A note on the effect of GCM tuning on climate sensitivity

F A-M Bender

Department of Meteorology, University of Stockholm, S-10691 Stockholm, Sweden

E-mail: frida@misu.su.se

Received 25 September 2007
Accepted 7 January 2008
Published 18 January 2008

Contents

• 1. Introduction
• 2. Experiment
• 3. Results
  • 3.1. Reference climates
  • 3.2. Climate sensitivity
• 4. Conclusions
• Acknowledgments
• References

1. Introduction

Satellite observations of the top-of-the-atmosphere (TOA) radiative budget are essential for restricting climate models. However, the observations are afflicted with errors and are not always in agreement with each other. This was made evident by, for example, Bender et al (2006) where ERBE (Earth Radiation Budget Experiment) (Barkstrom 1984, Barkstrom and Smith 1986) and CERES (Clouds and Earth's Radiant Energy System) (Wielicki et al 1996, Smith et al 2004) observations of the global albedo were compared with each other and with output from general circulation models (GCMs), displaying a difference in albedo at the top of the atmosphere of 0.01 (corresponding to ca. 3.4 W m^–2) between the two satellite estimates.

At present, climate models are tuned to achieve agreement with observations. This means that parameter values that are weakly restricted by observations are adjusted to generate good agreement with observations for those parameters that are better restricted, with the TOA radiative balance belonging to the latter category. However, with the discrepancy between the two satellites' estimates of the TOA radiative fluxes, the choice of reference dataset for the tuning process is not obvious. To date, ERBE is commonly used rather than CERES (Kiehl et al 1998, Hack et al 2006, Roeckner et al 2006), which was also indicated by Bender et al (2006), who found a more than twice as large deviation in TOA albedo between a multi-model mean and CERES as between a multi-model mean and ERBE.

The tuning of TOA fluxes to agree with satellite observations is done mainly via cloud parameters, as they are often poorly restricted by observations. However, cloud parameterizations and parameter values therein are known to be of importance in determining the climate sensitivity in a GCM (Senior and Mitchell 1992, Murphy et al 2004). Climate sensitivity is a quantification of how the climate system—modeled or real—responds to an imposed radiative forcing and is a key parameter in model projections of future climate change. Equilibrium climate sensitivity, studied here, is defined as the increase in global mean temperature resulting from a doubling of the atmospheric concentration of CO₂. The true sensitivity of the climate system is not accurately known and the climate sensitivity of a GCM is an inherent feature of the model physics. In this study we investigate if and how the climate sensitivity of a GCM, via differences in certain cloud parameters, may be dependent on which satellite dataset it is tuned to, ERBE or CERES.

2. Experiment

According to both ERBE and CERES the amount of short-wave (SW) radiation absorbed by the Earth is larger than the amount of long-wave (LW) radiation emitted, which would indicate a heating of the system. The measured radiative imbalance is of the order of 5 W m^–2, and is somewhat larger for CERES than for ERBE (see on-line data quality summaries at http://eosweb.larc.nasa.gov for details). Imbalances of this magnitude are not realistic and may, for instance, be compared with estimates of the total radiative forcing from anthropogenic greenhouse gases of 2.6 W m^–2 (IPCC 2007) and measurements of ocean heat storage, which indicate a heating representing less than 1 W m^–2 since the mid-1950s (Levitus et al 2000, Willis et al 2004). In a GCM, a large radiative imbalance is not tolerable and, given that the LW side of the satellite budget is more trustworthy than the SW side (Kiehl and Trenberth 1997), we create two model configurations where the TOA radiative balances agree with ERBE and CERES LW fluxes, respectively.

The tuning is done in the atmospheric component of a coupled GCM, the Community Atmosphere Model, CAM3.1 (Collins et al 2006), employing a T42 spectral truncation, which corresponds to a horizontal resolution of 2.8° × 2.8°. The model's treatment of cloud microphysics and precipitation processes is described by Boville et al (2006).

The cloud microphysics of the model, as in any GCM, is highly parameterized and the tuning is carried out through alterations of parameter values in these physics descriptions. There are numerous non-restricted parameters that affect the model cloud properties and thereby the radiative fluxes, and hence there are numerous ways to tune the model to a chosen level of radiative balance. We modify a number of parameters that are commonly used for tuning (Hack et al 2006), including relative humidity thresholds for cloud formation, thresholds for autoconversion of liquid and ice to rain and snow, efficiency of autoconversion in convective and stratiform clouds, efficiency of precipitation evaporation and adjustment timescales associated with convection. Some combinations of parameter values can easily be ruled out, yielding features in cloud properties that strongly disagree with observations. For instance, the LW cloud radiative forcing in the ITCZ is found to be very sensitive to alterations in the efficiency of autoconversion in deep convective clouds and to the autoconversion threshold of ice to snow. In other cases, e.g. for the parameters determining the re-evaporation of rain, the radiative budget is too sensitive on the SW side compared to the LW side to suit our purposes. Still, within the realm of reasonable agreement with reality there is no unique way to reach a certain level of TOA radiative balance. The tuning process is pragmatic and the two model configurations discussed here are achieved through alterations of parameters subjectively chosen because they are found to give a relatively clear-cut effect on the radiative fluxes and result in the desired radiative balance levels without displaying unreasonable cloud properties.

The ERBE-like model configuration is tuned to a global mean TOA balance of ca. 234 W m^–2 outgoing LW and net SW radiation. This is close to the default model configuration and only a slight alteration is made to the relative humidity threshold for formation of low clouds, which is decreased from 90% to 88%. The CERES-like model configuration is tuned to a global mean TOA balance of ca. 238 W m^–2 outgoing LW and net SW radiation. This is further from the default model configuration and the new balance is achieved by altering the relative humidity threshold for forming high clouds (from 80% to 85%), the relative humidity threshold for forming low clouds (from 90% to 87%) and the rate of autoconversion of cloud droplets to raindrops in shallow convective clouds (from 2 × 10^–4 to 1 × 10^–3). The magnitudes of these changes in parameter values do not exceed the differences in the default values of the same parameters in different horizontal resolution versions of the model (Collins et al 2004).

Increasing (decreasing) the relative humidity thresholds for formation of low and high clouds in the model results in a lower (higher) reflectivity, and thereby an increased (decreased) net SW radiation at the top of the atmosphere. It also leads to emission of LW radiation at a higher (lower) temperature, and thereby increased (decreased) outgoing LW radiation. Making the autoconversion of cloud droplets to rain more (less) efficient decreases (increases) the cloud amount and thereby leads to lower (higher) reflectivity and increased (decreased) net SW at the top of the atmosphere. The LW side of the budget is also affected, and the effective temperature, and thereby outgoing LW radiation, increased (decreased).

The two atmospheric model configurations are coupled to a slab ocean (Collins et al 2006) to calculate the equilibrium climate sensitivity, i.e. the change in global mean surface temperature at the equilibrium attained after an instantaneous doubling of the CO₂ concentration. The reference simulations are branched after 16 years, and the reference and branch simulations extend for 30 years and 60 years, respectively, to ensure stable present-day and doubled-CO₂ climates. The last 20 years of the reference and branch simulations are then compared with each other to determine the climate sensitivity in each case.

3. Results

3.1. Reference climates

The radiative balance levels in the two configurations differ by ca. 4 W m^–2. Despite this difference in radiation balance level and their different tunings, the climates of the two simulations are similar in many respects. For instance, the latitudinal distribution of the surface temperature is hardly affected (figure 1) and remains in good agreement with observations. This can be seen as a necessary, but not sufficient, condition for a reasonably tuned model—surface temperatures are well restricted by measurements and an altered distribution would be an indication of an unrealistic model.

The global distribution of clouds is also similar in the two configurations, but the annual mean total cloud cover is somewhat lower in the CERES-tuned case (60% globally averaged, compared to 63% in the ERBE-tuned case). Figure 2 displays the distribution of total cloud cover in the two model simulations and the same quantity given by ISCCP observations (Rossow et al 1996). The annual mean global mean total cloud cover is 67% according to ISCCP, and the main reason for the discrepancy relative to the model results is the greater observed cloud cover over the midlatitudes. The difference between the two model simulations lies primarily in high clouds and leads to maximum differences in LW cloud radiative forcing of ca. 10 W m^–2 locally in the ITCZ over the Indian and western Pacific Oceans (not shown). That the climates differ in cloud properties is expected, since this is where the changes have been made. The differences can also be exemplified by the grid-box total cloud water path, which is significantly smaller in the CERES-tuned than in the ERBE-tuned configuration. The higher autoconversion rate in the CERES-tuned model lowers the cloud water content, as does the higher threshold for formation of high clouds, while the lower threshold for low cloud formation in that configuration somewhat raises the cloud water content.

The global mean cloud water paths (liquid phase only) are 134 g m^–2 and 119 g m^–2, respectively, in the ERBE- and CERES-tuned configurations, with the largest discrepancies in the ITCZ and midlatitude stormtracks, as indicated by figure 3. Available observational estimates of global cloud water content vary widely (see, e.g., Penner et al 2006). A global estimate inferred from MODIS (Moderate Resolution Imaging Spectroradiometer) is 113 g m^–2 (Platnick et al 2003). Other satellite estimates over oceans only, derived from SSM/I (Special Sensor Microwave/Imager) with different retrieval algorithms, are 96 g m^–2 (Wentz 1997), 79 g m^–2 (Greenwald et al 1993) and 48 g m^–2 (Weng and Grody 1994). NVAP (NASA Water Vapor Project), combining SSM/I data with other soundings, suggests a value of 78 g m^–2 as the global ocean mean (Randel et al 1996). The corresponding ocean-only values for the two model simulations are 141 g m^–2 and 126 g m^–2 for the ERBE- and CERES-tuned configurations, respectively.

Due to the nature of the altered cloud parameters, the change in total cloud water content (liquid and ice phase) is primarily due to the change in liquid water path. The global mean total cloud water paths are 152 g m^–2 and 136 g m^–2, respectively, in the ERBE- and CERES-tuned configurations. This difference between the two model configurations is still small compared to how widely the estimates of cloud water path vary between different GCMs. Figure 4 shows the global mean cloud water content in 18 coupled GCMs, simulating the 20th century's climate (from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset) and the estimates are seen to range from ca. 40 g m^–2 to ca. 270 g m^–2. The MODIS estimate of this quantity is, according to Penner et al (2006), 193 g m^–2.

3.2. Climate sensitivity

Figure 5 shows the evolution of the surface temperature after the branching (i.e. the doubling of the CO₂ concentration) in the two cases. The difference in global mean temperature for the last 20 years of the two branch simulations is statistically significant at the 95% level, according to a Mann–Whitney test, and the equilibrium climate sensitivity (ΔT_{2 × CO2}) is found to be 2.50 K for the ERBE-tuned model and 2.26 K for the CERES-tuned model.

Although the sensitivity of the model tuned to CERES is lower than that of the ERBE-tuned model by 0.24 K, the tuning-induced difference is small when compared to estimates of equilibrium climate sensitivity in leading GCMs that span from 2.0 to 4.5 K (IPCC 2007). The difference is also well within the range of differences in climate sensitivity presented by Stainforth et al (2005), whose results, based on a large ensemble of model versions, are centered around 3.4 K, but range from less than 2 K to more than 11 K. However, rather than constraining the model to a certain level of radiative balance at the TOA, as in the present study, Stainforth et al (2005) vary model parameters within their respective estimated uncertainty ranges and the range of climate sensitivities found may therefore be seen as manifestations of maximum uncertainty, given that the individual parameter uncertainties are reasonably estimated.

The difference between the ERBE-tuned and CERES-tuned model configurations can also be compared to the results of Kiehl et al (2006), who investigated the dependence of climate sensitivity on horizontal resolution of the CAM. They found that ΔT_{2 × CO2} increased from 2.32 K for a T31 resolution, to 2.47 K for a T42 resolution and 2.71 K for a T85 resolution, i.e. variations of the same order of magnitude as that found here, that may partly be explained by the differences in tuning necessary to achieve energy balance at the different resolutions (Hack et al 2006).

4. Conclusions

We have shown that it is possible to tune the Community Atmosphere Model to CERES, with similar realism as when tuning it to ERBE, without largely impacting the model's climate sensitivity. Whether or not tuning to CERES is a better choice than tuning to ERBE is not a matter we discuss here, and our results do not make one set of observations more credible than the other. There are, however, documented improvements in CERES fluxes relative to ERBE fluxes, particularly related to reductions in angular biases, due to improved angular distribution models used to convert radiances to radiative fluxes (see, e.g., Wong et al 2000, Wielicki et al 1996). The different tuning does alter the climate sensitivity of the model, but the difference between ΔT_{2 × CO2} for the ERBE-tuned and the CERES-tuned configurations is not of the same order of magnitude as the range of climate sensitivities in state-of-the-art GCMs, and it is subtle compared to the extreme uncertainty in climate sensitivity due to model configuration that has previously been indicated. Rather, the difference is similar in size to that found between climate sensitivities for different horizontal resolutions of the same model.

Although this limited study offers no conclusive evidence, it indicates that the CAM is rather robust to tuning changes and that climate sensitivity is not strongly dependent on what level the TOA radiative balance is tuned to. This is a reassuring conclusion—practising tuning is necessary for achieving agreement with observations in key parameters, and possible since there are parameters that are still not well restricted by measurements. A strong dependence of climate sensitivity on TOA radiative balance level through the parameters determining it would make predictions of future warming no less arbitrary than the choice of dataset to tune the model to.

Nonetheless, the difference in climate sensitivity should not be repudiated. Clearly, the uncertainty in cloud parameters together with the ambiguity in the TOA radiative budget observations allow for differing model configurations and subsequently differing climate sensitivities, for which there is presently no way to determine which is more correct. The small magnitude of the difference must not be used as an excuse for not continuing to refine the measurements of the TOA radiative balance or imbalance and definitely not as an excuse for not making further attempts to restrict free parameters like cloud water content in models. The two realizations studied here may or may not be representative of a larger ensemble of differently tuned model configurations, but the fact that they display a difference in climate sensitivity calls for caution in the unavoidable tuning process.

Acknowledgments

The modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the World Climate Research Programme's (WCRP's) Working Group on Coupled Modelling (WGCM) are gratefully acknowledged for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. Thanks are due to Jeffrey Kiehl, James Hack and Christine Shields at NCAR for their support on the use of the model, and to Henning Rodhe and Annica Ekman at MISU for constructive discussion and criticism.

References

Barkstrom B R 1984 The earth radiation budget experiment (ERBE) Bull. Am. Meteorol. Soc. 65 1170–85 
CrossRef

Barkstrom B R and Smith G L 1986 The earth radiation budget experiment: science and implementation Rev. Geophys. 24 379–90 
CrossRef

Bender F A-M, Rodhe H, Charlson R J, Ekman A M-L and Loeb N 2006 22 views of the global albedo—comparison between 20 GCMs and two satellites Tellus A 58 320–30 
CrossRef

Boville B A, Rasch P J, Hack J J and McCaa J R 2006 Representation of clouds and precipitation processes in the Community Atmosphere Model Version 3 (CAM3) J. Clim. 19 2184–98 
CrossRef

Collins W D et al Description of the NCAR Community Atmosphere Model (CAM3), 2004 NCAR Technical Note NCAR/TN-464 + STR 

Collins W D, Rasch P J, Boville B A, Hack J J, McCaa J R, Williamson D L, Briegleb B P, Bitz C M, Lin S-J and Zhang M 2006 The formulation and atmospheric simulation of the Community Atmosphere Model version 3 (CAM3) J. Clim. 19 2144–61 
CrossRef

Greenwald T J, Stephens G L, Vander Haar T H and Jackson D L 1993 A physical retrieval of cloud liquid water over the global oceans using special sensor microwave/imager (SSM/I) observations J. Geophys. Res. 98 18471–88 
CrossRef

Hack J J, Caron J M, Danabasoglu G, Oleson K W, Bitz C and Truesdale J E 2006 CCSM-CAM3 climate simulation sensitivity to changes in horizontal resolution J. Clim. 19 2267–89 
CrossRef

IPCC 2007 Climate change 2007: the physical science basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ed S Solomon, D Qin, M Manning, Z Chen, M Marquis, K B Averyt, M Tignor and H L Miller (Cambridge: Cambridge University Press) 

Kalnay E et al 1996 The NCEP/NCAR 40-year reanalysis project Bull. Am. Meteorol. Soc. 77 437–70 
CrossRef

Kiehl J T, Hack J J and Hurrell J W 1998 The energy budget of the NCAR Community Climate Model: CCM3 J. Clim. 11 1151–78 
CrossRef

Kiehl J T, Shields C A, Hack J J and Collins W D 2006 The climate sensitivity of the Community Climate System Model Version 3 (CCSM3) J. Clim. 19 2584–96 
CrossRef

Kiehl J T and Trenberth K E 1997 Earth's annual global mean energy budget Bull. Am. Meteorol. Soc. 78 197–208 
CrossRef

Levitus S, Antonov J I, Boyer T P and Stephens C 2000 Warming of the world ocean Science 287 2225–9 
CrossRef

Murphy J M, Sexton D M H, Barnett D N, Jones G S, Webb M J, Collins M and Stainforth D A 2004 Quantification of modelling uncertainties in a large ensemble of climate change simulations Nature 430 768–72 
CrossRefPubMed

Penner J E, Quaas J, Storelvmo T, Takemura T, Boucher O, Guo H, Kirkevåg A, Kristjànsson J E and Seland Ø 2006 Model intercomparison of indirect aerosol effects Atmos. Chem. Phys. 6 3391–405 
CrossRef

Platnick S, King M D, Ackerman S A, Menzel W P, Baum B A, Riedi J C and Frey R A 2003 The MODIS cloud products: algorithms and examples from Terra IEEE Trans. Geosci. Remote Sens. 41 459–73 
CrossRef

Randel D L, Vander Haar T H, Ringerud M A, Stephens G L, Greenwald T J and Combs C L 1996 A new global water vapor dataset Bull. Am. Meteorol. Soc. 77 1233–46 
CrossRef

Roeckner E, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kornblueh L, Manzini E, Schlese U and Schulzweida U 2006 Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model J. Clim. 19 3771–91 
CrossRef

Rossow W B, Walker A W, Beuschel D E and Roiter M D 1996 International Satellite Cloud Climatology Project (ISCCP) Documentation of New Cloud Datasets WMO/TD-No. 737 World Meteorological Organization) 

Senior C A and Mitchell J F B 1992 Carbon dioxide and climate: the impact of cloud parameterization J. Clim. 6 393–418 
CrossRef

Smith G L, Wielicki B A, Barkstrom B R, Lee R B, Priestly K J, Charlock T P, Minnis P, Kratz D P, Loeb N and Young D F 2004 Clouds and the Earth radiant energy system: an overview Adv. Space Res. 33 1125–31 
CrossRef

Stainforth D A et al 2005 Uncertainty in predictions of the climate response to rising levels of greenhouse gases Nature 433 403–6 
CrossRefPubMed

Wielicki B A, Barkstrom B R, Harrison E F, Lee R B, Smith L and Cooper J E 1996 Clouds and the Earth's radiant energy system (CERES): an earth observing system experiment Bull. Am. Meteorol. Soc. 77 853–68 
CrossRef

Willis J K, Roemmich D and Cornuelle B 2004 Interannual variability in upper ocean heat content, temperature and thermosteric expansion on global scales J. Geophys. Res. 109 C12036 
CrossRef

Weng F and Grody N C 1994 Retrieval of cloud liquid water using the special sensor microwave imager (SSM/I) J. Geophys. Res. 99 25535–51 
CrossRef

Wentz F J 1997 A well-calibrated ocean algorithm for special sensor microwave/imager J. Geophys. Res. 102 8703–18 
CrossRef

Wong T, Young M, Haeffelin M and Weckmann S 2000 Validation of the CERES/TRMM ERBE-like monthly mean clear-sky longwave dataset and the effects of the 1998 ENSO event J. Clim. 13 4256–67 
CrossRef