Abstract

The atmospheric response to a hypothetical Tibetan ice sheet was tested with the T21 Atmospheric General Circulation Model (AGCM) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The model response is discussed in terms of an “autocycle” hypothesis of the ice ages proposed by Kuhle. According to this hypothesis, ice-albedo feedbacks associated with the growth and retreat of the Tibetan ice sheet are the mechanism that amplifies the variation of solar insolation on astronomical time scales, producing conditions that favor glaciation or deglaciation in North America and Eurasia.

The imposed Tibetan ice sheet forcing did not increase the annual snow balance at the locations of the Laurentide and Eurasian ice sheets. Analysis of the seasonal cycle results indicated that there were small areas of locally significant temperature decreases in July (at the ice sheet locations), but no corresponding precipitation increases in January. The upper-tropospheric response to the elevated Tibetan plateau is not confined to the vicinity of the forcing, but changes in the global energetics of the atmosphere are small (less than 5%) relative to the control.

The results of this experiment do not permit a conclusive decision regarding the validity of Kuhle's autocycle hypothesis. Future modeling studies need to consider ocean-atmosphere-ice sheet feedbacks and to investigate the transient response of the climate system over a complete ice age cycle.

Abstract

It has been suggested that the apparent warming of the earth's surface during the twentieth century may be biased by large changes in the coverage of surface temperature measurements since 1900. This issue is investigated using climate model simulations. By imposing observed coverage changes on simulated surface temperatures, estimates are obtained of twentieth-century temperature change for both full global coverage and for actual historical coverage. In 10 out of 16 simulations including human climate perturbations, the temperature change from the globally complete model output is significantly larger than that derived from the historically masked model output. The remaining six simulations show no significant difference between complete and masked model output. Thus, these results do not support the hypothesis that the increase in the earth's surface temperature has been overestimated because of incomplete observational data. Rather, if the simulations analyzed are realistic, the true temperature increase over the last century is slightly larger than that estimated from available observations. Eight simulations of natural internal climate variability, which omit human climate perturbations, were analyzed. In none of these simulations does the temperature change during 100 yr—whether obtained from globally complete or masked model output—come close to the observed twentieth-century temperature increase.

Abstract

This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.

Abstract

This paper investigates the impact of aerosol forcing uncertainty on the robustness of estimates of the twentieth-century warming attributable to anthropogenic greenhouse gas emissions. Attribution analyses on three coupled climate models with very different sensitivities and aerosol forcing are carried out. The Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3), Parallel Climate Model (PCM), and GFDL R30 models all provide good simulations of twentieth-century global mean temperature changes when they include both anthropogenic and natural forcings. Such good agreement could result from a fortuitous cancellation of errors, for example, by balancing too much (or too little) greenhouse warming by too much (or too little) aerosol cooling.

Despite a very large uncertainty for estimates of the possible range of sulfate aerosol forcing obtained from measurement campaigns, results show that the spatial and temporal nature of observed twentieth-century temperature change constrains the component of past warming attributable to anthropogenic greenhouse gases to be significantly greater (at the 5% level) than the observed warming over the twentieth century. The cooling effects of aerosols are detected in all three models.

Both spatial and temporal aspects of observed temperature change are responsible for constraining the relative roles of greenhouse warming and sulfate cooling over the twentieth century. This is because there are distinctive temporal structures in differential warming rates between the hemispheres, between land and ocean, and between mid- and low latitudes. As a result, consistent estimates of warming attributable to greenhouse gas emissions are obtained from all three models, and predictions are relatively robust to the use of more or less sensitive models. The transient climate response following a 1% yr⁻¹ increase in CO₂ is estimated to lie between 2.2 and 4 K century⁻¹ (5–95 percentiles).

Abstract

Previous work identified an anthropogenic fingerprint pattern in T _AC(x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T _AC(x, t) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T _AC(x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T _AC(x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T _AC(x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

Abstract

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.

Abstract

Temperature observations of the upper-air atmosphere are now available for more than 40 years from both ground- and satellite-based observing systems. Recent years have seen substantial improvements in reducing long-standing discrepancies among datasets through major reprocessing efforts. The advent of radio occultation (RO) observations in 2001 has led to further improvements in vertically resolved temperature measurements, enabling a detailed analysis of upper-troposphere/lower-stratosphere trends. This paper presents the current state of atmospheric temperature trends from the latest available observational records. We analyze observations from merged operational satellite measurements, radiosondes, lidars, and RO, spanning a vertical range from the lower troposphere to the upper stratosphere. The focus is on assessing climate trends and on identifying the degree of consistency among the observational systems. The results show a robust cooling of the stratosphere of about 1–3 K, and a robust warming of the troposphere of about 0.6–0.8 K over the last four decades (1979–2018). Consistent results are found between the satellite-based layer-average temperatures and vertically resolved radiosonde records. The overall latitude–altitude trend patterns are consistent between RO and radiosonde records. Significant warming of the troposphere is evident in the RO measurements available after 2001, with trends of 0.25–0.35 K per decade. Amplified warming in the tropical upper-troposphere compared to surface trends for 2002–18 is found based on RO and radiosonde records, in approximate agreement with moist adiabatic lapse rate theory. The consistency of trend results from the latest upper-air datasets will help to improve understanding of climate changes and their drivers.

Accurate and reliable predictions and an understanding of future changes in the stratosphere are major aspects of the subject of climate change. Simulating the interaction between chemistry and climate is of particular importance, because continued increases in greenhouse gases and a slow decrease in halogen loading are expected. These both influence the abundance of stratospheric ozone. In recent years a number of coupled chemistry–climate models (CCMs) with different levels of complexity have been developed. They produce a wide range of results concerning the timing and extent of ozone-layer recovery. Interest in reducing this range has created a need to address how the main dynamical, chemical, and physical processes that determine the long-term behavior of ozone are represented in the models and to validate these model processes through comparisons with observations and other models. A set of core validation processes structured around four major topics (transport, dynamics, radiation, and stratospheric chemistry and microphysics) has been developed. Each process is associated with one or more model diagnostics and with relevant datasets that can be used for validation. This approach provides a coherent framework for validating CCMs and can be used as a basis for future assessments. Similar efforts may benefit other modeling communities with a focus on earth science research as their models increase in complexity.