globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2136
论文题名:
Inhomogeneous forcing and transient climate sensitivity
作者: Drew T. Shindell
刊名: Nature Climate Change
ISSN: 1758-1385X
EISSN: 1758-7505
出版年: 2014-03-09
卷: Volume:4, 页码:Pages:274;277 (2014)
语种: 英语
英文关键词: Projection and prediction ; Climate and Earth system modelling
英文摘要:

Understanding climate sensitivity is critical to projecting climate change in response to a given forcing scenario. Recent analyses1, 2, 3 have suggested that transient climate sensitivity is at the low end of the present model range taking into account the reduced warming rates during the past 10–15 years during which forcing has increased markedly4. In contrast, comparisons of modelled feedback processes with observations indicate that the most realistic models have higher sensitivities5, 6. Here I analyse results from recent climate modelling intercomparison projects to demonstrate that transient climate sensitivity to historical aerosols and ozone is substantially greater than the transient climate sensitivity to CO2. This enhanced sensitivity is primarily caused by more of the forcing being located at Northern Hemisphere middle to high latitudes where it triggers more rapid land responses and stronger feedbacks. I find that accounting for this enhancement largely reconciles the two sets of results, and I conclude that the lowest end of the range of transient climate response to CO2 in present models and assessments7 (<1.3 °C) is very unlikely.

Modelled transient climate responses were in good agreement with the understanding of historical forcing and observed warming trends in most analyses to ~2006 (ref. 8). Recent measurements posed a problem, however: warming rates were slower during the past 10–15 years while positive forcing continued to increase rapidly and new observations led to reduced estimates of offsetting negative aerosol forcing4, 9. Although there are uncertainties in the recent forcing trends and at least part of the reduced warming rate could be due to internal variability, analyses in both the scientific literature1, 2 and the popular press3 accounting for those factors concluded that climate sensitivity is likely towards the low end of present models’ range.

Inferring climate sensitivity from recent observations requires a thorough understanding of both recent forcing and the global mean response to various forcing agents. Although the forcing has been studied in detail, the global mean response has conventionally been assumed to be the same for all forcing agents (in all such analyses, not only the most recent). I examine the response to historical anthropogenic inhomogeneous forcing in the most recent set of state of the art climate model simulations: the Coupled Model Intercomparison Project Phase 5 (CMIP5; ref.10). Simulations to examine the influence of aerosols and ozone on climate were part of CMIP5 (ref.10), but were relegated to a low priority and few results are available. Hence to examine the response to aerosols and ozone, I analyse CMIP5 historical simulations of the response to all forcings (histAll), to well-mixed greenhouse gas (WMGHG) forcing (histGHG) and to natural forcing (histNat), using the residual of histAll − (histGHG + histNat) following two methods. Method 1 assumes that stratospheric water vapour forcing induces a response similar to WMGHGs, so that the residual (with scaled histGHG) represents the response to aerosol + ozone + land-use (LU; representing anthropogenic changes in vegetation cover and land usage). Method 2 assumes that the response to positive stratospheric water vapour forcing offsets the response to negative LU forcing, leaving a residual representing only aerosols + ozone (Methods).

I include the eight models for which forcing due to aerosols and ozone has been documented11 and all of these transient historical climate simulations are available. I evaluate the transient climate response (TCR), defined as the global mean temperature change in response to gradually increasing (1% yr−1) CO2 at the time of its doubling in a given model12 (all values annual averages). For consistency, the response to other forcings, which I refer to more generally as transient climate sensitivity, is given using the same scale (that is, the response per unit forcing times a model’s doubled CO2 forcing). Uncertainty in the TCR for a particular model stems from both the responses and the forcings, with the poorly documented LU forcing contributing the largest fraction in these calculations.

All of the available CMIP5 models show greater TCR for historical inhomogeneous forcing than for WMGHG forcing (Fig. 1 and Supplementary Table 1). The TCR for WMGHG is 2.0 ± 0.3°C (mean and s.d. across model ensembles), whereas it is 2.9 ± 1.0°C for aerosol + ozone + LU (Method 1) and 3.0 ± 1.1°C for aerosol + ozone (Method 2). Thus, the results are robust to the methodology for treating these minor forcing agents (LU and stratospheric water vapour), and seem to be dominated by the response to aerosol and ozone forcing. The TCR for aerosol + ozone is 45 ± 38% (mean of Methods 1 and 2; Supplementary Table 1) greater than the TCR calculated from historical WMGHG simulations (histGHG). In comparison with independent TCR estimates from these same models from the response to 1% per year CO2 increases13, which are 2.0 ± 0.3°C, the aerosol + ozone TCR is 53 ± 46% greater.

Figure 1: Comparison of transient climate response for well-mixed greenhouse gas forcing and for aerosol + ozone + land-use forcing.
Comparison of transient climate response for
well-mixed greenhouse gas forcing and for aerosol + ozone + land-use forcing.

Response to well-mixed greenhouse gas (WMGHG) is based on the histGHG simulations (top) or 1% per year CO2 simulations (bottom), whereas the response to aerosol + ozone + land-use (LU) is based on histAll − (histGHG+histNat) (using Method 1). The solid line shows 1:1 correspondence, whereas the dashed line shows 50% greater transient climate response (TCR) for aerosol + ozone+ LU. Uncertainties are 95% confidence intervals incorporating uncertainties in forcing and modelled temperature change. Uncertainties in TCR for WMGHG are comparable to the symbol size (~12%). Some overlapping points have been very slightly displaced for clarity.

I examine all models for which both historical climate simulations and forcing diagnostics are available (see also Supplementary Information). I exclude NCAR-CAM3.5 and bcc-csm1-1 as they omit aerosol indirect effects. I also exclude GISS-E2-R as this model overestimates negative SH nitrate aerosol and ozone forcing11, 30, so that its NH and SH forcings are quite similar, making its historical runs unsuited to studying the impact of hemispherically asymmetric forcing (other models have substantially greater hemispheric forcing gradients). Although they may be biased, historical aerosol-only simulations with that model are included because there are only four available models and these results are used only in a single comparison complementing the primary analysis. This results in eight available models in the primary analysis (Supplementary Table 1).

The residual of histAll − (histGHG + histNat) includes not only the response to aerosols and ozone, but also to LU and stratospheric water, as well as any nonlinearities. The forcings are characterized as follows. Doubled CO2 forcing estimated using the fixed-sea surface temperature (SST) method in individual models is used when available12, with estimates derived by linear regression used in the few cases when fixed-SST simulations were not performed13. HistGHG forcing has been diagnosed from the CMIP5 simulations using linear regression13. I use these for all models except for CSIRO-Mk3-6-0, MRI-CGCM3 and HadGEM2, for which the regression-based analysis yielded values quite different from values using fixed-SSTs (which is used for aerosol forcing) in an earlier analysis12. For those models, fixed-SST results are used if available, and otherwise regression-method estimates are adjusted on the basis of the differences between the two methods (Supplementary Information). Ozone forcing is from ACCMIP analyses11, with the value for a few models that use prescribed ozone changes set to 0.27 ± 0.14Wm−2, based on forcing calculations for those data sets (Supplementary Information), and using the multi-model mean spatial pattern. For LU, I take the central estimate of forcing as −0.085Wm−2 with a range of the same magnitude so that −0.17Wm−2 is the high-end forcing and the low-end forcing is zero (see Supplementary Information). The exception to this is the NorESM1-M model, which does not have LU forcing and hence a value of zero is used. For stratospheric water, I use a value of 0.07 ± 0.05Wm−2 following the most recent assessment4. I then analyse the response to inhomogeneous forcing, histAll − (histGHG + histNat), in two ways: (Method 1) assuming the response to stratospheric water vapour is the same as that for WMGHG because it is similarly distributed globally (that is, the histGHG response is multiplied by (FWMGHG + 0.07)/FWMGHG, where F is forcing), and hence the residual represents aerosol + ozone + LU, and (Method 2) assuming the LU and stratospheric water forcings offset one another, and hence the unmodified residual represents aerosol + ozone (see also Supplementary Information).

For this analysis, I compare simulated temperatures for 2000 (average over 1996–2005) with those during 1850–1859 (although for GFDL-CM3 I use 1860–1869 for the first period, the earliest available; and for HadGEM2 I used December 1859–November 1869 and December 1995–November 2005), with model drift removed by subtracting changes over the same time periods in control runs. Temperature averages are taken over up to five available ensemble members. Values are very similar to those I obtain with these decadal differences when instead using linear regression over the full length of the simulations13.

TCR for the models is simply given by the simulated temperature change divided by the imposed forcing, all multiplied by the doubled CO2 forcing (Supplementary Information). To calculate TCR from observations, using the equation given in the text, forcing from solar and contrails is taken as 0.1Wm−2 in total4. Uncertainties in TCR are computed with a Monte Carlo approach incorporating uncertainties in historical temperature change, forcing and response enhancement for inhomogeneous forcing (the value of E), all of which are assumed to be independent. Given that the TCR incorporating the enhanced response to inhomogeneous aerosol forcing is quite sensitive to the magnitude of that forcing, the results are in turn quite sensitive to the assumed reduction of the modelled aerosol forcing values. For example, if I do not include the 0.3 W m−2 bias-adjustment to the aerosol forcing, the mean TCR increases by 0.7 °C. As I used the smaller aerosol forcing value including this adjustment in the TCR calculation from observed surface temperature changes, I reduced the range of the positive uncertainty on the aerosol forcing, but further work is needed to better constrain aerosol forcing.

  1. Ring, M. J., Lindner, D., Cross, E. F. & Schlesinger, M. E. Causes of the global warming observed since the 19th century. Atmos. Clim. Sci. 2, 401415 (2012).
  2. Otto, A. et al. Energy budget constraints on climate response. Nature Geosci. 6, 415416 (2013).
  3. The Economist, Climate Science: A Sensitive Matter (The Economist Group, 2012).
  4. Myhre, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 8 (IPCC, Cambridge Univ. Press, 2013).
  5. Fasullo, J. & Trenberth, K. A Less cloudy future: The role of subtropical subsidence in climate sensitivity. Science 338, 792794 (2012).
  6. Sherwood, S. C., Bony, S. & Dufresne, J-L. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 3742 (2014).
  7. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).
  8. Hegerl, G. C. et al. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) Ch. 9 (IPCC, Cambridge Univ. Press, 2007).
  9. Boucher, O. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 7 (IPCC, Cambridge Univ. Press, 2013).
  10. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485498 (2012).
  11. Shindell, D. T. et al. Radiative forcing in the ACCMIP historical and future climate simulations. Atmos. Chem. Phys. 13, 29392974 (2013).
  12. Andrews, T., Gregory, J., Webb, M. & Taylor, K. Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models. Geophys. Res. Lett. 39, L09712 (2012).
  13. URL:
http://www.nature.com/nclimate/journal/v4/n4/full/nclimate2136.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5206
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Drew T. Shindell. Inhomogeneous forcing and transient climate sensitivity[J]. Nature Climate Change,2014-03-09,Volume:4:Pages:274;277 (2014).
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