A Bivariate Time Series Approach to Anthropogenic Trend Detection in Hemispheric Mean Temperatures

A bivariate time series regression approach is used to model observed variations in hemispheric mean temperature over the period 1900-96. The regression equations include deterministic predictor variables and lagged values of the two predictands, and two different forms of this basic structure are employed. The deterministic predictors considered are simple linear trends, various climate model-generated time series based on different combinations of greenhouse gas, sulfate aerosol, and solar forcing, and the Southern Oscillation index (SOI). With linear trends as the only predictors, the best model is a fourth-order bivariate autoregressive model including lagged Southern Hemisphere (SH) to Northern Hemisphere (NH) dependence, as in previous work by Kaufmann and Stern. The estimated NH and SH trends are both + 0.67°C century-1, and both are highly statistically significant. If SOI is included as an additional predictor, however, a first-order time series model, with no SH to NH dependence, is an adequate fit to the data. This shows that SOI may be an important covariate in this kind of analysis. Further analysis uses climate model-generated forcing terms representing greenhouses gases, sulfate aerosols, and solar effects, as well as SOI. The statistical analysis makes extensive use of Bayes factors as a device for discriminating among a wide spectrum of possible models. The best fits to the data are obtained when all three forcing terms are included. Total sulfate aerosol forcing of 1.1 W m-2(with a corresponding climate sensitivity of ΔT2+ = 4.2cC) is preferred to -0.7 W m-2(with sensitivity of 2.3°C), but the Bayes factor discrimination between these cases is weak

Altitude dependence of atmospheric temperature trends: Climate models versus observation

As a consequence of greenhouse forcing, all state of the art general circulation models predict a positive temperature trend that is greater for the troposphere than the surface. This predicted positive trend increases in value with altitude until it reaches a maximum ratio with respect to the surface of as much as 1.5 to 2.0 at about 200 to 400 hPa. However, the temperature trends from several independent observational data sets show decreasing as well as mostly negative values. This disparity indicates that the three models examined here fail to account for the effects of greenhouse forcings.Comment: 9 pages, 3 figure

Observed multivariable signals of late 20th and early 21st century volcanic activity

The relatively muted warming of the surface and lower troposphere since 1998 has attracted considerable attention. One contributory factor to this “warming hiatus” is an increase in volcanically induced cooling over the early 21st century. Here we identify the signals of late 20th and early 21st century volcanic activity in multiple observed climate variables. Volcanic signals are statistically discernible in spatial averages of tropical and near-global SST, tropospheric temperature, net clear-sky short-wave radiation, and atmospheric water vapor. Signals of late 20th and early 21st century volcanic eruptions are also detectable in near-global averages of rainfall. In tropical average rainfall, however, only a Pinatubo-caused drying signal is identifiable. Successful volcanic signal detection is critically dependent on removal of variability induced by the El Nino–Southern Oscillation.National Science Foundation (U.S.) (Grant AGS-1342810

Volcanic Contribution to Decadal Changes in Tropospheric Temperature

Despite continued growth in atmospheric levels of greenhouse gases, global mean surface and tropospheric temperatures have shown slower warming since 1998 than previously. Possible explanations for the slow-down include internal climate variability, external cooling influences and observational errors. Several recent modelling studies have examined the contribution of early twenty-first-century volcanic eruptions to the muted surface warming. Here we present a detailed analysis of the impact of recent volcanic forcing on tropospheric temperature, based on observations as well as climate model simulations. We identify statistically significant correlations between observations of stratospheric aerosol optical depth and satellite-based estimates of both tropospheric temperature and short-wave fluxes at the top of the atmosphere. We show that climate model simulations without the effects of early twenty-first-century volcanic eruptions overestimate the tropospheric warming observed since 1998. In two simulations with more realistic volcanic influences following the 1991 Pinatubo eruption, differences between simulated and observed tropospheric temperature trends over the period 1998 to 2012 are up to 15% smaller, with large uncertainties in the magnitude of the effect. To reduce these uncertainties, better observations of eruption-specific properties of volcanic aerosols are needed, as well as improved representation of these eruption-specific properties in climate model simulations

The Detection and Attribution Model Intercomparison Project DAMIP v1.0) contribution to CMIP6

Detection and attribution (D&A) simulations were important components of CMIP5 and underpinned the climate change detection and attribution assessments of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The primary goals of the Detection and Attribution Model Intercomparison Project (DAMIP) are to facilitate improved estimation of the contributions of anthropogenic and natural forcing changes to observed global warming as well as to observed global and regional changes in other climate variables; to contribute to the estimation of how historical emissions have altered and are altering contemporary climate risk; and to facilitate improved observationally constrained projections of future climate change. D&A studies typically require unforced control simulations and historical simulations including all major anthropogenic and natural forcings. Such simulations will be carried out as part of the DECK and the CMIP6 historical simulation. In addition D&A studies require simulations covering the historical period driven by individual forcings or subsets of forcings only: such simulations are proposed here. Key novel features of the experimental design presented here include firstly new historical simulations with aerosols-only, stratospheric-ozone-only, CO2-only, solar-only, and volcanic-only forcing, facilitating an improved estimation of the climate response to individual forcing, secondly future single forcing experiments, allowing observationally constrained projections of future climate change, and thirdly an experimental design which allows models with and without coupled atmospheric chemistry to be compared on an equal footing

The Community Climate System Model version 3 (CCSM3)

Author Posting. © American Meteorological Society 2006. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 19 (2006): 2122–2143, doi:10.1175/JCLI3761.1.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.We would like to acknowledge the substantial contributions to and support for the CCSM project from the National Science Foundation (NSF), the Department of Energy (DOE), the National Oceanic and Atmospheric Administration, and the National Aeronautics and Space Administration

Attribution of Declining Western U.S. Snowpack to Human Effects

Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.Lawrence Livermore National Laboratory///Estados UnidosScripps Institution of Oceanography//SIO/Estados UnidosMinistry of Education, Culture, Sports, Science and Technology///JapónCalifornia Energy Commission///Estados UnidosProgram of Climate Model Diagnosis and Intercomparison/[DOE-W-7405-ENG-48]/PCMDI/Estados UnidosUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Causes of differences in model and satellite tropospheric warming rates

In the early twenty-first century, satellite-derived tropospheric warming trends were generally smaller than trends estimated from a large multi-model ensemble. Because observations and coupled model simulations do not have the same phasing of natural internal variability, such decadal differences in simulated and observed warming rates invariably occur. Here we analyse global-mean tropospheric temperatures from satellites and climate model simulations to examine whether warming rate differences over the satellite era can be explained by internal climate variability alone. We find that in the last two decades of the twentieth century, differences between modelled and observed tropospheric temperature trends are broadly consistent with internal variability. Over most of the early twenty-first century, however, model tropospheric warming is substantially larger than observed; warming rate differences are generally outside the range of trends arising from internal variability. The probability that multi-decadal internal variability fully explains the asymmetry between the late twentieth and early twenty-first century results is low (between zero and about 9%). It is also unlikely that this asymmetry is due to the combined effects of internal variability and a model error in climate sensitivity. We conclude that model overestimation of tropospheric warming in the early twenty-first century is partly due to systematic deficiencies in some of the post-2000 external forcings used in the model simulations