Characterising extratropical near‐tropopause analysis humidity biases and their radiative effects on temperature forecasts

Bland, Jake; Gray, Suzanne L.; Methven, John; Forbes, R. M.

doi:10.1002/qj.4150

Cited by 17 publications

(31 citation statements)

References 52 publications

(62 reference statements)

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“…The cold biases in wintertime polar cap temperatures (corresponding to stronger polar stratospheric winds), and cold biases in the extratropical upper troposphere/lower stratosphere are longstanding biases that are similar to what has been documented in other weather and climate models (Charlton-Perez et al, 2013;Bland et al, 2021). The stratospheric polar cap temperature biases generally point to a dynamical cause related to parameterized gravity wave drag, given that wave-mean flow interactions and the ensuing residual circulations are responsible for driving local zonal mean temperatures away from radiative equilibrium.…”

Section: Severalsupporting

confidence: 56%

“…The cold extratropical UTLS biases, on the other hand, are likely to be radiatively driven, related to excessive leakage of water vapor into the lower stratosphere (e.g., Bland et al, 2021). Both of these issues are dependent upon vertical resolution, which likely explains why the biases in the low-top systems (which have fewer levels in the stratosphere and coarser resolution in the UTLS) are generally more severe than those in the high-top systems.…”

Section: Severalmentioning

confidence: 99%

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Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Lawrence

Ábalos

Ayarzagüena

et al. 2022

Preprint

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Abstract. The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper troposphere/lower stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme vortex event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability of the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for sub-seasonal to seasonal forecast systems, and further our understanding of the role of the stratosphere for predictive skill in the troposphere.

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Section: Severalsupporting

confidence: 56%

Section: Severalmentioning

confidence: 99%

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Lawrence

Ábalos

Ayarzagüena

et al. 2022

Preprint

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“…ERA-5 forecasts are produced twice daily (00, 12 UTC) using ECMWF's Integrated Forecasting System (IFS). For the statistics of the lapse rate hourly ERA-5 reanalysis data were retrieved from the Copernicus Data Service (Copernicus Climate Change Service (C3S), Temperature data from ERA-5 agree well with MOZAIC in-situ data and a recent comparison with radiosonde data (Bland et al, 2021) confirms the good quality of the reanalysis' temperature in the upper troposphere and lower stratosphere.…”

Section: Reanalysis Datamentioning

confidence: 64%

The effect of ice supersaturation and thin cirrus on lapse rates in the upper troposphere

Gierens

Wilhelm

Hofer

et al. 2022

Preprint

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Abstract. In this paper, the effects of ice-supersaturated regions and thin, subvisual cirrus clouds on lapse rates are examined. For that, probability distribution and density functions of ten years of measurement data from the MOZAIC/IAGOS project and ERA-5 reanalysis data were produced, and an analysis of an example case of an ice supersaturated region with a large vertical extent is performed. For the study of the probability distribution and density functions, a distinction is made between ice-subsaturated, ice-supersaturated air masses and so-called Big Hits, which are situations of particularly high ice-supersaturation that allow the formation of optically thick and strongly warming contrails. The distribution functions show much higher lapse rates, which correspond to almost neutral stratification, for ice-supersaturated regions and Big Hits than for subsaturated air masses. The highest lapse rates are found for Big Hit situations, because of the strong interaction between radiation and high ice-supersaturation. For the examination of an example case, ERA-5 data and forecasts from ICON-EU (DWD) are compared. ERA-5 data, in particular, shows a large ice-supersaturated region below the tropopause, that was pushed up by uplifting air, while the data of ICON-EU indicates areas of saturation. The lapse rate in this ice-supersaturated region (ISSR), which is large, is associated with clouds and high relative humidity. Supersaturation and cloud formation result from uplifting of air layers. The temperature gradient within an uplifting layer steepens, both for dry and moist air, but for moist air there is an additional mechanism: it is the emission and absorption of radiation within the moist air: The upper part of this region emits longwave infrared radiation to space, while the bottom absorbs infrared radiation from lower and warmer layers, which consequently increases the lapse rate. This effect becomes even stronger, if ice crystals are involved (clouds).

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“…Above the ExTL, the concentration of water vapor approaches a low and vertically constant background value (e.g., Hintsa et al, 1994) which is determined by the stratospheric transport from tropics (Fueglistaler et al, 2009) within the Brewer-Dobson-Circulation (e.g., Dobson et al, 1946;Brewer, 1949) on time scales from months to years. The complexity of transport and mixing processes is mirrored in the high variability of water vapor in the extratropical UTLS on synoptic and seasonal time scales (e.g., Pan et al, 2000;Randel and Wu, 2010;Zahn et al, 2014;Dyroff et al, 2015;Bland et al, 2021;Schäfler et al, 2022, in prep. ).…”

Section: Introductionmentioning

confidence: 99%

“…Current operational analyses and forecasts are known to possess a distinct moist bias in the extratropical LS which is causing a co-located cold bias (Stenke et al, 2008;Diamantakis and Flemming, 2014;Shepherd et al, 2018). Recently, Bland et al (2021) used radiosonde observations of a two-month period in fall and confirmed the earlier documented moist bias (about 70 % in the LS) in current operational analyses and forecasts of the European Centre for Medium-Range Weather Forecast's (ECMWF) Integrated Forecast System (IFS) and the Met Office's Unified Model (METUM) and a co-located cold bias. For a comprehensive overview of the studies https://doi.org/10.5194/acp-2022-505 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes

Krüger

Schäfler

Wirth

et al. 2022

Preprint

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Abstract. Numerical weather prediction models (NWP) are known to possess a distinct moist bias in the mid-latitude lower stratosphere which is expected to affect the ability to accurately predict weather and climate. This paper investigates the vertical structure of this bias in the European Centre for Medium-Range Weather Forecast’s (ECMWF) latest global reanalysis ERA5 using a unique multi-campaign data set of highly-resolved water vapor profiles observed with a differential absorption lidar (DIAL) onboard the High Altitude and LOng Range Research Aircraft (HALO). In total, 41 flights in the midlatitudes provide more than 31000 humidity profiles varying by four orders of magnitude. The data set covers different synoptic situations and seasons and thus is suitable to characterize the strong vertical gradients in the upper troposphere and lower stratosphere (UTLS). The comparison to ERA5 indicates high positive and negative deviations in the UT which on average lead to a slightly positive bias (+20 %). In the LS, the bias rapidly increases up to a maximum of +55 % at 1.3 km altitude above the thermal tropopause (tTP), and decreases again to 15–20 % at 4 km altitude. This vertical structure is reproduced in all flights. The depth of the layer of increased bias is smaller at high tropopause altitudes and larger when the tropopause is located low. Our results also suggest a seasonality of the bias, with the maximum in summer exceeding fall by up to a factor of 3. During one field campaign, co-located ozone and water vapor profile observations enable a classification of the observations into tropospheric, stratospheric and mixed air using H2O-O3 correlations. It is shown that the bias is higher in the mixed air while being small in tropospheric and stratospheric air which highlights that excessive transport of moisture into the LS plays a decisive role for the formation of the bias. Future climatological studies should consider the analysed lower-stratospheric moist bias in ERA5. Our results show that a better representation of mixing processes in NWP models could lead to a reduced LS moist bias that, in turn, may have a positive impact on weather and climate forecasts. The moist bias should be borne in mind for climatological studies using reanalysis data.

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Characterising extratropical near‐tropopause analysis humidity biases and their radiative effects on temperature forecasts

Cited by 17 publications

References 52 publications

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

The effect of ice supersaturation and thin cirrus on lapse rates in the upper troposphere

Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes

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