Abstract
Early versions of satellite and radiosonde datasets suggested that the tropical surface had warmed more than the troposphere, while climate models consistently showed tropospheric amplification of surface warming in response to human-caused increases in greenhouse gases (GHGs). We revisit such comparisons here using new observational estimates of surface and tropospheric temperature changes. We find that there is no longer a serious discrepancy between modeled and observed trends in the tropics. Our results contradict a recent claim that all simulated temperature trends in the tropical troposphere are inconsistent with observations. This claim was based on the use of older radiosonde and satellite datasets and on two methodological errors: the neglect of observational trend uncertainties introduced by interannual climate variability and application of an inappropriate statistical “consistency test”.
This emerging reconciliation of models and observations has two primary explanations. First, because of changes in the treatment of buoy and satellite information, new surface temperature datasets yield slightly reduced tropical warming relative to earlier versions. Second, recently developed satellite and radiosonde datasets now show larger warming of the tropical lower troposphere. In the case of a new satellite dataset from Remote Sensing Systems (RSS) , enhanced warming is due to an improved procedure of adjusting for inter-satellite biases. When the RSS-derived tropospheric temperature trend is compared with four different observed estimates of surface temperature change, the surface warming is invariably amplified in the tropical troposphere, consistent with model results. Even if we use data from a second satellite dataset with smaller tropospheric warming than in RSS, observed tropical lapse rates are not significantly different from those in all model simulations.
Our results contradict a recent claim that all simulated temperature trends in the tropical troposphere and in tropical lapse rates are inconsistent with observations. This claim was based on the use of older radiosonde and satellite datasets and on two methodological errors: the neglect of observational trend uncertainties introduced by interannual climate variability and application of an inappropriate statistical “consistency test”.
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Notes
- 1.
See Table 3.4 in Lanzante et al. (2006). For the specific period 1979–2004, tropical (20 °N–20 °S) T 2 trends range from 0.05 °C/decade (UAH) to 0.19 °C/decade (UMd), while T 2LT trends span the range 0.05 °C/decade (UAH) to 0.15 °C/decade (RSS). The most important sources of uncertainty are likely to be “due to inter-satellite calibration offsets and calibration drifts” (Mears et al. 2006, page 78).
- 2.
The UMd and NOAA/NESDIS groups do not provide a T 2LT product. Because of their calibration procedure, the NOAA/NESDIS T 2 data are only available for a shorter period (1987 to present) than the T 2 products of the three other groups.
- 3.
A more recent version of the RSS T 2 and T 2LT datasets (version 3.1) now exists. RSS versions 3.0 and 3.1 are virtually identical over the primary analysis period considered here (1979–1999). For UAH data, a version 5.2 exists for T 2LT but not for T 2 data, for which only version 5.1 is available.
- 4.
RAOBCORE stands for RAdiosonde OBservation COrrection using REanalysis.
- 5.
All simulations included human-induced changes in well-mixed GHGs and the direct (scattering) effects of sulfate aerosols on incoming solar radiation. Other external forcings (such as changes in ozone, carbonaceous aerosols, indirect effects of aerosols on clouds, land surface properties, solar irradiance, and volcanic dust loadings) were not handled uniformly across different modeling groups. For further details of the applied forcings, see Santer et al. (2005, 2006).
- 6.
DCPS07 used a larger set of 20CEN runs (67 simulations performed with 22 different models) and incorporated model results that were not available at the time of the Santer et al. (2005) study. This difference in the number of 20CEN models employed in the two investigations is immaterial for illustrating the statistical problems in the consistency test applied by DCPS07. All 49 simulations employed in our current work were also analyzed by DCSP07.
- 7.
Amplification occurs due to the nonlinear effect of the release of latent heat by moist ascending air in regions experiencing convection.
- 8.
The 20CEN experiments analyzed here were performed with coupled atmosphere-ocean General Circulation Models (A/OGCMs) driven by estimates of historical changes in external forcing. Due to chaotic variability in the climate system, small differences in the atmospheric or oceanic initial conditions at the start of the 20CEN run (typically in the mid- to late nineteenth century) rapidly lead to different manifestations of climate noise. Within the space of several months, the state of the atmosphere is essentially uncorrelated with the initial state. This means that even the same model, when run many times with identical external forcings (but each time from slightly different initial conditions), produces many different samples of η m (t), each superimposed on the same underlying signal, ϕ m (t).
- 9.
Our \( {d}_1^{\ast } \)test involving the multi-model ensemble-mean trend [see Eq. (5.12)], also relies on an AR-1 model to estimate n e and adjust the observed standard error, and is therefore also likely to be too liberal.
- 10.
We use < > to denote an ensemble average over multiple 20CEN realizations performed with a single model. Double angle brackets, << >>, indicate a multi-model ensemble average.
- 11.
Under this assumption, the total uncertainty in << b m >> − b o is determined solely by inter-model trend differences arising from structural differences between the models [see Eqs. (5.9, 5.10, and 5.11)]. As discussed in Sect. 5.3, however, the total uncertainty in the magnitude of << b m >> − b o reflects not only these structural differences, but also inter-model differences in internal variability and ensemble size.
- 12.
Inter-model differences in the size of the confidence intervals in Fig. 5.3a are due primarily to differences in the amplitude and temporal autocorrelation properties of η m (t), but are also affected by neglect or inclusion of the effects of volcanic forcing (see Santer et al. 2005, 2006). Models with large ENSO variability (such as GFDL-CM2.1 and FGOALS-g1.0) have large adjusted confidence intervals, while A/OGCMs with relatively coarse-resolution, diffusive oceans (such as GISS-AOM) have much weaker ENSO variability and smaller values of s{b m }.
- 13.
We have explored the sensitivity of our adjusted standard errors and significance test results to choices of averaging period ranging from two to 12 months. These choices span a wide range of temporal autocorrelation behavior. Results for the d test are relatively insensitive to the selected averaging period, suggesting that our adjustment method is reasonable.
- 14.
Two layers (T 2LT and T 2) × two observational datasets (RSS and UAH).
- 15.
One of the assumptions underlying the d * 1 test (and all tests performed here) is that structural uncertainty in the observations is negligible (see Sect. 5.4.2). We know this is not the case in the real world (see, e.g., Seidel et al. 2004; Thorne et al. 2005; Lanzante et al. 2006; Mears et al. 2006). In the present study, we have examined the effects of structural uncertainties in satellite and radiosonde data by treating each observational dataset independently, and assessing the robustness of our model-versus-observed trend comparisons to different dataset choices. An alternative approach would be to explicitly include a structural uncertainty term for the observations in the test statistic itself.
- 16.
- 17.
Note that RATPAC-B is unadjusted after 1997. RATPAC-A, which we use here, accounts for inhomogeneities before and after 1997.
- 18.
Sherwood et al. (2008) argue that this procedure does not completely homogenize data from stations between 5°S and 20°N, since trends at these stations remained highly variable and (on average) unphysically low compared to those at neighboring latitudes that are much more accurately known. The implication is that gradual (rather than step-like) changes in bias at many tropical stations may not be reliably identified and adjusted by the IUK homogenization procedure. If this is the case, the IUK trends shown here are likely to be underestimates of the true trends.
- 19.
An error in the model average surface warming is entirely likely given the neglect of indirect aerosol effects in roughly half of the models analyzed here.
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Appendix: Statistical Notation
Appendix: Statistical Notation
Subscripts and indices | |
|---|---|
m | Subscript denoting model data |
o | Subscript denoting observational data |
t | Index over time (in months) |
i | Index over number of models |
j | Index over number of 20CEN realizations |
z | Index over number of atmospheric levels |
Sample sizes | |
|---|---|
n t | Total number of time samples (usually 252) |
n e | Effective number of time samples, adjusted for temporal autocorrelation |
n m | Total number of models (19) |
n r (i) | Total number of 20CEN realizations for the i th model |
N | Total number of synthetic time series |
Time series | |
|---|---|
y m(t) | Simulated T 2LT or T 2 time series |
ϕ m(t) | Underlying signal in y m (t) in response to forcing |
η m(t) | Realization of internally generated noise in y m (t) |
x(t) | Synthetic AR-1 time series |
z(t) | Synthetic noise time series |
Trends | |
|---|---|
b m | Least-squares linear trend in an individual y m (t) time series |
< b m (i) > | Ensemble-mean trend in the i th model |
<< b m >> | Multi-model ensemble-mean trend |
<< b m (z) >> | Multi-model ensemble-mean trend profile |
Standard errors and standard deviations | |
|---|---|
s{b m } | Standard error of b m |
s{y m (t)} | Temporal standard deviation of y m (t) anomaly time series |
s{< b m >} | Standard deviation of ensemble-mean trends |
s{< b m (z) >} | Standard deviation of ensemble-mean trends at discrete pressure levels |
σ SE | DCPS07 “estimate of the uncertainty of the mean” |
Other regression terms | |
|---|---|
e(t) | Regression residuals |
r 1 | Lag-1 autocorrelation of regression residuals |
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Santer, B.D. et al. (2018). Consistency of Modeled and Observed Temperature Trends in the Tropical Troposphere. In: A. Lloyd, E., Winsberg, E. (eds) Climate Modelling. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65058-6_5
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