Long‐term in situ observations are widely used in a variety of climate analyses. Unfortunately, most decade‐ to century‐scale time series of atmospheric data have been adversely impacted by inhomogeneities caused by, for example, changes in instrumentation, station moves, changes in the local environment such as urbanization, or the introduction of different observing practices like a new formula for calculating mean daily temperature or different observation times. If these inhomogeneities are not accounted for properly, the results of climate analyses using these data can be erroneous. Over the last decade, many climatologists have put a great deal of effort into developing techniques to identify inhomogeneities and adjust climatic time series to compensate for the biases produced by the inhomogeneities. It is important for users of homogeneity‐adjusted data to understand how the data were adjusted and what impacts these adjustments are likely to make on their analyses. And it is important for developers of homogeneity‐adjusted data sets to compare readily the different techniques most commonly used today. Therefore, this paper reviews the methods and techniques developed for homogeneity adjustments and describes many different approaches and philosophies involved in adjusting in situ climate data. © 1998 Royal Meteorological Society

Data set and methods. The station network shown in Fig. 1 has remained unchanged from that presented in Alexandersson et al. (1998). For each station 3 observations per day have been digitised or extracted from existing computer files. Most of the series started within the period 1860 to 1900, but for quality reasons we did not extend our analyses back before 1881. Geostrophic winds were calculated using triangles of stations. For each year 95 and 99 percentiles were derived. These percentiles give a measure of the synoptic-scale storminess of each year. Then area averages were calculated, not using the percentiles per triangle as such but standardised values, i.e. percentile values minus averages divided by standard deviations. Two areas were defined: one more maritime, westerly area called 'British Isles, North Sea, Norwegian Sea', one more continental, easterly area called 'Scandinavia, Finland, Baltic Sea'. The triangles within each area are given in Table 1. Results. Figs. 2 & 3 show the updated versions of the 2 area-averaged time series and low-pass filtered curves. Only 3 years have been added to the corresponding plots in Alexandersson et al. (1998), but it is interesting to update these curves as they ended at

The development of a daily historical European-North Atlantic mean sea level pressure dataset (EMSLP) for 1850-2003 on a 5°latitude by longitude grid is described. This product was produced using 86 continental and island stations distributed over the region 25°-70°N, 70°W-50°E blended with marine data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The EMSLP fields for 1850-80 are based purely on the land station data and ship observations. From 1881, the blended land and marine fields are combined with already available daily Northern Hemisphere fields. Complete coverage is obtained by employing reduced space optimal interpolation. Squared correlations (r 2 ) indicate that EMSLP generally captures 80%-90% of daily variability represented in an existing historical mean sea level pressure product and over 90% in modern 40-yr European Centre for Medium-Range Weather Forecasts Re-Analyses (ERA-40) over most of the region. A lack of sufficient observations over Greenland and the Middle East, however, has resulted in poorer reconstructions there. Error estimates, produced as part of the reconstruction technique, flag these as regions of low confidence. It is shown that the EMSLP daily fields and associated error estimates provide a unique opportunity to examine the circulation patterns associated with extreme events across the European-North Atlantic region, such as the 2003 heat wave, in the context of historical events.

The El Niño-Southern Oscillation (ENSO) is a pacemaker of global climate, and the accurate prediction of future climate change requires an understanding of the ENSO variability. Recently, much-debated aspects of the ENSO have included its long-term past and future changes and its associations with the North Atlantic and European sectors, potentially in interaction with the North Atlantic Oscillation and the Atlantic Multidecadal Oscillation. Here we present the fi rst European dendroclimatic precipitation reconstruction that extends through the alternating climate phases of the Medieval Climate Anomaly and the Little Ice Age. We show that northern Europe underwent a severe precipitation defi cit during the Medieval Climate Anomaly, which was synchronous with droughts in various ENSO-sensitive regions worldwide, while the subsequent centuries during the Little Ice Age were markedly wetter. We attribute this drought primarily to an interaction between the ENSO and the North Atlantic Oscillation, and to a lesser (or negligible) degree to an interaction between the ENSO and the Atlantic Multidecadal Oscillation.on June 6, 2015 geology.gsapubs.org Downloaded from

Making use of the Köppen-Trewartha (K-T) climate classification, we have found that a set of nine high-resolution regional climate models (RCM) are fairly capable of reproducing the current climate in Europe. The percentage of grid-point to grid-point coincidences between climate subtypes based on the control simulations and those of the Climate Research Unit (CRU) climatology varied between 73 and 82%. The best agreement with the CRU climatology corresponds to the RCM "ensemble mean". The K-T classification was then used to elucidate scenarios of climate change for 2071-2100 under the SRES A2 emission scenario. The percentage of land grid-points with unchanged K-T subtypes ranged from 41 to 49%, while those with a shift from the current climate subtypes towards warmer or drier ones ranged from 51 to 59%. As a first approximation, one may assume that in regions with a shift of two or more climate subtypes, ecosystems might be at risk. Excluding northern Scandinavia, such regions were projected to cover about 12% of the European land area.

No abstract

This study was aimed at assessing the potential impacts of climate change on the depth and duration of soil frost under snow cover in forests growing at different geographical locations in Finland. Frost simulations using a process-based forest ecosystem model (FinnFor) were made for Scots pine Pinus sylvestris L. stands (height 17 m, stand density 1100 stems ha -1 ) growing on a moraine sandy soil. The climate change forecast used in the computations was based on the global ocean-atmosphere general circulation model HadCM2 that was dynamically downscaled to the regional level. The simulated climate warming during the winter months was about 4 to 5°C by the end of the 21st century. Frost simulations showed that the length of the soil frost period would lessen all over the country. Though winters will be warmer, the associated decrease in snow cover in southern Finland will increase the probability of frozen ground there in the middle of winter compared with the current climate. In central and northern Finland there will be so much snow, even in the future, that the maximum annual soil frost depth will decrease there.KEY WORDS: Climate change · Soil frost · Soil freezing · Snow cover · Hydraulic frost model · Scots pine Resale or republication not permitted without written consent of the publisherClim Res 17: [63][64][65][66][67][68][69][70][71][72] 2001 The modelling of frost in the soil profile under snowfree surfaces can be done with the help of the frost sum and soil properties (e.g. Saarelainen 1992, McCormick 1993, Venäläinen et al. 2001. The frost sum is the sum of below-0°C daily mean temperatures calculated from the beginning of the frost period. In Scandinavia the frost period typically starts in October and ends in May, in northern Lapland in June. If there is snow on the ground, the modelling of soil temperature becomes more complex. Models must include many variables describing both meteorological conditions, such as air temperature, short and long wave radiation, amount and type of snow, and soil characteristics, such as thermal conductivity and soil heat capacity (e.g. Bonan 1991, Cox et al. 1999. The influence of snow cover on temperature is illustrated in Fig. 1. The daily variation of air temperature in the case of late winter conditions can be more than 20°C, whereas at a depth of 80 cm below the snow surface the daily cycle is practically negligible.Jansson (1991) introduced a comprehensive soil model known as SOIL that includes the processes relevant for the calculation of soil temperature. Kellomäki & Väi-sänen (1997) have integrated this SOIL model into a process-based forest ecosystem model (FinnFor), which links ecosystem dynamics with climate through selected physiological processes. Peltola et al. (1999) used this model when they studied the consequences of climate warming on soil frost and on the windthrow risk for trees in different geographical locations in Finland. Peltola et al. (1999) used 2 options for climate warming: the increase of temperature was estimated to be...

No abstract