Advection‐condensation paradigm for stratospheric water vapor

Abstract

[1] The advection-condensation (A-C) paradigm is a starting point for a theoretical framework for analysis of atmospheric water vapor distributions and changes therein in a changing climate. It postulates that water vapor concentrations are governed to leading order by the transport through the full four-dimensional temperature (and hence saturation mixing ratio) field. Brewer's (1949) qualitative deduction of the stratospheric circulation based on water vapor measurements was a first and prominently successful application of this paradigm. Here we examine the quantitative validity of the A-C paradigm by predicting stratospheric water vapor based on the saturation mixing ratio at the Lagrangian dry point of trajectories calculated using data from the European Centre for Medium-range Weather Forecasts. Using different data sets for the calculation, we show that results are sensitive to seemingly small differences in temperatures and wind fields and that interpretation of results (in terms of identification of effects of processes deliberately neglected by the advection-condensation paradigm) requires a careful error calculation. We introduce a semiempirical approach to analyze errors in the Lagrangian predictions of water vapor. We show that persistent (in time and space) errors in the temperature fields lead to similar errors in the Lagrangian model predictions. Conversely, biases in the variance of the temperature fields introduces a systematic bias in the model prediction. Further, model predictions are affected by dispersion and the time scale of troposphere-to-stratosphere transport. Our conclusion is that water vapor predictions for the stratospheric overworld based on the A-C paradigm have a dry bias of −40% ± 10% and −50% ± 10% when small-space-scale and short-time-scale temperature fluctuations not resolved by the ECMWF reanalyses are taken into account. We suggest that the correction to the A-C paradigm most likely to remove this dry bias is the inclusion of cloud microphysical processes (such as incomplete sedimentation of particles allowing reevaporation), which relax the assumption of instantaneous dehydration to the saturation mixing ratio. Interestingly, the bias attributed to the A-C paradigm in terms of water vapor concentration is found to be proportional to the measured water concentration, and a constant offset in terms of frost point temperature can account for much of the bias and its variability in water vapor mixing ratios.Citation: Liu, Y. S., S. Fueglistaler, and P. H. Haynes (2010), Advection-condensation paradigm for stratospheric water vapor,

Ice nucleation and dehydration in the Tropical Tropopause Layer

“…Even the high-concentration layers will only irreversibly dehydrate very narrow layers, given the necessarily small ice crystals in these clouds. These conclusions are consistent with the fact that the trajectory analyses assuming all vapor in excess of saturation is removed generally produce stratospheric water vapor concentrations lower than indicated by satellite measurements (38).…”

“…The results presented here have implications for assumptions made about TTL dehydration in models. Lagrangian trajectory analysis is commonly used to relate annual and interannual variability in stratospheric humidity to transport pathways and TTL temperatures (36)(37)(38). These analyses generally assume that any vapor in excess of ice saturation along the trajectories is completely removed from the TTL.…”

Insight from ozone and water vapour on transport in the tropical tropopause layer (TTL)

“…Trajectory dispersion may depend on the sampling frequency of velocity fields (compare e.g., Pisso et al, 2010). Note that for ERAInterim used here, the differences in transport between diabatic and kinematic trajectories are smaller than for the older ERA-40 data (Liu et al, 2010), likely due to the 4D-Var assimilation in ERA-Interim (compare Monge-Sanz et al, 2007), but the differences are still detectable and significant as we will show below.…”

“…The bulk transport characteristics for TST trajectories calculated from ERA-Interim data, like TTL residence times, are similar for kinematic and diabatic trajectories Liu et al, 2010). Figure 5 shows the position of the global (top panels) and in-situ trajectories (bottom panels) when tracing them back in time for 60 days.…”

“…1) based on condensation and complete fall-out of the condensate along the trajectory. It is known that stratospheric water vapour predictions based on the minimum saturation mixing ratio from ERA-Interim data are drier than observed (James et al, 2008;Liu et al, 2010). Following the approach of James et al (2008) condensation in our model occurs every time relative humidity exceeds 130%.…”