Last glacial maximum hydro-climate and cyclone characteristics in the Levant: a regional modelling perspective

Proxy-based hydro-climatic reconstructions over the Levant suggest enhanced water availability during the Last Glacial Maximum (LGM) compared to present-day conditions. To date, the governing hypothesis is that additional water availability may be directly linked to increased Cyprus Low frequency and intensity over the region. However, this paradigm has not been tested in a modelling framework. With this aim, we analyzed results from a weather type classification algorithm and regional climate simulations. The weather type classification is applied to ERA5 Reanalysis data for present-day (1979-2018) and two PMIP3/PMIP4 pre-industrial and LGM model runs. Dynamical downscaling of the two models with the regional WRF model shows that the present hydro-climate can largely be reproduced. Our simulations suggest that both evaporation and precipitation were lower in the LGM compared to pre-industrial conditions, and that their relative changes can thus most likely explain the additional water availability during that time. Indeed, evaporation in the eastern Mediterranean is reduced to a higher degree (~-33%) as compared to precipitation (~-20%) during the LGM. Particularly, lower evaporation during LGM summer may have sustained the year-round wetter conditions in the Levant. In addition, we find significant changes in Cyprus Low characteristics for the LGM. The simulated daily precipitation associated with Cyprus Lows is significantly lower than pre-industrial values (reduction of 26 - 29%), whereas the wind intensity is stronger (increase of 7 - 8%). Finally, a significant increase in Cyprus Low frequency during LGM winter is likely (+22%). Indeed, our findings are in line with a plethora of proxy-based reconstructions, and provide a reinterpretation of the driving mechanism of water availability, i.e., strong changes in evaporation rather than precipitation. This study places projected hydro-climatic drying of the Levant in a long timescale perspective. As such, it improves our understanding of the physical processes influencing the hydrological cycle in this vulnerable region, situated on the border between sub-tropical and mid-latitude climatic zones.

Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility

The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, $$f\left( {RH} \right)$$ f RH , and the hygroscopic mass growth, $$GM_{VIS}$$ G M VIS , which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled $$f\left( {RH} \right)$$ f RH agreed well with the observed $$f\left( {RH} \right)$$ f RH in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.

A Weather-Type Classification and Its Application to Near-Surface Wind Climate Change Projections over the Adriatic Region

The main goal of this study is to present a recently developed classification method for weather types based on the vorticity and the location of the synoptic centers relative to the Adriatic region. The basis of the present objective classification, applied to the Adriatic region, is the subjective classification developed by Poje. Our algorithm considered daily mean sea-level pressure and 500 hPa geopotential height to define one out of 17 possible weather types. We applied the algorithm to identify which weather type was relevant in the generation of the two typical near-surface winds over the Adriatic region, namely Bora and Sirocco. Two high-resolution (0.11°) EURO-CORDEX regional climate models were used, SMHI-RCA4 and DHMZ-RegCM4, forced by several CMIP5 global climate models and analyzed for two 30-year periods: near-present day and mid-21st century climate conditions under the high-end Representative Concentration Pathway (RCP8.5) scenario. Bora and Sirocco days were extracted for each weather type and a distribution over the 30-year period was presented. Our results suggest that in the winter season, climate model projections indicate a reduction in the main cyclonic types relevant in the formation of Bora over the entire Adriatic region and an increase in the number of anticyclonic types relevant in Sirocco events. In contrast, for the summer season, an increase in the main anticyclonic Bora-related weather types is found in the ensemble over the northern Adriatic region.

A low-cost post-processing technique improves weather forecasts around the world

Computer-generated weather forecasts divide the Earth’s surface into gridboxes, each currently spanning about 400 km2, and predict one value per gridbox. If weather varies markedly within a gridbox, forecasts for specific sites inevitably fail. Here we present a statistical post-processing method for ensemble forecasts that accounts for the degree of variation within each gridbox, bias on the gridbox scale, and the weather dependence of each. When applying this post-processing, skill improves substantially across the globe; for extreme rainfall, for example, useful forecasts extend 5 days ahead, compared to less than 1 day without post-processing. Skill improvements are attributed to creation of huge calibration datasets by aggregating, globally rather than locally, forecast-observation differences wherever and whenever the observed “weather type” was similar. A strong focus on meteorological understanding also contributes. We suggest that applications for our methodology include improved flash flood warnings, physics-related insights into model weaknesses and global pointwise re-analyses.

Assessing model performance by weather regime transitions

<p>Having the ability to stratify a model’s performance by weather type is not only beneficial to a weather forecaster when making decisions, but it is also important for end users, whether they be scientists looking to improve the model, or a customer wishing to know the value of a forecast under a specific set of circumstances.</p><p>At the MET Office, Decider is a tool which assigns a dominant weather type to a set of ensemble members, to predict the probability of a weather type occurring. The weather type is chosen from either a set of 30 or 8 sub-types, where a weather type is pre-determined objectively by clustering a 154 year record of sea level pressure anomaly fields.  </p><p>There is also a record of daily weather type classifications derived from analysis fields and so information of model performance for these weather types could be invaluable in reducing model error if combined with the predictions from Decider.</p><p>Early trials of assessing model performance by weather type revealed that larger errors occur when the weather type persisted for a single day, rather than longer timescales, and so this suggests that it would be beneficial to examine weather type transition periods.</p><p>To examine this, we expand the weather type methodology to include multiple time periods. The current methodology uses 12Z analyses to identify the weather type, and so we first assess model performance as a sensitivity study to the analysis time.</p><p>Transition days are identified when the weather type changes during a pre-defined validation period, which allows separation into either night/day weather type transitions, or a change in weather type over a full 24-hour period.</p><p>We will present early results of this work and demonstrate the impact of model performance when stratifying by regime transitions.</p>

Learning of weather-type transitions for risk assessment

<p>Persistence or sequences of critical weather patterns over Europe can trigger seasonally extreme hydroclimatic conditions in certain regions. In order to better estimate return periods of extremes across Europe, existing time series of sequences of weather-types over Europe were used to train monthly rules for the transition from one situation to another and their duration behaviour. This can be efficiently realized and tested by setting up decision trees and generating up to 10,000 year time series of weather-type sequences.</p><p>In an experiment carried out, large-scale weather situation types according to Hess/Brezowsky available from 1961 to 2020 were divided into two time periods and rules for the transition were derived for both by training decision trees. Based on the trained rules of transistions for the periods 1961-1990 and 1991-2020, 10,000-year weather-type sequences were then generated and analysed.</p><p>The comparison of the probability density functions of persistence for the 30 different large-scale weather situation types show that omega-like circultion patterns over Europe have a higher tendency to persist in the present time period. In connection with this, the risks of prolonged dry phases in Central Europe have increased. For the translation of different weather-types into local weather-type characteristics, long-term monthly mean daily precipitation values per weather-type was assigned from ERA5 reanalysis data and rearranged in a post-processing step according to the generated weather-type sequences. The analysis of the maximum duration of consecutive dry and wet months in Europe was the main focus and the identified long-term changes in hydroclimatic quantities can be thus exclusively attributed to dynamic factors.</p>