Jouni Räisänen

CO2-Induced Changes in Interannual Temperature and Precipitation Variability in 19 CMIP2 Experiments

CO2-induced changes in the interannual variability of monthly surface air temperature and precipitation are studied using 19 model experiments participating in the second phase of the Coupled Model Intercomparison Project (CMIP2). The magnitude of variability in the control runs appears generally reasonable, but it varies a great deal between different models, almost all of which overestimate temperature variability on low-latitude land areas. In most models the gradual doubling of CO2 leads to a decrease in temperature variability in the winter half-year in the extratropical Northern Hemisphere and over the high-latitude Southern Ocean. Over land in low latitudes and in northern midlatitudes in summer, a slight tendency toward increased temperature variability occurs. The standard deviation of monthly precipitation increases, on average, where the mean precipitation increases but also does so in some areas where the mean precipitation decreases slightly. The coefficient of variation of precipitation (i.e., the ratio between the standard deviation and the mean) also tends to increase in most areas, especially where the mean precipitation decreases. However, the changes in variability are less similar between the 19 experiments than the changes in mean temperature and precipitation, at least partly because they have a much lower signal-to-noise ratio. In addition, the changes in the standard deviation of monthly temperature are generally much smaller than the time-mean warming, which suggests that future changes in the extremes of interannual temperature variability will be largely determined by the latter.

21st Century changes in snow climate in Northern Europe: a high-resolution view from ENSEMBLES regional climate models

Changes in snow amount in northern Europe are analysed from 11 regional model simulations of 21st century climate under the Special Report on Emissions Scenarios A1B scenario. These high-resolution models collectively indicate a future decrease in the water equivalent of the snow pack (SWE). Although winter precipitation increases, this is insufficient to compensate for the increased fraction of liquid precipitation and increased snowmelt caused by higher temperatures. The multi-model mean results suggest a slight increase in March mean SWE only locally in mountains of northern Sweden, and even there, snow is reduced earlier in winter and later in spring. The nature of the changes remains the same throughout the 21st century, but their magnitude increases with time as the greenhouse gas forcing grows larger. The geographical patterns of the change support the physically intuitive view that snow is most vulnerable to warming in areas with relatively mild winter climate. A similar relationship emerges when comparing the 11 simulations with each other: the ratio between the relative SWE decrease and winter mean temperature change is larger (smaller) for simulations with higher (lower) late 20th century winter temperatures. Despite the decrease in long-term mean SWE, individual snow-rich winters do occur in the simulations, but they become increasingly uncommon towards the end of the 21st century.

Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations

Because of model biases, projections of future climate need to combine model simulations of recent and future climate with information on observed climate. Here, 10 methods for projecting the distribution of daily mean temperatures are compared, using six regional climate change simulations for Europe. Cross validation between the models is used to assess the potential performance of the methods in projecting future climate. Delta change and bias correction type methods show similar cross-validation performance, with methods based on the quantile mapping approach doing best in both groups due to their apparent ability to reduce the errors in the projected time mean temperature change. However, as no single method performs best under all circumstances, the optimal approach might be to use several well-behaving methods in parallel. When applying the various methods to real-world temperature projection for the late 21st century, the largest intermethod differences are found in the tails of the temperature distribution. Although the intermethod variation of the projections is generally smaller than their intermodel variation, it is not negligible. Therefore, it should be preferably included in uncertainty analysis of temperature projections, particularly in applications where the extremes of the distribution are important.

Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations

Abstract Due to inherent limitations in climate models, their output is biased in relation to observed climate and as such does not provide reliable climate projections. In this study, nine methods used to account for biases in daily precipitation are tested. First, cross-validation tests were made using a set of ENSEMBLES regional model simulations to gain insights in the potential performance of the methods in the future climate. The results show that quantile mapping type methods, being able to modify the shape of the precipitation distribution, often outperform other types of methods. Yet, as the performance depends on time of the year, location and part of the distribution considered, it is not possible to distinguish one universally best performing method. In addition, the improvement relative to the projections that would have been obtained assuming unchanged climate is relatively modest, particularly in the early twentyfirst century conditions. Further tests with different method combinations show that the projections could be potentially improved by using several well performing methods in parallel. In the second part of the study, contributions of method and model differences to the overall variation of precipitation projections are assessed. It is shown that although intermodel differences play an important role, uncertainties related to intermethod differences are substantial, particularly in the tails of the distribution. This suggests that method uncertainty should be taken into account when constructing daily precipitation projections, possibly by using several methods in parallel.

Towards modelling of decay risk of wooden materials

An empirical model for wood decay development which can be incorporated into a hygrothermal model of building physics is presented. The model is applied to the ERA-40 reanalysis data, based on six-hour weather observations in Europe, to estimate wood decay in different parts of Europe. These studies provide new tools for evaluating the durability and service life of wooden products and a preliminary European wood decay risk level map. The effects of the projected climate change on wood decay may also be considered by this methodology.ZusammenfassungVorgestellt wird ein empirisches Modell zur Holzfäuleentwicklung, welches sich in ein bauphysikalisches hygrothermisches Modell einbauen lässt. Zur Bestimmung der Holzfäule in verschiedenen Teilen Europas benutzt das Modell die aufbereiteten ERA-40 Daten, die auf sechsstündigen Wetterbeobachtungen in Europa basieren. Diese Untersuchungen liefern neue Möglichkeiten zur Bestimmung der Dauerhaftigkeit und der Nutzungsdauer von Holzprodukten sowie eine vorläufige Darstellung des Holzfäulerisikos in Europa. Die Einflüsse der erwarteten Klimaänderung auf die Holzfäule können mit diesem Verfahren ebenfalls untersucht werden.

How Much Should Climate Model Output Be Smoothed in Space

Abstract The general decrease in the quality of climate model output with decreasing scale suggests a need for spatial smoothing to suppress the most unreliable small-scale features. However, even if correctly simulated, a large-scale average retained by the smoothing may not be representative of the local conditions, which are of primary interest in many impact studies. Here, the authors study this trade-off using simulations of temperature and precipitation by 24 climate models within the Third Coupled Model Intercomparison Project, to find the scale of smoothing at which the mean-square difference between smoothed model output and gridbox-scale reality is minimized. This is done for present-day time mean climate, recent temperature trends, and projections of future climate change, using cross validation between the models for the latter. The optimal scale depends strongly on the number of models used, being much smaller for multimodel means than for individual model simulations. It also depends on the ...

Projections of Future Anthropogenic Climate Change

This chapter focuses on summarising projections of future anthropogenic climate change for the Baltic Sea Basin. This includes the science of climate change and how future projections are made, taking into account anthropogenic influence on greenhouse gases (GHG). Looking forward to-ward future climates requires using state-of-the-art modelling tools to represent climate processes.

CO2-Induced Changes in Atmospheric Angular Momentum in CMIP2 Experiments

The response of atmospheric angular momentum to a gradual doubling of CO2 is studied using 16 model experiments participating in the second phase of the Coupled Model Intercomparison Project (CMIP2). The relative angular momentum associated with atmospheric zonal winds increases in all but one of the models, although the magnitude of the change varies widely. About 90% of the 16-model mean increase comes from increasing westerly winds in the stratosphere and the uppermost low-latitude troposphere above 200 hPa. This increase in westerly winds reflects a steepening of the meridional temperature gradient near the tropopause and in the upper troposphere. The simulated temperature gradient at this height increases partly as an indirect consequence of the poleward decrease in the tropopause height, and partly because convection induces a maximum in warming in the tropical upper troposphere. The change in the omega angular momentum associated with the surface pressure distribution is in most models smaller than the change in the relative angular momentum, although its exact value is sensitive to the method of calculation.

Spatiotemporal distribution of threatened high-latitude snowbed and snow patch habitats in warming climate

We studied the interannual variation of late summer snow covered area (SCA), i.e. snowbeds and permanent snow patches, in northern Finland and analyzed the role of topographical factors and climatic conditions on the recent and future occurrence of summer snow. SCA for the years 2000, 2004, 2006 and 2009 was derived from Landsat images using a normalized difference snow index (NDSI). Late summer SCA varied notably between the years (1.5‐23:0 km 2 ). A major part of the late summer snow was located above 900‐1000 m and on northern and eastern slopes. A generalized additive model (GAM) showed that the number of years with snow present in 1 km grid squares was strongly positively related to altitude and terrain ruggedness. Parallel examination of interannual variation of SCA and climatic conditions showed that snow cover declines were linked to relatively low snowfall-to-rainfall ratios. Annual mean air temperatures, particularly spring and early winter temperatures, showed increasing trends during the study period. Projected increases in air temperatures and rainfall suggest earlier and more efficient snow melt in the future. This may threaten the occurrence of species and communities related to snowbeds and decrease the -diversity of the landscape, and could also affect ecosystem services of the region.

Can model weighting improve probabilistic projections of climate change

Recently, Räisänen and co-authors proposed a weighting scheme in which the relationship between observable climate and climate change within a multi-model ensemble determines to what extent agreement with observations affects model weights in climate change projection. Within the Third Coupled Model Intercomparison Project (CMIP3) dataset, this scheme slightly improved the cross-validated accuracy of deterministic projections of temperature change. Here the same scheme is applied to probabilistic temperature change projection, under the strong limiting assumption that the CMIP3 ensemble spans the actual modeling uncertainty. Cross-validation suggests that probabilistic temperature change projections may also be improved by this weighting scheme. However, the improvement relative to uniform weighting is smaller in the tail-sensitive logarithmic score than in the continuous ranked probability score. The impact of the weighting on projection of real-world twenty-first century temperature change is modest in most parts of the world. However, in some areas mainly over the high-latitude oceans, the mean of the distribution is substantially changed and/or the distribution is considerably narrowed. The weights of individual models vary strongly with location, so that a model that receives nearly zero weight in some area may still get a large weight elsewhere. Although the details of this variation are method-specific, it suggests that the relative strengths of different models may be difficult to harness by weighting schemes that use spatially uniform model weights.