It seemed that the obtained values were in the acceptable error limits. It is concluded that the determination of relative humidity values is possible at any target point of the region where the measurement cannot be performed.     

Criteria for heat and cold wave duration indexes

Many areas of society are susceptible to the effects of extreme temperatures. Without an adequate definition of what constitutes heat and cold waves, it is impossible to assess either their changes in the past or their possible consequences for the future. The Intergovernmental Panel on Climate Change recommended criteria for heat wave duration indexes based on two arbitrarily defined constants. The principal weakness of this approach is that it does not yield comparable results for different geographical locations. This paper remedies the current lack of a meteorologically based definition of heat and cold waves and offers a preliminary test of its performance. Having previously shown that maximum daily temperature values follow normal frequency distribution, we derive statistical thresholds (e.g., below and above normal) from that distribution. These thresholds are thus climate specific and their change can be compared across geographical locations. These criteria are then tested on the homogeneous time series of maximum daily temperature observed for the period 1961–2008 with respect to three different geographical locations. The results obtained show an increase in the frequency of heat waves for the period 1991–2008 in comparison with the normal climatological period 1961–1990.     

Determination of surface layer parameters at the edge of a suburban area

Vertical wind and air temperature profile related parameters in the surface layer at the edge of suburban area of Zagreb (Croatia) have been considered. For that purpose, adopted Monin–Obukhov similarity theory and a set of observations of wind and air temperature at 2 and 10?m above ground, recorded in 2005, have been used. The root mean square differences (errors) principle has been used as a tool to estimate the effective roughness length as well as standard deviations of wind speed and wind gusts. The results of estimation are effective roughness lengths dependent on eight wind direction sectors unknown before. Gratefully to that achievement, representativeness of wind data at standard 10-m height can be clarified more deeply for an area of at least about 1?km in upwind direction from the observation site. Extrapolation of wind data for lower or higher levels from standard 10-m height are thus properly representative for a wider inhomogeneous suburban area and can be used as such in numerical models, flux and wind energy estimation, civil engineering, air pollution and climatological applications.     

Performance of ENSEMBLES regional climate models over Central Europe using various metrics

We show the evaluation of ENSEMBLES regional climate models (RCMs) driven by reanalysis ERA40 over a region centered at the Czech Republic. Attention is paid especially to the model ALADIN-CLIMATE/CZ, being used as the basis of the new climate change scenarios simulation for the Czech Republic. The validation criteria used here are based on monthly or seasonal mean air temperature and precipitation. We concentrate not only on spatiotemporal mean values but also on temporal standard deviation, inter-annual variability, the mean annual cycle, and the skill of the models to represent the observed spatial patterns of these quantities. Model ALADIN-CLIMATE/CZ performs quite well in comparison to the other RCMs; we find its performance satisfactory for further use for impact studies. However, it is also shown that the results of evaluation of the RCMs’ skill in simulating observed climate strongly depend on the criteria incorporated for the evaluation.     

Interpolation of climate variables and temperature modeling

Geographic Information Systems (GIS) and modeling are becoming powerful tools in agricultural research and natural resource management. This study proposes an empirical methodology for modeling and mapping of the monthly and annual air temperature using remote sensing and GIS techniques. The study area is Gangetic West Bengal and its neighborhood in the eastern India, where a number of weather systems occur throughout the year. Gangetic West Bengal is a region of strong heterogeneous surface with several weather disturbances. This paper also examines statistical approaches for interpolating climatic data over large regions, providing different interpolation techniques for climate variables' use in agricultural research. Three interpolation approaches, like inverse distance weighted averaging, thin-plate smoothing splines, and co-kriging are evaluated for 4°?×?4° area, covering the eastern part of India. The land use/land cover, soil texture, and digital elevation model are used as the independent variables for temperature modeling. Multiple regression analysis with standard method is used to add dependent variables into regression equation. Prediction of mean temperature for monsoon season is better than winter season. Finally standard deviation errors are evaluated after comparing the predicted temperature and observed temperature of the area. For better improvement, distance from the coastline and seasonal wind pattern are stressed to be included as independent variables.