Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC.

The microwave radiometers HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds - Passive) continuously measured radiation emitted from the atmosphere throughout the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition on board the research vessel Polarstern. From the measured brightness temperatures, we have retrieved atmospheric variables using statistical methods in a temporal resolution of 1 s covering October 2019 to October 2020. The integrated water vapour (IWV) is derived individually from both radiometers. In addition, we present the liquid water path (LWP), temperature and absolute humidity profiles from HATPRO. To prove the quality and to estimate uncertainty, the data sets are compared to radiosonde measurements from Polarstern. The comparison shows an extremely good agreement for IWV, with standard deviations of 0.08-0.19 kg m-2 (0.39-1.47 kg m-2) in dry (moist) situations. The derived profiles of temperature and humidity denote uncertainties of 0.7-1.8 K and 0.6-0.45 gm-3 in 0-2 km altitude.

Impact on predictability of tropical and mid-latitude cyclones by extra Arctic observations.

Recent research has demonstrated that additional winter radiosonde observations in Arctic regions enhance the predictability of mid-latitude weather extremes by reducing uncertainty in the flow of localised tropopause polar vortices. The impacts of additional Arctic observations during summer are usually confined to high latitudes and they are difficult to realize at mid-latitudes because of the limited scale of localised tropopause polar vortices. However, in certain climatic states, the jet stream can intrude remarkably into the mid-latitudes, even in summer; thus, additional Arctic observations might improve analysis validity and forecast skill for summer atmospheric circulations over the Northern Hemisphere. This study examined such cases that occurred in 2016 by focusing on the prediction of the intensity and track of tropical cyclones (TCs) over the North Atlantic and North Pacific, because TCs are representative of extreme weather in summer. The predictabilities of three TCs were found influenced by additional Arctic observations. Comparisons with ensemble reanalysis data revealed that large errors propagate from the data-sparse Arctic into the mid-latitudes, together with high-potential-vorticity air. Ensemble forecast experiments with different reanalysis data confirmed that additional Arctic observations sometimes improve the initial conditions of upper-level troposphere circulations.

Multilevel cloud structures over Svalbard.

The presented picture of the month is a superposition of space-borne lidar observations and high-resolution temperature fields of the ECMWF integrated forecast system (IFS). It displays complex tropospheric and stratospheric clouds in the Arctic winter 2015/16. Near the end of December 2015, the unusual northeastward propagation of warm and humid subtropical air masses as far north as 80°N lifted the tropopause by more than 3 km in 24 h and cooled the stratosphere on a large scale. A widespread formation of thick cirrus clouds near the tropopause and of synoptic-scale polar stratospheric clouds (PSCs) occurred as the temperature dropped below the thresholds for the existence of cloud particles. Additionally, mountain waves were excited by the strong flow at the western edge of the ridge across Svalbard, leading to the formation of mesoscale ice PSCs. The most recent IFS cycle using a horizontal resolution of 8 km globally reproduces the large-scale and mesoscale flow features and leads to a remarkable agreement with the wave structure revealed by the space-borne observations.

Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route.

During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR.

Remote sensing of aerosols in the Arctic for an evaluation of global climate model simulations.

In this study Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua retrievals of aerosol optical thickness (AOT) at 555 nm are compared to Sun photometer measurements from Svalbard for a period of 9 years. For the 642 daily coincident measurements that were obtained, MODIS AOT generally varies within the predicted uncertainty of the retrieval over ocean (ΔAOT = ±0.03 ± 0.05 · AOT). The results from the remote sensing have been used to examine the accuracy in estimates of aerosol optical properties in the Arctic, generated by global climate models and from in situ measurements at the Zeppelin station, Svalbard. AOT simulated with the Norwegian Earth System Model/Community Atmosphere Model version 4 Oslo global climate model does not reproduce the observed seasonal variability of the Arctic aerosol. The model overestimates clear-sky AOT by nearly a factor of 2 for the background summer season, while tending to underestimate the values in the spring season. Furthermore, large differences in all-sky AOT of up to 1 order of magnitude are found for the Coupled Model Intercomparison Project phase 5 model ensemble for the spring and summer seasons. Large differences between satellite/ground-based remote sensing of AOT and AOT estimated from dry and humidified scattering coefficients are found for the subarctic marine boundary layer in summer. KEY POINTS: Remote sensing of AOT is very useful in validation of climate models.

The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data.

In the present study, we investigate the determination accuracy of the Universal Thermal Climate Index (UTCI). We study especially the UTCI uncertainties due to uncertainties in radiation fluxes, whose impacts on UTCI are evaluated via the mean radiant temperature (Tmrt). We assume "normal conditions", which means that usual meteorological information and data are available but no special additional measurements. First, the uncertainty arising only from the measurement uncertainties of the meteorological data is determined. Here, simulations show that uncertainties between 0.4 and 2 K due to the uncertainty of just one of the meteorological input parameters may be expected. We then analyse the determination accuracy when not all radiation data are available and modelling of the missing data is required. Since radiative transfer models require a lot of information that is usually not available, we concentrate only on the determination accuracy achievable with empirical models. The simulations show that uncertainties in the calculation of the diffuse irradiance may lead to Tmrt uncertainties of up to ±2.9 K. If long-wave radiation is missing, we may expect an uncertainty of ±2 K. If modelling of diffuse radiation and of longwave radiation is used for the calculation of Tmrt, we may then expect a determination uncertainty of ±3 K. If all radiative fluxes are modelled based on synoptic observation, the uncertainty in Tmrt is ±5.9 K. Because Tmrt is only one of the four input data required in the calculation of UTCI, the uncertainty in UTCI due to the uncertainty in radiation fluxes is less than ±2 K. The UTCI uncertainties due to uncertainties of the four meteorological input values are not larger than the 6 K reference intervals of the UTCI scale, which means that UTCI may only be wrong by one UTCI scale. This uncertainty may, however, be critical at the two temperature extremes, i.e. under extreme hot or extreme cold conditions.