James S. Boyle

Statistical significance of trends and trend differences in layer-average atmospheric temperature time series

This paper examines trend uncertainties in layer-average free atmosphere temperatures arising from the use of different trend estimation methods. It also considers statistical issues that arise in assessing the significance of individual trends and of trend differences between data sets. Possible causes of these trends are not addressed. We use data from satellite and radiosonde measurements and from two reanalysis projects. To facilitate intercomparison, we compute from reanalyses and radiosonde data temperatures equivalent to those from the satellite-based Microwave Sounding Unit (MSU). We compare linear trends based on minimization of absolute deviations (LA) and minimization of squared deviations (LS). Differences are generally less than 0.05°C/decade over 1959–1996. Over 1979–1993, they exceed 0.10°C/decade for lower tropospheric time series and 0.15°C/decade for the lower stratosphere. Trend fitting by the LA method can degrade the lower-tropospheric trend agreement of 0.03°C/decade (over 1979–1996) previously reported for the MSU and radiosonde data. In assessing trend significance we employ two methods to account for temporal autocorrelation effects. With our preferred method, virtually none of the individual 1979–1993 trends in deep-layer temperatures are significantly different from zero. To examine trend differences between data sets we compute 95% confidence intervals for individual trends and show that these overlap for almost all data sets considered. Confidence intervals for lower-tropospheric trends encompass both zero and the model-projected trends due to anthropogenic effects. We also test the significance of a trend in d(t), the time series of differences between a pair of data sets. Use of d(t) removes variability common to both time series and facilitates identification of small trend differences. This more discerning test reveals that roughly 30% of the data set comparisons have significant differences in lower-tropospheric trends, primarily related to differences in measurement system. Our study gives empirical estimates of statistical uncertainties in recent atmospheric temperature trends. These estimates and the simple significance testing framework used here facilitate the interpretation of previous temperature trend comparisons involving satellite, radiosonde, and reanalysis data sets.

Climatology and Interannual Variation of the East Asian Winter Monsoon: Results from the 1979-95 NCEP/NCAR Reanalysis

Abstract This paper presents the climatology and interannual variation of the East Asian winter monsoon based on the 1979–95 National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis. In addition to documenting the frequency, intensity, and preferred propagation tracks of cold surges and the evolution patterns of related fields, the authors discuss the temporal distribution of the Siberian high and cold surges. Further, the interannual variation of the cold surges and winter monsoon circulation and its relationship with ENSO were examined. There are on average 13 cold surges in each winter season (October–April), of which two are strong cases. The average intensity of cold surges, measured by maximum sea level pressure, is 1053 hPa. The cold surges originate from two source regions: 1) northwest of Lake Baikal, and 2) north of Lake Balkhash. The typical evolution of a cold surge occurs over a period of 5–14 days. Trajectory and correlation analyses indicate that, du...

Identification of human-induced changes in atmospheric moisture content

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/m2 per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data. Experiments in which forcing factors are varied individually suggest that this fingerprint “match” is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earths atmosphere.

Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators

AbstractSatellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are kn...

Forced and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis regions

Previous research has identified links between changes in sea surface temperature (SST) and hurricane intensity. We use climate models to study the possible causes of SST changes in Atlantic and Pacific tropical cyclogenesis regions. The observed SST increases in these regions range from 0.32°C to 0.67°C over the 20th century. The 22 climate models examined here suggest that century-timescale SST changes of this magnitude cannot be explained solely by unforced variability of the climate system. We employ model simulations of natural internal variability to make probabilistic estimates of the contribution of external forcing to observed SST changes. For the period 1906–2005, we find an 84% chance that external forcing explains at least 67% of observed SST increases in the two tropical cyclogenesis regions. Model “20th-century” simulations, with external forcing by combined anthropogenic and natural factors, are generally capable of replicating observed SST increases. In experiments in which forcing factors are varied individually rather than jointly, human-caused changes in greenhouse gases are the main driver of the 20th-century SST increases in both tropical cyclogenesis regions.

Evaluating Parameterizations in General Circulation Models: Climate Simulation Meets Weather Prediction

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be test...

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.

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.

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

Simulation of the northern summer monsoon in the ECMWF model: Sensitivity to horizontal resolution

Abstract The ability of the ECMWF model (cycle 33) to simulate the Indian and East Asian summer monsoons is evaluated at four different horizontal resolutions: T21, T42, T63, and T1O6. Generally, with respect to the large-scale features of the circulation, the largest differences among the simulations occur at T42 relative to T21. However, on regional scales, important differences among the high-frequency temporal variability serve as a further critical rest of the models ability to simulate the monsoon. T106 best captures both the spatial and temporal characteristics of the Indian and East Asian monsoons, whereas T42 fails to correctly simulate the sequence and development of synoptic-scale milestones that characterize the monsoon flow. In particular, T106 is superior at simulating the development and migration of the monsoon trough over the Bay of Bengal. In the T42 simulation, the development of the monsoon occurs one month earlier than typically observed. At this time the trough is incorrectly locate...