Impact of ocean resolution on storms in the North Atlantic region

<p>We study the impact of ocean horizontal resolution on storm tracks over the North Atlantic Ocean using the FOCI-OpenIFS climate model and the TRACK storm-tracking algorithm. We find that increasing ocean resolution from 1/2° to 1/10° reduces a cold bias over the North Atlantic which leads to a northward shift of the storm tracks, in particular in winter and spring seasons.<span> </span></p><p>Most climate models with non-eddying oceans, i.e. horizontal resolutions of 100 km or higher, suffer from a cold SST bias in the North Atlantic. Refining the horizontal resolution from 1/2° to 1/10° allows for a distinct Gulf Stream extension and better representation of the major current systems which reduces this cold bias. The associated warming of the ocean surface with increasing resolution also warms the troposphere and leads to a northward shift in the tropospheric eddy-driven jet. Overall, the increased ocean resolution thus improves the ocean circulation as well as the atmospheric circulation.<span> </span></p><p>We use two metrics to evaluate the storm track activity in the simulations. We calculate 2-8 day bandpass-filtered mean sea-level pressure (MSLP) and eddy heat flux (v’T’) which is an Eulerian metric that shows variability of low- and high-pressure systems as well as their associated heat flux, but says nothing about the genesis, lysis or life time of individual storms. We also use the TRACK storm-tracking algorithm with 12-hourly MSLP data to produce trajectories of individual storms, which allows us to study individual storms.<span> </span></p><p>The Eulerian approach using MSLP variance and eddy heat fluxes clearly shows a northward shift of the storm tracks as the ocean resolution is increased. Overall, the northward shift leads to reduced biases compared to ERA-Interim reanalysis. Storm-track trajectories show higher storm track and storm genesis densities around 60°N with the higher ocean resolution. Interestingly, a higher ocean resolution also results in longer life time of storms. We speculate that this is due to enhanced air-sea interactions where cyclones are fed more energy from the eddy-resolving ocean than from the non-eddying ocean.</p>

Quantifying Arctic Storm Risk in a Changing Climate

<p>The Arctic has undergone significant change over the past few decades, and there has been great reductions in Arctic sea ice extent. The Arctic ocean has become more accessible, and this has allowed for more human activity in the Arctic.  The risk of storms impacting human activities in the Arctic has consequently increased, and as sea ice extent continues to decline in the near-future, the risk of storms impacting human activities in the Arctic is likely to increase further.  In this study, the present climatology of Arctic storms is evaluated between modern reanalysis datasets, and the future climatology of Arctic storms is also evaluated in climate model simulations.</p><p>There are multiple reanalysis datasets available from different institutions, which each give an approximation of past atmospheric conditions over the last few decades.  In addition, there are multiple storm tracking methods, which may impact the climatology of Arctic storms that is identified in a reanalysis datasets.  In this study, we aimed to improve the understanding of Arctic storms by assessing their characteristics in multiple global reanalyses, the ECMWF-Interim Reanalysis (ERA-Interim), the 55-Year Japanese Reanalysis (JRA-55), the NASA-Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and the NCEP-Climate Forecast System Reanalysis (NCEP-CFSR), using the same storm tracking method based on 850 hPa relative vorticity and mean sea level pressure.  In addition, the response of Arctic storms to climate change has been evaluated in the UPSCALE climate simulations, and the affect of horizontal resolution on the representation of future Arctic storminess has been assessed.</p><p>The results show that there are no significant trends in Arctic storm characteristics between 1980-2017, even though the Arctic has undergone rapid change.  Although some similar Arctic storm characteristics are found between the reanalysis datasets, there are generally higher differences between the reanalyses in winter (DJF) than in summer (JJA).  In addition, substantial differences can arise between using the same storm tracking method based on 850 hPa relative vorticity or mean sea level pressure, which adds to the uncertainty associated with current Arctic storm characteristics.</p><p>The results also show that Arctic storms will change significantly in a future climate, particularly in their spatial distribution.  Differences have been found between the future simulations of Arctic storms between an ensemble of high resolution climate models (25km) and low resolution climate models (130km), which adds uncertainty to how Arctic storms may change in a future climate.  The possible reasons for why the representation of future climate Arctic storms may be different in climate models of differing horizontal resolution has been explored.</p>

In the eye of the STORM: Tracking the myosin-binding protein C N terminus in heart muscle

Colson discusses a recent investigation of the localization of N-terminal myosin-binding protein C in cardiac muscle.

Simulated hailstorms over Switzerland in May 2018 in current and future climate conditions

<p>Several remarkable hailstorms have occurred on the territory of Switzerland during the month<br>of May, 2018.<br>This period has been simulated, using the WRF4.0 model at a convection-permitting<br>resolution (1.5 km), using different microphysical schemes (Thompson, Morrison, P3).<br>The surrogate climate change approach has been used for imitating the climate conditions,<br>corresponding to the end of the 21st century (CMIP5 model data, RCP8.5 scenario).<br>The HAILCAST-1D model output has been used as a measure of simulated hail size and 5-<br>minute 3-D radar reflectivity field has been used for cell identification and tracking.<br>Hailstorms produced in the current climate and in surrogate climate change simulations have<br>been examined using neighborhood methods and a storm-tracking algorithm. Current-climate<br>simulated hailstorms were compared with the ground observations and MeteoSwiss radar<br>data.<br>The influence of microphysical schemes to the characteristics of simulated hailstorms has<br>been studied. </p>

An Inter-Comparison of Arctic Synoptic Scale Storms between Four Global Reanalysis Datasets

<p>Arctic sea ice has reduced significantly over recent decades and is projected to reduce further over this century. This has made the Arctic more accessible and increased opportunities for the expansion of business and industrial activities.  As a result, the exposure and risk of humans and infrastructure to extreme storms will increase in the Arctic.</p><p>Our understanding of the current risk from storms comes from analysing the past, for example, by using storm tracking algorithms to detect storms in reanalysis datasets.  However, there are multiple reanalysis datasets available from different institutions and there are multiple storm tracking methods.  Previous studies have found that there can be differences between reanalysis datasets and between storm tracking methods in the climatology of storms, particularly in mid-latitude regions rather than the Arctic.  In this study, we aimed to improve the understanding of Arctic storms by assessing their characteristics in multiple global reanalyses, the ECMWF-Interim Reanalysis (ERA-Interim), the 55-Year Japanese Reanalysis (JRA-55), the NASA-Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and the NCEP-Climate Forecast System Reanalysis (NCEP-CFSR), using the same storm tracking method based on 850 hPa relative vorticity and mean sea level pressure.</p><p>The results from this study show that there are no significant trends in Arctic storm characteristics between 1980-2017, even though the Arctic has undergone rapid change.  Although some similar Arctic storm characteristics are found between the reanalysis datasets, there are generally higher differences between the reanalyses in winter (DJF) than in summer (JJA).  In addition, substantial differences can arise between using the same storm tracking method based on 850 hPa relative vorticity or mean sea level pressure, which adds to the uncertainty associated with current Arctic storm characteristics.</p>

Regional fresh snowfall microbiology and chemistry are driven by geography in storm-tracked events, Colorado, USA

Snowfall is a global phenomenon highly integrated with hydrology and ecology. Forays into studying bioaerosols and their dependence on aeolian movement are largely constrained to either precipitation-independent analyses or in silico models. Though snowpack and glacial microbiological studies have been conducted, little is known about the biological component of meteoric snow. Through culture-independent phylogenetic and geochemical analyses, we show that the geographical location at which snow precipitates determines snowfall’s geochemical and microbiological composition. Storm-tracking, furthermore, can be used as a valuable environmental indicator to trace down what factors are influencing bioaerosols. We estimate annual aeolian snowfall deposits of up to ∼10 kg of bacterial/archaeal biomass per hectare along our study area of the eastern Front Range in Colorado. The dominant kinds of microbiota captured in an analysis of seven snow events at two different locations, one urban, one rural, across the winter of 2016/2017 included phylaProteobacteria,Bacteroidetes,Firmicutes, andAcidobacteria, though a multitude of different kinds of organisms were found in both. Taxonomically,Bacteroideteswere more abundant in Golden (urban plain) snow whileProteobacteriawere more common in Sunshine (rural mountain) samples. Chemically, Golden snowfall was positively correlated with some metals and anions. The work also hints at better informing the “everything is everywhere” hypotheses of the microbial world and that atmospheric transport of microbiota is not only common, but is capable of disseminating vast amounts of microbiota of different physiologies and genetics that then affect ecosystems globally. Snowfall, we conclude, is a significant repository of microbiological material with strong implications for both ecosystem genetic flux and general bio-aerosol theory.

Effects of City Size on Thunderstorm Evolution Revealed through a Multiradar Climatology of the Central United States

Five years of 0.01° latitude × 0.01° longitude multiradar multisensor grids of composite reflectivity and vertically integrated signals from the maximum expected size of hail (MESH) and vertically integrated liquid (VIL) were created to examine the role of city size on thunderstorm occurrence and strength around four cities: Dallas–Fort Worth, Texas; Minneapolis–St. Paul, Minnesota; Oklahoma City, Oklahoma; and Omaha, Nebraska. A storm-tracking algorithm identified thunderstorm areas every minute and connected them together to form tracks. These tracks defined the upwind and downwind regions around each city on a storm-by-storm basis and were analyzed in two ways: 1) by sampling the maximum value every 10 min and 2) by accumulating the spatial footprint over its lifetime. Beyond examining all events, a subset of events corresponding to favorable conditions for urban modification was explored. This urban favorable (UF) subset consisted of nonsupercells occurring in the late afternoon/evening in the meteorological summer on weak synoptically forced days. When examining all thunderstorm events, regions at variable ranges upwind of all four cities generally had higher areal mean values of reflectivity, MESH, and VIL relative to downwind areas. In the UF subset, the larger cities (Dallas–Fort Worth and Minneapolis–St. Paul) had a 24%–50% increase in the number of downwind thunderstorms, resulting in a higher areal mean reflectivity, MESH, and VIL in this region. The smaller cities (Oklahoma City and Omaha) did not show such a downwind enhancement in thunderstorm occurrence and strength for the radar variables examined. This pattern suggests that larger cities could increase thunderstorm occurrence and intensity downwind of the prevailing flow under unique environmental conditions.

Storm Tracking via Tree Structure Representation of Radar Data

An algorithm for automatic storm identification, tracking, and nowcasting using tree structure representation of radar reflectivity images is proposed. The algorithm aims to track and nowcast different kinds of storm objects (stratiform regions, convective storms, and storm cells) simultaneously and to preserve their spatial relationships in the tracking and nowcasting processes. The algorithm applies a region tree structure to represent intensity regions and their spatial relationships in radar reflectivity images. Storm objects are identified by clustering regions within the region tree structure. Storm tracking is accomplished using an iterative region tree matching algorithm. Storm nowcasting applies the tree structure to the nowcasting of the internal structures of storm objects. Using eight cases with different storm types, a comparative evaluation with the enhanced Thunderstorm Identification, Tracking, Analysis, and Nowcasting (ETITAN) method and the Storm Cell Identification and Tracking (SCIT) method has shown that the proposed tree-based storm-tracking algorithm achieves better performance in storm tracking and nowcasting. The critical success index (CSI) value of storm association is 78.16% for the tree-based method, as compared with 74.88% for SCIT and 74.71% for ETITAN. The CSI value of an 18-min nowcast is 29.02% for the tree-based method, as compared with 24.98% for SCIT and 24.44% for ETITAN. The evaluation also shows that the tree-based method is able to nowcast the internal structure of storms and therefore produces small mean absolute errors (MAE).