A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability

David P. Rowell

doi:10.1007/s00382-005-0068-6

Climate Dynamics

December 2005, Volume 25, Issue 7–8, pp 837–849 | Cite as

A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability

Authors
Authors and affiliations

David P. Rowell

Article

First Online: 14 October 2005

1 Shares
448 Downloads
106 Citations

Abstract

A scenario of European climate change for the late twenty-first century is described, using a high-resolution state-of-the-art model. A time-slice approach is used, whereby the atmospheric general circulation model, HadAM3P, was integrated for two periods, 1960–1990 and 2070–2100, using the SRES A2 scenario. For the first time an ensemble of such experiments was produced, along with appropriate statistical tests for assessing significance. The focus is on changes to the statistics of seasonal means, and includes analysis of both multi-year means and interannual variance. All four seasons are assessed, and anomalies are mapped for surface air temperature, precipitation and snow mass. Mechanisms are proposed where these are dominated by straightforward local processes. In winter, the largest warming occurs over eastern Europe, up to 7°C, mean snow mass is reduced by at least 80% except over Scandinavia, and precipitation increases over all but the southernmost parts of Europe. In summer, temperatures rise by 6–9°C south of about 50°N, and mean rainfall is substantially reduced over the same area. In spring and autumn, anomalies tend to be weaker, but often display patterns similar to the preceding season, reflecting the inertia of the land surface component of the climate system. Changes in interannual variance are substantial in the solsticial seasons for many regions (note that for precipitation, variance estimates are scaled by the square of the mean). In winter, interannual variability of near-surface air temperature is considerably reduced over much of Europe, and the relative variability of precipitation is reduced north of about 50°N. In summer, the (relative) interannual variance of both variables increases over much of the continent.

Keywords

Interannual Variability Ensemble Member Atmospheric General Circulation Model Snow Mass European Climate Change

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, to check access.

Notes

Acknowledgements

Discussions with Richard Jones have been much appreciated, and particular thanks is due to David Hassell for running the model. Financial support was provided by the UK Department for Environment, Food and Rural Affairs under contract PECD 7/12/37, and by the European Union Programme Energy, Environment and Sustainable Development under contract EVK2-CT-2001-00132 (PRUDENCE).

Appendix A

Statistical significance of changes in the multi-year means of time-slice ensembles

Here we describe a methodology to assess the significance of differences between multi-year seasonal means of future and control ensembles of HadAM3P. First it is necessary to build a conceptual model to characterise the data in each ensemble. For the control runs this is identical to that used by e.g. Rowell et al. (1995) and Rowell (1998):

$$x_{{ij}} = \mu _{i} + \varepsilon ^{x} _{{ij}} $$

(1)

where x _ij is a simulated seasonal mean quantity in year i=1, ..., N, ensemble member j=1, ..., n _x; μ_i is the forced component of x _ij in year i (this forcing primarily arises from SST variations, but to a lesser extent also arises from changes in atmospheric composition); and ɛ _ij ^x is the random component of x _ij due to internal atmospheric variations. For the future scenario runs this conceptual model is extended to become:

$$y_{{ij}} = \mu _{i} + g_{i} + m_{j} + i'\tau _{j} + \varepsilon ^{y} _{{ij}} $$

(2)

where y _ij is a simulated future seasonal mean for year i=1, ..., N, ensemble member j=1, ..., n _y; g _i is the time-evolving component of y _ij due to changes in atmospheric composition and ensemble mean SST in the future minus control scenario; m _j is the component due to the anomalous time-mean SST change in each ensemble member j (and has zero mean over an infinite ensemble); i′=i − i _mid, where i _mid is the central year (or half-year) of the period of interest; τ _j is the component due to the anomalous SST trend in each ensemble member (so that the average of i′ τ _j is zero over the period of interest, and τ _j has zero mean over an infinite ensemble), and ɛ _ij ^y is the random component of y _ij due to internal atmospheric variations.

Now the null hypothesis we wish to test is that the expectations of x and y are equal for the two periods of interest, or in other words that $\bar{g} = 0,$ where $\bar{g}$ is the time-mean of g. This is normally assessed using a t-test, in which a statistic (t) is computed from the x and y data, and then compared against a theoretical t-distribution with a specified number of degrees of freedom. This leads to an estimated probability that the null hypothesis holds true. However, the standard approach cannot be taken here, because the usual assumption required for a t-test, that x and y are independent, is violated by their common component of SST forcing. Thus, the calculation of the t-statistic must be specifically tailored to the character of the data described above.

First we note that under the null hypothesis:

$${\bar{\bar{x}}} - {\bar{\bar{y}}} = {\bar{\bar{\varepsilon}}}^{x} - {\bar{\bar{\varepsilon}}}^{y} - {\bar{m}}$$

(3)

where ${\bar{\bar{a}}}$ is the mean of a over all years and all ensemble members, and ${\bar{m}}$ is the mean of m over all ensemble members. Importantly, the right-hand side of Eq. 3 now satisfies the assumptions required for a t-test (e.g. von Storch and Zwiers 1999), viz. that all realisations of ɛ^x, ɛ^y and m are sampled from independent identical Gaussian distributions, with fixed variances $\sigma_{{\rm INT}x}^{2}, \sigma_{{\rm INT}y}^{2}$ and σ _m ² . Thus, a random variable with an approximate t-distribution can be constructed in the usual way for the case of unequal variances:

$$t = \frac{{\bar{\bar{\varepsilon}}}^{x} - {\bar{\bar{\varepsilon}}}^{y} - {\bar{m}}} {\sqrt{\frac{{\hat{\sigma}}^{2}_{{\rm INT}x}}{Nn_{x}} + \frac{{\hat{\sigma}}^{2}_{{\rm INT}y}}{Nn_{y}} + \frac{{\hat{\sigma}}^{2}_{m}}{n_{y}}}}$$

(4)

where ${\hat{a}}$ represents an estimate of a from sampled data.

Now, in order to compute t from the available model data, we require formulae to estimate ${\hat{\sigma}}^{2}_{{\rm INT}x}, {\hat{\sigma}}^{2}_{{\rm INT}y}$ and ${\hat{\sigma}}^{2}_{m}.$ For the first of these, we follow Rowell et al. (1995):

$${\hat{\sigma}}^{2}_{{\rm INT}x} = \frac{1}{N(n_{x} - 1)} {\sum\limits_{i = 1}^N {{\sum\limits_{j = 1}^{n_{x}} {(x_{{ij}} - {\bar{x}}_{i})^{2}}}}}$$

(5)

Next, to compute the remaining two variances, the following equality can be derived:

$${\hat{\sigma}}^{2}_{{\bar{y}}_{j}} = {\hat{\sigma}}^{2}_{m} + \frac{1}{N}{\hat{\sigma}}^{2}_{{\rm INT}y} $$

(6)

where

$${\hat{\sigma}}^{2}_{{\bar{y}}_{j}} = \frac{1}{n_{y} - 1} {\sum\limits_{j = 1}^{n_{y}} {{\bar{y}}_{j}^{2}}} \quad {\text{and}}\; {\bar{y}}_{j} = \frac{1}{N} {\sum\limits_{i = 1}^N {y_{ij}}}$$

(7)

Thus, by substituting Eqs. 3 and 6 into Eq. 4, we can compute a t-statistic from the model data using:

$$t = \frac{{\bar{\bar{x}} - \bar{\bar{y}}}} {{{\sqrt {\frac{{\hat{\sigma}^{2} _{{\rm INT}x}}}{Nn_{x}} + \frac{{\hat{\sigma}^{2} _{{\bar{y}_{j}}}}}{{n_{y}}}}}}}$$

(8)

along with Eqs. 5 and 7.

Following the usual approach for a t-test with unequal variances (e.g. von Storch and Zwiers 1999), this t-statistic has an approximate t-distribution with ν degrees of freezdom, where:

$$\nu = \frac{{{\left({\frac{{\hat{\sigma}^{2}_{{\rm INT}x}}}{Nn_{x}} + \frac{{\hat{\sigma}^{2}_{{\bar{y}_{j}}}}}{n_{y}}} \right)}^{2}}}{{\frac{{({\hat{\sigma}^{2}_{{\rm INT}x}} \mathord{\left/ {\vphantom {{\hat{\sigma}^{2}_{{\rm INT}x}}{Nn_{x}}}} \right. \kern-\nulldelimiterspace} {Nn_{x}})^{2}}}{{N(n_{x} - 1)}} + \frac{{({\hat{\sigma}^{2} _{{\bar{y}_{j}}}} \mathord{\left/ {\vphantom {{\hat{\sigma}^{2} _{{\bar{y}_{j}}}} {n_{y}}}} \right. \kern-\nulldelimiterspace} {n_{y}})^{2}}}{{n_{y} - 1}}}}$$

(9)

Finally, in order to verify that this formulation of the t-statistic provides a reliable test of the null hypothesis, thousands of synthetic ensemble timeseries were created using Eqs. 1 and 2. In each case we specified N=30 and n _x=n _y=3, g _i was specified as a linear trend with $\bar{g} = 0,$ and μ _i, m _j, τ _j, ɛ _ij ^x and ɛ _ij ^y were randomly selected from Gaussian distributions with variances $\sigma_{{\rm SST}}^{2}, \sigma_{m}^{2}, \sigma_{{\tau}}^{2}, \sigma_{{\rm INT}x}^{2}$ and $\sigma_{{\rm INT}y}^{2}$ respectively (where σ _SST ² is the variance of μ_i). A range of realistic values of the trend of g, and of $\sigma_{{\rm SST}}^{2}, \sigma_{m}^{2}, \sigma_{{\tau}}^{2}, \sigma_{{\rm INT}x}^{2}$ and $\sigma_{{\rm INT}y}^{2}$ were estimated from the HadAM3P data. Then for each of a variety of combinations of these values, 5000 randomly differing ensembles were produced. Next, the proportion of the resultant 5000 t-statistics that fell beyond the 5–95% range of the t-distribution was computed. For every combination of the values referred to above it was found that the rejection rate of the null hypothesis was close to 10%, i.e. exactly that expected from a well-behaved test. Conversely, it was noted that if a conventional t-test were applied by wrongly assuming that x and y derive from independent distributions, then the rejection rate is substantially underestimated, and the null hypothesis accepted far too frequently.

Appendix B

Estimation and statistical significance of changes in the internal atmospheric variance of seasonal mean data in time-slice ensembles

Here we formulate an estimate for the internal atmospheric interannual variance of seasonal mean data (hereafter, IAIV) for the control and scenario ensembles of HadAM3P, and describe a test to evaluate whether this IAIV changes significantly between the two ensembles. The decision to assess changes in internal variability, rather than total variability, is justified in the introduction to Sect. 3.

First, the estimation of IAIV for the control ensemble is straightforward, and its calculation is given by Eq. 5. This variance estimate has a χ² distribution with N(n _x − 1) degrees of freedom.

Next, the formula to estimate IAIV for the scenario ensemble $(\sigma_{{\rm INT}y}^{2})$ requires a brief derivation. The notation is that of Appendix A. To begin, we note that the variance of the deviations of y _ij from each year’s ensemble mean can be written as follows:

$$\hat{\sigma}^{2}_{y'} = \frac{1}{{N(n_{y} - 1)}}{\sum\limits_{i = 1}^N {{\sum\limits_{j = 1}^{n_{y}} {(y_{{ij}} - \bar{y}_{i})^{2}}}}}$$

(10)

Then, by substituting the conceptual model of Eq. 2 into Eq. 10, and assuming that the time-mean SST anomalies of each ensemble member (m _j) are uncorrelated with the anomalous SST trends of each ensemble member (τ _j) (which seems reasonable), it can be shown that:

$${\hat{\sigma}}^{2}_{y'} = {\hat{\sigma}}^{2}_{{\rm INT}y} + \hat{\sigma}^{2}_{m} + \alpha \hat{\sigma}^{2} _{\tau} $$

(11)

where:

$$\alpha = \frac{1}{N}{\sum\limits_{i = 1}^{N} {{i'}^{2}}}$$

(12)

Now, a biased estimate of σ _τ ² can be provided by $\sigma^{2}_{{\hat{\tau}}} ,$ which denotes the variance of the linear trends of y _ij estimated directly from the model data. The bias arises because each estimate of τ_j is ‘contaminated’ by internal atmospheric noise. Nevertheless, this bias can be estimated following Wilks (1995), and hence the following equality can be derived:

$$ \hat{\sigma }^{2}_{\tau } = \hat{\sigma }^{2}_{{\hat{\tau }}} - \frac{{\hat{\sigma }^{2}_{{{\rm INT}y }} }} {{N\alpha }} $$

(13)

Thus, by substituting this into Eq. 11, and also substituting Eq. 6 (which estimates σ _m ² ), it can easily be shown that:

$$\hat{\sigma}^{2} _{{{\rm INT}y}} = \frac{N}{{N - 2}}{\left({\hat{\sigma}^{2} _{{y'}} - \hat{\sigma}^{2} _{{\bar{y}_{j}}} - \alpha \hat{\sigma}^{2} _{{\hat{\tau}}}} \right)}$$

(14)

Importantly, this variance estimate is also close to a χ² distribution, with N(n _y − 1) degrees of freedom, if N is large. This has been verified using synthetic ensemble data with N=30 and n _y=3.

Thus, by computing the ratio ${\hat{\sigma }^{2}_{{{\text{INT}}y}} } \mathord{\left/ {\vphantom {{\hat{\sigma }^{2}_{{{\text{INT}}y}} } {\hat{\sigma }^{2}_{{{\text{INT}}x}} }}} \right. \kern-\nulldelimiterspace} {\hat{\sigma}^{2}_{{{\text{INT}}x}} }, $ a random variable is constructed which has an F-distribution with N(n _y − 1), N(n _x − 1) degrees of freedom (assuming that the two variance estimates are independent, which is expected). This can then be used to test whether changes in IAIV are statistically significant. Furthermore, since the number of degrees of freedom depends only on the ensemble size, and not in any way on the local climate, the variance ratio at which significance is achieved is spatially homogeneous. For the present study N=30 and n _x=n _y=3, so that variance ratios beyond the range 0.65 to 1.53 are significantly different from unity at the 10% level.

Following the approach of Appendix A, the reliability of this test was verified by computing 5,000 sets of synthetic ensemble data, repeatedly for a range of realistic combinations of $g, \sigma_{{\rm SST}}^{2}, \sigma_{m}^{2}, \sigma_{\tau}^{2}, \sigma_{{\rm INT}x}^{2}$ and $\sigma_{{\rm INT}y}^{2}.$ For each combination, and for $\sigma_{{\rm INT}y}^{2} = \sigma_{{\rm INT}x}^{2} ,$ it was found that the rejection rate of the null hypothesis (F-ratios falling beyond the 5–95% range) was close to 10%, as expected.

Appendix C

Statistical significance of changes in the coefficient of variation of internal atmospheric variability in time-slice ensembles

For zero-bounded fields, changes in variance (such as those formulated in Appendix B) tend to be similar, to first order, to changes in the mean. This occurs for simple statistical reasons, rather than ‘interesting’ physical reasons. Thus, in this study for precipitation, it is desirable to remove this effect and instead compare the squared coefficient of variation of internal atmospheric interannual variability (hereafter CV²-IAIV). Here a test is described to assess the statistical significance of changes in CV²-IAIV.

Unlike the approach used in Appendices A and B, it is not possible to develop an analytical test to assess changes in CV²-IAIV, because its differences or ratios do not follow a defined distribution. Instead, a test is developed whereby the ratio of the model’s scenario-to-control CV²-IAIV is compared against an estimate of the distribution of CV²-IAIV ratios that would be sampled from an underlying population with similar characteristics, and in which the control and scenario population CV²-IAIVs are identical (i.e. the null hypothesis is satisfied). This distribution is computed from a large set of ensembles of synthetic timeseries sampled from a population that is described by taking estimates $\hat{g}, {\hat{\sigma}^{2} _{{\rm SST}}} \mathord{\left/ {\vphantom {{\hat{\sigma}^{2} _{{\rm SST}}} {\hat{\sigma}^{2} _{{{\rm INT}x}}}}} \right. \kern-\nulldelimiterspace} {\hat{\sigma}^{2} _{{{\rm INT}x}}}, \hat{\sigma}^{2} _{m}$ and $\hat{\sigma}^{2} _{\tau}$ from the model data. The population CV²-IAIV used for this synthetic data is that of a joint estimate computed from both model ensembles. If the model’s actual CV²-IAIV ratio falls beyond the 5–95% range of the distribution of sampled CV²-IAIV ratios then the null hypothesis of an unaltered CV²-IAIV is rejected.

Again, synthetic data (now transformed to a zero-bounded distribution) was used to verify that this test produces the correct rejection rate when the CV²-IAIV of the underlying population is identical in control and scenario data. This verification was successful for precipitation, with rejection rates being only slightly overestimated (typically 10–15% rather than 10%).

References

Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrological cycle. Nature 419:224–232CrossRefPubMedGoogle Scholar
Boer GJ, Flato G, Ramsden D (2000) A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate change to the twenty-first century. Clim Dyn 16:427–450CrossRefGoogle Scholar
Christensen JH, Christensen OB (2003) Severe summer flooding in Europe. Nature 421:805–806CrossRefPubMedGoogle Scholar
Christensen JH, Carter T, Giorgi F (2002) PRUDENCE employs new methods to assess European climate change. EOS 83:147CrossRefGoogle Scholar
Cubasch U, Waszkewitz J, Hegerl G, Perlwitz J (1995) Regional climate changes as simulated in time-slice experiments. Clim Change 31:273–304CrossRefGoogle Scholar
Cubasch U, Meehl GA, Boer GJ, Stouffer RJ, Dix M, Noda A, Senior CA, Raper SCB, Yap KS (2001) Projections of future climate change. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden P, Dai X, Maskell K, Johnson CI (eds) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 525–582Google Scholar
Davey MK, Huddleston M, Sperber KR, Braconnot P, Bryan F, Chen D, Colman RA, Cooper C, Cubasch U, Delecluse P, DeWitt D, Fairhead L, Flato G, Gordon C, Hogan T, Ji M, Kimoto M, Kitoh A, Knutson TR, Latif M, Le Treut H, Li T, Manabe S, Mechoso CR, Meehl GA, Power SB, Roeckner E, Terray L, Vintzileos A, Voss R, Wang B, Washington WM, Yoshikawa I, Yu J-Y, Yukimoto S, Zebiak SE (2002) STOIC: a study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420CrossRefGoogle Scholar
Déqué M, Piedelievre JP (1995) High resolution climate simulation over Europe. Clim Dyn 17:321–339CrossRefGoogle Scholar
Déqué M, Marquet P, Jones RG (1998) Simulation of climate change over Europe using a global variable resolution general circulation model. Clim Dyn 14:173–189CrossRefGoogle Scholar
Déqué M, Rowell DP, Luthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellström E, de Castro M, van den Hurk B (2005) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Change (submitted)Google Scholar
Douville H, Chauvin F, Planton S, Royer J-F, Salas-Melia D, Tyteca S (2002) Sensitivity of the hydrological cycle to increasing amounts of greenhouse gases and aerosols. Clim Dyn 20:45–68CrossRefGoogle Scholar
Duffy PB, Govindasamy B, Iorio JP, Milovich J, Sperber KR, Taylor KE, Wehner MF, Thompson SL (2003) High-resolution simulations of global climate, part 1: present climate. Clim Dyn 21:371–390CrossRefGoogle Scholar
Durman CF, Gregory JM, Hassell DC, Jones RG, Murphy JM (2001) A comparison of extreme European daily precipitation simulated by a global and a regional climate model for present and future climates. Q J R Meteorol Soc 127:1005–1015CrossRefGoogle Scholar
Giorgi F, Francisco R (2000) Evaluating uncertainties in the prediction of regional climate change. Geophys Res Lett 27:1295–1298CrossRefGoogle Scholar
Giorgi F, Hewitson B, Christensen J, Fu C, Jones R, Hulme M, Mearns L, von Storch H, Whetton P (2001) Regional climate information - evaluation and projections. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden P, Dai X, Maskell K, Johnson CI (eds) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 583–638Google Scholar
Giorgi F, Bi X, Pal JS (2004) Mean, interannual variability and trends in a regional climate change experiment over Europe. II: climate change scenarios (2071–2100). Clim Dyn 23:839–858CrossRefGoogle Scholar
Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
Govinasamy B, Duffy PB, Coquard J (2003) High-resolution simulations of global climate, part 2: effects of increased greenhouse cases. Clim Dyn 21:391–404CrossRefGoogle Scholar
Gregory JM, Mitchell JFB, Brady AJ (1997) Summer drought in northern midlatitudes in a time-dependent CO₂ climate experiment. J Clim 10:662–686CrossRefGoogle Scholar
Hudson DA, Jones RG (2002) Simulations of present-day and future climate over southern Africa using HadAM3H. Hadley Centre Technical Note 38, Met Office, Exeter, UKGoogle Scholar
Hulme M, Jenkins GJ, Lu X, Turnpenny JR, Mitchell TD, Jones RG, Lowe J, Murphy JM, Hassell D, Boorman P, Macdonald R, Hill S (2002) Climate-change scenarios for the United Kingdom: The UKCIP02 scientific report. Tyndall Centre, University of East Anglia, NorwichGoogle Scholar
Johns TC, Gregory JM, Ingram WJ, Johnson CE, Jones A, Lowe JA, Mitchell JFB, Roberts DL, Sexton DMH, Stevenson DS, Tett SFB, Woodage MJ (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Clim Dyn 20:583–612Google Scholar
Jones RG, Murphy JM, Noguer M, Keen AB (1997) Simulation of climate change over Europe using a nested regional climate model. Part II. Comparison of driving and regional model responses to a doubling of carbon dioxide. Q J R Meteorol Soc 123:265–292CrossRefGoogle Scholar
Jones RG, Murphy JM, Hassell DC, Woodage MJ (2005) A high resolution atmospheric GCM for the generation of regional climate scenarios. Hadley Centre Technical Note 63, Met Office, ExeterGoogle Scholar
Kjellström E, Döscher R, Meier HEM (2005) Atmospheric response to different sea surface temperatures in the Baltic Sea: coupled versus uncoupled regional climate model experiments. Nordic Hydrol (in press)Google Scholar
Lenderink G, van Ulden A, van den Hurk B, van Meijgaard E (2005) Summertime inter-annual temperature variability in an ensemble of regional model simulations: analysis of the surface energy budget. Clim Change (submitted)Google Scholar
Manabe S, Wetherald RT (1975) The effects of doubling the CO₂ concentration on the climate of a general circulation model. J Atmos Sci 32:3–15CrossRefGoogle Scholar
May W (2001) Impact of horizontal resolution on the simulation of seasonal climate in the Atlantic/European area for present and future times. Clim Res 16:203–223CrossRefGoogle Scholar
May W, Roeckner E (2001) A time-slice experiment with the ECHAM4 AGCM at high resolution: the impact of horizontal resolution on annual mean climate change. Clim Dyn 17:407–420CrossRefGoogle Scholar
Merkel U, Latif M (2002) A high resolution AGCM study of the El Nino impact on the North Atlantic/European sector. Geophys Res Lett 29. DOI 10.1029/2001GL013726Google Scholar
Mitchell TD (2003) Pattern scaling. Clim Change 60:217–242CrossRefGoogle Scholar
Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Change 41:547–581CrossRefGoogle Scholar
Murphy JM (2000) Predictions of climate change over Europe using statistical and dynamical downscaling techniques. Int J Climatol 20:489–501CrossRefGoogle Scholar
Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefPubMedGoogle Scholar
Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grubler A, Jung TY, Kram T, La Rovere EL, Michaelis L, Mori S, Morita T, Pepper W, Pitcher H, Price L, Riahi K, Roehrl A, Rogner H-H, Sankovski A, Schlesinger M, Shukla P, Smith S, Swart R, van Rooijen S, Victor N, Dadi Z (2000) Special report on emissions scenarios. Cambridge University Press, Cambridge, p 599Google Scholar
Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley Centre climate model—HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
Raisanen J (2002) CO₂-induced changes in interannual temperature and precipitation variability in 19 CMIP2 experiments. J Clim 15:2395–2411CrossRefGoogle Scholar
Raisanen J, Hansson U, Ullerstig A, Doscher R, Graham LP, Jones C, Meier HEM, Samuelsson P, Willen U (2004) European climate in the late twenty-first century: regional simulations with two driving global models and two forcing scenarios. Clim Dyn 22:13–31CrossRefGoogle Scholar
Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108(D14):4407. DOI 10.1029/2002JD002670Google Scholar
Rowell DP (1998) Assessing potential seasonal predictability with an ensemble of multi-decadal GCM simulations. J Clim 11:109–120CrossRefGoogle Scholar
Rowell DP (2005) Uncertainty in projections of UK climate change resulting from regional model formulation. Clim Change (submitted)Google Scholar
Rowell DP, Folland CK, Maskell K, Ward MN (1995) Variability of summer rainfall over Tropical North Africa (1906–92): Observations and modelling. Q J R Meteorol Soc 121:669–704CrossRefGoogle Scholar
Stainforth DA, Aina T, Christensen C, Collins M, Faull N, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy JM, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefPubMedGoogle Scholar
von Storch H, Zwiers FW (1999) Statistical analysis in climate research. Cambridge University Press, Cambridge, p 484Google Scholar
Tebaldi C, Smith R, Nychka D, Mearns LO (2005) Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multimodel ensembles. J Clim 18:1524–1540CrossRefGoogle Scholar
Wetherald RT, Manabe S (1995) The mechanisms of summer dryness induced by greenhouse warming. J Clim 8:3096–3108CrossRefGoogle Scholar
Wilks DS (1995) Statistical methods in the atmospheric sciences. Academic, San Diego, p 467Google Scholar
Yang F, Kumar A, Wang W, Juang H-MH, Kanamitsu M (2001) Snow-Albedo feedback and seasonal climate variability over North America. J Clim 14:4245–4248CrossRefGoogle Scholar

Copyright information

Authors and Affiliations

David P. Rowell
- 1
Email author

1.Met OfficeHadley Centre for Climate Prediction and ResearchExeterUK

A scenario of European climate change for the late twenty-first century: seasonal means and interannual variability

Abstract

Keywords

Notes

Acknowledgements

References

Copyright information

Authors and Affiliations

Personalised recommendations

Cite article