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How weather impacts the forced climate response

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Abstract

The new interactive ensemble modeling strategy is used to diagnose how noise due to internal atmospheric dynamics impacts the forced climate response during the twentieth century (i.e., 1870–1999). The interactive ensemble uses multiple realizations of the atmospheric component model coupled to a single realization of the land, ocean and ice component models in order to reduce the noise due to internal atmospheric dynamics in the flux exchange at the interface of the component models. A control ensemble of so-called climate of the twentieth century simulations of the Community Climate Simulation Model version 3 (CCSM3) are compared with a similar simulation with the interactive ensemble version of CCSM3. Despite substantial differences in the overall mean climate, the global mean trends in surface temperature, 500 mb geopotential and precipitation are largely indistinguishable between the control ensemble and the interactive ensemble. Large differences in the forced response; however, are detected particularly in the surface temperature of the North Atlantic. Associated with the forced North Atlantic surface temperature differences are local differences in the forced precipitation and a substantial remote rainfall response in the deep tropical Pacific. We also introduce a simple variance analysis to separately compare the variance due to noise and the forced response. We find that the noise variance is decreased when external forcing is included. In terms of the forced variance, we find that the interactive ensemble increases this variance relative to the control.

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Notes

  1. It is important to emphasize that we are assuming these three components are independent (i.e., uncorrelated in time). Any dependence or two-way interactions, say, between the externally forced variability and the SST forced variability are error terms, which contribute to the noise variance.

References

  • Allen MR, Tett SFB (1999) Checking for model consistency in optimal fingerprinting. Clim Dyn 15:419–434

    Article  Google Scholar 

  • Ammann CM, Meehl GA, Washington WM, Zender CS (2003) A monthly and latitudinally varying volcanic forcing dataset in simulations of the 20th century climate. Geophys Res Lett 30(12):1657. doi:10.1029/2003GL016875

    Article  Google Scholar 

  • Barnett TP, Hegerl G, Knutson T, Tett S (2000) Uncertainty levels in predicted patterns of anthropogenic climate change. J Geophys Res 105(D12):15525–15542

    Article  Google Scholar 

  • Cash BA, Schneider EK, Bengtsson L (2007) Origin of climate sensitivity differences: role of shortwave parameterization in two GCMs. Tellus 59:155–170

    Article  Google Scholar 

  • Chen G, Held IM (2007) Phase speed spectra and the recent poleward shift of Southern Hemisphere surface westerlies. Geophys Res Lett 34:L21805. doi:10.1029/207GL031200

    Article  Google Scholar 

  • Collins WD, Rasch PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Briegleb BP, Bitz CM, Lin S-J, Zhang M (2006a) The formulation and atmospheric simulation of the Community Atmosphere Model Version 3 (CAM3). J Clim 19(11):2144–2161

    Article  Google Scholar 

  • Collins WD et al (2006b) The Community Climate System Model version 3 (CCSM3). J Clim 19:2122–2143

    Article  Google Scholar 

  • Crooks SA, Gray LJ (2005) Characterization of the 11-year solar signal using a multiple regression analysis of the ERA-40 dataset. J Clim 18:996–1015

    Article  Google Scholar 

  • Danabasoglu G, Large WG, Tribbia JJ, Gent PR, Briegleb BP, McWilliams JC (2006) Diurnal coupling in the tropical oceans of CCSM3. J Clim 19:2347–2365. doi:10.1175/JCLI3739.1

    Article  Google Scholar 

  • Hansen J et al (2005) Earth’s energy imbalance: confirmation and implications. Science 308:1431–1435

    Article  Google Scholar 

  • Hasselmann K (1976) Stochastic climate models. Tellus A 28:31–42

    Article  Google Scholar 

  • Hasselmann K (1993) Optimal fingerprints for the detection of time dependent climate change. J Clim 6:1957–1971

    Article  Google Scholar 

  • Hasselmann K (1997) On multifingerprint detection and attribution of anthropogenic climate change. Clim Dyn 13:601–611

    Article  Google Scholar 

  • Hegerl GC et al (1996) Detecting greenhouse gas induced climate change with an optimal fingerprint method. J Clim 9:2281–2306

    Article  Google Scholar 

  • Hegerl GC et al (2000) Optimal detection and attribution of climate change: sensitivity of results to climate model differences. Clim Dyn 16:737–754

    Article  Google Scholar 

  • Hegerl GC et al (2003) Detection of volcanic, solar and greenhouse gas signals in paleoreconstructions of Northern Hemispheric temperature. Geophys Res Lett 30. doi:10.1029/2002GL016635

  • Hurrell J, Meehl GA, Bader D, Delworth TL, Kirtman B, Wielicki B (2009) A unified modeling approach to climate system prediction. Bull Am Meteorol Soc 90:1819–1832

    Article  Google Scholar 

  • IDAG (The International ad hoc Detection and Attribution Group) (2005) Detecting and attributing external influences on the climate system: a review of recent advances. J Clim 18:1291–1314

    Article  Google Scholar 

  • Kiehl JT, Shields CA, Hack JJ, Collins WD (2006) The climate sensitivity of the community climate system model version 3 (CCSM3). J Clim 19:2584–2596. doi:10.1175/JCLI3747.1

    Article  Google Scholar 

  • Kirtman BP, Shukla J (2002) Interactive coupled ensemble: a new coupling strategy for CGCMs. Geophys Res Lett 29:17–20

    Article  Google Scholar 

  • Kirtman BP, Pegion K, Kinter S (2005) Internal atmospheric dynamics and climate variability. J Atmos Sci 62:2220–2233

    Article  Google Scholar 

  • Kirtman BP, Straus DM, Min D, Schneider EK, Siqueira L (2009) Understanding the link between weather and climate in CCSM3.0. Geophys Res Lett. doi:10.1029/2009GL038389

  • Large WG, Danabasoglu G (2006) Attribution and impacts of upper-ocean biases in CCSM3. J Clim 19:2325–2346. doi:10.1175/JCLI3740.1

    Article  Google Scholar 

  • Lean J, Beer J, Bradley R (1995) Reconstruction of solar irradiance since 1610: implications for climate change. Geophys Res Lett 22(23):3195–3198

    Article  Google Scholar 

  • Meehl GA, Washington WM, Ammann CM, Arblaster JM, Wigley TML, Tebaldi C (2004) Combinations of natural and anthropogenic forcings in twentieth-century climate. J Clim 17(19):3721–3727

    Article  Google Scholar 

  • Meehl GA, Washington WM, Santer BD, Collins WD, Arblaster JM, Hu A, Lawrence DM, Teng H, Buja LE, Strand WG (2006) Climate change projections for the twenty-first century and climate change commitment in the CCSM3. J Clim 19(11):2597–2616

    Article  Google Scholar 

  • Meehl GA, Stocker TF, Collins WD, Friedlingstein AT, Gaye AT, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB, Watterson IG, Weaver AJ, Zhao Z (2007) Global climate projections. In: Soloman S, Qin D, Manning M, Marquis M, Averyt K, Tignor MMB, Miller HJ, Chen Z (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 747–845 (ISBN: 9780521880091)

    Google Scholar 

  • Mitchell JFB, Karoly DJ, Hegerl G, Zwiers F, Allen MR, Marengo J (2001) Detection of climate change and attribution of causes. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, pp 695–738

    Google Scholar 

  • North GR, Wu Q (2001) Detecting climate signals using space-time eofs. J Clim 14:1839–1863

    Article  Google Scholar 

  • Pierce DW et al (2006) Anthropogenic warming of the oceans: observations and model results. J Clim 19:1873–1900

    Article  Google Scholar 

  • Power SB (1995) Climate drift in a global ocean general circulation model. J Phys Ocean 25:1025–1036

    Article  Google Scholar 

  • Ramaswamy V, Boucher O, Haigh J, Hauglustaine D, Haywood J, Myhre G, Nakajima T, Shi GY, Solomon S (2001) Radiative forcing of climate change. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, pp 349–416

    Google Scholar 

  • Rind D (1999) Complexity and climate. Science 284:105–107

    Article  Google Scholar 

  • Rowell DP, Folland CK, Maskell K, Ward MN (1995) Variability of summer rainfall over tropical North Africa (1906–92): observations and modeling. Q J R Meteorol Soc 121:69–704

    Google Scholar 

  • Santer BD, Wigley TLM, Barnett TP, Anyamba E (1996) Detection of climate change, and attribution of causes. In: Houghton JT et al (eds) Climate change, 1995: the science of climate change. Cambridge University Press, Cambridge, pp 407–443

    Google Scholar 

  • Schneider EK (2002) The causes of differences between equatorial Pacific SST simulations of two coupled ocean-atmosphere general circulation models. J Clim 15:449–469

    Article  Google Scholar 

  • Schneider EK, Fan M (2007) Weather noise forcing of surface climate variability. J Atmos Sci 64:3265–3280

    Article  Google Scholar 

  • Schneider EK, Kinter III JL (1994) An examination of internally generated variability in long climate simulations. Clim Dyn 10:181–204

    Article  Google Scholar 

  • Smith SJ, Pitcher H, Wigley TML (2001) Global and regional anthropogenic sulfur dioxide emissions. Global Planet Change 29(1–2):99–119

    Article  Google Scholar 

  • Smith SJ, Andres R, Conception E, Lurz J (2004) Historical sulfur dioxide emissions 1850–2000: methods and results. PNNL-14537, Pacific Northwest National Laboratory, Richland, WA

  • Stan C, Kirtman BP (2008) Internal atmospheric dynamics and tropical Pacific predictability in a coupled GCM. J Clim 21:3487–3503

    Article  Google Scholar 

  • Stone DA, Allen MR (2005) Attribution of global surface warming without dynamical models. Geophys Res Lett 32:L18711. doi:10.1029/2005GL023682

    Article  Google Scholar 

  • Stone PH, Yao M-S (1990) Development of a two-dimensional zonally averaged statistical-dynamical model. Part III: the parameterization of the eddy fluxes of heat and moisture. J Clim 7:726–740

    Article  Google Scholar 

  • von Storch H, Zwiers FW (1999) Statistical analysis in climate research. Cambridge University Press, Cambridge

    Google Scholar 

  • Wigley TML, Barnett TP (1990) Detection of the greenhouse effect in the observations. Scientific assessment of climate change: Intergovernmental Panel on Climate Change, IPCC working group I, WMO/UNEP, pp 239–255

  • Yeh SW, Kirtman BP (2004) Decadal North Pacific sea surface temperature variability and the associated global climate anomalies in a coupled general circulation model. J Geophys Res 109. doi:10.1029/2004JD004785

  • Yeh S-W, Kirtman BP (2009) Interannual atmospheric variability and interannual-to-decadal ENSO variability in a CGCM. J Clim 22:2335–2355

    Article  Google Scholar 

  • Zhang Y, Wallace JM, Battisti DS (1997) ENSO-like interdecadal variability: 1900–93. J Clim 10:1004–1020

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported through the Office of Science (BER), DOE DE-FG02-07ER64473 and NSF ATM-0653123. BPK acknowledges support from NSF OCI 0749165 and NOAA NA08OAR4320889. The investigators also thank NCAR and the NSF for computational support and access to CCSM3 and supporting data. The University of Miami Center for Computational Science also provided computational support.

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Correspondence to Ben P. Kirtman.

Appendix: Analysis of variance

Appendix: Analysis of variance

Analysis of variance has been used extensively in the climate modeling community to diagnose the atmospheric forced variance due to prescribed observed SST. With prescribed SST, the approach is to use ensembles of simulations to estimate the total variance as the sum of the internal variance (due to internal atmospheric dynamics) and the forced variance due to the prescribed SST. Since the sample sizes are finite some care must be taken to ensure that the estimates of variance are unbiased. For our purposes we follow the formalism described in the Appendix of Rowell et al. (1995), although there are many other similar formulations. Specifically, in the prescribed SST experiments of Rowell et al. (1995) it is assumed that precipitation is the sum of two independent components:

$$ {\text{P}}_{\text{ij}} = \mu_{\text{i}} + {\text{N}}_{\text{ij}} $$
(5)

where the subscript j corresponds to ensemble member, the subscript i corresponds to time, μi is the SST forced response for a particular moment in time (i) and Nij is the rainfall associated with internal atmospheric dynamics and is often referred to as the residual in the analysis of variance literature. Typically, the ensemble mean is used to estimate μi, and, as Rowell et al. (1995) show, an unbiased estimate of the SST forced variance must account for the finite ensemble size (n). Symbolically,

$$ \sigma^{2}_{\text{EM}} = \sigma^{2}_{\text{SST}} + \frac{1}{\text{n}}\sigma^{2}_{\text{N}} $$
(6)

where σ 2EM is the variance of ensemble means, is the variance due to internal atmospheric dynamics. Both and can be estimated from the ensemble of simulations to yield an estimate of the SST forced variance σ 2SST . The interactive ensemble provides an ideal tool for generalizing the prescribed SST analysis of variance approach to the coupled ocean–atmosphere system and we apply that here to PIES (see discussion of Fig. 15 in particular).

We also seek to answer the two questions introduce in Sect. 5 and in this case we will be applying the analysis of variance to FCS and FIES. In the case of FCS, as discussed below, it is easy to generalize the analysis for prescribed SST forcing into an analysis of the prescribed external forcing. Here, the noise or residual is due to both SST variability and internal atmospheric dynamics. Alternatively, when we apply the analysis of variance to FIES we can decompose the variance into the combined effects of the external forcing and the SST versus the variance due to internal atmospheric dynamics.

To separate the precipitation as the sum of three independent components: (1) variability due to externally prescribed forcing as in FCS and FIES, (2) variability due to SST forcing and (3) a residual which we view as variability due to internal atmospheric dynamics (noise)Footnote 1 is not possible without additional assumptions or experiments. For example, we can apply the Rowell et al. (1995) analysis of variance to the FCS ensemble to separately estimate the precipitation variance due to externally prescribed forcing (denoted as σ 2F ) and the combined precipitation variance due to internal atmospheric variability and SST variability (denoted as σ 2SST+N , which can be thought of as the internal variance analogously to Rowell et al. 1995). We cannot, however, use FCS to further decompose the internal variance into the precipitation variance associated with SST variability and the precipitation variance due to internal atmospheric dynamics. In this case, the externally prescribed forcing from the climate of the twentieth century protocol is analogous to the prescribed SST forcing in Rowell et al. (1995) and the internal variance is due to both atmospheric dynamics and SST variability. The estimate of the internal variance due to both SST and atmospheric dynamics is calculated as follows:

$$ \sigma^{2}_{\text{SST + N}} = \frac{1}{{{\text{T}}({\text{n}} - 1)}}\sum\limits_{{{\text{i}} = 1}}^{\text{T}} {\sum\limits_{{{\text{j}} = 1}}^{\text{n}} {\left( {{\text{P}}_{\text{ij}} - {\bar{\text{P}}}_{\text{i}} } \right)^{2} } } $$
(7)

where the subscript i corresponds to time (T = 1,560 months), subscript j corresponds to ensemble member (n = 6) and the over-bar denotes the ensemble mean. The estimate of the externally forced variance comes from re-writing (2) and is given by:

$$ \sigma^{2}_{\text{F}} = \frac{1}{{{\text{T}} - 1}}\sum\limits_{{{\text{i}} = 1}}^{\text{T}} {\left( {{\bar{\text{P}}}_{\text{i}} - \overline{\overline{\text{P}}} } \right)^{2} - \frac{1}{\text{n}}}\,\sigma^{2}_{\text{SST + N}} $$
(8)

where the double over-bar is the mean of all the data and the first term on the right hand side of (8) is the variance of ensemble means. The second term on the right hand side of (8) is required because the variance of ensemble means is a biased estimate of the forced variance as discussed above and in Rowell et al. (1995). The total variance is simply the sum σ 2T  = σ 2F  + σ 2SST+N .

Similarly, we can apply the analysis of variance using each atmospheric realization of FIES to separately estimate the precipitation variance associated with internal dynamics (i.e., σ 2N ) and the combined variance associate with the external forcing and the SST (i.e., σ 2F+SST ). In this case we are able to separate the internal dynamics from the SST and prescribed forcing since each atmospheric realization feels the same SST and the same external forcing. Further decomposition of would only be possible if we had ensembles of FIES simulations, which are not available at this time. We can also use the atmospheric realizations of PIES to estimate σ 2N and σ 2SST , which has not been done in the context of the coupled ocean–atmosphere model.

Finally, we can also calculate the sample (unbiased) precipitation variance from each PCS or FCS ensemble member separately. We denote this variance simply as and it is estimated as

$$ \sigma^{2}_{\text{j}} = \frac{1}{{{\text{T}} - 1}}\sum\limits_{{{\text{i}} = 1}}^{\text{T}} {\left( {{\text{P}}_{\text{ij}} - \left\langle {{\text{P}}_{\text{j}} } \right\rangle } \right)} $$
(9)

where the angle bracket indicate the time mean. The sample variance is an unbiased estimate of the total variance (σ 2T ), which is typically calculated using all the ensemble members. The sample variance estimate can also be applied to the individual atmospheric realizations in either PIES or FIES. As noted by Rowell et al. (1995) it would be tempting to calculate total variance by pooling all the ensemble members into a grand variance. This overestimates the variance since in the case of FCS, FIES and PIES it does not account for the correlation among the ensemble members via the forcing. All of these different estimated decompositions of the precipitation variance are summarized in Table 2. As noted in the table and as used throughout Sect. 5 we include the experiment name in parentheses with each variance estimate to avoid confusion.

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Kirtman, B.P., Schneider, E.K., Straus, D.M. et al. How weather impacts the forced climate response. Clim Dyn 37, 2389–2416 (2011). https://doi.org/10.1007/s00382-011-1084-3

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