Santer
Benjamin D.
Santer
Benjamin D.
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ArticleThe Community Climate System Model version 3 (CCSM3)(American Meteorological Society, 2006-06-01) Collins, William D. ; Bitz, Cecilia M. ; Blackmon, Maurice L. ; Bonan, Gordon B. ; Bretherton, Christopher S. ; Carton, James A. ; Chang, Ping ; Doney, Scott C. ; Hack, James J. ; Henderson, Thomas B. ; Kiehl, Jeffrey T. ; Large, William G. ; McKenna, Daniel S. ; Santer, Benjamin D. ; Smith, Richard D.The Community Climate System Model version 3 (CCSM3) has recently been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale dynamics, variability, and climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for the atmosphere and land and a grid with approximately 1° resolution for the ocean and sea ice. The new system incorporates several significant improvements in the physical parameterizations. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol radiative forcing, land–atmosphere fluxes, ocean mixed layer processes, and sea ice dynamics. There are significant improvements in the sea ice thickness, polar radiation budgets, tropical sea surface temperatures, and cloud radiative effects. CCSM3 can produce stable climate simulations of millennial duration without ad hoc adjustments to the fluxes exchanged among the component models. Nonetheless, there are still systematic biases in the ocean–atmosphere fluxes in coastal regions west of continents, the spectrum of ENSO variability, the spatial distribution of precipitation in the tropical oceans, and continental precipitation and surface air temperatures. Work is under way to extend CCSM to a more accurate and comprehensive model of the earth's climate system.
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ArticleInternal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming(National Academy of Sciences, 2022-11-22) Po-Chedley, Stephen ; Fasullo, John T. ; Siler, Nicholas ; Labe, Zachary M. ; Barnes, Elizabeth A. ; Bonfils, Céline J. W. ; Santer, Benjamin D.Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.
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ArticleExceptional stratospheric contribution to human fingerprints on atmospheric temperature(National Academy of Sciences, 2023-05-16) Santer, Benjamin D. ; Po-Chedley, Stephen ; Zhao, Lilong ; Zou, Cheng-Zhi ; Fu, Qiang ; Solomon, Susan ; Thompson, David W. J. ; Mears, Carl ; Taylor, Karl E.In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S25 − 50). To date, however, S25 − 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S25 − 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25 − 50 signal and noise patterns are accompanied by large cooling of S25 − 50 (1 to 2C over 1986 to 2022) and low S25 − 50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere.
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ArticleRobust human influence across the troposphere, surface, and ocean: a multivariate analysis(American Meteorological Society, 2023-10-20) Blackport, Russell ; Fyfe, John C. ; Santer, Benjamin D.Human influence has been robustly detected throughout many parts of the climate system. Pattern-based methods have been used extensively to estimate the strength of model-predicted “fingerprints,” both human and natural, in observational data. However, individual studies using different analysis methods and time periods yield inconsistent estimates of the magnitude of the influence of anthropogenic aerosols, depending on whether they examined the troposphere, surface, or ocean. Reducing the uncertainty of the impact of aerosols on the climate system is crucial for understanding past climate change and obtaining more reliable estimates of climate sensitivity. To reconcile divergent estimates of aerosol effects obtained in previous studies, we apply the same regression-based detection and attribution method to three different variables: mid-to-upper-tropospheric temperature, surface temperature, and ocean heat content. We find that quantitative estimates of human influence in observations are consistent across these three independently monitored components of the climate system. Combining the troposphere, surface, and ocean data into a single multivariate fingerprint results in a small (∼10%) reduction of uncertainty of the magnitude of the greenhouse gas fingerprint, but a large (∼40%) reduction for the anthropogenic aerosol fingerprint. This reduction in uncertainty results in a substantially earlier time of detection of the multivariate aerosol fingerprint when compared to aerosol fingerprint detection time in each of the three individual variables. Our results highlight the benefits of analyzing data across the troposphere, surface, and ocean in detection and attribution studies, and motivate future work to further constrain uncertainties in aerosol effects on climate.
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ArticleThe emerging human influence on the seasonal cycle of sea surface temperature(Nature Research, 2024-03-15) Shi, Jia-Rui ; Santer, Benjamin D. ; Kwon, Young-Oh ; Wijffels, Susan E.Gaining insight into anthropogenic influence on seasonality is of scientific, economic and societal importance. Here we show that a human-caused signal in the seasonal cycle of sea surface temperature (SST) has emerged from the noise of natural variability. Geographical patterns of changes in SST seasonal cycle amplitude (SSTAC) reveal two distinctive features: an increase at Northern Hemisphere mid-latitudes related to mixed-layer depth changes and a robust dipole pattern between 40° S and 55° S that is mainly driven by surface wind changes. The model-predicted pattern of SSTAC change is identifiable with high statistical confidence in four observed SST products and in 51 individual model realizations of historical climate evolution. Simulations with individual forcings reveal that GHG increases are the primary driver of changes in SSTAC, with smaller but distinct contributions from anthropogenic aerosol and ozone forcing. The robust human ‘fingerprint’ identified here is likely to have wide-ranging impacts on marine ecosystems.