Uncertainty in climate change projections: the role of internal variability
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Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.
KeywordsClimate change Uncertainty Annular modes Coupled climate models Climate detection and attribution
Characterizing and quantifying uncertainty in climate change projections is of fundamental importance not only for purposes of detection and attribution, but also for strategic approaches to adaptation and mitigation. Uncertainty in future climate change derives from three main sources: forcing, model response, and internal variability (e.g., Hawkins and Sutton 2009; Tebaldi and Knutti 2007). Forcing uncertainty arises from incomplete knowledge of external factors influencing the climate system, including future trajectories of anthropogenic emissions of greenhouse gases (GHG), stratospheric ozone concentrations, land use change, etc. Model uncertainty, also termed response uncertainty, occurs because different models may yield different responses to the same external forcing as a result of differences in, for example, physical and numerical formulations. Internal variability is the natural variability of the climate system that occurs in the absence of external forcing, and includes processes intrinsic to the atmosphere, the ocean, and the coupled ocean-atmosphere system.
Internal atmospheric variability, also termed “climate noise” (e.g., Madden 1976; Schneider and Kinter 1994; Wunsch 1999; Feldstein 2000), arises from non-linear dynamical processes intrinsic to the atmosphere. Although the atmosphere contains little memory beyond a few weeks, it exhibits long-time scale variability characteristic of a random stochastic process. Low frequency variability also arises from processes internal to the coupled ocean-atmosphere system via dynamic and thermodynamic interactions. Thermodynamic coupling between the atmosphere and upper ocean mixed layer produces slow climate fluctuations via the ocean’s integration of atmospheric “white noise” turbulent heat flux forcing (e.g., Frankignoul and Hasselmann 1977; Yukimoto et al. 1996; Barsugli and Battisti 1998; Deser et al. 2003; Dommenget and Latif 2008; Clement et al. 2010). Inclusion of dynamical ocean processes produces additional types of low-frequency coupled variability including wind-driven ocean-gyre fluctuations that have been found to play a role in the “Pacific Decadal Oscillation” (Mantua et al. 1997; Schneider and Miller 2001; Schneider and Cornuelle 2005; Kwon and Deser 2007; Alexander 2010). Finally, stochastic atmospheric forcing of internal oceanic variability may contribute to low-frequency climate fluctuations: for example variations in the Atlantic thermohaline circulation may underlie the “Atlantic Multi-decadal Oscillation” (e.g., Delworth et al. 1993).
The unprecedented assemblage of climate model projections from the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project Phase 3 (CMIP3) archive (Meehl et al. 2007) provides a unique opportunity for estimating uncertainty in climate change. This archive, consisting of forced twentieth and twenty-first century integrations from 23 coupled ocean-atmosphere models, forms the basis for much of the International Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) of Working Group I (Solomon et al. 2007). Uncertainty, as estimated by the spread of the responses across the CMIP3 ensemble relative to the ensemble mean response, has been assessed for a number of climate variables, including air temperature, precipitation and the large-scale atmospheric circulation (see Hegerl et al. 2007; Meehl et al. 2007; and references therein). These uncertainties, as well as those based on long model control integrations, have also been used for estimating the contribution of external forcing to observed climate changes over the twentieth century (Hegerl et al. 2007 and references therein). Recently, Hawkins and Sutton (2009) used the CMIP3 archive to quantify the relative contributions of each source of uncertainty for projected decadal-scale changes in global mean air temperature and precipitation over the twenty-first century. They found that model (forcing) uncertainty dominates before (after) ~2040, while internal variability plays a significant role for interannual air temperature changes before ~2010. A follow-up study by Hawkins and Sutton (2010) for regional-scale precipitation found that internal variability is the dominant source of uncertainty for decadal-scale changes in the first few decades, with model variability becoming dominant thereafter. In both studies, internal variability was defined as the residual from a 4th order polynomial fit to the regional or global mean time series for each model.
An underlying assumption of studies based on the CMIP3 archive is that the multi-model mean response to external forcing yields a more robust estimate of the forced climate signal than the response of any single model due to the reduction in uncertainty associated with model and internal variability (e.g., Tebaldi and Knutti 2007). However, this assumption has been difficult to verify in part due to the limited number of ensemble members for any given model and external forcing scenario (most of the CMIP3 models had fewer than 3 integrations for each forcing scenario). Thus, there is merit in performing a large number of simulations with a single climate model in order to provide a robust estimate of that model’s forced response in addition to its internal variability. One such ensemble is the 62-member “Dutch Challenge Project” (Selten et al. 2004) which employed Community Climate System Model Version 1 (CCSM1; Boville et al. 2001) forced by the “business-as-usual” GHG scenario (similar to the SRES A1 scenario) out to 2080. The individual ensemble members, which differ only in their atmospheric initial conditions, were found to exhibit large spread in the future state of the extra-tropical northern hemisphere wintertime atmospheric circulation (Selten et al. 2004; Branstator and Selten 2009).
Here we analyze a new 40-member ensemble for the period 2000–2060 performed with one of the CMIP3 models, Community Climate System Model Version 3 (CCSM3). Compared to the “Dutch Challenge Project”, this ensemble uses an improved and higher resolution state-of-the-art climate model and also stronger (and arguably more realistic) forcing consisting primarily of the SRES “A1B” GHG emissions and stratospheric ozone recovery scenarios. We use this ensemble to characterize the forced climate response and accompanying uncertainty due to internal variability. We consider three basic parameters, surface air temperature (TS), precipitation (Precip) and sea level pressure (SLP), for a broad view of the climate response. We also examine the responses as a function of season, highlighting any differences between winter and summer.
The following questions guide our investigation. What is the geographical distribution, magnitude and seasonal dependence of the ensemble mean (e.g., forced) response relative to the internal variability? Does this signal-to-noise ratio differ among the three climate parameters? What is the minimum number of ensemble members needed to detect the forced response with 95% statistical confidence? When can the forced response be detected given an ensemble of size n where n < 40? Is there a relationship between the patterns of the forced response and the leading patterns of internal variability? What are the sources of internal variability, and in particular, how large is the contribution from internal atmospheric variability (the latter being assessed from a 10,000 year control integration of the atmospheric component of CCSM3)? What are the relative contributions of internal and model variability to uncertainties in climate projections in the multi-model CMIP3 ensemble? Finally, what are the implications of the results based on the 40-member CCSM3 ensemble for detection and attribution studies of observed climate change and for investigations of future climate projections based on multi-model ensembles?
The rest of the paper is outlined as follows. The models and methods are given in Sect. 2. Results are presented in Sect. 3, structured following the sequence of questions listed above. A summary and discussion is provided in Sect. 4.
2 Models and methods
CCSM3, a coupled ocean-atmosphere-land-cryosphere general circulation model, has been extensively documented in the J. Climate CCSM3 Special Issue (2006). In general, CCSM3 realistically simulates the major patterns of internal climate variability, except for ENSO which exhibits higher regularity and frequency (2–3 year periodicity) than in nature (Deser et al. 2006; Stoner et al. 2009). The 40-member CCSM3 ensemble uses the T42 version (2.8° latitude by 2.8° longitude resolution for the atmosphere, land, and cryosphere components and nominal 1° latitude by 1° longitude resolution for the ocean model component; note that the version of CCSM3 used in the CMIP3 archive was at T85 resolution). Each ensemble member undergoes the same external forcing, the main components of which are the A1B GHG scenario in which CO2 concentrations increase from approximately 380 ppm in 2000 to approximately 570 ppm in 2060 and stratospheric ozone recovery by 2060, as well as smaller contributions from sulfate aerosol and black carbon changes (see Meehl et al. 2006). It is worth noting that for the period of interest, 2000–2060, the SRES A1B and A2 scenarios are very similar, and both are approximately 30% stronger than the B1 scenario. The ocean, land, and sea ice initial conditions are identical for each ensemble member, and are taken from the conditions on January 1, 2000 from a single 20th century CCSM3 integration. The atmospheric initial conditions differ for each ensemble member, and are taken from different days during December 1999 and January 2000 from the same twentieth century CCSM3 integration. Although the use of a single ocean initial condition may potentially underestimate the true internal variability of the simulated climate system, a recent predictability study using the same 40-member ensemble shows that the effect of ocean initial conditions is lost within 6–7 years for upper ocean (0–300 m) heat content, and even more rapidly for surface temperature (Branstator and Teng 2010). Thus, the full internal variability is likely to be sampled by perturbing only the atmospheric initial conditions.
In addition to the 40-member CCSM3 ensemble, we make use of a 10,000-year control integration of CAM3, the atmospheric component of CCSM3, at T42 resolution under present-day GHG concentrations. In this integration, sea surface temperatures (SSTs) and sea ice are prescribed to vary with a repeating seasonal cycle but no year-to-year variability. The SST and sea ice conditions are based on observations during the period 1980–2000 from the data set of Hurrell et al. (2008). As in CCSM3, CAM3 is coupled to the Community Land Model (CLM; Oleson et al. 2004).
For the purposes of this study, we form our own CMIP3 multi-model ensemble using a single integration from each of the 21 models forced with the SRES A1B forcing scenario (see Table 10.4 of the IPCC WG1 AR4 Report) excluding CCSM3 to avoid any overlap with present 40-member ensemble. Note that the ozone forcing scenario varies among the CMIP3 models, with nearly half prescribing no change over the twenty-first century (Son et al. 2008).
We used two methods to compute the climate response: (1) epoch differences between the last 10 years (2051–2060) and the first 10 years (2005–2014); and (2) linear least-squares trends fit to the period 2005–2060. Note that both approaches use data beginning in 2005, 6 years after the integrations start, so as to avoid any artificial reduction in ensemble spread due to the memory of ocean initial conditions (see Branstator and Teng 2010 and related discussion in the Introduction above). The two methods yield virtually identical results.
We evaluated the 95% statistical significance of the ensemble mean epoch differences and trends against a null hypothesis of zero change using a 2-sided Student’s t test (1-sided for TS since the sign of the response is known a priori), where the spread is computed using the individual epoch difference or trend values from the 40 ensemble members. Each ensemble member’s epoch difference (or trend) values are assumed to be independent.
3.1 Ensemble mean response and minimum ensemble size requirement
The ensemble mean response is statistically significant over most regions of the globe for all 3 variables. The large-scale SLP response over the Northern Hemisphere (NH) is characterized by generally negative (positive) values at high (middle) latitudes, with maximum amplitudes ~3 hPa in the Gulf of Alaska and northern Eurasia. A similar pattern with reversed polarity and somewhat weaker amplitude (~1 hPa) is found over the Southern Hemisphere (SH). These patterns project onto the zonally-symmetric Northern and Southern Annular Modes (NAM and SAM, respectively; e.g., Thompson and Wallace 2000). The global distribution of SLP changes is broadly consistent with that from the set of 22 CMIP3 models reported in Solomon et al. (2007). We note that the reversed polarity of the response in the SH compared to the NH is due to stratospheric ozone recovery (e.g., Son et al. 2009).
The tropical Precip response consists of mainly positive values along the equator flanked by compensating negative values, especially to the south, with maximum amplitudes ~ 2 mm day−1. The subtropics (extra-tropics) generally exhibit reduced (enhanced) Precip, with magnitudes ~ < 0.5 mm day−1. Surface temperature increases everywhere, with larger warming over land than ocean and maximum warming over the ice-covered Arctic Ocean and adjacent continents (maximum values ~ 6°C), the latter attributable to Arctic sea ice loss in late autumn (Deser et al. 2010). The Precip and TS responses are similar to those documented from other models (Solomon et al. 2007) and the 21 CMIP3 multi-model mean (not shown).
Ensemble mean epoch difference maps and ensemble size requirements for the June–July–August (JJA) season are shown in Fig. 1b. The SLP response pattern is considerably different in JJA compared to DJF. For example, over the SH the quasi-zonally symmetric pattern in DJF (e.g., the SAM) is replaced with a regional meridional dipole over the Pacific sector. In addition, the positive SLP response centered over the Mediterranean region in DJF is replaced by negative values in JJA. SLP decreases over the Arctic Ocean in both seasons, although the maximum negative anomalies are centered over the western Arctic in JJA compared to the eastern Arctic in DJF. The Precip response pattern in JJA is similar to that in DJF except that the tropical signals are largest within the NH following the position of the sun. The biggest difference between the TS responses in JJA and DJF is the lack of northern hemisphere polar amplification in summer, consistent with the muted influence of Arctic sea ice loss during this season (Deser et al. 2010).
In general, fewer ensemble members are needed for detecting significant SLP changes in JJA compared to DJF (Fig. 1b, right). For example, Nmin < 3 over the entire tropical Pacific, and <12 over the Arctic and portions of the Southern Ocean. On the other hand, larger (smaller) values of Nmin are needed to detect the enhanced Precip over the Arctic (Southern Ocean) in JJA compared to DJF, in part related to the weaker (stronger) amplitude of the signal. The ensemble size requirements for TS in JJA are similar to those in DJF.
3.2 Characterization and mechanisms for uncertainties in future climate trends: the role of “weather noise”
What mechanisms contribute to the spread of the trends across the ensemble members? First we consider the role of internal atmospheric variability using the 10,000-year CAM3 control integration. In this integration, the specified repeating seasonal cycles of SST and sea ice are based on observations from the period 1980–2000. Ideally, the CAM3 control integration should be forced with the SST and sea ice conditions simulated by the CCSM3 40-member ensemble mean during 2000–2060 to obtain identical boundary conditions for the two sets of experiments; however, the differences between atmospheric internal variability under observed present-day (1980–2000) and simulated future (2000–2060) SST and sea ice conditions are likely to be small compared to the magnitude of the internal variability itself.
The spatial pattern of the ensemble mean SLP response (Fig. 1) bears some similarity to the leading EOF of the trends in CCSM3 and the CAM3 control integration in both seasons. In particular, the SH response in DJF exhibits a high spatial correlation (0.88) with CAM3 EOF1 in the SH; there is also some correspondence between the NH response in DJF and the NH EOF1 from CAM3 especially over the Atlantic-Eurasian sector (spatial correlation of 0.64 for all longitudes, and 0.82 in the longitude band 20°W–140°E). In JJA, the NH ensemble mean response resembles the NH EOF1 from CAM3 (spatial correlation of 0.79), while the SH response bears some similarity to the SH EOF1 from CAM3 especially over the Pacific sector (spatial correlation of 0.61 over all longitudes and 0.84 in the longitude band 135°E–45°W). The spatial correspondence between the annular modes of atmospheric circulation variability in twentieth century coupled model integrations and the forced response to increasing concentrations of greenhouse gases and tropospheric sulfate aerosols has been documented by Miller et al. (2006) for the models in the CMIP3 archive.
The second EOFs of annual SLP trends over the NH and SH (Fig. 14, upper right) bear a close resemblance to the SLP regressions associated with the leading EOF of tropical SLP trends in their respective hemispheres, suggesting that they are due at least in part to coupled ocean-atmosphere variability within the tropics. For example, EOF2 of the NH exhibits negative SLP trend anomalies over the North Pacific and over northern Eurasia in the vicinity of the Arctic coastline, similar albeit not identical to the NH teleconnection pattern associated with tropical EOF1. The second EOF of the SH exhibits a NE-SW oriented dipole over the South Pacific and negative SLP trend anomalies over the Indian Ocean sector of the Southern Ocean and Antarctica, generally consistent with the SH teleconnection pattern associated with tropical EOF1. There are some differences in the shapes and relative amplitudes of the centers of action of the extra-tropical EOF2 patterns and those associated with tropical EOF1, most notably over the South Pacific. These differences may indicate that internal atmospheric variability also contributes to the former. Indeed, EOF2 in the NH and SH from the CAM3 control integration (Fig. 14, lower right) exhibits centers of action over northern Eurasia and in the South Pacific north of West Antarctica, respectively.
3.3 Comparison with nature
The overall shape and magnitude of the observed and simulated spectra are similar, with a rapid increase in power with decreasing frequency for periods shorter than a few months, and approximately constant or slightly increasing power for periods longer than about 1 year. CAM3 overestimates the power in the NAM for periods between about 30 days and 10 years. The daily annular mode power spectra from a 200-year segment of a CCSM3 pre-industrial control integration, indicated by the dashed gray curves in Fig. 15 (only periods longer than 2 years are plotted for clarity), does not differ significantly from CAM3 over the range of periods relevant for this study (<60 years), confirming that interannual-to-decadal variability of the simulated annular modes is predominantly due to processes internal to the atmosphere. Thus, the null hypothesis of intrinsic atmospheric variability is a useful benchmark against which to test for the presence of externally forced trends in the annular modes in both coupled models and nature.
The spatial distributions of the standard deviations of the 8-year low-pass filtered data from observations (left) and CCSM3 (right) are similar for each variable and season, and the magnitudes are of the same order. For example, the standard deviations of SLP are largest at high latitudes of the winter hemisphere, with values < 0.4 hPa within the tropics increasing to ~2 hPa and greater in polar regions. The model tends to overestimate low-frequency SLP variability over the extra-tropical NH by approximately 30% in DJF and 50% in JJA. Like SLP, Precip low-frequency variability is comparable in the model and observations except for the double ITCZ-bias over the western two-thirds of the tropical Pacific in the model that is reflected in the pattern of simulated variability especially in JJA. Simulated near-surface air temperature also exhibits realistic patterns and magnitudes of low-frequency variability, with larger values over land and the marginal sea ice zones compared to ocean. The highest amplitude variability occurs the NH continents in winter, with values ~ 1.2–1.5°C in nature compared to 1.5–1.8°C in the model. The overestimate of low-frequency wintertime air temperature variability over Eurasia and Alaska in the model may be partly due to the stronger-than-observed atmospheric circulation (e.g., SLP) variability. In summary, the 40-member CCSM3 ensemble generally simulates a realistic order-of-magnitude for low-frequency (>8 years) variability in near-surface air temperature, Precip and SLP.
3.4 Contribution of internal variability to the CMIP3 multi-model ensemble
As mentioned in the Introduction, uncertainties in the forced climate change signals simulated by the multi-model mean in the IPCC WG1 4th Assessment Report (Solomon et al. 2007) contains contributions from model uncertainty and internal variability. As a first step in separating the two contributions, we have compared the internal variability of trends during 2005–2060 from the 40-member CCSM3 ensemble with the model-plus-internal variability of similarly-computed trends from the 21-model CMIP3 ensemble forced by the SRES A1B GHG scenario (see Table 8.1 in Solomon et al. 2007 for a list of models). To help mitigate seasonal biases between different models, we have used annual mean values in our trend calculations. While the internal variability estimated from one model does not necessarily represent the internal variability averaged across all models, our estimate of the contribution of internal variability to the spread of trends within the CMIP3 ensemble is intended to serve as a benchmark until sufficiently large ensembles are completed for the other models.
4 Summary and discussion
We have investigated the forced climate response and associated uncertainties from a new 40-member ensemble of CCSM3 simulations forced with the SRES A1B GHG and ozone recovery scenarios during 2000–2060. The large ensemble size has enabled not only a robust estimate of the model’s forced response, but also an evaluation of the spread in the response due to internal (natural) variability of the climate system. The contribution of intrinsic atmospheric variability to uncertainty in the forced response was assessed using a long (10,000-year) control integration of the atmospheric model component of CCSM3. The response was characterized for 3 basic climate parameters (surface air temperature, precipitation, and sea level pressure) and two seasons (DJF and JJA). The main results are summarized below.
Similar to the average response of the 21 models in the CMIP3 archive (Hegerl et al. 2007), the 40-member CCSM3 ensemble mean response is characterized by: increased Precip along the equator and at high latitudes, and decreased Precip within the tropics and subtropics; more warming over land than ocean, and strongest warming over the Arctic and adjacent high latitude continents in winter; and a general pattern of decreased SLP at high latitudes and increased SLP in middle latitudes, except for the SH in summer which exhibits a response of the opposite sign as a result of prescribed recovery of the stratospheric ozone hole.
Due to the relative amplitudes of the forced response and natural variability, fewer ensemble members are needed to detect a significant response in TS compared to either Precip or SLP. More specifically, only 1 realization is needed to detect a significant (at the 95% confidence level) warming in the 2050s decade compared to the 2010s at nearly all locations, compared to approximately 3–6 (>15) ensemble members for tropical and high latitude (middle latitude) Precip, and approximately 3–6 (9–30) members for tropical (extra-tropical) SLP, depending on location and season. Larger ensemble sizes are needed to detect a significant response in the 2030s, even for TS at middle and high latitudes where 3–12 members are required. With a 40-member ensemble, significant decadal TS changes are detectable within the next few years over most regions, while decadal SLP and Precip changes are detectable within approximately 5–10 years over portions of the tropics (and the Arctic and Southern Ocean for Precip) and around 2030 elsewhere. With a 5-member ensemble, detection of the forced signal is delayed to 2025–2040 for tropical SLP and for Precip over the Arctic, equatorial and Southern Oceans, and 2020–2030 for TS over Eurasia and North America; no detection is possible for extra-tropical SLP and middle latitude Precip. Although the spatially-uniform component of the forced tropical SST warming emerges within a few years and with a small number of realizations (<3), the spatially-varying component is subject to a lower signal-to-noise ratio that is commensurate with the characteristics of the tropical precipitation response. The forced decadal-scale responses of the NAM and SAM require a relatively large number of realizations for detection (~25 and 15, respectively, in DJF) and a time horizon of detection of 2–3 decades (~2040 and 2030, respectively, in DJF), underscoring the low signal-to-noise ratios in even the large-scale patterns of extra-tropical atmospheric circulation response.
The leading pattern of uncertainty in the extra-tropical responses of TS, Precip and SLP, as determined from EOF analysis of the 40 individual responses, is associated with the annular mode of atmospheric circulation variability in both seasons and hemispheres. This mode, in turn, is primarily due to intrinsic atmospheric dynamics (e.g., “weather noise”: Madden 1976) and, as such, contains no predictability beyond a few months. The leading mode of uncertainty in the extra-tropical SLP response bears some resemblance to the forced (ensemble mean) SLP response, especially in the summer hemisphere. The leading pattern of uncertainty in the tropics displays a spatial structure reminiscent of the “Inter-decadal Pacific Oscillation” (Power et al. 1999) or “Pacific Decadal Oscillation” (e.g., Zhang et al. 1997). This pattern does not occur in the atmospheric control integration of CAM3, and thus owes its existence to ocean-atmosphere coupling. We note that thermodynamic coupling between the global atmosphere-ocean mixed layer system is sufficient to produce much of the spatial structure of this pattern (e.g., Yukimoto et al. 1996; Dommenget and Latif 2008; Clement et al. 2010). This tropical mode affects the extra-tropical atmospheric circulation via precipitation-induced teleconnections which in turn impact TS and Precip over middle and high latitudes. Indeed, the second EOF of the extra-tropical SLP response is linked in part to the leading EOF of the tropical SLP response.
The fact that forced changes in TS are more readily detectable than those in SLP over the extra-tropics by the middle of the twenty-first century indicates that the thermodynamically-induced signal in the TS response is larger than the dynamically-induced (via atmospheric circulation changes) uncertainty in the TS response (similar comments apply to the winter precipitation response over the northern high latitudes). For changes in earlier decades, the effect of circulation uncertainty on detection of the forced TS response is more evident (e.g., the minimum number of ensemble members needed to detect a significant TS response in the 2030s is markedly larger over the NH continents and Antarctica, regions affected by the annular modes, than other areas; recall Fig. 3). These results are in keeping with the studies of Yiou et al. (2007), Boé et al. (2009) and Vautard and Yiou (2009) focused on western Europe.
Our results have implications for detection and attribution of the twenty-first century climate response to anthropogenic forcing in nature and in the multi-model CMIP3 archive used in the AR4 IPCC assessment, and may also be useful for strategic guidance of the upcoming CMIP5 protocol of model experiments in support of the AR5. To the extent that the 40-member CCSM3 ensemble exhibits generally realistic levels of decadal variability as estimated from observational data sets spanning the past 30–50 years, attribution of observed future decadal changes in TS, Precip and SLP to anthropogenic forcing will be subject to similar levels of uncertainty reported here, taking into account any differences in climate sensitivity and forcing amplitude between CCSM3 and nature. The observed atmospheric circulation response over the extra-tropical NH may exhibit less uncertainty than indicated by CCSM3 due to the model’s overestimation of decadal SLP variability in this region (by approximately 30% in DJF). We have shown that internal variability as estimated from the 40-member CCSM3 ensemble makes an appreciable contribution to the total (model plus internal) uncertainty in the future climate response simulated by the 21-model mean from the CMIP3 archive. In particular, internal variability was shown to be more important than model variability for annual-mean SLP and Precip responses in the extra-tropics, while the two sources of uncertainty are of the same order for the annual-mean TS responses over North America, Eurasia and Antarctica. The magnitude of uncertainty due to internal variability is rarely less than half that due to model variability for forced linear climate trends during 2005–2060. Given our results, the planned set of CMIP5 model projections of twenty-first century climate in support of the AR5 IPCC Assessment should take into account the relatively high levels of uncertainty due to internal climate variability (of which internal atmospheric variability is an important component) by running enough ensemble members to provide robust assessments of the forced response in each model, perhaps by taking an adaptive approach based on the time horizon and climate parameter of interest. Similarly, given the inevitable competition between ensemble size and model resolution for a fixed level of computational resources, the former should not be sacrificed at the expense of the latter.
We have shown that the response to anthropogenic forcing is more detectable in surface temperature than in precipitation or atmospheric circulation. Thus, monitoring of observed climate change may be best served by focusing on thermodynamic components of the climate system such as air temperature and integrated quantities such as top-of-atmosphere radiation or ocean heat storage, rather than on dynamical components related to atmospheric and oceanic circulation changes.
We thank Dr. Grant Branstator for useful discussions during the course of this work and for providing the CAM3 control integration. Dr. Michael Alexander provided helpful comments on the original manuscript. We also thank the two anonymous reviewers, Dr. Laurent Terray and Dr. Shang-Ping Xie for constructive suggestions. NCAR is sponsored by the National Science Foundation. Haiyan Teng is supported by the Department of Energy under Cooperative Agreement No. DE-FC02-97ER62402. VB’s was supported by Meteo-France.
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- Alexander MA (2010) Extratropical air–sea interaction, SST variability and the Pacific Decadal Oscillation (PDO). In: Sun D, Bryan F (eds) Climate dynamics: why does climate vary. AGU MonographGoogle Scholar
- Branstator G, Teng H (2010) Two limits of initial-value Decadal predictability in a CGCM. J Clim (in press)Google Scholar
- CCSM3 special issue 2006. J Clim 19:2121–2630Google Scholar
- Clement A, DiNezio P, Deser C (2010) Rethinking the ocean’s role in the Southern Oscillation. J Clim (accepted with minor revisions)Google Scholar
- Hegerl GC et al (2007) Understanding and attributing climate change. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 663–745Google Scholar
- Meehl GA et al (2007) Global climate projections. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 747–845Google Scholar
- Oleson KW et al (2004) Technical description of the community land model (CLM). NCAR Technical Note NCAR/TN-461+STR, 173 ppGoogle Scholar
- Solomon S, Qin D, Manning M, Chen Z, Marquis M et al (2007) The physical science basis. Contribution of Working Group I to the 4th Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
- Trenberth KE et al (2007) Observations: surface and atmospheric climate change. In: Solomon S et al (eds) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, pp 235–336Google Scholar