Influence of changes in humidity on dry temperature in GPS RO climatologies

Abstract

Abstract. Radio occultation (RO) data are increasingly used in climate research. Accurate phase (change) measurements of Global Positioning System (GPS) signals are the basis for the retrieval of near-vertical profiles of bending angle, microwave refractivity, density, pressure, and temperature. If temperature is calculated from observed refractivity with the assumption that water vapor is zero, the product is called "dry temperature", which is commonly used to study earth's atmosphere, e.g., when analyzing temperature trends due to global warming. Dry temperature is a useful quantity, since it does not need additional background information in its retrieval. However, it can only be safely used as proxy for physical temperature, where moisture is negligible. The altitude region above which water vapor does not play a dominant role anymore, depends primarily on latitude and season.In this study we first investigated the influence of water vapor on dry temperature RO profiles. Hence, we analyzed the maximum altitude down to which monthly mean dry temperature profiles can be regarded as being equivalent to physical temperature. This was done by examining dry temperature to physical temperature differences of monthly mean analysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), studied from 2006 until 2010. We introduced cutoff criteria, where maximum temperature differences of −0.1, −0.05, and −0.02 K were allowed (dry temperature is always lower than physical temperature), and computed the corresponding altitudes. As an example, a temperature difference of −0.05 K in the tropics was found at an altitude of about 14 km, while at higher northern latitudes in winter it was found at an altitude of about 9-10 km, in summer at about 11 km.Furthermore, regarding climate change, we expect an increase of absolute humidity in the atmosphere. This possible trend in water vapor could yield a wrongly interpreted dry temperature trend. As a consequence, we performed a model study, investigating the increase in height of the transition region between moist and dry air. We used data from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), analyzing again monthly mean dry temperature to physical temperature differences, now from the years 2006 to 2050. We used the highest emission scenario RCP8.5 (representative concentration pathway), studying all available models of the CMIP5 project, analyzing one internal run per model, with the goal to identify the altitude region where trends in dry temperature can be safely regarded as reflecting trends in physical temperature. From all models we therefore choose a selection of models ("max 8" CMIP5 models), which showed the largest trend differences. As a result, our trend study suggests that the lower boundary of the region where dry temperature is essentially equal to physical temperature rises about 150 m decade −1 .

The Effects of Deep Convection on Regional Temperature Structure in the Tropical Upper Troposphere and Lower Stratosphere

“…Journal of Geophysical Research: Atmospheres 10.1002/2017JD027120 Figure 3 shows the ERA-I specific humidity (q) for mean background conditions and after deep convection, along with the corresponding differences between ERA-I temperature and COSMIC dry temperature that occurs due to the presence of water vapor that is not considered in the dry temperature. COSMIC dry temperature has a cool bias compared to the real temperature within 10-13 km due to the presence of significant moisture content within the tropics (Danzer et al, 2014), which is amplified near deep convection. The background specific humidity at 10 km is roughly 0.35 g/kg (PWP) and 0.25 g/kg (TACO), which contributes to a Journal of Geophysical Research: Atmospheres 10.1002/2017JD027120 temperature-dry temperature difference of about 3 K (PWP) and 2 K (TACO).…”

Climate intercomparison of GPS radio occultation, RS90/92 radiosondes and GRUAN from 2002 to 2013

“…As noted above, the physical temperature will diverge significantly from the GPSRO dry temperature measurement at low altitudes, where the background information dominates in the optimal estimation scheme (Scherllin-Pirscher et al, 2011a;Danzer et al, 2014). Dry temperature, while not a physical parameter, does not need background information in the GPSRO retrieval process and is a valid measure for physical temperature as long as moisture is negligible.…”

“…In this section, we use the comparison to observational (RS) temperature data to determine where we can safely assume them to be equivalent. For a thorough investigation of this question using model data as reference see Danzer et al (2014). An initial illustration based on monthly climatological fields was included in Scherllin-Pirscher et al (2011a).…”

Characteristics of tropopause parameters as observed with GPS radio occultation

“…This method fits well to the physical structure of temperature profiles close to the tropopause. We apply the tropopause algorithm to dry temperature profiles as differences between dry and physical temperatures become negligible at tropopause altitudes for most latitudes (ScherllinPirscher et al, 2011;Danzer et al, 2014). However, high concentrations of water vapor in the lower troposphere can lead to temperature gradients, which may be interpreted as tropopauses by the tropopause algorithm.…”