Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.     

Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios

We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System—recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827–840, 2013)—we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region.     

Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071–2100)

We present an analysis of climate change over Europe as simulated by a regional climate model (RCM) nested within time-slice atmospheric general circulation model (AGCM) experiments. Changes in mean and interannual variability are discussed for the 30-year period of 2071–2100 with respect to the present day period of 1961–1990 under forcing from the A2 and B2 IPCC emission scenarios. In both scenarios, the European region undergoes substantial warming in all seasons, in the range of 1–5.5°C, with the warming being 1–2°C lower in the B2 than in the A2 scenario. The spatial patterns of warming are similar in the two scenarios, with a maximum over eastern Europe in winter and over western and southern Europe in summer. The precipitation changes in the two scenarios also show similar spatial patterns. In winter, precipitation increases over most of Europe (except for the southern Mediterranean regions) due to increased storm activity and higher atmospheric water vapor loadings. In summer, a decrease in precipitation is found over most of western and southern Europe in response to a blocking-like anticyclonic circulation over the northeastern Atlantic which deflects summer storms northward. The precipitation changes in the intermediate seasons (spring and fall) are less pronounced than in winter and summer. Overall, the intensity of daily precipitation events predominantly increases, often also in regions where the mean precipitation decreases. Conversely the number of wet days decreases (leading to longer dry periods) except in the winter over western and central Europe. Cloudiness, snow cover and soil water content show predominant decreases, in many cases also in regions where precipitation increases. Interannual variability of both temperature and precipitation increases substantially in the summer and shows only small changes in the other seasons. A number of statistically significant regional trends are found throughout the scenario simulations, especially for temperature and for the A2 scenario. The results from the forcing AGCM simulations and the nested RCM simulations are generally consistent with each other at the broad scale. However, significant differences in the simulated surface climate changes are found between the two models in the summer, when local physics processes are more important. In addition, substantial fine scale detail in the RCM-produced change signal is found in response to local topographical and coastline features.     

Interdecadal variability of regional climate change: implications for the development of regional climate change scenarios

Summary Illustrative examples are discussed of the interdecadal variability features of the regional climate change signal in 5 AOGCM transient simulations. It is shown that the regional precipitation change signal is characterized by large variability at decadal to multidecadal scales, with the structure of the variability varying markedly across regions. Conversely, the regional temperature change signal shows low interdecadal variability. Results are compared across scenarios, models and different realizations with the same model. Our analysis indicates that, at the decadal scale, linear scaling of the regional climate change signal by the global temperature change works relatively well for temperature but less so for precipitation. The nonlinear fraction of the climate change signal tends to decrease with the magnitude of the signal. The implications of interdecadal variability for the generation of regional climate change scenarios are discussed, in particular concerning the use of multi-experiment ensembles to produce such scenarios.     

Twenty-first century changes in snowfall climate in Northern Europe in ENSEMBLES regional climate models

Changes in snowfall in northern Europe (55–71°N, 5–35°E) are analysed from 12 regional model simulations of twenty-first century climate under the Special Report on Emissions Scenarios A1B scenario. As an ensemble mean, the models suggest a decrease in the winter total snowfall in nearly all of northern Europe. In the middle of the winter, however, snowfall generally increases in the coldest areas. The borderline between increasing and decreasing snowfall broadly coincides with the ?11 °C isotherm in baseline (1980–2010) monthly mean temperature, although with variation between models and grid boxes. High extremes of daily snowfall remain nearly unchanged, except for decreases in the mildest areas, where snowfall as a whole becomes much less common. A smaller fraction of the snow in the simulated late twenty-first century climate falls on severely cold days and a larger fraction on days with near-zero temperatures. Not only do days with low temperatures become less common, but they also typically have more positive anomalies of sea level pressure and less snowfall for the same temperature than in the present-day climate.     

Internal variability of regional climate models

 Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. Received: 26 October 1999 / Accepted: 18 December 2000     

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.     

Intraseasonal and interannual climate variability

Major advances in our understanding of intraseasonal-interannual climate variability during the past three decades are reviewed. Prospects for prediction on these time-scales are briefly discussed.Paper based on talk presented at the session honoring J. Murray Mitchell at the December 1986 AGU meeting in San Francisco, CA.     

European climate in the late twenty-first century: regional simulations with two driving global models and two forcing scenarios

A basic analysis is presented for a series of regional climate change simulations that were conducted by the Swedish Rossby Centre and contribute to the PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects) project. For each of the two driving global models HadAM3H and ECHAM4/OPYC3, a 30-year control run and two 30-year scenario runs (based on the SRES A2 and B2 emission scenarios) were made with the regional model. In this way, four realizations of climate change from 1961–1990 to 2071–2100 were obtained. The simulated changes are larger for the A2 than the B2 scenario (although with few qualitative differences) and in most cases in the ECHAM4/OPYC3-driven (RE) than in the HadAM3H-driven (RH) regional simulations. In all the scenario runs, the warming in northern Europe is largest in winter or late autumn. In central and southern Europe, the warming peaks in summer when it locally reaches 10 °C in the RE-A2 simulation and 6–7 °C in the RH-A2 and RE-B2 simulations. The four simulations agree on a general increase in precipitation in northern Europe especially in winter and on a general decrease in precipitation in southern and central Europe in summer, but the magnitude and the geographical patterns of the change differ markedly between RH and RE. This reflects very different changes in the atmospheric circulation during the winter half-year, which also lead to quite different simulated changes in windiness. All four simulations show a large increase in the lowest minimum temperatures in northern, central and eastern Europe, most likely due to reduced snow cover. Extreme daily precipitation increases even in most of those areas where the mean annual precipitation decreases.     

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.     

Simple general atmospheric circulation and climate models with memory

This article examines some general atmospheric circulation and climate models in the context of the notion of “memory”. Two kinds of memories are defined： statistical memory and deterministic memory. The former is defined through the autocorrelation characteristic of the process if it is random （chaotic）, while for the latter, a special memory function is introduced. Three of the numerous existing models are selected as examples. For each of the models, asymptotic （at t →∞） expressions are derived. In this way, the transients are filtered out and that which remains concerns the final behaviour of the models.     

Stratospheric climate and variability from a general circulation model and observations

The climate and natural variability of the large-scale stratospheric circulation simulated by a newly developed general circulation model are evaluated against available global observations. The simulation consisted of a 30-year annual cycle integration performed with a comprehensive model of the troposphere and stratosphere. The observations consisted of a 15-year dataset from global operational analyses of the troposphere and stratosphere. The model evaluation concentrates on the simulation of the evolution of the extratropical stratospheric circulation in both hemispheres. The December–February climatology of the observed zonal mean winter circulation is found to be reasonably well captured by the model, although in the Northern Hemisphere upper stratosphere the simulated westerly winds are systematically stronger and a cold bias is apparent in the polar stratosphere. This Northern Hemisphere stratospheric cold bias virtually disappears during spring (March–May), consistent with a realistic simulation of the spring weakening of the mean westerly winds in the model. A considerable amount of monthly interannual variability is also found in the simulation in the Northern Hemisphere in late winter and early spring. The simulated interannual variability is predominantly caused by polar warmings of the stratosphere, in agreement with observations. The breakdown of the Northern Hemisphere stratospheric polar vortex appears therefore to occur in a realistic way in the model. However, in early winter the model severely underestimates the interannual variability, especially in the upper troposphere. The Southern Hemisphere winter (June–August) zonal mean temperature is systematically colder in the model, and the simulated winds are somewhat too strong in the upper stratosphere. Contrary to the results for the Northern Hemisphere spring, this model cold bias worsens during the Southern Hemisphere spring (September–November). Significant discrepancies between the model results and the observations are therefore found during the breakdown of the Southern Hemisphere polar vortex. For instance, the simulated Southern Hemisphere stratosphere westerly jet continuously decreases in intensity more or less in situ from June to November, while the observed stratospheric jet moves downward and poleward.This paper was presented at the Third International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 4–8 Sept. 1995 under the auspice of the Max Planck Institute for Meteorology, Hamburg. Editor for these papers is L. Dümenil.     

The Interannual Variability and Predictability in a Global Climate Model

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Decadal climate variability simulated in a coupled general circulation model

A 1000 year integration of the CSIRO coupled ocean-atmosphere general circulation model is used to study low frequency (decadal to centennial) climate variability in precipitation and temperature. The model is shown to exhibit sizeable decadal variability for these fields, generally accounting for approximately 20 to 40% of the variability (greater than one year) in precipitation and up to 80% for temperature. An empirical orthogonal function (EOF) analysis is applied to the model output to show some of the major statistical modes of low frequency variability. The first EOF spatial pattern looks very much like that of the interannual ENSO pattern. It bears considerable resemblance to observational estimates and is centred in the Pacific extending into both hemispheres. It modulates both precipitation and temperature globally. The EOF has a time evolution that appears to be more than just red noise. Finally, the link between SST in the Pacific with Australian rainfall variability seen in observations is also evident in the model. Received: 29 August 1998 / Accepted: 31 July 1999     

Atmospheric circulation in regional climate models over Central Europe: links to surface air temperature and the influence of driving data

The study examines simulation of atmospheric circulation, represented by circulation indices (flow direction, strength and vorticity), and links between circulation and daily surface air temperatures in regional climate models (RCMs) over Central Europe. We explore control simulations of five high-resolution RCMs from the ENSEMBLES project driven by re-analysis (ERA-40) and the same global climate model (ECHAM5 GCM) plus of one RCM (RCA) driven by different GCMs. The aims are to (1) identify errors in RCM-simulated distributions of circulation indices in individual seasons, (2) identify errors in simulated temperatures under particular circulation indices, and (3) compare performance of individual RCMs with respect to the driving data. Although most of the RCMs qualitatively reflect observed distributions of the airflow indices, each produces distributions significantly different from the observations. General biases include overestimation of the frequency of strong flow days and of strong cyclonic vorticity. Some circulation biases obviously propagate from the driving data. ECHAM5 and all simulations driven by ECHAM5 underestimate frequency of easterly flow, mainly in summer. Except for HIRHAM, however, all RCMs driven by ECHAM5 improve on the driving GCM in simulating atmospheric circulation. The influence on circulation characteristics in the nested RCM differs between GCMs, as demonstrated in a set of RCA simulations with different driving data. The driving data control on circulation in RCA is particularly weak for the BCM GCM, in which case RCA substantially modifies (but does not improve) the circulation from the driving data in both winter and summer. Those RCMs with the most distorted atmospheric circulation are HIRHAM driven by ECHAM5 and RCA driven by BCM. Relatively strong relationships between circulation indices and surface air temperatures were found in the observed data for Central Europe. The links differ by season and are usually stronger for daily maxima than minima. RCMs qualitatively reproduce these relationships. Effects of the driving model biases were found on RCMs’ performance in reproducing not only atmospheric circulation but also the links to surface temperature. However, the RCM formulation appears to be more important than the driving data in representing the latter. Differences of the circulation-to-temperature links among the RCA simulations are smaller and the links tend to be more realistic compared to the driving GCMs.     

Sensitivity of terrestrial carbon storage to CO2-induced climate change: Comparison of four scenarios based on general circulation models

The potential impacts of CO₂-induced climate change on terrestrial carbon storage was estimated using the Holdridge Life-Zone Classification and four climate change scenarios derived from general circulation models. Carbon values were assigned to life-zones and their associated soils from published studies. All four scenarios suggest an increase in area occupied by forests although details of predicted patterns vary among the scenarios. There is a poleward shift of the forested zones, with an increase in the areal extent of tropical forests and a shift of the boreal forest zone into the region currently occupied by tundra. Terrestrial carbon storage increased from 0.4% (8.5 Gt) to 9.5% (180.5 Gt) above estimates for present conditions. These changes represent a potential reduction of 4 to 85 ppm on elevated atmospheric CO₂ levels.     

Modes of interannual variability of Southern Hemisphere atmospheric circulation in CMIP3 models: assessment and projections

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere 500 hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset. The analysis is done for both the intraseasonal and slow components of the geopotential height. When the CMIP3 models are assessed against reanalysis data, the spatial structure and variance of the leading modes in the intraseasonal component are generally well reproduced. There are systematic differences between the models in their reproduction of the leading modes in the slow component. An overall score using the leading modes in the slow component allows a categorisation of CMIP3 model performance. Using an ensemble from four models that suitably reproduce the twentieth century modes, modes of variability in the slow-internal and slow-external components are estimated. The leading mode of the slow-external component is shown to be related to observed changes in greenhouse gas concentrations. In this ensemble, there is little change in the leading modes in the intraseasonal component in the twenty-first century. Larger changes in variance, and subtle changes in regional-scale structure, are found for the leading modes in the slow-internal component. These are related to changes in the slowly varying dynamics of the Southern Annular Mode and the El Niño-Southern Oscillation. By far the biggest change is in the leading mode of the slow-external component. The spatial structure becomes uniform in the twenty-first century, and the variance increases with increasing greenhouse gas concentrations.