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John R. Lanzante

Abstract

The local association between ocean and atmosphere was examined statistically by correlating the anomalous sea surface temperature (SST) gradient and the anomalous geostrophic wind at the 700 mb level using 30 years (1949–78) of monthly data. Under the assumption that anomalous oceanic thermal gradients are transmitted to the lower troposphere via anomalous fluxes of latent and sensible heat, and by applying the thermal wind relationship, a significant positive correlation is expected. This analysis is an extension of earlier work by Harnack and Broccoli and includes results for both the Atlantic and Pacific, for both zonal and meridional components, for lags as well as contemporaneous associations, and includes an examination of spatial variability. The major findings are: 1) the expected association is found in both oceans and for both components, although it is somewhat stronger in the Pacific and when relating the zonal wind to the meridional SST gradient, 2) the best association is found in the zonal band of 35–45°N, although some seasonal variability is experienced in the Pacific, 3) the lag relationships are significant only at zero lag or with atmosphere leading ocean and 4) the effect of the association is enhanced by time averaging (over 3 months).

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Melissa Free and John Lanzante

Abstract

Both observed and modeled upper-air temperature profiles show the tropospheric cooling and tropical stratospheric warming effects from the three major volcanic eruptions since 1960. Detailed comparisons of vertical profiles of Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) and Hadley Centre Atmospheric Temperatures, version 2 (HadAT2), radiosonde temperatures with output from six coupled GCMs show good overall agreement on the responses to the 1991 Mount Pinatubo and 1982 El Chichón eruptions in the troposphere and stratosphere, with a tendency of the models to underestimate the upper-tropospheric cooling and overestimate the stratospheric warming relative to observations. The cooling effect at the surface in the tropics is amplified with altitude in the troposphere in both observations and models, but this amplification is greater for the observations than for the models. Models and observations show a large disagreement around 100 mb for Mount Pinatubo in the tropics, where observations show essentially no change, while models show significant warming of ∼0.7 to ∼2.6 K. This difference occurs even in models that accurately simulate stratospheric warming at 50 mb. Overall, the Parallel Climate Model is an outlier in that it simulates more volcanic-induced stratospheric warming than both the other models and the observations in most cases.

From 1979 to 1999 in the tropics, RATPAC shows a trend of less than 0.1 K decade−1 at and above 300 mb before volcanic effects are removed, while the mean of the models used here has a trend of more than 0.3 K decade−1, giving a difference of ∼0.2 K decade−1. At 300 mb, from 0.02 to 0.10 K decade−1 of this difference may be due to the influence of volcanic eruptions, with the smaller estimate appearing more likely than the larger. No more than ∼0.03 K of the ∼0.1-K difference in trends between the surface and troposphere at 700 mb or below in the radiosonde data appears to be due to volcanic effects.

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John R. Lanzante and Melissa Free

Abstract

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 time-varying 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.

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John R. Lanzante and Robert P. Harnack

Abstract

The specification of summer season precipitation in the contiguous United States from summer season fields of 700 mb height, sea level pressure (SLP) and Pacific sea surface temperature (SST) was carried out using stepwise multiple linear regression. The specifier fields were characterized by their first five Empirical Orthogonal Functions (EOF's). The objectives were to assess the overall skill in specifying summer season precipitation, examine the differences among predictands with regard to both spatial averaging and type of statistic, compare the usefulness of the specifier fields, and to look at spatial variations in specification skill.

Overall, the strongest relationships between actual summer season precipitation and the predictors were found for 700 mb heights (R 2 ∼ 0.24) followed by Pacific SST’s (R 2 ∼ 0.21) and SLP (R 2 &sim 0.12). The use of large area averages (∼ 105 km2) for the predictand produced slightly greater R 2 values than for individual climatic division averages (∼ 1O4 km2).

The use of transformed summer season precipitation statistics to account for precipitation skewness, did not improve upon the use of actual summer season precipitation as the predictand. However, frequency of precipitation greater than 0.1 inch resulted in an almost doubling of explained variances over actual precipitation (0.47 versus 0.24) when 700 mb heights were used as the specifier field.

The areas of weakest relationship (west of the Rockies and southern states) between predictor and summer precipitation statistic generally had R 2 values less than 0.3, even for the best models. Elsewhere, the R 2 values generally ranged from 0.5 to 0.7 for the best model (700 mb heights and precipitation frequency). After accounting for artificial predictability which results from imperfect estimates of the statistics, skill values (explained variances) cast of the Rockies ranged from 0.01 to 0.44.

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Robert Harnack, Jeremi Harnack, and John R. Lanzante

Abstract

The prediction of seasonal temperatures in the United States farm Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Predictions were made using both a one mouth and a one season lead. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast skill to be better quantified than in pan studies, by greatly increasing the effective independent sample size. The procedures were repeated on three data sets for each season: 1) all year in the period 1930–79 (29 or 30 years); 2) high intraseasonal variability index (VI) years; and 3) low intraseasonal VI years. The VI was constructed to measure the intraseasonal variability of 5-day period mean 700 mb heights for a portion of the Northern Hemisphere. The following results obtained from the study: 1) for winter and summer, significant models were found, though skill is modest (less than 60% correct for two-class forecasts), but the relationship between intraseasonal variability and skill is not consistent; 2) generally no significant skill was found for spring or fag models; 3) the use of the jackknife procedure for increasing the number of independent tests available for short data sell appears to be a real asset, which may allow more accurate assessment of true skill.

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Robert P. Harnack and John R. Lanzante

Abstract

North Pacific and North Atlantic SST (sea surface temperature) were used separately and in combination to specify seasonal-mean North American 700 mb heights. One of the goals was to quantify these relationships so that the importance of North Atlantic versus North Pacific SST could be assessed. Sea surface temperature predictors were in the form of EOF (empirical orthogonal function) amplitudes while the predictands consisted of seasonal-mean 700 mb heights at each of 25 locations over North America. Linear regression analysis was used in the data period 1949–77 to build three kinds of models: 1) using the first five North Pacific SST EOFs, 2) using the fist five North Atlantic SST EOFs and 3) using five EOFs from each field, but screening to produce the best five predictor models.

The principal findings can be summarized as:

1) Based on area-averaged skill and percent area of significant skill, North Pacific SST is a better specifier of 700 mb height than North Atlantic SST.

2) Pacific SST models have significant overall skill for all seasons except spring, with area-averaged true skill being greatest in winter (¯S = 0.247) and least in spring (¯S = 0.061).

3) Atlantic SST models do not attain field significance in any season, but perform best overall in winter (¯S = 0.095).

4) A portion of the region studied for winter and summer contained grid point locations where testing indicated that Atlantic SST adds significant information to that of Pacific SST in explaining variations of 700 mb height. This amounted to 13 and 15% of the total area, respectively, which was not enough to declare field significance.

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Brian J. Soden and John R. Lanzante

Abstract

This study compares radiosonde and satellite climatologies of upper-tropospheric water vapor for the period 1979–1991. Comparison of the two climatologies reveals significant differences in the regional distribution of upper-tropospheric relative humidity. These discrepancies exhibit a distinct geopolitical dependence that is demonstrated to result from international differences in radiosonde instrumentation. Specifically, radiosondes equipped with goldbeater's skin humidity sensors (found primarily in the former Soviet Union, China, and eastern Europe) report a systematically moister upper troposphere relative to the satellite observations, whereas radiosondes equipped with capacitive or carbon hygristor sensors (found at most other locations) report a systematically drier upper troposphere. The bias between humidity sensors is roughly 15%–20% in terms of the relative humidity, being slightly greater during summer than during winter and greater in the upper troposphere than in the midtroposphere. However, once the instrumentation bias is accounted for, regional variations of satellite and radiosonde upper-tropospheric relative humidity are shown to be in good agreement. Additionally, temporal variations in radiosonde upper-tropospheric humidity agree reasonably well with the satellite observations and exhibit much less dependence upon instrumentation.

The impact that the limited spatial coverage of the radiosonde network has upon the moisture climatology is also examined and found to introduce systematic errors of 10%–20% relative humidity over data-sparse regions of the Tropics. It is further suggested that the present radiosonde network lacks sufficient coverage in the eastern tropical Pacific to adequately capture ENSO-related variations in upper-tropospheric moisture. Finally, we investigate the impact of the clear-sky sampling restriction upon the satellite moisture climatology. Comparison of clear-sky and total-sky radiosonde observations suggests the clear-sky sampling limitation introduces a modest dry bias (<10% relative humidity) in the satellite climatology.

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Robert P. Harnack and John R. Lanzante

Abstract

Seasonal precipitation is specified for the United States by matching various area-averaged precipitation statistics as predictands with three different predictors in turn: 700 mb heights, North Pacific SST and North Atlantic SST. Predictors are in the form of empirical orthogonal function (EOF) amplitude time series. The predictands used in trials include total precipitation and precipitation frequencies derived using three different critical values: 2.5, 12.7 and 25.4 mm. Screening multiple linear regression is used to relate predictands to predictors for samples ranging from 24 to 35 years in length; initially trials are compared in terms of area-averaged true skill and percent area of local significance. In order to assess specification skill on an independent sample, additional tests are made using a jackknife regression approach.

Results suggest that skillful seasonal precipitation prediction will continue to be very difficult using predictors and methods presently in common use based on the use of specification equations on an independent sample. Generally, area-averaged explained variances are less than 10% and the area of significant local skill is less than 50%. Based on the low level of specification skill, predictive skill for precipitation using specification equations with imprecisely known specifier fields (like 700 mb heights) as input would be effectively zero.

Other conclusions are:

  1. 700 mb heights specify seasonal precipitation about equally well in winter, spring and summer, but worse in fall.

  2. Among the three predictor types employed, 700 mb heights are best for all seasons but fall, when Pacific SST does best. Specification using Atlantic SST is poor in all instances and inferior to the use of the other predictor fields.

  3. Overall among the four precipitation statistics used as predictands, the frequency statistics have a slightly better relationship with 700 mb heights or Pacific SST than do precipitation totals.

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John R. Lanzante and Gregory E. Gahrs

Abstract

A temporal sampling bias may be introduced due to the inability of a measurement system to produce a valid observation during certain types of situations. In this study the temporal sampling bias in satellite-derived measures of upper-tropospheric humidity (UTH) was examined through the utilization of similar humidity measures derived from radiosonde data. This bias was estimated by imparting the temporal sampling characteristics of the satellite system onto the radiosonde observations. This approach was applied to UTH derived from Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder radiances from the NOAA-10 satellite from the period 1987–91 and from the “Angell” network of 63 radiosonde stations for the same time period. Radiative modeling was used to convert both the satellite and radiosonde data to commensurate measures of UTH.

Examination of the satellite temporal sampling bias focused on the effects of the “clear-sky bias” due to the inability of the satellite system to produce measurements when extensive cloud cover is present. This study indicates that the effects of any such bias are relatively small in the extratropics (about several percent relative humidity) but may be ∼5%–10% in the most convectively active regions in the Tropics. Furthermore, there is a systematic movement and evolution of the bias pattern following the seasonal migration of convection, which reflects the fact that the bias increases as cloud cover increases. The bias is less noticeable for shorter timescales (seasonal values) but becomes more obvious as the averaging time increases (climatological values); it may be that small-scale noise partially obscures the bias for shorter time averages. Based on indirect inference it is speculated that the bias may lead to an underestimate of the magnitude of trends in satellite UTH in the Tropics, particularly in the drier regions.

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John R. Lanzante and Robert P. Harnack

Abstract

An investigation of the January thaw phenomenon, a period of unseasonable warmth, was conducted using daily maximum temperatures recorded at New Brunswick, New Jersey, from 1858–1981. Student's t-tests, comparing long-term means of daily maximum temperature to values from a fitted seasonal trend curve, indicate temperatures higher on 22–23 January and lower on the 29th than seasonally expected.

It was found that the January thaw does not have a fixed time of occurrence but occurs most frequently from the 19th to the 28th. During this time the interannual variability of daily maximum temperature is significantly higher than during the remainder of the month.

Evidence of a tendency for a secondary thaw maximum to occur, centered on the 26th, is evident in several different analyses. Examination of daily temperature curves for 10-, 20- and 40-year periods reveals a shift in the mean thaw date from 22–23 January to the 26th. This change has evolved over the last 30–40 years. It was concluded that the January thaw is more pronounced when the mean circulation is characterized by a contracted polar vortex over North America and abnormally strong midlatitude westerlies.

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