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    The evolution from 2000 to 2300 of globally and annually averaged temperature T (°C) and precipitation P (mm day−1) simulated by models in the CMIP3 data archive as listed in Table 1. The simulations follow the B1 and A1B forcing scenarios until year 2100, after which forcing is kept constant at the year 2100 value for 200 yr. Deviations from the average over the period are plotted. The thick red line is each ensemble average.

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    (top) Observation-based standard deviations of annual mean temperature σT (°C) and precipitation σP (mm day−1) based on data from the ERA-40 (Uppala et al. 2005) and Xie and Arkin (1996), respectively. (bottom) The corresponding multimodel standard deviations.

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    Zonal averages of the standard deviation of annual mean temperature σT (°C) and precipitation σP (mm day−1). Black lines are observation-based estimates and solid red lines are the multimodel values for preindustrial climate. Long- and short-dashed red lines give the values obtained from the last 150 yr of the B1 and A1B stabilization simulations, respectively, after removal of trend. The corresponding blue lines are the standard deviations for decadal mean temperature and precipitation.

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    (top) The multimodel standard deviations of annual mean temperature σT (°C) and precipitation σP (mm day−1) as simulated by the models in Table 1 for preindustrial conditions. (middle), (bottom) The percentage change in these standard deviations for the last 150 yr of the B1 and A1B stabilization simulations after removal of the forced component. Values are statistically significantly different from zero at the 1% level, except for a narrow band about the zero lines.

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    (top) The multimodel estimate of internally generated decadal potential predictability pν for temperature and precipitation, based on the unforced preindustrial control simulations of the models in Table 1. Values of pν are not plotted if not deemed to be significantly different from zero at the 1% level. (middle), (bottom) The change in pν for the last 150 yr of the B1 and A1B stabilization simulations after removal of trend. Values of pν differences are plotted only when the probability that the control and stabilization pν values are the same is estimated to be less than 1% based on their confidence intervals.

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Changes in Interannual Variability and Decadal Potential Predictability under Global Warming

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  • 1 Canadian Centre for Climate Modelling and Analysis, Environment Canada, University of Victoria, Victoria, British Columbia, Canada
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Abstract

Global warming will result in changes in mean temperature and precipitation distributions and is also expected to affect interannual and longer time-scale internally generated variability as a consequence of changes in climate processes and feedbacks. Multimodel estimates of changes in the variability of annual mean temperature and precipitation and in the variability of decadal potential predictability are investigated based on the collection of coupled climate model simulations in the Coupled Model Intercomparison Project phase 3 (CMIP3) data archive. Pooled, multimodel standard deviations of annual mean temperature and precipitation for the unforced preindustrial control climates of the models show good resemblance to observation-based estimates. The internally generated variability of the unforced climate is compared with that of the warmer conditions for simulations with the B1 and A1B climate change scenarios with forcing stabilized at year 2100 values. The standard deviation of annual mean temperature generally decreases with global warming at extratropical latitudes, with the largest percentage decreases over the oceans and largest percentage increases in the tropics and subtropics, although the magnitudes of these increases are smaller. The standard deviation of annual mean precipitation increases almost everywhere, with larger increases in the tropics. Changes are generally larger for the more strongly forced, warmer A1B scenario than for the B1 scenario. The characterization of decadal variability changes in terms of potential predictability stems from the growing interest in producing forecasts for the next decade or several decades. The potential predictability identifies that fraction of the long time-scale variability that is, at least potentially and with enough information, predictable on decadal time scales. There is a general decrease in the internally generated decadal variability of temperature and its potential predictability in the warmer world. The decrease tends to be largest where the decadal potential predictability of the unforced control climate is largest over the high-latitude oceans. The potential predictability of precipitation is small to begin with and generally decreases further. Therefore, there is a potential decrease in the decadal potential predictability of the internally generated component in a warmer world.

Corresponding author address: G. J. Boer, CCCMA, University of Victoria, Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada. Email: george.boer@ec.gc.ca

Abstract

Global warming will result in changes in mean temperature and precipitation distributions and is also expected to affect interannual and longer time-scale internally generated variability as a consequence of changes in climate processes and feedbacks. Multimodel estimates of changes in the variability of annual mean temperature and precipitation and in the variability of decadal potential predictability are investigated based on the collection of coupled climate model simulations in the Coupled Model Intercomparison Project phase 3 (CMIP3) data archive. Pooled, multimodel standard deviations of annual mean temperature and precipitation for the unforced preindustrial control climates of the models show good resemblance to observation-based estimates. The internally generated variability of the unforced climate is compared with that of the warmer conditions for simulations with the B1 and A1B climate change scenarios with forcing stabilized at year 2100 values. The standard deviation of annual mean temperature generally decreases with global warming at extratropical latitudes, with the largest percentage decreases over the oceans and largest percentage increases in the tropics and subtropics, although the magnitudes of these increases are smaller. The standard deviation of annual mean precipitation increases almost everywhere, with larger increases in the tropics. Changes are generally larger for the more strongly forced, warmer A1B scenario than for the B1 scenario. The characterization of decadal variability changes in terms of potential predictability stems from the growing interest in producing forecasts for the next decade or several decades. The potential predictability identifies that fraction of the long time-scale variability that is, at least potentially and with enough information, predictable on decadal time scales. There is a general decrease in the internally generated decadal variability of temperature and its potential predictability in the warmer world. The decrease tends to be largest where the decadal potential predictability of the unforced control climate is largest over the high-latitude oceans. The potential predictability of precipitation is small to begin with and generally decreases further. Therefore, there is a potential decrease in the decadal potential predictability of the internally generated component in a warmer world.

Corresponding author address: G. J. Boer, CCCMA, University of Victoria, Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada. Email: george.boer@ec.gc.ca

1. Introduction

The changes in internally generated natural climate variability that accompany global warming may be characterized in several ways. These include changes in the following: standard deviation (Räisänen 2002), variance (Stouffer and Weatherald 2007), the properties of the modes of variability (Meehl et al. 2007a, section 10.3.5 and references therein), and long time-scale “potential predictability.” Here, we obtain multimodel estimates of changes in the interannual standard deviation of temperature and precipitation as climate warms together with changes in climate variability at longer time scales in terms of changes in decadal potential predictability.

For climate, the temperature, precipitation, or other variable may be considered as the sum of an externally forced component Ω and an internally generated natural variability ω. The internally generated component may be further decomposed into a long time-scale potentially predictable component ν and a remaining natural variability noise component ε produced by instability and nonlinearity. Symbolically,
i1520-0442-22-11-3098-e1
with associated variances σ2 = σΩ2 + σω2 = σΩ2 + σν2 + σε2.
The forced change component Ω includes the “commitment” to further change that remains as the system approaches a new equilibrium, even if the forcing is stabilized at some constant value. The forced component, as simulated by currently available coupled climate models, is studied in considerable detail in Meehl et al. (2007a, chapter 10, hereafter M07) and elsewhere. Here, however, we concentrate on changes in the internally generated variability
i1520-0442-22-11-3098-e2
which remain after removing the externally forced climate change component. We compare the internally generated variability in the warmer world with that of the unforced control simulations.
Changes in overall internally generated natural variability are characterized by changes in σω and changes in the long time-scale component of this variability are characterized by changes in pv, which is obtained by dividing (2) by the variance to give
i1520-0442-22-11-3098-e3
Here, pν = σν2/σω2 = γ/(1+γ) is the long time-scale potential predictability variance fraction (ppvf) and q is the unpredictable fraction of the variance. The ppvf may also be written in terms of the signal-to-noise ratio γ = σν2/σε2 and will be small if γ is small, either due to a weak signal component σν2 or a large noise component σε2. We consider how the internally generated variability of decadal means may change by investigating changes in pv.

The interest in changes in decadal potential predictability stems from the growing interest in producing forecasts for the next decade or several decades. Skill will depend on forecasting both the externally forced component [due to, e.g., greenhouse gases (GHGs), aerosols, and volcanoes] but also the predictable part of the long time-scale internally generated variability [due to oceanic, atmospheric, or coupled phenomena, e.g., thermohaline oscillations, the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO)]. Decadal potential predictability, as characterized here, estimates the fraction of low-frequency variability that exists and how it changes with climate change.

As such, decadal potential predictability does not treat how anomalies evolve with time in the presence of error but rather represents an idealized potential level of skill that might be attained for the long time-scale component of the variability. Studies of potential predictability in real and modeled systems include those of Madden (1976), Zwiers (1996), Rowell (1998), Rowell and Zwiers (1999), Boer and Lambert (2008), and Boer (2008, manuscript submitted to Climate Dyn.). Potential predictability may be seen to be related to standard measures of predictability by comparison with the predictability studies of Griffies and Bryan (1997), Boer (2000), Collins (2002), and Collins and Sinha (2003), for instance.

Projections for the twenty-first century foresee the evolution of a forced component of considerable magnitude. If the forcing is subsequently stabilized, in the sense that GHGs and other forcing components are no longer changing, then the system will adjust toward a new state consistent with the stabilized forcing. The internally generated variability for a new warmer equilibrium climate will differ from the internally generated variability of the preindustrial equilibrium climate. Even though such a stabilization of forcing is unlikely to occur, we may nevertheless take advantage of simulations of this kind, in which the forced component is no longer changing rapidly and the system is approaching a new equilibrium, to analyze the expected behavior of the natural variability in a warmer world.

Changes in the overall internally generated variance σω2, as well as in the partition between the long decadal time-scale variance σν2 and the noise variance σε2, as measured by changes in pν, are of interest. To the extent that the ensemble of coupled models in phase 3 of the Coupled Model Intercomparison Project (CMIP3) reflects the working of the actual climate system, the results indicate the direction of the changes that may be expected.

2. Data and calculations

The CMIP3 archive (Meehl et al. 2007b) contains data from unforced preindustrial climate simulations, from forced climate change simulations for the twentieth and twenty-first centuries, and from simulations for stabilized forcings. The simulations referred to here as Special Report on Emissions Scenarios (SRES) A1B and B1 simulations follow scenarios that begin from the control simulation, use observationally based climate forcing from 1850 to 2000, adopt A1B and B1 projected forcings from 2000 to 2100, and hold the resulting forcings fixed after 2100 for an additional 200 simulated years. The forcings are introduced into the models as evolving values of greenhouse gas concentrations and aerosol loadings, which affect the climate via radiative calculations. The A1 family of scenarios reflects “rapid economic growth, population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies…balanced across all sources,” whereas the B1 scenario describes a world with the same population but with the introduction of clean and resource efficient technologies with an emphasis on sustainability. The A1B scenario exhibits stronger forcing and more rapid climate change than the B1 scenario. Both extended scenarios include the period from 2100 to 2300, for which the forcing is fixed at the respective year 2100 values and during which the climate system evolves toward a new state in equilibrium with the fixed forcing.

Only about a third of the models in the CMIP3 archive provided simulations of climate change for the twenty-first century followed by stabilization simulations to the year 2300 for the A1B and B1 scenarios at the time the data were processed. These models and simulations are listed in Table 1. Figure 1 displays the evolution of the globally and annually averaged temperature (°C) and precipitation (mm day−1) as deviations from their temporal means for the two scenarios. The multimodel ensemble mean values are shown as thick red lines. Results from a particular model preserve their color from diagram to diagram but different models contribute differently to the results, as listed in Table 1. The stronger forcing in the A1B scenarios than the B1 scenarios is apparent from the temperature and precipitation responses of the models. The spread of the results across models is comparatively large in absolute terms but considerably smaller in relative terms with means removed. Comparable figures for the control simulations of CMIP3 models are given in Boer and Lambert (2008).

a. Multimodel statistics of variability and decadal potential predictability

We adopt a multimodel ensemble (MME) approach, which pools the statistics of individual models to better estimate population parameters. The justification is that pooled multimodel statistics are generally in better agreement with observation-based estimates for current climate than are the results from individual models. This has been demonstrated, for instance, for climatological mean quantities (Lambert and Boer 2001) and also for the second-order climatological statistics (variances and covariances) that arise in the Lorenz energy cycle (Boer and Lambert 2007). The MME approach is also adopted in seasonal forecasting applications, highlighted in a special issue of Tellus (2005, Vol. 57A, No. 3) devoted to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project, and in the climate assessments of M07.

In the 200-yr stabilization period after 2100, Fig. 1 indicates that the rate of climate change decreases markedly as the system adjusts toward a new equilibrium. If we assume that the slow commitment component may be removed by fitting a low-order polynomial to the data, we may calculate the usual variance and potential predictability statistics about this trend and compare them with those of the control simulations. The difference between them gives an indication of how the internally generated interannual variability and the long time-scale predictability is expected to change with global warming. We write X = Xc + X′, where Xc is the committed response to the now-stabilized forcing as the system evolves toward a new equilibrium. It is identified by fitting second-order polynomials, orthogonal in time, to the data at every grid point over the last 150 yr of the simulations. We follow Stouffer and Weatherald (2007) and Vinnikov and Robock (2002), who justify the fitting of a quadratic at each point as a suitable representation of the evolution of the forced component.

The variance of the natural variability component Xi′ that remains after removing Xc, for i = 1 … 150 (the year number), is further decomposed into components following (3) as
i1520-0442-22-11-3098-e4
where time is now identified by the decade number j and the “within decade” year number k for i = (j − 1)m + k and m = 10 yr, is the decadal average, and the overbar is the average over all years and decades for the analysis period. Taking the expectations of the sample variances under the assumption that the various components are largely independent (as discussed in Boer 2004) gives unbiased estimators of the variances as
i1520-0442-22-11-3098-e5
The decadal potential predictability is estimated as
i1520-0442-22-11-3098-e6
following the general approach of Rowell (1998) and Rowell and Zwiers (1999), termed the RZ approach. The statistics are compared with like quantities obtained for the control simulations. Boer and Lambert (2008) discuss the removal of residual drift and the calculation of these quantities for the CMIP3 control simulations.

The usual F test may be applied to the ratio of the variances σω2 in the warmer world to those from the control simulations with the view to rejecting the null hypothesis that the ratio is 1. Because of the considerable amount of data (11 realizations of 150 yr each), the hypothesis is rejected in general except for a narrow band about the zero lines (in Fig. 4). For decadal potential predictability, the statistical situation is not so straightforward, but approximate confidence intervals around the ppvf are available following RZ as described in Boer (2004) and we deem the ppvfs to be statistically distinct and plot their differences for probabilities less than 1% (in Fig. 5).

There are only 11 simulations for each of the A1B and B1 scenarios in which simulation results are available for the entire 2000–2300 period and, although there is considerable overlap, the models in the two sets of simulations are not exactly the same. There are two options in comparing the potential predictability of the stabilization and the control simulations. The additional data and the assumption that multimodel statistics are more robust than those for a particular model or group of models suggest the option of comparing the multimodel control statistics based on 27 simulations with the statistics of the 11 particular simulations available for the A1B and B1 scenarios. The alternative is to consider subsets of the control simulations that correspond to the models in the stabilization cases. We adopt the second approach as being more conservative in that data from the same set of models enter the control and stabilization estimates of potential predictability. However, because of the smaller amounts of data involved, the confidence bands about the estimates of potential predictability will be broader, and hence we will be less likely to conclude that there is a difference in potential predictability.

3. Variability and potential predictability

a. Unforced interannual variability

Multimodel estimates of the standard deviations of annual mean temperature σT and precipitation σP, obtained by pooling the simulated data from the control runs of the coupled climate models in Table 1, are shown in Fig. 2 together with observation-based estimates of these quantities. In each case, trends have been removed to minimize the inclusion of model drift and twentieth-century climate change in the estimates. The observation-based values for temperature are from the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005), whereas those for precipitation are from the 17-yr Xie and Arkin (1996) dataset. Figure 3 plots the multimodel- and observation-based zonal averages of these standard deviations as solid red and black lines, respectively.

The intent here is not to make a detailed comparison of the simulated and observation-based variances, which has difficulties of its own, but to indicate that the simulated values capture the essential features of the observationally based values as a necessary condition for the analysis of changes in variability. The main area of disagreement is over the very high southern latitudes. Although the area covered is small and not well observed, the source of the differences in standard deviations is puzzling and worthy of further study. Despite these differences, the reasonable correspondence of the multimodel standard deviations with observationally based values over the remainder of the globe supports further analysis.

b. Changes in variability

Figure 3 plots the zonal averages of the observation-based estimates of the standard deviations of annual mean temperature σT and precipitation σP as black lines and the model-based preindustrial values as solid red lines. The values for the B1 and A1B scenarios are the long- and short-dashed red lines, respectively. The corresponding blue lines are the standard deviations of decadal mean temperature and precipitation. Figure 3 supports Fig. 2 in that the multimodel control-run standard deviations (solid red) are quite comparable to the observation-based estimates (solid black) with the exception of the high southern latitudes.

Figure 3 also indicates that the standard deviation of annual mean temperatures generally decreases with global warming at extratropical latitudes, whereas the reverse is broadly the case in the tropics, although the magnitudes involved in the tropics are much smaller. Standard deviations of annual mean precipitation generally increase everywhere, with larger increases in the tropics. The changes (increases or decreases) are generally larger for the scenario with the largest forcing and warming; that is, changes for the A1B scenario are greater than for the more modest B1 scenario. The situation for the standard deviation of decadal means (the blue lines) is similar, although the changes are typically larger as a percentage of the control-run values.

Figure 4 plots the multimodel standard deviations of annual mean temperature and precipitation for the preindustrial control simulations and the percentage differences for the B1 and A1B stabilization scenarios. For temperature, as anticipated from Fig. 3, there is a general decrease in σT at extratropical latitudes and an increase at tropical latitudes. The patterns are not zonal however, especially in the Northern Hemisphere where land–sea differences are notable. It is unnecessary to describe the diagrams, but we may note that σT tends to increase over land except for higher Northern Hemisphere land and that σT decreases are particularly notable for high-latitude oceans, although increases can be found elsewhere, including the equatorial eastern Pacific. Perhaps not surprisingly, the patterns of change for the two scenarios are very similar.

The patterns of the percentage change in the standard deviation of annual mean precipitation for the two stabilization scenarios are also shown in Fig. 4. Again, as anticipated from Fig. 3, there is a general increase in precipitation variability but there are regions at the latitudes of the subtropical highs that show decreases over both land and ocean. It is generally the case that the percentage change in standard deviation increases with latitude, as does the change in annual mean precipitation itself according to Fig. 10.9 in M07. An increase in σP (and σT) in the equatorial eastern Pacific is presumably associated with the models’ version of the ENSO mechanism although, according to Merryfield (2006) for example, models do not show consistent ENSO change behavior under global warming.

These results for temperature are broadly similar to those of Räisänen (2002, Fig. 6), who analyzes changes in standard deviation for the climate change forced by doubling atmospheric CO2 (2 × CO2) in results from an earlier generation of climate models in the CMIP2 archive. There are several differences, including the larger percentage changes in variability due to, presumably, the stronger forcing, especially for the A1B simulation. There are differences in detail, such as a broader region of increased temperature variability in the southern part of North America and modest increases in temperature variability over India and in the equatorial eastern Pacific, instead of decreases.

Stouffer and Weatherald (2007) investigate the changes in temperature variability with global warming in two versions of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model: an older version (R30) and a more recent version [Climate Model, version 2.1 (CM2.1)], which is included in the multimodel collection in Table 1. Their Fig. 2d shows F ratios, which may formally be transformed into the percentage standard deviation changes of Fig. 4 with δσ/σ = F − 1. There is a broad similarity of their results with Fig. 4 but also some notable differences such as, for instance, the lack of a strong decrease of variability over the southern ocean, which is a feature of the multimodel analysis.

For precipitation, a strong increase in precipitation variability is seen in the equatorial eastern Pacific here and in Räisänen (2002), despite the difference in the changes in temperature variability, and there are differences in other regions, such as over Australia for instance. Nevertheless, the broad agreement of the overall patterns of change in variability despite differences in forcing and in the CMIP3 versus the CMIP2 generation of coupled models indicates the general robustness of the results.

Some of the relationship between the pattern of changes in standard deviation δσ and the pattern of standard deviation in the control simulations σ can be seen in the spatial correlations of Table 2. For temperature, the spatial correlations r(δσ, σ) are negative, indicating that temperature variability, at least as measured by the standard deviation, generally decreases most where it is largest. The correlations are stronger (larger negative) for the more strongly forced A1B scenario compared to the B1 scenario. The reverse is the case for precipitation variability, which tends to increase where it is largest, albeit with a smaller correlation coefficient than for temperature. The correlations are larger for the variability of decadal means of temperature than for annual means and the reverse is again the case for precipitation. The pattern of the changes in standard deviation is similar for the B1 and A1B scenarios, as already apparent in Fig. 4, with spatial correlations r(δσ, δσ) greater than 0.8 in all cases except for the decadal means of precipitation. Finally, the spatial patterns of changes in the variability of temperature are poorly correlated with those of precipitation over the globe.

The implication from these results is that there is not only a shift in mean temperature and precipitation rates, as described in M07, for instance, but also a shift in the widths of the frequency distributions in different parts of the globe. Although it is beyond the scope of the present investigation to identify the mechanisms associated with changes in the variance of annual means, Räisänen (2002) and Stouffer and Weatherald (2007) discuss this briefly and provide some references, whereas M07 provide indirect information largely through a discussion of changes in extremes. In brief, the increase in precipitation variability is consistent with the increase in precipitation intensity, which is a relatively robust feature of the climate projections and is explained as a consequence of the increased moisture capacity of the warmer atmosphere. The decrease of temperature variability at higher latitudes is generally ascribed to the retreat of snow and ice with a resulting less winterlike variability. The replacement of sea ice by open water, for instance, acts to damp temperature variability locally. Increases of temperature variability over lower-latitude land may be linked to generally dryer conditions whereby changes in energy input at the surface are less able to be damped by latent heat fluxes.

c. Changes in decadal potential predictability

Figure 5 plots the multimodel decadal internally generated potential predictability pν of the unforced preindustrial control simulation and its difference from the pν of the 150-yr stabilization period (nominally 2150–2300) for the two scenarios. Values of pν are not plotted unless they are deemed to be significantly different from zero at the 1% level, and differences are plotted only when the probability that the control and stabilization pν values are the same is estimated to be less than 1% based on their confidence intervals.

The first result is that differences in pν are very similar in the two scenarios and, although a slightly larger area of the globe exhibits statistically significant differences in the more strongly forced A1B case, the results are not strongly scenario dependent, which also attests to their general robustness. The main result is an overall decrease in decadal variability and potential predictability in a warmer world. The decrease tends to be largest where the ppvfs of the unforced preindustrial control climate are largest.

For temperature, decadal potential predictability is largest over middle-to-high-latitude oceans but small over land. The smallness of the ppvf over land is due not so much to the smallness of σν2 but rather to the small signal-to-noise ratio there. Over higher latitudes, the longer time scales of the oceans provide larger values of pν and these are also where decreases in decadal potential predictability are largest in the warmer world.

The potential predictability of precipitation is small to begin with and only a pale reflection of that for temperature. This is because the decadal variability σν2 for precipitation is comparatively small and the noise variance σε2 is generally large, giving a small signal-to-noise ratio so that pν = γ/(1 + γ) is small almost everywhere. The decrease in pν for precipitation occurs despite the modest increase in decadal standard deviation (see, e.g., Fig. 3) because it is outweighed by the overall increase in variability, which implies a reduction in the signal-to-noise ratio γ.

The high-latitude ocean regions of diminished potential predictability are typically those where the surface connects to the deeper oceans and/or where variations in important ocean currents, such as the Kuroshio, Gulf Stream, and the Antarctic Circumpolar Current, can provide the long time scales of interest. There is, however, a bewildering array of modes and analyses that may be called upon to provide the long time-scale variability that might change in the warmer world. These are discussed by M07 (sections 10.3.4 and 10.3.5), who also provide copious references. They include the PDO and the related interdecadal Pacific oscillation (IPO) in the Pacific, the meridional overturning circulation (MOC) and the AMO in the Atlantic, as well as decadal components in the strengths and envelopes of mechanisms, such as ENSO, the North Atlantic Oscillation (NAO), and the northern and southern hemisphere annular modes (NAM and SAM, respectively).

The analysis of internally generated decadal potential predictability undertaken here cannot make a direct connection with these modes but does indicate a general decrease of decadal variability (at least in the model world). This is of interest in itself as well as indicating where to look for mechanisms responsible for the decrease. The potential predictability results also connect with some of the results discussed in M07. In the North Atlantic, for instance, the weakening of the MOC and associated changes in heat transports are noted as a general result in simulations of a warmer world. In the South Atlantic, a poleward shift, strengthening and narrowing of the SH westerlies with enhanced equatorward surface transport, and deeper poleward return flows are documented. These are mean changes in circulations but are apparently also associated with changes in long time-scale variability as indicated by the pν changes in Fig. 5 for temperature and, to some extent, precipitation.

Parker et al. (2007) argue for a connection between the long time-scale variations of the MOC and the AMO by regressing decadal mean temperatures on a decadal MOC index in results from a long simulation with the third climate configuration of the Met Office Unified Model (HadCM3). The zero-lag regression pattern (their Fig. 7a) resembles the patterns of pν difference for temperature in Fig. 5 over both the North Pacific and Atlantic (even though the HadCM3 model does not appear in Table 1). The coherence of these patterns suggests a decrease in this mode of variability in the warmer world as a possible cause of the pattern of pν change. However, the corresponding regression for decadal mean precipitation (their Fig. 8) gives a patchy pattern only vaguely reminiscent of that in Fig. 5 for the change in pν for precipitation.

For the North Pacific, Kwon and Deser (2007) analyze decadal variability in a version of the National Center for Atmospheric Research (NCAR) coupled model. They note long time-scale variability of temperature associated with the Kuroshio extension region and suggest a coupled mechanism for its existence. If the decadal potential predictability in the North Pacific can be associated with such a mechanism, then Fig. 5 suggests a decrease in this variability in a warmer world.

Actual decadal predictions, as an initial and boundary value problem, are made in Smith et al. (2007) and Keenlyside et al. (2008). These predictions include the externally forced signal as well as the internally generated decadal component. To the extent that pν reflects the behavior of the coupled climate system, the results in Fig. 5 give some indication as to where we may hope to find predictive skill at long time scales for the internally generated long time-scale component and how it might change as a consequence of changes in climate process and feedbacks in a warmer world. In other words, a prediction of the mean temperature anomaly for the next decade will include contributions from both forced and internally generated components of variability. The general decrease of pν implies an expected loss of decadal predictability in this latter component in a warmer world.

4. Summary

The collection of coupled climate model simulations in the CMIP3 data archive includes simulations of the unforced preindustrial control climate and simulations using B1 and A1B projected forcings from 2000 to 2100, with subsequent simulations having the forcings held fixed at year 2100 values for another 200 yr (nominally from 2100 to 2300). The internally generated variability of the unforced control climate is characterized by multimodel estimates of the standard deviations of annual mean temperature σT and precipitation σP. The decadal variability of these variables is characterized in terms of the decadal potential predictability variance fraction pν. These control climate variability statistics are compared with those from the last 150 yr of simulations with year 2100 fixed forcings, for the B1 and A1B scenarios, after removing the commitment-trend component. Differences in σT and σP characterize the expected changes in interannual variability with global warming, whereas changes in pν characterize changes in decadal potential predictability. The change in pν has at least indirect implications for decadal prediction in a warmer world.

Multimodel estimates of the standard deviations of annual mean temperature and precipitation for the unforced preindustrial control simulations compare well with observation-based estimates. The main area of disagreement is at very high southern latitudes, which cover a comparatively small area. The standard deviations of annual mean temperatures are largest over land and especially over high-latitude land and over the tropical eastern Pacific. Changes in interannual variability are characterized as percentage changes in the standard deviations. The largest percentage decreases in interannual variability are seen over high-latitude oceans. Temperature variability generally increases over land except at high latitudes. The patterns of the percentage change in the standard deviation of annual mean precipitation show a general increase with the exception of modest regions at the latitudes of the subtropical highs, which show decreases over both land and ocean. Percentage increases are largest where the variability of the control climates are largest. An increase in σP (and σT) is seen in the equatorial eastern Pacific. Changes in standard deviations are larger for the more strongly forced and warmer A1B simulations than for the B1 simulations.

For longer time scales, there is a general decrease in decadal potential predictability for temperature in a warmer world. The decrease tends to be largest where the decadal potential predictability of the unforced preindustrial control climate is largest over the high-latitude oceans. The potential predictability of precipitation is small to begin and generally decreases further. The overall decrease in decadal potential predictability indicates a potential loss of predictability in the internally generated component of decadal variability in a warmer world.

Multimodel estimates of changes in the standard deviation of annual mean temperature and precipitation from the CMIP3 archive of coupled model simulations for the B1 and A1B stabilization scenarios are broadly similar to those of Räisänen (2002) based on 2 × CO2 simulations with an earlier generation of coupled models in the CMIP2 archive. The results are also consistent with aspects of the analyses of changes in variability reported in M07, indicating their general robustness. The presumption is that the results for decadal variability and potential predictability are similarly robust but this must await further analyses with other datasets and analysis methods.

Acknowledgments

Steve Lambert has been instrumental in downloading the data from the CMIP3 archive for this and other purposes. John Fyfe and others provided helpful comments.

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Fig. 1.
Fig. 1.

The evolution from 2000 to 2300 of globally and annually averaged temperature T (°C) and precipitation P (mm day−1) simulated by models in the CMIP3 data archive as listed in Table 1. The simulations follow the B1 and A1B forcing scenarios until year 2100, after which forcing is kept constant at the year 2100 value for 200 yr. Deviations from the average over the period are plotted. The thick red line is each ensemble average.

Citation: Journal of Climate 22, 11; 10.1175/2008JCLI2835.1

Fig. 2.
Fig. 2.

(top) Observation-based standard deviations of annual mean temperature σT (°C) and precipitation σP (mm day−1) based on data from the ERA-40 (Uppala et al. 2005) and Xie and Arkin (1996), respectively. (bottom) The corresponding multimodel standard deviations.

Citation: Journal of Climate 22, 11; 10.1175/2008JCLI2835.1

Fig. 3.
Fig. 3.

Zonal averages of the standard deviation of annual mean temperature σT (°C) and precipitation σP (mm day−1). Black lines are observation-based estimates and solid red lines are the multimodel values for preindustrial climate. Long- and short-dashed red lines give the values obtained from the last 150 yr of the B1 and A1B stabilization simulations, respectively, after removal of trend. The corresponding blue lines are the standard deviations for decadal mean temperature and precipitation.

Citation: Journal of Climate 22, 11; 10.1175/2008JCLI2835.1

Fig. 4.
Fig. 4.

(top) The multimodel standard deviations of annual mean temperature σT (°C) and precipitation σP (mm day−1) as simulated by the models in Table 1 for preindustrial conditions. (middle), (bottom) The percentage change in these standard deviations for the last 150 yr of the B1 and A1B stabilization simulations after removal of the forced component. Values are statistically significantly different from zero at the 1% level, except for a narrow band about the zero lines.

Citation: Journal of Climate 22, 11; 10.1175/2008JCLI2835.1

Fig. 5.
Fig. 5.

(top) The multimodel estimate of internally generated decadal potential predictability pν for temperature and precipitation, based on the unforced preindustrial control simulations of the models in Table 1. Values of pν are not plotted if not deemed to be significantly different from zero at the 1% level. (middle), (bottom) The change in pν for the last 150 yr of the B1 and A1B stabilization simulations after removal of trend. Values of pν differences are plotted only when the probability that the control and stabilization pν values are the same is estimated to be less than 1% based on their confidence intervals.

Citation: Journal of Climate 22, 11; 10.1175/2008JCLI2835.1

Table 1.

CMIP3 models providing data for the twenty-first-century A1B and B1 scenarios with stabilization integrations to 2300.

Table 1.
Table 2.

Spatial correlations r(δσ, σ) between changes in standard deviation δσ and control-run values σ and between changes r(δσ, δσ) in standard deviations.

Table 2.
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