J. J. Hnilo

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.

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.

Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements

Received 11 November 2005 revised 14 June 2006 ; accepted 10 August 2006; published 16 March 2007. [i] Temperature change of the lower troposphere (LT) in the tropics (20°S-20°N) during the period 1979-2004 is examined using 58 radiosonde (sonde) stations and the microwave-based satellite data sets of the University of Alabama in Huntsville (UAH v5.2) and Remote Sensing Systems (RSS v2.1). At the 29 stations that make both day and night observations, the average nighttime trend (+0.12 K decade -1 ) is 0.05 K decades -1 more positive than that for the daytime (+0.07 K decade -1 ) in the unadjusted observations, an unlikely physical possibility indicating adjustments are needed. At the 58 sites the UAH data indicate a trend of +0.08 K decade -1 , the RSS data, +0.15. When the largest discontinuities in the sondes are detected and removed through comparison with UAH data, the trend of day and night releases combined becomes +0.09, and using RSS data, +0.12. Relative to several data sets, the RSS data show a warming shift, broadly occurring in 1992, of between +0.07 K and +0.13 K. Because the shift occurs at the time NOAA-12 readings began to be merged into the satellite data stream and large NOAA-11 adjustments were applied, the discrepancy appears to be due to bias adjustment procedures. Several comparisons are consistent with a 26-year trend and error estimate for the UAH LT product for the full tropics of +0.05 ± 0.07, which is very likely less than the tropical surface trend of+0.13 K decade -1 .

Cloud and radiation budget changes associated with tropical intraseasonal oscillations

[1] We explore the daily evolution of tropical intraseasonal oscillations in satellite-observed tropospheric temperature, precipitation, radiative fluxes, and cloud properties. The warm/rainy phase of a composited average of fifteen oscillations is accompanied by a net reduction in radiative input into the ocean-atmosphere system, with longwave heating anomalies transitioning to longwave cooling during the rainy phase. The increase in longwave cooling is traced to decreasing coverage by ice clouds, potentially supporting Lindzen’s ‘‘infrared iris’’ hypothesis of climate stabilization. These observations should be considered in the testing of cloud parameterizations in climate models, which remain sources of substantial uncertainty in global warming prediction. Citation: Spencer, R. W., W. D. Braswell, J. R. Christy, and J. Hnilo (2007), Cloud and radiation budget changes associated with tropical intraseasonal oscillations, Geophys. Res. Lett., 34, L15707, doi:10.1029/2007GL029698.