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Measurements of the U K 0 37 index and the absolute abundance of alkenones in marine sediments are increasingly used in paleoceanographic research as proxies of past sea surface temperature and haptophyte (mainly coccolith-bearing species) primary productivity, respectively. An important aspect of these studies is to be able to compare reliably data obtained by different laboratories from a wide variety of locations. Hence the intercomparability of data produced by the research community is essential. Here we report results from an anonymous interlaboratory comparison study involving 24 of the leading laboratories that carry out alkenone measurements worldwide. The majority of laboratories produce data that are intercomparable within the considered confidence limits. For the Geochemistry GeophysicsGeosystems G 3 G 3 rosell-melé et al.: alkenones interlaboratory comparison 2000GC000141measurement of alkenone concentrations, however, there are systematic biases between laboratories, which might be related to the techniques employed to quantify the components. The maximum difference between any two laboratories for any two single measurements of U K 0 37 in sediments is estimated, with a probability of 95%, to be <2.18C. In addition, the overall within-laboratory precision for the U K 0 37 temperature estimates is estimated to be <1.68C (95% probability). Similarly, from the analyses of alkenone concentrations the interlaboratory reproducibility is estimated at 32%, and the repeatability is estimated at 24%. The former is compared to a theoretical estimate of reproducibility and found to be excessively high. Hence there is certainly scope and a demonstrable need to improve reproducibility and repeatability of U

Precipitation forecast data from the ERA-Interim reanalysis (33 years) are evaluated using the daily England and Wales Precipitation (EWP) observations obtained from a rain-gauge network. Observed and reanalysis daily precipitation data are both described well by Weibull distributions with indistinguishable shapes but different scale parameters, such that the reanalysis underestimates the observations by an average of 22%. The correlation between the observed and ERA-Interim time series of regional daily precipitation is 0.91. ERA-Interim also captures the statistics of extreme precipitation including a slightly lower likelihood of the heaviest precipitation events (> 15 mm day −1 for the regional average) than indicated by the Weibull fit. ERA-Interim is also closer to EWP for the high precipitation events. Since these carry weight in longer accumulations, a smaller underestimation of 19% is found for monthly mean precipitation. The partition between convective and stratiform precipitation in the ERA-Interim forecast is also examined. In summer both components contribute equally to the total precipitation amount, while in winter the stratiform precipitation is approximately double the convective. These results are expected to be relevant to other regions with low orography on the coast of a continent at the downstream end of midlatitude stormtracks.

Abstract. Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3∘ N, 153.2∘ E). The eruption emitted around 1.5±0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high-SO2-VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

Abstract. Volcanic eruptions can cause significant disruption to society and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3° N, 153.2° E). The eruption emitted around 1.5 &amp;pm; 0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the Tropospheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption, but is unable, in its standard configuration, to capture the extent and precise location of very high-concentration regions within the volcanic cloud. Using the Fractional Skill Score as metric for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 days after the eruption where vertical column densities (VCD) of SO2 (in Dobson Units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 days only. The lower skill for these high-concentration regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard diffusion parameters used in NAME by a factor of four results in a slightly increased model skill during the first five days of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. We find that the temporal evolution of the NH-mean SO2 mass burden simulated by NAME strongly depends on the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), both NAME and TROPOMI show a similar peak in SO2 mass burden (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 days in the NH. Our work demonstrates the large potential of using high-resolution satellite retrievals to identify and rectify limitations in dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

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A novel Lagrangian framework is developed to attribute monthly precipitation variability to physical processes. Precipitation variability is partitioned into a combination of five factors: airmass origin location, origin surface temperature variation, ascent intensity, mass fraction of ascending air, and the number of “wet” analysis times per month [&gt;1 mm (6 h)−1]. Precipitation in a target region is linked to “origin” locations of air masses where the water vapor mixing ratio was last set by boundary layer moistening and is a maximum along back trajectories. Applying the technique to the England and Wales region, the factors together account for 83%–89% of the observed summer precipitation variability. The dominant contributor is the number of wet analyses, which is shown to be associated with cyclone statistics. The wettest summer months are mainly associated with anomalous cyclone duration rather than the number of cyclones. In addition, surface temperature and saturation humidity at the origin locations are found to be below their climatological averages (1979–2013). Therefore, the direct thermodynamic effect of anomalous surface temperature on marine boundary layer humidity acts to reduce monthly precipitation anomalies. The decadal precipitation change between phases of the Atlantic multidecadal oscillation is approximately 20% of the interannual variability between summer months. Changes in cyclone statistics have an effect 6 times larger than the direct thermodynamic factor in both monthly and decadal precipitation variability.