Charles Doutriaux

Uncertainties in observationally based estimates of temperature change in the free atmosphere

Uncertainties are quantified in atmospheric temperature changes derived from satellites, radiosondes, and the reanalyses of the National Center for Environmental Prediction and European Centre for Medium-Range Weather Forecasts (NCEP and ERA). To facilitate intercomparison, we compute from the reanalyses and radiosonde data deep layer temperatures equivalent to those estimated from the satellite-based Microwave Sounding Unit (MSU). Equivalent MSU temperatures generated using global mean weighting functions and a radiative transfer code give similar results. NCEPs pre-1979 global mean lower stratospheric temperature anomalies diverge markedly from radiosonde data. A smaller offset occurs in the midtroposphere. These differences are attributed to a likely warm bias in the tropical lower stratosphere in the temperature retrievals used by NCEP from November 1978 onward, and changes in the error characteristics of the assimilation models simulation of the lower stratosphere. In the lower troposphere, ERA and NCEP show different global mean trends due to differences in assimilation strategy, input observational data, quality control procedures, and model physics. Over 1979–1993, ERA warms by 0.106°C/decade, while NCEP cools by 0.028°C/decade. Applying the HadRT1.1 (radiosonde) data availability mask to NCEP improves the agreement between these data sets. Neglecting coverage differences can yield misleading results in MSU-radiosonde trend comparisons. Substantial trend uncertainties also arise from coverage differences between various radiosonde data sets. Version c of the MSU lower tropospheric temperature retrieval fails to adjust explicitly for orbital decay. If this were applied without any additional adjustments, it would resolve an important discrepancy: in MSUc the lower troposphere has cooled in relation to the midtroposphere, while the reverse is the case for both reanalyses and for the radiosonde data examined here.

Separating signal and noise in atmospheric temperature changes: The importance of timescale

We compare global-scale changes in satellite estimates of the temperature of the lower troposphere (TLT) with model simulations of forced and unforced TLT changes. While previous work has focused on a single period of record, we select analysis timescales ranging from 10 to 32 years, and then compare all possible observed TLT trends on each timescale with corresponding multi-model distributions of forced and unforced trends. We use observed estimates of the signal component of TLT changes and model estimates of climate noise to calculate timescale-dependent signal-to-noise ratios (S/N). These ratios are small (less than 1) on the 10-year timescale, increasing to more than 3.9 for 32-year trends. This large change in S/N is primarily due to a decrease in the amplitude of internally generated variability with increasing trend length. Because of the pronounced effect of interannual noise on decadal trends, a multi-model ensemble of anthropogenically-forced simulations displays many 10-year periods with little warming. A single decade of observational TLT data is therefore inadequate for identifying a slowly evolving anthropogenic warming signal. Our results show that temperature records of at least 17 years in length are required for identifying human effects on global-mean tropospheric temperature. Copyright 2011 by the American Geophysical Union.

Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data

Scientists increasingly use ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. In this article, we present Ensemble-Vis, a framework consisting of a collection of overview and statistical displays linked through a high level of interactivity. Ensemble-Vis allows scientists to gain key scientific insight into the distribution of simulation results as well as the uncertainty associated with the scientific data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate our framework using driving problems from climate modeling and meteorology and discuss generalizations to other fields.

Accounting for the effects of volcanoes and ENSO in comparisons of modeled and observed temperature trends

Several previous studies have attempted to remove the effects of explosive volcanic eruptions and El Nino-Southern Oscillation (ENSO) variability from time series of globally averaged surface and tropospheric temperatures. Such work has largely ignored the nonzero correlation between volcanic signals and ENSO. Here we account for this collinearity using an iterative procedure. We remove estimated volcano and ENSO signals from the observed global mean temperature data, and then calculate trends over 1979-1999 in the residuals. Residual trends are sensitive to the choice of index used for removing ENSO effects and to uncertainties in key volcanic parameters. Despite these sensitivities, residual surface and lower tropospheric (2LT) trends are almost always larger than trends in the raw observational data. After removal of volcano and ENSO effects, the differential warming between the surface and lower troposphere is generally reduced. These results suggest that the net effect of volcanoes and ENSO over 1979-1999 was to reduce globally averaged surface and tropospheric temperatures and cool the troposphere by more than the surface. ENSO and incomplete volcanic forcing effects can hamper reliable assessment of the true correspondence between modeled and observed trends. In the second part of our study, we remove these effects from model data and compare simulated and observed residual trends. Residual temperature trends are not significantly different at the surface. In the lower troposphere the statistical significance of trend differences depends on the experiment considered, the choice of ENSO index, and the volcanic signal decay time. The simulated difference between surface and tropospheric warming rates is significantly smaller than observed in 51 out of 54 cases considered. We also examine multiple realizations of model experiments with relatively complete estimates of natural and anthropogenic forcing. ENSO and volcanic effects are not removed from these integrations. As in the case of residual trends, model and observed raw trends are in good agreement at the surface but differ significantly in terms of the trend differential between the surface and lower troposphere. Observed and simulated lower tropospheric trends are not significantly different in 17 out of 24 cases. Our study highlights the large uncertainties inherent in removing volcano and ENSO effects from atmospheric temperature data. It shows that statistical removal of these effects improves the correspondence between modeled and observed temperature trends over the satellite era. Accounting for volcanoes and ENSO cannot fully explain the observed warming of the surface relative to the lower troposphere, or why this differential warming is not reproduced in the model simulations considered here.

Detection and Attribution of Temperature Changes in the Mountainous Western United States

Abstract Large changes in the hydrology of the western United States have been observed since the mid-twentieth century. These include a reduction in the amount of precipitation arriving as snow, a decline in snowpack at low and midelevations, and a shift toward earlier arrival of both snowmelt and the centroid (center of mass) of streamflows. To project future water supply reliability, it is crucial to obtain a better understanding of the underlying cause or causes for these changes. A regional warming is often posited as the cause of these changes without formal testing of different competitive explanations for the warming. In this study, a rigorous detection and attribution analysis is performed to determine the causes of the late winter/early spring changes in hydrologically relevant temperature variables over mountain ranges of the western United States. Natural internal climate variability, as estimated from two long control climate model simulations, is insufficient to explain the rapid increase in...

Identification of Anthropogenic Climate Change Using a Second-Generation Reanalysis

[1] Changes in the height of the tropopause provide a sensitive indicator of human effects on climate. A previous attempt to identify human effects on tropopause height relied on information from ‘first-generation’ reanalyses of past weather observations. Climate data from these initial model-based reanalyses have well-documented deficiencies, raising concerns regarding the robustness of earlier detection work that employed these data. Here we address these concerns using information from the new second-generation ERA-40 reanalysis. Over 1979 to 2001, tropopause height increases by nearly 200 m in ERA-40, partly due to tropospheric warming. The spatial pattern of height increase is consistent with climate model predictions of the expected response to anthropogenic influences alone, significantly strengthening earlier detection results. Atmospheric temperature changes in two different satellite data sets are more highly correlated with changes in ERA-40 than with those in a first-generation reanalysis, illustrating the improved quality of temperature information in ERA-40. Our results provide support for claims that human activities have warmed the troposphere and cooled the lower stratosphere over the last several decades of the 20th century, and that both of these changes in atmospheric temperature have contributed to an overall increase in tropopause height. INDEX TERMS: 0350 Atmospheric Composition and Structure: Pressure, density, and temperature; 0370 Atmospheric Composition and Structure: Volcanic effects (8409); 1620 Global Change: Climate dynamics (3309); 1640 Global Change: Remote sensing; KEYWORDS: climate change, detection, reanalysis

Identifying human influences on atmospheric temperature.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data

Abstract The Earth System Grid Federation (ESGF) is a multi-agency, international collaboration that aims at developing the software infrastructure needed to facilitate and empower the study of climate change on a global scale. The ESGF’s architecture employs a system of geographically distributed peer nodes, which are independently administered yet united by the adoption of common federation protocols and application programming interfaces (APIs). The cornerstones of its interoperability are the peer-to-peer messaging that is continuously exchanged among all nodes in the federation; a shared architecture and API for search and discovery; and a security infrastructure based on industry standards (OpenID, SSL, GSI and SAML). The ESGF software stack integrates custom components (for data publishing, searching, user interface, security and messaging), developed collaboratively by the team, with popular application engines (Tomcat, Solr) available from the open source community. The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the entire Fifth Coupled Model Intercomparison Project (CMIP5) output used by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and a suite of satellite observations (obs4MIPs) and reanalysis data sets (ANA4MIPs). This paper presents ESGF as a successful example of integration of disparate open source technologies into a cohesive, wide functional system, and describes our experience in building and operating a distributed and federated infrastructure to serve the needs of the global climate science community.

Human and natural influences on the changing thermal structure of the atmosphere

Significance Observational satellite data and the model-predicted response to human influence have a common latitude/altitude pattern of atmospheric temperature change. The key features of this pattern are global-scale tropospheric warming and stratospheric cooling over the 34-y satellite temperature record. We show that current climate models are highly unlikely to produce this distinctive signal pattern by internal variability alone, or in response to naturally forced changes in solar output and volcanic aerosol loadings. We detect a “human influence” signal in all cases, even if we test against natural variability estimates with much larger fluctuations in solar and volcanic influences than those observed since 1979. These results highlight the very unusual nature of observed changes in atmospheric temperature. Since the late 1970s, satellite-based instruments have monitored global changes in atmospheric temperature. These measurements reveal multidecadal tropospheric warming and stratospheric cooling, punctuated by short-term volcanic signals of reverse sign. Similar long- and short-term temperature signals occur in model simulations driven by human-caused changes in atmospheric composition and natural variations in volcanic aerosols. Most previous comparisons of modeled and observed atmospheric temperature changes have used results from individual models and individual observational records. In contrast, we rely on a large multimodel archive and multiple observational datasets. We show that a human-caused latitude/altitude pattern of atmospheric temperature change can be identified with high statistical confidence in satellite data. Results are robust to current uncertainties in models and observations. Virtually all previous research in this area has attempted to discriminate an anthropogenic signal from internal variability. Here, we present evidence that a human-caused signal can also be identified relative to the larger “total” natural variability arising from sources internal to the climate system, solar irradiance changes, and volcanic forcing. Consistent signal identification occurs because both internal and total natural variability (as simulated by state-of-the-art models) cannot produce sustained global-scale tropospheric warming and stratospheric cooling. Our results provide clear evidence for a discernible human influence on the thermal structure of the atmosphere.

Visualization of uncertainty and ensemble data: Exploration of climate modeling and weather forecast data with integrated ViSUS-CDAT systems

Climate scientists and meteorologists are working towards a better understanding of atmospheric conditions and global climate change. To explore the relationships present in numerical predictions of the atmosphere, ensemble datasets are produced that combine time- and spatially-varying simulations generated using multiple numeric models, sampled input conditions, and perturbed parameters. These data sets mitigate as well as describe the uncertainty present in the data by providing insight into the effects of parameter perturbation, sensitivity to initial conditions, and inconsistencies in model outcomes. As such, massive amounts of data are produced, creating challenges both in data analysis and in visualization. This work presents an approach to understanding ensembles by using a collection of statistical descriptors to summarize the data, and displaying these descriptors using variety of visualization techniques which are familiar to domain experts. The resulting techniques are integrated into the ViSUS/Climate Data and Analysis Tools (CDAT) system designed to provide a directly accessible, complex visualization framework to atmospheric researchers.