Evaluation of NWP results for wintertime nocturnal boundary‐layer temperatures over Europe and Finland

Abstract

The results demonstrated a T2m bias increasing with decreasing temperature and strengthening temperature inversion. When a strong temperature inversion was observed in Sodankylä, the models underestimated it, whereas in nearneutral conditions the stratification was overestimated. Comparison of observed and modelled 3 h temperature tendencies showed that the T2m tendency in the models was on average only 17-20% of the observed one. The warm bias in T2m forecast in Sodankylä during periods of observed temperature inversion partly resulted from a warm bias in the initial conditions. This was due to problems in data assimilation in IFS and HIRLAM, in initialization in AROME, and in either or both procedures in GFS. In particular, the IFS data assimilation increased the T2m bias. Evaluation of modelled T2m against grid-averaged T2m observed at Helsinki Testbed demonstrated that the T2m model error dominated over the spatial variability of observed T2m. This suggests that over an almost flat terrain horizontal resolution is not a major factor for the accuracy of T2m forecast at low T2m typically associated with temperature inversions.

Surface energy budget on Larsen and Wilkins ice shelves in the Antarctic Peninsula: results based on reanalyses in 1989–2010

“…Focusing on biases of seasonal means, our validation results include three interesting issues: (1) all three reanalyses had warm temperature biases in all seasons, (2) all three reanalyses yielded positive biases for the eastward wind component in all seasons, and (3) reanalyses mostly yielded negative biases for the northward wind component. The first issue is in agreement with many previous validation studies over snow and ice surfaces: Jakobson et al (2012) obtained similar results for the same (and other) reanalyses over the Arctic sea ice, Vihma et al (2002) observed a year-round warm bias for the ECMWF operational analyses over the Antarctic sea ice, and Atlaskin and Vihma (2012) observed that several numerical weather prediction models yielded warm biases under conditions of stable boundary layer over snowcovered boreal forest. The second and third issue are probably at least partly due to the resolution of reanalyses, which is not high enough to accurately represent the complex orography of the Antarctic Peninsula.…”

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

“…Considering ABL modelling, it is well understood that the ABL schemes commonly applied in climate models and NWP yield excessive heat and momentum fluxes in the SBL (Cuxart et al, 2006;Tjernström et al, 2005), typically resulting in a warm bias near the surface (Atlaskin and Vihma, 2012). In the Arctic, Byrkjedal et al (2007) demonstrated the importance of a high vertical resolution: not surprisingly, model experiments with 90 levels in the vertical yielded much better results than those with 31 levels, the latter being typical for climate models contributing to the IPCC AR4.…”

Sensitivity of local air quality to the interplay between small- and large-scale circulations: a large-eddy simulation study

“…Thus, the problem could be partially related to an insufficient spatial model resolution with respect to the relevant topographic features. But partially, it should be also related to the breakdown of inadequate turbulent mixing schemes for the atmospheric stably stratified boundary layer (Atlaskin and Vihma, 2012;Fernando and Weil, 2010;Mahrt, 2014;Zilitinkevich et al, 2015). These and other inaccuracies in the air quality assessment and modelling motivated the Pan-Eurasian Experiment (PEEX) community to include air pollution in the stably stratified boundary layers in the list of research priorities addressing the environmental and socio-economic challenges of the northern high latitudes (Lappalainen et al, 2016).…”