Lars Bärring

Monthly mean pressure reconstructions for Europe for the 1780–1995 period

Monthly grid-point pressure data are reconstructed from station records of pressure for Europe since 1780. The region encompasses 35-70°N to 30°W-40°E. The reconstructions are based on a principal components regression technique, which relates surface pressure patterns to those of the station pressure data. The relationships are derived over a calibration period (1936-1995) and the results tested with independent data (the verification period, 1881-1935). The reconstructions are of excellent quality, although this is slightly lower for regions with poor station coverage in the early years, particularly during the summer months. The reconstructions are compared with other monthly mean pressure maps produced by Lamb and Johnson (1966) for the years 1780-1872 and by Kington (1980, 1988) for 1781-1785. Both of these map series show systematic biases relative to the present reconstructions.

Monthly mean pressure reconstruction for the Late Maunder Minimum Period (AD 1675–1715)

The Late Maunder Minimum (LMM; 1675-1715) delineates a period with marked climate variability within the Little Ice Age in Europe. Gridded monthly mean surface pressure fields were reconstructed for this period for the eastern North Atlantic-European region (25°W-30°E and 35-70°N). These were based on continuous information drawn from proxy and instrumental data taken from several European data sites. The data include indexed temperature and rainfall values, sea ice conditions from northern Iceland and the Western Baltic. In addition, limited instrumental data, such as air temperature from central England (CET) and Paris, reduced mean sea level pressure (SLP) at Paris, and monthly mean wind direction in the Oresund (Denmark) are used. The reconstructions are based on a canonical correlation analysis (CCA), with the standardized station data as predictors and the SLP pressure fields as predictand. The CCA-based model was performed using data from the twentieth century. The period 1901-1960 was used to calibrate the statistical model, and the remaining 30 years (1961-1990) for the validation of the reconstructed monthly pressure fields. Assuming stationarity of the statistical relationships, the calibrated CCA model was then used to predict the monthly LMM SLP fields. The verification results illustrated that the regression equations developed for the majority of grid points contain good predictive skill. Nevertheless, there are seasonal and geographical limitations for which valid spatial SLP patterns can be reconstructed. Backward elimination techniques indicated that Paris station air pressure and temperature, CET, and the wind direction in the Oresund are the most important predictors, together sharing more than 65% of the total variance. The reconstructions are compared with additional data and subjectively reconstructed monthly pressure charts for the years 1675-1704. It is shown that there are differences between the two approaches. However, for extreme years the reconstructions are in good agreement.

Future climate impact on spruce bark beetle life cycle in relation to uncertainties in regional climate model data ensembles

In this study, we quantify the effect of uncertainties in climate projections on an impact model (IPS) that describes the temperature-dependent swarming and development of Ips typographus. Three forcing climate data sets (ensembles) were used: (1) E-Obs gridded observations, (2) ERA-40 reanalysis data downscaled by eight regional climate models (RCMs) and (3) regional scenarios from one RCM forced by seven GCM simulations representing SRES-A1B, for the period of 1961–2097. The IPS_RCM_ERA40 ensemble members, including IPS_RC3_ERA40, were generally within the IPS_E-Obs confidence limits. The IPS model is however sensitive to the warming during spring and cooling during autumn, and deviations in simulated swarming were related to known climate model biases. The variation between the IPS_RCA3_GCM ensemble members was particularly high in regions where warmer summers (temperature increase from +2 ◦C to +4 ◦C) will induce an additional generation per year, for example a shift from one to two generations per year in south Scandinavia, and an increased frequency of three generations per year in central Europe. Impact assessments based on an ensemble of climate data gives more robust decision support than a single climate model approach because it allows a probabilistic assessment of the geographical areas experiencing a transition in biological response.

Selecting regional climate scenarios for impact modelling studies

In climate change research ensembles of climate simulations are produced in an attempt to cover the uncertainty in future projections. Many climate change impact studies face difficulties using the full number of simulations available, and therefore often only subsets are used. Until now such subsets were chosen based on their representation of temperature change or by accessibility of the simulations. By using more specific information about the needs of the impact study as guidance for the clustering of simulations, the subset fits the purpose of climate change impact research more appropriately. Here, the sensitivity of such a procedure is explored, particularly with regard to the use of different climate variables, seasons, and regions in Europe. While temperature dominates the clustering, the resulting selection is influenced by all variables, leading to the conclusion that different subsets fit different impact studies best. Display Omitted We present a method to reduce ensembles of climate models.We use hierarchical clustering, SVD, Silhouettes and mean distances.To minimise the information loss the selection is fitted to the data application.The method shows strong sensitivity to the choice of variables/information provided.Strong need for a careful and thoughtful selection process is shown.

Past and current climate change

This section describes long-term observed climatic changes in atmospheric parameters. The focus is on surface climate conditions, but changes in atmospheric circulation are discussed as they often are behind climatic variability seen on regional and local scales. For a summary introduction on mean atmospheric states and conditions in the Baltic Sea Basin see Annex 1.2 with sections on the general atmospheric circulation (A.1.2.1), surface air temperature (A.1.2.2), precipitation (A.1.2.3), clouds (A.1.2.4), and global radiation (A.1.2.5).

Estimation of local near-surface wind conditions - a comparison of WASP and regression based techniques

This study compares the performance of different models used to assess the local wind near-surface conditions at an agricultural site in Scania, southern Sweden. The methods are: (a) the WASP model (Wind Analysis and Application Program), (b) separate linear regressions of the two wind vector components, (c) a regression model based on vector correlation, and (d) linear regression of scalar wind. Each method was tested with three different data sets over nine months: wind measurements from the nearby Sturup airport SYNOP station, 10 m surface wind and surface geostrophic wind produced by the operational Swedish Mesoscale Analysis system (Mesan). The wind climate estimations were compared with observed winds at the field site, with respect to mean wind speed, wind direction, wind speed frequency distribution and the relative frequency of winds above 6 m s(-1). All models performed reasonably well with data from Sturup and Mesan surface wind, but gave less reliable results with the Mesan geostrophic data. The estimated frequency of winds above 6 m s(-1) was in general lower than the observed frequency. Overall, best results were obtained with WASP in combination with measurements from Sturup. (Less)

The Lund instrumental record of meteorological observations: reconstruction of monthly sea-level pressure 1780–1997

The reconstructed surface air pressure series from Lund, southern Sweden, covers the period 1780-1997 and comprises mon than 234000 valid observations (three observations per day), i.e. > 98% of all possible observation occasions. For the Early Instrumental Period (EIP; 1780-1860) data were digitised from the original records. For most of the Modern Instrumental Period (MIP; 1861-) a series was compiled from various databases containing instrument corrected data. During EIP, the series of raw monthly means show several substantial inhomogeneities. With the aid of a detailed reconstruction of the station history, it was possible to remove almost all inhomogeneities during EIP by applying the correct instrument corrections (for barometer temperature, to standard gravity and to mean sea-level pressure) to the series of original observations. In particular, corrections for the temperature and altitude of the barometer eliminated several inhomogeneities. A prerequisite for applying these corrections is the availability of high-resolution data (actual raw observations or daily averages). Further homogenisation was attained by intercomparison of the monthly mean pressure with acknowledged homogeneous series (mainly the UKMO monthly grid, station records from Copenhagen and Edinburgh). Statistical tests of homogeneity showed that no substantial inhomogeneities remain in the final version. The modern part of the final monthly pressure series largely follows that of the southern Baltic Sea region. Furthermore, it shows relatively high pressure during spring (MAM) in the period 1780-1820, which was paralleled by severe wind erosion in southern Scandinavia during this time. Relatively high pressure throughout the year is also notable during a period of precipitation deficit in 1970s. Copyright (C) 1999 Royal Meteorological Society.

Zonal index variations, 1899-1992 : links to air temperature in southern Scandinavia

Monthly zonal indices (ZI:s) from January 1899 to August 1992 in a North Atlantic area (40° W-5° W) and a north European area (5° E-40° E) are analysed. The ZI:s are calculated as the sea-level pre...

Production and use of regional climate model projections – A Swedish perspective on building climate services

We describe the process of building a climate service centred on regional climate model results from the Rossby Centre regional climate model RCA4. The climate service has as its central facility a web service provided by the Swedish Meteorological and Hydrological Institute where users can get an idea of various aspects of climate change from a suite of maps, diagrams, explaining texts and user guides. Here we present the contents of the web service and how this has been designed and developed in collaboration with users of the service in a dialogue reaching over more than a decade. We also present the ensemble of climate projections with RCA4 that provides the fundamental climate information presented at the web service. In this context, RCA4 has been used to downscale nine different coupled atmosphere-ocean general circulation models (AOGCMs) from the 5th Coupled Model Intercomparison Project (CMIP5) to 0.44° (c. 50 km) horizontal resolution over Europe. Further, we investigate how this ensemble relates to the CMIP5 ensemble. We find that the iterative approach involving the users of the climate service has been successful as the service is widely used and is an important source of information for work on climate adaptation in Sweden. The RCA4 ensemble samples a large degree of the spread in the CMIP5 ensemble implying that it can be used to illustrate uncertainties and robustness in future climate change in Sweden. The results also show that RCA4 changes results compared to the underlying AOGCMs, sometimes in a systematic way.

Disentangling effects of uncertainties on population projections: climate change impact on an epixylic bryophyte

Assessment of future ecosystem risks should account for the relevant uncertainty sources. This means accounting for the joint effects of climate variables and using modelling techniques that allow proper treatment of uncertainties. We investigate the influence of three of the IPCCs scenarios of greenhouse gas emissions (special report on emission scenarios (SRES)) on projections of the future abundance of a bryophyte model species. We also compare the relative importance of uncertainty sources on the population projections. The whole chain global climate model (GCM)—regional climate model—population dynamics model is addressed. The uncertainty depends on both natural- and model-related sources, in particular on GCM uncertainty. Ignoring the uncertainties gives an unwarranted impression of confidence in the results. The most likely population development of the bryophyte Buxbaumia viridis towards the end of this century is negative: even with a low-emission scenario, there is more than a 65 per cent risk for the population to be halved. The conclusion of a population decline is valid for all SRES scenarios investigated. Uncertainties are no longer an obstacle, but a mandatory aspect to include in the viability analysis of populations.