Causes of differences in model and satellite tropospheric warming rates

Santer, Benjamin D.; Fyfe, John C.; Pallotta, Giuliana; Flato, Gregory M.; Meehl, Gerald A.; England, Matthew H.; Hawkins, Ed; Mann, Michael; Painter, Jeffrey F.; Bonfils, C.; Cvijanović, Ivana; Mears, C. A.; Wentz, F. J.; Po‐Chedley, Stephen; Fu, Qiang; Zou, Cheng Zhi

doi:10.1038/ngeo2973

Cited by 45 publications

(56 citation statements)

References 61 publications

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“…This will result in an overestimate of the current and future GHGA-produced warming. Within the last few decades this may have contributed to climate models failing to predict the actual warming slowdown [30][31][32][33][34]. Similarly, the absence of the AMO (negative) signal in the ensemble mean of the CMIP5 simulations of the eastern US precipitation will lead to a decreased GHGA precipitation influence in the eastern US, leading to an underestimate of the models' projected precipitation over the eastern US.…”

Section: Statistical Regression Modelsmentioning

confidence: 98%

Observed and Projected Precipitation Changes over the Nine US Climate Regions

et al. 2017

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Abstract:We analyze the past temperature and precipitation changes in nine separate US climate regions. We find that the temperature increased in a statistically significant (95% confidence level equivalent to alpha level of 0.05) manner in all of these regions. However, the variability in the observed precipitation was much more complex. In the eastern US (east of Rocky Mountains), the precipitation increased in all five climate regions and the increase was statistically significant in three of them. In contract, in the western US, the precipitation increased in two regions and decreased in two with no statistical significance in any region. The CMIP5 climate models (an ensemble mean) were not able to capture properly either the large precipitation differences between the eastern and the western US, or the changes of precipitation between 1900 and 2015 in eastern US. The statistical regression model explains the differences between the eastern and western US precipitation as results of different significant predictors. The anthropogenic greenhouse gases and aerosol (GHGA) are the major forcing of the precipitation in the eastern part of US, while the Pacific Decadal Oscillation (PDO) has the major influence on precipitation in the western part of the US. Our analysis suggests that the precipitation over the eastern US increased at an approximate rate of 6.7%/K, in agreement with the Clausius-Clapeyron equation, while the precipitation of the western US was approximately constant, independent of the temperature. Future precipitation over the western part of the US will depend on the behavior of the PDO, and how it (PDO) may be affected by future warming. Low hydrological sensitivity (percent increase of precipitation per one K of warming) projected by the CMIP5 models for the eastern US suggests either an underestimate of future precipitation or an overestimate of future warming.

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Section: Statistical Regression Modelsmentioning

confidence: 98%

Observed and Projected Precipitation Changes over the Nine US Climate Regions

et al. 2017

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“…Instead of a trend ratio of 3–4 between modeled and observed TMT trends, applying the stratospheric correction reduced this ratio to about 2 for the tropics and 1.7 for the global average (Santer et al, ). Additionally, once taking observational and statistical uncertainties and natural variability into account, the difference in trends between models and observations did not appear to be significant, at least up to the beginning of the 21st century (Santer et al, ; Suárez‐Gutiérrez et al, ).…”

Section: Introductionmentioning

confidence: 99%

Explaining Differences Between Recent Model and Satellite Tropospheric Warming Rates With Tropical SSTs

Tuel

2019

Geophysical Research Letters

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Global climate models generally overestimate recent tropospheric warming trends. While a number of explanations have been suggested, their relative impacts have not been quantified. In particular, interannual and long‐term variability of tropospheric temperatures (TTT) is known to be strongly constrained by near‐surface conditions in ocean regions of deep convection. Here, we analyze the role played by tropical sea surface temperature (SST) variability in recent decades in setting TTT. We find that Coupled Model Intercomparison Project Phase 5 models and observations agree on the interannual relationship between SSTs in regions of deep, tropical convection and TTT. Over the 1979–2018 period, most of the difference between model and satellite‐based TTT trends can be explained by respective differences in SST warming trends in regions of deep convection. While large multidecadal patterns of SST variability certainly play a role, notably in the Pacific Ocean, other mechanisms may also contribute to the overestimation of recent SST warming in climate models.

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“…Air temperature variability and trends at various atmospheric levels represent a crucial indicator of the warming or cooling of the Earth's atmosphere both at a global and regional scale [1][2][3][4]. In addition, adequate and in-depth knowledge of temperature fluctuations is essential for validating the accuracy of climate models' simulations [5][6][7]. The development of a climate data record of suitable length and reliability is required for the reliable detection of changes in the Earth's atmospheric temperature [8].…”

Section: Introductionmentioning

confidence: 99%

Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

et al. 2020

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Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) was applied to time series of global methane concentrations and remotely-sensed temperature anomalies of the global lower and mid-troposphere, with the purpose of investigating the multifractal characteristics of their cross-correlated time series and examining their interaction in terms of nonlinear analysis. The findings revealed the multifractal nature of the cross-correlated time series and the existence of positive persistence. It was also found that the cross-correlation in the lower troposphere displayed more abundant multifractal characteristics when compared to the mid-troposphere. The source of multifractality in both cases was found to be mainly the dependence of long-range correlations on different fluctuation magnitudes. Multifractal Detrended Fluctuation Analysis (MF-DFA) was also applied to the time series of global methane and global lower and mid-tropospheric temperature anomalies to separately study their multifractal properties. From the results, it was found that the cross-correlated time series exhibit similar multifractal characteristics to the component time series. This could be another sign of the dynamic interaction between the two climate variables.

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Causes of differences in model and satellite tropospheric warming rates

Cited by 45 publications

References 61 publications

Observed and Projected Precipitation Changes over the Nine US Climate Regions

Observed and Projected Precipitation Changes over the Nine US Climate Regions

Explaining Differences Between Recent Model and Satellite Tropospheric Warming Rates With Tropical SSTs

Multifractal Detrended Cross-Correlation Analysis of Global Methane and Temperature

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