Trends in radiosonde-based temperatures and lower-tropospheric lapse rates are presented for the time periods 1959–97 and 1979–97, including their vertical, horizontal, and seasonal variations. A novel aspect is that estimates are made globally of the effects of artificial (instrumental or procedural) changes on the derived trends using data homogenization procedures introduced in a companion paper (Part I). Credibility of the data homogenization scheme is established by comparison with independent satellite temperature measurements derived from the microwave sounding unit (MSU) instruments for 1979–97. The various analyses are performed using monthly mean temperatures from a near–globally distributed network of 87 radiosonde stations. The severity of instrument-related problems, which varies markedly by geographic region, was found, in general, to increase from the lower troposphere to the lower stratosphere, although surface data were found to be as problematic as data from the stratosphere. Except for the surface, there is a tendency for changes in instruments to artificially lower temperature readings with time, so that adjusting the data to account for this results in increased tropospheric warming and decreased stratospheric cooling. Furthermore, the adjustments tend to enhance warming in the upper troposphere more than in the lower troposphere; such sensitivity may have implications for ‘‘fingerprint’ ’ assessments of climate change. However, the most sensitive part of the

This paper addresses the question of where we now stand with respect to detection and attribution of an anthropogenic climate signal. Our ability to estimate natural climate variability, against which claims of anthropogenic signal detection must be made, is reviewed. The current situation suggests control runs of global climate models may give the best estimates of natural variability on a global basis, estimates that appear to be accurate to within a factor of 2 or 3 at multidecadal timescales used in detection work. Present uncertainties in both observations and model-simulated anthropogenic signals in near-surface air temperature are estimated. The uncertainty in model simulated signals is, in places, as large as the signal to be detected. Two different, but complementary, approaches to detection and attribution are discussed in the context of these uncertainties. Applying one of the detection strategies, it is found that the change in near-surface, June through August air temperature field over the last 50 years is generally different at a significance level of 5 % from that expected from modelbased estimates of natural variability. Greenhouse gases alone cannot explain the observed change. Two of four climate models forced by greenhouse gases and direct sulfate aerosols produce results consistent with the current climate change observations, while the consistency of the other two depends on which model’s anthropogenic fingerprints are used.

Current attribution analyses that seek to determine the relative contributions of different forcing agents to observed near-surface temperature changes underestimate the importance of weak signals, such as that due to changes in solar irradiance. Here a new attribution method is applied that does not have a systematic bias against weak signals. It is found that current climate models underestimate the observed climate response to solar forcing over the twentieth century as a whole, indicating that the climate system has a greater sensitivity to solar forcing than do models. The results from this research show that increases in solar irradiance are likely to have had a greater influence on global-mean temperatures in the first half of the twentieth century than the combined effects of changes in anthropogenic forcings. Nevertheless the results confirm previous analyses showing that greenhouse gas increases explain most of the global warming observed in the second half of the twentieth century. 1.

A fingerprint-based method for climate change detection and attribution with some novel features is proposed. The method is based on a functional ANOVA (ANalysis Of VAriance) decomposition of a time and space signal, further decomposed into global time-trend and time-trend anomaly as a function of space. The method estimates the signal as a component of forced minus background climate model output, and then uses a partial spline model to estimate and test for the existence of signal in historical data. The method is based on the classical detection of signal in noise, however there are several features apparently novel to the fingerprint literature, in particular, the analysis takes place directly in observation space, anomalies are tted directly and there is possibility for estimating certain parameters of covariance models for the historical data as part of the analysis. Simulation studies using climate model runs from GFDL and NCAR and historical data for NH Winter average...

An approach to identifying climate changes is presented that does not hinge on simulations of natural climate variations or anthropogenic changes. Observed interdecadal climate variations are decomposed into several discriminants, mutually uncorrelated spatiotemporal components with a maximal ratio of interdecadal-to-intradecadal variance. The dominant discriminants of twentieth-century variations in surface temperature exhibit large-scale warming in which, particularly in the Northern Hemisphere summer months, localized cooling is embedded. The structure of the large-scale warming is consistent with expected effects of increases in greenhouse gas concentrations. The localized cooling, with maxima on scales of 1000–2000 km over East Asia, eastern Europe, and North America, is suggestive of radiative effects of anthropogenic sulfate aerosols. 1.

this document we provide details of both.

We present an approach to identifying climate changes that does not hinge on simulations of natural climate variations or anthropogenic changes. We decompose observed interdecadal climate variations into several discriminants, mutually uncorrelated spatiotemporal components with maximal ratio of interdecadal to intradecadal variance. The dominant discriminants of twentieth-century variations in surface temperature exhibit large-scale warming in which, particularly in the Northern Hemisphere summer months, localized cooling is embedded. The structure of the large-scale warming is consistent with expected effects of increases in greenhouse gas concentrations. The localized cooling, with maxima on scales of 1,000--2,000 km over East Asia, eastern Europe, and North America, is suggestive of radiative effects of anthropogenic sulphate aerosols.

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 twentiethcentury 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

levels in predicted patterns of anthropogenic

In comparisons of radiosonde vertical temperature trend profiles with comparable profiles derived from selected Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) general circulation models (GCMs) driven by major external forcings of the latter part of the twentieth century, model trends exhibit a positive bias relative to radiosonde trends in the majority of cases for both time periods examined (1960–99 and 1979–99). Homogeneity adjustments made in the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) and Hadley Centre Atmospheric Temperatures, version 2 (HadAT2), radiosonde datasets, which are applied by dataset developers to account for timevarying biases introduced by historical changes in instruments and measurement practices, reduce the relative bias in most cases. Although some differences were found between the two observed datasets, in general the observed trend profiles were more similar to one another than either was to the GCM profiles. In the troposphere, adjustment has a greater impact on improving agreement of the shapes of the trend profiles than on improving agreement of the layer mean trends, whereas in the stratosphere the opposite is true. Agreement between the shapes of GCM and radiosonde trend profiles is generally better in the stratosphere than the troposphere, with more complexity to the profiles in the latter than the former. In the troposphere the tropics exhibit the poorest agreement between GCM and radiosonde trend profiles, but also the largest improvement in agreement resulting from homogeneity adjustment. In the stratosphere, radiosonde trends indicate more cooling than GCMs. For the 1979–99 period, a disproportionate amount of this discrepancy arises several months after the eruption of Mount Pinatubo, at which time temperatures in the radiosonde time series cool abruptly by �0.5 K compared to those derived from GCMs, and this difference persists to the end of the record. 1.